ORIGINAL_ARTICLE
Optimal Robust Control Based on State-Dependent Differential Riccati Equation with Application on Ducted Fan Aircraft
In this paper, optimal robust controllers are developed for tracking control of a ducted fan engine of a thrust-vectored aircraft in the presence of external disturbances. First, by applying a nonlinear regulator based on the state-dependent differential Riccati equation (SDDRE) approach, an optimal control law is designed that is not robust against external disturbances. The second design, a VSC (variable structure control) with an NTV (nonlinear time-varying) sliding sector, is proposed. The sliding sector is a subset of the state space which is obtained by the SDDRE. As the final design, to guarantee the system's robustness against external disturbances and achieve optimal performance, a robust optimal sliding mode controller based on SDDRE (ROSMC) is designed, which integrates the sliding mode control (SMC) theory with the SDDRE approach. For each design, the global asymptotic stability is proved using the Lyapunov stability theorem. Also, the SDDRE is solved by a change of variable and converting it to a differential Lyapunov equation (DLE). Numerical simulations are presented considering different types of external disturbances and several scenarios. Simulation results show that ROSMC has stronger robustness and demonstrates optimal performance compared to SDDRE and VSC designs.
https://tjee.tabrizu.ac.ir/article_14372_82781754a843d5d6e756e8d67247309d.pdf
2021-10-23
303
313
Optimal
State-dependent differential Riccati equation (SDDRE)
Variable structure control (VSC)
sliding mode
Ducted fan engine
محمد
عباسی
kasra3p@yahoo.com
1
Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
AUTHOR
سید سجاد
موسی پور
s.moosapour@scu.ac.ir
2
Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
LEAD_AUTHOR
سید سعید اله
مرتضوی
mortazavi_s@scu.ac.ir
3
Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
AUTHOR
[1] B. Fazeli Asl, S. S. Moosapour, "Adaptive backstepping fast terminal sliding mode controller design for ducted fan engine of thrust-vectored aircraft," Aerospace Science and Technology, vol. 71, pp. 521-529, 2017
1
[2] Fan, X. Changle, X. Bin, "Modelling, attitude controller design and flight experiments of a novel micro-ducted-fan aircraft", Advances in Mechanical Engineering, vol. 10, no. 3, pp. 1-16, 2018.
2
[3] Wang, C. Xiang, Y. Ma, and B. Xu, "Comprehensive nonlinear modeling and simulation analysis of a tandem ducted fan aircraft," in Guidance, Navigation and Control Conference (CGNCC), 2014 IEEE Chinese, pp. 255-261: IEEE, 2014.
3
[4] Chadli, S. Aouaouda, H.-R. Karimi, and P. Shi, "Robust fault tolerant tracking controller design for a VTOL aircraft," Journal of the Franklin Institute, vol. 350, no. 9, pp. 2627-2645, 2013.
4
[5] Cimen, "State-dependent Riccati equation (SDRE) control: A survey," IFAC Proceedings Volumes, vol. 41, no. 2, pp. 3761-3775, 2008.
5
[6] Pearson, "Approximation methods in optimal control I. Sub-optimal control," International Journal of Electronics, vol. 13, no. 5, pp. 453-469, 1962.
6
[7] Korayem, S. Nekoo, "Finite-time state-dependent Riccati equation for time-varying nonaffine systems: Rigid and flexible joint manipulator control," ISA transactions, vol. 54, pp. 125-144, 2015.
7
[8] Heydari and S. N. Balakrishnan, "Approximate closed-form solutions to finite-horizon optimal control of nonlinear systems," in American Control Conference (ACC), 2012, 2012, pp. 2657-2662: IEEE.
8
[9] Heydari, S. Balakrishnan, "Path planning using a novel finite horizon suboptimal controller", Journal of guidance, control, and dynamics, vol. 36, no. 4, pp. 1210-1214, 2013.
9
[10] G. Lin, M. Xin, "Impact Angle Guidance Using State-Dependent (Differential) Riccati Equation: Unified Applicability Analysis", Journal of Guidance, Control, and Dynamics, vol. 43, no. 11, pp. 2175-2182, 2020.
10
[11] Ashish, A.J. Sinclair, "Nonlinear control for spacecraft pursuit-evasion game using the state-dependent Riccati equation method", IEEE Transactions on Aerospace and Electronic Systems, vol. 53, no. 6, pp. 3032-3042, 2017.
11
[12] Pan, K. D. Kumar, G. Liu, and K. Furuta, "Design of variable structure control system with nonlinear time-varying sliding sector," IEEE Transactions on Automatic Control, vol. 54, no. 8, pp. 1981-1986, 2009.
12
[13] Xu, D. Zhou, and S. Sun, "Finite time sliding sector guidance law with acceleration saturation constraint," IET Control Theory & Applications, vol. 10, no. 7, pp. 789-799, 2016.
13
[14] Cucuzzella, G.P. Incremona, A. Ferrara, "Event-triggered variable structure control", International Journal of Control, vol. 93, no. 2, pp. 252-260, 2020.
14
[15] Hongmei, "Simulation Research on Ship Electric Propulsion Speed Regulation System Based on Variable Structure Control and FPGA", Microprocessors and Microsystems, In press, 2020.
15
[16] Tourajizadeh and S. Zare, "Robust and optimal control of shimmy vibration in aircraft nose landing gear," Aerospace Science and Technology, vol. 50, pp. 1-14, 2016.
16
[17] Pukdeboon and P. Kumam, "Robust optimal sliding mode control for spacecraft position and attitude maneuvers," Aerospace Science and Technology, vol. 43, pp. 329-342, 2015.
17
[18] -H. Zheng, J.-J. Xiong, and J.-L. Luo, "Second order sliding mode control for a quadrotor UAV," ISA transactions, vol. 53, no. 4, pp. 1350-1356, 2014.
18
[19] H. Korayem, S. R. Nekoo & M. H. Korayem, "Sliding mode control design based on the state-dependent Riccati equation: theoretical and experimental implementation", International Journal of Control, vol. 92, no. 9, pp. 2136-2149, 2017.
19
[20] H. Korayem, S.R. Nekoo, M. H. Korayem, "Optimal sliding mode control design based on the state-dependent Riccati equation for cooperative manipulators to increase dynamic load carrying capacity." Robotica, vol. 37, no. 2, pp. 321-337, 2019.
20
[21] Farkh, M. Ksouri, and F. Bouani. "Optimal Robust Control for Unstable Delay System," Comput. Syst. Sci. Eng., vol. 36, no. 2, pp. 307-321, 2021.
21
[22] Pujol-Vazquez, S. Mobayen, and L. Acho. "Robust control design to the furuta system under time delay measurement feedback and exogenous-based perturbation," Mathematics, vol. 8, no. 12, pp. 21-31, 2020.
22
[23] Gkizas, "Optimal robust control of a Cascaded DC–DC boost converter," Control Engineering Practice, vol. 107, 2021.
23
[24] Xu, Q. Wang, and Y. Li. "Adaptive Optimal Robust Control for Uncertain Nonlinear Systems Using Neural Network Approximation in Policy Iteration," Applied Sciences, vol. 11, no. 5, 2312, 2021.
24
[25] Kiamini, A. Jalilvand, and S. Mobayen, "LMI-based robust control of floating tension-leg platforms with uncertainties and time-delays in offshore wind turbines via TS fuzzy approach," Ocean Engineering vol. 154, pp. 367-374, 2018.
25
[26] Batmani, M. Davoodi, and N. Meskin, "Nonlinear suboptimal tracking controller design using state-dependent Riccati equation technique," IEEE Transactions on Control Systems Technology, vol. 25, no. 5, pp. 1833-1839, 2016.
26
[27] Batmani, and S. Khodakaramzadeh., "Nonlinear estimation and observer-based output feedback control," IET Control Theory & Applications, vol. 14, no. 17, pp. 2548-2555, 2020.
27
[28] Batmani, " Event-triggered Observer Design for Nonlinear Networked Control Systems," Tabriz Journal of Electrical Engineering, vol. 49, no. 1, pp. 71-77, 2019.
