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Significant enhancement in the electrical conductance properties of ethylene propylene diene monomer using the nano‐SiO_(2) particles

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High Voltage 2021年第3期6卷 487-494页

作者： Chunyu Xu Ruixue Zhao Chuanyang Li Yuanyuan Li Guodong Zhang Tohid Shahsavarian Mengqiang Yuan Zhipeng Lei Shanxi Key Laboratory of Mining Electrical Equipment and Intelligent Control College of Electrical and Power EngineeringTaiyuan University of TechnologyTaiyuanChina Electrical and Computer Engineering University of ConnecticutStorrsConnecticutUSA State Grid Jincheng Power Supply Company JinchengChina Shanxi Jincheng Anthracite Mining Group Co. LtdJinchengChina School of Electrical Engineering Shandong UniversityJinanChina

To study the effect of nano‐silica particles on the conductivity characteristics of ethylene propylene diene monomer(EPDM)insulation,EPDM nanocomposites with different mass fractions of nano silica were prepared via the melt‐blending *** electron microscopy was used to analyze the dispersion of nanoparticles in *** on the Fourier‐transform infrared spectrum,the bonding properties between the nanoparticles and EPDM were *** steady‐state current at 30-100°C and 1-20 kV/mm was ***,the conductivity characteristic was *** experimental results show that as the content of nano‐silica increases,the electrical conductivity of EPDM nanocomposite as well as the threshold field strength gradually *** with a small number of nanoparticles will increase the activation energy of the carrier conductivity,and the interface region effect introduced by nanoparticles will reduce the carrier mobility and concentration,resulting in a lower *** the calculated value of the dielectric constant with the measured value can conclude that the high‐field conductance process is a combination of the electrode effect and the body effect,which in this case,Schottky effect dominates.

关键词： propylene conductivity conductance

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Hybrid Disassembly Line Balancing for Human-robot Collaborative Remanufacturing

Hybrid Disassembly Line Balancing for Human-robot Collaborat...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Feng Guo XiWang Guo MengChu Zhou Weitian Wang ShuJin Qin Qi Kang College of Information and Control Engineering Liaoning Petrochemical University Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Collaborative Robotics and Smart Systems Laboratory (CRoSS Lab) Montclair State University Montclair NJ USA College of Economics and Management Shangqiu Normal University Shangqiu P R. China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

As technologies advance rapidly, the urgency of remanufacturing is escalating. Efficient disassembly processes are crucial at the outset of remanufacturing. This work proposes an innovative disassembly scheme aimed at amplifying efficiency through the utilization of parallelization and human-robot collaboration in the context of a hybrid disassembly line. A single-objective mixed-integer programming model is developed to optimize disassembly profit. A discrete salp swarm algorithm is proposed to solve it since it is NP-hard. We have proposed and integrated uniform variation and two-point crossover operators in this algorithm. After comparing its results with those of the exact solver and other intelligent optimization methods, we conclude its competitive performance in both solution quality and efficiency.

关键词： computer aided software engineering Automation Optimization methods Human-robot interaction Collaboration Programming

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A reliable spacecraft power supply subsystem based on discrete water cycle multi-objective optimization

Franklin Open

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Franklin Open 2024年 7卷

作者： Mokhtar, Ahmed Ibrahim, Mohamed Hanafy, Mohamed E. ElTohamy, Fawzy H. Amer Murtada, Wael A. Elhalwagy, Yehia Z. Aircraft Electric Equipment Department Military Technical College Cairo Egypt Automatic Control Systems Department Military Technical College Cairo Egypt National Authority for Remote Sensing & Space Sciences Cairo Egypt Electrical and Computer Engineering Military Technical College Cairo Egypt

The successful execution of spacecraft missions relies heavily on the performance and reliability of their power supply subsystems. Achieving optimal component size and configuration is crucial for efficient power generation, distribution, and storage. Designing an efficient power supply subsystem for spacecraft missions is a complex and multi-objective optimization problem. Traditional optimization techniques, such as genetic algorithm and particle swarm optimization, have been employed, but their limitations in handling discrete variables and achieving global optima necessitate the exploration of alternative methods. In this context, this research paper explores the application of discrete water cycle algorithm (DWCA) optimization as a novel approach for optimizing the component size and configuration of spacecraft power supply subsystems. By emulating the dynamic flow and distribution of water, DWCA seeks to efficiently explore the design space and identify optimal solutions. Through comprehensive experimentation and comparative analysis, this research demonstrates that the proposed DWCA outperforms/surpasses other traditional optimization techniques in optimizing component size and configuration for spacecraft power supply subsystems. It consistently determines designs that are more efficient in power generation and storage. Moreover, DWCA enhances the efficiency and reliability of spacecraft power supply systems, especially in mission-critical scenarios. The successful optimization of spacecraft power supply subsystems is paramount to the overall success of space missions. Therefore, this advancement contributes significantly to the ongoing efforts to ensure the success of future space missions. © 2024 The Author(s)

