Yixin Zhong
email: zyx@bupt.edu.cn

Ecological Methodology and Mechanism Approach --New perspective to Intelligence Science

Keywords: Ecological Methodology, Mechanism Approach, Unified Theory of Intelligence

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

Introduction

The research on artificial intelligence, AI for short, has made a number of splendid progresses recently while also confronted with severe controversies. Some researchers declare that the great breakthrough in AI is coming in few decades and AI machines will take the domination over human beings. Other group of researchers, however, insists on the existence of huge difficulties in AI research in which many fundamental issues related to intelligence are still remained unclear, or even almost unknown [START_REF] Zhong | AI: The Unknown behind The Hot Wave[END_REF]. This situation led the theme of intelligence science, instead of intelligence technology (or AI), to receive more and more attentions from academic circles worldwide.

It is well accepted that the first important issue one should take care in mind in the academic research is the question of whether the scientific methodology being adopted is appropriate or not. This is because of the fact that scientific methodology is the macroscopic guideline for research that will inspire the proper approach to the specific field of research. Therefore, whether or not the methodology is suitable for intelligence study will determine whether or not the research can be successful [START_REF] Zhong | Principles of Information Science[END_REF].

According to the principles mentioned above, we have noted that the methodology adopted in intelligence research has long been the reductionism featured with "divide and conquer" which had been an extremely powerful, and also successful, methodology in classical physics in modern times. This led AI research to the situation where a number of different, and mutually isolated, approaches being employed, namely the structuralism, the functionalism, and the behaviorism all in parallel and separate. As result, researchers employing different approaches have different understanding on what AI should be. In other words, reductionism is the major root that generates the controversies in AI [START_REF] Zhong | Principles of Advanced AI[END_REF].

In order to effectively improve the situation, we have carried out a long term investigations based on which we have summed up and would now like to propose a new methodology, named ecological methodology, for the intelligence science study from which a new approach to the intelligence research, termed mechanism approach, is also inspired [START_REF] Zhong | The Law of "Information Conversion and Intelligence Creation[END_REF].

The concepts of ecological methodology will be introduced in section 2 and then the basic model of the typical intelligence process, based on ecological methodology, will be presented in section 3. The mechanism approach to the study of intelligence science will be explained and compared with the other approaches in section 4. The major results due to the new methodology and approach will be exposited in section 5. Some conclusions will be given in the final section 2 Ecological Methodology vs. Reductionism Methodology

Definition 1 Methodology

Methodology, scientific methodology more precisely, is not methods for dealing with specific problems but is a general term that refers to the general criterion, or the general guideline, for the proper selection of the specific methods to a field of study.

Therefore, the first, and also the most important, issue among other any things for scientific study should be making an effort in doing an assessment on whether the methodology to be employed is appropriate or not. Only the proper methodology employed could give guarantee to the success of the research.

One of the most influential, and also the most successful, methodologies in the history of natural sciences development is the "reductionism". It says that, a complex system can be divided into a number of sub-systems, which must be simpler and easier to handle, and that the solution for the system can then be obtained by summing up the ones of all sub-systems. So, this methodology can well be characterized as "divide and conquer".

Reductionism as a methodology has achieved numerous successes, particularly in the physical science study, or mechanical science study in modern times. The major characteristic for performing the reductionism, as was mentioned above, is that the complex problem must first be divided into a number of simpler, and mutually isolated, sub-problems and then each of the sub-problem be individually solved and finally all the sub-solutions are summed up. This caused no problem in classical physical science study.

However, if the system in consideration is a kind of information systems, the performing of reductionism will certainly cause serious problem. This is because of the fact that there exist complex information links among the sub-systems of the system and between the system and its environment and that all the information links are the 'life lines' of the information system. The 'life lines' of the information system may be cut-off when improperly dividing the system, hence leading the information system being "dead system". Therefore, when dealing with the problems in information systems, intelligence systems in particular, which are usually complex systems, one should be very much careful on how to properly dividing on one hand and, on the other hand, should carefully seek for the new, and appropriate, methodology for recovering the life lines of the system.

Definition 2 Ecology and Ecosystem

As a kind of methodology, ecology is referred to the general guideline that emphasizes the study of interrelationships among the organisms of the system and between the system and their environment. All the interrelated organisms constitute the ecosystem.

Obviously, the methodology of ecology, or more conveniently the ecological methodology, is the methodology that is exactly what needed for the study of complex information systems, through which the life lines, or the interrelations, among all the parts of the ecosystem and between the ecosystem and its environment can hopefully be recovered. Consequently, the global properties and laws governing the complex information system can hence be recovered and studied.

It is known that intelligence is derived out from knowledge which in turn is from information. In other words, there will be no intelligence at all if there is no information provided.

Therefore, intelligence systems in all cases are complex information systems, or equivalently information processing systems, in nature. In other words, ecological methodology is the one needed, and suitable, for the study of intelligence science.

The Model of Intelligence Process in Perspective of Ecological Methodology

In accordance with the definition of ecological methodology and the investigation of various kinds of intelligence activities, it is found that the most typical intelligence process can be abstracted as the model in Fig. 1.

Fig. 1. Abstract Model of Typical Intelligence Process

As can be seen from Fig. 1 that the intelligence process must be within the framework of the subject-object interaction: First, the object in environment presents its object information to the subject. And then the subject handles the object information and produces, based on the results of the handling, an intelligence action that acts on the object in environment. The action must be intelligent enough. Otherwise the subject may suffer from risks of danger.

The process of producing intelligent action in Fig. 1 can be expressed in more detail. This leads to the model in Fig. 2. The entirety within the thick-line-box in Fig. 2 represents the subject who performs all the functions of intelligence: perception, cognition, strategy creation and strategy execution. The attributes that characterize the subject include various kinds of knowledge, K1 (innate and commonsense knowledge), K2 (empirical knowledge), K3 (regular knowledge), K4 (Art and other knowledge needed for strategy creation), and the goal for problem solving, G, all stored in the knowledge base at the center.

As can be seen from Fig. 2 that there exist various kinds of interrelations among the units of the subject and between the subject and the environment. The studies of the interrelations meet the requirements of ecological methodology.

More specifically, the major interrelations that the ecological methodology concerns the most include the followings (see in Fig. 2):

(1) The interrelation between the object in environment and the subject's unit of perception,

(2) The interrelation between the unit of perception and the unit of cognition,

(3) The interrelation between the unit of cognition and the unit of intelligence the latter of which consists of the unit of primary consciousness, the unit of emotion, the unit of intellect, and the unit of strategy creation, [START_REF] Zhong | The Law of "Information Conversion and Intelligence Creation[END_REF] The interrelation between the unit of intelligence and the unit of execution, and [START_REF] He | Principles of the universal Logic[END_REF] The interrelation between the unit of execution and the object in environment.

Mechanism Approach vs. Other Approaches

The next issue is to develop the proper approach to the study of intelligence science based on the general guideline from the ecological methodology and the associated model. That means that we should understand and study these interrelations among the units of the system and their environment so that the laws and rules that govern the intelligence processes and activities can be established.

There have been many different approaches existed in history. Particularly in AI research, there are structuralism approach for dealing with neural networks, functionalism approach for dealing with physical symbol systems and expert systems, and behaviorism approach for dealing with sensor-motor systems. As mentioned above, these approaches are resulted from the reductionism methodology and can only explore some of the partial knowledge of the intelligence process.

Note that the mainstay in intelligence system is the series of interrelations among the units and their environment and that all the units of the subject cannot be treated separately from each other as mutually isolated ones. We should explore and discover the inherent mechanism that reasonably organizes all the units to fulfill the task of generating intelligence. This leads to the mechanism approach to intelligence science study.

To investigate the mechanism approach, Let's look back the model in Fig. 2:

The function of interrelation ( 1) is to converse object information to perceived information; The function of interrelation ( 2) is to converse perceived information to knowledge; The function of interrelation ( 3) is to converse perceived information to intelligent strategy; The function of interrelation ( 4) is to converse the intelligent strategy to intelligent action; and The function of interrelation ( 5) is to apply intelligent action to the object in environment.

This can briefly be expressed as a series of conversion, that is, (1) The conversion: object information → perceived information, (2) The conversion: perceived information → knowledge, (3) The conversion: knowledge → intelligent strategy, (4) The conversion: intelligent strategy → intelligent action, and

The conversion: intelligent action → object.