28
[29] Nazari, "State Dependent Riccati Equation based Model Reference Adaptive Control for Finite Duration Cancer Treatment by using a Mixed Therapy," Tabriz Journal Of Electrical Engineering, vol. 48, no. 1, pp. 369-380, 2018
29
ORIGINAL_ARTICLE
DynamicEvoStream: An EvoStream based Algorithm for Dynamically Determining The Number of Clusters in Data Streams
EvoStream is a stream clustering algorithm which gradually clusters data in the idle times of the stream. In comparison with other algorithms in this field, EvoStream has a lower computation overload in the offline phase and has better accuracy. Also, in this algorithm, the number of clusters is taken as constant whereas in an authentic stream this number varies with the complexity of input data. In this work, we present DynamicEvoStream as an improved version of the original EvoStream. In this algorithm, we detect and exploit variations in the distribution and speed of the stream. Also, we modified the cleanup function to merge overlapping clusters. Therefore, in contrast to the basic EvoStream, DynamicEvoStream identifies the number of clusters in a dynamic manner. Also, the speed of evolutionary steps is increased while improving the quality of the clusters. Finally, experiments using DynamicEvoStream on different streams showed that it can cluster the stream up to four times faster than the original EvoStream with fewer computation and memory resources. In the worst case, the quality of clusters is competitive to the original EvoStream, however improves the quality of clusters up to 30% in the majority of cases.
https://tjee.tabrizu.ac.ir/article_14374_856817018a2cf179c30b92efc17f2543.pdf
2021-10-23
315
326
Data stream
Evostream Algorithm
Data stream clustering
Dynamic clustering
Online Clustering
زهرا
عمیقی
amighi.zahra.ac@gmail.com
1
Department of Computer Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
AUTHOR
M.
Yousef Sanati
mysanati@basu.ac.ir
2
Department of Computer Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
LEAD_AUTHOR
میرحسین
دزفولیان
dezfoulian@basu.ac.ir
3
Department of Computer Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
AUTHOR
[1] Shayesteh, V. Hakami, S.A. Mostafavi, A. Akbari Azirani, “A Novel Trust Computation Scheme for Internet of Things Applications”, Tabriz Journal of Electrical Engineering, vol 50, no. 2, pp. 743-755, 2020 (in persian).
1
[2] Chen, S. Mao, Y. Liu, “Big Data: A Survey”, Mobile Networks and Applications, vol. 19, pp. 171-209, 2014.
2
[3] Kolajo, O. Daramola, A. Adebiyi, “Big data stream analysis: a systematic literature review”, Journal of Big Data, vol.47, no. 6, 2019.
3
[4] Gurusamy, S. Kannan, K. Nandhini, "The Real Time Big Data Processing Framework: Advantages and Limitations", International Journal of Computer Sciences and Engineering, vol. 5, no. 12, pp. 305-312, 2017.
4
[5] Namiot, “On Big Data Stream Processing”, International Journal of Open Information Technologies, vol. 3, no. 8, pp. 48-51, 2015.
5
[6] Carnein, H. Trautmann, "EvoStream Evolutionary Stream Clustering Utilizing Idle Times", Big Data Research, vol. 14, pp. 101–111, 2018.
6
[7] K. Jain, M.N. Murty, P.J. Flynn, “Data clustering: a review”, ACM Computing Surveys, vol.31, no. 3, pp. 264-323, 1999.
7
[8] Ester, H.P. Kriegel, J. Sander, X. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise”, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 226–231, 1996.
8
[9] Behravan , S.H. Zahiri, S.M. Razavi, R. Trasarti, “Using Gray Wolf Optimization Algorithm in Big Data Clustering”, Tabriz Journal of Electrical Engineering, vol. 50, no. 1, 2020 (in persian).
9
[10] Leite DF, Costa P, Gomide F (2009) Evolving granular classification neural networks, IJCNN 2009, pp 1736–1743
10
[11] Smith, D. Alahakoon, “Growing self-organizing map for online continuous clustering”, Studies in computational intelligence, vol. 204, pp. 49–83, 2009.
11
[12] Dang, V. Lee, W. Ng, A. Ciptadi, K. Ong, “An EM-based algorithm for clustering data streams in sliding windows”, Lecture notes in computer science, vol. 5463, pp 230–235, 2009.
12
[13] Guha, N. Mishra, R. Motwani, L. O’Callaghan, “Clustering data streams”, Proceding 41st Annual Symposium Foundations Computer Science, 2000.
13
[14] Cao, M. Ester, W. Qian, and A. Zhou, “Density-based clustering over an evolving data stream with noise”, Proceeding of the Sixth SIAM Conference on Data Mining, 2006.
14
[15] Zhou, F. Cao, Y. Yan, C. Sha, X. He, “Distributed Data Stream Clustering: A Fast EM-Based Approach”, IEEE 23rd International Conference on Data Engineering, pp. 736-745, 2007.
15
[16] Tu, Y. Chen, “Stream data clustering based on grid density and Attraction”, ACM Transactions on Knowledge Discovery from Data, vol. 3, no. 3, pp. 1–27, 2009.
16
[17] H. Dang, V.C. Lee, W.K. Ng, K. Ong, “Incremental and Adaptive Clustering Stream Data Over Sliding Window”, 20th International Conference on Database and Expert Systems Applications. pp. 660-674, 2009.
17
[18] P. Barddal, H.M. Gomes, F. Enembreck, “SNCStream: A Social Network-Based Data Stream Clustering Algorithm”, Proceedings of the 30th Annual ACM Symposium on Applied Computing, pp.935–940, 2015.
18
[19] Khalilian, N. Mustapha, N. Sulaiman, “Data Stream Clustering by Divide and Conquer Approach Based on Vector Model”, Journal of Big Data, vol. 3, no. 1, 2016.
19
[20] Hyde, P. Angelov, A. MacKenzie, “Fully Online Clustering of Evolving Data Streams Into Arbitrarily Shaped Clusters”, Information Sciences, vol. 382, pp. 96–114, 2017.
20
[21] Gu, P. Angelov, D. Kangin, J. Principe, “Self-Organised Direction Aware Data Partitioning Algorithm”, Information Sciences, vol. 423, pp. 80–95, 2017.
21
[22] Su, Y. Li, X. Zhao, “Data Stream Clustering by fast Density-Peak-Search”, Statistics and its Interface, vol. 11, no. 1, pp. 183–189, 2018.
22
[23] Ahmed, G. Dalkılıç, Y. Erten, “DGStream: High quality and efficiency stream clustering algorithm”, Expert Systems with Applications, vol. 141, 2020,
23
[24] Fahy, S. Yang and M. Gongora, "Ant Colony Stream Clustering: A Fast Density Clustering Algorithm for Dynamic Data Streams", IEEE Transactions on Cybernetics, vol. 49, no. 6, pp. 2215-2228, 2019.
24
[25] Longnguyen, Y. Kwongwoon,W. Keongng, "A Survey On Data Stream Clustering And Classification", Knowledge And Information Systems, vol. 45, pp. 535–569, 2014.
25
[26] Mansalis, E. Ntoutsi, N. Pelekis, Y. Theodoridis, "An Evaluation of Data Stream Clustering Algorithms", Statistical Analysis and Data Mining: The ASA Data Science Journal, vol. 11, no. 4, 2018.
26
[27] Carnein, H. Trautmann, "Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms", Business & Information Systems Engineering, vol. 61, no. 3, pp. 277–297, 2019.
27
[28] Mohiuddin, "Data Summarization: A Survey", Knowledge and Information Systems, vol. 58, pp 249-273, 2019.
28
[29] Whitley, Darrell. "A genetic algorithm tutorial", Statistics and Computing, vol.4, no.2, pp. 65–85, 1994.
29
[30] M. Spears, K.A. De Jong, T. Bäck, D.B. Fogel, H. de Garis, “An overview of evolutionary computation”, Lecture Notes in Computer Science, vol. 667, pp. 442-459, 1993.
30
[31] Maulik, S. Bandyopadhyay, “Genetic algorithm-based clustering technique”, Pattern Recognition, vol. 33, no. 9, pp. 1455–1465, 2000.
31
[32] C. Aggarwal, J. Han, J. Wang, P.S. Yu, “A framework for projected clustering of high dimensional data streams”, Proceedings of the thirtieth international conference on very large data bases, vol. 30, pp. 852–863, 2004.
