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检索条件"机构=Computer and Control Engineering"

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Disturbances Observer-Based Fixed-Time Sliding Mode control for Three-Level NPC Converters in Microgrid 22

Disturbances Observer-Based Fixed-Time Sliding Mode Control ...

引用

22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Liu, Guangxin Shen, Xiaoning Zhang, Jianhua Gao, Yabin Liu, Jianxing School of Astronautics Harbin Institute of Technology Harbin China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798331527471

This paper introduces a fixed-time sliding mode control (FTSMC) scheme that utilizes a fixed-time disturbance observer (FTDOB) for three-level neutral-point-clamped (3L-NPC) converters. Therein, a FTSMC with adaptive exponential coefficient is developed to regulate the dc-link voltage. Compared with existing methods, the proposed approach features a settling time upper bound that is independent of the initial error, which ensures the dynamic performance of the 3L-NPC converters. Furthermore, a FTDOB is employed to estimate external disturbances and act as feedforward compensation of the 3L-NPC converters to enhance its disturbances rejection ability. Finally, a series of simulation results illustrate the advantages of the proposed scheme by compared with several representative methods used in 3L-NPC converters. © 2024 IEEE.

关键词： Sliding mode control

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A Novel Hybrid DC Circuit Breaker Topology with Current Limiting Capability and Rapid Fault Isolation Time 7

A Novel Hybrid DC Circuit Breaker Topology with Current Limi...

引用

7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Yin, Laicheng Liu, Jiayi Wu, Yucheng Liu, Wei Zhaobo Liu, Yiqi College of Mechanical and Electrical Engineerding Northeast Forestry University Harbin China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9798350359558

When fault currents in Modular Multilevel Converter High Voltage Direct Current (MMC-HVDC) transmission systems rise too rapid, the DC circuit breakers (DCCBs) often have insufficient capacity. Therefore, this paper proposes a current-limited hybrid DC circuit breaker (CL-HCB) with fault current limiting and fast fault isolation of the dc grid to reduce the capacity requirement of the DCCB. The topology of the proposed CL-HCB discusses the operation process, metal-oxide arrester (MOA) properties, and parameter design. Besides, the energy dissipation circuit (EDC) is parallel to reduce the energy dissipation in MOA and decrease fault isolation time (FIT), which could reduce the thermal effects of the MOA and improve its reliability. Extensive simulation results verify the working principle and advantages of the proposed CL-HCB. Compared to conventional HCB under the same simulation parameters, the proposed CL-HCB has more advantages in limiting fault current and fault isolation. © 2024 IEEE.

关键词： Energy dissipation

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DFE-SLAM: Dynamic SLAM based on improved feature extraction

DFE-SLAM: Dynamic SLAM based on improved feature extraction

引用

2024 China Automation Congress, CAC 2024

作者： Cheng, Guiying Jia, Jianfang Pang, Xiaoqiong Wen, Jie Shi, Yuanhao Zeng, Jianchao School of Electrical and Control Engineering North University of China Taiyuan China School of Computer Science and Technology North University of China Taiyuan China

ISBN: (纸本)9798350368604

Traditional visual localization algorithms often assume a static world, making them susceptible to inaccuracies and reduced robustness in real environments with dynamic objects. Additionally, these algorithms struggle to maintain high localization accuracy in low-texture settings. To overcome these challenges, we propose a dynamic SLAM system with enhanced feature extraction capabilities, called DFE-SLAM (Dynamic SLAM based on improved feature extraction). DFE-SLAM employs a lightweight YOLOv5s dynamic target detection network to filter out dynamic features from detection results. It also utilizes adaptive thresholding during the feature point detection stage to enhance accuracy. Moreover, the system incorporates a dense map construction thread, which creates dense point cloud maps using keyframes with corresponding bit poses. We evaluated DFE-SLAM using the TUM and Bonn datasets, benchmarking it against several advanced SLAM systems. The results demonstrate that DFE-SLAM excels in dynamic environments, delivering superior accuracy and robustness compared to most existing SLAM systems. © 2024 IEEE.

关键词： dense map Dynamic scenes feature extraction simultaneous localization and map building YOLOv5s

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Fault Detection for Battery Energy Storage Systems in Microgrids Under Event-Triggered Protocols: An Interval Observer Approach 7

Fault Detection for Battery Energy Storage Systems in Microg...

