Ivana Strumberger
email: istrumberger@singidunum.ac.rs

Eva Tuba
email: etuba@singidunum.ac.rs

Miodrag Zivkovic
email: mzivkovic@singidunum.ac.rs

Nebojsa Bacanin
email: nbacanin@singidunum.ac.rs

Marko Beko
email: mbeko@uninova.pt

Milan Tuba
email: tuba@ieee.org

Dynamic Search Tree Growth Algorithm for Global Optimization

Keywords: tree growth algorithm, swarm intelligence, global, unconstrained, metaheuristics, dynamically adjusted parameters

This paper presents dynamic version of the tree growth algorithm. Tree growth algorithm is a novel optimization approach that belongs to the group of swarm intelligence metaheuristics. Only few papers addressed this method so far. This algorithm simulates the competition between the trees for resources such as food and light. The dynamic version of the tree growth algorithm introduces dynamical adjustment of exploitation and exploration search parameters. The efficiency and robustness of the proposed method were tested on a well-known set of standard global unconstrained benchmarks. Besides numerical results obtained by dynamic tree growth algorithm, in the experimental part of this paper, we have also shown comparative analysis with the original tree growth algorithm, as well as comparison with other methods, which were tested on the same benchmark set. Since many problems from the domains of industrial and service systems can be modeled as global optimization tasks, dynamic tree growth algorithm shows great potential in this area and can be further adapted for tackling many real-world unconstrained and constrained optimization challenges.

Introduction

Motivation

In the todays era of digitalization, Internet of Things (IoT) and Cloud Computing, the industry and service sectors are transforming by introducing many innovations, where the most emphasized features of these innovations are so-called "exponential technologies". This transformation is usually being expressed with the terms Industry 4.0, Smart Manufacturing and Economy 4.0.

In order to implement and to establish innovations in industry and service sectors, many problems need to be addressed. Most of such problems can be mathematically modelled and can be categorized as NP hard problems. For example, in the domains of Industry 4.0, cloud computing and IoT, many scheduling problems exist, that are characterized with NP hardness [START_REF] Leusin | Solving the Job-Shop Scheduling Problem in the Industry 4.0 Era, Technologies[END_REF]. Also, the implementation of the wireless sensor networks (WSN) is very important in conducting innovations of industry and service sectors. Many problems, such is the problem of localization, from this domain belong to the NP hard group [START_REF] Strumberger | Elephant Herding Optimization Algorithm for Wireless Sensor Network Localization Problem[END_REF].

Another area which belongs to the group of NP hard problems is data clustering, along with data mining. Modern industry often relies on large amount of data. Large datasets are stored on the clusters, and focus of the algorithms is partitioning any dataset into an optimal number of groups through one run of optimization [START_REF] Abraham | Swarm Intelligence Algorithms for Data Clustering[END_REF]. Modern industry also heavily depends on telecommunication networks. In order to keep the cost of the network reasonable, it is not possible to directly connect all communication nodes. Routing process is responsible for selection of the path between source and destination, by optimizing the objectives together with constraints. Any routing problem has the main objective to maximize the network performance [START_REF] Ducatelle | Principles and applications of swarm intelligence for adaptive routing in telecommunications networks[END_REF].

For solving NP hard problems, it is not enough to use classic deterministic algorithms, since the results cannot be generated in a reasonable amount of time. For such purposes, it is better to employ stochastic methods like metaheuristics. Metaheuristic algorithms can be roughly divided into those who are inspired by the nature, and those who are not nature inspired. Nature-inspired metaheuristics can be further divided into evolutionary algorithms (EA) and swarm intelligence (SI).

Swarm intelligence is population-based, stochastic and iterative search methods that try to improve the population of candidate solutions in a predetermined number of iterations. These algorithms simulate the groups of natural organisms such as flock of birds and fish, groups of fireflies and bats, etc. One of the first swarm intelligence approaches was particle swarm optimization (PSO) that was developed in 1990s [START_REF] Kennedy | Particle swarm optimization[END_REF]. After the PSO, many other swarm intelligence algorithms emerged that were adapted for solving benchmark, as well as real-world NP hard optimization tasks. Some of the examples include artificial bee colony (ABC) [START_REF] Bacanin | Artificial bee colony (ABC) algorithm for constrained optimization improved with genetic operators[END_REF], firefly algorithm (FA) [START_REF] Yang | Firefly Algorithm: Recent Advances and Applications[END_REF], fireworks algorithm (FWA) [START_REF] Strumberger | Bare Bones Fireworks Algorithm for the RFID Network Planning Problem[END_REF], monarch butterfly optimization (MBO) [START_REF] Wang | Monarch butterfly optimization[END_REF], and many others [START_REF] Tuba | Hybridized bat algorithm for multi-objective radio frequency identification (RFID) network planning[END_REF].

According to the literature review, many swarm intelligence algorithms were successfully applied to the domains that are crucial for innovations of industry and service sectors. The PSO metaheuristics obtained satisfying results for solving job-shop scheduling problem in the era of industry 4.0 [START_REF] Leusin | Solving the Job-Shop Scheduling Problem in the Industry 4.0 Era, Technologies[END_REF], as well as in some problems from the domain of IoT [START_REF] Nouiri | Using IoT in breakdown tolerance: PSO solving FJSP[END_REF] and cloud computing [START_REF] Masdari | A Survey of PSO-Based Scheduling Algorithms in Cloud Computing[END_REF], MBO algorithm can be applied to localization of unknown sensor nodes in some wireless sensor network topologies [START_REF] Strumberger | Monarch butterfly optimization algorithm for localization in wireless sensor networks[END_REF], etc.

Besides mentioned, swarm intelligence algorithms were also successfully applied to the domains of neural networks training [START_REF] Tuba | Cuckoo Search and Bat Algorithm Applied to Training Feed-Forward Neural Networks[END_REF], and image processing [START_REF] Tuba | Multilevel image thresholding using elephant herding optimization algorithm[END_REF]. In recent years, the adaptations of swarm intelligence methods for image classification with convolutional neural networks (CNN) emerged [START_REF] França Da Silva | Convolutional neural network-based PSO for lung nodule false positive reduction on CT images[END_REF]. These implementations may be crucial for digitalization.

Since the role of metaheuristics will be important in solving NP hard problems that will have to be tackled in the process of transformation of industry and service sectors, and due to the fact that many of such problems can be formulated as bound-constrained and constrained problems global optimization problems, our motivation behind this work is to improve recently proposed tree growth algorithm (TGA) [START_REF] Cheraghalipour | Tree Growth Algorithm (TGA): An Effective Metaheuristic Algorithm Inspired by trees' behavior, 13[END_REF] for global optimization assignments.

In this paper, we propose dynamic version of the TGA swarm intelligence metaheuristics for solving global optimization problems. The approach was tested on a set of standard global benchmarks, and with only few modifications, enhanced TGA can be successfully applied to NP hard problems from the domain of innovations of industry and service sectors.

Research Question

According to the literature survey [START_REF] Cheraghalipour | Tree Growth Algorithm (TGA): A novel approach for solving optimization problems[END_REF], [START_REF] Cheraghalipour | Tree Growth Algorithm (TGA): An Effective Metaheuristic Algorithm Inspired by trees' behavior, 13[END_REF], we concluded that the TGA can be successfully applied to solving range types of NP hard problems. The research question, which is the stepping stone of this paper, can be formulated as:

How to design and implement an efficient and robust TGA algorithm that will be capable of solving bound constrained and constrained global optimization NP hard problems and that will outperform other similar approaches tested for the same problem formulations?

In order to address the research question, we have formulated the following hypothesis:

TGA metaheuristics, as novel and promising method, can be further improved for solving global optimization NP hard problems. Once improved, this method can be easily adapted for solving different kinds of problems that innovations in industry and service systems face.

Related Work

TGA algorithm is novel approach that was developed in 2017 by Armin Cheraghalipour and Mostafa Hajiaghaei-Keshteli [START_REF] Cheraghalipour | Tree Growth Algorithm (TGA): An Effective Metaheuristic Algorithm Inspired by trees' behavior, 13[END_REF]. The algorithm was tested on standard global optimization benchmarks and on some engineering (constrained) benchmarks [START_REF] Cheraghalipour | Tree Growth Algorithm (TGA): An Effective Metaheuristic Algorithm Inspired by trees' behavior, 13[END_REF]. According to the literature review, only two papers that deal with this specific method exist in the literature.

On the other hand, since many NP hard problems that are crucial for innovations in industry and service systems were successfully tackled by employing swarm intelligence methods [START_REF] Abraham | Swarm Intelligence Algorithms for Data Clustering[END_REF], [START_REF] Nouiri | Using IoT in breakdown tolerance: PSO solving FJSP[END_REF], [START_REF] Masdari | A Survey of PSO-Based Scheduling Algorithms in Cloud Computing[END_REF], and according to our analysis of the TGA metaheuristics, we concluded that the TGA can be further improved, and in the future research can be adapted for solving various kinds of problems from the domain innovations of industry and service systems.

