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共 24667 条记录，以下是161-170 订阅

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Research on electrochemical impedance spectroscopy analysis method based on sparse MLP

Research on electrochemical impedance spectroscopy analysis ...

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2024 international conference on Mechatronic Engineering and Artificial Intelligence, MEAI 2024

作者： Song, Hongjie Liu, Xianda Chen, Xi Yan, Bingjun Gang, Mingxu Hu, Bo Shenyang University of Technology Software College Shenyang110870 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9781510689176

EIS data are typically presented in the form of complex impedance spectra, which require interpretation through equivalent circuit models. Traditional methods for selecting equivalent circuits often rely on manual expertise, leading to inefficiencies and subjectivity in the modeling process. To address this, this paper integrates deep learning techniques by introducing the classic Multi-Layer Perceptron (MLP) into a sparse neural network framework, forming the Sparse MLP model, and incorporates the Gaussian Error Linear Unit (GeLU) activation function for equivalent circuit selection. In this study, the model's performance is comprehensively evaluated using multiple metrics, including precision, recall, F1 score, and AUC. Experimental results demonstrate that, compared to traditional MLP and other benchmark models, the Sparse MLP model exhibits improved classification performance. The sparsification design reduces model complexity and training time, not only enhancing classification accuracy but also providing an efficient and reliable analytical approach for electrochemical impedance spectroscopy research. © 2025 SPIE.

关键词： Image coding

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Reinforcement Learning Based Data-Driven Control Method for Dynamic Systems and Its Application to FLUIDMechatronix Platform 16th

Reinforcement Learning Based Data-Driven Control Method for...

引用

16th international conference on Modelling, Identification and Control, ICMIC 2024

作者： Zou, Zonghan Gao, Jingjing Yang, Xu Huang, Jian Zhang, Tao Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9789819617760

With the increasing complexity of industrial process, accurate models of automatic control systems are difficult to be established, which makes model-based control methods become less effective. On this premise, a reinforcement learning (RL) based data-driven control method for dynamic systems is proposed in this paper. For our purposes, a model-free RL controller with an Actor-Critic structure is developed by using neural networks (NN), combining the merits of both value-based and policy-based RL algorithms. Policy of the actor is assessed by the critic, updating parameters of RL controller in real-time for online optimization. The effectiveness of the RL controller is demonstrated by an experimental study on FLUIDMechatronix platform. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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19th international conference on Computer Aided Systems Theory, EUROCAST 2024

19th International Conference on Computer Aided Systems Theo...

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19th international conference on Computer Aided Systems Theory, EUROCAST 2024

ISBN: (纸本)9783031829598

The proceedings contain 104 papers. The special focus in this conference is on Computer Aided Systems Theory. The topics include: Influence of Spike Encoding, Neuron models and Quantization on SNN Performance;adaptive Combination in Frequency Domain: An Approach for Robust Nonlinear Acoustic Echo Cancellation;using of a Robotic Platform to Detect Acoustic Events for Indoor Environments;modeling Wildlife Accident Risk with Gaussian Mixture models;towards a Unified Incident Detection and Response System for Autonomous Transportation;edge-Processing of Myoelectric Signals for the Control of Hand and Arm-Prostheses;AI-Driven Gesture and Action Recognition for Learning Medicine Through Virtual Reality;Medical Protocols and AI-Driven Algorithms for Enhanced Monitoring of Cardiac Implantable Electronic Devices;a Survey of Machine Learning methods for Analyzing Synovitis Arthritis in Human Joints;motion Tracking in Augmented and Mixed Realities for Healthcare and Medicine Applications;advancements and Applications of Medical Human Digital Twin Technology in Cerebral Palsy Diagnosis, Therapy, and Rehabilitation;Transformation of IEC 61131-3 onto an Embedded Platform Using LLVM;machine Learning Based Parameter Estimation of Energy models in Digital Production Environments;efficient Classification of Live Sensor Data on Low-Energy IoT Devices with Simple Machine Learning methods;machine Learning Using a Hybrid Quantum Classical Algorithm with Amplitude Data Encoding;quantitative Trend Analysis of Reinforcement Learning Algorithms in Production Systems;Using automationML for Advanced Simulation in Industrial automation;AR Digital Twin Demonstrator for Industrial robotics Education;Accelerating Manual Pick-and-Place Operations with AR-Projected CAD Plans and AI-Assisted Object Recognition;variety Engineering – A Cybernetic Concept with Practical Implications;using a System Archetype to Explore a Business Model for Digital Textile Microfactories;interacting with the Water Cycle -

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19th international conference on Computer Aided Systems Theory, EUROCAST 2024

19th International Conference on Computer Aided Systems Theo...

