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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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Deep Self-Supervised Learning for Oracle Bone Inscriptions Features Representation 4

Deep Self-Supervised Learning for Oracle Bone Inscriptions F...

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4th IEEE International Conference on information Systems and computer Aided Education, ICISCAE 2021

作者： Du, Bingxin Liu, Guoying Ge, Wenying School of Media and Communications Anyang Normal University Anyang China School of Computer and Information Engineering Anyang Normal University Anyang China

ISBN: (纸本)9781665441247

In this paper, we design a two-branch deep learning framework to tackle the problem of self-supervised representation learning for Oracle Bone Inscriptions (OBIs). This problem is very complicated in that, unlike natural-photos, OBI images present more abstract content and suffer from different drawing styles, resulting in the failure of many existing self-supervised learning methods to describe them accurately. The core idea of our framework is that we design two OBI-specific pretext tasks, i.e. rotation and deformation. These two kinds of pretext tasks can provide strong supervision signals for OBI features learning. And we perform OBI recognition downstream task to evaluate our self-supervised learned features. Experimental results show that, under the same dataset, our proposed method outperforms jigsaw and matting based self-supervised learning methods. © 2021 IEEE.

关键词： Supervised learning

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Traffic Anomaly Detection Using Optimized Faster R-CNN

Traffic Anomaly Detection Using Optimized Faster R-CNN

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2024 International Conference on Electronics, Electrical and information engineering, ICEEIE 2024

作者： Karim, Shahid Qadir, Akeel Han, Gao Li, Jing Ibrar, Muhammad Bibi, Irfana School of Information Engineering Xi'an Eurasia University 710065 China Software College Shenyang Normal University Shenyang China Department of Computer Science Faculty of Computing and Information Technology University of Punjab Punjab Lahore Pakistan

ISBN: (数字)9781510686809

ISBN: (纸本)9781510686793

This paper introduces a new method for traffic anomaly identification within the Faster R-CNN framework by combining the DenseNet module with the region proposals (RPs) processing block. The suggested approach seeks to improve feature extraction and RP creation in aerial imagery or CCTV frames to get beyond the drawbacks of the current methods. Discriminative features are extracted through DenseNet, and the RPs block makes it easier to provide a wide range of accurate and varied area proposals. Contrast correction techniques are applied to overcome the difficulties presented by low-resolution and low-contrast images frequently seen in surveillance scenarios. Comparing experimental results to state-of-the-art techniques, one can observe notable resilience and detection accuracy increases. The suggested method works exceptionally well in various traffic scenarios and environmental circumstances, indicating its potential for practical implementation in traffic surveillance systems. Overall, the study contributes to advancing the efficacy and reliability of anomaly detection in urban traffic environments. © 2024 SPIE.

关键词： Aerial photography

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Periodicity and bifurcation of single pulse and soliton molecules

Periodicity and bifurcation of single pulse and soliton mole...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on information Photonics and Optical Communications, IPOC 2024

作者： Ban, Xiaoqiang Li, Xiaohui Li, Qian Zhong, Ming School of Electronic and Computer Engineering Peking University Shenzhen China School of Physics and Information Technology Shaanxi Normal University Xi'an China Faculty of Education Shenzhen University Shenzhen China

Period bifurcation of single pulse and soliton molecule are studied in fiber lasers, where period-timing bifurcation reveals the abundant nonlinear pulse dynamics and may be applied to optical switch. © 2024 IEEE.

ISBN: (纸本)9798350379266

关键词： Fiber lasers

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User Influence Evaluation Based on Behavioral Event Model

User Influence Evaluation Based on Behavioral Event Model

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2021 IEEE International Conference on Artificial Intelligence and computer Applications, ICAICA 2021

作者： Wang, Nan Lei, Guang Anyang Normal University School of Computer Information Engineering Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Anyang China Anyang Normal University School of Computer Information Engineering Anyang China

ISBN: (纸本)9781665418676

In Social networks, user influence can be evaluated and calculated based on the network topology, user identity and user behavior. The corresponding user influence value can be obtained through the evaluation by simple user characteristic such as follower count or retweeted count. However, the user influence is affected by many factors, simple method is not enough to fully reflect the influence of a user, which needs a more comprehensive evaluation process. 'Retweet' and 'Comment' are the core functions of online social networks which can reflect the user behavior pattern and are the important basis of influence research. We proposed a behavior model based on user 'Retweet', 'Comment' and 'like' behaviors as well as related behavior tendency. Based on the model a user influence evaluation method is proposed. The experimental results show that the influence evaluation method can reasonably identify which users have greater influence. © 2021 IEEE.