The series of conversion above can be expressed as "information → knowledge → intelligence", in which "object information" and "perceived information" are abstracted as "information" and similarly, "intelligent strategy" and "intelligent action" are abstracted as "intelligence".

Therefore, the most compact expression for the mechanism approach to intelligence science is the series of conversion: information → knowledge → intelligence (

The formula (4.1) means that, by employing mechanism approach to the study of intelligence science, the most important thing should be to have the information needed for the problem solving and to handle the series of the conversion from information to knowledge and further to intelligence, instead of the specific structure, functions, and behaviors of the system.

The formula (4.1) is the most important and significant result because of the fact that the system's structure, functions and behaviors must be determined by the mechanism of the intelligence system. In other words, mechanism of intelligence generation is the only dominating factor in the study of intelligence science whereas the system's structure, functions, and behaviors are the dominated factors.

Major Results Due to the Mechanism Approach

It is easy to see from the formula (4.1) that the essence of the mechanism approach can be named as "Information Conversion and intelligence Creation" provided that the following three conditions are given beforehand: [START_REF] Zhong | AI: The Unknown behind The Hot Wave[END_REF] The object information that represents the problem to be solved, [START_REF] Zhong | Principles of Information Science[END_REF] The goal that indicates the final state for the problem solved, and (3) The knowledge that is needed for solving the problem in hand. This can also be clearly expressed in Fig. 3 below [START_REF] Zhong | The Law of "Information Conversion and Intelligence Creation[END_REF]. The meaning one can see from Fig. 3 can be expressed in words that, as long as the three conditions are provided, the study of intelligence science is to perform the function of (4.1), the function of information conversion and intelligence creation, with the intelligent action being produced and executed.

If the result of the intelligent action is satisfied the problem is satisfactorily solved. Otherwise the error between the actual state and the goal state, as a kind of new information, should be feedback to the input of the system and the new knowledge should be learned and added for producing a new, and better, strategy. This "feedback-leaningoptimization" loop may be carried on a certain number of times until the result is satisfied. This is the universal law as well as the unified theory of intelligence science.

Conclusions

(1) Scientific methodology is the first, and the most important, issue in scientific research. The reductionism methodology employed in intelligence research the past is not sufficient. The scientific methodology appropriate to the study of intelligence science should be the one of ecology, or more precisely the methodology of information ecology.

(2) The secondly important issue in any scientific research is to, under the guideline of the methodology employed, formulate an appropriate model representing the research. The model applied in AI in the past has been much too narrowly isolated. The appropriate models of typical intelligence process should be the model of subject-object interaction expressed in Fig. 1 and Fig. 2 respectively, instead of the model of brain only.

(3) The thirdly important issue in any scientific research is the approach selection, under the general guideline of the methodology employed and also based on the model selected. The structural approach, the functionalism approach, and the behaviorism approach applied in AI in the past are locally and shallowly valid. The universal approach to the study of intelligence science is the mechanism approach expressed in the formula (4.1).

(4) The mechanism approach to intelligence science study has been identified as the series of conversion: "object information → perceived information → knowledge → intelligent strategy → intelligent action", or more briefly "information → knowledge → intelligence" in which the conversions need to be supported by new logic [START_REF] He | Principles of the universal Logic[END_REF] and new mathematics [START_REF] Wang | A factor space approach to knowledge representation[END_REF].

(5) The most interesting, and also most significant, result is the universal law governing the intelligence science that is the Law of Information Conversion for Intelligence Creation expressed in Fig. 3 and Fig. 2 which is derived from the methodology of information ecology, ecological model of intelligence process, and the mechanism approach. The law is universally valid, that is whenever the three conditions (the object information about the problem to be handled, the goal for the problem solving, and the knowledge needed for the problem solving) are provided, the task of intelligence science study is to perform the mechanism approach.

Fig. 2 .

 2 Fig. 2. The Detailed Version of Fig.1

Fig. 3 .

 3 Fig. 3. Another Expression for the Formula (4.1)


Di Zang
email: zangdi@tongji.edu.cn

Dehai Wang

Jiujun Cheng

Keshuang Tang3

Xin Li

Traffic Parameters Prediction Using a Three-channel Convolutional Neural Network

Keywords: Deep Learning, Convolutional Neural Network, Traffic Prediction, Intelligent Transportation System

Traffic three elements consisting of flow, speed and occupancy are very important parameters representing the traffic information. Prediction of them is a fundamental problem of Intelligent Transportation Systems (ITS). Convolutional Neural Network (CNN) has been proved to be an effective deep learning method for extracting hierarchical features from data with local correlations such as image, video. In this paper, in consideration of the spatiotemporal correlations of traffic data, we propose a CNN-based method to forecast flow, speed and occupancy simultaneously by converting raw flow, speed and occupancy (FSO) data to FSO color images. We evaluate the performance of this method and compare it with other prevailing methods for traffic prediction. Experimental results show that our method has superior performance.

Introduction

Currently, real time traffic information prediction has got more and more attention of individual drivers, business sectors and governmental agencies with the increasing numbers of vehicles and the development of ITS. Flow, speed and occupancy as three elements of traffic describe the different characteristics of traffic and record the spatiotemporal evolution over a period of time. Accurate prediction of them can facilitate people's travel and reduce traffic congestion and accidents. Meanwhile, it can help traffic managers allocate traffic resources systematically and improve regulatory efficiency.

There exist the spatiotemporal correlations of traffic data due to the consecutive evolution of traffic state on the time-space dimension. It's necessary to retain and leverage inherent spatiotemporal correlations when forecasting traffic information. In addition, there are undoubtedly inner correlations between traffic parameters such as flow, speed and occupancy within a time unit, most of which have been explained theoretically and mathematically. Intuitively, when traffic flow is high, speed usually won't be low. Therefore, it's reasonable to take these important correlations into account in the modeling to make the prediction more robust.

Existing traffic prediction methods can be mainly divided into three categories: time-series methods, nonparametric methods and deep leaning methods.

Autoregressive moving average (ARIMA) [START_REF] Ahmed | Analysis of Freeway Traffic Time-series Data by Using Box-Jenkins techniques[END_REF] model is one of the representative time-series models, which focuses on finding the patterns of the temporal evolution formation by two steps: moving average (MA) and autoregressive (AR) considering the essential traffic information characteristics. Many variants of this model, such as subset ARIMA, seasonal ARIMA, KARIMA, ARIMAX, were proposed to improve prediction accuracy. However, these models are inept at extracting spatiotemporal feature for prediction because of ignoring spatiotemporal correlations.

Nonparametric methods were widely used because of its advantages, such as their ability to deal with multi-dimensional data, implementation flexibility. In [START_REF] Jeong | Supervised Weighting-online Learning Algorithm for Short-term Traffic Flow Prediction[END_REF], a prediction model was built by support vector regression (SVR) and optimized by particle swarm algorithm. Chang et al proposed a dynamic multi-interval traffic flow prediction using k-nearest neighbors (KNN) [START_REF] Chang | Dynamic Near-term Traffic Flow Prediction: System Oriented Approach Based on Past Experiences[END_REF]. In addition, various artificial neural network (ANNs) [START_REF] Chan | Neural-network-based Models for Short-term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg-Marquardt Algorithm[END_REF][START_REF] Kumar | Short Term Traffic Flow Prediction for a Non Urban Highway Using Artificial Neural Network[END_REF] were designed to predict traffic information. Unfortunately, these shallow architectures failed to learn deep features.

Recently, deep learning models such as deep belief network (DBN) [START_REF] Huang | Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning[END_REF], recurrent neural network (RNN) [START_REF] Ma | Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory[END_REF], and long short-term memory (LSTM) [START_REF] Tian | Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network[END_REF] have been used for traffic prediction owe to their excellent capability of extracting complex features and generalizability and strong forecasting performance. However, these models usually put the time and space into same dimension and they violate the twodimensional basis of spatiotemporal features. CNN has been demonstrated to have excellent performance in large-scale image-processing tasks [START_REF] Krizhevsky | ImageNet Classification with Deep Convolutional Neural Networks[END_REF]. Ma et al. [START_REF] Ma | Learning Traffic as Images: A Deep Convolution Neural Network for Large-scale Transportation Network Speed Prediction[END_REF] first applied CNN to traffic speed prediction by converting network traffic to gray images and achieved significant improvement on average accuracy.

