Liang Zhou
email: lzhou@shmeea.edu.cn

Shengming Jiang

A Composite Weight Based Access Network Selection Algorithm in Marine Internet

Keywords: Marine Internet, Access method, Analytic Hierarchy Process(AHP), Entropy, Exata simulation

The 21 st century is the ocean century, and ocean informatization is one of the key requirements. Marine Internet (MI) is the basic infrastructure of ocean informatization, so developing the MI technology is of great significance. There are several access networks maybe available in a MI system, and a network user has to choose the best one to support the application. The selection scheme needs to consider may factors in order to make a smart decision, which is a multi-index decision problem. These factors include user preferences, business types, and the performance as well as cost of alternative access networks. Combining with the Analytic Hierarchy Process (AHP) algorithm and entropy theory, we develop a method for the user to select access networks in a smart way. It adopts subjective and objective weights and a utility function to select the target access network. The simulation results carried out on the Exata platform show that the proposed scheme can improve the network performance with reduced cost.

Introduction

The 21 st century is the ocean century, and ocean informatization is one of the key requirements. Marine Internet (MI) is the basic infrastructure of ocean informatization, so developing the MI technology is of great significance In the marine Internet , there are multiple access networks maybe available for selection, which typically include shore-based networks, self-organizing ship networks, high-altitude communication platforms, satellite networks and so on [START_REF] Jiang | On the marine internet and its potential applications for underwater internetworking[END_REF]. Network users often need to choose one from a number of access networks to support the current application, considering the user preferences, business type and the performance as well as cost of alternative access networks and other factors.

When multiple access networks are available, a network selection is usually based on a mathematical algorithm that gives a weight to each metric. With the utility function, the comprehensive utility value of each index corresponding to each alternative network is calculated. Finally, the access network with the largest utility value is selected as the best solution. The problem with such kind of algorithm is that the assignment of weights is dependent on the subjective experience of the operator as mentioned in [START_REF] Sun | Research on heterogeneous network selection algorithms based on network performance and service feature perception[END_REF], which then proposed an algorithm based on the combination of subjective and objective weight to determine the weight of the network attributes, taking into account the user preferences and different business types and Quality of Service(QoS). The algorithm works well in a dense user scenario. However, the marine Internet is characterized by sparse distribution of users, which mainly consist of ships. So far, such kind of scenario has not been investigated in the literature. Therefore, this paper combines the Analytic Hierarchy Process (AHP) [START_REF] Yang | General link layer technology in heterogeneous wireless access networks[END_REF] and the entropy theory to set the subjective and objective weights respectively, and then selects the target network through the utility function.

2

Access Network Selection Algorithm Based on Compound Weights

Framework of the proposed algorithm

Trigger network selection

Network performance parameters business type

User preference parameters

Network availability check

Build a hierarchical model

Right decision parameter values

Subjective weight of decision attribute

Objective attribute of judgment attribute

The utility function based on compound weights

Optimal network

Fig. 1. Framework of the proposed network selection algorithm. It shows the framework of the algorithm. The Analytic Hierarchy Process (AHP) is an effective method to solve the complex multi-index decision problem, and the entropy weight method has objective fairness and can judge the change of the judgment index objectively [START_REF] Jia | Research on access selection and admission control in heterogeneous wireless networks[END_REF]. Therefore, this paper combines these two algorithms to deal with the access network selection in the marine Internet. The algorithm considers the network performance requirements and user preferences, makes the algorithm have a good applicability.

Network Selection Model Based on Analytic Hierarchy Process

Many factors affect the heterogeneous network selection, and it is unrealistic to choose an optimal network without considering them comprehensively. We can focus on several common, important factors as an indicator. The users of the same business costs have different tendencies, and some hope to have high-quality services, and some with a pursuit of economic benefits. Furthermore, different types of applications have different basic requirements of network performance. For example, for marine emergency search and rescue applications, the network needs to ensure the stability of communication and timeliness. Based on the above considerations, the AHP algorithm is used to establish the hierarchical model. With consideration of the user preference in the criterion layer, the sub-criterion layer considers the application requirements of the network type.

1) Hierarchical Model of Network Selection Based on User Preference

As shown in Fig. 2, first, select the QoS level (α), the network available load (β) and the price charge (γ) as the network performance indicators, and then combine them with the user's network performance bias, QoS priority mode and price priority mode. We first establish the two models of the decision matrix, set the two indicators of the importance of each in Table 1, where the values depend on the experience of the decision maker, and can be appropriately adjusted to suit the specific needs of the algorithm.

Table 1. Setting for different strategies under the decision matrix

(a)QoS priority QoS priority QoS level(α) Load(β) Price(γ) QoS level(α) 1 2 4 Load(β) 1/2 1 2 Price(γ) 1/4 1/2 1 (b)Price priority Price priority QoS level(α) Load(β) Price(γ) QoS level(α) 1 2 1/2 Load(β) 1/2 1 1/4 Price(γ) 2 4 1
The judgment matrix is sorted and the consistency is verified. The maximum Eigen values of the two matrices are calculated as 3, and the calculated values are all zero. Note that the settings of the matrix conform to the consistency requirements. Then, the weights of the indexes in the two modes are obtained and normalized, and the calculated values are all four decimal places.

{ W QoS = {ω α , ω β , ω γ } = {0.5714,0.2857,0.1429} W Price = {ω α , ω β , ω γ } = {0.2857,0.1429,0.5714} (1)
2) QoS hierarchical model based on service type There are many types of network applications with different network service quality requirements. For example, at present, 3GPP is divided into four categories according to the different QoS features of network application: session class, streaming class, interactive class and background class service [START_REF] Buddhikot | Integration of 802.11 and thirdgeneration wireless data networks[END_REF].

Different services corresponding to the network performance indicators have different requirements. Therefore, we use QoS (α) in Figure 3 as the target layer of the criterion layer to establish the QoS hierarchical model based on the service type. The quasi-side layer is divided into session class (T1), streaming media class (T2), interactive class (T3), and background class(T4).The subordinate criterion layer includes four parameters: delay (α 1 ), jitter (α 2 ), packet loss rate (α 3 ) and rate (α 4 ).Take Session class (T1) as an example, the network performance indicators between the decision matrixes are set as shown in Table 2. According to the AHP algorithm, the maximum Eigen values of the decision matrix corresponding to the four business types are calculated, which are 4.0735, 3.8480,4 and 4, respectively. The four decision matrices pass the consistency test; then calculate the QoS sub-parameters based on different types of business weights as listed in Table 3.

W QoS_i ={ω Q_α1 ,ω Q_α2 ,ω Q_α3 ,ω Q_α4 } i∈{T1,T2,T3,T4} (2
) Among them, the corresponding weight of the parameters are shown in Table 3. 3) Ranking the total order and calculating the subjective weight An overall multi-level hierarchical model based on the network selection problem is drawn in Fig. 3, consisting of two modes of QoS priority and price priority target layer in the model, which represent different user preferences. QoS, available network load and price factors are the center of the rule layer, the weights corresponding to the target layer are calculated with Formula 1.In addition, the sub-rule layer under QoS criterion includes delay, jitter, packet loss rate, traffic rate and other factors, also correspond to different business types with different weights.

C T C T C T C T W W W W W W W W         , , , , , , , , , , , ,   (3)
0343 0.1512 0.3514 0.2857 0.1429

       , , , ,       1 2_ 1 1 2_ 2 1 2_ 3
C T C T C T C T W W W W W W W W         , , , , , , , , , , , , , , , ,   (4)
0.0172 0.0756,0.1757 0.1429 0.5714

       , , ,
After the layered model is built up, should calculate the weight of the single layer firstly, and calculate the total weight of the hierarchy finally. Because the decision matrix is set by the decision-maker itself, it has some limitations.

Objective weight based on information entropy

In order to eliminate the subjective unilateralism of the total weight obtained by AHP, the proposed method uses entropy weight as the objective weight of judgment. Before calculating the entropy weight, the dimensionless decision attributes are introduced, followed by the method of the dimensionless of the decision attributes.

In a decision problem with m alternative network schemes and n network metrics, the given parameter evaluation matrix is:

R ' =(
For some network parameter indicators, the smaller the better, such as delay; while for the other, the greater the better, such as speed. In order to describe the changing trend of two kinds of indexes in a unified way, normalization is needed.

