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Finite-time synchronization control for a class of delayed neural networks:an improved two-step control method

Science China(Information Sciences) 2025年

作者： Yue CHEN Song ZHU Mouquan SHEN Xiaoyang LIU Shiping WEN School of Mathematics China University of Mining and Technology College of Electrical Engineering and Control Science Nanjing Technology University School of Computer Science and Technology Jiangsu Normal University Faculty of Engineering and Information Technology University of Technology Sydney

In this paper,a two-step control method is proposed,leveraging the generalized Halanay inequality and existing finite-time stability theorems,to achieve finite-time synchronization for a class of neural networks with bounded time-varying *** the first step,the system state is attenuated from V （t0） to γV （t0） using the generalized Halanay inequality,where0<γ 1 is a free *** the second step,by applying existing finite-time stability theorems,the system state further decays from γV （t0） to *** on the above ideas,two novel finite-time stability lemmas for the error system are presented,and the convergence rate as well as the settling time is ***,the value of γ that results in the shortest settling time for the error system is also *** the help of the derived lemmas,several sufficient algebraic criteria are established to achieve finite-time synchronization between the considered delayed neural *** results of this paper not only improve the existing two-step control method but also overcome the limitations of certain one-step finite-time control ***,the validity and practical applicability of the obtained theoretical results are demonstrated through two numerical examples and an image protection experiment.

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A Review of Deep Convolutional Neural Networks in Mobile Face Recognition

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第23期17卷 4-19页

作者： Chi, Jing On, Chin Kim Zhang, Haopeng Chai, Soo See Faculty of Computing and Informatics University Malaysia Sabah Sabah Malaysia School Information and Electrical Engineering Hebei University of Engineering Hebei China Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak Sarawak Malaysia

With the emergence of deep learning, Convolutional Neural Network (CNN) models have been proposed to advance the progress of various applications, including face recognition, object detection, pattern recognition, and number plate recognition. The utilization of CNNs in these areas has considerably improved security and surveillance capabilities by providing automated recognition solutions, such as traffic surveillance, access control devices, biometric security systems, and attendance systems. However, there is still room for improvement in this field. This paper discusses several classic CNN models, such as LeNet-5, AlexNet, VGGNet, GoogLeNet, and ResNet, as well as lightweight models for mobile-based applications, such as MobileNet, ShuffleNet, and EfficientNet. Additionally, deep CNN-based face recognition models, such as DeepFace, DeepID, FaceNet, and SphereFace, are explored, along with their architectural characteristics, advantages, disadvantages, and recognition accuracy. The results indicate that many scholars are researching lightweight face recognition, but applying it to mobile devices is impractical due to high computational costs. Furthermore, noise label learning is not robust in actual scenarios, and unlabeled face learning is expensive in manual labeling. Finally, this paper concludes with a discussion of the current problems faced by face recognition technology and its potential future directions for development. © 2023 by the authors of this article. Published under CC-BY. All Rights Reserved.

关键词： Neural network models

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Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data

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computers, Materials & Continua 2023年第4期75卷 1883-1900页

作者： Ghada Atteia El-Sayed M.El-kenawy Nagwan Abdel Samee Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Ahmad Taher Azar Nima Khodadadi Reham A.Ghanem Mahmoud Y.Shams 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 Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Civil Engineering University of Science and TechnologyMiami33101USA Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Oral Biology Department Faculty of Oral and Dental MedicineDelta University for Science and TechnologyGamasaEgypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

The rapid population growth results in a crucial problem in the early detection of diseases inmedical *** all the cancers unveiled,breast cancer is considered the second most severe ***,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical *** recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death *** this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated *** proposed algorithm is evaluated using four publicly available breast cancer *** evaluation results show the effectiveness of the proposed approach from the accuracy and speed *** prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted *** addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed *** best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.