In this paper, we propose a CNN-based method to forecast traffic information from a comprehensive perspective. We design a novel way in which quite complete spatiotemporal features can be extracted from the data by converting raw flow, speed and occupancy (FSO) data to FSO images. Furthermore, we use the features to forecast the traffic flow, speed, occupancy simultaneously. Finally, we demonstrate the performance of the proposed model and compare it with other prevailing methods.

The rest of the paper is organized as follows: In section 2, converting raw FSO data to FSO images and CNN model for traffic prediction are introduced. In Section 3, experiments and results are detailed. Finally, conclusions are drawn in section 4.

Methodologies

Raw FSO data with space and time dimensions can be integrated and converted to FSO color images as shown in Fig. 1. Then a CNN model based on LeNet-5 architecture is designed to learn the mapping relationship between spatiotemporal features and FSO images. Finally, we compute predictive performance indicators by mapping the learned features back to original FSO data space.

Data conversion

Traffic data such as flow, speed, occupancy collected by sensors such as inductive loop detectors at a certain time interval, which is usually not more than 5 minutes, record the evolution of traffic conditions in a particular region. For the width of the image, time sequences are fitted linearly into the width-axis chronologically. Thus, the length of time interval determines the width of the image. For a 5-minute interval, there will be 288 data sequences on the time dimension corresponding to the image width of 288 pixels. In general, short intervals like 5 seconds are meaningless for traffic prediction. Therefore, in most cases data sequences need to be aggregated to generate available data by dealing with several adjacent time intervals.

For the height of the image, we simply fit the number of sensors ordered spatially into the height-axis. We can also make height-axis compact and informative, in the same way as width dimension, by aggregating data from serval adjacent sensors. Notably, different traffic data may use different aggregation methods. For example, flow use accumulation while speed and occupancy use mean.

Finally, three time-space matrix generated by the method mentioned above are directly merged into FSO color images for flow as green channel, speed as red channel and occupancy as blue channel. A FSO image can be denoted as:

𝑃 = [ 𝑝 11 𝑝 12 ⋯ 𝑝 1𝑁 𝑝 21 𝑝 22 ⋯ 𝑝 2𝑁 ⋮ ⋮ ⋱ ⋮ 𝑝 𝑀1 𝑝 𝑀2 ⋯ 𝑝 𝑀𝑁 ] ( 1
)
where 𝑀 is the number of sensors, 𝑁 is the length of time units and pixel 𝑝 𝑖𝑗 is a triple consisting of flow, speed and occupancy.

CNN architecture

Fig. 2 illustrates the proposed three-channel convolutional neural network model. In this case, traffic data of flow, speed and occupancy are respectively encoded into three channels corresponding to the red, green and blue color channels. These three traffic parameters are then learned by different convolutional kernels to generate feature maps which represent the fusion of traffic information. After the consecutive processing of convolution and pooling, the fully-connected layer maps the extracted traffic features back to the original space for final prediction as the output of the model.

Input layer and output layer

First, raw FSO data are converted to FSO images. However, different from CNN models for classification, for an FSO image as input, the output of the model is also a FSO image right next to it. This means that the network not only learns hierarchical features but also learns to map the extracted features back to original space, which is similar to the mechanism of the autoencoder (AE).

Given the lengths of input and output time units as 𝜏 and 𝜀, respectively and prediction interval between input and output as π, which is set to 1 in this paper, the input of the model can be written as:

𝑥 𝑖 = [ 𝑝 1,𝑖 𝑝 1,𝑖+1 ⋯ 𝑝 1,𝑖+𝜏-1 𝑝 2,𝑖 𝑝 2,𝑖+1 ⋯ 𝑝 2,𝑖+𝜏-1 ⋮ ⋮ ⋱ ⋮ 𝑝 𝑀,𝑖 𝑝 𝑀,𝑖+1 ⋯ 𝑝 𝑀,𝑖+𝜏-1 ] ( 2
)
where 𝑖 is the sample index in the range of [1, 𝑁 -𝜏 -𝜋 -𝜀 + 2], and 𝑀 is defined as in formula (1), 𝑝 𝑖,𝑗 is a triple consisting of flow, speed and occupancy. Accordingly, the predicted FSO image can be written as:

𝑦 𝑖 = [ 𝑝 1,𝑖+𝜏+π-1 𝑝 1,𝑖+𝜏+π ⋯ 𝑝 1,𝑖+𝜏+π+ε-2 𝑝 2,𝑖+𝜏+π-1 𝑝 2,𝑖+𝜏+π ⋯ 𝑝 2,𝑖+𝜏+π+ε-2 ⋮ ⋮ ⋱ ⋮ 𝑝 𝑀,𝑖+𝜏+π-1 𝑝 𝑀,𝑖+𝜏+π ⋯ 𝑝 𝑀,𝑖+𝜏+π+ε-2 ] (3)

Convolutional layer and pooling layer

Convolutional layer plays a major role in extracting the spatiotemporal features. The previous layer's features are convolved with learnable kernels and put through the activation function to form more complex features. Generally, a convolutional layer is usually followed by a pooling layer to reduce parameters of the model and make the learned features more robust. Rectified linear unit (ReLU) activation function and max pooling procedure are used in our model because of their respective superior performance. The output of convolutional and pooling layers can be written as:

𝑥 𝑗 𝑙 = 𝑚𝑝 (𝜑 (∑ 𝑥 𝑖 𝑙-1 𝑐 𝑙-1 𝑖=1 * 𝑘 𝑖𝑗 𝑙 + 𝑏 𝑗 𝑙 )) (4)
where 𝑙 is the index of layers and j is the index of feature maps in the 𝑙 th layer, 𝑥 𝑖 𝑙-1 , 𝑥 𝑗 𝑙 , 𝑘 𝑖𝑗 𝑙 and 𝑏 𝑗 𝑙 denote the input, output, kernels and bias of the layer, respectively. * indicates convolution operation and 𝑚𝑝 denotes max pooling procedure. 𝜑 is ReLU activation function defined as:

𝜑(𝑥) = 𝑚𝑎𝑥 (0, 𝑥).

(5)

Fully-connection layer

Fully-connection layer provides an effective way to map the spatiotemporal features back to original FSO image space. The output of this layer as predictive value of the model can be written as:

𝑦 ̂= 𝜎(𝑤 𝑙 𝑥 𝑙-1 + 𝑏 𝑙 ) (6)
where 𝑤 𝑙 and 𝑏 𝑙 are the weights and bias of the layer, respectively. 𝑦 ̂ are predicted values ,that is, the output of the model. 𝜎 is sigmoid activation function defined as:

𝜎(𝑥) = 1 1 + 𝑒 -𝑥 (7)
Finally, Mean squared errors (MSEs) are employed as loss function to measure the distance between predictions and ground-truth, which are optimized by mini-batch gradient descent (mini-batch GD) algorithm. MSE can be defined as:

𝑀𝑆𝐸 = 1 𝑁 ∑(𝑦 ̂𝑖 -𝑦 𝑖 ) 2 𝑁 𝑖=1 (8)
where 𝑦 ̂𝑖 and 𝑦 𝑖 are predicted values and true values of the 𝑖th sample, respectively, and 𝑁 is the number of samples.

Experiments and Results

In this section, we first detail the generation of samples. Then we display the trained model from different aspects. Finally, we give the experimental results.

Data Description

The proposed method is evaluated on the data of West Yan'an Road in Shanghai, which were collected every 5 minutes from 35 individual inductive loops during the whole year of 2012. In fact, there are a total of 361 days of data because of the absence of data from March 20 to March 23. For practical reasons, there are inevitably some missing pieces in the data. Therefore, a proper mending method is applied to the data with spatiotemporal adjacent records. As shown in Fig. 1, the green area from 21:00 pm of previous day to 7:00 am of next day corresponds to the traffic state that there are few vehicles on the road. It is incompatible with the characteristics of CNN model and almost impossible to extract spatiotemporal features because of lacking representational and differentiable patterns when using image patches to train the network. Therefore, the data with obvious traffic patterns from 7:00 am to 21:00 pm of a day are chosen to generate samples. For the time interval of 5 minutes as a time unit, there are 168 time sequences from 7:00 am to 21:00 pm. Accordingly, when converting these sequences to FSO images, the width of the images will be 168. As a result, there are 361 FSO images of 35×168×3 available for intercepting image patches as samples. When the lengths of input and output are set to 𝜏 and 𝜀, respectively, which means using 5τ-minute FSO data to forecast next 5ε-minute FSO data, the number of the image patches of a day will be 168 -𝜏 -𝜀 + 1. In total, there are 361 • (168 -𝜏 -𝜀 + 1) samples. First 90% of them are used as training data and the rest are used as test data.