关键词： Collaborative optimization Component-based system analysis Design performance Discrete Water Cycle Algorithm (DWCA) Multi-objective design Multidisciplinary design optimization Space electric power system Trade-off analysis

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Energy Internet:Redefinition and categories

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Energy Internet 2024年第1期1卷 3-8页

作者： Hongbin Sun Qinglai Guo Xinwei Shen Yixun Xue Mohammad Shahidehpour Nikos Hatziargyriou Department of Electrical Engineering Tsinghua UniversityBeijingChina College of Electrical and Power Engineering and Key Laboratory of Cleaner Intelligent Control on Coal&Electricity Ministry of EducationTaiyuan University of TechnologyTaiyuanChina Shenzhen International Graduate School Tsinghua UniversityShenzhenChina Department of Electrical and Computer Engineering Illinois Institute of TechnologyChicagoIllinoisUSA National Technical University of Athens AthensGreece

The concept of‘Energy Internet’(EI)has been widely accepted by both academic and industry experts after more than a decade of *** it was proposed,EI has been discussed and applied to many technical works in power and energy *** specific definitions were proposed for EI by those who have applied it to respective fields of engineering,but a comprehensive and widely accepted definition of EI is still being *** this paper,we propose the redefinition of EI,based on a comprehensive literature review,some latest trends and driving forces in the global energy industry,as well as its development in the past *** addition,we summarise the EI framework and features for future applications,where EI is categorised by its scale into local‐and wide‐area applications to manifest its effectiveness in power and energy.

关键词： categories Energy Internet redefinition

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The Improved Decoding Algorithm of the (71, 36, 11) Quadratic Residue Code

The Improved Decoding Algorithm of the (71, 36, 11) Quadrati...

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IEEE International Conference on Electronics and Communication engineering (ICECE)

作者： Chunlan Luo Xiaoxia Zhu College of Computer and Control Engineering Minjiang University Fuzhou Fujian China

ISBN: (纸本)9781728188577

In the context of informatization and big data, the demands for the reliability and real-time of information transmission in wireless communication channels are increasing. Quadratic residue (QR) codes have a high prospect on error correction for reliable data conveyance over channel with noise. This paper proposes an optimized algebraic scheme for decoding (71, 36, 11) QR code for correcting up to 5 errors on one-case-by-one-case basis. This scheme is mainly to improve its algebraic decoding algorithm based on the Lin’s algorithm, only in this paper, the Lin’s algorithm is called LTC71 algorithm. The key technology is to use the algebraic structure of QR code and give a new discriminant condition by mathematical derivation, which can quickly detect whether there are four errors in the code. Simulation results show that the proposed decoding scheme achieves the same bit error-rate performance as LTC71 algorithm, and the decoding time is reduced by about 28.72% owing to the reduced complexity of the decoder when there are four errors in the code.

关键词： Wireless communication Simulation Information processing Numerical simulation Real-time systems Mathematical models Decoding

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lncRNA-Disease Association Prediction Based On Weight Matrix And Projection Score

Research Square

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Research Square 2021年

作者： Wang, Bo Zhang, Chao Du, Xiao-Xin Zhang, Jian-Fei College of Computer and Control Engineering Qiqihar University Qiqihar161006 China

Background: with the development of medical science, lncRNA, originally considered as a noise gene, has been found to participate in a variety of biological activities. Nowadays, more and more studies show that lncRNA is involved in various human diseases, such as gastric cancer, prostate cancer, lung cancer, etc. However, obtaining lncRNA-disease association only through biological experiments not only costs manpower and material resources, but also gains little. Therefore, it is very important to develop effective computational models for predicting lncRNA-disease association. Results: In this paper, a new lncRNA-disease association prediction model LDAP-WMPS based on weight distribution and projection score is proposed. Based on the existing research results of disease semantic similarity, the integrated lncRNA similarity matrix and the integrated disease similarity matrix are calculated according to the disease semantic similarity and the association information between data. On this basis, the weight algorithm is combined with the improved projection algorithm to predict the lncRNA-disease association through the known lncRNA-miRNA association and miRNA-disease association. The simulation results show that under the loocv framework, the AUC of LDAP-WMPS can reach 0.8822. Better than the latest results. Through the case study of adenocarcinoma and colorectal cancer, it is proved that LDAP-WMPS can effectively infer lncRNA-disease association. Conclusions: The simulation results show that LDAP-WMPS has good prediction performance, which is an important supplement to the research of lncRNA-disease association prediction without lncRNA-disease association data. © 2021, CC BY.