Contributions

In this paper, we show an implementation of the improved tree growth algorithm (TGA) adopted for solving global optimization problems. Due to the fact that many problems from the domains of industry and service sectors innovation can be formulated as global NP hard problems, the improved TGA in the unmodified, or slightly adjusted implementations, can be easily applied to these problems as well, and this represents the clear contribution of this paper for this research domain. Also, we should emphasize that the TGA was not explored well, since only two papers that deal with this approach exist in the literature.

Relationship to Technological Innovation for Industrial and Service Systems

Currently, the 4th industrial revolution is taking place. The idea of this new revolution is represented by the terms Industry 4.0, Smart Manufacturing and Economy 4.0. During this revolution, many innovations of industrial and service systems will take place. Cloud computing, Internet of things, WSNs, and the integration of "exponential technologies" play an important role in these innovations. Also, during these innovations, increasing digitalization and interconnection of systems, products, value chains, and business models is needed. However, in order for innovations to be successfully implemented, many problems should be solved. As already, stated in the Section 1, most of such problems can be mathematically modelled and such they belong to the category of NP hard optimization. Since the swarm intelligence methods have proved to be efficient in solving NP hard problems, these methods can be applied for tackling NP hard problems when innovating industrial and service systems.

For example, cloud computing may be the production platform for technological innovations for industrial and service systems. According to the literature review, load balancing [START_REF] Li | A hybrid particle swarm optimization algorithm for load balancing of MDS on heterogeneous computing systems[END_REF], as well as scheduling problems [START_REF] Kalra | A review of metaheuristic scheduling techniques in cloud computing[END_REF] in cloud computing environments were successfully tackled by using swarm intelligence metaheuristics. Swarm intelligence has also been successfully applied in numerous optimizations in telecommunications and routing systems [START_REF] Ducatelle | Principles and applications of swarm intelligence for adaptive routing in telecommunications networks[END_REF].

Basic and Enhanced Tree Growth Algorithm

TGA is novel swarm intelligence metaheuristics developed in 2017 by Armin Cheraghalipour and Mostafa Hajiaghaei-Keshteli [START_REF] Cheraghalipour | Tree Growth Algorithm (TGA): An Effective Metaheuristic Algorithm Inspired by trees' behavior, 13[END_REF]. TGA models the competition between the trees in the nature for acquiring light and food sources.

Inspired by the nature, one iteration of the TGA is divided into four phases. In the first phase, N1 better solutions (individuals) in the population perform the local search process. In the second phase, N2 solutions are moved to the distance between the close best solutions under different α angles. Third phase is conducted by discarding N3 worst solutions from the population, which are replaced by randomly generated solution from the feasible domain of the search space. Finally, in the fourth phase, N4 new solutions are generated, when each newly created solution is modified by the mask operator respect to the best solution in the set N1 that consists of best solutions in the population.

One execution (run) of the TGA can be summarized in the eight following steps:

Step 1: Generate random initial population of N candidate solutions within the values of lower and upper parameters' bound and calculate fitness of each solution. In the case of minimization problems, the fitness is reverse proportional to the value of objective function.

Step 2: Sort population according to the value of fitness and find the current best solution T j GB, where j-th denotes the current iteration.

Step 3: For N1 better solutions in the population perform the local search for all solutions' parameters according to the Eq. ( 1) [START_REF] Cheraghalipour | Tree Growth Algorithm (TGA): An Effective Metaheuristic Algorithm Inspired by trees' behavior, 13[END_REF].

𝑻 𝒊 𝒋+𝟏 = 𝑻 𝒊 𝒋 𝜽 + 𝒓𝑻 𝒊 𝒋 (1)
where 𝑇 𝑖 𝑗 and 𝑇 𝑖 𝑗+1 denote the i-th solution in the population in the iterations j (old solution i) and j+1 (new solution i), respectively, 𝜃 is the reproduction rate parameter, and r is uniformly distributed pseudo-random number between 0 and 1. When the new solution is generated, the selection between old and new solution is performed using greedy mechanism (the solution with the higher fitness value is retained).

Step 4: N2 solutions from the population are moved towards the two closest better solutions from the subpopulation N1 under different α angles. To find the closest best solutions, the following equation is applied for all solutions' parameters [START_REF] Cheraghalipour | Tree Growth Algorithm (TGA): An Effective Metaheuristic Algorithm Inspired by trees' behavior, 13[END_REF]:

𝑑 𝑖 = √ ∑(𝑇 𝑁 2 𝑗 -𝑇 𝑖 𝑗 ) 2 𝑁 1 𝑖=1 , where 𝑑 𝑖 = { 𝑑 𝑖 𝑖𝑓 𝑇 𝑁 2 𝑗 ≠ 𝑇 𝑖 𝑗 ∞ 𝑖𝑓 𝑇 𝑁 2 𝑗 ≠ 𝑇 𝑖 𝑗 (2)
When the distance is calculated, then two solutions x1 and x2 with the minimal distance (di) from each solution 𝑇 𝑁 2 𝑗 are selected for producing linear combination by using the equation [START_REF] Cheraghalipour | Tree Growth Algorithm (TGA): An Effective Metaheuristic Algorithm Inspired by trees' behavior, 13[END_REF]:

𝑦 = 𝜆𝑥 1 + (1 -𝜆)𝑥 2 (3)
where 𝜆 represents the TGA control parameter and its value is between 0 and 1. Finally, all solutions 𝑇 𝑁 2 𝑗 from the subpopulation N2 are moved between two adjacent solutions with α different angles by employing Eq. ( 4) [START_REF] Cheraghalipour | Tree Growth Algorithm (TGA): An Effective Metaheuristic Algorithm Inspired by trees' behavior, 13[END_REF].

𝑇 𝑁 2 𝑗 = 𝑇 𝑁 2 𝑗 + 𝛼 𝑖 𝑦 (4)
Step 5: The N3 worst solutions are discarded from the population and they are replaced with the randomly generated solutions from the feasible region of the search space.

Step 6: New population N is created, where N= N1 + N2 + N3

Step 7: In this step, new set of N4 randomly distributed solutions are created and each solution is modified by using the mask operator respect to the best solution from the subpopulation N1. Solutions from the N4 subpopulation are then added to the population N.

Step 8: Population N+ N4 is sorted according to the fitness and the best N solutions are chosen as the initial population for the next iteration of the algorithm. In this step, tournament or roulette wheel selection process is employed.

In the presented steps, N, N1, N2, N4, 𝜃 and 𝜆 are the control parameters of the TGA.

Dynamic Tree Growth Algorithm

Two basic processes of any swarm intelligence algorithm, that guide the search, are the exploitation (intensification) and exploration (diversification). To obtain the satisfactory results, the balance between these two processes should be adjusted well.

If the balance between exploration and exploitation in not properly balanced in favor to exploitation, the algorithm may converge to suboptimal solutions in early iterations, and may be trapped in the local optimum, which is called the premature convergence. At the other hand, if this balance is in favor of exploration, it can happen that the algorithm may find the right part of the search space, but may not be able to fine tune around the current best solutions. As a consequence, the algorithm cannot find the optimum solution.

The exploration and exploitation in the original TGA are controlled by two control parameters 𝜃 and 𝜆, and the number of solutions N3, which are discarded from the population in each iteration. The 𝜃 parameter directs the local search process, while the 𝜆 parameter directs solutions towards the better solutions in the population. Both parameters conduct the process of exploitation. At the other hand, by adjusting N3, the process of exploration is conducted. All three parameters are static during the whole course of algorithm's execution.

By conducting empirical tests, we concluded that the TGA performance can be improved by dynamically adjusting the values of 𝜃 and N3. In early iterations of algorithm's execution, the value of the 𝜃 parameter should be lower, and the newly generated solution should be further from the current solution (Eq. ( 1)). Basic assumption is that the algorithm in early iterations has not found the right part of the search space. However, in later iterations, the value of the 𝜃 parameter should be higher, with the assumption that the algorithm has found the proper part of the search space. The value of the parameter 𝜆 should be static, since the nature of Eq. (3).

Also, the number of solutions that are discarded from the population (parameter N3) should be higher in early iterations, since in this phase more exploration power is needed. The change of N3 parameter is directly influenced by the values of N1 and N2.

By incorporating dynamic behavior of 𝜃 and N3, we devised dynamic search TGA (dynsTGA) metaheuristics. Pseudo-code of dynsTGA is shown in Algorithm 1.

Algorithm 1 Pseudo-code of the dynsTGA metaheuristics

Initialization. Generate pseudo-random population, set the iteration counter t=1, maximum iteration number MaxIter, and initial values for 𝜃 and N3 control parameters while t < MaxIter do Evaluate population and sort all solutions according to their fitness for all solutions in N1 Perform local search by using Eq. ( 1) Apply greedy selection between old and new solution end for for all solutions in N2 Algorithm was tested in 30 independent runs. We performed comparative analysis with outstanding stochastic algorithms from the literature that were tested on the same benchmark set, and compared obtained values for best, mean and standard deviation performance indicators. Comparative analysis is presented in Table 2, where the best results for each performance indicator and each benchmark test are bolded.

