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12th International Conference on Information technology in Medicine and Education, ITME 2022

作者： Jin, Guonian Lu, Shejie Hu, Siping Xu, Haihong Gao, Jinfeng Liu, Fang Wu, Haitao Hubei University of Science and Technology School of Computer Science and Technology Institute of Engineering and Technology Xianning437100 China Huanghuai University International Joint Laboratory of Behavior Optimization Control for Smart Robots College of Computer and Artificial Intelligence Zhumadian463000 China

ISBN: (纸本)9798350310153

In IEEE 802.11 wireless LAN, as the number of nodes increases, collisions will increase correspondingly, channel utilization will decline, and the total throughput will decline instead of increasing, leading to system performance degradation. Through in-depth analysis of the root cause of this problem, this paper designs a simple and effective algorithm, which uses ARMA filtering algorithm to measure the conditional collision probability and then calculate the number of nodes in the network. The AP tells all nodes in the network in the form of broadcast when the number of nodes in the network changes, after all nodes receive the broadcast sent by the AP, they dynamically adjust the network parameters accordingly. Finally, NS2 simulation software is used to conduct simulation experiments in a variety of network scenarios, and the experimental results verify that the algorithm in this paper is simple and effective, with less changes to IEEE 802.11, and is suitable for use and promotion in wireless networks. It can optimize the network system performance based on the number of nodes, improve the system throughput, and significantly improve the network performance. According to the number and density of nodes, the algorithm reduces the collision probability by dynamically adjusting the timeslot, accordingly increases the total network throughput, and achieves the purpose of effectively improving the network performance. © 2022 IEEE.

关键词： IEEE Standards

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Event-Triggered-Based Robust control of Heterogeneous Connected Vehicles Platoon with Communication Delay and Random Packet Loss 8

Event-Triggered-Based Robust Control of Heterogeneous Connec...

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8th CAA International Conference on Vehicular control and Intelligence, CVCI 2024

作者： Liu, Chen Ru, Shuangkun Qi, Lin Fang, Jiayi Zhang, Ting Yanshan University Hebei Key Laboratory of Industrial Computer Control Engineering Hebei Province Qinhuangdao066004 China School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China School of Vehicle and Mobility Tsinghua University Beijing100081 China

ISBN: (纸本)9798331504892

With the development of vehicle autonomous driving technology and wireless network communication technology, the mix platoon of connected autonomous vehicles (CAVs) and connected human-driven vehicles (CHVs) is becoming inevitable. The abnormal V2V communication and uncertain vehicle dynamics may affect the CAV control performance. Considering time-varying communication delays and random packet loss, an event-trigger-based robust control strategy is proposed based on the stability theory of stochastic time-delay systems. The Bernoulli packet loss model is constructed to solve the random packet loss problem. The robust controller with the event-triggered mechanism designed using the Lyapunov functional stability theory of stochastic systems ensures the stability of the mix platoon with vehicle heterogeneity and the stability of CAV. The simulation comparison has verified that the proposed control strategy can better suppress the fluctuation of the preceding vehicle and more quickly reach the desired value when both time delay and packet loss are present. © 2024 IEEE.

关键词： Stochastic systems

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Research on Preventing Commutation Failure in MMC and LCC Parallel DC Systems

Research on Preventing Commutation Failure in MMC and LCC Pa...

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International Conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Xiong, Lingfei Zheng, Kuan Wu, Yucheng Liu, Yiqi State Grid Economic and Technology Research Institute Co. Ltd. Beijing102209 China College of Mechanical and Electrical Engineering Northeast Forestry University Harbin150040 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China

ISBN: (纸本)9789819618675

Offshore wind power transmission systems require high capacity, and LCC-based HVDC transmission systems are well-suited to meet this demand. Nevertheless, LCCs are dependent on the grid to supply commutation voltage, and in the event of voltage dips or distortions in the grid, commutation failures can readily occur. This results in significant oscillations between the AC and DC sides, thereby compromising the system’s safe and stable operation. This paper introduces a method to enhance the system’s resilience to commutation failures by integrating an MMC-based VSC-HVDC system in parallel with an LCC-HVDC inverter station. The MMC offers the advantage of decoupled control of active and reactive power, enabling it to stabilize the AC bus voltage. By maintaining the stability of the AC bus voltage, the resilience of the parallel system to commutation failures is augmented, thereby improving the overall stability of the combined transmission system. Subsequently, a parallel hybrid HVDC system model was developed in PSCAD/EMTDC to simulate AC grid faults, and a coordinated control strategy was formulated. The simulation results demonstrate that the proposed control method significantly enhances the system’s capability to withstand commutation failures. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： HVDC power transmission

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Prioritization Method of Test Data for Intelligent Software based on Multi-objective Optimization 9

Prioritization Method of Test Data for Intelligent Software ...