32
[33] López-Ibáñez, J. Dubois-Lacoste, L. Pérez Cáceres, T. Stützle, M. Birattari, “The irace package: iterated racing for automatic algorithm configuration”, Operation Research Perspectives, vol. 3, pp. 43–58, 2016
33
ORIGINAL_ARTICLE
Joint Resource Allocation and Position Optimization in NOMA-based Multi-UAV Wireless Communication Networks
In this paper, we study an uplink multi-unmanned aerial vehicle (UAV) wireless communication network where multi-UAV are deployed to serve the ground users by utilizing the non-orthogonal multiple access (NOMA) technology. The goal is to minimize the total transmit power of users by jointly optimizing the user association, sub-channel assignment, power allocation and UAVs’ position. The formulated problem is a mixed integer non-convex optimization that is difficult to solve in optimal approach. By applying the convex optimization tools, successive convex approximation (SCA) and Lagrange dual approaches, we solve the optimization problem then we propose an efficient iterative algorithm. Numerical results confirm that the proposed scheme can provide a better performance compared to the orthogonal multiple access (OMA), random position of UAVs in NOMA (RP-NOMA) and OMA (RP-OMA) schemes in both of the transmit power and sum-rate performance metrics.
https://tjee.tabrizu.ac.ir/article_14371_2d9c8e77a5ddc625f42c62cb9d367927.pdf
2021-10-23
327
336
unmanned aerial vehicle
Non-orthogonal Multiple Access
Mixed integer non-convex
Successive convex approximation
Lagrange dual
مریم
عیدزاده
maryam.eidzadeh@birjand.ac.ir
1
Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran
AUTHOR
رضا
قاضی زاده
rghazizade@birjand.ac.ir
2
Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran
LEAD_AUTHOR
مجید
هادی
majid.h71@birjand.ac.ir
3
Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran
AUTHOR
[1] Gan et al., “Energy efficient switch policy for small cells”, China Communincations, vol. 12, no. 1, pp. 78-88, 2015.
1
[2] Abdelnasser, E. Hossain, D. I. Kim, “Tier-aware resource allocation in ofdma macrocell-small cell networks”, IEEE Transactions on Communications, vol. 63, no. 3, pp. 695-710, 2015.
2
[3] Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Unmanned aerial vehicle with underlaid device-to-device communications: Performance and tradeoffs”, IEEE Transactions on Wireless Communications, vol. 15, no. 6, pp. 3949-3963, 2016.
3
[4] Orfanus, E. P. de Freitas, and F. Eliassen, “Self-organization as a supporting paradigm for military UAV relay networks”, IEEE Communications Letters, vol. 20, no. 4, pp. 804-807, 2016.
4
[5] P. Valavanis and G. J. Vachtsevanos, “Handbook of unmanned aerial vehicles”, Springer, 2015.
5
[6] Zeng, R. Zhang, and T. J. Lim, “Wireless communications with unmanned aerial vehicles: Opportunities and challenges”, IEEE Communications Magazine, vol. 54, no. 5, pp. 36-42, 2016.
6
[7] M. Azari, F. Rosas, K.-C. Chen, and S. Pollin, “Joint sum-rate and power gain analysis of an aerial base station”, In 2016 IEEE Globecom Workshops (GC Wkshps), 2016: IEEE, pp. 1-6.
7
[8] Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Wireless communication using unmanned aerial vehicles (UAVs): Optimal transport theory for hover time optimization”, IEEE Transactions on Wireless Communications, vol. 16, no. 12, pp. 8052-8066, 2017.
8
[9] Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Mobile unmanned aerial vehicles (UAVs) for energy-efficient Internet of Things communications”, IEEE Transactions on Wireless Communications, vol. 16, no. 11, pp. 7574-7589, 2017.
9
[10] Wu, Y. Zeng, and R. Zhang, “Joint trajectory and communication design for multi-UAV enabled wireless networks”, IEEE Transactions on Wireless Communications, vol. 17, no. 3, pp. 2109-2121, 2018.
10
[11] Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage”, IEEE Communications Letters, vol. 20, no. 8, pp. 1647-1650, 2016.
11
[12] Alzenad, A. El-Keyi, F. Lagum, and H. Yanikomeroglu, “3-D placement of an unmanned aerial vehicle base station (UAV-BS) for energy-efficient maximal coverage”, IEEE Wireless Communications Letters, vol. 6, no. 4, pp. 434-437, 2017.
12
[13] Zeng and R. Zhang, “Energy-efficient UAV communication with trajectory optimization”, IEEE Transactions on Wireless Communications, vol. 16, no. 6, pp. 3747-3760, 2017.
13
[14] Zeng, R. Zhang, and T. J. Lim, “Throughput maximization for UAV-enabled mobile relaying systems”, IEEE Transactions on Communications, vol. 64, no. 12, pp. 4983-4996, 2016.
14
[15] Li, W. Ni, X. Wang, R. P. Liu, S. S. Kanhere, and S. Jha, “Energy-efficient cooperative relaying for unmanned aerial vehicles”, IEEE Transactions on Mobile Computing, vol. 15, no. 6, pp. 1377-1386, 2015.
15
[16] Ahmed, M. Z. Chowdhury, and Y. M. Jang, “Energy-Efficient UAV-To-User Scheduling to Maximize Throughput in Wireless Networks,” IEEE Access, vol. 8, pp. 21215–21225, 2020.
16
[17] Zeng et al., “Resource Allocation and Trajectory Optimization for QoE Provisioning in Energy-Efficient UAV-Enabled Wireless Networks,” IEEE Transactions on Vehicular Technology, vol. 69, no. 7, pp. 7634–7647, 2020.
17
[18] Dai, B. Wang, Z. Ding, Z. Wang, S. Chen, and L. Hanzo, “A survey of non-orthogonal multiple access for 5G”, IEEE communications surveys & tutorials, vol. 20, no. 3, pp. 2294-2323, 2018.
18
[19] نیما نوری، علیاکبر تدین، «بهینه سازی چندهدفه بهمنظور تخصیص منابع محاسباتی و مخابراتی مبتنی بر دسترسی نامتعامد، مشارکت سرور ابری و سرور لبه در شبکههای نسل پنج»، مجله مهندسی برق دانشگاه تبریز، جلد 50، شماره 1، صفحات 462-451، 1399.
19
[20] فرزاد دهقانی، جعفر پوررستم، «بهبود شاخص عدالت جین و بهینهسازی مصرف توان فرستنده در سیستمهای NOMA»، مجله مهندسی برق دانشگاه تبریز، جلد 49، شماره 2، صفحات 586-577، 1398.
20
[21] Yang, Z. Ding, P. Fan, and N. Al-Dhahir, “A General Power Allocation Scheme to Guarantee Quality of Service in Downlink and Uplink NOMA Systems”, IEEE Transactions on Wireless Communications, vol. 15, no. 11, pp. 7244-7257, 2016.
21
[22] F. Sohail, C. Y. Leow, and S. Won, “Non-orthogonal multiple access for unmanned aerial vehicle assisted communication”, IEEE Access, vol. 6, pp. 22716-22727, 2018.
22
[23] Cui, Y. Cai, Z. Qin, M. Zhao, and G. Y. Li, “Joint Trajectory Design and Power Allocation for UAV-Enabled Non-Orthogonal Multiple Access Systems”, In 2018 IEEE Global Communications Conference (GLOBECOM), 2018: IEEE, pp. 1-6.
23
[24] K. Sharma and D. I. Kim, “UAV-enabled downlink wireless system with non-orthogonal multiple access”, In 2017 IEEE Globecom Workshops (GC Wkshps), 2017: IEEE, pp. 1-6.
24
[25] Hu, Q. Zhang, Q. Li, and J. Qin, “Joint Position, Decoding Order, and Power Allocation Optimization in UAV-Based NOMA Downlink Communications,” IEEE Systems Journal, vol. 14, no. 2, pp. 2949–2960, 2020.
25
[26] Masaracchia, L. D. Nguyen, T. Q. Duong, C. Yin, O. A. Dobre, and E. Garcia-Palacios, “Energy-Efficient and Throughput Fair Resource Allocation for TS-NOMA UAV-Assisted Communications,” IEEE Transactions on Communications, vol. 68, no. 11, pp. 7156–7169, 2020.
26
[27] Tse and P. Viswanath, “Fundamentals of Wireless Communication”, Cambridge University Press, 2005.
27
[28] Masaracchia, L. D. Nguyen, T. Q. Duong, C. Yin, O. A. Dobre, and E. Garcia-Palacios, “Energy-Efficient and Throughput Fair Resource Allocation for TS-NOMA UAV-Assisted Communications,” IEEE Transactions on Communications, vol. 68, no. 11, pp. 7156–7169, 2020.
