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A Novel Bi-Level VSC-DC Transmission Expansion Planning Method of VSC-DC for power system Flexibility and Stability Enhancement

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Energy engineering 2024年第11期121卷 3161-3179页

作者： Weigang Jin Lei Chen Shencong Zheng Yuqi Jiang Yifei Li Hongkun Chen Support Center of Power System Technology Central China Branch of State Grid Corporation of ChinaWuhan430077China Hubei Engineering and Technology Research Center forAC/DC Intelligent Distribution Network School of Electrical Engineering and AutomationWuhan UniversityWuhan430072China

Investigating flexibility and stability boosting transmission expansion planning(TEP)methods can increase the renewable energy(RE)consumption of the power *** this study,we propose a bi-level TEP method for voltage-source-converter-based direct current(VSC-DC),focusing on flexibility and stability ***,we established the TEP framework of VSC-DC,by introducing the evaluation indices to quantify the power system flexibility and ***,we propose a bi-level VSC-DC TEP model:the upper-level model acquires the optimal VSC-DC planning scheme by using the improved moth flame optimization(IMFO)algorithm,and the lower-level model evaluates the flexibility through time-series production ***,we applied the proposedVSC-DC TEPmethod to the modified IEEE-24 and IEEE-39 test systems,and obtained the optimalVSCDC planning *** results verified that the proposed method can achieve excellent RE curtailment with high flexibility and ***,the well-designed IMFO algorithm outperformed the traditional particle swarm optimization(PSO)and moth flame optimization(MFO)algorithms,confirming the effectiveness of the proposed approach.

关键词： VSC-DC transmission expansion planning renewable energy consumption line delivery flexibility short-circuit ratio improved moth flame optimization algorithm

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MBTDiff: Multi-segmentation Brain Tumor Model with Diffusion Probabilistic Model 9

MBTDiff: Multi-segmentation Brain Tumor Model with Diffusion...

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9th International Conference on Image, Vision and Computing, ICIVC 2024

作者： Zeng, Shuqi Liu, Jun Xu, Ji Luo, Yuan School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China Key Laboratory of Biomarkers and In Vitro Diagnosis Translation Zhejiang province Hangzhou China Hubei Engineering Research Center for Intelligent Digital Technology in New Power System Hubei Central China Technology Development of Electric Power Co. Ltd Wuhan China

ISBN: (纸本)9798350385991

The diffusion probability model has recently garnered remarkable achievements in image generation, emerging as a prominent research focus within computer vision. It has been observed through numerous studies that the diffusion probability model holds potential for application across various domains within computer vision. Drawing inspiration from these models, we present a novel approach to extend their utility to the realm of brain tumor medical image segmentation. Our method entails training the model utilizing a diffusion framework, followed by iterative refinement of segmentation outcomes by incorporating additional encoding layers and decoders. This method employs an end-to-end training approach without relying on pre-trained backbone networks. Given the probabilistic nature of the results generated by the diffusion model and to simulate the actual decision-making process of doctors, we will generate multiple segmentation results and apply denoising techniques to obtain a stable output. These results will then be integrated into the final segmentation map to achieve more precise segmentation. © 2024 IEEE.

关键词： Image segmentation

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intelligent Manufacturing Flexibility Scheduling Optimization Based on Hybrid NSGA-II Algorithm

Intelligent Manufacturing Flexibility Scheduling Optimizatio...

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IEEE International Conference on Mechatronics, Robotics and Automation (ICMRA)

作者： Zhuoya Hu Jun Liu Ji Xu Chaoqian Ye School of Computer Science & Engineering Wuhan Institute of Technology Wuhan China Key Laboratory of Biomarkers and In Vitro Diagnosis Translation of Zhejiang province Hangzhou China Hubei Engineering Research Center for Intelligent Digital Technology in New Power System Hubei Central China Technology Development of Electric Power Co. Ltd Wuhan China

ISBN: (数字)9798350352474

ISBN: (纸本)9798350352481

The flexible job-shop problem is one of the classical problems that have attracted much attention in the field of industrial automation and control. For the Multi-Objective Flexible Job-shop Scheduling Problem (MOFJSP), this paper proposes a hybrid particle swarm Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve the problem. The high-quality solution of the problem is obtained through particle swarm optimization as part of the initial population of NSGA-II, and an elite strategy is used to prevent the loss of outstanding individuals by mixing all the individuals of the parent and the offspring in a non-dominated sorting method. This approach not only facilitates automation in manufacturing and production but also enhances the control and optimization of complex systems. Furthermore, integrating intelligent transportation systems and the Internet of Things (IoT) into the scheduling process ensures real-time fault diagnosis and fault-tolerant control, thereby improving the overall efficiency and reliability of the production system. The experimental results show that the hybrid optimization approach combining PSO and NSGA-II exhibits superior performance in solving MOFJSP. This research contributes to the advancement of cyber-physical systems and the automation of complex industrial processes.

