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Abnormal Behavior Detection in Video Using BiFPN-EfficientNet based on Cosine distance optimization 3

Abnormal Behavior Detection in Video Using BiFPN-EfficientNe...

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3rd International conference on Signal Image Processing and Communication, ICSIPC 2023

作者： Zhao, Haili Song, Junfang Xu, Xiaoyu Wang, Tengjiao Pu, Yuanyuan Wu, Wenzhe College of Information Engineering Xizang Minzu University Shaanxi Xianyang712082 China

ISBN: (纸本)9781510670945

With the explosive growth of the number of videos on surveillance and the Internet. How to intelligently detect the large amount of video data has become a core task in the current video security surveillance system and network security, which is of great significance to urban security control and network environment governance. This paper introduce the idea of metric learning and design a video abnormal behavior detection network EBML applied to small sample size to address the problem that specific abnormal behavior cannot be detected and abnormal behavior samples are scarce in most methods. Firstly, EfficientNet is used as the backbone network for feature extraction, then, bringing in an improved BiFPN to mitigate the feature loss caused by too many networks and information loss, and enhances the ability to fuse low-level semantic information. Finally, the scalable Cosine distance metric is introduced into the Softmax of BiFPN-EfficientNet, which makes the gap between similar features shrink continuously and the gap between dissimilar features increase continuously, thus improving the accuracy of video anomalous behavior detection. By testing on the VCAD dataset, the accuracy reaches 96.3% and the F1 value is 0.94, which has an increase of 7.8% and 0.09 respectively, compared with the benchmark model EfficientNet. Experiments show that the algorithm can better balance speed and accuracy in video anomaly detection and can meet the demand of video anomalous behavior detection under small samples. © 2023 SPIE.

关键词： Anomaly detection

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optimization Design of Dual Redundant networks Based on Packet Delay and Dropping During Switching

Optimization Design of Dual Redundant Networks Based on Pack...

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Intelligent Communications and Computing (ICC), International conference on

作者： Xiangying Kong Cuntian Hu Jiangsu Automation Research Institute Lianyungang China

Mission critical control systems such as electronics, rail transit, and weapon equipment typically use dual redundant networks to improve communication high availability, which means switching between two networks to ensure that the system can continue to operate in the event of a network failure. However, both link on/off detection and new link establishment require a certain amount of time, which is the time for dual network switching. During this time, the pending messages submitted by the protocol stack to the faulty network card were not actually sent to the network, resulting in a “frame loss” phenomenon during switching. On the basis of analyzing the switching mechanism of dual redundant network cards, this article proposes an optimization mechanism for packet dropout processing based on packet delay during switching. By increasing the sending packet buffer and resending packets stuck in the sending buffer of the faulty network card, the number of frame dropouts caused by switching can be effectively reduced, and the reliability of network communication can be improved.

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3rd International conference on control, Instrumentation and Mechatronics Engineering, CIM 2022

3rd International Conference on Control, Instrumentation and...

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3rd International conference on control, Instrumentation and Mechatronics Engineering, CIM 2022

ISBN: (纸本)9789811939228

The proceedings contain 73 papers. The special focus in this conference is on control, Instrumentation and Mechatronics Engineering. The topics include: Optimizing PID controller Coefficients Using an Improved Biogeography-Based optimization to Stabilize Movements of Quadcopters;prediction of Occupant’s Head Movement During Slalom Driving via Artificial Neural network with Multiple Training Algorithms;Flapping Induced Piezoelectric Transduction for Novel Disaster Management UAVs;analysis of the Hybrid Adhesion Mechanism of the Wall Climbing Robot;Real-Time Data Collection Technique for UAVs Propeller Inspection;model Reference Adaptive controller Design for Electrohydraulic Actuator System with Varying Disturbance;Fabrication of a Low Cost Three-Wheel Electric Scooter Using PSoC Technology;a Design of Portable IoT Vital Signs Monitoring Device;ioT System Design of Thermoelectric Generator for Harvesting Motorcycle Exhaust Heat Energy;Flow Visualisation Over the Low Sweep Unmanned Combat Aerial Vehicle (UCAV) Configuration;enhancement of Robust control Law for Active Front Steering control Strategy;low-Cost Multiple-Pass Confocal Surface Profiler;self-healing Fluidic Dielectric Elastomer Actuator;empirical Based Irrigation Model Using Predicted Soil Moisture for Durian Plantation;a Simple Energy Management System for a Micro Grid System Using Rule-Based Algorithm;an Optimal Fuzzy Logic controller Design for a Single-Linked Inverted Pendulum System;Adaptive Super Twisting Sliding Mode control of Quadrotor MAV;Industrial SPECT System for Imaging Water Level Inside a Horizontal Pipe;neural network-Based Battery Management System for Through-the-Road Hybrid Electric Vehicle;Hybrid Core Power control Using PI, Fuzzy and MPC for TRIGA Nuclear Reactor;dynamic Modeling and Analysis for Parallel Robots Actuated by Pneumatic Artificial Muscles.