28
[29] Yang, X. Cao, C. Yin, Z. Xiao, X. Xi, and D. Wu, “Proactive dronecell deployment: Overload relief for a cellular network under flash crowd traffic,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 10, pp. 2877-2892, 2017.
29
[30] Hadi and R. Ghazizadeh, “Sub-channel assignment and power allocation in OFDMA-NOMA based heterogeneous cellular networks,” AEU - International Journal of Electronic and Communications, vol. 120, p. 153195, 2020.
30
[31] C. Research, “CVX: Matlab software for disciplined convex programming, version 2.0”, http://cvxr.com/cvx, Aug 2012.
31
[32] Wang and L. Vandendorpe, “Iterative Resource Allocation for Maximizing Weighted Sum Min-Rate in Downlink Cellular OFDMA Systems”, IEEE Transactions on Signal Processing, vol. 59, no. 1, pp. 223-234, 2011.
32
[33] Boyd and L. Vandenberghe, “convex optimization”, Cambridge University Press, 2004.
33
[34] W. K. Ng, E. S. Lo, and R. Schober, “Energy-Efficient Resource Allocation in OFDMA Systems with Large Numbers of Base Station Antennas”, IEEE Transactions on Wireless Communications, vol. 11, no. 9, pp. 3292-3304, 2012.
34
[35] H. Kha, H. D. Tuan, and H. H. Nguyen, “Fast Global Optimal Power Allocation in Wireless Networks by Local D.C. Programming”, IEEE Transactions on Wireless Communications, vol. 11, no. 2, pp. 510-515, 2012.
35
ORIGINAL_ARTICLE
Adaptive Robust H∞ finite-time congestion control design for TCP/AQM Network System with parametric uncertainties
The congestion control problem is one of the most essential subjects in the Transmission Control Protocol (TCP) Network because of the complex nonlinear model, uncertainties, and external disturbances. This paper extends the adaptive robust H∞ control finite-time approach to TCP network and presents a new solution to solve the congestion control problem employing Active Queue Management (AQM). Firstly, a modified nonlinear model of TCP network system with parametric uncertainties and external disturbance is given. Then, by several variable changes based on the backstepping method and Lyapunov function, adaptation and control laws were derived. Stability analysis is given to prove that all the signals of the closed-loop system are finite-time bounded. In addition, the results show that the proposed controller can guarantee both the transient and steady-state performance of the system, the queue of the TCP network system can track the desired queue and the disturbance is rejected satisfactory based on H∞ control part of the controller. Finally, a comparison example is considered to demonstrate the feasibility and superiority of the presented scheme.
https://tjee.tabrizu.ac.ir/article_14373_11090423c96486e406958c08567282cd.pdf
2021-10-23
337
346
adaptive control
Robust H∞ control
TCP/AQM
finite-time
parametric uncertainties
مجتبی
حسینی تودشکی
mhosseini.t@hut.ac.ir
1
Electrical Engineering Group, Hamedan University of Technology, Hamedan, Iran
LEAD_AUTHOR
[1] R. Adams, “Active Queue Management: A Survey”, IEEE Communications Surveys and Tutorials, vol. 15, no. 3, pp. 1425-1476, 2013.
1
[2] S. Floyd, V. Floyd, “Random Early Detection Gateways for Congestion Avoidance”, IEEE-Acm Transactions on Networking, vol. 1, no. 4, pp. 397-413, 1993.
2
[3] K. Chavan, R.G. Kumar, M.N. Belur, A. Karandikar, “Robust Active Queue Management for Wireless Networks”, IEEE Transactions on Control Systems Technology, vol. 19, no. 6, pp. 1630-1638, 2011.
3
[4] D.W. Ding, X.M. Qin, T.T. Wu, N. Wang, D. Liang, “Hopf bifurcation control of congestion control model in a wireless access network”, Neurocomputing, vol. 144, pp. 159-168, 2014.
4
[5] W.B. Zhao, X.K. Sun, H.C. Wang, “Hopf bifurcation and stability analysis of a congestion control model with delay in wireless access network”, Abstract and Applied Analysis, 2014.
5
[6] S.J. Wen, L.B. Deng, S.Shi, X.Y. Fan, H. Li, “Distributed congestion control via outage probability model for delay-constrained flying Ad Hoc networks”, Wireless Communications & Mobile Computing, 2020.
6
[7] L.J. Ma, X.P. Liu, H.Q. Wang, X.P. Deng XP, “Congestion tracking control for multi-router TCP/AQM network based on integral backstepping”, Computer Networks vol. 175, 2020.
7
[8] D.Z. Wang, S.J. Wu, “Design of the Congestion Control for TCP/AQM Network with Time-Delay”, Mathematical Problems in Engineering, 2014.
8
[9] W.J. Chang,P.H. Chen, C.T. Yang, “Robust Fuzzy Congestion Control of TCP/AQM Router via Perturbed Takagi-Sugeno Fuzzy Models”, International Journal of Fuzzy Systems, vol. 15, no. 2, pp. 203-213, 2013.
9
[10] W.C. Feng, K.G. Shin, D.D. Kandlur, D. Saha, “The blue active queue management algorithms”, IEEE-Acm Transactions on Networking, vol. 10, no.4, pp. 513-528, 2002.
10
[11] S. Athuraliya, S.H. Low, V.H. Li, Q.H. Yin, “REM: Active queue management”, IEEE Network, vol. 15, no. 3, pp. 48-53, 2001.
11
[12] V. Misra, W.B. Gong, D. Towsley, “Fluid-based analysis of a network of AQM routers supporting TCP flows with an application to RED”, In Proceedings of the Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, 2000, pp. 151-160.
12
[13] C.V. Hollot, V. Misra, D. Towsley, W.B. Gong “On designing improved controllers for AQM routers supporting TCP flows”, In Proceedings of IEEE Conference on Computer Communications, 12th Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No. 01CH37213), 2001.
13
[14] C.V. Hollot, V. Misra, D. Towsley, W.B. Gong “Analysis and design of controllers for AQM routers supporting TCP flows”, IEEE Transactions on automatic control, vol. 47, no. 6, pp. 945-959, 2002.
14
[15] Y. Hong, O.W. Yang, “Design of adaptive PI rate controller for best-effort traffic in the Internet based on phase margin”, IEEE Transactions on Parallel and Distributed Systems, vol. 18, no. 4, pp. 550-561, 2007.
15
[16] S. Ryu, B. Ryu, M. Jeong, S. Park, “PI-PD controller for adaptive and robust active queue management for internet congestion control”, Simulation vol. 81, no. 6, pp. 437-459, 2005.
16
[17] S.K. Bisoy, P.K. Pattnaik, “Design of feedback controller for TCP/AQM networks”, Engineering Science and Technology, vol. 20, no. 1, pp. 116-132, 2017.
17
[18] M. Jalili-Kharaajoo, “Sliding mode queue management in TCP/AQM networks”, In International Conference on Telecommunications, 2004. Springer, pp 638-643.
18
[19] L.J. Ma, X.P. Liu, H.Q. Wang, Y.C. Zhou, “Congestion Tracking Control for Wireless TCP /AQM Network Based on Adaptive Integral Backstepping”, International Journal of Control Automation and Systems, vol. 18, no. 9, pp. 2289-2296, 2020.
19
[20] Y. Liu, X.P. Liu, Y.W. Jing, Z.Y. Zhang, X.Y. Chen, “Congestion tracking control for uncertain TCP/AQM network based on integral backstepping”, ISA Transactions, vol. 89, pp. 131-138, 2019.
20
[21] S.K. Bisoy, P.K. Pattnaik, “An AQM Controller Based on Feed-Forward Neural Networks for Stable Internet”, Arabian Journal for Science and Engineering, vol. 43, no. 8, pp. 3993-4004, 2018.
21
[22] K. Wang, Y. Liu, X.P. Liu, Y.W. Jing, S.Y. Zhang, “Adaptive fuzzy funnel congestion control for TCP/AQM network”, ISA Transactions, vol. 95, pp. 11-17, 2019.
22
[23] علیرضا مدیر روستا و مهدی خدابنده، " طراحی یک روش کنترل مد لغزشی انتگرالی تطبیقی برای پایدارسازی زمان محدود و مقاوم پرنده چهارملخه" ، مجله مهندسی برق دانشگاه تبریز، دوره 46، شماره 1، صفحه 332-321، 1395.
