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Amplitude-frequency-aware deep fusion network for optimal contact selection on STN-DBS electrodes

science China(Information sciences) 2022年第4期65卷 48-63页

作者： Linxia XIAO Caizi LI Yanjiang WANG Weixin SI Hai LIN Doudou ZHANG Xiaodong CAI Pheng-Ann HENG College of Control Science and Engineering China University of Petroleum (East China) Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Department of Neurosurgery Shenzhen Second People's Hospital School of Medicine Shenzhen University Department of Computer Science and Engineering The Chinese University of Hong Kong

Parkinson's disease(PD) is treated effectively by deep brain stimulation(DBS) of the subthalamic nucleus(STN), using an electrode inserted into the head of a PD patient. The electrode has multiple electrical contacts along its length, so the best may be chosen for selectively stimulating the STN. Neurosurgeons usually determine the optimal stimulated contact via the clinical experience of the neurosurgeon and the motor improvement of PD patients. This is a time-consuming and labor-intensive trial-and-error process. The selection of optimal stimulated contact highly depends on the locations of sweet spots, which are manually identified by the characteristic features of microelectrode recordings(MERs). This paper presents an amplitude-frequency-aware deep fusion network for optimal contact selection on STN-DBS *** method first obtains the amplitude-frequency fusion features by combining the MERs time sequence features and the amplitude sequence features, and then uses the convolutional neural network(CNN) with convolutional block attention module(CBAM) to identify both the border of the STN and the sweet spots to implant the electrode. The optimal stimulated contact can be selected according to the distribution of the sweet spots. Experimental results indicate that, for successful surgeries, neurosurgeons and the proposed AI solution selected the same optimal contacts. Furthermore, the proposed method outperforms the state-of-the-art methods for STN and sweet spot identification. The proposed method shows great potential for optimal contact selection to improve the efficiency of STN-DBS surgery and reduce the dependence on clinicians' experience.

关键词： optimal contact selection sweet spots microelectrode recordings amplitude-frequency feature deep fusion network

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Developed of special CNC system for dental implant based on spiral trajectory smooth compression 5

Developed of special CNC system for dental implant based on ...

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5th International Conference on Mechatronics and computer technology engineering, MCTE 2022

作者： Xu, Qingping Zhao, Gang Huang, Bo Shao, Hua He, Kai Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518005 China Shenzhen Key Laboratory of Precision Engineering Shenzhen518005 China

ISBN: (纸本)9781510661073

The demand for dental implants has grown rapidly in recent years, but they are expensive. One of the prime reasons is its manufacturing costs. In order to realize low cost and high efficiency of dental implant processing, this paper presents a seven-axis machine tool and develops a special numerical control system of the machine tool. Firstly, the structure of machine designed by us is introduced. Then, the hardware structure and software function of the control system were designed according to the processing technology of dental implant, including turning-milling conversion and back processing. In addition, a spiral path smooth compression algorithm was proposed to improve the machining efficiency and quality. Finally, the performance of the machining center is tested to verify that it has a high degree of automation, processing efficiency and machining quality, which meets the performance requirements. © 2022 SPIE.

关键词： Machine tools

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An Efficient and Secured Energy Management System for Automated Guided Vehicles

An Efficient and Secured Energy Management System for Automa...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Mrozek, Dariusz National Taiwan University Department of Computer Science and Information Engineering Taiwan Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Norway Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Poland

ISBN: (纸本)9781665480451

In this paper, we propose a Secure Energy Management System (SEMS) with anomaly detection and Q-Learning decision modules for Automated Guided Vehicles (AGV). The anomaly detection module is a multi-task learning network to simultaneously classify suppliers and predict the real supply quantities. The Q-learning decision module can then determine operating reserve and subsidies to manage the energy grid. Experimental results illustrate that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers, excels at shaping supply distribution, and outperforms the existing benchmark systems. © 2022 IEEE.