关键词： Diseases

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Optimizing machine learning algorithms to enhance blood donation for liver patients: a case study

Optimizing machine learning algorithms to enhance blood dona...

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7th IET Smart Cities Symposium (SCS 2023)

作者： Maad M. Mijwil El-Sayed M. El-kenawy Indu Bala Mohammad Aljanabi Mostafa Abotaleb Abdelhameed Ibrahim Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood.

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Contextual Multi-Armed Bandit-Based Dynamic Cooperative Link Configuration for AUV in UWASNs With Energy Harvesting

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao066004 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

This paper investigates the cooperative link configuration problem for Autonomous Underwater Vehicle (AUV) in Underwater Acoustic (UWA) sensor networks with Energy Harvesting (EH), which aims to maximize long-term cumulative capacity by jointly optimizing cooperation relay, AUV transmission power, and relay transmission power. Subject to unknown time-varying Channel State Information (CSI), unpredictable stochastic EH, and variable communication topology caused by AUV mobility, the proposed problem is an unknown dynamic combination decision optimization problem. To resist the change of the dynamic UWA communication topology with unknown time-varying CSI and EH, a novel collaborative contextual multi-armed bandit learning framework is proposed, which allows relay nodes to learn cooperatively and enriches the learning information of relays. Consequently, the proposed learning framework can resist the change of the dynamic UWA communication topology efficiently, thereby achieving the superior link configuration quickly. Besides, a learning-rate adjustment rule for the dynamic UWA communication topology is proposed to adaptively balance the exploration-exploitation, thereby avoiding missing the real superior joint relay-power configuration. Finally, simulation results show the significantly superiority of the proposed scheme. © 2015 IEEE.

关键词： Cooperative communication

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Modeling and control of Intermediate Point Temperature of A 1000-WM Ultra Supercritical Thermal Power Plant 40

Modeling and Control of Intermediate Point Temperature of A ...

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40th Chinese control Conference, CCC 2021

作者： Peng, Chen Xxx Shanghai University School of Mechatronic Engineering and Automation Shanghai201900 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China Nanjing University of Posts and Telecommunications College of Automation and College of Artificial Intelligence Nanjing210023 China

ISBN: (纸本)9789881563804

This paper proposes an improved fuzzy particle swarm optimization algorithm (FPSO) to model intermediate point temperature. In FPSO, the plant-wide operation region with high nonlinearity is divided into several local regions by clustering algorithm. Following an improved particle swarm optimization algorithm is used to obtain local linear models. Then, fuzzy strategy is introduced to fuse the local models to get the model of the whole system. Besides, an improved generalized predictive control (GPC) is introduced to improve the control performance based on the identified modes. Finally, the simulation results show that the proposed modeling and control method can achieve accurate model identification and effective control of the intermediate point temperature. © 2021 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Clustering algorithms

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Energy Consumption Prediction for Manufacturing in Industrial IoT Based on Heterogeneous GNN

Energy Consumption Prediction for Manufacturing in Industria...

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IEEE International Conference on Communications in China (ICCC)

作者： Xuan Su Mingyan Li Qihao Li Chao Chen Songtao Guo Xu Wang College of Mechanical and Vehicle Engineering Chongqing University Chongqing P. R. China College of Computer Science Chongqing University Chongqing P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China College of Communication Engineering Jilin University Changchun P. R. China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Energy consumption prediction plays a crucial role in green manufacturing for industry, providing a theoretical foundation for energy-saving planning, workshop scheduling, and process optimization. However, owing to the processing complexity of mechanical products and the multiplicity of production information, previous methods can only achieve prediction for individual devices, which are quite restrictive to collaborative manufacturing planning. To deal with product-level energy management, this paper first proposes a heterogeneous manufacturing association graph (HetMG) which contains various manufacturing elements including product projects, decomposed orders and processing devices. Then, with HetMG, an energy consumption prediction method, HetEP, is introduced based on heterogeneous graph neural network (GNN), which incorporates Relational Graph Convolutional Networks (R-GCNs), and Long Short-Term Memory (LSTM) to mine the spatial-temporal relation of heterogeneous elements during the manufacturing process. Moreover, Graph Attention Network (GAT) is utilized in HetEP to adaptively learn the graph-structured data so that informative representations of each element can be obtained. The experimental results based on a manufacturing dataset from a generator factory demonstrate that the proposed HetEP yields superior prediction performance compared to the device-oriented prediction methods in terms of prediction accuracy.

关键词： Representation learning Performance evaluation Energy consumption Green manufacturing Job shop scheduling Prediction methods Production facilities

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