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Neighborhood Combination Search for Single-Machine Scheduling with Sequence-Dependent Setup Time

Journal of computer Science & Technology 2024年第3期39卷 737-752页

作者：刘晓路徐宏云陈嘉铭苏宙行吕志鹏丁俊文 College of System Engineering National University of Defense TechnologyChangsha 410015China School of Artificial Intelligence Jianghan UniversityWuhan 430056China Xi’an Satellite Control Center Xi’an 710043China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China CCF

In a local search algorithm,one of its most important features is the definition of its neighborhood which is crucial to the algorithm's *** this paper,we present an analysis of neighborhood combination search for solv-ing the single-machine scheduling problem with sequence-dependent setup time with the objective of minimizing total weighted tardiness(SMSWT).First,We propose a new neighborhood structure named Block Swap(B1)which can be con-sidered as an extension of the previously widely used Block Move(B2)neighborhood,and a fast incremental evaluation technique to enhance its evaluation ***,based on the Block Swap and Block Move neighborhoods,we present two kinds of neighborhood structures:neighborhood union(denoted by B1UB2)and token-ring search(denoted by B1→B2),both of which are combinations of B1 and ***,we incorporate the neighborhood union and token-ring search into two representative metaheuristic algorithms:the Iterated Local Search Algorithm(ILSnew)and the Hybrid Evolutionary Algorithm(HEA_(new))to investigate the performance of the neighborhood union and token-ring ***-sive experiments show the competitiveness of the token-ring search combination mechanism of the two *** on the 120 public benchmark instances,our HEA_(new)has a highly competitive performance in solution quality and computational time compared with both the exact algorithms and recent *** have also tested the HEA,new algorithm with the selected neighborhood combination search to deal with the 64 public benchmark instances of the single-machine scheduling problem with sequence-dependent setup *** is able to match the optimal or the best known results for all the 64 *** particular,the computational time for reaching the best well-known results for five chal-lenging instances is reduced by at least 61.25%.

关键词： single-machine scheduling sequence-dependent setup time neighborhood combination search token-ring search hybrid evolutionary algorithm

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Short-term Load Prediction of Integrated Energy System Based on MCNN-LSTM Neural Network 6

Short-term Load Prediction of Integrated Energy System Based...

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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Lv, You Shi, Yijun Tian, Helu School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9798350346671

The high coupling characteristics between various energy of integrated energy system (IES) and the uncertainty of each link of power-grid-load make the dynamic process of IES more complex than that of the traditional single-load system, which brings great difficulties to the effective operation of IES. To solve the above problems, this paper proposes a method for short term multivariate load prediction based on deep neural network. Firstly, the correlation between each single load is used to divide the categories, and the load data at the same time is reorganized into an image mode to maximize the use of correlation;then, the multi-channel convolutional neural networks (MCNN) are used to extract and fuse the features of the images, and the fused feature data is input into the long short-term memory (LSTM) neural network to realize the prediction of multi-load data from the time level;finally, the feasibility of the proposed method is verified by an actual example analysis. © 2023 IEEE.

关键词： Long short-term memory

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Research on Fine-grained Access control Technology for Energy Data Based on Blockchain

Research on Fine-grained Access Control Technology for Energ...

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2023 International Conference on Intelligent Communication and Networking, ICN 2023

作者： Liang, Yin Li, Jianbin Li, Shike North China Electric Power University School of Control and Computer Engineering Beijing102206 China

ISBN: (纸本)9798350357677

In data-sharing scenarios in the energy sector, it is essential to establish a sound and reliable access control mechanism to protect energy resources and make informed decisions. However, traditional access control has flaws such as centralization, low transparency, and low flexibility, making it challenging to meet the current security needs of energy data sharing. In addition, due to the distributed, dynamic, and sensitive nature of energy data, it is necessary to ensure its security and auditability. To solve the above problems, this paper proposes an auditable access control model for energy data based on blockchain. The model adopts the attribute-based access control (ABAC) authorization model, which uses attributes as the adjudication element to build access control policies while adding data and user hierarchy attributes to improve adjudication efficiency and meet the need for fine-grained authorization. Based on this authorization model, blockchain technology ensures the data-sharing process is decentralized and transparent. The access control process is automated through four types of smart contracts, and the traceability of blockchain is used to store the access control operation records to monitor the illegal behavior of data requesters. The experimental results show that the model can control the access rights of data requesters with different attributes and ensure the security and controllability of data. © 2023 IEEE.