关键词： Energy management

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Identification of Magnetorheological Damper Model for Off-Road Vehicle Suspension 21st

Identification of Magnetorheological Damper Model for Off-Ro...

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21st Polish control Conference, PCC 2023

作者： Krauze, Piotr Placzek, Marek Zmudka, Zbigniew Bauke, Dawid Olszowka, Przemyslaw Turek, Jakub Wycislok, Artur Ziaja, Maciej Zosgornik, Szymon Janusz, Wojciech Przybyla, Grzegorz Wychowanski, Michal Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Akademicka 16 Gliwice44-100 Poland Silesian University of Technology Faculty of Mechanical Engineering Konarskiego 18A Gliwice44-100 Poland Silesian University of Technology Faculty of Energy and Environmental Engineering Konarskiego 18 Gliwice44-100 Poland Engineering Office Ozarow27-530 Poland Gliwice Poland

ISBN: (纸本)9783031351693

The article presents results of experimental identification of MR (magnetorheological) damper model for an off-road vehicle. Original shock-absorbers of the vehicle were replaced by suspension MR dampers supplied by a dedicated measurement and control system. The vehicle includes several sensors which allow to observe electric currents controlling MR dampers, forces generated in the front vehicle suspension columns and other sensors which measure deflection of these suspension parts. The front part of the semi-active suspension was tested using laboratory setup with vibration mechanical exciters generating sinusoidally variable excitation at frequency equal to 12 Hz. MR dampers were directly controlled by peripheral electronic units generating different constant currents equal to 0, 0.11 and 0.37 A. As a result, evaluated force-velocity characteristics and analysis of MR damper behaviour in the target vehicle were carried out. Further steps of analysis included mathematical description of the selected MR damper using several types of MR damper models, i.e.: Bingham and hyperbolic tangent function models as well as their variants including first-order output dynamics. It was shown that the dynamic model based on hyperbolic tangent function offers the best fitness to the response of the actual MR damper with respect to the mean-squared error. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Off road vehicles

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Construction, modeling, and control of a three-beam prototype using interactive fiber rubber composites

Construction, modeling, and control of a three-beam prototyp...

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93rd Annual Meeting of the International Association of Applied Mathematics and Mechanics, GAMM 2023

作者： Acevedo-Velazquez, Aline Iobana Keshtkar, Najmeh Mersch, Johannes Katzer, Konrad Cherif, Chokri Zimmermann, Martina Gerlach, Gerald Röbenack, Klaus Institute of Control Theory Faculty of Electrical and Computer Engineering TUD Dresden University of Technology Dresden Germany Institute of Solid-State Electronics Faculty of Electrical and Computer Engineering TUD Dresden University of Technology Dresden Germany Fraunhofer Institute for Material and Beam Technology IWS Dresden Germany Institute for Material Science Faculty of Mechanical Engineering TUD Dresden University of Technology Dresden Germany Institute of Textile Machinery and High Performance Material Technology Faculty of Mechanical Engineering TUD Dresden University of Technology Dresden Germany

In this contribution, we discuss the construction, modeling, and control of a three-beam prototype that utilizes interactive fiber rubber composites integrated with shape memory alloys (SMAs) as actuators. These actuators are integrated into a textile layer and covered with an elastomer layer, providing flexibility and protection to the prototype. A mathematical model was developed to describe the behavior of the prototype, employing system identification techniques. However, precise position control of systems actuated by SMAs is challenging due to their inherent nonlinearities and hysteretic behavior. To tackle this challenge, a proportional-integral (PI) controller was designed based on robust stability conditions. The effectiveness of the designed PI controller was validated through both numerical simulations and real experiments. © 2023 The Authors. Proceedings in Applied Mathematics & Mechanics published by Wiley-VCH GmbH.