First thing that we want to emphasize is that from the Table 2, it is clear that the dynsTGA outperforms original TGA metaheuristics. For example, in F3 (BL Problem) benchmark, dynsTGA showed better performance for best, mean, as well as for standard deviation indicator. Similar, in the case of F8 (GRP) test, dynsTGA showed significantly better robustness than the basic TGA. The improvements over unmodified TGA are also significant in the F6 test, where dynsTGA obtained better values.

As can be seen from Table 2, comparative analysis with IBPA [START_REF] Cheraghalipour | Tree Growth Algorithm (TGA): An Effective Metaheuristic Algorithm Inspired by trees' behavior, 13[END_REF], LADA [START_REF] Cheraghalipour | Tree Growth Algorithm (TGA): An Effective Metaheuristic Algorithm Inspired by trees' behavior, 13[END_REF], TS [START_REF] Cheraghalipour | Tree Growth Algorithm (TGA): An Effective Metaheuristic Algorithm Inspired by trees' behavior, 13[END_REF] and WSA [START_REF] Cheraghalipour | Tree Growth Algorithm (TGA): An Effective Metaheuristic Algorithm Inspired by trees' behavior, 13[END_REF] was performed. In average, dynsTGA approach performs better than all other metaheuristics that are included in comparative analysis. The superiority of dynsTGA can be best pictured in the F3 (BL Problem) benchmark, where dynsTGA completely outperformed all other approaches for best, mean and standard deviation indicators.

Conclusion

In this paper, a modified and improved version of relatively new tree growth algorithm (TGA) metaheuristics was presented. Only few papers from the literature deal with the TGA approach. Original TGA was enhanced by introducing dynamical adjustment of exploitation and exploration search parameters. The performance of enhanced TGA metaheuristics was measured on standard set of global optimization benchmarks. In the experiments, similar parameter setup as in [START_REF] Cheraghalipour | Tree Growth Algorithm (TGA): An Effective Metaheuristic Algorithm Inspired by trees' behavior, 13[END_REF] was applied. To prove the robustness and solutions' quality of the dynamic TGA, comparative analysis with the basic TGA, as well as with other state-of-the-art algorithms for global optimization was conducted. From the presented side-by-side comparison, it is obvious that the dynamic TGA significantly improves original TGA and performs better than other algorithms that were included in analysis.

Since many problems from the domains of industrial and service systems can be modeled as global optimization tasks, dynamic TGA metaheuristics shows great potential in this area and can be adapted for tackling many real-world unconstrained and constrained optimization challenges.

Table 2 .

 2 Simulation results for performance indicators and comparative analysis.

	ID	Indicator	IBPA	LADA	TS	WSA	TGA	dynsTGA
		Best	0.00815	0.00088	0.14185	0.888E-15	0.00	0.00
	F1	Mean	0.02260	0.00473	0.38528	0.888E-15	0.00	0.00
		StdDev	0.01021	0.00157	0.07488	1.0029E-31	0.00	0.00
		Best	-0.35238	-0.35238	-0.35238	-0.35238	-0.35239	-0.35239
	F2	Mean	-0.35238	-0.35238	-0.35238	-0.35236	-0.35239	-0.35239
		StdDev	1.067E-6	5.576E-7	2.183E-5	8.761E-6	6.09E-07	3.53E-09
		Best	3.217E-9	1.259E-9	3.955E-7	5.589E-8	1.07E-08	0.00
	F3	Mean	2.826E-7	2.486E-7	7.637E-6	1.267E-7	3.70E-07	2.04E-09
		StdDev	2.838E-7	2.704E-7	6.302E-6	3.877E-8	5.84E-07	5.11E-10

Acknowledgements: This research is supported by the Ministry of Education, Science and Technological Development of Republic of Serbia, Grant No. III-44006. The work of M. Beko was supported in part by Fundao para a Cincia e a Tecnologia under Projects PEst-OE/EEI/UI0066/2014 (UNINOVA) and Program Investigador FCT (IF/00325/2015).

Move solutions towards the closest best solutions in N1 by using Eqs. ( 2) -(4) Apply greedy selection between old and new solution end for Discard N3 worst solutions from the population and replace them with pseudo-random solutions Generate N4 randomly distributed solutions and modify each solution in respect to the best solutions in N1 by using the mask operator Evaluate population and sort all solutions according to their fitness Choose N solutions for the new iteration Adjust the values of 𝜃 and N3 along with N2 and N1 control parameters end while return the best solution in the population

Empirical Results and Discussion

For the purpose of research presented in this paper we devised our own software framework by using .NET 4.5 technology and C# in Visual Studio 2017 Integrated Development Environment. In the framework we incorporated both, original TGA and dynsTGA, along with the benchmark functions.

To evaluate the performance of the dynsTGA, we used similar parameter values as in [START_REF] Cheraghalipour | Tree Growth Algorithm (TGA): A novel approach for solving optimization problems[END_REF] and [START_REF] Cheraghalipour | Tree Growth Algorithm (TGA): An Effective Metaheuristic Algorithm Inspired by trees' behavior, 13[END_REF]. The size of initial population (N) was set to 100, while N1 and N2 were set to 20. In the beginning of the algorithm's execution, the value of N3 was set to 60 (N3 = N-(N1 + N2)). The value of N4 was set to 30 and it was fixed during the whole course of the algorithm's execution. The maximum iteration number (MaxIter) was set to 250, and the algorithm was executed in 30 independent runs.

In the first 210 iterations, the values of N1, N2 and N3 were fixed. Then, in each of the following iterations (from iteration 211 to iteration 250), the value of N3 was decremented by one, and the values of N1 and N2 were incremented by 1 in even and odd iterations, respectively. The value of the 𝜆 parameter was set to 0.5, while the initial value of θ was set to 0.2. In each iteration, the value of 𝜃 was adjusted according to the following expression: 𝜃 𝑗+1 = 𝜃 𝑗 • 1.002 , until the threshold value of 1.5 is reached.

We tested dynsTGA on a standard set of global optimization benchmarks and compared results with the original TGA, as well as with other state-of-the-art approaches that were tested on the same benchmark test. Details of benchmark functions are given in Table 1.


João P Matos-Carvalho

André Mora

Raúl T Rato

Ricardo Mendonça

José M Fonseca

UAV Downwash-based Terrain Classification Using Wiener-Khinchin and EMD Filters

Keywords: Image Processing, Wiener-Khinchin, EMD, IMF, Machine Learning, Terrain Classification, UAV

Knowing how to identify terrain types is especially important in the autonomous navigation, mapping, decision making and detect landings areas. A recent area is in cooperation and improvement of autonomous behavior between robots. For example, an unmanned aerial vehicle (UAV) is used to identify a possible landing area or used in cooperation with other robots to navigate in unknown terrains. This paper presents a computer vision algorithm capable of identifying the terrain type where the UAV is flying, using its rotors' downwash effect. The algorithm is a fusion between the frequency Wiener-Khinchin adapted and spatial Empirical Mode Decomposition (EMD) domains. In order to increase certainty in terrain identification, machine learning is also used. The system is validated using videos acquired onboard of a UAV with an RGB camera.

Introduction

Nowadays, Unmanned Aerial Vehicles (UAVs) have a huge impact in the area of research and industry. In the research area since it is a recent topic and therefore little studied. In the area of industry, being a lightweight and low-power robot, it can be used in various situations, such as precision agriculture, emergency landings and rescue missions.

In this case, the task of the UAV is to cooperate with the Unmanned Surface Vehicle (USV) so that it reaches its intended destination. This is the purpose of this paper.

With an RGB camera mounted on the UAV it is possible to receive the images from the outside from the effect of the downwash caused by the UAV. This effect is only felt at low altitudes and has unique effects on the terrain (e.g. the circular wave effect on the water terrain).

Two different techniques were merged to classify the terrain type: Wiener-Khinchin Theorem and Empirical Mode Decomposition (EMD). These techniques, although well developed for 1-D signals, are seldom applied in image processing domain. Thus, many of the following questions are still unknown: How to apply both algorithms for image processing? Is it necessary to evaluate the whole image or may it be necessary to find methods to reduce computational complexity? Knowing that the EMD technique decomposes the signal into several intrinsic mode functions (IMFs), which of the IMFs best classifies the type of terrain? How can the Wiener-Khinchin theorem and the EMD algorithm complement each other? These challenges are addressed throughout this work and how they have been resolved to have the best results possible.

Contribution to Industrial and Service Systems

The behavior of different terrains when exposed to wind turbulence (downwash effect) is discernable and, by processing that behavior using Wiener-Khinchin (W-K) and EMD filters, we can generate singular results to identify a terrain type. Exploiting this knowledge, a novel terrain classifier is proposed and presented as a scalable service that can be used simultaneously by several applications and systems. Therefore, the herein presented work is focused on the Industrial and Service Systems domain, specifically, in Robotics and Integrated Manufacturing and Signal Processing topic.

