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Metaheuristic Optimization of Time Series Models for Predicting Networks Traffic

Computers, Materials & Continua 2023年第4期75卷 427-442页

作者： Reem Alkanhel El-Sayed M.El-kenawy D.L.Elsheweikh Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia

Traffic prediction of wireless networks attracted many researchersand practitioners during the past decades. However, wireless traffic frequentlyexhibits strong nonlinearities and complicated patterns, which makes it challengingto be predicted accurately. Many of the existing approaches forpredicting wireless network traffic are unable to produce accurate predictionsbecause they lack the ability to describe the dynamic spatial-temporalcorrelations of wireless network traffic data. In this paper, we proposed anovel meta-heuristic optimization approach based on fitness grey wolf anddipper throated optimization algorithms for boosting the prediction accuracyof traffic volume. The proposed algorithm is employed to optimize the hyperparametersof long short-term memory (LSTM) network as an efficient timeseries modeling approach which is widely used in sequence prediction *** prove the superiority of the proposed algorithm, four other optimizationalgorithms were employed to optimize LSTM, and the results were *** evaluation results confirmed the effectiveness of the proposed approachin predicting the traffic of wireless networks accurately. On the other hand,a statistical analysis is performed to emphasize the stability of the proposedapproach.

关键词： Network traffic soft computing LSTM metaheuristic optimization

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A Method for Solving Camera Pose Estimation Problems Considering the Pairwise Constraints and Polar Coordinates 8

A Method for Solving Camera Pose Estimation Problems Conside...

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8th International Conference on Computer and Communication Systems, ICCCS 2023

作者： Uematsu, Kohta Matsushima, Kousuke Advanced Engineering School National Institute of Technology Kurume College Fukuoka Japan National Institute of Technology Department of Control and Information Systems Engineering Kurume College Fukuoka Japan

ISBN: (纸本)9781665456128

The camera pose estimation problem using correspondences involves outliers in most cases. In recent years, Random Sample Consensus (RANSAC) is often used as a well-known outlier removal method, however, RANSAC does not guarantee to generate an optimal model due to the characteristics of its processing. Therefore, a method that separates rotation and translation parameters by using the Pairwise Constraints has been proposed. Since this method has no randomness, it can estimate the optimal rotation parameters. In the conventional method, the rotation parameters are expressed in terms of the rotation axis, and estimated using the Branch-and-Bound method. In the proposed method, the rotation parameters are expressed in polar coordinates and searched for. Experimental results show that the proposed method achieves better accuracy in most cases than the conventional methods on synthetic data. The proposed method also succeeded in reducing the execution time. © 2023 IEEE.

关键词： Branch and bound method

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Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

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Computers, Materials & Continua 2023年第1期74卷 1905-1921页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and *** more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the ***,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole *** proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering *** is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor *** hybridmethod is primarily designed to combat active black-hole *** the LEACH clustering phase,however,can also detect passive black-hole *** effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested *** diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution ***,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

关键词： Black-hole attack mobile ad-hoc network optimization dipper throated optimization

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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced control Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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Stacked Dual-stream LSTM based Feature Extraction Network for Hyperspectral Image Classification 42

Stacked Dual-stream LSTM based Feature Extraction Network fo...

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42nd Chinese control Conference, CCC 2023

作者： Yu, Wenbo Huang, He Zhang, Miao Shen, Yi School of Electronics and Information Engineering Soochow University Suzhou215021 China Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China

ISBN: (纸本)9789887581543

Remote sensing technology plays a significant role in classifying and detecting on-ground objects accurately. Specifically, hyperspectral imagery outperforms other remote sensing tools in terms of its abundant spatial and spectral information. However, analyzing hyperspectral images is always hampered by their redundant spectral dimensionality. In this paper, we proposed a novel stacked dual-stream long short-term memory (LSTM) based feature extraction network (SDLSTMnet) for hyperspectral image classification. The whole network consists of two streams for spatial and spectral information, respectively. In this network, convolutional layers are absolutely removed because of their time-consuming calculating and optimizing processes. As a substitute, stacked LSTM layers are utilized to capture the spatial information within sample neighbourhoods. Notably, the latent sequential features are captured to simulate the neighbouring regions of samples. Furthermore, the sequential features along the spectral dimension are also extracted by stacked LSTM layers because these bands are continuous locally. Finally, the presented fusion module is capable to fuse and align these extracted features for classification. Multiple experiments are conducted to prove the effectiveness of the proposed SDLSTMnet. Compared with other state-of-the-art techniques, SDLSTMnet is capable to reduce the spectral dimension and increase the classification accuracies simultaneously. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Remote sensing

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Comparison of Various Mathematical Models of 8/20 μs Current Surge and their Influence on RFID Marker 14

Comparison of Various Mathematical Models of 8/20 μs Curren...