The delay(α 1 )、jitter(α 2 )、 packet loss rate(α 3 )、rate (α 4 )、available load(β) and network price (γ)are chosen as decision criteria in network parameters. Among them, for the delay(α 1 )、jitter (α 2 )、 packet loss rate(α 3 ) and the network price(γ), the smaller the better, and its normalization is:

(6)
For the rate(α 4 ) and the available load(β), the bigger the better, and the following formula is used for the normalization processing:

r ij = r′ ij
max(r′ 1j ,r′ 2j ,…r′ mj )+min(r′ 1j ,r′ 2j ,…r′ mj )

(7)
After the parameter is dimensionless, the normalized evaluation matrix R is obtained:

R=( r 11 r 12 r 21 r 22 ⋯ r 1n r 21 ⋮ ⋱ ⋮ r m1 r m1 ⋯ r mn ) (8)
Then calculate the network attributes corresponding to entropy weight, composed of objective weight vector W 2 as follows:

W 2 ={ω" α1 ,ω" α2 ,ω" α3 ,ω" α4 ,ω" β ,ω" γ } (9)

Utility function

Selection algorithm is based on a combination of subjective and objective weights from the user perspective and network management. The user preferences and different requirements for network performance by different services are considered firstly. The hierarchical model using the AHP algorithm is built, and the subjective weight of each index is calculated. Then entropy method is used to reflect the dynamic relationship between the various indicators of competition, and reduce the error caused by subjective judgment from the objective standpoint. The selection algorithm is based on the combination of subjective and objective weights,aiming to the process of network selection more reasonably. The algorithm itself is not complicated and is beneficial to generalization. The form of compound weights is as follows:

ω j =θ • ω ′ j +(1 -θ) • ω " j ,j=α 1 ,α 2 ,α 3 ,α 4 ,β,γ (10) W ={ω α 1 ,ω α 2 ,ω α 3 ,ω α 4 ,ω β ,ω γ } (11)
Among themθ∈(0,1),θ=0.5 in this paper. For a network with N candidates, evaluation matrix of the network parameter normalization is R=(r ij ) n×6 , and we define the utility of each network as follows:

U=[u 1 , u 2 , … u n ]=R• W T (12) u i = ∑ (w j j • r ij ) j=α 1 ,α 2 ,α 3 ,α 4 ,β,γ ,i=1,2,…n (13)
Finally, the network with the highest utility value is chosen as the switching access network as follows:

𝐮 𝐁𝐞𝐬𝐭 =argmax(𝐮 𝐣 ),𝐢=1,2,…n (14)

Simulation and analysis

In order to verify the performance of the proposed access selection method (En-AHP), we simulated En-AHP on the Exata platform, comparing with, the satellite network priority algorithm (Simple) and the unmodified AHP algorithm. The simulation scenario follows a real ship distribution with a total of 29 ships, and other infrastructure network equipment. The moving speed of ship follows the random moving model, and the velocity distribution is from 6 to 13m/s with the moving granularity of 10m. The simulation time of the scene is 30s, and the application traffic type is constant bit rate (CBR). The data packet delivered by the application is 512bytes, and the maximum number of packets is 100.

As shown in Fig. 4, where the simulation time and the size of data packet remain unchanged, as the contract rate decreases, the total number of contracts is reduced, and the throughput of the two links shows a downward trend. When the transmission interval is 0.8s, the trend becomes flat, and the packet delivery rate decreases gradually with respect to the throughput per unit time. The throughput of En-AHP per unit time is more than Simple and AHP. The reason is the EN-AHP selects the ship ad hoc network, and the speed of base network in EN-AHP is faster than the Simple and the unimproved AHP, which select the satellite communication. As a result, the data processing capabilities of EN-AHP are superior to those of Simple and the unmodified AHP. As shown in Fig. 5, the end-to-end delay of the Simple algorithm is always higher than that of EN-AHP. The primary variable is the transmission distance. In En-AHP, the data exchange between ships increases the delay, but the delay of Simple (satellite communication) is too large, accounting for the main proportion. With the reduce of packet sending rate, the end-to-end delay of Simple and unimproved AHP remain stable at 0.29s basically, because for satellite, transmission distance of Simple algorithm is mainly composed of a satellite transmission path, and the characteristics of satellite communication is the delay remain stable. As shown in Fig. 6, the Jitter of EN-AHP is always higher than Simple and unmodified AHP. Due to the number of ships is limited, the impact of link congestion is not considered. In EN-AHP, the movements of nodes in the ship self-organizing network lead to the instability of the link state, which is not as good as the satellite system. With the decrease of the packet transmission rate and the increase of the ship's moving distance, the data transmission jitter will intensify. The jitter of Simple remained stable, because the jitter of satellite communication is almost zero. Fig. 6. Comparison of Jitter. We found that the packet loss rate of EN-AHP is always higher than that of Simple and not improved AHP. The movement of the nodes in the ship's self-organizing network causes the link state to be unstable. In terms of s the delay, jitter, and throughput, the benefits of EN-AHP are higher than Simple and unmodified AHP significantly.

Conclusion

Multiple access networks may coexist in the Marine Internet. Considering the different preferences of users and different needs of different applications, we proposes an access network selection algorithm based on the composite weights. The algorithm is simulated on Exata, and the simulation results show that the network performance of the access network using the proposed algorithm is better with lower cost.

Fig. 2 .

 2 Fig.2.The hierarchical model based on the user's preferred access network. Users in the QoS priority mode in the access network selection give priority to application requirements with an inclination to the performance of the access network an acceptable price. Under this strategy, the priority of the three network attribute indicators is as follows: QoS level> network load> price. Users in the price priority mode is more concerned about the cost of the access network, while the sensitivity of the network performance is low. Then the three network attribute indicators priority is price> QoS level> network load.

Fig. 4 .

 4 Fig.4. Comparison of throughput. The throughput shows a downward trend mainly.

Fig. 5 .

 5 Fig.5. End-to-end delay. There is little difference between the En-AHP and AHP algorithm.

Table 2 .

 2 Judgment matrices based on various business types

			Session class -T1
		α1	α2	α3	α4
	Delay(α1)	1	1	3	7

Table 3 .

 3 The QoS (α) weights of different service types

	Type of service	α 1	α 2	α 3	α 4
	(T1)	0.3966	0.3966	0.16	0.0468
	(T2)	0.0615	0.2273	0.2583	0.4529
	(T3)	0.4091	0.0455	0.4091	0.1364
	(T4)	0.0601	0.0601	0.2647	0.6150

  Network selection hierarchy model. The total rank weights are calculated by single rank weight at each level. Formulas 1 and 2 are used to calculate the total order of each index under different user preference model, different business types, namely for the time delay (α 1 ), jitter (α 2 ), the rate of packet loss(α 3 ),Rate(α 4 ),price(γ) and subjective weight vector based on different cases: W 1 ={ω′ α1 ,ω′ α2 ,ω′ α3 ,ω′ α4 ,ω′ β ,ω′ γ } The weight vectors for different scenes are shown in Formulas 3 and 4.

	Heterogeneous network Heterogeneous network Qos priority Price priority QoS(α) Jitter Packet loss Rate Satellite communication system    1_ 1 1_ 2 1_ 3 0.2266,0.2266,0.0914 0.0267,0.2857,0.1429 Price(γ) Transmit Rate Ship ad-hoc network   0.0351 0.1299 0.1476 0.2588 0.2857 0.1429 0.2338 0.0260 0.2338 0.0779 0.2857 0.1429 Fig.3.  Load(β) Time delay Shore-based network 1 1 1 T1: T2 : QoS priority T3: 1 1_ 4 T4: 0.0343 0.


Shengjun Liu

Shengbing Chen

Hu Meng

A Dynamic Mining Algorithm for Multi-granularity User's Learning Preference Based on Ant Colony Optimization

Keywords: Quotient Space, Preference Mining, Ant Colony Optimization, Personalized Learning, Granular Computing

Mining user's learning preference is one of the key issues in the personalized online learning system, which is of great significance technology for modern educational. In this paper, using the hierarchical characteristics of the knowledge points in the course domain, we defined the equivalence relation and equivalence of knowledge points, and defined the structure of the knowledge points quotient space. Then, the functions of support, pheromone concentration and preference were defined on various levels, and an improved ant colony optimization was proposed to handle the multi granularity data structure of quotient space. An algorithm of multi-granularity Learning Preference Mining based on Ant Colony Optimization (ACO-LPM) was proposed to address the problems about too many learning knowledge points and too few user's test data in the online personalized learning system. The pheromone has the characteristic of dynamic evaporation, so, the preference patterns mined by ACO-LPM can be changed with the change of user interest in real time. The experimental results show that the algorithm can mining the user's learning preferences in online learning system effectively and efficiently.

Online learning, which breakthrough the constraints of time and space, provides a convenient and efficient learning platform for learners. It has become an important means of modern education for its three characteristics: the various learning modalities, the multiple teachers and students' role, and the rich learning resources. Personalized learning is the hotspot of online learning, which makes learners achieve the best learning effect under the minimum time and the best learning experience according to learners' learning characteristics and preference model. Researchers have done a lot of work in this domain. Jiunn used neural network to analyze students' online browsing behavior and get students' learning styles and learning preferences [START_REF] Lo | Identifying Learning Styles by Observing Learners' Browsing Behavior[END_REF]. Du studied the personality traits of learners and the association between learning behaviors, and used the data mining algorithm to obtain the learner behavior model [START_REF] Jin | The Research of Mining Association Rules between Personality and Behavior of Learner under Web-Based Learning Environment[END_REF]. Qiu employed solomon learning style scale for pre-test, and obtained user interest model through the data mining of user learning history [START_REF] Baishuang | Research on user model in adaptive learning system based on Semantic Web[END_REF]. Lin and Yan studied the news recommendation in the mobile network environment, constructed the keyword vector by using the spatial model, and clustered the document according to the similarity degree, to obtain the gravity vector of each document cluster and build the user preference model [START_REF] Hongfei | The user table and update mechanism[END_REF][START_REF] Shukui | Design and implementation of user interest extraction system for mobile network news[END_REF]. Ren put forward a U-I-C user interest model, which obtained scenario user preferences by adding the scene information in the user-project matrix [START_REF] Ziting | Research on Personalized Recommendation user interest modeling in mobile environment[END_REF]. Wang put forward the idea and method of user preference based on ontology and label [START_REF] Hongming | Design and implementation of user preference extraction system based on ontology and tag[END_REF]. Wolfgang studied the scenario information of the users in the mobile environment and found the user preference information by using the collaborative filtering algorithm [START_REF] Woemd | A Hybrid Recommender System for Context-aware Recommendations of Mobile Applications[END_REF]. Chen presented logistic curve model and hyperbolic model to analyze the user behavior, and proposed a user preference model based on multi-vector tree [START_REF] Shuran | Research on personalized service oriented user interest modeling and Application[END_REF]. Pazzan used the expected information gain to analysis the annotations of users when they were browsing the pages and get user interest preferences [START_REF] Pazzani | Learning and Revising User Profiles: The identification of interesting Web sites[END_REF]. Adomavicius mined the user's individual access records to construct the user model by using the associated association rules and user's personal information [START_REF] Adomavicius | Using Data Mining Methods to Build Customer Profiles[END_REF]. These methods have greatly improved the efficiency of the users' preferences in different backgrounds and applications. However, because of the characteristics of the massive knowledge points and the few test data, the problems of learning preferences on knowledge points has not been solved yet, and becomes a hot issue.