23
[24] فاطمه پیشکاری و طاهره بینازاده، "طراحی کنترلکننده مقاوم برای دستهای از سیستمهای غیرخطی متغیر با زمان با رویکرد بهینگی"، مجله مهندسی برق دانشگاه تبریز، دوره 50، شماره 4، صفحه 1531-1521، 1399.
24
[25] J-S. Fang, J.S. Tsai, J-J. Yan, P. Chen, S-M. Guo, “H-Infinity Model Following Control for Uncertain Systems by Using Digital Redesign Sliding Mode Control”, IEEE Access 8:147199-147210, 2020.
25
[26] B. Kharabian, H. Mirinejad, “Hybrid Sliding Mode/H-Infinity Control Approach for Uncertain Flexible Manipulators”, IEEE Access 8:170452-170460, 2020.
26
[27] S.P. Bhat, D.S. Bernstein, “Finite-time stability of continuous autonomous systems”, SIAM Journal on Control and Optimization, vol. 38, no. 3, pp. 751-766, 2000.
27
[28] Y. Liu, D. Yao, H. Li, R. Lu, “A Novel Active Queue Management Algorithm: Finite-Time H∞ Congestion Tracking Control”. In 39th Chinese Control Conference (CCC), IEEE 2020, pp 496-501.
28
[29] R.M. Yang, F.Y. Zang, L.Y. Sun, P. Zhou, B.H. Zhang, “Finite-time adaptive robust control of nonlinear time-delay uncertain systems with disturbance” International Journal of Robust and Nonlinear Control, vol. 29, no. 4, pp. 919-934, 2019.
29
[30] Z.L. Zhao, Z.P. Jiang, “Finite-time output feedback stabilization of lower-triangular nonlinear systems”, Automatica, vol. 96, pp.259-269, 2018.
30
[31] R. Yang, G. Zhang, L. Sun, “Observer-based finite-time robust control of nonlinear time-delay systems via Hamiltonian function method”, International Journal of Control, (just-accepted), pp. 1-32, 2020.
31
[32] B. Zhou, “Finite-time stability analysis and stabilization by bounded linear time-varying feedback”, Automatica, vol. 121, pp. 109-191, 2020.
32
[33] J. Liang, B. Wu, L. Liu, Y-E. Wang, C. Li, “Finite-time stability and finite-time boundedness of fractional order switched systems”, Transactions of the Institute of Measurement and Control, vol. 41, no. 12, pp. 3364-3371, 2019.
33
[34] K. Wang, Y.W. Jing, Y. Liu, X.P. Liu, G.M. Dimirovski, “Adaptive finite-time congestion controller design of TCP/AQM systems based on neural network and funnel control”, Neural Computing & Applications, vol. 32, no. 13, pp. 9471-9478, 2020.
34
[35] W.M. Zheng, Y.X. Li, X.W. Jing, S.K. Liu, “Adaptive Finite-Time Congestion Control for Uncertain TCP/AQM Network with Unknown Hysteresis”, Complexity, 2020.
35
[36] K. Wang, X. Liu, Y. Jing, “Robust finite-time H∞ congestion control for a class of AQM network systems”, Neural Computing and Applications, pp. 1-8, 2020.
36
[37] Y. Liu, X. Liu, Y. Jing, S. Zhou, “Adaptive backstepping H∞ tracking control with prescribed performance for internet congestion”, ISA transactions vol. 72, pp. 92-99, 2018.
37
[38] B.A. Sadek, T. El Houssaine, C. Noreddine, “Small-gain theorem and finite-frequency analysis of TCP/AQM system with time varying delay”, IET Control Theory & Applications, vol. 13, no. 13, pp. 1971-1982, 2019.
38
[39] J. M. Amigó, G. Duran, A. Giménez, J. Valero, O. M. Bonastre, “Modeling a new AQM model for Internet chaotic behavior using Petri Nets”, Applied Sciences-Basel, vol. 11, no. 13, pp. 5877- 5880, 2021.
39
[40] J. Shen, Y. Jing, T. Ren, “Adaptive finite time congestion tracking control for TCP/AQM system with input-saturation”, International Journal of Systems Science, pp. 1-12, 2021.
40
[41] C. Wang, X. Chen, J. Cao, J. Qiu, Y. Liu, Y. Luo, “Neural network-based distributed adaptive pre-assigned finite-time consensus of multiple TCP/AQM networks”, IEEE Transactions on Circuits and Systems, vol. 68, no. 1, pp. 387-395, 2021.
41
[42] O. Menacer, A. Messai, L. Kassa-Baghdouche, “Improved variable structure Proportional–Integral Controller for TCP/AQM network systems”, Journal of Electrical Engineering & Technology, vol. 16, no. 4, pp. 2235-2243, 2021.
42
ORIGINAL_ARTICLE
Approximate Fault Simulation for Quick Evaluation of Test Patterns in Digital Circuit Testing
Simulation-based test pattern generation methods are an interesting alternative to deterministic methods because of lower time complexity. In these methods, test patterns are evaluated and those with higher efficiency are selected. Traditionally, test pattern selection is based on fault coverage, which is an accurate merit indicator, but its calculation is time-consuming. Instead of fault coverage, approximate indicators can be used to assess efficiency of test patterns. In this paper, an approximate indicator called APXD is proposed, which is more efficient than existing approximate methods. Our experimental results show that APXD indicator has a strong correlation with fault coverage. In addition, APXD simulation is 1900x, 63x, and 56x faster than serial, sampling, and parallel fault simulation, respectively. Exploiting APXD indicator instead of fault coverage, in a pruning-based test generation method, leads to about 700x, 24.2x, and 18.4x speedup, respectively compared to pruning based methods that use serial, sampling, or parallel fault simulation for test pattern evaluation, at fault coverage of 80%. Speedup at fault coverage of 95% is about 111.3x, 11.1, and 3.6x, respectively. While, the use of APXD indicator instead of fault coverage increases the number of test vectors by 2% at most, confirming the efficiency of APXD indicator compared with probabilistic and statistical approximate indicators.
https://tjee.tabrizu.ac.ir/article_14456_95a8dfeacdd5b150dc7678b613251c05.pdf
2021-10-23
347
357
Approximate fault simulation
Test pattern generation
Probabilistic fault simulation
Fault sampling
لیلی
خسروی
leyli.khosravi536@gmail.com
1
Department of Computer Engineering and Information Technology, Razi University, Kermanshah, Iran
AUTHOR
آرزو
کامران
kamran@razi.ac.ir
2
Department of Computer Engineering and Information Technology, Razi University, Kermanshah, Iran
LEAD_AUTHOR
[1] E. O. Osimiry, R. Ubar, S. Kostin, and J. Raik, "A novel random approach to diagnostic test generation," in 2016 IEEE Nordic Circuits and Systems Conference (NORCAS), 2016, pp. 1-4.
1
[2] A. Kamran, M. S. Jahangiry, and Z. Navabi, "Merit based directed random test generation (MDRTG) scheme for combinational circuits," in 2010 East-West Design & Test Symposium (EWDTS), 2010, pp. 416-419.
2
[3] A. Kamran, "HASTI: hardware-assisted functional testing of embedded processors in idle times," IET Computers & Digital Techniques, vol. 13, no. 3, pp. 198-205, 2019.
3
[4] S. Esfandyari, V. Rafe, “A Hybrid solution for Software testing to minimum test suite generation using hill climbing and bat search algorithms”, Tabriz Journal of Electrical Engineering, vol. 46, no. 3, pp. 25-35, 2016 (in persion).
4
[5] M. M. Dejam Shahabi, S. E. Beheshtian, P. Badiei, R. Akbari, S. M. R. Moosavi, “Adapting Swarm Intelligence Based Methods for Test Data Generation”, Tabriz Journal of Electrical Engineering, vol. 51, no. 2, pp. 183-193, 2021.
5
[6] E. M. Rudnick, J. G. Holm, D. G. Saab, and J. H. Patel, "Application of simple genetic algorithms to sequential circuit test generation," in Proceedings of European Design and Test Conference EDAC-ETC-EUROASIC, 1994, pp. 40-45.
6
[7] E. M. Rudnick, J. H. Patel, G. S. Greenstein, and T. M. Niermann, "A genetic algorithm framework for test generation," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 16, no. 9, pp. 1034-1044, 1997.
7
[8] H. Harmanani and B. Karablieh, "A hybrid distributed test generation method using deterministic and genetic algorithms," in Fifth International Workshop on System-on-Chip for Real-Time Applications (IWSOC'05), 2005, pp. 317-322.