Model display

The model is designed based on LeNet-5 architecture. The parameters of the model as listed in Table 1 are set based on the principle that the network converges to a better solution and that the train time of the network is acceptable.

Results and comparison

We first evaluate the proposed method for short-term traffic prediction with the lengths of input and output are set to 35 and 1, respectively. This means we use 175-min FSO data to forecast next 5-min data. As mentioned in section 3. As shown in Table 3, we further evaluate the performance of our model on forecasting longer time span when setting the input length to 35 i.e. 175 minutes. From the table we can see that, with the increase of time span, the prediction accuracy is decreasing but within the acceptable range. This is due to the fusion of three-channel information which can effectively filter out influences of the outliers in channels.

T Y P E CNN ANN DBN RW M A E R M S E M R E (%) M A E R M S E M R E (%) M A E R M S E M R E M A E R M S E M R E (
Finally, we present the prediction curves of the 343 th day at the 26 th loop and the 357 th day at the 22 th loop from 10:00 am to 21:00 pm as shown in Fig. 4.

Conclusion

In this paper, we proposed a CNN-based method to forecast traffic information. Unlike most single prediction models, traffic flow, speed and occupancy are simultaneously predicted by the model to provide more complete traffic information. Spatiotemporal traffic features of traffic data can be learned automatically by converting flow, speed, and occupancy data to color images as the input of the model. We evaluated the proposed method on the data of West Yan'an road in Shanghai and compared it with ANN, DBN and RW methods. And results show that the proposed method is superior to others. In addition, we explored the prediction performance on different tasks of 5-min prediction, 15-min prediction and 30-min prediction and results show that proposed model has certain robustness as the prediction accuracy descends but within an acceptable range.

Fig. 1 .

 1 Fig. 1. FSO image converted from FSO data of a day. The area containing purple blocks corresponds to the period when the traffic volume is large i.e. day time whereas the green area corresponds to the opposite side.

Fig. 2 .

 2 Fig. 2. Proposed CNN model for extracting traffic spatiotemporal features. C, MP and FC represent convolution, max pooling and fully-connected layer, respectively.

Fig. 3 .

 3 Fig. 3. (a): FSO input image. (b): Feature map of the first convolutional layer. (c): Feature map of the second convolutional layer. (d): Feature map of the third convolutional layer. Hierarchical spatiotemporal features of FSO data from simple to complex and concrete to abstract are extracted automatically through the model. (e): Kernels of the first convolutional layer. The model has learnt homogeneous kernels through training, which is consistent with the characteristics of traffic data.

Fig. 4 .

 4 Fig. 4. (a), (c) and (e) are the flow, speed and occupancy predictions of the 343th day at the 26th loop, respectively. (b), (d) and (f) are the flow, speed and occupancy predictions of the 357th day at the 22th loop, respectively.

Table 1 .

 1 Parameters of the model.

	Layer	Parameter Dim	Feature Dim	Parameter Num
	Input	-	(3,35,35)	-
	C1	Kernel(8,3,5,5)	(8,31,31)	608
	MP1	Pooling(2,2)	(8,16,16)	-
	C2	Kernel(16,8,3,3)	(16,14,14)	1168
	MP2	Pooling(2,2)	(16,7,7)	-
	C3	Kernel(32,16,3,3)	(32,5,5)	4640
	MP3	Pooling(2,2)	(32,3,3)	-
	FC4	Weight(288,105)	105	147968
	Output

Table 2 .

 2 Prediction performance of CNN and other models on test data.

Table 3 .

 3 Prediction performance of CNN on different time span.

	%)

Acknowledgments. This work has been supported by the Fundamental Research Funds for the Central Universities of China and by National Natural Science Foundation of China under grant 61472284.


Hong-Xu Qiu
email: qiuhongxu93@163.com

Huang Xiao-Xia
email: lhuangxiaoxia@163.com

An Improved Image Transformation Network for Neural Style Transfer

Keywords: Style Transfer, Batch Normalization, Residual Block, Convolutional Neural Networks

By using Convolutional Neural Networks(CNNs), the semantics and styles of images can be separated and recombined to create fascinating images. In this paper, an image transformation network for style transfer is proposed, which consists of convolution layers, deconvolution layers and Fusion modules composed of two 1x1 convolution layers and a residual block. The output of each layer in the network is normalized using batch normalization to speed up the training process. Compared with other networks, our network has fewer parameters and better real-time performance while generating similar quality images..

Introduction

For centuries, painting has been a popular form of art, having produced plenty of valuable masterpieces which attract people's attention. But in the past, it would take a long time for a well-trained artist to draw a painting of a particular style.

Recently, Gatys et al. first studied how to use CNN to reproduce famous painting styles in the natural picture. They obtained the representations of the image from the CNN and found that the content of image and the style of image were separable. Based on the above findings, Gatys et al. proposed a Neural Style Transfer algorithm [START_REF] Gatys | A neural algorithm of artistic style[END_REF] to recombine the contents of a given image and the style of famous artworks. However, the efficiency of his algorithm can't meet the real-time requirements. Johnson et al. introduced a fast method based on the algorithm proposed by Gatys et al. Firstly, they trained an equivalent feed-forward generator network by using the perceptual loss function [START_REF] Johnson | Perceptual Losses for Real-Time Style Transfer and Super-Resolution[END_REF] they proposed for each particular style. The perceptual loss function calculates the loss by using high-level features extracted from the images using the 16-layer VGG network [3] pretrained on ImageNet dataset [START_REF] Russakovsky | ImageNet large scale visual recognition challenge[END_REF]. When there is a content image to be stylized, only a single forward transfer is required to produce the result.

Due to the amazing stylized results, the study of Neural Style Transfer has led to many successful industrial applications. The mobile application Prisma [START_REF] Labs | Prisma: Turn memories into art using artificial intelligence[END_REF] is one of the first industrial applications that offer the Neural Style Transfer algorithm as a service. Before Prisma appeared, people never thought that one day they could turn their images into artworks in just a few minutes. In order to meet the growing needs of the mobile end, a smaller and faster network is urgently needed.

In this paper, a new module which can be used to construct an image transformation network is proposed. In order to train the network, the pretrained 16-layer VGG network is used to extract the advanced features of the image, then train our network by minimizing the perceptual loss function. At test-time, compared with the network of Johnson et al., our transformation networks reduce network parameters by 62.3% and the running time reduced by 12%.

Related Work

Feed-forward Image Transformation

In recent years, training a deep convolutional neural network can solve many image transformation problems. Since the purpose of our image transformation network is to convert an image into a stylized image, we referred to the architecture of the Fully Convolutional Networks [START_REF] Long | Fully convolutional networks for semantic segmentation[END_REF] and the Deconvolution Network [START_REF] Noh | Learning deconvolution network for semantic segmentation[END_REF]. In the architecture of our image transformation network, instead of using the pooling layer, the convolution layer and the deconvolution layer are used to perform the down-sampling and up-sampling operations.

Neural Style Transfer

The method proposed by Gatys et al. starts with random noise and updating the stylized image constantly by back propagation. The method of Johnson et al. is to train a feedforward network on a large image dataset for each particular style of image. Gradient descent is used to optimize the network by iteratively updating the network. Those two methods use similar objective function.

The perceptual loss function is improved by Johnson et al. on the basis of Gatys et al. Given the style and content images 𝑦 𝑠 and 𝑦 𝑐 , and the layer j and J used in the network ϕ for feature and style reconstruction, the stylized images can be generated by minimizing the total loss. loss = 𝜆 𝑐 𝑙 𝑓𝑒𝑎𝑡 𝜙,𝑗 (𝑦, 𝑦 𝑐 ) + 𝜆 𝑠 𝑙 𝑠𝑡𝑦𝑙𝑒 𝜙,𝐽 (y, 𝑦 𝑠 ) + 𝜆 𝑇𝑉 𝑙 𝑇𝑉 (𝑦) (1) 𝜆 𝑐 , 𝜆 𝑠 and 𝜆 𝑇𝑉 are scalars representing the weights of feature reconstruction loss, style reconstruction loss and total variation regularizer in the total loss. In this paper, the network ϕ is the pretrained 16-layer VGG network.