Granular computation can reduce the complexity of the solution, which inspired by mankind who can solve complex problems at different levels and solve them at the appropriate size [START_REF] Zhang | Dynamic Quotient Space Model and Its Basic Properties[END_REF]. In recent years, Granular computation (such as quotient space, rough set and fuzzy set ) have been successfully applied to complex problems in many fields such as industrial control, transportation, graphic image processing, decision support, and biological information [START_REF] Wang | An overview of granular computing[END_REF]. With the consideration of all the facts ( the universe, structure, projection, etc.), quotient space can meet all needs of online learning system, such as the domain of knowledge and the dependence analysis. In this paper, we used quotient space theory to explore a dynamic mining algorithm for user's learning preference based on Ant Colony Optimization.

Quotient Space Structure of Knowledge Points

In online learning system, each knowledge point corresponds to a concept, which comes from the domain knowledge ontology, the knowledge points have a certain hierarchical structure and complex dependencies. As shown in Figure 1, there is a inclusion relation between the knowledge points in different level. At same level, the knowledge has three relationships: pre-order, brotherhood, and equivalent. The knowledge point KP is defined as follows: Definition 1: Knowledge point K is a triple (C, T, f), where C is the corresponding concept of knowledge point K; T is the topology of various relationships between knowledge points. The online learning system mainly has inclusion and pre-order structure; f is the property of knowledge. Definition 2: If the knowledge of the learning point K i need to use the knowledge point K j , then K j is K i 's pre-order knowledge (also known as preparatory knowledge), expressed as K j < K i . Definition 3: If K u is the upper level knowledge point of K i and K j , and C u ⊇C i + C j , T u ⊇T i + T j , then K u contains K i and K j , K i and K j are brotherhood relations, K u is K i and K j 's Father knowledge points. Since K u is not the same level as K i and K j , the internal relationship between K i and K j disappears naturally in the upper level, so T u ⊇T i + T j is the operation of the parent node K u level.

From the above definition we can see that if the brotherhood of the knowledge points is denoted by R, then R has both reflexivity, symmetry and transitivity, that is, R is the equivalence relation. Using the brotherhood relationship R constructs the equivalence class, the new triples ([C],[T],[f] ) form a larger granularity of knowledge space, which is identified by the parent knowledge point.

Constructing the equivalence class mapping p: (C, T) → ([C], [T]

) which is the continuous natural projection of the knowledge point concept space. So, it meet the conditions of false warranty principle and fidelity principle define as follows:

1. False warranty principle: If the problem in the quotient space has no solution, it must also has no solution in any finer space. 2. Fidelity principle: Assuming that the problem has solution in the semaphore quotient space {C1, T1, f1}, {C2, T2, f2}, then it has a solution in its synthetic quotient space {C3, T3, f3}.

Using the false warranty principle and the fidelity principle, we can mining users' preferences on knowledge points in different granularity. The number of knowledge points were reduced by the equivalence. At the same time, the data sparse problems have also been cut down because the equivalent knowledge points have larger size.

The knowledge point space constructed by the brotherhood relationship R is shown in Fig. 2, the top is the root node, and its child node is called the inner node (corresponding to the equivalence class which R constructed). The inner node can also contain other inner nodes (finer equivalence), the bottom level is the leaf node, corresponding to the specific knowledge point. We call the sub-nodes of the root for the 1th level, the sub-nodes of the 1th level node for the 2th level, and so on, the specific knowledge points for the n-th level. In the structure of quotient space shown in Fig. 2, each level is a set of equivalence of a certain granularity. When a user study a leaf node (a specific knowledge point), each inner node on the path is considered to be accessed.

Functions Definition of Multi-granularity Ant Colony Optimization

Ant Colony Optimization

Ant Colony Optimization (ACO) proposed by Dr. Marco Dorigo who inspired by the natural ant colony foraging process. Ants can find the shortest path from the food source to the nest in un-visual conditions. During the foraging process, the ants release pheromone, which is proportional to the quality of the food source, and sense the pheromone. Then ants tend to move in the direction of high pheromone concentration. Thus, the group behavior of the ant colony shows a positive feedback: the more ants travel on a path, the more probability the latter choose the path, so, the path with good quality and short distance will attract more ants, and the pheromone concentration grows faster [START_REF] Chen | Offensive Strategy in the 2D Soccer Simulation League Using Multi-group Ant Colony Optimization[END_REF].

Functions Definition of ACO in Multi-granularity Data

In order to apply the ACO to the mining of multi-granularity knowledge points of hierarchical structure, several functions such as support degree, pheromone concentration and preference function in ACO are defined as follows:

Boot K11 K12 … K21 K22 K23 K24 K2b … Pk Pk+1 … 1th level 3th level K1a 2th level
Definition 4: Support  l represents the probability that the user accesses path l, ex- pressed as the access frequency of the path. Let K ij 、K im be the two knowledge points (equivalence class) nodes of the i-th level, the number of nodes in i-th level is n i , path l(i, j, m) represents a certain path from node K ij to node K im , the number of times the user visits the path l(i, j, m) is expressed as C l (i, j, m). Then for the hierarchical interest pattern, its support  l (i,j,m) is:

 l (i,j,m) =   n k l l i k j i C m j i C 1 ) , , ( ) , , ( ( 1 )
Definition 5: The pheromone concentration  l (t) represents the user's interest in access to a path l, fades with time and increase with the user accesses the path l.

 l (t+1) = (1-) l (t) +⊿ l ( 2 ) In formula (2),  is pheromone volatilization coefficient,  is pheromone concentration increment adjustment coefficient, ⊿ l is pheromone increased value, the formula is as follows:

⊿ l = ) ( 1
) ( t t F t t l b l       ( 3 )
In formula (3), F b is the feedback value of the user's access to the node at time t+1. Definition 6: The preference function P l (t) represents the user's preference for path l, including two factors which are pheromone and support. Let  w (t),  w represent the pheromone and support of w-th path. Then the preference function P l (t) is defined as:

             n 1 w ) ( ) ( ) (         w w l l l t t t P ( 4 )
In formula (4), n is the total number of paths in this level. If the threshold of the preference function is given and the value of the preference function P of all the path nodes is checked by (4), then the user's learning preference on multi-granularity knowledge points can be obtained.

Dynamic Mining Algorithm for Multi-granularity Learning Preferences

In this paper, we use the dynamic volatility of pheromone to dynamically mining the learning preferences on multi-granularity knowledge points. The main idea is that the user's knowledge learning activity corresponds to the ant's foraging behavior, the process of the users' learning knowledge points corresponds to the ants' foraging activity cycle. All of the users' learning actions are recorded in log files. Based on these log files , we can use ACO to find preferred function values of path nodes that formed by all knowledge points, and dynamically mine learning preferences for each knowledge point of the users, and then offer the users needed content for further study. Multi-granularity Learning Preference Mining algorithm based on Ant Colony Optimization (ACO-LPM) described as follows: Input: Pre-processed m learning records, preference function threshold T p Output: learning preferences H of all levels in Multi-granularity knowledge space

Step 1: Initialize the ACO parameter, t=0,  l (t)=0,  l =0, C l =0;

Initialize the knowledge point space tree, Tree=NULL; Initialize the learning preference set, H = (H In step 4 of the above algorithm, Tree.High represents the height of the tree Tree.

The time complexity of the algorithm: according to the definition of the quotient space structure, if the average of each equivalence class is composed of m elements, then the number of nodes in the upper lever is m 2 times less than the number of nodes in the lower level in the multi-granularity knowledge quotient space structure. While the time complexity of standard ACO is O(I * N 2 * k), in which I is the number of iterations, N is the number of vertices, and k is the number of ants in the ant colony. So the time complexity of the i-th level is O(I *( N/m 2i ) 2 * k), which has a greater reduction compared to the original space. Meanwhile, because the data of user online learning is the same, so the training data of each node in i-level increased m 2i times, which can effectively solve the problem of sparse data in the online learning system.