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9th International Conference on Dependable Systems and Their Applications, DSA 2022

作者： Xia, Chunyan Huang, Yifan Huang, Song Zheng, Changyou Wang, Yutong Wang, Yu College of Command and Control Engineering Army Engineering University of Pla China College of Computer and Information Technology Mudanjiang Normal University China Illinois Institute of Technology United States

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

With the rapid development of the information industry, intelligent software testing has become one of the hot research. This paper studies how to extract useful data from the original test set to test the modified model for the intelligent software. First, the initial test data are obtained according to the label classification of the original structured documents. Second, the non-dominated sorting genetic algorithm is used to prioritize the test data. Finally, the experimental results of intelligent software show that compared with the random method, the accuracy of the penalty_money prediction model is reduced by 0.04% 34.91 %, and the accuracy of the penalty_imprisonment prediction model is reduced by 0.08% 32.63 %, indicating that the proposed method can find more defects of the penalty prediction model earlier, to reduce the cost of intelligent software testing. © 2022 IEEE.

关键词： Multiobjective optimization

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The Most Powerful Unfalsified Linear Parameter-Varying Model with Shifted-Affine Scheduling Dependence

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SSRN 2024年

作者： Markovsky, Ivan Verhoek, Chris Tóth, Roland Gran Capità Barcelona08034 Spain Control Systems Group Eindhoven University of Technology Eindhoven5600 Netherlands Control Systems Group Eindhoven University of Technology and Systems and Control Lab Institute for Computer Science and Control Budapest1111 Hungary

The paper considers exact identification of linear parameter-varying (LPV) systems from the behavioral perspective. In the case of linear time-invariant (LTI) systems, the notion of the most powerful unfalsified model (MPUM), i.e., the least complex exact model for a given data trajectory, is well established. The MPUM, however, has not been generalized for LPV systems. In order to do this, we define the notions of model complexity for LPV systems with shifted-affine scheduling dependence. The MPUM leads to identifiability conditions and a method for exact LPV system identification. The method is based on lifting the LPV system to a higher dimensional space and LTI embedding in the lifted space. It is made rigorous by proving a formal connection between the parameters of the LTI embedding and the original LPV system. © 2024, The Authors. All rights reserved.

关键词： Embeddings

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Long Term Load Forecasting Using Adaptive Neural Fuzzy Inference System: Case Study on Woldia Power Distribution Systems

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SSRN 2024年

作者： Sinishaw, Getaye Yeshaneh Mitiku, Hunachew Moges Molla, Tesfahun Desale School of Electrical and Computer Engineering Electrical Power Woldia Institute of Technology Woldia University Woldia Ethiopia School of Electrical and Computer Engineering Industrial Control Woldia Institute of Technology Woldia University Woldia Ethiopia

Abstract: - Now a day’s power consumption in Woldia city is becoming increased. As a result, there is a need of load forecasting to know the electrical load consumption and be ready to supply the customers of woldia city. Therefore, load forecasting strengthens the power systems' dependability and energy efficiency. Power systems are highly distributed, highly complex graphically, and subject to numerous unforeseen events, making power system problems difficult to solve. It has considered the different demographic aspects, such as population growth, time, historical load, economic growth of the city and current load of the distribution system. In this thesis, Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models have been used to determine the forecasted load using data collection obtained from woldia city and the results of the two models have been compared. The data sets cover a seven-year period (2016/17- 2022/23) on monthly basis and forecast for ten years (2023/24-2032/33). The performance evaluations of ANFIS model have been executed better when compared with ANN. The performance specification of load forecasting MAPE and RMSE in ANFIS were improve by 2.0196% and 2.859984 respectively as compared with ANN. The prediction performance of the ANFIS model for load forecasting based on the previous data trained was investigated and a good performance of future load forecasting has been recoded. It can be decided that the future load consumption of woldia city is directly related with the population growth and the GDP of the city. © 2024, The Authors. All rights reserved.

关键词： Energy efficiency

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Range-Limited Pursuit-Evasion

Range-Limited Pursuit-Evasion

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2023 IEEE National Aerospace and Electronics Conference, NAECON 2023

作者： Weintraub, Isaac E. Von Moll, Alexander Pachter, Meir Control Science Center Air Force Research Laboratory Wright-Patterson AFBOH United States Air Force Institute of Technology Electrical and Computer Engineering Wright-Patterson AFBOH United States

ISBN: (纸本)9798350338782

In pursuit-evasion the objective of the pursuer is to capture the evader. In this work, the faster pursuer is modeled to have limited range and therefore optimal strategies for the pursuer and evader change. Depending upon the range limits of the pursuer the evader may evade capture by the pursuer. This paper describes the optimal strategies and nuances that appear for point-capture or when the pursuer is endowed with a non-zero capture radius. © 2023 IEEE.