Related Work

UAV applications is a topic that lately has arisen a lot and is becoming more important in several areas and is predicted that it will soon have a great impact on society [START_REF] Bestaoui Sebbane | Intelligent Autonomy of UAVs: Advanced Missions and Future Use[END_REF]. One of the areas that is causing a great impact is in the area computer vision with the aim of making an UAV in an autonomous robot. For this, and concretely in this paper, the UAV must be able to capture, process and analyze the images of the environment in order to classify the type of terrain where it is flying over. Many researchers have proposed several methods in the classification of terrain, such as texture algorithms [START_REF] Linderhed | Image Empirical Mode Decomposition: A New Tool For Image Processing[END_REF], [START_REF] Feng | UAV Remote sensing for urban vegetation mapping using random forest and texture analysis[END_REF], [START_REF] Khan | Grid-based visual terrain classification for outdoor robots using local features[END_REF], [START_REF] Pietikäinen | Computer Vision Using Local Binary Patterns[END_REF], color information [START_REF] Ebadi | Road Terrain detection and Classification algorithm based on the Color Feature extraction[END_REF] and other types of sensors that help in the classification decision, such as LIDAR type sensors [START_REF] Yan | Urban land cover classification using airborne LiDAR data: A review[END_REF], [START_REF] Wallace | Assessment of forest structure using two UAV techniques: A comparison of airborne laser scanning and structure from motion (SfM) point clouds[END_REF], [START_REF] Gruszczynski | Comparison of lowaltitude UAV photogrammetry with terrestrial laser scanning as data-source methods for terrain covered in low vegetation[END_REF]. Currently, on terrain classification using the downwash effect concept there are two recent works [START_REF] Pombeiro | Water detection from downwash-induced optical flow for a multirotor UAV[END_REF], [START_REF] Matos-Carvalho | UAV downwash dynamic texture features for terrain classification on autonomous navigation[END_REF]. The first one, used the concept of optical flow to detect the dynamic features of an image (Lucas Kanade algorithm [START_REF] Pombeiro | Water detection from downwash-induced optical flow for a multirotor UAV[END_REF]), from an RGB camera that was placed in an UAV. The objective of this work was to decide whether the terrain under study was water or not water. However, it takes at best 4 seconds to classify whether the terrain is water-type or not. Thus, it forces the UAV to hover in a steady position to classify the terrain. Another work that exploits the downwash effect to classify the type of terrain, takes advantage, not only of the dynamic part but also the static part of the image. In relation to [START_REF] Pombeiro | Water detection from downwash-induced optical flow for a multirotor UAV[END_REF], this work in addition to classifying water-type terrains can also differentiate vegetation and sand terrains in real time (less than 0.03 second). To classify the terrain type, the algorithm uses the Gabor concept [START_REF] Matos-Carvalho | UAV downwash dynamic texture features for terrain classification on autonomous navigation[END_REF] to remove the static texture of the terrain, complementing with the concept of optical flow (Farneback algorithm [START_REF] Matos-Carvalho | UAV downwash dynamic texture features for terrain classification on autonomous navigation[END_REF]) to remove the dynamic part of the terrain under study.

Color information was also used in [START_REF] Ebadi | Road Terrain detection and Classification algorithm based on the Color Feature extraction[END_REF] to classify four types of terrain. Converting each pixel of the image by the square root of the three channels of the pixel itself, the result will emphasize the color that most represents the type of terrain (e.g., blue for water). Other methods that rely on laser scanners also proved to differentiate between water and non-water terrain with good accuracy [START_REF] Yan | Urban land cover classification using airborne LiDAR data: A review[END_REF], [START_REF] Wallace | Assessment of forest structure using two UAV techniques: A comparison of airborne laser scanning and structure from motion (SfM) point clouds[END_REF], [START_REF] Gruszczynski | Comparison of lowaltitude UAV photogrammetry with terrestrial laser scanning as data-source methods for terrain covered in low vegetation[END_REF]. However, shallow water terrain will increase the decision error due to laser reflection and thus the sensor classifies as non-water-type terrain.

Proposed Model

Two algorithms were used in the proposed classifier: Wiener-Kinchin (W-K) and Empirical Mode Decomposition (EMD). Since these two techniques are independent of each other (one works in the spatial domain and the other in the frequency domain), it is possible to operate in parallel mode to increase the processing speed. From the proposed classifier model, as depicted in Figure 1, it is possible to identify six fundamental stages:

1. Rectified Image: For the proposed classifier to be generic enough to work for all RGB cameras (depending on the resolution), it is first necessary to calibrate the image and mitigate its distortion;

Dimensionality reduction:

To increase the classification speed only a small piece (i.e., region delimited by the red square) of information was extracted in the image (in this case the information was taken in a square format);

Empirical Mode Decomposition (EMD) filter:

In the spatial domain, the EMD algorithm is applied in the image; 4. Computation of the first Three IMFs: Having the EMD obtained previously, the first three Intrinsic Mode Functions (IMFs) are taken to analyze the signal coming from the terrain under study; 5. Wiener-Khinchin (W-K) filter: Vertical and horizontal projections are applied to the thresholded image, extracting unique features that help differentiate the different types of terrains; 6. Terrain Classification: Certain decisions can be improved by using machine learning [START_REF] Mora | Land Cover Classification from Multispectral Data Using Computational Intelligence Tools: A Comparative Study[END_REF], [START_REF] Heung | An Overview and Comparison of Machine-Learning Techniques for Classification Purposes in Digital Soil Mapping[END_REF], [START_REF] Giusti | A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots[END_REF]. It will contribute to a more automated and reproducible process and possibly to increase the decision accuracy. Five different terrain types representing the most common natural landscapes were selected to be the output of the proposed classifier: lake, pool, vegetation, dirt and sand terrains. When exposed to wind turbulence (downwash effect) distinct behaviors emerge according to the type of terrain. For instance, in water-like terrains, a circular wave movement outwards a center point is observed. In the vegetation, the movement occurs linearly from the inside to the outside. In sand and dirt, the movement is almost static.

Experimental Results

Empirical Mode Decomposition

According to Huang [START_REF] Huang | The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[END_REF], [START_REF] Oonincx | Empirical mode decomposition: a new tool for S-wave detection[END_REF], the principle of EMD is to decompose the signal into several frequencies, called Intrinsic Mode Functions (IMFs) and each IMF represents a zero-mean AM-FM function [START_REF] Rato | On the HHT, its problems, and some solutions[END_REF]. EMD is performed as follows:

1. Initialize vector h(t) and k(t) for a given image I;

2. Get local maxima and minima from h(t) to build an upper s + (t) and a lower s(t) envelopes;

3. Calculate the mean envelope:

meanenvelope = 0.5 x [s + (t) + s -(t)]
(1)

4. The mean is subtracted from signal c(t) (initially c(t) is equal to vector h(t)):

c(t) = c(t) -meanenvelope (2)
5. c(t) is verified against a defined threshold. If it is above it, then h(t) = c(t) and repeat step 2-4. If it is below, then an IMF is found and h(t)= k(t) -c(t); k(t)= c(t); and repeat the steps 2-4 to find the next IMF.

Only the first three IMFs and the baseline are taken. Next, two heuristics are computed to identify the terrain: the number of local maximums given by an IMF and the number of times the IMF has crossed zero along the vector. These two heuristics were calculated for a set of 332 frames. From Fig. 2

Wiener-Khinchin Filter

The Wiener-Khinchin filter (W-K) is performed in the frequency domain while the algorithm in section 5.1 is presented in the spatial domain. To increase computational speed, the algorithm is not used in the total image but only in sub-region of it. To compare different types of terrain, it is necessary to compute the average value from that sub-region and normalize it apply general thresholds. As the Wiener-Khinchin theorem uses the concept of energy in the frequency domain, it becomes intuitive to normalize according to energy. These same values were then passed to the frequency domain. Next, frequency values are signal crossed with themselves. Finally, an inverse transform is performed. In last, it is possible to use the same heuristic characteristics that were used in section 5.1. This algorithm was also applied to the same set of 332 frames (90 frames are pool water, 35 frames are lake, 74 frames are vegetation, 55 frames are sand and 78 frames are dirt). As depicted in Fig. 3, with the Wiener filter it is also possible to clearly identify the five terrain types. However, lakes present greater uncertainty. Due to this situation, to increase the probability of a correct classification, machine learning was also added to the proposed model.

Classification

To automate the classification process, feed-forward neural network (NN) was used.

Several tests were done to assess the best combination between the Wiener-Khinchin filter and Empirical Mode Decomposition. The first step was to create a neural network composed of two layers: the first layer is hidden and contains 10 neurons; the second layer contains 5 neurons that represents the terrain types. A sigmoidal function was used as activation function and the final classification of the terrain under study is validated by the neuron that gives a greater result (the training dataset was composed by 334 samples, from which 70% were for training, 15% for testing and 15% for validation). Table 1 was produced showing the classification accuracy in all possible combinations. The best was the Wiener-Khinchin filter with the first IMF, yielding a 93% terrain classification accuracy.