Experiment and Result

This section verified the effectiveness of the multi-granularity mining algorithm from two aspects: dynamic mining process display and practical application system. The experimental data was got from the online learning system, and the user learning behavior is recorded in log files. In order to record the user learning behavior, we added knowledge points information on the basis of the W3C extended log ExLF, which format is: "c-ip date time cs-username cs-method cs-uri-stem cs-user-agent sc-knowledgepoint cs-iscorrect sc-status sc-bytes". For example: "205.12.15.179 [01/Jul/2015:09:10:12] utest1 'GET /index/ course/?courseid=10379 HTTP/4.0' M050311 0 200 598". Table 1 listed the log identification and description:

Dynamic Change Process Experiment of User Learning Preference

Dynamic mining is one of the advantages of the ACO-LPM algorithm. We took some log information to do simulation test on the situation of user's interest change. After the pre-processing of log records, knowledge that involved in one learning action combined with the case of right or wrong answer (F: wrong, T: right) as a record, such as: "K01F" represents the answer of knowledge K01 is wrong. Selected part of the knowledge of learning behavior data as follows: Assuming that the minimum support is 2, the traditional mining algorithm will get frequent itemsets in consideration of the correctness of the answer{{K01K02: 3}, {K03K07: 2}}( In order to facilitate the expression, we call item {K01K02} for item-set1, item set {K03K07} for itemset2). However, if you carefully analyze the data flow, you will find two facts: First, In the long run, the user has the preferences of knowledge point K01K02, itemset1 coincidence occurs 3 times, and the error times with the correct times of 2: 1; Second, recent user learning has changed, itemset1 did not appear in the last 5 learning, but the itemset2 appeared 2 times, indicating that the the user was concern about itemset2 recently(Comprehensive practice of knowledge points K03 and K07). In order to show the changes of frequent item sets more clearly and intuitive, we added value of pheromone volatilization coefficient and concentration incremental coefficient , the parameters of ACO are defined as:= 0.15, = 0.3,  = 1,  = 0.5.

The preference function curve of each item is shown in Fig. 3. It can be seen that at the beginning of the training, the preference function value of the node is exponentially increasing due to the user's continuous error at the knowledge point K01K02, and the preference function value of the other knowledge points is zero; In the second half of the training, the knowledge point K01K02 is gradually reduced because it has not been accessed, but the preference value of the knowledge point K03K07 is gradually increased with the access, and even more than the knowledge point K01K02. It can be concluded that this algorithm can dynamically capture the current user's preference when the user's preference changes.

Experiment Data Analysis of Practical Application System

In order to verify the effectiveness of this method, we use the learning resources and system logs of the online education online system of Anhui Education Publishing Network Company (http://www.timeep.com/cms/index.html) on line in 2015 .We have extracted the user learning logs of junior high school mathematics, physics, chemistry, a total of 12,000 log information, and recorded them in Math, Physics, Chemistry three data sets. After preprocessing above data, we used the current mainstream mining algorithms: BP neural network (referred to as the BP-NN) and FP-growth algorithm for mining frequent items (referred to as FP-growth), and multi-size paper ACO-LPM learning preferences mining Algorithm, respectively, to test and verify the accuracy rate. In order to unify the format, ACO-LPM algorithm in this paper only mine for the first three granularity level. We set the ACO parameters as: = 0.05, = 0.5,  = 1,  = 0.5. The accuracy of each learning preferences mining algorithms is shown in the following table: It can be seen from Table 3 that the ACO-LPM mining algorithm in this paper is superior to BP-NN and FP-growth algorithm as a whole. And with the level of the study increased, the accuracy of mining increases. This situation is not random, but by the character of hierarchical structure user interest, the higher the level, the user interest model contains more content, and the more vague concept, the higher the hit rate.

Conclusion

In online learning system, it is a great challenge to mining learning preferences on knowledge points because of the massive knowledge points, the few single user test data, and the change of user's learning preferences. Based on the Multi-granularity feature of knowledge points, this paper defines a quotient space structure of the knowledge points. On this basis, the ACO is introduced into the quotient space, and the multi-granularity dynamic mining algorithm is proposed by using the characteristics of pheromone dynamic volatility. Experiments show the effectiveness of the method.

Fig. 1 .

 1 Fig. 1. Topological relationship of knowledge points in junior middle school

Fig. 2 .

 2 Fig. 2. The structure of knowledge points quotient space with brotherhood relationship

Fig. 3 .

 3 Fig. 3. The curve of user learning preference function

  1 ,…, H n ) = NULL;Step 2: According to the domain knowledge ontology, construct the quotient space tree of domain knowledge Tree;Step 3: FOR (i=1 to m )Find the first i log records corresponding to the path l in the tree Tree; Calculate F b of all nodes on path l, update C l ,  l (t).

	END FOR
	Step 4: Calculate  l of all path nodes from equation (1);
	Step 5: Initialize , 
	FOR (i=1 to Tree.High)
	Calculate P

l (t) of all path nodes in the i-th level; If P l (t) >T p , then P l (t)→H i ; END FOR Step 6: Return H.

Table 1 .

 1 The log format definition based on W3C extended standard

	Field identifier	Description
	c-ip	IP address of client that accessed the server
	date	The date when the transaction was completed
	time	The time when the transaction was completed(use UTC standard by default)
	cs-username	Authenticated user name (anonymous user with '-')
	cs-method	Action method that the client performed(GET and POST)
	cs-uri-stem	Full URL of the data source that been accessed
	cs(Referer)	Previous URL that the user browsed, the current page is linked from the URL
	cs-user-agent	The browser version information used by the user client
	sc-knowledgepoint	The knowledge points that the user visited
	cs-iscorrect	Answer correctness (1 is correct, 0 is wrong)
	sc-status	Returned status after the execution, described by HTTP or FTP
	sc-bytes	The number of bytes sent by the server to the client

Table 2 .

 2 The data of user learning behavior

	Learning Record	Knowledge point learning situation list
	S001	K01F, K02F, K08T
	S002	K01F, K02F, K06T, K09T, K12T
	S003	K01T, K02F
	S004	K01T, K02T, K07F
	S005	K01T, K04T, K05F, K07F, K10T
	S006	K03F, K07F, K08F
	S007	K03T, K05T, K07F, K08F
	S008	K02T, K03T, K07F, K08T, K15T
	S009	K01T, K03T, K07T, K11T

Table 3 .

 3 Accuracy comparison of the ACO-LPM with other algorithms

	Knowledge field	BP-NN	FP-growth	Original space	ACO-LPM Level-2	Level-1
	Math	69.5%	72.3%	73.7%	80.2%	85.7%
	Physics	66.8%	68.6%	70.1%	75.5%	80.9%
	Chemistry	66.2%	67.0%	68.7%	76.1%	81.7%

Acknowledgments

This work was supported by the National Natural Science Foundation of China(Grant No. 61672204), Natural Science Foundations of Higher Education Institutions of Anhui Province (Grant Nos. KJ2012B149, KJ2013A226, KJ2015A229, KJ2015A257), Key Projects of Domestic Visiting and Training for Middle-aged and Young Scholar in Anhui Province (Grant No. gxfxZD2016211).


Xin Zhang

Zhiying Yang
email: zzyang@shmtu.edu.cn

An Improved Algorithm for Redundant Readers Elimination in Dense RFID Networks

Keywords: Radio Frequency Identification (RFID), Redundant Reader Elimination, CBA, RRE, LEO

In a dense RFID sensor network system, in order to prolong the life of the sensor network and reduce the interference between readers and the problem of eliminating redundant readers has always been the concern of the people. Now there are many algorithms and optimization technology to eliminate redundant reader in the RFID system. Inspired by the previous algorithms such as SCBA and RRE, this paper presents an improved algorithm, ICBA(Improved Count Based Algorithm) which based on the RRE and SCBA, using the count of covered tags and the Greedy Algorithm to eliminate redundant readers. The simulation experiments show that the algorithm ICBA can eliminate more redundancy readers than other algorithms. The same case, ICBA algorithm compared with other algorithm is increased by 3.2% ~ 15% of redundant reader eliminating rate.

Introduction

Radio Frequency Identifier (RFID) is a kind of commonly used wireless communication technology, which can read and write target's data through the radio signal, and in the identifying process the identify system does not need to establish any contact with the identified targets. Because the RFID system is low cost, high safety, large capacity, nowadays it is widely used in all aspects of people's life, such as automation and identity identification, access control, supply chain inventory tracking. In a densely deployed RFID network, redundant readers can cause a waste of energy and easily lead to mutual interference, because the radio frequencies of two or more neighboring readers may overlap and interfere. Therefore how to use the minimum number of readers effectively cover every tag, which is called redundant readers elimination problem. Redundant readers elimination problem is a basic problem in the RFID system, which has proved to be NP -hard problem [START_REF] Bogdan | Redundant-Reader Elimination in RFID Systems[END_REF] .

In this paper, an Improved Count Based Algorithm(ICBA) is designed to detect and eliminate more redundant RFID readers in the complicated RFID networks. ICBA is based on the every tag's count of readers which covered this tag and the every reader's count of tags which are covered by this reader. Simulation results illustrate that ICBA has a better capacity to detect more redundant readers, a maximum of up to 15% more effectively than LEO and RRE in the conditions we set,and increased by 3.2% of redundant reader eliminating rate compared with the CBA which is a good algorithm for redundant readers elimination.

Relevant Research

The RFID system mainly is composed by two components, tags and readers. The RFID tag can store information and be detected by readers .The readers can read and write data which stored at tag's memory through radio signal. For reader's convenience, a definition of redundant reader is given as following.

Definition of redundant reader: In a RFID network, a RFID reader covers a group of RFID tags, which are also covered by the other RFID readers simultaneously. This reader is called as the redundant reader [START_REF] Ma | A novel distributed algorithm for redundant reader elimination in RFID networks [C] // RFID -Technologies and Applications[END_REF][START_REF] Yu | A density-based algorithm for redundant reader elimination in a RFID network[C]//Future Generation Communication and Networking[END_REF][START_REF] Shen | Three-Count Based Algorithm for Redundant Readers Elimination in the Complicated RFID System[END_REF] .

