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Tangentially-Magnetized-Slot-PM-Assisted Magnet Saturation Relieving Design for Doubly Salient Reluctance Machine Position Sensorless Drive Electric Propulsion Applications 6

Tangentially-Magnetized-Slot-PM-Assisted Magnet Saturation R...

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6th International Conference on Electrical engineering and control Technologies, CEECT 2024

作者： Wang, Weiyu Zhang, Xiaodong Niu, Shuangxia Zhao, Xing Pan, Jianfei Fu, Weinong Great Bay University School of Engineering Dongguan China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Hong Kong University of York Department of Electronic Engineering York United Kingdom Shenzhen University College of Mechatronics and Control Engineering Shenzhen China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen China

ISBN: (纸本)9798331528096

For doubly salient reluctance machines, the magnetic saturation effect easily occurs, thereby the saturated inductance with complex analytical expression brings difficulties to the inductance modeling procedure during position sensorless control. To tackle this issue, a direct solution from electrical machine design is introduced to regulate magnetic flux distribution. The key is to insert tangentially-magnetized-slot-permanent-magnet into stator slots, thus relieving the magnetic saturation in the stator teeth. Consequently, unsaturated inductance characteristics can be acquired through the magnetic saturation relieving effect, thereby the inductance modeling effort can be significantly decreased, and such design is friendly for doubly salient reluctance machine position sensorless drive application. © 2024 IEEE.

关键词： Stators

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Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury:predictors of patient prognosis

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Neural Regeneration Research 2024年第7期19卷 1553-1558页

作者： Sihong Huang Jungong Han Hairong Zheng Mengjun Li Chuxin Huang Xiaoyan Kui Jun Liu Department of Radiology The Second Xiangya HospitalCentral South UniversityChangshaHunan ProvinceChina Department of Computer Science Aberystwyth UniversityAberystwythCeredigionUK Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhenGuangdong ProvinceChina School of Computer Science and Engineering Central South UniversityChangshaHunan ProvinceChina Department of Radiology Quality Control Center of Hunan ProvinceChangshaHunan ProvinceChina Clinical Research Center for Medical Imaging of Hunan Province ChangshaHunan ProvinceChina

Patients with mild traumatic brain injury have a diverse clinical presentation,and the underlying pathophysiology remains poorly *** resonance imaging is a non-invasive technique that has been widely utilized to investigate neuro biological markers after mild traumatic brain *** approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury.G raph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and ***,most previous mild traumatic brain injury studies using graph theory have focused on specific populations,with limited exploration of simultaneous abnormalities in structural and functional *** that mild traumatic brain injury is the most common type of traumatic brain injury encounte red in clinical practice,further investigation of the patient characteristics and evolution of structural and functional connectivity is *** the present study,we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain *** this longitudinal study,we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 wee ks of injury,as well as 36 healthy ***-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network *** the acute phase,patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention *** than 3 months of followup data revealed signs of recovery in structural and functional connectivity,as well as cognitive function,in 22 out of the 46 ***,better cognitive function was associated with more efficient ***,our data indicated that small-worldness in the acute sta

关键词： cognitive function cross-section follow-up functional connectivity graph theory longitudinal study mild traumatic brain injury prediction small-worldness structural connectivity subnetworks whole brain network

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Adaptive particle swarm optimization model for resource leveling

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Evolving Systems 2023年第4期14卷 593-604页

作者： Lin, Jerry Chun-Wei Lv, Qing Yu, Dehu Srivastava, Gautam Chen, Chun-Hao School of Information and Control Engineering Qingdao University of Technology Qingdao China Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway School of Civil Engineering Qingdao University of Technology Qingdao China School of Civil Engineering Shandong Jianzhu University Jinan China Department of Math and Computer Science Brandon University Brandon Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Department of Information and Finance Management National Taipei University of Technology Taipei Taiwan