Conclusions

The goal of this work was to design a terrain classifier using two algorithms that exploit the UAV's downwash effect. The two algorithms are the Wiener-Khinchin filter and Empirical Mode Decomposition. It was proved throughout this work that both algorithms worked well together by presenting good results in the terrain classification. Regarding the EMD algorithm, it was emphasized that, despite the high accuracy in the NN decision, the probability of correctly classifying a terrain worsens from IMF to IMF and therefore it is always better use the first IMF. However, it is always possible to improve certainty in the decision and therefore the EMD algorithm was complemented with the theory of Wiener-Khinchin. With this addition, the certainty in the classification of terrains managed to extend to 93%.

Fig. 1 .

 1 Fig. 1. Proposed terrain classifier model.

Fig. 2 .

 2 Fig. 2. Intrinsic Mode Function -The zero crossings in relation to the total number of local maxima. a) IMF-A; b) IMF-B; c) IMF-C.

Fig. 3 .

 3 Fig. 3. Wiener-Kinchin Filter -Number of times the signal crosses the zero in relation to the total number of local maxima.

Table 1 .

 1 Combination of the two algorithms developed for the proposed classifier.

	Combination	W-K	IMF-A	IMF-B	IMF-C	Result
	1	x				90.0 %
	2		x			88.0 %
	3			x		74.0 %
	4				x	74.0 %
	5	x	x			93.0 %
	6	x		x		77.0 %
	7	x			x	82.0 %
	8		x			79.0 %
	9		x		x	85.0 %
	10			x	x	84.0 %
	11	x	x			82.0 %
	12	x	x		x	81.3 %
	13	x		x	x	80.0 %
	14		x	x	x	83.3 %
	15	x	x	x	x	84.0 %

Acknowledgments

This work was partially funded by FCT Strategic Program UID/EEA/00066/203 of the Center of Technologies and System (CTS) of UNINOVA -Institute for the Development of new Technologies.


Fabrício Junqueira

Paulo E Miyagi
email: pemiyagi@usp.br

Felipe A Coda
email: coda.felipe@usp.br

Rafael M Salles
email: rafaelmachadosalles@usp.br

Henrique A Vitoi
email: hvitoi@usp.br

Marcosiris A O Pessoa
email: marcosiris@usp.br

Lucas Antônio Moscato
email: lamoscat@usp.br

Diolino J Santos Filho
email: diolinos@usp.br

Diolino J Santos

Fabrício Filho

Paulo E Junqueira

Miyagi

Big Data on Machine to Machine Integration's Requirement Analysis Within Industry 4.0

Keywords: Industry 4.0, requirements engineering, KAOS modeling, big data, Machine to Machine, interoperability

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

Introduction

Industry 4.0 (I4.0), or fourth industrial revolution, is an expression that was displayed in 2011 at the Hannover fair and started in April 2013 with the project "Plattform Industrie 4.0" [START_REF] Kagermann | Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry[END_REF]. Basically, Industry 4.0 uses technologies such as RFID (Radio Frequency IDentificator), to identify and track all of its elements or assets, such as machines, tools, and products. These identifiers [START_REF] Pisching | An architecture based on RAMI 4.0 discover equipment to process operations required by products[END_REF]. Ploner [START_REF] Ploner | Industry Insiders Report: Industry 4.0. Renesas Edge[END_REF] describes how the title "Industry 4.0" indicates an industrial revolution marked by the connection of three main technologies: Internet of Things (IoT), Cyber-Physical Systems (CPS), hings (IoT) and Information and Communication Technology (ICT). Computational elements are used to control or monitor cyber-physical systems. In these systems, the physical and software elements are interconnected and also act closely integrated with the Internet and its users. Organization assets can be defined as a CPS with their physical and virtual functionalities, and the communication between the functionalities of these assets is carried out by IoT and ICT. From these functionalities, Machine to Machine (M2M) communication also can be achieved through IoT, which must satisfy all the needs of I4.0, relating the efficiency and monitoring criteria of these machines. Through Big Data analytical techniques, it is possible to understand how the machines work and how their performance can be optimized.

The Machine to Machine communication is accomplished by integrating any machine to any machine that shares a common data exchange service. For this, sensors, actuators, and identifiers are coupled on these machines, thus representing communication through the IoT [START_REF] Salles | Requirements Analysis for Machine to Machine Integration within Industry 4[END_REF].

For the development of this communication and the data analysis, it arises the need to develop criteria for its implementation. The first stage of this process begins with the analysis of requirements, responsible for specifying objectives and possible obstacles. In order to obtain a cohesive requirement analysis, three points must be considered [START_REF] Salles | Requirements Analysis for Machine to Machine Integration within Industry 4[END_REF]:

• The viewpoint of various stakeholders and development engineers should be analyzed in order to have no subsequent problems with wrong or incomplete requirements [START_REF] Liu | An analytic framework for specifying and analyzing imprecise requirements[END_REF];

• Establishment of methods for requirements management and validation;

• Creation of organized processes that lead to a specification with a natural language.

In this context, the focus of work is the modeling of requirements in both communications between machines and big data systems.

A hypothesis raised by the authors is that modeling the requirements described by initiatives associated with the I4.0 like the RAMI 4.0 (Reference Architectural Model for Industry 4.0) [START_REF] Adolphs | Reference architecture model Industrie 4.0 (RAMI 4.0). ZVEI and VDI[END_REF] using the KAOS (Keep All Objectives Satisfied) [START_REF] Horkoff | Analyzing goal models: different approaches and how to choose among them[END_REF] methodology can assist in developing systems that effectively reflect the needs of the industry.

Relationship to Industrial and Service Systems

As described [START_REF] Salles | Requirements Analysis for Machine to Machine Integration within Industry 4[END_REF], requirements analysis remains the first step of any type of project. It has the responsibility of defining goals and predicting hindrances and therefore assisting on creating a resilient system. It is necessary that the requirements for a system are accurately identified, enabling its development to be effectively oriented to fulfill the necessities of the involved stakeholders (the individuals committed with the benefits of the company's business) associated with the I4.0 context.

In recent years, efforts to establish models and standards for the structure of the Industry 4.0 were made. Some of them decompose the properties of a productive system in layers, from the physical assets, at the bottom, to the business model and regulatory conditions, at the top. According to [START_REF] Adolphs | Reference architecture model Industrie 4.0 (RAMI 4.0). ZVEI and VDI[END_REF], the most advanced of these structures is the German initiative "RAMI 4.0". Within this context, previous works [START_REF] Salles | Requirements Analysis for Machine to Machine Integration within Industry 4[END_REF][8] characterize the objectives, requirements, and challenges from the viewpoints of stakeholders associated with the Industry 4.0 context. Using the layers described in RAMI 4.0 as a base, [START_REF] Salles | Requirements Analysis for Machine to Machine Integration within Industry 4[END_REF] presents the modeling of the interaction between machines and [START_REF] Coda | Big data systems requirements for Industry 4[END_REF] alludes to the data analysis functionalities and information capacities needed. This work relates to industrial and service systems, providing a take the first step towards the development of systems capable of dealing with the heterogeneity of the entities in the Industry 4.0. Another valuable contribution of this research is to propose the discussion of the current efforts surrounding Industry 4.0 from the angle of the requirements engineering, orienting the productive system development towards the solving of the existing problems.

Literature Review

The foundation concepts utilized in the present work are displayed in this section. A comprehensive review of the concepts of Machine to Machine, Big Data, and the KAOS methodology can be found in previous works [START_REF] Salles | Requirements Analysis for Machine to Machine Integration within Industry 4[END_REF] [8].

RAMI 4.0

A reference architecture is a document that represents the structures and integrations recommended to form a solution. It incorporates industry-accepted practices and therefore answers the most common questions that arise during the development of solutions and technologies [2][9]. The use of reference architectures is important for orienting the technological development towards the needs and requirements of the stakeholders of a production process, ensuring that the solutions developed are focused on solving existing problems.

The German Electrical and Electronic Manufacturers Association (ZVEI) established the RAMI 4.0 (Reference Architectural Model for Industry 4.0) as a standard design for I4.0 [START_REF] Adolphs | Reference architecture model Industrie 4.0 (RAMI 4.0). ZVEI and VDI[END_REF]. The RAMI 4.0 was conceived from current standards of the productive sector aiming to group different specifications on a single threedimensional model (Figure 1) to match the integration, both vertical, horizontal, and of distinct stages within the engineering process [START_REF] Pisching | An architecture based on RAMI 4.0 discover equipment to process operations required by products[END_REF].

With these three dimensions, all significant features of I4.0 can be outlined, allowing the elements like the machines to be classified according to the model. The concepts of high flexibility proposed by I4.0 can be represented and implemented using RAMI 4.0, enabling a step-by-step departure from the current industry to the Industry 4.0 [START_REF] Hankel | The reference architectural model Industrie 4.0 (RAMI 4.0)[END_REF].