关键词： Authorization

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A Surface Defect Detection Method based on Information Entropy 26

A Surface Defect Detection Method based on Information Entro...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Zhang, Guoqing Sun, Hongbo Liu, Xin Liu, Lei Yantai University School of Computer and Control Engineering Shandong China Shandong University Shandong China

ISBN: (纸本)9798350331684

Surface defect detection is important in the industrial field. Most factories use the difference method to solve the problem of defect detection. However, difference method can't solve misjudgments caused by shooting angle and location. In this paper, using the information entropy to solve the problem of the misjudgment for qualified products caused by product position and camera Angle. At the same time, 30 experiments were carried out to determine the threshold, and then 100 experiments were carried out to compare the accuracy of information entropy and difference methods, including the images of qualified products and unqualified products. Finally, the information entropy method is better than the difference method, and its detection accuracy is 97%. © 2023 IEEE.

关键词： Image processing

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Assessment and Enhancement of FRC of Power Systems Considering Thermal Power Dynamic Conditions

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CSEE Journal of Power and Energy Systems 2024年第4期10卷 1371-1383页

作者： Feng Hong Yalei Pang Weiming Ji Lu Liang Fang Fang Junhong Hao Jizhen Liu State Key Laboratory of Alternate Electric Power System with Renewable Energy Sources and School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China State Key Laboratory of Alternate Electric Power System with Renewable Energy Sources School of Energy Power and Mechanical EngineeringNorth China Electric Power UniversityBeijing 102206China

Frequency stability and security have been a vital challenge as large-scale renewable energy is integrated into power *** contrast,the proportion of traditional thermal power units decreases during the decarbonization transformation process,resulting in poor frequency *** paper aims to explore the potential of frequency regulation support,dynamic assessment,and capacity promotion of thermal power plants in the transition *** the dynamic characteristics of the main steam working fluid under different working conditions,a nonlinear observer is constructed by extracting the main steam pressure and valve opening degree *** real-time frequency modulation capacity of thermal power units can provide a dynamic state for the power grid.A dynamic adaptive modification for primary frequency control(PFC)of power systems,including wind power and thermal power,is proposed and *** power dynamic allocation factor is adaptively optimized by predicting the speed droop ratio,and the frequency modulation capability of the system is improved by more than 11%under extreme ***,through the Monte Carlo simulation of unit states of the system under various working conditions,the promotion of the frequency regulation capacity with high wind power penetration(WPP)is verified.

关键词： Dynamic evaluation frequency regulation power system predictive modeling wind penetration

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Attention Based Temporal Convolutional Networks for Short-Term Wind Power Forecasting 19th

Attention Based Temporal Convolutional Networks for Short-T...

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19th Chinese Intelligent Systems Conference, CISC 2023

作者： Li, Qingwei Hou, Guolian Yu, Qi School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819968855

As wind is characterized by its inherent stochasticity and volatility, accurate wind power forecasting has become increasingly crucial for efficient operation of the power system. In this paper, we propose a deep learning method combining temporal convolutional networks (TCNs) and attention mechanism for wind power forecasting. Temporal convolutional networks are employed for extracting features, and then attention mechanism is utilized to concentrate on the important timesteps. In data preprocessing part, we adopt sliding windows approach to generate training samples from the normalized data, which facilitates processing and learning for the model. The comparison results demonstrate that the proposed model outperforms the comparison models, indicating the effectiveness of the proposed model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Transmission Line Fault Diagnosis Method Based on SDA-ISSA-XGBoost under Meteorological Factors 3

Transmission Line Fault Diagnosis Method Based on SDA-ISSA-X...