Batch Normalization

Because of change in the parameters of the previous layer, the distribution of each layer's inputs changes during training. It makes the depth neural network difficult to train.

To solve this problem, Ioffe et al. proposed batch normalization [START_REF] Ioffe | Batch normalization: accelerating deep network training by reducing internal covariate shift[END_REF]. By using batch normalization, the internal covariate shift is reduced and the training process of network is shortened greatly. Batch normalization is implemented by normalizing each feature map so that the mean is zero and the variance is one. For a layer with d-dimensional input x = (𝑥 (1) … 𝑥 (𝑑) ), each dimensional will be normalized

𝑥 ̂(𝑘) = 𝑥 (𝑘) -𝐸[𝑥 (𝑘) ] √𝑉𝑎𝑟 [𝑥 (𝑘) ] (2)
where the expectation and variance are computed over the training data set.

In this paper, batch normalization is added after the output of each convolutional and deconvolutional layer.

Residual Connection

He et al. found the use of residual connections [START_REF] He | Deep residual learning for image recognition[END_REF] in the network which made it possible to train deep convolutional neural networks. They used residual connections on various datasets and proved the effect of residual connections. The residual block is defined as: y = F(𝑥, {𝑊 𝑖 }) + 𝑥 (3) Here x and y are the input and output vectors of the layers. The function F(𝑥, {𝑊 𝑖 }) is a residual mapping that needs to be learned. The structure of the residual block is shown in Fig. 2.

Image Transformation Network

A module we designed with fewer parameters is proposed in this part. First, we introduced design ideas of modules with fewer parameters. Then, we proposed a module called Fusion which enabled us to build an image transformation.

Design Idea

Our objective is to define a CNN module with fewer parameters while keeping the image transformation network capability of generating similar quality images. To achieve it, the following ideas are used in the design of module: Use 1x1 filters. Given a certain number of filters, the module will use more of the 1x1 filters because the parameters of 1x1 filters are 9 times less than the parameters of 3x3 filters.

Reduce the number of input channels to the 3x3 filters. When the layer contains 3x3 filters, the convolution layer will have (number of input channels) * (number of filters) * (3 * 3) parameters. Obviously, the parameters of the convolution layer can be effectively reduced by reducing the number of filters and the number of input channels. It is crucial to reduce the number of input channels and the number of filters to reduce the convolution layer parameters. These ideas will be applied to getting a novel module and utilizing it on the main body of the image transformation network. The first layers of the network are the convolution layers used to downsampling, and the last layers of the network are the deconvolution layers for upsampling.

The Fusion Module

The Fusion module is defined as follows. y = joint{𝑊 1×1 2 (𝑊 1 ×1 1 𝑥), 𝐹(𝑊 1×1 1 𝑥) + 𝑊 1×1 1 𝑥} (4) Here, x and y are module inputs and outputs, the function F is the residual mapping mentioned above. 𝑊 1×1 1 is the first 1x1 convolution layer used to reduce the number of input channel. Then, the output of the first 1x1 convolution layer is fed into second 1x1 convolution layer 𝑊 1 ×1

2 and a residual block respectively. Finally, the output of the second 1x1 convolution layer and the output of the residual block are jointed as output of the module.

In the Fusion module, the residual block consists of two 3x3 convolution layers. The output of each convolution layer is normalized by batch normalization and the activation function is ReLU. The Fusion module is illustrated in Fig. 3.

The Transformation Network Architecture

The transformation network architecture is shown in Fig. 4. The image transformation network starts with three convolution layers used for downsampling, followed by 5 Fusion modules, and finally ends with three deconvolution layers used for upsampling to generate the final image.

In Fig. 5, it is obvious that parameters in our network are much lower than those in

Experiments

We use the perceptual loss function as the objective function to train the image transform network. The Microsoft COCO dataset [START_REF] Lin | Microsoft COCO: common objects in context[END_REF] is used to train our image transformation network. Our implementation uses Tensorflow [START_REF] Abadi | TensorFlow: A system for large-scale machine learnin[END_REF] and cuDNN [START_REF] Chetlur | cuDNN: efficient primitives for deep learning[END_REF]. The whole training process is accomplished on a single GTX 950M GPU. At the same time, we also have reimplemented the work of Johnson et al. In Fig. 6, our image transformation network has generated images with similar quality to Johnson et al.

In Table 1, we compared the runtime of two networks on different sizes of images. In different sizes of images, our network has a faster speed. Compare to the network of Johnson et al., our network is 12% faster on average. Experiments show that our network can meet the higher real-time requirements.

Since the image transformation network is a fully convolutional neural network, it can be applied to any resolution image as long as the machine memory is enough.

Conclusion

In this paper, Fusion module is proposed based on some design ideas, and an image transformation network is constructed with the module to obtain a network with fewer parameters and better real-time performance. Experimental results show that, compared with the network of Johnson et al., the number of parameters in our network is greatly reduced and the runtime is shortened when similar quality images are generated.

In the future, we would like to explore a better network architecture in image transformation tasks. We hope that an image transformation network with smaller and higher real-time performance can be applied to smart phones, which enable them to partly break away from the constraints of mobile phone hardware conditions.

Fig. 1 .

 1 Fig. 1. Convolution and deconvolution operations for downsampling and upsampling.

Fig. 2 .

 2 Fig. 2. Residual block.

Fig. 3 .

 3 Fig. 3. The architecture of Fusion module.

Fig. 5 .

 5 Fig. 5. Parameters for each layer of the network. (a)The network of Johnson et al. (b) Our network.

Fig. 6 .

 6 Fig. 6. (a) The content images. (b) The style images. (c) Results of Johnson et al. (d) Our results.

  Johnson et al. Compared with their network, our network parameters decreased by 62.3%.

	Layer name	output size	filter size/stride	depth	parameter	Layer name	output size	filter size/stride	depth	parameter
	input image	256×256×3				input image	256×256×3
	conv1	256×256×32	9×9/1	1	7,808	conv1	256×256×32	9×9/1	1	7,808
	conv2	128×128×64	3×3/2	1	18,496	conv2	128×128×64	3×3/2	1	18,496
	conv3	64×64×128	3×3/2	1	73,856	conv3	64×64×128	3×3/2	1	73,856
	resid1	64×64×128		2	295,168	fusion1	64×64×128		3	86,272
	resid2	64×64×128		2	295,168	fusion2	64×64×128		3	86,272
	resid3	64×64×128		2	295,168	fusion3	64×64×128		3	86,272
	resid4	64×64×128		2	295,168	fusion4	64×64×128		3	86,272
	resid5	64×64×128		2	295,168	fusion5	64×64×128		3	86,272
	conv5	128×128×64	3×3/(1/2)	1	73,792	conv5	128×128×64	3×3/(1/2)	1	73,792
	conv6	256×256×32	3×3/(1/2)	1	18,464	conv6	256×256×32	3×3/(1/2)	1	18,464
	conv7	256×256×3	9×9/1	1	7,779	conv7	256×256×3	9×9/1	1	7,779
	output image	256×256×3				output image	256×256×3
				16	1,676,035				21	631,555
				(total)	(total)			(total)	(total)
			(a)					(b)
		256×256×32					256×256×32
			Convolution network	Fusion modules	Deconvolution network
		128×128×64					128×128×64
			64×64×128		64×64×128	64×64×128
	256×256×3									256×256×3
			Fig. 4. The architecture of transformation network.

Table 1 .

 1 For different sizes of images, the table lists the speed of our network and the speed of the network of Johnson et al. Our network gets stylized images of similar quality but is faster than the network of Johnson et al. Both methods are implemented on GTX 950M GPU.

	Image size	Johnson et al	Ours	Speedup
	256×256	0.098s	0.084s	14.3%
	512×512	0.335s	0.296s	11.6%
	1024×1024	1.244s	1.1s	11.6%

 Fund project: The 48 th Project Sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry; Shanghai Science and Technology Commission Innovation Project (16DZ1201402).