8
[9] M. Azimipour, M. R. Bonyadi, and M. Eshghi, "Using immune genetic algorithm in ATPG," Australian Journal of Basic and Applied Sciences, vol. 2, no. 4, pp. 920-928, 2008.
9
[10] A. N. Nagamani, S. N. Anuktha, N. Nanditha, and V. K. Agrawal, "A Genetic Algorithm-Based Heuristic Method for Test Set Generation in Reversible Circuits," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 37, no. 2, pp. 324-336, 2018.
10
[11] A. Bhar, S. Chattopadhyay, I. Sengupta, and R. Kapur, "GA based diagnostic test pattern generation for transition faults," in 2015 19th International Symposium on VLSI Design and Test, 2015, pp. 1-6.
11
[12] J. P. Anita and P. T. Vanathi, "Genetic algorithm based test pattern generation for multiple stuck-at faults and test power reduction in VLSI circuits," in 2014 International Conference on Electronics and Communication Systems (ICECS), 2014, pp. 1-6.
12
[13] R. Farah and H. Harmanani, "An Ant Colony Optimization approach for test pattern generation," 2008 Canadian Conference on Electrical and Computer Engineering, pp. 001397-001402, 2008.
13
[14] M. M. Alateeq and W. Pedrycz, "Analysis of optimization algorithms in automated test pattern generation for sequential circuits," in 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017, pp. 1834-1839.
14
[15] G. Yuan-Liang and X. Wen-Bo, "Study on Automatic Test Generation of Digital Circuits Using Particle Swarm Optimization," in 2011 10th International Symposium on Distributed Computing and Applications to Business, Engineering and Science, 2011, pp. 324-328.
15
[16] Z. Jiali, Z. Lin, Y. Yun, N. Tianlin, Z. Long, and X. Xiaodong, "The Test Pattern Generation for Digital Integrated Circuits Based on CA-IA-PSO Algorithm," in 2015 Seventh International Conference on Measuring Technology and Mechatronics Automation, 2015, pp. 1316-1320.
16
[17] M. Santos, H. Braga, I. Teixeira, J. P. Teixeira, "Dynamic Fault Injection Optimization for FPGA-Based Harware Fault Simulation, " Design and Diagnostics of Electronic Circuits and Systems Workshop (DDECS), 2002, pp. 370-373.
17
[18] A. Parreira, J. P. Teixeira, A. Pantelimon, M. B. Santos, and J. T. de Sousa, "Fault Simulation Using Partially Reconfigurable Hardware," vol. 2778, pp. 839-848, 2003.
18
[19] L. Kafka and O. Novak, "FPGA-based fault simulator," in 2006 IEEE Design and Diagnostics of Electronic Circuits and systems, 2006, pp. 272-276.
19
[20] M. Haghbayan, S. Teräväinen, A. Rahmani, P. Liljeberg, and H. Tenhunen, "Adaptive fault simulation on many-core microprocessor systems," in 2015 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFTS), 2015, pp. 151-154.
20
[21] S. Hadjitheophanous, S. N. Neophytou, and M. K. Michael, "Scalable parallel fault simulation for shared-memory multiprocessor systems," in 2016 IEEE 34th VLSI Test Symposium (VTS), 2016, pp. 1-6.
21
[22] M. Li and M. S. Hsiao, "3-D Parallel Fault Simulation With GPGPU," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 30, no. 10, pp. 1545-1555, 2011.
22
[23] M. Beckler and R. D. Blanton, "Fault simulation acceleration for TRAX dictionary construction using GPUs," in 2017 IEEE International Test Conference (ITC), 2017, pp. 1-9.
23
[24] E. Schneider and H. Wunderlich, "SWIFT: Switch-Level Fault Simulation on GPUs," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 38, no. 1, pp. 122-135, 2019.
24
[25] J. T. Xiao, T. S. Hsu, C. M. Fuchs, Y. T. Chang, J. J. Liou, and H. H. Chen, "An ISA-level Accurate Fault Simulator for System-level Fault Analysis," in 2020 IEEE 29th Asian Test Symposium (ATS, pp. 1-6), 2020.
25
[26] M. Karami, M. H. Haghbayan, M. Ebrahimi, A. Miele, H. Tenhunen, and J. Plosila, "Hierarchical Fault Simulation of Deep Neural Networks on Multi-Core Systems," in 2021 IEEE European Test Symposium (ETS), pp. 1-2, 2021.
26
[27] P. R. Maier, U. Sharif, D. Mueller-Gritschneder, and U. Schlichtmann, "Efficient Fault Injection for Embedded Systems: As Fast as Possible but as Accurate as Necessary," in 2018 IEEE 24th International Symposium on On-Line Testing And Robust System Design (IOLTS, pp. 119-122), 2018.
27
[28] F. M. Goncalves, M. B. Santos, I. C. Teixeira, and J. P. Teixeira, "Self-checking and fault tolerance quality assessment using fault sampling," in 17th IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems, 2002. DFT 2002. Proceedings., 2002, pp. 216-224.
28
[29] S. Mirkhani, J. A. Abraham, T. Vo, H. Jun, and B. Eklow, "FALCON: Rapid statistical fault coverage estimation for complex designs," in 2012 IEEE International Test Conference, 2012, pp. 1-10.
29
[30] M. Fooladi and A. Kamran, "Speed-Up in Test Methods Using Probabilistic Merit Indicators," Journal of Electronic Testing, vol. 36, no. 2, pp. 285-296, 2020/04/01 2020.
30
[31] S. A. Al-Arian and M. A. Al-Kharji, "Fault simulation and test generation by fault sampling techniques," in Proceedings 1992 IEEE International Conference on Computer Design: VLSI in Computers & Processors, 1992, pp. 365-368.
31
[32] G. Asadi and M. B. Tahoori, "An analytical approach for soft error rate estimation in digital circuits," in 2005 IEEE International Symposium on Circuits and Systems, 2005, pp. 2991-2994 Vol. 3.
32
[33] M. M. Mukaka, "Statistics corner: A guide to appropriate use of correlation coefficient in medical research," Malawi medical journal : the journal of Medical Association of Malawi, vol. 24, no. 3, pp. 69-71, 2012.
33
ORIGINAL_ARTICLE
Influence Maximization using Time Delay based Harmonic Centrality in Social Networks
With the extension of social networks, research on influence maximization (IM) in time-sensitive graphs has increased in recent years. IM is a problem to find a seed set with k nodes to maximize the information propagation range in the graph. Most of the research in this area consists of greedy, heuristic, meta-heuristic methods. However, most of these methods ignore the time-sensitivity to propagation delay and duration. The preceding time-sensitive centrality measures as a part of heuristic approaches take the propagation delay but only consider the nodes locally so that each graph node considers only the direct neighbors. Based on the above analysis, this article focuses on the time-sensitive IM problem. Here, a propagation value for each path in the graph is defined in terms of the probability of affecting through the edge and freshness amount of the edge. To solve the problem, we propose time-sensitive centrality measures that consider propagation value and both the direct and the indirect neighbors. Therefore, four measures of time-sensitive closeness centrality (TSCloseness), time-sensitive harmonic (TSHarmonic), time-sensitive decay centrality (TSDecay), and time-sensitive eccentricity centrality (TSEccentricity) were proposed. The experiments on five datasets demonstrate the efficiency and influence performance of the TSHarmonic measure on evaluation metrics.
https://tjee.tabrizu.ac.ir/article_14274_09c413d370f8b5d4b8563690a142891b.pdf
2021-10-23
359
370
Influence Maximization
propagation delay
Closeness centrality
Harmonic centrality
Decay centrality
Eccentricity centrality
سلمان
مختارزاده
mokhtarzadeh@iauahvaz.ac.ir
1
Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran
AUTHOR
بهزاد
زمانی دهکردی
bzamani@iaushk.ac.ir
2
Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran/ Department of Engineering, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran
LEAD_AUTHOR
محمد
مصلح
mosleh@iaud.ac.ir
3
Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran
AUTHOR
علی
براتی
abarati@iaud.ac.ir
4
Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran
AUTHOR
[1] H. Huang, H. Shen, Z. Meng, H. Chang, H. He, “Community-based influence maximization for viral marketing”, Applied Intelligence, vol. 49, pp. 2137-2150, 2018.
1
[2] C. Budak, D. Agrawal, A. El Abbadi, “Limiting the spread of misinformation in social networks”, In the Proceedings of the 20th international conference on World Wide Web, 2011, pp. 665-674.