As the simulative RFID network showed in Figure .1, Tag T1 and tag T2 are covered by reader R1 and they also are covered by R2. All tags in R1 are covered by other readers, so the R1 is a redundant reader. Similarly, R2 and R3 are also redundant readers due to the same reason [START_REF] Shen | Three-Count Based Algorithm for Redundant Readers Elimination in the Complicated RFID System[END_REF] . Although all the readers are redundant readers in this RFID network, we can only eliminate at most two redundant readers without affecting the work of the RFID system. Nowadays there are many algorithms and optimization technique to eliminate the redundant reader of the RFID system. Some introduction and analysis of algorithms for eliminating redundant readers are given as following:

Algorithm RRE

The Redundant Reader Elimination(RRE) is the first distributed algorithm for eliminating redundant readers, which was proposed by Carbunar B, Ramanathan M K et al in 2005 [START_REF] Bogdan | Redundant-Reader Elimination in RFID Systems[END_REF] . The main idea of Algorithm RRE is using the greedy algorithm: If a tag T1 is covered by multiple readers, find the reader which covered T1 and covered most

R2 R3 T2 R1 T1 T3
of tags, then set T1's holder to the reader's ID. With this strategy, the tags will be greatly covered by the few readers and the goal of eliminating some redundant readers can be reached. Algorithm RRE steps are described below: ①Each reader delivers write commands that write the tagcount(the number of tags which are covered by this readers) to all tags which this reader covers. Each tag stores only the highest value of tagcount that it received and the ID of the corresponding reader.

②The reader queries each of the tags in the inquiry area and reads the tag's holder of each tag in the coverage area. A non-redundant reader should be locked at least to one tag and this reader will remain active. While readers that do not lock any of the tags are marked as redundant readers and can be safely turned off.

Figure.2 An Example of RFID system network

The RRE algorithm has its flaw that the selection of tag's holder is random and unreliable [START_REF] Shen | Three-Count Based Algorithm for Redundant Readers Elimination in the Complicated RFID System[END_REF] . Because when a tag is in the overlapping areas of two or more reader and the number of tags covered by the two or more readers is the same, each reader of them has a same chance to become this tag's holder and this tag randomly chooses a reader of them as its holder. In Figure .1 R1 and R2 have same chance to become the holder of T1,T2 and T3 according to Algorithm RRE, because R1's tagcount is same as the R2's tagcount. R1 and R2 randomly become the holder of these tags. The time-complexity of RRE is O(n logβ logm ) m is the number of readers, n is the number of tags and β is the bit length of RFID tag identifiers [START_REF] Bogdan | Redundant-Reader Elimination in RFID Systems[END_REF][START_REF] Hung | The First Search Right Algorithm for Redundant Reader Elimination in RFID Network[END_REF] .

Algorithm LEO

In order to effectively reduce the read and write operations of readers, Ching -Hsien et al proposed the LEO (Layered Elimination Optimization) algorithm [START_REF] Hsu | A Layered Optimization Approach for Redundant Reader Elimination in Wireless RFID Networks[END_REF] .This algorithm is based on the principle of "first come, first served".

Algorithm RRE steps are described below: ①RFID reader (Ri) delivers a query signal that check the tag's holder (holder) to all tags in its query area and the tags respond the corresponding readers.

②There are two possibilities for receiving a tag response, the tag's holder = "NULL" or the tag's holder = "Rk". If the holder = "NULL", the reader delivers a write command that set the reader's Id to the tag holder. If the holder = "Rk" and Rk ≠ Ri , the reader skips the reply information of this tag.

T1 R1 R2 T2 T3
③When a reader receives all tags' response that their holder ≠ Ri, that means all tags have belonged to other readers, the reader is a redundant reader, close the reader.

LEO algorithm can effectively reduce the read and write operation compared with other algorithms [START_REF] Ma | A novel distributed algorithm for redundant reader elimination in RFID networks [C] // RFID -Technologies and Applications[END_REF] . But the LEO algorithm randomly determines the holder of the tag, so the algorithm is unreliable. In Figure .1 R1 and R2 have same chance to become the holder of T1,T2 and T3 and which is the holder depends on the first query command. The time-complexity of LEO is O(mn), m is the number of readers and n is the number of tags [START_REF] Ma | A novel distributed algorithm for redundant reader elimination in RFID networks [C] // RFID -Technologies and Applications[END_REF] .

Algorithm CBA

Algorithm CBA is based on the number of covered readers and involves the influence of the neighboring readers. In algorithm CBA, the redundant readers will be eliminated according to the sum of the count of the neighboring readers and the count of tags covered [START_REF] Pan | A Count Based Algorithm for Redundant Reader Elimination in RFID Application System, Intelligent System Design and Engineering Applications(ISDEA)[END_REF] .

Algorithm RRE steps are described below: ①RFID reader (Ri) delivers a query signal that check the tag's holder (holder) to all tags in its query area and records the number of tags covered by this reader as tagcount. Each tag records how many readers cover it and writes this value as its readercount.

②If the reader queries a tag's readercount = 1 in the inquiry area, change the holder of all tags that are covered by this reader to the reader ID. If there is readercount> 1 for all tags in the reader's query area, mark the reader as a redundant reader and close the reader. Decrement the reader count of all tags in the query area.

③When all readers in the RFID system have run step 2,the algorithm ends and some redundant readers are eliminated.

Algorithm CBA involves the influence of the neighboring readers and finds the unredundant readers in the first step while RRE and LEO don't, but the RRE algorithm also has its flaw that the elimination rate is connected with the order of reader selection. reader selection. The time-complexity of CBA algorithm is O(mn 2 ), m is the number of readers and n is the number of tags [START_REF] Pan | A Count Based Algorithm for Redundant Reader Elimination in RFID Application System, Intelligent System Design and Engineering Applications(ISDEA)[END_REF] .

Algorithm ICBA

Algorithm ICBA (Improved Count Based Algorithm) is designed by improving CBA and RRE. Algorithm ICBA concerns about the influence of the neighboring readers while RRE and LEO don't. In algorithm ICBA, the redundant readers will be eliminated according to readercount from small to large order while CBA don't. So Algorithm ICBA can solve the problem that the elimination rate is connected with the order of reader selection in the Algorithm CBA and the problem that the selection of tag's holder is random and unreliable in Algorithm LEO and RRE.

Algorithm ICBA

Input: The information of readers and tags, such as reader coordinates and tag coordinates(The value of readercount and tagcount will be calculated by the reader coordinates and tag coordinates ).

Output: The ID of redundant readers and the number of the redundant readers

Process

Algorithm ICBA steps are described below: ①The first step is information gathering phase. Every reader broadcasts a query command to tags which are covered by this reader and the reader records the number of tags it covers as the value of tagcount. Meanwhile each tag records the number of readers cover it as the value of readercount. Then readers deliver a query command again and readers send the data of their tagcount and every tag's readercount to the centralized processing host.

② All tags are sorted by the value of readercount from small to large in the centralized processing host. Meanwhile, the centralized processing host assigns a list of reader to each tag that used to record which readers cover this tag.

③According to readercount from small to large order, find every tag's holder. If the tag's readercount=0,it means no reader cover this tag and we set its holder to -1. If the tag's readercount=1,it means this tag is only covered by a unique reader and this reader is non-redundant reader. All the tags in this non-redundant reader's interrogation zones are held by this reader. The tag that has the holdid value only responds to the reader access that corresponds to the holdid value (for other readers do not respond to). When each tag set the tag' holder, the tagcount of the corresponding readers also update. If a reader's tagcount=0,this reader can be safely turned off, because no tag is covered by this reader.

④To those tags whose readercount>=2, using the ideas of RRE greedy algorithm, set the tag's holdid to the ID of reader which is maximum tagcount value and covers this tag.

⑤When all tags have their holders, the algorithm ends and some redundant readers are eliminated.

Analysis

Algorithm ICBA is based on CBA and RRE. Through the order of tags according the value of readercount, the ICBA can solve the problem of the eliminating rate is related with the order of reader selection. And the second step of ICBA is a kind of improved Greedy Algorithm that tags which has holder only respond the query commands of theirs holders, so Algorithm ICBA can avoid the happening of random selection in the RRE. The algorithm firstly calculates the value of readercount and tagcount. The timecomplexity of the first step is O(mn).Then the time-complexity of sorting tags is O(nlogn)(Using the Quicksort).The time-complexity of finding tag's holder and updating the value of other readers is O(mn 2 ).In summary, the time-complexity of ICBA is O(mn 2 ), m is the number of readers and n is the number of tags. The algorithm ICBA has the same time complexity as the algorithm CBA. Compared with other algorithms such as RRE(O(n logβ logm)) and LEO(O(nm)), the ICBA(O(mn 2 )) needs more time but has a better elimination effect.

Simulation Experiments and Analysis

A RFID network simulation environment was designed and then compared with the classic algorithms RRE , LEO and CBA. ICBA algorithm turns out to be highly efficiency.

The following experiment data are all from the average of 3 experiments.

Experiment I

The first experiment is designed to illustrate the performance of different algorithms under different number of tags. At the invariable regional size of 1000 × 1000, randomly deploying 500 readers, and the working radius of readers are 50m. The numbers of tags increases from 1000 to 5000. The following Figure 4 shows the comparison of results among RRE, LEO,CBA and ICBA under the condition of the above variables.