关键词： Social networking (online)

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Optimized Flame Detection Using a YOLOv5 Neural Network

Optimized Flame Detection Using a YOLOv5 Neural Network

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2024 International Conference on Computing, Robotics and System Sciences, ICRSS 2024

作者： Karim, Shahid Ibrar, Muhammad Bilal, Anas Qadir, Akeel Bibi, Irfana School of Information Engineering Xi'an Eurasia University Xi'an710065 China Software College Shenyang Normal University Shenyang China College of Information Science and Technology Hainan Normal University Haikou571158 China Faculty of Computing and Information Technology University of Punjab Department of Computer Science Punjab Lahore Pakistan

ISBN: (纸本)9798331511296

With the rising prevalence of smart homes, there's an increasing demand for comprehensive automation solutions to mitigate fire risks, especially when homeowners are absent or in homes with elderly residents. This study focuses on enhancing flame detection techniques, particularly for smaller, hard-to-detect fire sources. A customized Yolov5 neural network was developed to improve detection precision. This enhancement was facilitated using a dataset of over 2,000 images from lab camera observations and online platforms. These images were then manually labeled and clustered for anchor dimensions using the K-means++ algorithm. The CIOU and diou - nms techniques were integrated to optimize the network's performance. Upon training, the refined model was tested on the Jet Nano, revealing its high efficacy in flame detection. © 2024 IEEE.

关键词： Smart homes

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Research on Oracle Bone Inscriptions Recognition From Multi-Source Data Based on Deep Learning

Research on Oracle Bone Inscriptions Recognition From Multi-...

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IEEE Advanced information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Nan Wang Zhenyang Qi Fengxia Du School of Computer & Information Engineering Anyang Normal University Anyang Henan China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Anyang Henan China School of Fine Art Anyang Normal University Anyang Henan Province China

How to carry out recognition process in a quick and effective way is important to the oracle bone inscriptions research area. In this paper, we proposed a recognition method which adopt a skeletonization process to normalize handwriting and rubbing forms of oracle bone inscriptions into the same expression. Then by using models based on CNN such as VGG19, AlexNet and ResNet34, the proposed method can simultaneously handle recognition work with mixed input. The experiment results showed that our method achieve a good performance applied in the real oracle bone inscriptions data set.

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Study on Different Strategies to Solve the Problem of Addition and Subtraction: FMRI Combined with ACT-R 5

Study on Different Strategies to Solve the Problem of Additi...

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5th IEEE Advanced information Technology, Electronic and Automation Control Conference, IAEAC 2021

作者： Wang, Nan Yang, Xiaojing Anyang Normal University School of Computer Information Engineering Anyang Henan455000 China

ISBN: (纸本)9781728180281

Addition calculation and subtraction calculation are the most basic and common arithmetic operations. Different computational equations use different strategies, and the range and intensity of brain regions are also different. The research on cognitive processing strategies of addition calculation and subtraction calculation is helpful to explore the brain activity regularity of different cognitive processing strategies of human brain, provide reference for the further development of artificial intelligence and computer intelligence, and put forward cognitive model to realize the research on it when it is difficult to obtain a certain cognitive task data. © 2021 IEEE.

关键词： Brain

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Broadband Spectrum Sensing Based on Exponential Sum Decomposition 11

Broadband Spectrum Sensing Based on Exponential Sum Decompos...

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11th Joint International information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Li, Peijun Qian, Tao-Xin Wang, Hai-Quan Wang, Tong School of Computer and Information Engineering Fuyang Normal University Anhui Fuyang236037 China Hangzhou Dianzi University Faculty of Communication Engineering Zhejiang Hangzhou310018 China

ISBN: (纸本)9798350333664

In this paper, a blind broadband spectrum sensing based on Hankel matrix and Kung method is proposed. Specifically, first, the entire broadband spectrum is sampled by a sampling filter, and sampled signals are transformed into a Hankel matrix to display signal subspace. Second, using singular value decomposition (SVD) technique to construct the Hankel matrix, and the number of occupied subbands can be evaluated. Third, centers of the occupied subbands are estimated by applying the Kung method. The proposed method can determine the number and the centers of occupied subbands at the same time. Simulations show that the proposed method has a great detecting performance, especially in low signal-to-noise ratio (SNR) region. Moreover, complexity of the method is also relatively low. © 2023 IEEE.

关键词： Broadband spectrum sensing Cognitive radio Exponential Sum Decomposition Hankel matrix Kung method

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Vision based intelligent traffic light management system using Faster R‐CNN

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 932-947页

作者： Syed Konain Abbas Muhammad Usman Ghani Khan Jia Zhu Raheem Sarwar Naif R.Aljohani Ibrahim A.Hameed Muhammad Umair Hassan Department of Computer Science and Engineering University of Engineering and TechnologyLahorePakistan Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal UniversityJinhuaChina OTHEM Manchester Metropolitan UniversityManchesterUK Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of ICT and Natural Sciences Norwegian University of Science and Technology(NTNU)ÅlesundNorway

Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

关键词： access control artificial intelligence computer vision intelligent control

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