2
[3] M. A. Manouchehri, M. S. Helfroush, H. Danyali, “A Theoretically Guaranteed Approach to Efficiently Block the Influence of Misinformation in Social Networks”, IEEE Transactions on Computational Social Systems, vol. 8, pp. 716-727, 2021.
3
[4] D. Kempe, J. Kleinberg, É. Tardos, “Maximizing the spread of influence through a social network”, In the Proceedings of the 9th ACM SIGKDD international conference on Knowledge discovery and data mining, 2003, Washington, DC, USA, pp. 137-146.
4
[5] A. Mohammadi, M. Saraee, A. Mirzaei, “Time-sensitive influence maximization in social networks”, Journal of Information Science, vol. 41, no. 6, pp. 765-778, 2015.
5
[6] M. Adineh, M. Nouri-Baygi, “High Quality Degree Based Heuristics for the Influence Maximization Problem”, arXiv preprint arXiv:1904.12164, 2019.
6
[7] P. Domingos, M. Richardson, “Mining the network value of customers”, In the Proceedings of the 7th ACM SIGKDD international conference on Knowledge discovery and data mining, 2001, San Francisco, California, pp. 57-66.
7
[8] J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen, and N. Glance, “Cost-effective outbreak detection in networks”, In the Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, 2007, San Jose, California, USA, pp. 420-429.
8
[9] A. Goyal, W. Lu, L.V.S. Lakshmanan, “CELF++: optimizing the greedy algorithm for influence maximization in social networks”, In the Proceedings of the 20th international conference companion on World wide web, 2011, Hyderabad, India, pp. 47-48.
9
[10] Y. Wang, G. Cong, G. Song, K. Xie, “Community-based greedy algorithm for mining top-K influential nodes in mobile social networks”, In the Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010, Washington, DC, USA, pp. 1039-1048.
10
[11] K. Jung, W. Heo, and W. Chen, “IRIE: Scalable and Robust Influence Maximization in Social Networks”, In 2010 IEEE 12th International Conference on Data Mining, 2012, Brussels, Belgium, pp. 918-923,.
11
[12] A. Sheikhahmadi, M. A. Nematbakhsh, A. Zareie, “Identification of influential users by neighbors in online social networks”, Physica A: Statistical Mechanics and its Applications, vol. 486, pp. 517-534, 2017.
12
[13] W. Chen, C. Wang, Y. Wang, “Scalable influence maximization for prevalent viral marketing in large-scale social networks”, In the Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010, Washington, DC, USA, pp. 1029-1038.
13
[14] W. Chen, Y. Yuan, L. Zhang, “Scalable Influence Maximization in Social Networks under the Linear Threshold Model”, In 2010 IEEE International Conference on Data Mining, 2010, Sydney, Australia, pp. 88-97.
14
[15] A. Zareie, A. Sheikhahmadi, M. Jalili, “Identification of influential users in social network using gray wolf optimization algorithm”, Expert Systems with Applications, vol. 142, 2020.
15
[16] A. Zareie, A. Sheikhahmadi, M. Jalili, M.S.K. Fasaei, “Finding influential nodes in social networks based on neighborhood correlation coefficient”, Knowledge-based systems, vol. 194, 2020.
16
[17] Ch. Salavati, A. Abdollahpouri, Zh. Manbari, “A Multi-objective Algorithm for Identifying Influential Nodes in Social Networks”, Tabriz Journal of Electrical Engineering, vol. 50, no. 3, pp. 1293-1304, 2020 (in persian).
17
[18] M. Kitsak, L.K. Gallos, S. Havlin, F. Liljeros, L. Muchnik, H.E. Stanley, H.A. Makse, “Identification of influential spreaders in complex networks”, Nature physics, vol. 6, no. 11, pp. 888-893, 2010.
18
[19] W. Chen, Y. Wang, S. Yang, “Efficient influence maximization in social networks”, In the Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 2009, Paris, pp. 199-208.
19
[20] X. Wang, Y. Su, C. Zhao, D. Yi, “Effective identification of multiple influential spreaders by DegreePunishment”, Physica A: Statistical Mechanics and its Applications, vol. 461, pp. 238-247, 2016.
20
[21] A. Sheikhahmadi, M. A. Nematbakhsh, and A. Shokrollahi, “Improving detection of influential nodes in complex networks”, Physica A: Statistical Mechanics and its Applications, vol. 436, pp. 833-845, 2015.
21
[22] A. Zareie, A. Sheikhahmadi, K. Khamforoosh, “Influence maximization in social networks based on TOPSIS”, Expert Systems with Applications, vol. 108, pp. 96-107, 2018.
22
[23] L. Katz, “A new status index derived from sociometric analysis”, Psychometrika, vol. 18, no. 1, pp. 39-43, 1953.
23
[24] A. Zareie, A. Sheikhahmadi, and R. Sakellariou, “A composite centrality measure for improved identification of influential users”, arXiv preprint arXiv:2111.04529, 2021.
24
[25] K. Saito, M. Kimura, K. Ohara, H. Motoda, “Learning continuous-time information diffusion model for social behavioral data analysis”, In Asian Conference on Machine Learning, 2009, Berlin, Heidelberg, pp. 322-337.
25
[26] W. Chen, W. Lu, N. Zhang, “Time-critical influence maximization in social networks with time-delayed diffusion process”, In the Proceedings of the 26th AAAI Conference on Artificial Intelligence, 2012, Toronto, Ontario, Canada.
26
[27] J. Kim, W. Lee, H. Yu, “CT-IC: Continuously activated and time-restricted independent cascade model for viral marketing”, Knowledge-Based Systems, vol. 62, pp. 57-68, 2014.
27
[28] R. Yan, Y. Li, D. Li, Y. Zhu, Y. Wang, H. Du, “Activation probability maximization for target users under influence decay model”, in International Computing and Combinatorics Conference, 2019, Springer, pp. 603-614.
28
[29] N. Ohsaka, Y. Yamaguchi, N. Kakimura, K.I. Kawarabayashi, “Maximizing time-decaying influence in social networks”, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2016, Springer, Cham, pp. 132-147.
29
[30] M. Hu, Q. Liu, H. Huang, X. Jia, “Time-sensitive influence maximization in social networks”, in 18th International Conference on Communication Technology (ICCT), 2018, IEEE, pp. 1351-1356.
30
[31] M. Huiyu, C. Jiuxin, Y. Tangfei, B. Liu, “Topic based time-sensitive influence maximization in online social networks”, World Wide Web, vol. 23, no. 3, pp. 1831-1859, 2020.
31
[32] Y. Wang, Y. Zhang, F. Yang, D. Li, X. Sun, J. Ma, “Time-sensitive positive influence maximization in signed social networks”, Physica A: Statistical Mechanics and its Applications, vol. 584, pp. 126353, 2021.
32
[33] A. Goyal, F. Bonchi, L.V.S. Lakshmanan, “Learning influence probabilities in social networks”, In the Proceedings of the 3rd ACM international conference on web search and data mining, 2010, New York, USA, pp. 241-250.
33
[34] B. Liu, G. Cong, D. Xu, Y. Zeng, “Time constrained influence maximization in social networks”, in 12th international conference on data mining, 2012, IEEE, Brussels, Belgium, pp. 439-448.
34
[35] B. Liu, G. Cong, Y. Zeng, D. Xu, Y.M. Chee, “Influence spreading path and its application to the time constrained social influence maximization problem and beyond”, IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 8, pp. 1904-1917, 2013.
35
[36] L.C. Freeman, “Centrality in social networks conceptual clarification”, Social Networks, vol. 1, no. 3, pp. 215-239, 1978.
36
[37] L.C. Freeman, “A set of measures of centrality based on Betweenness”, Sociometry, pp. 35-41, 1977.
37
[38] M.O. Jackson, “Social and economic networks”, Princeton university press, 2010.
38
[39] P. Crescenzi, G. d’Angelo, L. Severini, Y. Velaj, “Greedily improving our own centrality in a network”, in International Symposium on Experimental Algorithms, 2015, Springer, pp. 43-55.
39
[40] U. Brandes, “A faster algorithm for Betweenness centrality”, Journal of mathematical sociology, vol. 25, no. 2, pp. 163-177, 2001.