Each dimension can be summarized as:

•
The dimension "Layers" is used to represent asset characteristics, making it possible for them to be fully mapped virtually.

• The dimension "Life Cycle and Value Stream" is used to visualize and normalize the relationships of Industry 4.0 components along their life cycle.

The dimension "Hierarchical Levels" describes the integration of business control systems. It was based on the ISA-95 standard, which defines the interface between the control functions and the other functions of the enterprise [START_REF] Pisching | An architecture based on RAMI 4.0 discover equipment to process operations required by products[END_REF]. According to [START_REF] Pisching | An architecture based on RAMI 4.0 discover equipment to process operations required by products[END_REF], the dimension Layers is divided into six levels to illustrate the breakdown of machines and physical entities, mapping those elements to their respective virtual representations, this virtual represented entity can also be referred as Industry 4.0 Component.

Using these levels, an overview to data mapping, functional descriptions, communication protocols, hardware and assets, and business processes can be represented. Each level and its interrelationships can be described as [START_REF] Adolphs | Reference architecture model Industrie 4.0 (RAMI 4.0). ZVEI and VDI[END_REF]:

• Business: Contains the rules that the system must follow as legal and regulatory conditions, mapping the business model of the system and orchestrating the services of the functional level; • Functional: Contains a formal description of all the functionalities of a system and it is the stage responsible for their horizontal integration; • Information: Contains the necessary data source for the control of assets, ensuring that the data of an asset is treated and made available. In this layer Big Data analysis is performed according to the specific application; • Communication: Guarantees that all the data directed to the above layer have a uniform format, allowing the access of information; • Integration: It is the virtual mapping of the real system. Here, the information on the physical assets is provided by the sensors as RFID (Radio-Frequency IDentification) and the Human Machine Interface (HMI); • Asset: Represents physical components such as machines, actuators, parts, documents, and people.

This "Layer" dimension on RAMI 4.0 represents the main change on the way to view and understand a physical asset. Here the asset is seen as a CPS and the IoT is the tool that enables the connection among the physical asset and its digital counterpart on the Integration Layer.

KAOS Methodology

The KAOS methodology is a "goal-oriented requirements engineering approach" elaborated at the Universities of Oregon and Louvain. The benefit of KAOS over other methods is its ability to align goals to its requirements, improving the odds of the modeling adding value to the business [8] [11].

The KAOS modeling uses a graph with:

Requirements Modeling

In this section, the objectives, operations, entities, and actors are demonstrated and discussed.

GOALS

With the purpose of understanding the problem, it is first necessary to design the objectives. In this paper, the central goal is to implement Machine to Machine integration by monitoring the performance of products and machines with Big Data analysis. Thus, some points must be considered:

• The proper use of IoT for communication;

• Interoperability between the machines;

• Integrate M2M within CPS steps, using RAMI 4.0.

• In the Big Data analysis, it must be verified whether the decision-making speed for the manufacturing process is satisfactory; if the amount of data transmitted is enough; if there is a possibility of working with various types of data; if there is reliable information; and if the analysis can improve production [START_REF] Coda | Big data systems requirements for Industry 4[END_REF].

Interaction Between Tiers of "Layers" Dimension

Machine to Machine integration and Big Data analysis happens on RAMI 4.0 "Layers" dimension. The operation begins when physical assets are initially represented as virtual assets. This step in RAMI 4.0 is called integration. After the virtualization of the assets, the standardization of the data occurs in the Communication level, in order to guarantee that they are standardized before passing them to other levels.

The level of Information is next. At this level, machines can be evaluated to improve production performance. Big Data analytical methods can be employed to direct the production to the machine with the best performance for the required procedure.

The last level considered is the Functional one. It is at this level that commands are directed to the selected machine. At the end of the process, the machine receives the information and executes the required command. In this level, in fact, that occurs the interaction between the machines (M2M), supported by Big Data techniques. The requirements model is exemplified in Fig. 3.

Discussion about the Model

It is considered in the model, the steps of asset, integration, communication, information and functional. Machine to Machine communication starts at the asset, goes through the integration, collects information, and performs the function. Big Data occurs in the Information step, where the data are studied for the highest performance of the process. It is noted in [START_REF] Polyantchikov | Virtual enterprise formation in the context of a sustainable partner network[END_REF] that the communication between elements can occur not only within a single company but also throughout a "partner network". In the context of risk assessment, the model presented in this work can be improved by considering the concepts presented in [START_REF] Mahmood | Risk assessment approach for a virtual enterprise of small and medium-sized enterprises[END_REF] for risks involving "virtual enterprises" (i.e. a temporary entity created through a collaborative network of enterprises for the fulfillment of a goal).

Another important point to note is that there is no level of "Business" in the model. The legal and regulatory conditions and the orchestration of the level of functional services are represented in the level of "Business". The model represents each company with its operating rules guided by its level of "Business". In addition, each company has specifics functions, communication structures, databases, physical and virtual resources. Each company has its "partner network" who can communicate with each other, if necessary, and share a database or even Machine to Machine communication if there is interoperability.

Model Checking

For validation, it is necessary to know if all the requirements are correctly defined in the model. This problem can be solved with interactive questions directed to the stakeholders that aim to show if all the requirements have been fulfilled according to the viewpoint of each stakeholder. Some of these questions are verified in the papers [START_REF] Horkoff | Analyzing goal models: different approaches and how to choose among them[END_REF] and [START_REF] Lopes | A checklist for peer knowledge validation in project-based organizations[END_REF]. The key issues addressed in [START_REF] Horkoff | Analyzing goal models: different approaches and how to choose among them[END_REF] and [START_REF] Lopes | A checklist for peer knowledge validation in project-based organizations[END_REF] that are relevant to the present paper can be summarized in:

• Does the model need to further detail the system requirements?

• Would you like to check out the model's critical properties?

• Is it necessary to determine the scope of the system?

• Does the model need to consider non-functional system requirements that are difficult to quantify?

Final Remarks

The requirements analysis is an essential step required to understand the global and initial aspects of a project. The adoption of the methodology to model these requirements depends on the intricacy of the system to be included. With the KAOS methodology, the requirements of the big data analysis on the M2M integration are accurately recognized, supporting its development to be adequately oriented to answer the needs of the interested stakeholders associated within the Industry 4.0 context. Validation of the requirements can be further performed with the statement of the aforementioned questions to be answered by the stakeholders and end users involved in a specific business model within the Industry 4.0 context. In this case, further details will be added to the model by incorporating characteristics sought by a specific application or system.

An interactive process must be carried out if any point not previously considered is identified on the model when answering the questions. A new solution is then proposed with a further corrected interpretation of requirements. The cycle must be carried out until the model is completely defined without issues.

Fig 1 .

 1 Fig 1. RAMI 4.0 [6].

•:

  GoalsCentral Work Objective (white parallelograms); • SoftGoal: Secondary objectives to achieve the central objective (gray parallelograms); • Requirements: Establish the requirements for achieving the Goals (bold white parallelograms); • Operation: Act that shows the procedures for obtaining the result (ellipses); • Actors: Responsible for system action (hexagons); • Event: Effect of the operation and demonstrated requirements (pentagons).

Figure 2 Fig 2 .

 22 Figure 2 represents an example of how the requirements modeling are divided in this paper:

Fig 3 .

 3 Fig 3. Goals on the Machine to Machine integration and Big Data analysis.

Acknowledgement

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal no Nível Superior -Brazil (CAPES), Fundação de Amparo à Pesquisa do Estado de São Paulo -Brazil (FAPESP), and Conselho Nacional de Desenvolvimento Científico e Tecnológico -Brazil (CNPq).


Shakib Zohrehvandi
email: shakib.zohrehvandi@srbiau.ac.ir

Alexandra Tenera

Proposing a Risk Management Model in Construction of Combined-Cycle Power Plant Projects

Keywords: Project management, Risk management, combined cycle power plant (CCPP) projects

Companies are increasingly focusing on identifying risks and managing them even before they affect project executing. Risk management can play a key part in identifying problems and taking preventive measures in this regard. This research focused on risk management in the implementation of a combined cycle power plant project. This paper aims at exploring the role of risks and risk management practices in construction of this type of projects by considering how they can impact the project's time, quality, and cost. First, the project risk types were identified. The extent to which the project's objectives were achieved was then considered on the basis of three criteria: time, cost, and quality. Next, the risks were categorized and prioritized through a risk breakdown structure. The obtained results revealed the most influential risks in the project. Finally, the proposed risk management model was used in the project which led to strengthening the performance impact of risk management practices in the risky project environment.