关键词： Actuators

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Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration

arXiv

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arXiv 2024年

作者： Zhang, Xiaoqing Nie, Qiushi Xiao, Zunjie Zhao, Jilu Wu, Xiao Guo, Pengxin Li, Runzhi Liu, Jin Wei, Yanjie Pan, Yi Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Actuarial Science The University of Hong Kong 999077 Hong Kong Cooperative Innovation Center of Internet Healthcare Zhengzhou University Zhengzhou450001 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830046 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Spatial pooling (SP) and cross-channel pooling (CCP) operators have been applied to aggregate spatial features and pixel-wise features from feature maps in deep neural networks (DNNs), respectively. Their main goal is to reduce computation and memory overhead without visibly weakening the performance of DNNs. However, SP often faces the problem of losing the subtle feature representations, while CCP has a high possibility of ignoring salient feature representations, which may lead to both miscalibration of confidence issues and suboptimal medical classification results. To address these problems, we propose a novel dual-view framework, the first to systematically investigate the relative roles of SP and CCP by analyzing the difference between spatial features and pixel-wise features. Based on this framework, we propose a new pooling method, termed dual-view pyramid pooling (DVPP), to aggregate multi-scale dual-view features. DVPP aims to boost both medical image classification and confidence calibration performance by fully leveraging the merits of SP and CCP operators from a dual-axis perspective. Additionally, we discuss how to fulfill DVPP with five parameter-free implementations. Extensive experiments on six 2D/3D medical image classification tasks show that our DVPP surpasses state-of-the-art pooling methods in terms of medical image classification results and confidence calibration across different DNNs. © 2024, CC BY.

关键词： Deep neural networks

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Global scale solar energy harnessing: An advanced intra-hourly diffuse solar irradiance predicting framework for solar energy projects

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Neural Computing and Applications 2024年第18期36卷 10585-10598页

作者： El-kenawy, El-Sayed M. Bailek, Nadjem Bouchouicha, Kada Zerouali, Bilel Hassan, Muhammed A. Kuriqi, Alban Jamil, Basharat Colak, Ilhami Khalil, Adel Ibrahim, Abdelhameed Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura35111 Egypt Laboratory of Mathematics Modeling and Applications Department of Mathematics and Computer Science Faculty of Sciences and Technology Ahmed Draia University of Adrar Adrar01000 Algeria Energies and Materials Research Laboratory Faculty of Sciences and Technology University of Tamanghasset Tamanrasset10034 Algeria MEU Research Unit Middle East University Amman Jordan Adrar01000 Algeria Vegetal Chemistry-Water-Energy Laboratory Faculty of Civil Engineering and Architecture Department of Hydraulic Hassiba Benbouali University of Chlef Ouled Fares Chlef02180 Algeria Mechanical Power Engineering Department Faculty of Engineering Cairo University Giza12613 Egypt School of Architecture University of Waterloo 7 Melville St S CambridgeONN1S 2H4 Canada CERIS Instituto Superior Tecnico Universidade de Lisboa Lisbon Portugal Civil Engineering Department University for Business and Technology PristinaKosovo10000 High Temperature Processes Unit IMDEA Energy Institute Av. Ramón de La Sagra 3 Móstoles Madrid28935 Spain Faculty of Engineering and Architectures Nisantasi University Istanbul Turkey Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