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2023 3rd International Conference on Electrical engineering and computer Technology, ICEECT 2023

作者： Zhang, Kun North China Electric Power University School of Control and Computer Engineering Beijing102206 China

Transmission lines are directly exposed to the natural environment and are prone to failure due to meteorological factors. A novel approach for diagnosing transmission line faults under various meteorological conditions has been introduced. This method, known as SDA-ISSA-XGBoost, combines the power of Stacked Denoising Autoencoder (SDA), an improved Sparrow Search Algorithm (ISSA) enhanced with chaotic mapping sequences, adaptive weights, improved iterative local search, and a random differential mutation strategy, and eXtreme Gradient Boosting (XGBoost). The process begins with SDA, which extracts essential features from the initial meteorological data. Subsequently, ISSA is applied to optimize the parameters of the XGBoost model. This results in the ISSA-XGBoost fault diagnosis model. The performance of this model is compared to PSO-XGBoost and SSA-XGBoost. The experimental findings demonstrate that the ISSA-XGBoost model achieves an impressive fault diagnosis accuracy of 94.39%, surpassing both PSO-XGBoost and SSA-XGBoost by 6.54% and 3.74%, respectively. © Published under licence by IOP Publishing Ltd.

关键词： Failure analysis

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Development of High Accurate Family-use Digital Refractometer Based on CMOS

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Instrumentation 2023年第3期10卷 12-22页

作者： WANG Zhenxing JIA Zhenyuan School of Computer and Information Engineering Shanghai Polytechnic UniversityShanghai 201209China School of Intelligent Manufacturing and Control Engineering Shanghai Polytechnic UniversityShanghai 201209China

This study aims to develop a low-cost refractometer for measuring the sucrose content of fruit juice,which is an important factor affecting human *** laboratory-grade refractometers are expensive and unsuitable for personal use,existing low-cost commercial options lack stability and *** address this gap,we propose a refractometer that replaces the expensive CCD sensor and light source with a conventional LED and a reasonably priced CMOS *** analyzing the output waveform pattern of the CMOS sensor,we achieve high precision with a personal-use-appropriate accuracy of 0.1%.We tested the proposed refractometer by conducting 100 repeated measurements on various fruit juice samples,and the results demonstrate its reliability and *** on a 48 MHz ARM processor,the algorithm can acquire data within 0.2 *** low-cost refractometer is suitable for personal health management and small-scale production,providing an affordable and reliable method for measuring sucrose concentration in fruit *** improves upon the existing low-cost options by offering better stability and *** accessible tool has potential applications in optimizing the sucrose content of fruit juice for better health and quality control.

关键词： Brix Meter High-precision Refractometer Oversampling 1D CMOS Sensor

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A Machine Vision-based Deep Learning Method in Hemodialysis Filter Defects Detection 26

A Machine Vision-based Deep Learning Method in Hemodialysis ...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Liu, Xin Sun, Hongbo Zhang, Guoqing Liu, Lei Yantai University School of Computer and Control Engineering Shandong China Shandong University Shandong China

ISBN: (纸本)9798350331684

Hemodialysis filters are widely used in the treatment of kidney diseases. In order to reduce the occurrence of medical accidents, they need to go through a strict inspection process before being put into use to avoid defective products from entering the market, so it is crucial to locate and classify hemodialysis filters defects. In this paper, a deep learning method based on machine vision is proposed to solve the problem that the background of the inspected products is very similar to the defects and difficult to distinguish them. First, image denoising is performed, then the center of the product is located and the circle is expanded into a rectangle, the edge is extracted using a firstorder difference operator to distinguish the background from the defects, and finally the image after the extended channel is provided to the lightweight U-Net for training by online sample sampling. The test results show that the proposed method have achieved 0.78 mean IoU, 4.67% false detection rate and 0.67% missing rate on the hemodialysis filter dataset, which demonstrates the effectiveness of the proposed method. © 2023 IEEE.

关键词： Defects

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Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 66-75页

作者： Lingkang Zeng Wei Yao Ze Hu Hang Shuai Zhouping Li Jinyu Wen Shijie Cheng State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronics EngineeringHuazhong University of Science and TechnologyWuhan 430074China Dispatching and Control Center Central China Branch of State Grid Corporation of ChinaWuhan 430077China. Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA

Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and ***,GTS is composed of offline predetermination and real-time scenario ***,it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power *** improve efficiency of predetermination,this paper proposes a framework of knowledge fusion-based deep reinforcement learning(KF-DRL)for intelligent predetermination of ***,the Markov Decision Process(MDP)for GTS problem is formulated based on transient instability ***,linear action space is developed to reduce dimensionality of action space for multiple controllable ***,KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making *** can enhance the efficiency and learning ***,the graph convolutional network(GCN)is introduced to the policy network for enhanced learning *** simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.

关键词： Deep reinforcement learning generator tripping scheme graph convolutional network invalid action masking knowledgefusion

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