As it is shown in Figure .4, ICBA algorithm can detect and eliminate the most of redundant readers in all situations. As the number of tags increases, the number of redundant readers eliminated by each algorithm has declined. Because more readers might become non-redundant readers, with more tags covered by a certain reader.

Experiment 2

In the second experiment is designed to figure out the performance of different algorithms under different number of readers. At the invariable regional size of 1000 × 1000, we randomly generate 1000 tags and the numbers of readers increases from 200 to 600 and their working distance is 50m. when the number of randomly deployed RFID. The following Figure 5 shows the comparison of results among RRE, LEO,CBA and ICBA under the condition of the above variables. As it is shown in Figure 5, the numbers of redundant readers eliminated in all four algorithms increase gradually when the number of readers deploying in this area is rising. When more readers come into the system, the network becomes denser and the probability of every reader with more neighbors gets larger. ICBA algorithm involves a condition of neighbor count, therefore an increasing number of redundant readers will be detected.

Experiment 3

The third experiment is designed to figure out the performance of different algorithms under different length of working radius of RFID readers. At the invariable regional size of 1000 × 1000, 500 readers and 1000 tags are randomly generated. The length of radius of readers in the experiments are from 20m to 60m. The following Figure 6 shows the comparison of results among RRE, LEO,CBA and ICBA under the condition of the above variables.

As it is shown in Figure 6, the tendencies of all algorithms are similar in this experiment. At the beginning, with the increase of working radius of readers , the count of tags each reader covering gradually rises and the probability of a reader becoming redundant. Later, when the working radius goes up to 30m, more multiple readers might cover the same tags simultaneously, which causes more readers become redundant.

Conclusion

In this paper, we have designed a new centralized algorithm named ICBA (Improved Count Based Algorithm) which based on the value of readercount(how many reader covers this tag) and tagcount(how many tags are covered by this reader) to solve the redundant reader elimination problem in wireless RFID network system.

Algorithm ICBA solves the problems of eliminating rate is relate with order in CBA and the randomness in RRE and LEO. Simulations show that compared to other algorithms the performance in redundant reader elimination of ICBA algorithm is more efficient in most of the situations and maximum of up to 15% more effectively than Algorithm LEO.

The data of simulation experiments is based on the ideal experimental environment. Although it is made close to the deployment of RFID system actual applying environment as much as possible, there is still a difference from the actual situations, which needs further testing. Meanwhile it is also necessary to consider the indoor environment and other interferential factors on the signal attenuation.

Figure. 1

 1 Figure.1 Redundant reader

Figure. 3

 3 Figure.3 An Example of RFID system network

Figure. 4

 4 Figure.4 Performance comparison of each algorithm under different number of tags.

Figure. 5

 5 Figure.5 Performance comparison of each algorithm under different number of readers

Figure. 6 Figure. 7

 67 Figure.6 Performance comparison of each algorithm under different radius of readers


Hengzhen Zhang
email: zhanghz@shmtu.edu.cn

Lihua Lu
email: lulihua@sdju.edu.cn

Xiaofeng Wang
email: xfwang@shmtu.edu.cn

Two Stages Empty Containers Repositioning of Asia-Europe Shipping Routes Under Revenue Maximization

Keywords: empty containers repositioning, laden containers transportation, across-region, revenue maximization, dynamic programming

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Containers transportation has increasingly become popular in international trade transportation activities in the last few decades. As various reasons consisting of the history, political and economy the world trades are getting more imbalanced in recent years especially the Trans-Pacific and Asia-Europe shipping routes [START_REF] Unctad | Review of maritime transport[END_REF]. The growth of containerized shipping has presented challenges inevitably, in particular to the management of empty containers arising from the highly imbalanced trade between continents. Some ports have accumulated many surplus empty containers, which are called the surplus ports. At the same time, other ports (deficit ports) need many empty containers to load the cargoes and have to lease some ones to meet the customers demand. For example, the Europe region can be regarded as surplus region while the Asia is deficient region. Under such imbalanced situation, efficiently and effectively repositioning empty containers by using the residual vessel space has become an important strategy to fortify the competitive market of liner company.

Basically, the liner shipping has fixed service route with a number of vessels deployed in fixed schedules with the weekly service frequency. Reasonable arrangement plan of empty and laden containers not only guarantee the arrival of laden ones on time but ensure to reposition empty containers as possible as reducing the operation cost. However the factors influencing the ECR policy are rather complex including the sail schedule, various costs, laden containers, vessel capacity etc. So this problem is a typical combinational optimization problem.

2

Literature review

There have been many literatures related to the ECR. Crainic et al. [START_REF] Crainic | Dynamic and stochastic models for the allocation of empty containers[END_REF] consider the factors of long-term leasing containers for attacking the dynamic random ECR. Cheung and Chen [START_REF] Raymond | Two-stage stochastic network model and solution methods for the dynamic empty container allocation problem[END_REF] model the ECR as a two-stage stochastic optimization model. They propose a time-space network model which is the opening the maritime ECR network modeling for discussion. Lam et al. [START_REF] Shao-Wei | An Approximate Dynamic Programming Approach for the Empty Container Allocation Problem[END_REF] apply the actual service schedule so that the general networking techniques to the shipping industry can be developed. Bell et al. [START_REF] Bell | A frequency-based maritime container assignment model[END_REF] focus on the assignment of laden and empty containers over a given shipping service network. Erera et al. [START_REF] Erera | Global intermodal tank container management for the chemical industry[END_REF] and Brouer et al. [START_REF] Brouer | Liner shipping cargo allocation with repositioning of empty containers[END_REF] confirmed the economic benefits of simultaneously considering laden and empty containers when modeling cargo allocation in a shipping network. Both above literatures do not consider the asynchronism between planning repositioning and actual repositioning. Song and Dong [START_REF] Song | Flow balancing-based empty container repositioning in typical shipping service routes[END_REF] formulate the problem of ECR for general shipping service routes based on container flow balancing. Imai et al. [START_REF] Imai | Multi-port vs. hub-and-spoke port calls by containerships[END_REF] and Meng and Wang [START_REF] Meng | Liner shipping service network design with empty container repositioning[END_REF] are the only two papers found in the related literatures that explicitly consider ECR decisions together with shipping network design or ship routing, in which the shipping routes are limited within a few pre-specified options.

To incorporate uncertainties in the operational model, Long et al. [START_REF] Long | The sample average approximation method for empty container repositioning with uncertainties[END_REF] formulate a twostage stochastic programming model with random demand, supply, ship weight, and ship capacity.

To solve long-haul journey of ECR, we firstly divide one circle journey into several stages to attack the asynchronism of the planned repositioning and actual repositioning. Through analyzing the numerical changes of empty and laden containers in ports and on board in different stages, we can dynamically redistribute the empty containers to meet the current actual demand of different ports. Then the dynamic region-to-region ECR(DRR-ECR) optimization model is built. Fig. 1 shows the details of liner shipping, ports and regions, where the circle represents the port, the directed arc the two successive visited ports, and the rectangle the surplus or deficit region. Since the Asia is the deficit region, it sends the total number of demand empty containers of all ports to the control center in stage 0 t . Then each port in Europe firstly loads the laden containers to the vessel according to the control center. The number of loading empty containers of each port equals to the minimum value of demand ones of Asia, supply ones of Europe and residual vessel space in 0 t . Meanwhile the number of con- tainers in each leg of liner shipping should be less than the vessel space. In addition, the empty containers in surplus ports which do not temporarily be transported to the Asia or other ports will incur the storage costs.

Phase 1 t : on board stage

After the liners successively visit the ports in Europe, they will sail into stage 1 t . In this stage, the numbers of empty and laden containers on board are unchanged until the liners reach the first port of Asia: Shanghai. But note that the demand or supply empty containers in all ports are constantly changing with the time evolving because the other liners may visit the corresponding ports. This will cause the changes of ports states and their number of empty and laden containers.

Phase 2 t : dynamic repositioning stage

When the liners arrive the first port of Asia in 2 t , it can observe that the actual states of ports have changed compared with the corresponding ones in 0 t . It is mainly caused by the unpredictable demand/supply in ports. For the laden containers, which reach the destination ports, they will be discharged and release their space. For the empty containers, the control center will dynamically redistribute and discharge them to meet the actual demands. Moreover, the vessels also load the laden containers, which will be shipped to their corresponding destination ports in Europe.

Phase 3 t : return trip stage

The liners visit each port in Asia and discharge the laden and empty containers as well as loading the laden ones whose destination ports are the ones in Europe. In general, it does not involve the problem of ECR since the Asia region is the deficit one. The above four phases go round and round until the shipping line is altered caused by the tactical level or the seasonal change.

In this paper, we firstly consider the ECR coupled with the laden containers transportation in shipping service. Then the one circulation trip is divided into four phases to analyze the changes of empty/laden containers including the ports and vessels. Finally a maximized revenue optimization model: DRR-ECR is proposed to dynamically solve the empty/laden containers transportation problem.

The remainder of this paper is as follows. In the next section, the ECR and laden containers transportation problem are formulated. In the subsequent section, it presents the experimental results. Finally, conclusions are drawn and future researches are derived.

3

Problem formulation

Problem description

In this paper suppose that all the proposed models are subject to the following assumptions. The schedules of services are given and fixed in advance. In the different stages, the numbers of deficit/surplus empty containers in all ports have been dealt with by the branch lines and they refer to the integrate ones. In 0 t , the number of surplus/deficit empty containers in their corresponding regions is also known a prior according to the history data of ports. There is no limit on the number of leasing containers for each port in any moment. All the containers are measured in TEU. The demand empty containers must be satisfied. All costs are calculated in dollars.