关键词： Multi agent systems

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Optimizing Electrocardiogram Signal Augmentation for Realistic Synthetic Data in Deep Learning Model

Optimizing Electrocardiogram Signal Augmentation for Realist...

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Signal Processing Algorithms, Architectures, Arrangements and Applications (SPA)

作者： Muhammad Farhan Safdar Piotr Pałka Ahmed Al Faresi Robert Marek Nowak Institute of Computer Science Warsaw University of Technology Poland Institute of Control and Computation Engineering Warsaw University of Technology Poland College of Information Technology United Arab Emirates University UAE

ISBN: (数字)9788362065486

ISBN: (纸本)9798350373806

Electrocardiograms (ECG) are non-invasive signals and have proven useful in assessing the heart condition. Given the necessity for extensive datasets in ECG classification using deep learning (DL) models, there is a critical imperative to devise data augmentation methods capable of generating synthetic but realistic dataset suitable for training DL model. In this study, we propose a novel approach for augmenting ECG signals, aiming to produce realistic signals while optimizing memory usage and resource requirements. Building upon our previous work in ECG signal augmentation, we revisit the methodology to address limitations observed in the generation of synthetic signals. The existing method segmented ECG signals into fixed-length segments and combined them, occasionally resulting in unrealistic heart cycles within the signals in extreme condition. To address this issue, our proposed technique incorporates R peak detection, signal segmentation, and reordering based on the R-peaks information. We evaluated the proposed method using three benchmark datasets, including PTB-XL, Chapman-Shaoxin from PhysioNet, and the dataset from China Physiological Signal Challenge 2018 (CPSC-2018), for classifying atrial fibrillation from normal samples. Our approach achieved an accuracy of 0.83, sensitivity of 0.86, specificity of $0.80, F_{1}$-score of 0.83, and precision of 0.80. These results underscore the effectiveness and efficiency of our method in augmenting ECG signals for various applications in healthcare and biomedical research.

关键词： Training Heart Deep learning Biological system modeling Signal processing algorithms Electrocardiography Data augmentation Data models Classification algorithms Synthetic data

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A New Approach of Intrusion Detection with Command Sequence-To-Sequence Model 6th

A New Approach of Intrusion Detection with Command Sequence-...

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6th Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2019

作者： Liu, Wei Mao, Yu Ci, Linlin Zhang, Fuquan School of Computer Science and Technology Beijing Institute of Technology Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350117 China

ISBN: (纸本)9789811650352

Traditionally, researchers have focused on network level intrusion detection and program level intrusion detection to improve computer security. However, neither approach is foolproof. We argue that the internal and external security of a computer system are equally important. Typically, a successful attacker manifests in the form of the attacker becoming a user on the host either with elevated or normal user privileges. At this point, user-level intrusion detection attempts to deter and curtail an attacker even after the system has been compromised. In this work, we introduce a new approach of intrusion detection based on recurrent neural networks (RNNs) to solve the long sequential problem. We build a robust user command sequence-to-sequence model by semantic modeling command. Our model implements the prediction of user command sequence and the prophesying of user behaviors. The experimental results on data sets of Purdue University, SEA and self-collected data show that an accurate, effective and efficient detection can be achieved by using the proposed approach. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recurrent neural networks

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Load balancing transmission technology of network communication based on phase spectrum compensation 7

Load balancing transmission technology of network communicat...

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2021 7th International Conference on Manufacturing technology and Applied Materials, ICAMMT 2021

作者： Zhai, Qian Lu, Xing Hua Huali College Guangdong University of Technology Institute of Computer Control Technology Guangzhou511325 China

In order to improve the load balance scheduling transmission ability of multi-frequency network communication in high-order nonlinear system, load balance design is needed. A load balance transmission method of multi-frequency network communication in high-order nonlinear system based on phase spectrum compensation is proposed. The time domain model of high-order nonlinear system multi-frequency network communication load is constructed by multi-frequency modulation phase spectrum compensation method. The feature detection of high-order nonlinear system multi-frequency network communication load is realized by high-order statistical feature connection identification and communication load balancing method. The autocorrelation constraint parameters of high-order nonlinear system multi-frequency network communication load are analyzed. The multi-dimensional array networking structure model of high-order nonlinear system multi-frequency network communication load is constructed by spatial spectral density analysis method. Combined with phase spectrum compensation and signal detection methods, the extracted high-order nonlinear system multi-frequency network communication load spectrum features are adaptively scheduled and filtered. In the wireless communication networking environment, load balance transmission of communication load in multi-frequency network of high-order nonlinear system is realized. The simulation results show that this method has better balance and lower error rate in multi-frequency network communication load balancing transmission, which improves the accuracy of multi-frequency network communication load balancing scheduling in high-order nonlinear systems. © Published under licence by IOP Publishing Ltd.

关键词： Time domain analysis

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