Haitao Qi

Zhijing Xu

Ship Identification based on Ship Blade Noise

Keywords: Blade noise, Digital hydrophone, Softmax, CDBN

In this paper, we propose a new method about ship identification, which is based on paddle noise for classification and recognition. Blade noise is the feature that is used to identify ships, blade noise is collected by digital hydrophone, then the signal is converted to spectrum. We extract features based on the difference of the pixel in the picture. What's more, the softmax classification function is used to training blade noise samples by combining with CDBN technology. The maximal output is the sample that satisfies the minimum cost function. The results of our experiment show that the method can be applied for ships identification by combining CDBN, spectrum and softmax functions, the classification recognition rate increases with the increase of the convolution kernel.

Introduction

In recent years, more and more ship identification methods are forming, because of the development of pattern recognition and artificial intelligence. For example: wavelet analysis theory, support vector machine (SVM) [START_REF] Suykens | Least squares support vector machine classifiers[END_REF], neural network and deep Learning model [START_REF] Tang | Deep learning using linear support vector machines[END_REF]. Of course, there are other research methods, such as AIS and XAIRCRAFT XCope UAV vision system and Ship identification based on ship blade noise [START_REF] Can | Recognition of vessel acoustic signatures using non-linear teager energy based features[C]//Computational Intelligence for Multimedia Understanding (IWCIM)[END_REF].

The traditional ship identification generally uses the theory of wavelet analysis to analyze the characteristics and characteristics of ship noise. The wavelet packet technique is used to separate the characteristics of different frequency bands, and the maximum and outband energy is extracted in the wavelet band. This method is cumbersome and complex, and SVM is valid for small samples. In the neural network of ship identification, the DEMON spectrum is extracted by spectral estimation method, and then BP neural network is used to classify and identify, but the feature extraction can not represent the ship noise well, which is not conducive to the actual operation.

At present, the deep learning technology development is rapid, it can forms a more abstract high-level representation of attribute categories or characteristics by combining low-level features. Convolutional Deep Belief Networks(CDBN) [START_REF] Lee | Unsupervised feature learning for audio classification using convolutional deep belief networks[END_REF] is a method of deep learning, it has been successfully applied in recognition tasks, such as handwritten character recognition, face recognition and pedestrian detection. The experiment of the results show that CDBN model can extract features effectively.

In this paper, the noise of different types of ship blades are simulated by setting the number and size of the blades. The traditional neural network and the deep learning model are used respectively. Speedboat model's blades can produce different noise signals and simulate the cavitation noise [START_REF] Harrison | An experimental study of single bubble cavitation noise[END_REF] by using different blades, and then signals are processed by the convolutional belief neural network (CDBN).

Noise Collection of Ship Blade Signal

Ship radiation noise is mainly composed of propeller noise, mechanical noise and hydrodynamic noise. During the high speed of the ship, the noise generated by the propeller blades is divided into vacuosity and non-voiding noise. Vacuum noise is the main noise in ship sailing. The ship blade noise signal can be divided through the number of blades and the blade size of the propeller. In this experiment, five different blades of the propeller: 2 leaves, 3 leaves, 4 leaves, irregular 4 leaves, irregular 7 leaves. The rules of the ship propeller blades are divided to 24mm, 28mm, 32mm, 36mm, 40mm, 44mm according to the size; Irregular 4 leaves and irregular 7 leaves are not required in size. The signals of irregular blades are added to the rules signal to enhance the generalization ability of the deep neural network. Table 1 shows the number of signals collected for different types of propeller blades. 3 Blade Noise spectrum DHP8501 digital hydrophone collects the blade noise signal in the form of binary. Since the binary data is not suitable for filtering and feature extraction, the file format must be converted into a wav format, it is convenient to feature extraction and deep learning, so the binary file is converted to wav format files.

The sound signal is subjected to a windowing function and then subjected to Fourier transform. The resulting amplitude is represented by a spectrum with different shades of color, which is the phonogram of the sound signal we have obtained. In this paper, the blade noise signal is processed in the form of a spectrum similar to the speech signal processed in the form of speech spectrum. From the nature of the spectrum, we can see that the two-dimensional spectrum expresses the three-dimensional characteristics of the sound signal, which is the time characteristic of the horizontal axis, the frequency characteristic of the vertical axis and the energy characteristic of the gray scale in the spectrum.

Taking the pulse signal of the speedboat model as an example, this signal collects one of the 20 kinds of blade noise signals of the speedboat model. The number of blades is 4 leaves, the blade size is 32mm, and the time of blade noise signal is 30s. The wav format sound signal is represented by a spectrum, as shown in Figure 3. In order to solve the problem that the feature value is too large to fitted for the classifier, the pool layer is added to the classifier. Pooling generally uses the method of aggregate statistics to find the characteristic mean for different positions [START_REF] Adhikari | Construction of CNN Template via Learning with Random Weight Change Algorithm[END_REF]. The pooling process not only reduces the dimensionality of the feature but also enhances the generalization ability of the deep model, which can effectively prevent overfitting.

In the deep learning model [START_REF] Sainath | Deep convolutional neural networks for large-scale speech tasks[END_REF], there are three types of pooling, which are named average pooling, maximum pooling and random pooling. The average pooling is the average of the numbers in the pooled domain. The maximum pooling is the characteristic point of the neighborhood. The random pool is probability assigns a variable to sampling point and sub-sampled by probability. Convolutional Restricted Boltzmann Machines (CRBM) [START_REF] Krizhevsky | Factored 3-way restricted boltzmann machines for modeling natural images[END_REF] uses random pooling.

As can be seen from Figure 4, the hidden layer and the pooling layer have the same number of units, it set as k group according to the mentioned above, the total of pool layer binary is

P P N N  . Defining a binary interval )} , {( j i B    : ij h belong to  . The area block 
B of the hidden layer is fastened to the cell of the pooled layer.

Once the hidden layer of cell area block is activated, which is equals to the corresponding cell block is activated. Then, the maximum pooled CRBM energy function is:

       j i j i k j i k j i k j i k k j i v c h b v W h h v E , , , , , , ) ) * ( ( ) , ( (1)
The corresponding conditional probability is:

          B j i k j i k j i k j i h I h I v h P ) , ( , , ,
)) ( exp( 1)

) ( exp( ) | 1 ( (2)            B j i k j i k h I v p P ) , ( , )) ( exp( 1
1 ) | 0 ( (3)
In the above equation,

) ( , k j i h I 
is the energy increment expression of the activation function, the form follow as:

j i k k k j i v W b h I , , ) * ( ) (    (4)

Softmax Classification Function

The softmax classifier is a logistic regression function classifier that can be classified as multiple categories, which is a commonly used classifier for the deep learning classification model. The basic principle is to calculate the probability value of all independent samples, the probability value which meets the minimum sample regard as the optimal sample output, so as to achieve the effect of classification. Sigmoid is the activation function of the softmax classifier:

k j i , h c k P k W w N v N h N p N                                  )) ( exp( )) ( exp( )) ( exp( 1 ) ; | ( ) ; | 2 ( ) ; | 1 ( ) ( ) ( ) ( 2 ) ( 1 ) ( ) ( ) ( ) ( ) ( ) ( ) ( i T k i T i T i i i i i i i x x x Z x k y p x y p x y p x h          (6)
Where the model parameter is

k     , , 2 1
and the normalized function is

)) ( exp( ) ( 1 1 i T k j x Z    
. Now solving the probabilistic problem is transformed into solving the model parameters. In order to solve the model function, the cost function of the regression model is introduced. If the parameter makes the value of the cost function reach the minimum, the parameter is the solution. The cost function is set to:

        m i k j i T k j i T i x x j y m J 1 1 ) ( 1 1 ) ( 1 ) ( )) ( exp( )) ( exp( lg } { 1 1 ) (    (7)
For solving the cost function, the usual method is to find the partial derivative of the cost function, that is ) ( j for each parameter j  partial derivative, so that the partial derivative is 0, solving its value, and reversing to solve the sample parameter probability value.

Experimental Setup and Analysis of Results

Spectral Classification and Recognition Model

Using the web tutorial to produce the generated blade noise map into a data set similar to CIFAR-10.

Taking 30,000 samples from the data set and dividing the sample into two cases:

In first case, 10,000 samples will be divided into two groups, one group selected 8,000 samples as a training sample, leaving the 2000 samples as a test sample.