Parameters

To simplify the narrative, the following notations are introduced to formulate the containers transportation problems. tr ij CL : transportation cost of unit laden containers from i to j .

tr ij PL : profit of unit laden container from i to j .

Capacity : the vessel capacity.

Decision variables

According to the problem, we give the decision variables as follows. sA : number of storage empty containers in Asia port i and in 2 t .

Objective function

The objective function of our paper is to maximize the total profit minus the various costs. The formal model is described in [START_REF] Unctad | Review of maritime transport[END_REF], which is subject to constraints (2)- [START_REF] Long | The sample average approximation method for empty container repositioning with uncertainties[END_REF]. The first two terms in [START_REF] Unctad | Review of maritime transport[END_REF] represent the profits between Europe and Asia. The next two terms describe the corresponding transportation costs, loading costs and discharging costs. The fifth and sixth terms show various costs in Europe region. The last three terms explain the cost of redistribution empty containers from liner to the ports of Asia, storage cost of additional ones and leasing cost of deficit ones.

0 3 0 3 0 0 2 , , , , , , , , * *( )

4

Experimental Results and Analysis

Dataset

Table 1. Characteristics of the instances.

Instance subset transportation cost in t 3 demand of Europe leasing cost of Asia base instance

I11 I12 I13 I14 I15 +5% +10% +20% +50% +70% - - - - - - - - - - I21 I22 I23 I24 I25 - - - - - [-10%,10%] [±10%,±20%] [±20%,±50%] [±50%,±80%] [±80%,±100%] - - - - - I31 I32 I33 I34 I35 - - - - - [-10%,±10%] [±10%,±20%] [±20%,±50%] [±50%,±80%] [±80%,±100%]
To evaluate DRR-ECR, one case, which simulates the shipping company business, is conducted in this section. Assume that the surplus region has 5 ports and the deficit region 4 ports. The number of transportation laden containers from surplus region to deficit one or in the opposite direction are randomly ranged from 100 to 200. Both two directional laden containers flows have the same profits which is random generated within [650,1500].The laden containers transportation costs are changed between 100 and 150 since they are different among ports. Suppose that they discount 20%~30% compared with the transportation costs of laden containers and are within [20,50].The supply empty containers in surplus region are from 150 to 400 and the demand ones in deficit ports [150,200] in 0 t as the imbalance exists. The actual demand empty con- tainers in deficit region in 3

t are the fluctuation with 5%  based on the supply ones in surplus region in 0 t , where the positive value represents the increasing rate and the negative one the decreasing rate. To test the performances of DRR-ECR, many instances with varying characteristics are generated. The entire set of instances is divided into some subsets so that different cost parameters influencing the total cost can be tested. We refer to the data generated with the above method as base instance. The other instances are deduced from it. Table 1 show the details for each subset, where instances I11-I15 are changed in the transportation cost, instances I21-I25 the demand of Europe and instances I31-I35 the leasing cost. The symbol "-"' represents the corresponding values are the same as the base instance.

Compared model

To discuss the DRR-ECR, a static port-to-port ECR model (SPP-ECR) is also designed. .Its objective function and constraints conditions are given as ( 13)-( 21). We use CPLEX 12.6 to solve these mathematical models. For each instance, we run 10 replications and achieve the average value. Two indicators are employed to analyze them. One is the values of objective functions. Another is the decreasing rates of total cost for 8 extracted instances which are the combination of four parameters.

0 3 0 3 0 0 0 , , , , , , , * *( ) *( ) *( ) ( ) *

Objective function

Table 2 gives the details when various costs fluctuate in deficit region. In the third line of Table 2, the cost, total profit and net profit of the base instance are first shown in order to compare with other instances. As we do not change the numbers of laden containers transportation in the whole operation and they are less than the vessel space, all the laden containers can be transportation from Europe to Asia. The instances I11-I35 have the same total profits in column 3 and 6.And it is no difficulty to observe that the DRR-ECR is always superior to the SPP-ECR with no relationship with any cost parameter. The reason is mainly that both the storage costs and leasing costs in DRR-ECR are lower than the ones of ECR-SPP because we use the dynamic redistribution strategy in 3 t .

4.4

The cost, total profit and net profit under different circumstances Considering with the actual numbers of ECR, which is limited by the residual space of liners excluding the laden containers, the supply ones of surplus region in 0 t ,and the demand ones of deficit region in 0 t and 2 t ,we divide the whole process into two stages. The first stage is planned stage and the second stage the implementation stage. We combine with these parameters and deduce eight instances shown as Table 3 describes that the whole demand of all ports in deficit region is larger than the residual capacity. Table 4 describes the details. From it, we can discover that the RTEC always equals to the minimum value of RC, the whole supply empty containers of surplus region in 0 t , and the whole demand ones of deficit region in 0 t .

Table 3. Eight combined instances according to the whole supply empty containers in surplus region in 0 t , the demand ones in deficit region in 0 t and in 2 t .

Compared with other instances, both I45 and I46 have higher costs reducing, because the RTEC, which equals to the surplus empty containers of surplus region in 0 t , fluctu- ates wildly than the demand ones of deficit region in 2 t .So, for SPP-ECR it directly leads to that both the storage cost in deficit region and the transportation cost increase. However, for DRR-ECR the dynamic redistribution policy among the ports of deficit region brings down the storage cost to some extent. Table 4. the percentage of each cost in the total operation cost of these two models.

Conclusion and future work

This paper presents a dynamic across-region ECR model: DRR-ECR, in which we use the region-region redistribution strategy to reduce the possible increasing costs which commonly exist in tradition static port-port ECR policy. To evaluate these models, we compare them through some deductive instances. Moreover, the DRR-ECR has significant impact on the safety stock of ports.

In this paper, our model considers the numbers of supply/demand empty containers given in advance. This cannot match the practice. So, one of the extensions is to develop the uncertainties. Another possible future work is to study by combining the problem of ECR with the tactical level decision even the strategic level decision. If we take the two level decisions or three level decisions into account, the meta-heuristic algorithms have to be involved to face this complex problem.

Fig. 1 .

 1 Fig. 1. Shipping line between Europe and Asia.

  (

					ji 	trLaden	, ji t			Capactity	;	(18)
						ij trEmp	00 ,, t ij t Capactity trLaden  	;	(19)
					i				j													i	j
						00 ,, tr ij t ij t trLaden LadenTrans  	;	(20)
						i			j											i	j
						trLaden	tr ji	33 ,, t ji t LadenTrans  	;	(21)
					j				i													j	i
	max imize
		ij trLaden	t			tr ij PL			trLaden	ji	t	tr ji PL			ij trLaden	t	tr ij CL		l i CL		u j CL
	i	j													j				i		i	j
			trLaden	ji	t				tr ji CL		l j CL		u i CL			ij trEmp	t	C	tr ij E		l i CE		CE	u j
		j	i																		i	j
		j  t D 	ij trEmp			CE	r j		i  t S 	ij trEmp	)* S i CE
		j			i																	i	j
																						(13)
						00 ,, tr ij t i t trEmp S 	;	i	,  PE j	PA	(14)
					j
						00 ,, tr ij t j t trEmp D 	;	j		PA	(15)
					i
						0 ij t , tr trEmp		min(	0 i t , S   ,	D	0 j t ,	)	(16)
					i				j													i	j
						0 ij t , trLaden			Capactity	;	(17)
					ij

Table 2 .

 2 Cost, total profit and net profit when the transportation cost, the demand of deficit region and leasing cost in deficit region fluctuate.

  to explain above models, where the RTEC refers to the real transportation empty containers and the RC the residual capacity. For example, the

						00 t deficit t ,, SD  surplus	means the whole
	supply in surplus region is larger than the total demands ones in deficit region. The
	S	defict	,	0 t		RC

The "*" refers to the corresponding author.


Xueyan Duan
email: xyduan@sspu.edu.cn

Xiaolin Xu
email: xlxu@sspu.edu.cn

Jiali Feng
email: jlfeng@189.cn

Research of the Evaluation Index System of Green Port Based on Analysis Approach of Attribute Coordinate

Keywords: green port, Analysis Approach of Attribute Coordinate, index system, quantification methods

With the coming of low-carbon era the concept of green port becomes more and more important due to the occurrence of abnormal global environmental change. Founded on the previous literatures, the Analysis Approach of Attribute Coordinate is used to analyze evaluation of green port. An index system including three attributes and ten factors is put forwarded. The quantification methods of different indicators are also proposed according to the different property and scoring methods of indicators. The green port evaluation index system can be used to evaluate the port's green operation performance. It is also a good instrument to construct a green port operation for decision makers of port organizations.

Introduction and Literature review

Port is an important infrastructure and strategic resource in the economic and social development. In the low-carbon economy era Green Port development has become the important field of sustainable development in the world transportation today. How to boost port service capabilities, as well as achieve green sustainable development, has important strategic significance for each port. During green port development, science evaluation and policy guidance is the key point.

In recent years, the domestic and foreign scholars have carried out a large number of research on green port evaluation.

Stone(1975) [START_REF] Stone | Environmental Impact of a Super port in the Gulf of Mexieo[END_REF] studies environmental pollution problems in port construction. Knight(1983) [START_REF] Knight | Conceptual Ecological Modeling and Interaction Matrices as Environmental Assessment Tools in Coastal Planning[END_REF] ,Brooke(1990) [START_REF] Brooke | Environmental Appraisal for Ports and Harbours[END_REF] and Peris-Moraa E. etc.(2005) [START_REF] Peris-Mora | Development of a System of Indicators for Sustainable Port Management[END_REF] build the evaluation index system of port based on the concept of green and ecological.