关键词： Production systems Job shop scheduling Automation Process control Real-time systems Manufacturing Internet of Things Particle swarm optimization Optimization Sorting

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MBTDiff: Multi-segmentation Brain Tumor Model with Diffusion Probabilistic Model

MBTDiff: Multi-segmentation Brain Tumor Model with Diffusion...

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International Conference on Image, Vision and Computing (ICIVC)

作者： Shuqi Zeng Jun Liu Ji Xu Yuan Luo School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China Key Laboratory of Biomarkers and In Vitro Diagnosis Translation of Zhejiang province Hangzhou China Hubei Engineering Research Center for Intelligent Digital Technology in New Power System Hubei Central China Technology Development of Electric Power Co Ltd Wuhan China

ISBN: (数字)9798350385991

ISBN: (纸本)9798350386004

关键词： Training Image segmentation Computer vision Three-dimensional displays Computational modeling Magnetic resonance imaging Brain tumors Brain modeling Diffusion models Biomedical imaging

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Bidirectional generative diffusion model with cascaded symmetric attention for brain connectivity-to-connectivity translation

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Biomedical Signal Processing and Control 2025年 108卷

作者： Zuo, Qiankun Di, Yi Ye, Conghuan Shi, Binghua Chen, Junyi Wei, Hui Li, Ruiheng Lei, Bangjun Hubei Key Laboratory of Digital Finance Innovation Hubei University of Economics Hubei Wuhan430205 China School of Information Engineering Hubei University of Economics Hubei Wuhan430205 China The State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing400044 China Hubei Internet Finance Information Engineering Technology Research Center Hubei University of Economics Hubei Wuhan430205 China

Understanding the complex relationship between structural and functional connectivity is fundamental to discovering the pathogenesis of brain diseases. However, it is challenging to establish nonlinear connective relationships between structural and functional modalities. In this study, a novel bidirectional generative diffusion model (BGDM) is proposed to construct bidirectional mapping for connectivity-to-connectivity translation. The proposed BGDM is an AIGC framework to learn bidirectional translation between structural and functional connectivity. By designing the cascaded symmetric attention module, the BGDM can learn multi-channel and cascaded edge features to estimate diffusion noise, which improves computational efficiency and enhances the model's ability to predict brain connectivity patterns. Furthermore, the connectivity noise-balanced loss is devised to adaptively adjust the importance of individual noisy connections and ensure more accurate and reliable translations between diverse brain connectivity representations. We demonstrate the effectiveness of BGDM through comprehensive experiments on the HCP datasets. Our model achieves the best translation performance with the mean MAE/SSIM of 0.065/0.835 for SC-to-FC translation, and the mean MAE/SSIM of 0.050/0.932 for FC-to-SC translation. Compared with the state-of-the-art method, the results of our model show improvements of 0.014 (MAE) and 0.06 (SSIM) for the SC-to-FC task, 0.014 (MAE) and 0.04 (SSIM) for FC-to-SC task. Our model can generate incomplete multimodal brain networks, which can be used to improve the accuracy of brain disease diagnosis and provide clinicians with disease-related biomarkers. Moreover, the proposed model has the potential to bridge the gap between structural connectivity and functional connectivity, offering new opportunities for understanding the brain's working mechanisms and revealing brain disease's pathogenesis. © 2025 Elsevier Ltd

关键词： Mapping

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A Component Construction Progress Surveillance Method Using LiDAR and BIM 6

A Component Construction Progress Surveillance Method Using ...