Introduction

Electrical energy is the most convenient form of energy for most human uses. For this reason, demand for electricity is set to grow even further over the next few years. This research was focused on risk management in the implementation of Samangan combined cycle power plant (SCCPP) project. Samangan is the name of this project that located in Sirjan, Iran. The country's increasing demand for electricity has led to massive investment on building power plants. However, uncertainties, different layers of decision-making, and the increasing complication of technologies in this industry has created a multitude of risks and challenges in achieving the project's objectives [START_REF] Ashtiyani | A Guide to the Project Management Body of Knowledge[END_REF]. The aim of project risk management is to decrease the likelihood of failure of the project. According to research, risk management in project portfolios must be carried out by adopting a broader standpoint than that of the individual risk in the project [START_REF] Teller | Risk management in project portfolios is more than managing project risks: A contingency perspective on risk management[END_REF]. Managing the project risks is one of the main responsibilities of the project manager. However, if risk management has not been properly done from the start of the project, the responsibility becomes complicated and even ineffectual [START_REF] Serpella | Risk management in construction projects: a knowledge-based approach[END_REF]. The goal of this research is to identify and prioritize the risks and explore the role of risks and risk management practices in construction of SCCPP project in terms of their impact on the project's time, quality, and cost. Based on the proposed approach applied in this research, the risks involved in the SCCPP project will be identified and analyzed. So, the research question is, does the risk management model used in SCCPP project lead to strengthening the performance impact of risk management practices in the project risky environments? Through the remainder of the paper, the relationship to industrial and service systems, literature review, methodology, case study, and the finding sections will be presented respectively. The paper continues with results, main discussion, and lastly, research conclusions.

Relationship to Industrial and Service Systems

The power generation industry and its service sectors are going through profound transformation towards digitalization and integration of new levels of smartness. Risks have significant impacts on power generation projects' execution, systems, products, value chains, and business models, including exceeding the estimated cost, failing to complete the project in the due date, and decline in quality and productivity. Considering the large amount of investment required in these projects, failing to reach the time, cost, and quality objectives in power plant projects can cause huge financial losses. This shows the importance of risk identification and management in this industry which is requiring increased level of real time data availability and exchange to continuously monitor and control risk changes and support smart risk plan implementations facing mostly human skills and knowledge deficiencies, improper contracts and import constraints and sanctions.

It is quite characteristic that electric power industry, as a key part of economy in any country, has a deep and versatile effect on the performance of industrial outfits. The power sector appears to be a crucial factor in enhancing universal corporate competitiveness and a driver of economic development [START_REF] Domnikov | A risk-oriented approach to capital management at a power generation company in Russia[END_REF]. So, in order to ensure the stepwise integrated growth of the power sector, it is essential to create an efficient sectorial risk management system to enable the power generating firms to instantly identify and minimize the influence of appearing threats [START_REF] Domnikov | Development of risk management for power generating companies in developing countries[END_REF]. This case study is carried out on a CCPP project by Mapna Group in Samangan project. Mapna is an umbrella name for a group of industrial companies tasked with construction and installation of power generation equipment, including wind, steam, and gas turbines, electrical generators, heat recovery steam generators, electrical and control systems, conventional boilers, and railway locomotives.

Literature Review

Risk management is indeed a fundamental component of project management. Many research projects have been conducted with different methods in various domains focusing on risk management. The results of these studies led to a categorization of risks within this domain. Nie et al. launched an interval-stochastic method for risk management to consider the notion of risk in stochastic programing and to control the resource cost variability. The model was formulated to identify the optimal power generation mix for Beijing's energy system. The obtained results revealed that the decision makers' risk attitude along with the inherent uncertainty of system components can significantly affect the power-generation schemes, the city's energy supply, the probabilistic penalty, and the system's total cost [START_REF] Nie | Risk management of energy system for identifying optimal power mix with financial-cost minimization and environmental-impact mitigation under uncertainty[END_REF]. A new financing tool called hybrid bond was introduced by Lee and Zhong to launch projects related to renewable energy. The instrument can support the initial capital costs, and manage the risks involved in renewable energy investment [START_REF] Lee | Financing and risk management of renewable energy projects with a hybrid bond[END_REF]. Serpella et al. considered two kinds of risk management techniques: preventive risk management techniques applied prior to the start of a project to manage anticipated risks that are likely to occur during the execution of the project; and remedial risk management techniques used during the project's execution phase when a risk has occurred already [START_REF] Iqbal | Risk management in construction projects[END_REF]. Gatzert and Kosub identified recurrent risks and presented risk management solutions to be applied in renewable energy projects with the aim of identifying critical gaps in risk transfer, and thereby distinguishing between offshore and onshore wind parks with focus on the European market [START_REF] Gatzert | Risks and risk management of renewable energy projects: The case of onshore and offshore wind parks[END_REF].

Hwang et al. investigated risk management in small scale projects in Singapore with respect to barriers, status, and the impact which risk management can have on project performance. They provided a detailed explanation of risk management in those projects and marked the main benefits of risk management for small projects participants [START_REF] Hwang | Risk management in small construction projects in Singapore: Status, barriers and impact[END_REF]. Desai and Kashiyani reviewed construction projects works exposing the knowledge about Insurance as a risk management instrument in construction industry [START_REF] Desai | Role of insurance as a risk management tool in construction projects[END_REF]. More recently Cheraghi et al. established a mathematical programing model for choosing risk response strategies in construction projects. The presented model is centered on the project classical triangle of time, quality, and cost in order to find the best risk response strategy in construction projects [START_REF] Cheraghi | A mathematical Model to select the Risk Response Strategies of the Construction Projects: Case Study of Saba Tower[END_REF]. Liu and Zeng used the system dynamics method to study the risks involved in renewable energy investment. The results showed that the investment in the early developmental stage was mainly affected by policy risk, that gradually declines the same as technology risk, while market risk gradually becomes the main uncertainty that affects the investment in the mature developmental stage [START_REF] Liu | Renewable energy investment risk evaluation model based on system dynamics[END_REF]. Qazi et al. considered identifying the critical risks and selecting appropriate risk mitigation strategies as decision problems at the start of a project, taking account of the decision maker's utility function regarding the significance of project goals and the holistic interaction between project risk and complexity [START_REF] Qazi | Project Complexity and Risk Management (ProCRiM): Towards modelling project complexity driven risk paths in construction projects[END_REF].

Carvalho and Rabechini exposed how risk management contributes to project success, taking into account the contingent effect of project complexity [START_REF] Carvalho | Impact of risk management on project performance: the importance of soft skills[END_REF]. Violante et al. highlighted the difficulties small construction companies have dealing with risk management and tried to describe how they manage risks and which aspects of their practice in risk management can be enhanced [START_REF] Violante | Risk Management in Construction Projects: Are Small Companies Prepared[END_REF]. Titarenko et al. described the statistical and probabilistic methods applied in managing the investment risks in construction projects and emphasized the necessity of adopting robust procedures when dealing with conditions of significant uncertainty [START_REF] Titarenko | Robust and traditional methods of risk management in investment and construction projects[END_REF]. Reddy attempted to gain a general understanding of risks and their consequences in construction projects and the process through which they are managed. The impact of risk on project assessment is discussed as well as the risk management instruments and methods in construction industry [START_REF] Reddy | Risk Management in Construction Industry-A Case Study[END_REF]. Zhao et al. identified the critical obstacles to enterprise risk management implementation in Chinese construction companies and explored the interrelationships amongst these obstacles [START_REF] Zhao | Reducing hindrances to enterprise risk management implementation in construction firms[END_REF]. Kang et al. studied the current status of risk management practiced in the Malaysian construction industry in an attempt to assess the process, as well as the various techniques and tools currently used to manage the projects [START_REF] Kang | Current Practice of Risk Management in the Malaysia Construction Industry-The Process and Tools/Techniques[END_REF].

Methodology

As exposed, there are various risk management models for construction projects. Despite the considerable differences among them, they are all applied to serve the same purpose reduce or prevent negative results on the project objectives. In this study the risk management proposed model was carried out using the project operational requirements into account. The manner of directing risk management activities in the project was defined in risk management planning. Next, different types of risks involved in building power plants in the company's projects were identified based on experts' opinions, modeling, and examining previous projects. In this phase, which is referred to as risk identification, a list of events that might probably occur was developed. The project's success was then assessed on the basis of the three criteria of time, execution quality, and cost. Mahjoub et al. examined the role of strategic talent management in project success, considering the organizational commitment, job satisfaction and motivation as mediators [START_REF] Mahjoub | Linking "Project Success" and "Strategic Talent Management": satisfaction/motivation and organizational commitment as mediators[END_REF]. The prioritization of the risks for further analysis and evaluation was carried out combining each probability of occurrence and their impact. The Probability and Impact Matrix, which is a prevalent risk analysis tool, was also used in this study. The impact of each risk will be separately examined using the probability and impact matrix; however, a simultaneous risk affects of the three criteria of time, quality, and cost of the project was also undertaken. In this research, the quantitative method for data collection were applied using interviews and questionnaires conducted by different project managers and experts (45 experts). First, the experts working on the sites of the projects, and the experts working in the project's central office were identified. The experts all had more than 12 years of experience in risk management and executing power plant construction projects with bachelor or master degree. The closed interviews have been conducted with the average duration of 30 minutes. The whole interview process took about 5 weeks.