In second case, 20,000 samples will be divided into two groups, one group selected 16,000 samples as a training sample, leaving 4000 samples as a test sample.

First, preprocessed the data set: the first step is to whiten the data, reduce the correlation of adjacent pixels, so as to reduce the correlation between the features, and between the characteristics has the same variance. The second step, the data is divided into several copies, the number of each sample is 100. In this experiment, the CDBN structure is set as: the input layer is . In this experiment, the number of layers and convolution kernel, and the size of the convolution kernel are not specifically prescribed.

Classification and Recognition on Samples

In this experiment, the classification recognition rate was tested separately, in the first case tested by 10,000 samples, in the second case tested by 20,000 samples. The network structure of CDBN is set as: the input layer is As can be seen from the table, in general, the sample classification recognition rate is not very high, and the number of samples is high and the classification rate of the model is higher.

Recognition Rate about CDBN Network Structure

Since the recognition rate of the spectrum increases with the increase of the test sample, so in the test CDBN network structure on the impact of the impact of the experiment, only the second group of samples were tested. In this experiment, the experimental data are set as follows: The experiment divided 30,000 samples into two groups, 22,500 as training samples, and 7,500 as test samples. CDBN is a single layer structure, the input layer is

3 32 32   , the pool layer is 2 2 2  
. Learning rate is 0.05, the number of iterations are 200 times. The experimental results are shown in Figure 5 and Table 3. It can be seen from Figure 5 that as the number of convolution nuclei is from less to more, the classification recognition is increased firstly, and the classification recognition rate is almost constant at a certain stage. In the case of a certain number, the classification recognition rate is higher. The classification of the kernel increases the classification rate, but the classification recognition rate does not fluctuate in the case of a large number.

Conclusions

In this paper, we provide a new method of ship identification, which is based on CDBN technology to carry out ship classification and identification. In the experiment, we demonstrated the possibility of CDBN, spectrum and softmax to identify ships. Experimental results on identification rate of ship show that recognition rate of ship will increase when increasing the samples of the training; the number of convolution kernel is different, the recognition rate is different, too. Correspondingly, when we increase the number of convolution kernel, the recognition rate will increase. Therefore, if we want to improve the recognition rate of ship, we must increase the number of training samples and convolution kernel.

Fig. 1 .

 1 Fig. 1. Speedboat model As the experimental conditions are limited, this experiment can not collect propeller noise signal, the signals are simulated sampling in still water by a speedboat model, speedboat model is shown in Figure 1. The experiment simulated different ships By changing the type and size of the propeller blades constantly. Noise of the software control interface is shown in Figure 2.The ship blade noise signal can be divided through the number of blades and the blade size of the propeller. In this experiment, five different blades of the propeller: 2 leaves, 3 leaves, 4 leaves, irregular 4 leaves, irregular 7 leaves. The rules of the ship propeller blades are divided to 24mm, 28mm, 32mm, 36mm, 40mm, 44mm according to the size; Irregular 4 leaves and irregular 7 leaves are not required in size. The signals of irregular blades are added to the rules signal to enhance the generalization ability of the deep neural network. Table1shows the number of signals collected for different types of propeller blades.

Fig. 2 .

 2 Fig. 2. software control interfaceTable 1. Number of different types of blades collected signal

Fig. 3 .

 3 Fig. 3. Impeller noise spectrum

Fig. 4 .

 4 Fig. 4. Added the pool layer to conditional Restricted Boltsman Machine(CRBM)

Fig. 5 .

 5 Fig. 5. Influence of Network Structure on Classification Recognition Rate

Table 2 .

 2 . The specific parameters of the network are: learning rate is 0.05, the number of iterations is 100 times. According to the above data to get the spectrum CDBN model classification recognition rate as shown in Table2: The number of samples and the impact of ICA and ASSASE on the classification recognition

	32		32		3	; the convolution

Table 3 .

 3 The Influence of CDBN Network Structure on Classification Recognition Rate

Single layer structure :A layer of convolution +A layer of pooling Convolution count Convolution kernel size Classification recognition rate

		3 	3	59.8%
		5 	5	60.0%
		7 	7	60.3%
		3 	3	60.0%
	7	5 	5	60.3%
		7 	7	60.7%

Acknowledgments. This study was funded by the National Natural Science Foundation of China (Grant No.61404083).

the way of mathematical description is the same as below. Supposing there are m training sample :

For sample x, there is always a corresponding k-dimensional vector y as classification label. Then for

, then the probability that the class is judged to be k in the input is x is the

, and the probability is chosen among all the probabilities. The category is the optimal solution. The K input classifier will get a k-dimensional output vector, and the sum of all the elements of the vector is 1. Output set to:


Jianguo Huang

Lang Sun

Hu Meng

Beam Bridge Health Monitoring Algorithm Based on Gray Correlation Analysis

Keywords: Beam bridge, bridge health assessment, Gray Correlation Analysis

Bridge construction investment is huge and the service cycle is long. During the service cycle, the bridge structure not only beared the load effect caused by fatigue damage, but also effected by the natural environment and human damage. Beam bridge is the most kind of bridge built on the highway and had a long-term service in China. The main beam of beam bridge is the main load-bearing component. Real-time evaluation of main beam's health degree will greatly improve the safety of highway transportation. Through the rapid assessment of the main beam of the bridge, it can not only directly reflect whether the deflection of the main beam is beyond the dangerous range and the overall condition of the main beam, but also observe the long-term variation rule of the main beam. The current assessment algorithm only stays in the monitoring of whether the deflection of the main beam is beyond the dangerous range, without a complete assessment combined with massive historical data. Based on the theory of Gray Correlation Analysis and combined with the realtime data and historical data of bridge monitoring, we calculate the statistical indicator and morphological indicator of the main beam quickly, and evaluate the comprehensive health indicator of the bridge according to the technical specification in this paper.

Introduce

In recent years, with the continuous development of computer technology, communication technology, embedded sensors and other technologies, the use of computer systems for automatic health monitoring has become the main method of bridge monitoring. The linear evaluation of bridge main beam is an important indicator which can reflect the safety of the bridge. Through the linear monitoring of the main beam of the bridge, it can not only directly reflect whether the deflection of the main beam is beyond the dangerous range and the working condition of the main beam in the operating state, but also observe the long-term variation rule of the main beam.

And the linear evaluation of the main beam of the bridge is of great significance to the bridge bearing capacity detection and the bridge earthquake disaster mitigation.

The linear evaluation of bridge main beam theory is still in the exploratory stage and without unified evaluation method in our country. The method remains on the level of monitoring whether the original data of the main beam deflection exceeds the risk threshold. There is no standardized assessment of the bridge data.

Gray system theory is a discipline with good theoretical research and application value. Gray Correlation Analysis plays a significant role in theoretical research and application study, which is one of the important parts of Gray system theory. Gray Correlation Analysis is a method of analyzing and determining the degree of correlation between factors or factors on the main behavior of the system through the calculation of gray relational degree. The calculation of Gray Correlation Grade is the basement and an important tool of Gray Correlation Analysis. Therefor the establishment and improvement of the Gray Correlation Model is an important discussion topic of the Gray Correlation Analysis. Gray Correlation Grade theories now catch the attention of the scholars at home and abroad, and become an important branch of future developments and researches of the Gray system study area. Applying the Gray Correlation Grade theory to evaluate the morphological characteristics of the main beam can provide reliable analysis theory support for bridge monitoring.

In this paper, a health assessment algorithm for the main beam of beam bridge based on Gray Correlation Grade is proposed, which can solve the shortcomings that evaluation cannot be synthetically in existing technologies.

Health Monitoring Algorithm

The algorithm mainly includes the following steps :

Step 1: Define the evaluation strategy Define the range of the outliers and the reference time range of the main beam deflection data.

Step 2: Obtain the original data of deflection Read original data obtained by the i sensors installed on the main beam in real time, and store them as arrays in memory. The array is defined as rawdata i {{id0, time, value}…{idn, time, value}}.

Step 3: Filter the deflection data Classify the scrolling raw data based on the evaluation strategy, dividing the array rawdata i into the array of reference values refdata i and the array to be evaluated candata i .

The value of the attribute value in the array rawdata i is filtered based on the range of the outliers of the main beam deflection data, and the arrays in the range of the outliers are discarded and the arrays in the range of the outliers are kept in the array rawdata i .