40
[41] A. Raychaudhuri, S. Mallick, A. Sircar, S. Singh, “Identifying Influential Nodes Based on Network Topology: A Comparative Study”, in Information, Photonics and Communication, 2020, Springer, pp. 65-76.
41
[42] P. Jia, J. Liu, C. Huang, L. Liu, C. Xu, “An improvement method for degree and its extending centralities in directed networks”, Physica A: Statistical Mechanics and its Applications, vol. 532, pp. 121891, 2019.
42
[43] S. Babaei S. Molaei M. Salehi, “Modeling Information Diffusion in Bibliographic Multilayer Networks”, Tabriz Journal of Electrical Engineering, vol. 49, no. 2, pp. 503-515, 2019 (in persian).
43
[44] J. Kunegis, “Konect: the koblenz network collection”, in Proceedings of the 22nd international conference on World Wide Web, 2013, pp. 1343-1350.
44
[45] J. Leskovec, D. Huttenlocher, J. Kleinberg, “Predicting Positive and Negative Links in Online Social
45
Networks”, In Proceedings of the 19th international conference on World Wide Web, 2010, North Carolina, USA, pp. 641-650.
46
ORIGINAL_ARTICLE
Outdoor fire detection on the video frames including fire zones close to the fire-like objects recorded by a fixed camera
In this paper, an automatic outdoor fire detection method is proposed for the fire videos recorded by a fixed camera. First, a new set of color rules is introduced to eliminate the non-fire pixels as much as possible while detecting the fire zone pixels completely. Next, the texture and flicker effect features are extracted from the detected fire zone, to remove the remainder of non-fire pixels if still any non-fire pixel exists. The texture feature is extracted by using the angular second moment. To extract the flicker effect feature, the time prehistory signal of color components of each fire zone pixel is obtained and passed through a half band high pass filter. Finally, the Ward classifier clusters the fire features to separate the fire zone pixels from the non-fire. At the various steps of the proposed method, the morphology process is also used to improve the accuracy of fire detection. The proposed method is applied to the 200 different fire videos including the fire-like objects. Simulation results indicate 6% to 56% improvement on performance of the proposed method in comparison to the similar ones.
https://tjee.tabrizu.ac.ir/article_14457_187915ecedf666ca1f57cbc2126b3060.pdf
2021-10-23
371
380
Fire detection
Outdoor fire video frames
Fixed camera
feature extraction
Flicker effect feature
Clustering
محمود
طالبیان مشهدی
mahmood22805.talebiyan@gmail.com
1
Department of Communication Engineering, University of Sistan and Baluchestan, Zahedan, Iran
AUTHOR
فرحناز
مهنا
f_mohanna@ece.usb.ac.ir
2
Department of Communication Engineering, University of Sistan and Baluchestan, Zahedan, Iran
LEAD_AUTHOR
[1] O. Gunay, K. Tasdemir, B. U. Toreyin, and A, E. Cetin, “Video based wildfire detection at night”, Fire Safety, Vol. 44, No. 6, pp. 860-868, 2009.
1
https://doi.org/10.1016/j.firesaf.2009.04.003
2
[2] J. Zhao et al., “SVM based forest fire detection using static and dynamic features”, Computer Science and Information Systems, Vol. 8, No. 3, pp. 821-841, 2011.
3
https://doi.org/10.2298/CSIS101012030Z
4
[3] A. Gupta, N. Bokde, D. Marathe and Kishore, “A novel approach for video based fire detection system using spatial and texture analysis”, Indian Journal of Science and Technology, Vol. 11, No. 19, pp. 1-17, 2018.
5
https://doi.org/10.17485/ijst/2018/v11i19/88825
6
[4] B. Ugur Toreyin, Y. Dedeoglu, U. Gudukbay, and A. Enis Cetin, “Computer vision based method for real-time fire and flame detection”, Pattern Recognition Letters, Vol. 27, pp. 49-58, 2006. https://doi.org/10.1016/j.patrec.2005.06.015.
7
[5] D. H. Lee, S. H. Lee, T. Byun, N. IK. Cho, “Fire detection using color and motion models”, IEEE Transaction on Smart and Computing, Vol. 6, No. 4, pp. 237-245, 2017.
8
https://doi.org/10.5573/IEIESPC.2017.6.4.237
9
[6] F. Gong, C. Li, W. Gong, X. Li, X. Yuan, Y. Ma, and T. Song, “A real-time fire detection method from video with multifeature fusion”, Computational Intelligence and Neuroscience, Vol. 2019, No. 1, pp. 1-17, 2019.
10
https://doi.org/10.1155/2019/193971
11
[7] F. Karimi Zarchi, V. Derhami, A. Latif, and A. Ebrahimi, “Fire detection using video sequences in urban out-door environment”, Signal and Data Processing, Vol. 16, No. 3, pp. 61-78, 2019.
12
[8] A. Mouelhi, M. Bouchouicha, M. Sayadi, and E. Moreau, “Fire tracking in video sequences using geometric active contours controlled by artificial neural network”, 4th International Conference on Advanced Systems and Emergent Technologies, Tunisia, 2020. https://doi.org/10.1109/IC_ASET.2020.9318289
13
[9] M. Ajith, and M. Martinez-Ramon, “Unsupervised segmentation of fire and smoke from infra-red videos”, IEEE Access, Vol. 17, pp. 182381-182394, 2019. https://doi.org/10.1109/ACCESS.2019.2960209
14
[10] A. Gaur et al., “Fire Sensing Technologies: A Review”, IEEE Sensors, Vol. 19, No. 9, pp. 3191 – 3202, 2019. https://doi.org/10.1109/JSEN.2019.2894665
15
[11] X. Chen, A. Qing, K. Yu, and Y. Ban, “A novel fire identification algorithm based on improved color segmentation and enhanced feature data”, IEEE Transaction on Instrumentation and Measurement, Vol. 70, 2021. https://doi.org/10.1109/TIM.2021.3075380
16
[12] Y. Xie, J. Zhu, Y. Cao, Y. Zhang, D. Feng, Y. Zhang, and M. Chen, “Efficient video fire detection exploiting motion-flicker-based dynamic features and deep static features”, IEEE Access, Vol. 8, pp. 81904-81917, 2020. https://doi.org/10.1109/ACCESS.2020.2991338
17
[13] K. Muhammad, S. Khan, M. Elhoseny, S. H. Ahmed, and S. Wook Baik, “Efficient fire detection for uncertain surveillance environment”, IEEE Transaction on Industrial Informatics, Vol. 15, No. 5, pp. 3113-3122, 2019. https://doi.org/10.1109/TII.2019.2897594
18
[14] K. Muhammad, J. Ahmad, Z. Lv, P. Bellavista, P. Yang, S. Wook Baik, “Efficient deep CNN-based fire detection and localization in video surveillance applications”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 49, No. 7, pp. 1419 – 1434, 2018. https://doi.org/10.1109/TSMC.2018.2830099
19
[15] X. Wu, X. Lu, and H. Leung, “A video based fire smoke detection using robust AdaBoost”, Sensors, Vol. 18, No. 11, 3780, 2018.
20
https://doi.org/10.3390/s18113780
21
[16] N. Randriamihamison, N. Vialaneix, and P. Neuvial, “Applicability and interpretability of Ward’s hierarchical agglomerative clustering with or without contiguity constraints”, Journal of Classification, Vol. 38, pp. 363-389, 2021.
22
https://doi.org/10.1007/s00357-020-09377-yff
23
[17] G. F. Shidik, F. N. Adnan, C. Supriyanto, and R. A. Pramunendar, and P. Andono, “Multi-color feature, background subtraction and time frame selection for fire detection”, Int. Conf. on Robotics, Biomimetic, Intelligent Computational Systems, 2013.
24
https://doi.org/10.1109/ROBIONETICS.201306743589
25
[18] V. Vipin, “Image processing based forest fire detection”, Int. Journal of Emerging Technology and Advanced Engineering, vol. 2, no. 2, pp. 87-95, 2012.
26
[19] H. Jin, and R. B. Zhang, “A fire and flame detecting method based on video”, Int. Conf. on Machine Learning and Cybernetics, pp. 2347-2352, 2009. https://doi.org/10.1109/ICMLC.2009.5212165
27
[20] Ti Nguyen-Ti, Thuan Nguyen-Phuc, and Tuan Do-Hong, “Fire detection based on video processing method”, Int. Conf. on Advanced Technologies for Communications, pp. 106-110, 2013.
28
https://doi.org/10.1109/ATC.2013.6698087
29