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14th International Conference ELEKTRO, ELEKTRO 2022

作者： Vestenicky, Peter Kanalikova, Alzbeta University of Žilina Faculty of Electrical Engineering and Information Technology Department of Control and Information Systems Žilina Slovakia

ISBN: (纸本)9781665467261

In technical praxis the underground facility networks such as gas and water pipes or telecommunication and energetic cables are marked by special RFID markers to enable localization and identification of the network. Due to influence of climatic effects such as lightning part of the lightning surge current may be induced into the markers. This paper gives a comparison of various mathematical models of the surge current induced into conductors and these mathematical models are used to calculate current waveform in the markers. Finally, the voltage waveform across the capacitor in the marker resonant circuit is calculated to enable proper dimensioning of the capacitor and to increase the long term marker reliability. © 2022 IEEE.

关键词： Differential equations

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14th International Conference ELEKTRO, ELEKTRO 2022

作者： Zdansky, Juraj Hrbcek, Jozef Rastocny, Karol University of Žilina Faculty of Electrical Engineering and Information Technology Department of Control and Information Systems Žilina Slovakia

ISBN: (纸本)9781665467261

Safety PLCs have become a popular device for implementing safety functions in industrial applications. Their correct use is the prerequisite for achieving the required safety integrity level (SIL). This is closely related to the parametrization of the safety PLC (setting of the parameters). This paper deals mainly with parameters influencing the safety of two-channel connection of sensors to the safety PLC. © 2022 IEEE.

关键词： Accident prevention

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Safety Aspects of the Wheeled Mobile Robot 14

Safety Aspects of the Wheeled Mobile Robot

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14th International Conference ELEKTRO, ELEKTRO 2022

作者： Nemec, Dusan Andel, Jan Simak, Vojtech Pirnik, Rastislav University of Zilina Faculty of Electrical Engineering and Information Technology Department of Control and Information Systems Zilina Slovakia

ISBN: (纸本)9781665467261

This paper discusses the safety of mobile robots and proposes improvements by specific methods of collision avoidance, and by changes of logical architecture or the communication protocol. The proposed architecture is semi-distributed, which allows optimalization of the load balance among components while supporting strict safety rules. Such approach allows development of small-size mobile intelligent robots using low-cost computational hardware. © 2022 IEEE.

关键词： Mobile robots

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Design and Realization of Unmanned Vehicle Autonomous Navigation System Based on Binocular Camera 3

Design and Realization of Unmanned Vehicle Autonomous Naviga...

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3rd International Conference on Computer Science, Electronic information engineering and Intelligent control technology, CEI 2023

作者： Guo, Tianxing Lin, Lianlei Liu, Huanyu Li, Junbao School of Electronics and Information Engineering Harbin Institute of Technology Department of Automatic Test and Control Harbin China Information Countermeasure Technique Institute School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350372052

Aiming at the navigation problem of unmanned vehicles in extreme environments such as communication interference and limited GPS signals, this study proposes an autonomous navigation method based on binocular cameras. The method enables the unmanned vehicle to complete the task of localization and map building using only binocular images, without the need for other sensors or signal sources. Meanwhile, this study also proposes a local path planning obstacle avoidance method based on depth map, which, when combined with global localization information, can take into account both local obstacle avoidance and global path navigation, and can cope with random environmental changes. The system uses a single sensor for autonomous obstacle avoidance and navigation, which reduces the computational requirements and ensures the low cost of the navigation system. In order to verify the reliability and effectiveness of the system under signal-constrained conditions, the system is evaluated in a simulation environment and a real field scenario, respectively. The experimental results show that the system is able to achieve reliable localization and path planning under signal-constrained conditions. © 2023 IEEE.

关键词： Cameras

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Deep convolutional autoencoder thermography for artwork defect detection

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Quantitative InfraRed Thermography Journal 2024年第6期21卷 367-383页

作者： Liu, Yi Wang, Fumin Liu, Kaixin Mostacci, Miranda Yao, Yuan Sfarra, Stefano Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China Shanxi Key Laboratory of Signal Capturing & Processing North University of China Taiyuan China Professional Restorer Celano Italy Department of Chemical Engineering National Tsing Hua University Hsinchu Taiwan Department of Industrial and Information Engineering and Economics University of L’Aquila L’Aquila Italy

Infrared thermography is a cost-effective non-destructive evaluation technique that plays a critical role in extracting information about defects in cultural heritage such as works of art. However, in-depth studies on the internal structure of contemporary artworks using infrared thermography remain lacking, and therefore, a deep convolutional autoencoder thermography (DCAT) data analysis method is proposed for defect detection of contemporary artworks. In the proposed method, original data are enhanced by convolution to reduce the noise and inhomogeneous background in the original thermal images;subsequently, a deep autoencoder is used to extract nonlinear features from the enhanced thermographic data, and the results of the hidden layer are visualised. These visualised images highlight information regarding the internal structure and defects of the artwork. The case study on a handmade replica of Picasso’s ‘La Bouteille de Suze’, reproduced exactly as the original, has shown that DCAT significantly improved the accuracy of defect detection when compared with that of commonly used methods. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Nondestructive examination

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