引用

7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Su, Mengmeng Zhang, Yong Liu, Zhibin Li, Xin North China Branch Of State Grid Corporation Of China Beijing China Northeast Forestry University College of Computer and Control Engineering Harbin China

ISBN: (纸本)9798350359558

In this paper, we investigate a method to realize fault detection using interval observer for battery energy storage systems containing actuator faults in microgrids. In order to reduce the data communication burden of the battery energy storage system, an improved event triggering mechanism with time-varying threshold is proposed to control the received signal of the interval observer. In addition, the desired interval observer gain is obtained by solving a matrix inequality. The output of the interval observer is utilized to construct the upper and lower bounds of the output of the system when the system does not contain actuator faults, and the actuator fault detection is achieved by whether the output of the system exceeds the constructed upper and lower bounds. Finally, the correctness and effectiveness of the proposed method are verified by numerical simulation. © 2024 IEEE.

关键词： Fault detection

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DialogNTM: Context Reconstruction in Multi-Turn Dialogue Generation using Neural Turing Machines

DialogNTM: Context Reconstruction in Multi-Turn Dialogue Gen...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Haohao Liu, Meiling Zhou, Jiyun Fu, Kaiquan College of Computer and Control Engineering Northeast Forestry University Harbin China Lieber Institute Johns Hopkins University Baltimore United States South Dakota State University Department of Electrical Engineering and Computer Science BrookingsSD United States

ISBN: (纸本)9781665410205

In actual conversational scenarios, we can often determine which parts of the previous dialogue are more critical based on the current inquiry. However, the existing contextual modeling methods often encode the query sentence and the dialogue history in a unified manner, which fails to effectively highlight the inference effect of the query sentence. Moreover, these methods typically process the dialogue history only at the information extraction level, neglecting the treatment of the context itself. In this paper, we propose a novel conversational context modeling technique called DialogNTM. Based on the guidance of the query sentence, the technology can effectively eliminate redundant information by reconstructing the representation of the context. Specifically, we have tweaked the memory and input flow of the Neural Turing Machine (NTM) to encode contextual information in memory and guide the read, write, and erase operations of memory through query sentence. This design simulates the human brain's dynamic retrieval and renewal mechanism of previous memories when dealing with current problems. We have conducted extensive experiments on three publicly available datasets to verify the effectiveness of the DialogNTM model. Compared to the benchmark model, DialogNTM showed significant performance improvements ranging from 11% to 73% across multiple automated evaluation metrics (3.52% to 8.68% in absolute terms). © 2024 IEEE.

关键词： Turing machines

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Wave refrigeration control via deep reinforcement learning 4

Wave refrigeration control via deep reinforcement learning

引用

2024 4th International Conference on Artificial Intelligence and Industrial Technology Applications, AIITA 2024

作者： Cui, Xuesong Hu, Xiaopeng Wang, Fan Li, Qi School of Computer Science and Technology Dalian University of Technology Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China

Wave refrigerating technology is a new type of refrigerating technology that performs more efficiently and is more environmentally friendly. Nowadays, the commonly used control method is the PID control method. However, although the above control methods are simple in structure, they have the problems of difficult parameter tuning, large overshoot, and unstable and inaccurate output response. Therefore, to improve the above problems, we propose a framework of deep reinforcement learning-based wave refrigerating control system. An improved SAC algorithm is used to learn the control system. The evaluation method is established according to the working condition requirements as well as the safety requirements in the operation process. Experiments show that this method can effectively improve the control efficiency as well as the control quality, which can make the control system operate stably. © Published under licence by IOP Publishing Ltd.

关键词： Deep reinforcement learning

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Configuration Optimization of Hybrid Electrolyzer for Wind-Based Green Hydrogen Production System 7

Configuration Optimization of Hybrid Electrolyzer for Wind-B...

引用

7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Lin, Jiahu Hao, Wenbo Ban, Mingfei Northeast Forestry University College of Computer and Control Engineering Harbin China State Grid Heilongjiang Electric Power Research Institute Harbin China

ISBN: (纸本)9798350359558

As the development of renewable energy progresses, the prospects for the application of green hydrogen are gradually expanding, with countries strengthening the construction of industries related to green hydrogen. This paper investigates the hybrid electrolytic hydrogen production system and its configuration optimization problem, considering wind power decomposition and multiple types of electrolyzers. Firstly, a model for wind power redistribution is established based on the characteristics of different types of electrolyzers to determine the power loads of each type. Subsequently, based on the solution results of the redistribution model, a hybrid electrolyzer system configuration optimization model with the objective of maximizing annual revenue is proposed. Finally, case studies show that Hybrid electrolyzer system offers advantages in terms of energy efficiency, reliability and economy. Compared to alkaline electrolyzer system, hybrid system can improve the efficiency and stability of energy utilization and better cope with the uncertainty of renewable energy sources. The hybrid system can significantly reduce the initial capital investment and number of configurations compared to proton exchange membrane electrolyzer system, thereby lowering the operating cost of the overall energy system. © 2024 IEEE.

关键词： Wind power

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Artificial Intelligence Based Employee Attrition Analysis and Prediction 6

Artificial Intelligence Based Employee Attrition Analysis an...