关键词： Medical dataset breast cancer guided whale optimizer dipper throated optimizer feature selection meta-heuristics

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Novel soft robotic finger model driven by electrohydrodynamic(EHD)pump

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Journal of Zhejiang University-Science A(Applied Physics & engineering) 2024年第7期25卷 596-604页

作者： Xuehang BAI Yanhong PENG Dongze LI Zhuochao LIU Zebing MAO Department of Mechanical Automotive and Materials EngineeringFaculty of EngineeringUniversity of WindsorWindsorN9B 3P4Canada Department of Information and Communication Engineering Graduate School of EngineeringNagoya UniversityNagoya464-8601Japan Department of Intelligent Science and Technology College of Computer Science and TechnologyQingdao UniversityQingdao266071China Design and Research Institute of University of Science and Technology Beijing Co.Ltd. Beijing100083China Faculty of Engineering Yamaguchi UniversityYamaguchi755-8611Japan

1Introduction In the field of robotic-human interactions,soft robotics offers enhanced safety and adaptability.A major challenge in this area is the integration of soft actuators with pump systems,which often increases the system volume and *** study presents the development and testing of a robotic finger powered by electrohydrodynamic(EHD)*** leveraging the electric field-induced flow of dielectric fluids.

关键词： robotic complexity. pump

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An integrated strategy of AEF attribute evaluation for reliable thunderstorm detection

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Digital Communications and Networks 2025年第1期11卷 234-245页

作者： Xu Yang Hongyan Xing Xinyuan Ji Xin Su Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada College of Internet of Things Engineering Hohai UniversityChangzhou 213022China Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye UniversitySariyer/IstanbulTurkey

Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain *** is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and *** this paper,a strategy is proposed to integrate three currently competitive WA's evaluation ***,a conventional evaluation method based on AEF statistical indicators is *** evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy *** AEF attributes contribute to a more accurate AEF classification to different *** resulting dynamic weighting applied to these attributes improves the classification *** evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation *** integration in the proposed strategy takes the form of a score *** cumulative score levels correspond to different final WA *** imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm *** results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA *** is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.

关键词： Atmospheric electric field(AEF) Thunderstorm Attribute Fuzzy c-means Imaging

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Numerical Integration Study for Spherical Near-Field to Far-Field Transformations 12

Numerical Integration Study for Spherical Near-Field to Far-...

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12th International Electrical engineering Congress, iEECON 2024

作者： Sukpreecha, Kitiphon Lertwiriyaprapa, Titipong Torrungrueng, Danai Akkaraekthalin, Prayoot Phaebua, Kittisak Faculty Of Technical Education Bangkok Thailand King Mongkut's University Of Technology North Bangkok Faculty Of Electrical Engineering Department Of Electrical And Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350383591

This paper presents a numerical integration study for spherical near field (NF) to far field (FF) transformations. The trapezoidal and Simpson 1/3 numerical integration methods are employed in the NF to FF transformations algorithm. The performance of both numerical integrations is investigated. In this study, a seven patch array antenna is employed. Simulated FF results are performed by three dimensional (3D) simulation program (CST Microwave Studio) and used as the reference. The collected NF sampling data is divided into two cases;1) over-sampled NF angles of ? = f = 2° and 2) under-sampled NF angles of ? = f = 12°. The investigation reveals that, in the case of under-sampled near field (NF) angles of ? = f = 12°, the trapezoidal demonstrates lower errors of the FF radiation pattern in comparison to those of Simpson 1/3. Particularly, the trapezoidal is suitable for the under-sampled NF measurement case. © 2024 IEEE.

关键词： Microstrip antennas

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Improving Sales Predictions with Autoformer by Integrating ARIMA for Anomaly Detection 5

Improving Sales Predictions with Autoformer by Integrating A...

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5th International Conference on Electrical, Communication and computer engineering, ICECCE 2024

作者： Saghafi, Mohammad Hasan Moshavash, Monireh Mohammadpur, Davud Amirkabir University of Technology Chief Technology Officer at *** Faculty of Electrical Engineering Tehran Iran Shahid Bahonar University of Kerman Member of AI Group at *** Faculty of Computer Engineering Tehran Iran University of Zanjan Chief AI Officer at *** Faculty of Engineering Zanjan Iran

ISBN: (纸本)9798331529437

Accurate sales forecasting is essential for online businesses to optimize inventory and marketing strategies. This paper addresses a significant and timely problem in e-commerce forecasting by proposing a hybrid model combining ARIMA for anomaly detection and Autoformer for long-term prediction. The integration of these models results in improved forecast accuracy, particularly in handling anomalous sales patterns. The Autoformer model, known for its performance in capturing long-term dependencies and seasonal trends, is enhanced by filtering out anomalies in the sales data using ARIMA before training. Our approach leverages historical sales data from an online shop, Technolife, to demonstrate how the detection of anomalous data leads to more accurate predictions. We provide key performance metrics such as Mean Absolute Error (MAE) and R-squared (R2) and demonstrate that handling anomalies improves forecasting accuracy. © 2024 IEEE.