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Modification of methods for optimization of electrical network modes in order to reduce power losses for corona discharge 3

Modification of methods for optimization of electrical netwo...

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3rd International Youth conference on Radio Electronics, Electrical and Power Engineering, REEPE 2021

作者： Rakhaev, Alexander Shishkov, Evgeny Samara State Technical University Department of Electric Stations Samara Russia

ISBN: (纸本)9781728183985

The article shows the need to reduce corona losses in electrical networks. For this, we studied the statistics of network companies, as well as the results of scientific research. optimization of the modes of electrical networks is considered as a measure to reduce losses. The existing optimization methods are described, their drawback is indicated - the lack of accounting for the control actions implemented by FACTS devices. A modification of optimization methods is proposed to eliminate the neglect of FACTS devices in the optimization problem. The mathematical model of the modified optimization method is shown. The implemented method is tested on the design model in the software packages 'RastrWin3', 'Matlab'. Based on the results of a successful test, the efficiency of the proposed modification of the optimization methods was established.. © 2021 IEEE.

关键词： Flexible AC transmission systems

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Dangerous Goods Vehicle Routing optimization Model Considering Time-Varying Conditions

Dangerous Goods Vehicle Routing Optimization Model Consideri...

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Cloud Computing, Big Data Applications and Software Engineering (CBASE), International conference on

作者： Jinyu Han Senlai Zhu Nantong University Nantong China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

To achieve safety control and minimize transportation costs in the transportation of dangerous goods vehicles, a multi-objective path optimization model is established. The transportation risk is evaluated by the population exposure number of passing road sections. A time-dependent function based on travel speed is employed to capture the dynamic characteristics of the urban road network. An optimization model with the goals of minimizing total transportation cost and transportation risk is constructed. A multi-objective evolutionary algorithm is designed based on NSGA-II combined with the simulated annealing algorithm. The model and algorithm are verified by simulating the dangerous material transportation network with 19 demand nodes. The results show that the algorithm can be used to obtain Pareto solutions, which will provide decision support for the scheduling of dangerous material transportation vehicles.

关键词： Costs Roads optimization models Heuristic algorithms Transportation Simulated annealing Routing Safety Vehicle dynamics optimization

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Stochastic Scheduling for Stability of networked control Systems – A Short Review

Stochastic Scheduling for Stability of Networked Control Sys...

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Intelligent Technologies (CONIT), International conference on

作者： Jahnavi Guduru S. M. A K. Azad School of Electronics Engineering VIT AP University Amaravati India

With the increasing adoption of networked control systems in industrial process automation, it is crucial to ensure their effectiveness in data transmission. The challenges such as time delays, packet dropouts and bandwidth allocation are involved in data transmission over a networked control system. To maintain the stability of process control, the network connecting field devices is essentially analysed. This paper aims to provide informative trends on challenges in networked control systems supported to process control. It includes the review of methods used to improve stability and maintain the time delays, packet dropouts, and other intermediate challenges like bandwidth allocation collected from the literature. Moreover, the paper delivers the stochastic nature of network scheduling through various theories like Markov, Lyapunov, Swarm optimization, Monte Carlo, NBM, DNS, Dempster’s combination etc. Overall, this study can assist readers in comprehending the potential for stochastic scheduling in networked control systems for process automation.

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Computing Force network self-intelligence Based on Intent Driven and Digital Twin

Computing Force Network self-intelligence Based on Intent Dr...

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Electronic Information Engineering and Computer Science (EIECS), 2021 International conference on

作者： Xiangning Li Yuqian Cai Jingyue Tian 6G Research Centre China Telecom Beijing Research Institude Beijing China

Introducing intent-driven network technology and digital twin network technology into the computing force network can solve the problem of unified management and scheduling of the computing force network and the high delay of data routing. Intent-driven network, based on technologies such as AI (artificial intelligence) and nfV (network function virtualization), can translate the service, performance and networking intents expected by users or operators into actual networking policies, so as to realize efficient and flexible reconfiguration of computing force networks. Digital twin network realizes efficient analysis, diagnosis and control of the computing force network by high-fidelity virtual real-time interaction. Combining intent-driven and digital twin, a self-intelligent optimization process of computing force network is proposed, which realizes the low delay self-organizing closed-loop control of user intent-based computing force network and the cross-domain regulation of computing force resources.

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Modified Whale Optimisation Technique for Combined Objective of Torque Ripple Minimization & Speed control of SRM Drive 3

Modified Whale Optimisation Technique for Combined Objective...