The value of the attribute time in the array rawdata i is filtered based on the reference time range of the main beam deflection data, and the arrays in the time range of the reference value are divided into the array of reference values refdata i , and the array is not within the time range of the reference values candata i .

Step 4: Calculate reference values Get the reference mean value a i0 and the reference variance value b i0 the reference displacement value d i0 and the reference numerical sequence L 0 from refdata i .

Calculate the base mean a i0 as follows:

n refdata refdata refdata in i     … a 1 0 i i0 (1)
In formula [START_REF] Lee | Fatigue Testing and Analysis(Theory and Practice)[END_REF], i is the number of measured points, refdata in is the n-th measured value of the i-th measure.

Calculate the benchmark variance b i0 as follows:

    n efdata efdata efdata 1 j i0 i0 ij i0 r r r n 1 b (2)
In formula [START_REF] Stuart | Data Analysis for Science and Engineering[END_REF], i is the number of measured points, refdata ij is the j-th measured value of the i-th measuring point.

Calculate the reference displacement value d i0 as follows:

n refdata refdata refdata refdata refdata refdata i i i in 1) - n ( i2 1 i1 0 i0 ... d        (3)
In formula (3), i is the number of measured points, refdata in is the n-th measured value of the i-th measure.

Calculate the displacement monitoring data L 0 .The baseline sequence is calculated as follows:

  0 i 10 00 0 a , … … , a , a  (4)
In formula (4), d 00 is the reference mean of the first measurement point, a i0 is the reference mean of the i-th measurement point.

Step 5: Evaluate the health characteristics of the main beam indicators According to the health data of the main beam, calculate the variance variation coefficient, the maximum variance variation coefficient, the maximum displacement change coefficient, the ride coefficient and the curvature change coefficient.

First, calculate the mean change coefficient. Calculate the mean value of the deflection monitoring point along the main beam distribution, and define the mean change coefficient Δa as follows:

    n 1 i i0 i a candata n 1 Δa (5)
In formula [START_REF] Ying | Parameter Estimation and Test of Fatigue Stress Probability Distribution Function of Large Span Bridge Based on Monitoring Samples[END_REF], n is point numbers, candata i is the measured value of the i-th measuring point, a i0 is the reference mean of the i-th measurement point.

Then calculate the variance change coefficient. Calculate the variance change of the overall deflection of the data at the deflection monitoring point along the main beam, and define the variance change coefficient Δb as follows:

    n 1 i i0 i0 i b b b n 1 Δb (6)
In formula [START_REF] Aj | Foundations of Structural Optimization: A Unified Approach[END_REF], Δb is the variance change coefficient of the deflection point, b i is the measured variance of the i-th measuring point, b i0 is the reference variance.

Then calculate the maximum variance change coefficient. Define the maximum variance change coefficient Δc as follows:

i i0 i0 b b Δc max b   (7)
In formula [START_REF] Fox | Rate of change of eigenvalues and eigenvectors[END_REF], Δc is the maximum variance change coefficient, b i is the measured variance of the i-th measuring point, b i0 is the reference variance.

Then calculate the maximum displacement change coefficient. Define the maximum displacement change coefficient Δd as follows:

i0 i0 i d d d max Δd   (8)
In formula [START_REF] Mo11er | Updating Large Finite Element Models in Structural Dynamics[END_REF], d i is the measured displacement of the i-th measuring point, d i0 is the reference displacement value.

According to the theory of Gray Correlation Analysis, the ride coefficient p is cal- culated, and the sequence of the j-th of the monitoring points is defined as

L j           i data c , … … , 3 data c , 2 data c , 1 candata j j j j j an an an L  (9)
Define the correlation coefficient as pj, the formula is as follows:

                            1 1 0 0 0 0 j ) 1 ( ) ( ) 1 ( ) 1 ( ) ( ) 1 ( 1 1 1 1 ) , ( p i k j j j j k L k L k L k L k L k L i L L r (10) k=1, 2, 3…, i-1
In formula [START_REF] Pmdey | Damage Detection in Structures Using Changes in Flexibility[END_REF], L 0 (k+1) is the k + 1 value of the reference numerical sequence, L 0 (k) is the k-th value of the reference numerical sequence, L j (k+1) is the k + 1 value of the data sequence to be evaluated, L j (k) is the k-th value of the data sequence to be evaluated.

Then normalize the degree of correlation. Take the j p average as a ride coeffi- cient, define smoothness coefficient of p as follows:

j j n    0 n p p (11)
In formula (11), j is the number of sampling points, j p is the correlation coeffi- cient obtained for the vector corresponding to the j-th sample point, p is the ride coefficient.

Then calculate the curvature change coefficient. Through the reference value data to obtain the reference deflection deformation curvature s i0 , define the data to be evaluated deflection deformation curvature s i as follows:

2 1 1 i 2 d d x d d s i i i i          (12)
In formula (12), s i is the deformation curvature of the i-th measuring point, d i+1 , d i , d i-1 are the i + 1 measuring point, i point, i-1 measuring point deflection deformation value, Δx is the distance between two adjacent measuring points.

The mean value of the difference of the vector is taken as the variation coefficient of curvature, and the variation coefficient of curvature is defined as Δs, and its formula is as follows:

    n i i i i s s s 1 0 0 n 1 Δs (13)
The data are scored based on the scoring rules and the weight table. Calculate the health status score F, the formula is as follows:

s p d c a w S w S w S w S w S w S F ) s ( ) p ( ) d ( ) c ( ) b ( ) a ( b            (

Experiment and Result

The experiment data were randomly selected from July 4 to July 18, 2016, a total of 9 days data of Wohe Bridge in Anhui Province for analysis. The data acquisition frequency is 1HZ. After statistical analysis, calculate the maximum displacement of the monitoring points as shown in Table 3.1, the displacement mean as shown in Table 33.2.

Conclusion

The evaluation algorithm proposed in this paper can make the bridge supervisors quick to judge the overall health status of the bridge and the damage status of the main beams compared with the existing technology. This method makes the monitoring data of the bridge have the characteristics of standardization and high efficiency, and improves the robustness and ease of use of the system, and has high engineering application value.

  14) In formula (14), F is the total score of linear evaluation, full score is 100 points, S(Δa) is the mean change coefficient score value, wa is the mean change coefficient weight, S(Δb) is the variance change coefficient score value, wb is the variance change coefficient weight, S(Δc) is the maximum variance change coefficient wc is the maximum variance change coefficient weight, S(Δ p )is the ride factor coefficient score value, wp is the ride coefficient weight, S(Δs) is the curvature change coefficient score value, ws is the curvature change coefficient weight.

Fig. 3 . 1 .

 31 Fig. 3.1. Line chart of main beam As you can see from Fig 3.1, there was a large fluctuation of displacement at No. 4monitoring point at 80m of the bridge which is under permanent load condition during the monitoring period. So we initially suspected there was something wrong of this bridge. Through the calculation of basic grey relational degree algorithm, the real health data of the bridge was obtained after analysis, the evaluation results are listed in Table3.4.

  .

  4.

Table 3 . 1 .

 31 The maximum displacement monitoring table(mm)

	Date	Monitoring point1	Monitoring point2	Monitoring point3	Monitoring point4	Monitoring point5

Table 3 .4. Linear evaluation results table

 3 According to the calculated values, the line chart of the bridge can be evaluated. Referring to《Rules for inspection and assessment of bearing capacity of Highway Bridges》(2003)and《Technical code for highway maintenance》(JTGH11-2004), the technical state of bridge is divided into five categories. The first class is intact and in good state; the second class is in good state; the third class is in poor state; the fourth class is in bad state; the fifth class is in dangerous state. The specific classification criteria are shown in the Table3.5.

	Ref	∆a	∆b	ai0	Curv
	Def1	-2.11	0.70	-2.11	2.00
	Def2	-1.54	0.50	-1.54	2.00
	Def3	-0.34	0.53	-0.34	2.00
	Def4	-16.45	3.65	-16.45	3.00
	Def5	-16.10	0.43	-16.10	2.00

Table 3 .5. Technical condition evaluation

 3

	Grade	Ⅰ	Ⅱ	Ⅲ	Ⅳ	Ⅴ
	value	80-100	60-80	40-60	20-40	0-20
	From

Table 3

 3 .5, this bridge's Technical condition evaluation is Grade Ⅲ, a poor state. It needs a maintenance work.