In the process of engineering project construction, the balanced allocation of resources has an important impact on the purchase of actual materials, the progress of the site construction and the arrangement of temporary facilities. Although there have been many studies on the use of evolutionary computation (EC) to solve the fixed duration of the resource-leveling problem, the optimization effect of standard EC to solve the resource-balancing problem in large-scale network design planning is reduced due to the phenomenon of premature convergence of PSO and the complexity of large-scale network planning. Therefore, in this paper, an adaptive dissipative particle swarm optimization (ADPSO) algorithm is used to solve the resource balancing optimization problem for different network-plans scales, and the feasibility of the developed model is verified using four different scaled network plans (databases). At the same time, a solution to the dynamic resource-leveling problem with optimization for a fixed duration is proposed. The results show that the proposed models are suitable for solving the problem of resource-leveling with a fixed duration of optimization of different scale network plans, and their accuracy and stability are significantly improved compared to the state-of-the-art approaches. Moreover, the proposed models are accurate and suitable for combining the calculation method of network planning time parameters with the ADPSO algorithm to realize dynamic resource balancing optimization. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Particle swarm optimization (PSO)

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A network-based bioinformatic analysis for identifying potential repurposable drugs for obesity avoiding hepatic steatosis side-effect

A network-based bioinformatic analysis for identifying poten...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Brunetti, Martina Fiscon, Giulia Costanzo, Alessia Di Arca, Marcello Paci, Paola Sapienza University of Rome Department of Experimental Medicine Rome Italy Sapienza University of Rome Institute for Systems Analysis and Computer Science "a. Ruberti" National Research Council of Rome Department of Computer Control and Management Engineering "a. Ruberti" Rome Italy Sapienza University of Rome Policlinico Umberto I Department of Translational and Precision Medicine Rome Italy Sapienza University of Rome Department of Computer Control and Management Engineering "a. Ruberti" Rome Italy Karolinska Institutet Stockholm Sweden

ISBN: (纸本)9798350386226

Obesity is a complex multifactorial disorder characterized by the excess accumulation of body fat that impairs human health due to the risk of developing other diseases, including cardiovascular and hepatic diseases, hypertension, diabetes, hyperlipidemia. Its spread has been progressively accelerating, resulting in an unprecedented epidemic with no significant signs of slowing down any time soon. Drug therapy via the proposal repurposing solutions can represent an actionable treatment strategy, even if the emergence of drug-induced adverse effects can affect the treatment of this pathology. In this study, we propose a network-based analysis to identify a list of drug candidates predicted to be repurposable for obesity that are unlikely to produce specific adverse side-effect, such as hepatic steatosis. © 2024 IEEE.

关键词： Nutrition

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Differential gene correlation analysis to investigate evolutionary divergence between Cardamine hirsuta and Arabidopsis thaliana

Differential gene correlation analysis to investigate evolut...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： D'Annunzio, Federica Fiscon, Giulia Bertolotti, Gaia Dello Ioio, Raffaele Paci, Paola Sapienza University of Rome Department of Translational and Precision Medicine Rome Italy Sapienza University of Rome Institute for Systems Analysis and Computer Science 'A. Ruberti' National Research Council of Rome Department of Computer Control and Management Engineering 'A. Ruberti' Rome Italy Sapienza University of Rome Department of Biology and Biotechnology 'Charles Darwin' Rome Italy Department of Computer Control and Management Engineering 'A. Ruberti' Sapienza University of Rome Rome Italy Karolinska Institutet Stockholm Sweden

ISBN: (纸本)9798350386226

Dissecting the intricate regulatory dynamics between genes stands as a critical step towards the development of precise predictive models within biological systems. A highly effective strategy in this pursuit involves an examination of variations in correlation among gene pairs across different conditions trough differential gene correlation analysis. We anticipated that the application of this method to phylogenetically close plant species could reveal powerful insights on how genes' relationships have a fundamental role in evolutionary dynamics. Building upon this principle, our investigation focused primarily on conducting a comprehensive differential correlation analysis of the roots' orthologous genes of two closely related plant species: Cardamine hirsuta and Arabidopsis thaliana. By leveraging their shared phylogenetic proximity, we were able to draw comparisons that shed light on how evolutionary pressures might have shaped their gene regulatory networks. By integrating differential expression analysis, differential correlation analysis and functional enrichment analysis, we offer a robust framework for future studies aiming to unravel the complexities of adaptation and evolution across a wide range of biological systems. © 2024 IEEE.