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3rd IEEE International conference on Energy, Power and Environment - Towards Clean Energy Technologies (ICEPE)

作者： Saha, Nutan Panda, Sidhartha Sahoo, Dillip Ku. VSSUT Dept Elect Engn Burla 768018 Odisha India

ISBN: (纸本)9781665425360

In this work metaheuristic technique such as modified Hybrid Whale optimization Algorithm (mWOA) is used for control of speed along with ripple reduction in output torque of 75 KW, 4-phase 8/6 Switched Reluctance Motor (SRM) drive. The objective is to develop a controller for combined goal of speed control and ripple reduction in output torque of SRM. Various performance factors such as gain of proportional controller & integral controller of speed controller as well as of current controller commutation angle values arc considered for performance assessment of SRM. A comparison is made of the performance of SRM with implementation of modified Whale optimization Algorithm (mWOA) & Whale optimization Algorithm (WOA). It is seen that coefficient of torque ripple, ISE of current and objective function is reduced by mWOA algorithm as compared to WOA

关键词： Whale optimization Algorithm (WOA) Modified Whale optimization Algorithm (mWOA) Switch reluctance motor (SRM) Torque ripple Proportional integral (PI) controller

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Ship Intrusion Detection Technology Based on Bayesian optimization Algorithm and XGBoost

Ship Intrusion Detection Technology Based on Bayesian Optimi...

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Electrical Engineering and control Science (IC2ECS), International conference on

作者： Aobo Zhou Qi Zhu Jundong Zhang Ke Meng School of Marine Engineering Dalian Maritime University Dalian Liaoning China

Cybersecurity is a prerequisite for maritime safety. This study focuses on intelligent ship integrated automation system, analyzing the principles and shortcomings of these system. We propose a machine learning-based intrusion detection solution that utilizes Recursive Feature Elimination (RFE) as a feature selection algorithm and XGBoost as a classification algorithm. The Bayesian optimization algorithm is applied to optimize the hyperparameters of the XGBoost model for detecting anomalous network data. Results indicate that, compared to decision trees, logistic regression, random forests, KNN, and recent research findings, the proposed method exhibits smaller estimation errors with an overall accuracy of 99.8%. It effectively reduces vulnerabilities in ship networks, achieving a F1-score of 96.37% and a recall rate of 96.33% under sufficient dataset conditions. This approach enhances network security performance and elevates the cybersecurity level of intelligent ships.

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Pest Detection in Olive Groves Using YOLOv7 and YOLOv8 Models 3rd

Pest Detection in Olive Groves Using YOLOv7 and YOLOv8 Model...

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3rd International conference on optimization, Learning Algorithms and Applications (OL2A)

作者： Alves, Adilia Pereira, Jose Khanal, Salik Morais, A. Jorge Filipe, Vitor Univ Tras os Montes & Alto Douro P-5000801 Vila Real Portugal Univ Aberta P-1269001 Lisbon Portugal Inst Politecn Braganca Centro Invest Mt CIMO Campus Santa Apolonia P-5300253 Braganca Portugal Inst Politecn Braganca Lab Associado Sustentabilidade & Tecnol Reg Mt Su Campus Santa Apolonia P-5300253 Braganca Portugal INESC TEC LIAAD P-4200465 Porto Portugal Washington State Univ Ctr Precis & Automated Agr Syst Biol Syst Engn Prosser WA USA

ISBN: (纸本)9783031530357;9783031530364

Modern agriculture faces important challenges for feeding a fast-growing planet's population in a sustainable way. One of the most important challenges faced by agriculture is the increasing destruction caused by pests to important crops. It is very important to control and manage pests in order to reduce the losses they cause. However, pest detection and monitoring are very resources consuming tasks. The recent development of computer vision-based technology has made it possible to automatize pest detection efficiently. In Mediterranean olive groves, the olive fly (Bactrocera oleae Rossi) is considered the key-pest of the crop. This paper presents olive fly detection using the lightweight YOLO-based model for versions 7 and 8, respectively, YOLOv7-tiny and YOLOv8n. The proposed object detection models were trained, validated, and tested using two different image datasets collected in various locations of Portugal and Greece. The images are constituted by sticky yellow trap photos and by McPhail trap photos with olive fly exemplars. The performance of the models was evaluated using precision, recall, and mAP.95. The YOLOV7-tiny model best performance is 88.3% of precision, 85% of Recall, 90% of mAP.50, and 53% of mAP.95. The YOLOV8n model best performance is 85% of precision, 85% of Recall, 90% mAP.50, and 55% of mAP.50 YOLO8n model achieved worst results than YOLOv7-tiny for a dataset without negative images (images without olive fly exemplars). Aiming at installing an experimental prototype in the olive grove, the YOLOv8n model was implemented in a Ubuntu Server 23.04 Raspberry PI 3 microcomputer.

关键词： Olives sustainable production Convolutional Neural network Deep Learning YOLOv7 YOLOv8

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