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6th International Conference on Robotics, Control and Automation engineering, RCAE 2023

作者： Nie, Zijie Ding, Lei Peng, Qi Chu, Xuezheng Yuan, Chuming Li, Wenlong Huazhong University of Science and Technology State Key Laboratory of Intelligent Manufacturing Equipment and Technology Wuhan China China Power Construction New Energy Group Co. Ltd. Beijing China WISDRI Engineering and Research Incorporation Limited Wuhan China Huazhong University of Science and Technology Nation Nc System Engineering Research Center Wuhan China

ISBN: (纸本)9798350315301

Laser imaging detection and ranging (LiDAR) and Building Information Model (BIM) are widely used in building construction. But there are problems such as inconvenient interaction and inaccurate data registration due to different data coordinate systems. This paper proposes a method for component construction progress surveillance using LiDAR and BIM. Component-level interactions can be performed by splitting the model into components and converting them into point clouds. Then, the problem of inaccurate registration between BIM and LiDAR can be solved by filtering and feature point calibration. Finally, the construction progress can be obtained by performing the k-neighborhood algorithm. To verify the validity and reliability of the proposed method, this paper uses the actual data of the factory under renovation to conduct experiments. The results show that the proposed method achieves a registration tolerance of less than 0.05m. The proposed method has good robustness on outlier points for evaluating the construction progress of components. © 2023 IEEE.

关键词： Architectural design

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Tensor and Minimum Connected Dominating Set based Confident Information Coverage Reliability Evaluation for IoT

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第03期10卷 547-561页

作者： Xiao, Ziheng Zhu, Chenlu Feng, Wei Liu, Shenghao Deng, Xianjun Lu, Hongwei Yang, Laurence T. Park, Jong Hyuk Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China Artificial Intelligence and Intelligent Transportation Joint Technical Center of HUST and Hubei Chutian Intelligent Transportation Company Ltd United States Network and Industrial Control Information Security Technology Department China Nuclear Power Operation Technology Company Ltd. 1011 Xiongchu Avenue Hubei Wuhan430070 China The Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of

Internet of Things (IoT) reliability evaluation contributes to the sustainable computing and enhanced stability of the network. Previous algorithms usually evaluate the reliability of IoT by enumenating the states of nodes and networks, which are difficult to handle IoT with hundreds of nodes because the computational cost. In this paper, a novel algorithm, TMCRA, is proposed to evaluate the reliability of IoT in complex network environment, which consider both coverage and connectivity. For coverage, TMCRA employs the Confident Information Coverage (CIC) model to divide the target area into independent grids and calculates the coverage rate. In terms of connectivity, TMCRA forming the Virtual Backbone Network (VBN) based on two proposed methods: TMA and MGIN, and evaluate connectivity by analyzing the VBN rather than the whole network. The TMA and MGIN are two algorithms for constructing Minimum Connected Dominant Sets (MCDS), which are suitable for different scale networks. Finally, based on the data of coverage and connectivity, TMCRA utilizes tensors for the unified modeling and representation of network structure, and calculates IoT reliability based on the tensors. Simulations are carried out for various sizes of IoT to show the advantages and effectiveness of the proposed approach in reliability evaluation. © 2016 IEEE.

关键词： Data reliability

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High Purity Hydrogen Production by Metal Hydride system:A Parametric Study Based on the Lumped Parameter Model

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Journal of Wuhan University of technology(Materials Science) 2021年第1期36卷 127-135页

作者： KOUA Koua Alain Jesus TONG Liang YANG Tianqi XIAO Jinsheng School of Materials Science and Engineering Wuhan University of TechnologyWuhan 430070China Hubei Key Laboratory of Advanced Technology for Automotive Components Hubei Collaborative Innovation Center for Automotive Components Technology and Hubei Research Center for New Energy&Intelligent Connected VehicleWuhan University of TechnologyWuhan 430063China Reliability Engineering Institute School of Energy and Power EngineeringWuhan University of TechnologyWuhan 430070China Hydrogen Research Institute Universitédu QuébecàTrois-RivièresQC G9A 5H7Canada

The simulation of hydrogen purification in a mixture gas of hydrogen/carbon dioxide (H2/CO2) by metal hydride system was *** lumped parameter model was developed and *** validated model was implemented on the software Matlab/Simulink to simulate the present *** simulation results demonstrate that the purification efficiency depends on the external pressure and the venting *** increase in the external pressure and enough venting time makes it possible to effectively remove the impurities from the tank during the venting process and allows to desorb pure *** impurities are partially removed from the tank for low external pressure and venting time during the venting process and the desorbed hydrogen is *** parameters such as the overall heat transfer coefficient,solid material mass,supply pressure,and the ambient temperature influence the purification system in terms of the hydrogen recovery *** increase in the overall heat transfer coefficient,solid material mass,and supply pressure improves the hydrogen recovery rate while a decrease in the ambient temperature enhances the recovery rate.