The first and second levels of the risk breakdown structure of the SCCPP project is shown in Fig. 1 which represent an organized and hierarchical illustration of the main potential risks of the project, ordered in the form of sets and subsets. Generally, there are different classifications of the risks with regards to the project's objectives. So, the risk breakdown structure helps to identify the causes of occurrence and the sources of different types of project risks, if categorized according to their potential source of occurrence.

SCCPP project's risks

The SCCPP Case Study

The manner of directing the risk management activities in the SCCPP project was determined through risk management planning. Considering the fact that there should be no power outage throughout the power grid, projects must be conducted immediately within high quality level requirements, and based on the estimated cost. Therefore, the main goals of the projects under investigation pertained to quality, cost, and time. Hosseini et al. suggested a multi-objective mathematical model to discover the optimal balance amongst time, quality, and cost of construction projects which involved using reinforced concrete in underground structures according to existing risks [START_REF] Hosseini | Balance of Cost, Time, and Quality Related to Construction Projects Regarding the Reinforced Concrete of Underground Structures using a Meta-Heuristic Algorithm[END_REF]. It should be noted that the importance of these goals vary according to the type and nature of the projects and their particular circumstances.

The main phases of the SCCPP projects are as follows: preliminary studies, selection and occupation of land, selection of a consultant and signing a contract, selecting a contractor, project execution, pre-commissioning, commissioning, and final delivery. The nature of the problems in CCPP projects is generally related to investment, high technology, and high volume of projects. Zohrehvandi et al. proposed a procedure for going through the closing process group in mega projects. This research was done on a CCPP project as a case study [START_REF] Zohrehvandi | Planning project closure phase in combined cycle power plant projects[END_REF]. Some experts believe that in risk management process, the most important phase is risk identification. Stosic et al. addressed the improvement of innovation project risk identification by applying Risk Breakdown Structure methods, and present a follow-up analysis of the subject [START_REF] Stosic | Risk identification in product innovation projects: new perspectives and lessons learned[END_REF]. The major output of this phase is identification and categorization of the risks and their sources. In this paper, the parties involved in the projects including the employers, contractors, supervisors, and consultants were interviewed with the aim of identifying the risks. The documents produced in the previously conducted projects were also examined in order to determine the reasons why some projects' objectives were not met. They were also studied for benchmarking purposes.

The examination of the SCCPP project led to the identification of a few risks, and each risk was assigned to a subset of the already prepared risk breakdown structure based on its source. In fact, Fig. 2 shows a more detailed level of risks compared to Fig. 1. In order to manage and properly confront the identified risks, they must then, categorized in order of importance. In this way the more serious risks, and their impacts in a greater focus on projects' risky aspects and parts, will be exposed. The methods of risk analysis and assessment are generally classified into two main groups: qualitative and quantitative methods. In qualitative risk analysis, typically the risks are categorized according to their probability of occurrence and their possible impact on the project, measured through a descriptive or relative approach. Therefore, qualitative approaches are a suitable tool for ranking and determining the relative priority of the risks for further actions. Therefore, in this research, the quantitative method for data collection were applied detailed in next section.

SCCPP Study Main Findings

Kırılmaz and Erol investigated the supply chain risk management process and suggested a procedure in the risk mitigation phase. The model was tested and analyzed by Probability and Impact Matrix [START_REF] Kırılmaz | A proactive approach to supply chain risk management: Shifting orders among suppliers to mitigate the supply side risks[END_REF], which is a prevalent risk analysis tool, also used in the case. This approach considers the probability of each risk and its possible impacts on project goals with respect to time, quality, and cost. In order to determine risk significance and its impact on project objectives, the risk assessment or Probability and Impact Matrix is applied. This matrix helps to determine the combination of probability and impacts in a way that the risks can be categorized in low, medium, and high-priority risks.

In this research, questionnaires were designed to gather the required data and information from SCCPP project experts and stakeholder and a quantitative method for data collection were applied. The risks impact on the criteria were examined through a five-point Likert scale (ranging from too little to too much). As an example, Table 1 shows the ranking of the risks impact on project duration.

Permits and conf irmations

Late removal of the opponents, Permits problems

Documentstions and agreement

Delay in contract conclusion

Improper def ined payments in different phases of contract

Improper contracts type chosen

Inclination of some project crew such as the supervisor f or project delay in order f or a higher f ee It must be mentioned that the risks are mapped in different areas of the matrix according to the average value obtained from each criterion and the explanation of each area. The probability-impact matrix with regard to project duration for the SCCPP is shown in Fig. 3

Fig. 3. SCCPP project probability-impact matrix

As shown in Fig. 3, the red area represents the highest risks: the green area represents lowers, and the yellow area represents moderate risks. A risk with a high and very high probability of occurring and which will have a high impact on the project objectives will likely need further quantitative analysis and response plan. As also if a particular risk has a moderate probability with a higher estimated impact. A risk with a low and very low probability of occurring and which will have a low impact on the project objectives so no response plan will be developed. According to the probability of risks occurrence and their impact on SCCPP project duration, about 36 percent of the identified risks are in the area of high-priority, 48 percent in the area of medium-priority, and 12 percent are in the area of low-priority. This distribution shows that most of potential project risks might threat the project with completion delays. Pfeifer et al. created an optimization problem to maximize project delay due to certain stochastic task disruptions, and proposed a genetic algorithm to identify the critical tasks which may cause the maximum risk of delay in the project [START_REF] Pfeifer | Quantifying the risk of project delays with a genetic algorithm[END_REF].

The probability and impact matrix examines the impact of each risk separately; however, a risk simultaneously affects the three criteria of project time, quality, and cost. Based on the specific conditions of a given project, one criterion of assessing a project's success might be considered relatively important. In these cases, the assessment and analysis of the risks impact on the project success criteria must be conducted simultaneously. Weighting is one of the common ways of performing multiple criteria analyses and determining the relative significance of variables in decision making. Normalized weighting was applied in this research to determine the relative significance of risk assessment criteria. Table 2 shows the relative weighting of the three criteria, determined according to interviews and questionnaires carried out by different project managers and experts as mentioned in methodology section. To determine the risks' priority and final ranking, the average points of each criterion was multiplied by its weight, and the products were added together. The risks were divided into three groups of medium priority, high priority and low priority based on their final rankings. Also the obtained results show that the problems posed by the stakeholders match the rankings to a great extent. In SCCPP project, Lack of skilled manpower, Improper contracts type chosen and Imports constraints and sanctions are the high-priority risks according to Fig. 4. Therefore, a smarter planning is deemed necessary to resolve these issues.

Main Conclusions

Risk management is a process through which the threats to an organization's earnings and capital are identified, assessed and controlled. The main purpose in risk management is to understand, analyze, and address risk in order to make sure that organizations and firms achieve their objectives. The goal of this study was to identify and prioritize the risks and explore the role of risks and risk management practices in construction of SCCPP project in terms of their effect on the project's time, quality, and cost. As a part of the project risk management process, different types of risks were identified after the planning phase of risk management. The results of these examinations led to making the projects' risk breakdown structure in groups of planning, resource constraint, legal and contractual and others. Each of these main groups were classified into subgroups. The identified risks were put in their respective groups. The results indicated that in SCCPP project, significant risks such as Lack of skilled manpower, Improper contracts type chosen and Imports constraints and sanctions were among the high-priority risks which. Considering the results of this study project managers and stakeholders should take appropriate measures to reduce the undesirable risks' impacts by this risks developing proper mitigation risk strategy plan development in order to achieve project higher resilience on SCCPP projects. Eventually, according to the research question, SCCPP project complemented and strengthened the performance impact of classical risk management practices in risky environments. For future research, the scope of the study can be expanded to examine the risk management of similar projects in different countries. It is also suggested that the handling of risk management in other types of projects be considered seriously by academics and project practitioners as an area in need of further research.

Fig. 1 .

 1 Fig. 1. The SCCPP project's risk breakdown structure

Fig. 4 .Fig. 5 .

 45 Fig. 4. The relative weighting of the three criteria

Table 1 .

 1 SCCPP project's risk impact on project duration

	Lack of transparency in contract's terms

  Similar diagrams were also developed for the other two selected criteria

	Probability Rating	Very High 5 High 4 Moderate 3 Low 2	. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .	.
		Very Low 1
		1	2	3	4	5
		Very Low	Low	Moderate	High	Very High
				Impact Rating

Table 2 .

 2 Relative significance of risk assessment criteria in SCCPP project

	Impact of risk on	Impact of risk on	Impact of risk on
	project Quality	project Cost	project Duration
	30%	33%	37%

Acknowledgements

The authors are grateful to all of those with whom they had the pleasure to work during this and other related projects. This work has been partially supported by UNIDEMI -Research and Development Unit for Mechanical and Industrial Engineering by the Portuguese FCT-PEST program UID/EMS/00667/2019.