引用

6th International Conference on Computing and Informatics, ICCI 2024

作者： Ozakca, Nazli Sila Bulus, Ali Cetin, Aydin Havelsan Inc.Command Control and Defence technologies Ankara Turkey Computer Engineering Department Gazi University Faculty of Technology Ankara Turkey

ISBN: (纸本)9798350373875

The use of classification models to predict the probability of an employee leaving the job can greatly improve the ability of human resources to intervene in a timely manner and rectify the situation to prevent attrition. After the so-called "The Great Resignation", which took place after the pandemic period, studies on attrition have increased in order to regain the lost workforce and increase the loyalty of existing employees. In Turkey, however, studies in this field remain insufficient. The aim of this study is to predict the possible loss of employees, to take relevant measures and to reduce the financial losses of companies. The study was made available as SaaS (Software as a Service) based in order to facilitate accessibility to the target audience. For the analysis phase, the columns in the data set were analyzed with Pandas and Scikit-Learn library. Exploratory Data Analysis was performed by visualizing the analyzed data with Plotly and Seaborn libraries. Various insights were obtained with these inferences. The data was subjected to pre-processing stages such as cleaning, missing data completion, scaling, feature selection, data set balancing, dimension reduction, etc. to make it useful. Logistic Regression, KNN, SVM, Desicion Tree, Random Forest, ADABoost and Naive Bayes were used to train the model. In order to enrich the data set and increase the efficiency of the model, artificial intelligence-based synthetic data generation (Data Augmentation) was performed. In case of missing columns in the trained model, KNN-Data Imputation, one of the missing data completion methods, was used. In the optimization process of the model, hyperparameter optimizations were performed to achieve maximum efficiency with improvements. 5-fold cross-validation prevented the model from over-learning. Performance was analyzed on the basis of Accuracy, Recall, Precision and F1-score metrics and the success criterion was determined as F1-Score. The model was presented as a web se

关键词： Efficiency

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A hybrid algorithm based on ABC algorithm and its application in Hpcf pump fuzzy control 5

A hybrid algorithm based on ABC algorithm and its applicatio...

引用

5th International Conference on computer Communication and Network Security, CCNS 2024

作者： Xu, Lei Xu, Yang Sun, Ming Colledge of Computer and Control Engineering Qiqihar University Qiqihar161006 China ZhongKeWoYe Jiangsu Biological Co. Ltd. Suqian China

ISBN: (纸本)9781510681842

The Artificial Bee Colony (ABC) algorithm excels in solving optimization challenges but faces slow convergence, low precision, and local optima *** mitigate these limitations, a novel hybrid optimization algorithm, termed the IABC algorithm, was developed to improve the global search capability and speed up convergence. Initially, the employed bee phase adopts a sine law-based dimension-changing strategy to boost search and exploitation capabilities, accelerate convergence, and prevent premature convergence;in the onlooker bee phase, it follows the global optimum, altering its step size-large for broad searches in the early stages and small for precise searches later on. Finally, in the scout bee phase, the hunting strategy from the SWO algorithm(Spider Bee Optimization) is utilized to strengthen global and local search abilities. Experimental conducted on the CEC2017 dataset confirm enhanced strategies' success and performance efficiency. Comparisons with other ABC algorithms and competitive algorithms show that the IABC significantly enhances the performance of the ABC. In nearly all test functions, the IABC attained the superior solution quality, quickest rate of convergence, and most robust performance. Its effectiveness is further proven by successful application in pulsation suppression for the HPCF(high-pressure constant flow) pump pulse systems, indicating its potential for solving diverse optimization problems. © 2024 SPIE.

关键词： Fuzzy control

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A GRU-based Automatic Weighting Factors Optimization Strategy for PMSM

A GRU-based Automatic Weighting Factors Optimization Strateg...

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2024 China Automation Congress, CAC 2024

作者： Li, Jiaao Jin, Jie Zhang, Jianhua Liu, Zhuang Shen, Xiaoning School of Astronautics Harbin Institute of Technology Harbin China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798350368604

The performance of model predictive control (MPC) in permanent magnet synchronous motor (PMSM) still remains a challenging problem due to the large torque ripple in lower speed. The cost function in conventional MPC generally use fixed weighting factors, which is not easy to adapt different operation scenario and keep balance between torque ripple suppression and current tracking accuracy. To address this issue, a gated recurrent units (GRU) network based weighting factors automated-regulation strategy is proposed for the MPC in PMSM. Through offline training, the weighting factors in MPC are tuned in real time and the current tracking accuracy as well as torque ripple in different conditions are ensured, which is superior to the conventional fixed weighting factors-based MPC. Finally, some simulation results verify the effectiveness of the proposed method. © 2024 IEEE.

关键词： Predictive control systems

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