关键词： Plant diseases

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Finite-time robust performance improvement for non-linear systems in the presence of input non-linearity and external disturbance

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IET control Theory and Applications 2023年第6期17卷 672-682页

作者： Wang, Haoping Ghaffari, Valiollah Mobayen, Saleh Bartoszewicz, Andrzej Fekih, Afef Assawinchaichote, Wudhichai School of Automation Nanjing University of Science and Technology Nanjing China Department of Electrical Engineering Persian Gulf University Bushehr Iran Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Douliou Taiwan Institute of Automatic Control Lodz University of Technology Lodz Poland Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA United States Department of Electronic and Telecommunication Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand

The main focus of this research is on improving the performance of dynamic systems with actuator non-linearities and time-varying disturbances. To this end, using the concept of finite-time stability, a novel observer is presented to estimate the disturbances in systems with input non-linearities. Then, introducing an innovative sliding manifold, a robust observer-based technique is derived to guarantee the finite-time stability of the sliding surface. Hence, the closed-loop system would be stable in the presence of hard non-linearities and time-varying disturbances. The performance of the proposed approach was assessed in using a simulation study of two numerical examples. The obtained results and comparison with a conventional method confirmed the benefits of the suggested approach. © 2022 The Authors. IET control Theory & Applications published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Closed loop systems

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Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning 27

Video2Reward: Generating Reward Function from Videos for Leg...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zeng, Runhao Zhou, Dingjie Liang, Qiwei Liu, Junlin Li, Hui Huang, Changxin Li, Jianqiang Hu, Xiping Sun, Fuchun Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Mechatronics and Control Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science and Technology Tsinghua University China

ISBN: (纸本)9781643685489

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running. © 2024 The Authors.

关键词： Robots

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Pothole-YOLO: A Single-Stage Instance Segmentation Method for Pothole Detection 3rd

Pothole-YOLO: A Single-Stage Instance Segmentation Method fo...

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3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Cheng, Jintao Chen, Xingming Chen, Weiwen Huang, Zhuoxu Wu, Jin Fan, Rui Tang, Xiaoyu School of Physics South China Normal University Guangzhou510006 China School of Electronic and Information Engineering South China Normal University Foshan528225 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China Department of Control Science and Engineering Tongji University Shanghai201804 China Department of Computer Science Aberystwyth University AberystwythSY23 3DB United Kingdom

ISBN: (纸本)9789819710867

Potholes pose a significant hazard to traffic safety and transportation infrastructure. Traditional methods of human-based pothole detection present challenges in terms of cost and safety. However, instance segmentation technology offers a promising solution by accurately locating potholes and providing high-resolution features. This paper addresses the task of pothole detection by introducing a novel single-stage instance segmentation network called Pothole-YOLO. Our approach incorporates an efficient plug-and-play module called CEA-block to enhance the YOLO backbone and layers. Furthermore, we propose a robust and lightweight segmentation head named Pothole-Protonet to improve the segmentation prediction performance. Additionally, we enhance the pothole boundary features by modifying the Wise-IOU method, instead of using the common IOU method. Experimental results demonstrate that our Pothole-YOLO achieves the highest accuracy in pothole detection compared to other state-of-the-art methods on publicly available pothole datasets. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Deep learning

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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Unveiling drug-induced side effects through network-based analysis: An update

Unveiling drug-induced side effects through network-based an...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Funari, Alessio Conte, Federica Paci, Paola Sapienza University of Rome Institute for Systems Analysis and Computer Science 'Antonio Ruberti' National Research Council Department of Translational and Precision Medicine Rome Italy National Research Council Institute for Systems Analysis and Computer Science 'Antonio Ruberti' Rome Italy Sapienza University of Rome Department of Computer Control and Management Engineering Rome17177 Italy

ISBN: (纸本)9798350386226

In this article, we propose an updated version of our previously developed model for predicting drug-side effect associations, applied to two case studies: long QT syndrome and asthma. The classifier accepts the name of a specific drug side effect as input and outputs a list of drugs potentially associated with this side effect. By simulating how drug effects propagate within the interactome using the Random Walk with Restart algorithm, the classifier identifies genes potentially associated with the onset of the side effect. Based on the rationale that the more a drug perturbs these genes, the more likely it is to cause the side effect, the model identifies drugs potentially linked to the onset of the side effect. Moreover, the model enables the categorization of drugs into chemical subclasses using the ClassyFire schema, facilitating the analysis of complex side effects, such as asthma, through more specific mechanisms. The results show that the model identifies both drugs known to be associated with certain side effects, as well as drugs not officially reported by the FDA, demonstrating its generalizability and practical relevance. This method is also adaptable for analyzing other side effects. © 2024 IEEE.

关键词： drug side effect network medicine random walk

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