关键词： hydrogen production hydrogen purification carbon dioxide metal hydride lumped parameter parametric study

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Artificial intelligence for hydrogen-based hybrid renewable energy systems: A review with case study 4

Artificial intelligence for hydrogen-based hybrid renewable ...

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2021 4th International Symposium on Hydrogen Energy and Energy Technologies, HEET 2021

作者： Su, Shenglin Yan, Xianglin Agbossou, Kodjo Chahine, Richard Zong, Yi Hubei Research Center For New Energy And Intelligent Connected Vehicle School Of Automotive Engineering Wuhan University Of Technology Hubei430070 China Hydrogen Research Institute Université Du Québec À Trois-Rivières QCG8Z 4M3 Canada Center For Electric Power And Energy Technical University Of Denmark Roskilde4000 Denmark

In recent years, with the progress of computer technology, artificial intelligence has been rapidly developed and begun to be applied in industry, economy and other aspects. Besides, with the pursuit of green hydrogen, hydrogen-based hybrid renewable energy systems have become the focus of the development of the hydrogen industry. This paper compares different artificial intelligence applications in hydrogen-based hybrid renewable energy systems and carries out a case study in a typical area. Firstly, this paper summarizes important works in literature, which use artificial intelligence methods to predict the supply chain of the renewable energy system, including the prediction of renewable energy system resources, output power, load demand and terminal electricity price. Secondly, main articles about artificial intelligence optimization algorithms used in renewable energy systems are also summarized, including swarm and non-swarm biological heuristics, physical or chemical heuristics and hybrid optimization algorithms. Finally, a case study is carried out in Tikanlik, Xinjiang, China. Tikanlik's weather and load data train the artificial neural network to predict system output power. It shows that 99.32% of the relative error of the test set is less than 3%, which proves that this model can achieve good prediction results. © Published under licence by IOP Publishing Ltd.

关键词： Renewable energy resources

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Artificial neural network based optimization of a six-step two-bed pressure swing adsorption system for hydrogen purification

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Energy and AI 2021年第3期5卷 38-52页

作者： Liang Tong Pierre Bénard Yi Zong Richard Chahine Kun Liu Jinsheng Xiao Reliability Engineering Institute School of Energy and Power EngineeringWuhan University of TechnologyWuhanHubei 430063China Hubei Research Center for New Energy&Intelligent Connected Vehicle and Hubei Key Laboratory of Advanced Technology for Automotive Components School of Automotive EngineeringWuhan University of TechnologyHubei 430070China Hydrogen Research Institute Universitédu QuébecàTrois-RivièresQuebec G8Z 4M3Canada Center for Electric Power and Energy Department of Electrical EngineeringTechnical University of Denmark4000RoskildeDenmark Green Island Environment and Resources Institute School of Architectural and Civil EngineeringShenyang City UniversityLiaoning 110112China

The pressure swing adsorption(PSA)system is widely applied to separate and purify hydrogen from gaseous *** extended Langmuir equation fitted from the extended Langmuir-Freundlich isotherm has been used to predict the adsorption isothermal of hydrogen and methane on the zeolite 5A adsorbent bed.A six-step two-bed PSA model for hydrogen purification is developed and validated by comparing its simulation results with other *** effects of the adsorption pressure,the P/F ratio,the adsorption step time and the pressure equalization time on the performance of the hydrogen purification system are studied.A four-step two-bed PSA model is taken into consideration,and the six-step PSA system shows higher about 13%hydrogen recovery than the four-step PSA *** performance of the vacuum pressure swing adsorption(VPSA)system is compared with that of the PSA system,the VPSA system shows higher hydrogen purity than the PSA *** on the validated PSA model,a dataset has been produced to train the artificial neural network(ANN)*** effects of the number of neurons in the hidden layer and the number of samples used for training ANN model on the predicted performance of ANN model are ***,the well-trained ANN model with 6 neurons in the hidden layer is applied to predict the performance of the PSA system for hydrogen ***-objective optimization of hydrogen purification system is performed based on the trained ANN *** artificial neural network can be considered as a very effective method for predicting and optimizing the performance of the PSA system for hydrogen purification.

关键词： Hydrogen purification Pressure swing adsorption Isotherm Artificial neural network Machine learning Optimization

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