Shao etc.(2009) [START_REF] Chaofeng | Study on index system of eco-ports based on DPSIR model[END_REF] establishes ecological port evaluation index system based on Driver-Pressure-State-Impact-Response (DPSIR) model.

Ge(2008) [START_REF] Zhenming | Preliminary Study on the Comprehensive Evaluation Index System for Ecological Ports---Shanghai Port as the example[END_REF] builds a comprehensive evaluation index system of ecological port. Ling(2010) [START_REF] Ling | The Preliminary Study of Evaluation Index System for the Shanghai Green Port[END_REF] introduces the evaluation index system of green port taking Shanghai port as an example.

Essence(2014) [8] analyses factors and performance of green port and put forward an index system including five dimensions and thirteen factors based on Fuzzy AHP model.

Ouyang(2016) [9] discusses the connotation and features of green and low-carbon port and establishes an evaluation index system including four categories of indexes.

However existing studies somewhat have some difficulties in indicator scoring and do not take the experts' psychological preferences and evaluation process into account. The Analysis Approach of Attribute Coordinate can make up for this deficiency. Therefore, this article attempts to construct a green port evaluation index system based on the Analysis Approach of Attribute Coordinate in order to provide research ideas for related research.

Analysis Approach of Attribute Coordinate

According to the Attribute Theory, everything that exists objectively has two kinds of stipulation: quality and quantity. Therefore, the attributes of things also have two characteristic values of quantity and quality. The sensory feature extraction system is a converter that transforms the quantitative characteristics x of sensory attributes () ao into their corresponding quality characteristics ( , ) p x o .The Attribute Theory calls this process Qualitative Mapping(QM). Conversely, it is Converse Qualitative Mapping(CQM) (See as Fig. 1). The definition of QM and CQM can be extended from 1 dimension to n dimension.

Fig. 1. QM and CQM model of Attribute Theory

For human assessment and decision making, different decision attributes have different decision weights (or preferences). An evaluation and decision model called Analysis Approach of Attribute Coordinate is proposed [START_REF] Qinpan | A Kind of Evaluation and Decision Model Based on Analysis and Learning of Attribute Coordinate[END_REF] . The Analysis Approach of Attribute Coordinate reflects the psychological standard of the decision-maker and the process of evaluating. It considers the evaluation criteria for nonlinear problems. The nonlinear change of judgment criterion is tracked dynamically by human-computer interaction in order to make appropriate evaluation decisions. It is more consistent with the nonlinear characteristics of human decision-making process.

Meanwhile the Analysis Approach of Attribute Coordinate defines a mental weight which is easier to express psychological preference of decision makers. The meaning of mental weight is the score of corresponding decision attributes when total score equals a certain value. The psychological weight is directly linked to the psychological preference of the decision maker, and the distribution is the most reasonable or satisfactory when corresponding to the mental weight of the decision maker.

In recent years the Analysis Approach of Attribute Coordinate is applied to the emergency decision-making evaluation system of college entrance examination [START_REF] Xiaolan | Admissions decisions in the university entrance exam system[END_REF] , the evaluation of third-party logistics enterprises [START_REF] Xueyan | Evaluation on 3PL's core competence based on method of attribute theory[END_REF] , the evaluation of Chinese Software Enterprises [START_REF] Guanglin | Evaluation of Chinese Software Enterprises' Core Competence in Attribute Theory[END_REF] and the evaluation of bullwhip effect in supply chain [START_REF] Jianli | Comprehensive Assessment & Optimized Selection of Supplier in SCM Based on Attribute Theory[END_REF] .These applications have achieved very good results.

Attribute characteristics of Green Port

Green port means the organic combination of port development and ecological environment protection. Through rational utilization of resources, ports can achieve low energy consumption and low pollution, and achieve harmonious and unified development between people and ports and the environment.

On the one hand, ports need to the scientific integration of existing resources and to ensure the improvement of port development speed; on the other hand, ports also need to pay attention to the quality and efficiency of development, low resource consumption, less environmental pollution, growth advantages and scale effect of the road of sustainable development.

As a systematic concept, green port can deepen its understanding of the three attributes of economy, society and environment (See as Fig. 2).

Fig. 2. Three attributes of Green Port

(1) Economic attribute: The economic attribute is the basic index in the evaluation index system of green port, which can fully reflect the economic development level of a port. Green ports also seek an increase in economic returns.The characteristics of port economic benefits can be drawn from the indexes of port GDP, profit and tax ratio, input output ratio, etc.

(2) Society attribute: On the premise of ensuring proper economic development social attribute of port is another important criterion for measuring green ports . The port should be able to play an important role in promoting regional social Economy Sociality Green Port Environment development , promoting the employment of the population in the port area and improving the quality of the population near the port area.

(3) Environment attribute: In essence, the development of green port is resourceefficient and environment-friendly. Resource-efficiency involves conservation of energy, land, coastline and materials, while the environment-friendliness involves objects such as atmosphere, water, sound and ecology.

Evaluation Index System of Green Port

According to the principles of the index system and the objectives of this study, we establish a recursive hierarchy evaluation index system of attribute coordinate. Evaluation index system of green port is set up including target layer, criterion layer and indicator level. At criterion layer we divide green port system into three dimensions which are economic benefit, social efficiency and ecological environment. Each dimension separately includes several specific indicators. In all there are 10 evaluation indicators.

The index system of green port is shown in Table1.

Indicator Quantification Method

In order to carry out the assessment, we must first quantify the 10 indicators of evaluation index system of green port, that means to convert each index value to a standard uniform score. According to Analysis Approach of Attribute Coordinate 100 point system is adopted.

Qualitative indicators are quantified by Converse Qualitative Mapping (CQM).

Let (0) a be an attribute of something (or object).

x is a quantity characteristic for (0) a , x XR. (0

) i p is a quality characteristic for (0) a , ( 0
) i p  0 p . A=( ,
ii  ) is qualitative benchmark (or "degree") of (0) x based on ( , ii  ) which can be written into:

  : 0 1 i X     , ,making: , ( , )) ( , ) ( )
i i i i i x x x          ( . In consideration of [ , ii  ] is a topological neighborhood with ( ) / 2 i i i    
as the center and ( -) / 2

i i i    
as the radius, the QM can also be written into:

  : 0 1 i X     , ,making :
, ( , )) ( , ) ( )

i i i i i x N x N x          ( .
Then some qualitative mappings that can be transformed to the Converse Qualitative Mapping (CQM) can be written.

When an expert evaluates a qualitative indicator, the evaluation results are divided into four grades which correspond to four ranges of scores. "Excellent" corresponds to [90-100], "Good " corresponds to [80-90], "General" corresponds to [60-80] and "Inferior" corresponds to [0-60]. Meanwhile experts also need to give the degree of evaluation to the extent of the range.

If the evaluation results are "excellent", "good" and "general", Formula( 1) is used to obtain the quantization results. ( )

i i i i x        ( 1)
If the evaluation results are "Inferior", Formula (2) is used to obtain the quantization results.

( ) (1-)

i i i i x      (2)
The quantitative indicators that are percentage are quantified by linear interpolation. If the indicator are positive when the index value is bigger it is the better, Formula (3) is used to obtain the quantization results.

) ( ) ( ) [ ( )] f j f i p f i f i ji      (3)
If the indicator are negative when the index value is smaller it is the better,, Formula (4) is used to obtain the quantization results.

) ( ) ( ) [ ( )] f j f i p f j x f j ji      (4)
The Indicator Property and Quantification Method are shown in Table 2.

Conclusion

Ports play an important role in economic development.The evaluation of green ports is of great significance to the development of the world ports. Based on the Analysis Approach of Attribute Coordinate, economic attributes, social attributes and environmental attributes of green port is deeply analyzed. The evaluation index system of the green port is constructed and the corresponding indicator properties and quantization method are put forward. The index system of green ports can be applied to the objective evaluation of the current situation of the development of green ports, so as to guide and promote the construction and development of green ports all over the world. In the follow-up research, the index system can be used in the evaluation of specific ports, and the practical research can also be improved on the basis of the characteristics of the development trend of green ports.

  the Qualitative Mapping(QM) of

(

(

Table 1 .

 1 The Evaluation Index System of Green Port

		Economical	Port GDP(u11)
		benefit	Investment profit rate (u11)
		(u1)	Input-output Ratio(u13)
		Social	CargoThroughput(u21)
	Green Port	efficiency	Passenger Throughput(u22)
	(U)	(u2)	Port employment(u23)
		Ecological environment (u3)	Qualified rate of wastewater discharge(u31) Air quality qualification rate in harbor area(u32) Qualified rate of noise pollution in port area(u33) Green degree of port area(u34)

Table 2 .

 2 Indicator Property and Quantification Method

	Indicator	Property	Quantification Method
	U11	Positive	expert evaluation
	U12	Positive	Linear interpolation method
	U13	Negative	Linear interpolation method
	U21	Positive	expert evaluation
	U22	Positive	expert evaluation
	U23	Positive	expert evaluation
	U31	Positive	Linear interpolation method
	U32	Positive	Linear interpolation method
	U33	Positive	Linear interpolation method
	U34	Positive	Linear interpolation method