关键词： Prediction models

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An Advanced Modulation Technique Featuring Common Mode Voltage Suppressio for Three-Phase Neutral Point Clamped Back to Back Converters

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International Journal of engineering, Transactions B: Applications 2022年第11期35卷 1184-1191页

作者： Tavassoli, F. Ghoreishy, H. Adabi, J. Rezanejad, M. Faculty of Electrical and Computer Engineering Babol Noshirvani University of Technology Mazandaran Iran Faculty of Electrical Engineering University of Mazandaran Mazandaran Babolsar Iran

Three-phase back-to-back converters are widely applied in various industrial, commercial, and domestic applications, such as AC motor drives. Due to the non-sinusoidal voltages they generate, a common mode voltage (CMV) appears, leading to problems in electrical drive systems and high-frequency applications. The CMV and rapid voltage changes can cause serious problems, including leakage currents flowing through the parasitic capacitors inside the motor, electromagnetic interference, shaft voltage, and bearing currents that reduce the motor's lifespan. In general, research to reduce these effects is divided into two methods: modifying the drive system's physical structure or improving the inverter's control algorithm. Pulse Width Modulation (PWM) methods are commonly used in control algorithms of converters to reduce the CMV. However, adding pulse amplitude modulation to the PWM helps reduce the CMV. The technique of simultaneous pulse width and amplitude modulation of space vectors is proposed in this paper to reduce the CMV and its destructive effects in drive systems. The proposed technique is based on the elimination of zero vectors and the inherent reduction of DC link voltage by amplitude modulation leading to a further reduction of the CMV;The obtained results of applying the proposed strategy to a three-phase back-to-back NPC converter with 738-watt steady-state operating point power showed the system's sufficient behavior with the efficiency of 98.62 percent. Finally, the transient performance of the converter from no-load to full-load condition ensures its sufficient behavior for industrial applications. © 2022 Materials and Energy Research Center. All rights reserved.

关键词： Pulse width modulation

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Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4027-4041页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences 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 School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target *** are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard ***,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been *** using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to *** of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization *** the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed *** to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance *** other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and *** results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

关键词： Metaheuristics adaptive squirrel search algorithm optimization methods binary optimizer

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Deep learning-based method for detection of copy-move forgery in videos

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Neural Computing and Applications 2025年第14期37卷 8451-8464页

作者： Elbarougy, Reda Abdelfatah, Osama Behery, G.M. El-Badry, Noha M. Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Ha’il Ha’il81481 Saudi Arabia Department of Information Technology Faculty of Computer and Artificial Intelligence Damietta University New Damietta Kafr Saad Egypt Department of Computer Science Faculty of Computer and Artificial Intelligence Damietta University New Damietta Kafr Saad Egypt Department of Mathematics Faculty of Science Damietta University New Damietta Kafr Saad Egypt

Video forgery is one of the most serious problems affecting the credibility and reliability of video content. Therefore, detecting video forgery presents a major challenge for researchers due to the diversity of forgery types, the modernity of the programs used in forgery operations, and the abundance of information and content present in videos. The seriousness of this issue arises from the widespread use of videos in vital fields that require very high accuracy with no room for doubt or error, such as courtrooms, journalism, and others. Copy-move forgery is one of the most common and dangerous types of video forgery because of the difficulty in identifying it with the naked eye and the great diversity of forgery techniques involved in this particular type. One of the biggest challenges researchers have faced in the past is the complexity of the steps required to detect forgery in videos, leading to significant computational complexity and time consumption. The proposed method also aims to achieve better results than previous methods while reducing computational operations. Ultimately, forgery is detected with great efficiency. Compared to previous methods used for detecting copy-move forgery in the Rewind dataset, the proposed method achieves the highest F1, reaching 0.86, with a significant difference of 0.13 compared to the best result of the previous methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Deep learning

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