Diffuse horizontal irradiance (DHI) forecasts are critical for adopting solar photovoltaic technology. Yet, they can lack reliability given the limited and uncertain meteorological data available for desert areas. This research develops a new sine cosine dipper throated optimization (SCDTO) technique, targeting DHI prediction even given such data constraints. SCDTO uniquely hybridizes sine cosine metaheuristics, adept at exploration, with dipper throat optimization, providing focused exploitation. This hybridization aims to create a robust ensemble model capable of delivering reliable DHI predictions despite climatic uncertainty. The ensemble model, employing various input combinations, was rigorously evaluated across multiple meteorological stations. The SCDTO algorithm exhibited remarkable performance improvements, yielding substantial reductions exceeding 93% in root mean squared error and 98% in mean absolute error at key stations in Morocco, including Tan-Tan, Zagora, Erfoud, and Oujda. Comparative analyses against established optimization algorithms consistently underscored the superior predictive capabilities of SCDTO. Visualizations, including box plots and histograms, demonstrated SCDTO’s efficacy in minimizing prediction errors, particularly for Tan-Tan, Zagora, and Erfoud stations. Also, statistical validation through one-way analysis of variance (ANOVA) further affirmed the significance of the proposed SCDTO method. Therefore, this hybrid metaheuristic optimization enables more accurate DHI predictions from limited meteorological data. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Solar energy

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Hierarchical Retrieval of High-Resolution Fingerprints Based on Pore Feature

Hierarchical Retrieval of High-Resolution Fingerprints Based...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jing Ma Yuanrong Xu Suyu Dong Wei Wang School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Computer Science and Technology Harbin Institute of Technology Weihai China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Faced with an escalating number of fingerprint images, most existing retrieval approachs suffer from a common problem: diminishing computational efficiency. This paper presents a hierarchical retrieval system tailored for high-resolution fingerprint images that utilizes abundant pore features and robust recognizability to improve retrieval performance. The framework comprises two core components. Firstly, a CNN-based feature extraction network is established, incorporating an attention mechanism to capture pore features in fingerprint images comprehensively. Subsequently, a hierarchical fingerprint retrieval approach is introduced, involving connection graph construction and a hierarchy of jump table structures for efficient retrieval of query pores. Empirical experiments conducted on high-resolution fingerprint image datasets underscore the system’s effectiveness. Compared with other advanced pore-based fingerprint retrieval methods, the proposed method exhibits a notable rise in the hit rate with reduced penetration rates, significantly reducing the retrieval time.

关键词： Accuracy Image recognition Attention mechanisms Image matching Fingerprint recognition Feature extraction Robustness Computational efficiency Bioinformatics

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

arXiv

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arXiv 2024年

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

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. Copyright © 2024, The Authors. All rights reserved.

关键词： Robots

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Joining Edge-Enabled Metaverse Services with Network Externality: A Stackelberg Game Approach

Joining Edge-Enabled Metaverse Services with Network Externa...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Jiang, Yuna Kang, Jiawen Niyato, Dusit Ge, Xiaohu Xiong, Zehui Xu, Minrui Deng, Ruilong Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China Guangdong University of Technology School of Automation Guangzhou China Nanyang Technological University School of Computer Science and Engineering Singapore Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou China Shenzhen Huazhong University of Science and Technology Research Institute China

ISBN: (纸本)9798350333336

The metaverse is defined as a three-dimensional virtual-real fusion network focused on social connection. Edge computing can empower the metaverse by providing computing resources to realize real-time motion tracking, visual or auditory rendering and virtual scene dynamic change at the edge of networks. Users enjoy the immersive experience provided by the Metaverse Service Provider (MSP), and interact through digital avatars in the virtual world by requesting metaverse services. In this paper, we formulate the edge computing resources pricing, metaverse service rendering bitrate pricing and request problem in the metaverse. Especially, we model the interactions among edge servers, the MSP and Metaverse Service Users (MSUs) as a hierarchical three-stage Stackelberg game. The problem of joining edge-enabled metaverse services is also studied with the consideration of network externality, including the network effect and congestion effect. Based on the backward induction method, we derive the optimal computing resources pricing of edge servers, rendering bitrate pricing of the MSP, and rendering bitrate requests of MSUs. We also prove the existence and uniqueness of the Stackelberg game equilibrium. Simulation results show that the network effect can bring more positive utilities to entities when the social tie coefficient among MSUs is higher. Besides, all entities in the edge-enabled metaverse are affected negatively by the congestion effect. © 2023 IEEE.

关键词： Edge computing

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