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19th international conference on Computer Aided Systems Theory, EUROCAST 2024

ISBN: (纸本)9783031829512

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A Review of Degradation Prediction methods for Proton Exchange Membrane Fuel Cells

A Review of Degradation Prediction Methods for Proton Exchan...

引用

2025 international conference on Electrical automation and Artificial Intelligence, ICEAAI 2025

作者： Ma, Jun Liu, Xiaoqian Wu, Ziheng Shen, Rui Liu, Haining Li, Fajia AECC Hunan Aviation Powerplant Research Institute Hunan Zhuzhou China School of mechanical Engineering University of Jinan Jinan China

ISBN: (纸本)9798331506797

Proton exchange membrane fuel cells (PEMFCs), as a new type of clean and pollution-free power generation device, have the advantages of high energy conversion efficiency, low noise and fast start-up. However, the operation of PEMFCs is affected by multiple factors, and degradation problems are inevitable. Such degradation issues may cause safety hazards, reduce energy conversion efficiency and shorten their service life. Therefore, accurately predicting the degradation process is crucial for developing reliable and durable PEMFCs. This study reviews fuel cell degradation prediction models from three perspectives: model-based, data-driven, and fusion approaches. The study mentioned above is finally discussed. © 2025 IEEE.

关键词： Data-driven Degradation prediction Fusion Model-based PEMFC

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A Machine Learning-Driven Model for Predicting the Productivity of Internet of Mechanical Things 2nd

A Machine Learning-Driven Model for Predicting the Productiv...

引用

2nd international conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Sedik, Ahmed Nasralla, Moustafa M. Esmail, Maged Abdullah Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt

ISBN: (纸本)9789819783472

This study delves into the utilization of artificial intelligence (AI) for predicting the performance of Hemispherical Solar Stills (HSS), aiming to transcend reliance on empirical methods. Employing real-world experimental data, five distinct prediction models were developed. The investigation revealed that the DT model emerged as the most efficacious in estimating both hourly productivity and instantaneous efficiency, showcasing remarkable accuracy and efficiency in its predictions. Statistical metrics demonstrated that the DT model's estimates for HSS productivity and efficiency were closely aligned with unity. This research accentuates the transformative potential of AI models in streamlining and enhancing predictive techniques for estimating HSS performance. The findings advocate for the adoption of AI-driven approaches as cost-effective and precise solutions for researchers and practitioners within the realm of mechanical engineering. By substantiating the efficacy of the DT model, this study contributes valuable insights that not only advance our understanding of AI applications in solar technology but also offer practical tools for optimizing the design and performance assessment of Hemispherical Solar Stills. The integration of AI in this context promises to usher in a new era of efficiency and accuracy, addressing challenges in the field and fostering advancements in sustainable energy technologies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Collective Intelligence Unleashed: Exploring the Dynamics and Applications of Swarm Robotic Systems

Collective Intelligence Unleashed: Exploring the Dynamics an...

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international conference on Autonomous Robots and Agents, ICARA

作者： Nada Masood Mirza Adnan Ali Nura Shifa Mohamad Khairi Ishak Khalid Ammar Shafiz Affendi Mohd Yusof United Arab Emirates University Al Ain UAE Universiti Sains Malaysia Penang Malaysia Information Technology Center University of Khorfakkan Khorfakkan UAE College of Engineering Al Ain University Al Ain UAE Ajman University Ajman UAE Department of Computer Engineering Ajman University Ajman United Arab Emirates College of Engineering and Information Technology Ajman University Ajman United Arab Emirates

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Swarm robotics is a branch of robotics that studies the idea of many uncontrolled robots operating in sizable groupings. These swarms engage in cooperative behavior and may handle difficult problems through small-scale interactions. Swarm robotics is a potential method for many applications since it has benefits, including adaptability, resilience, and scalability. This research focuses on the practical applications of swarm robotics. A thorough description of current applications in navigation, foraging, exploration, aggregation, and more is given, focusing on the characteristics of swarm robotics and the value of simulators for algorithm validation. The research also presents a data-driven model for drainage monitoring and alert systems in smart cities, highlighting the potential use of swarm robotics to resolve practical issues.

关键词： Adaptation models Smart cities Navigation Robot kinematics Scalability Swarm robotics Transforms Collective intelligence Space exploration Testing

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Network anomaly traffic detection method based on ensemble learning and feature fusion

Network anomaly traffic detection method based on ensemble l...

引用

2024 international conference on Computer Application and Information Security, ICCAIS 2024

作者： Tian, Yu Yin, Shaoping Liang, Shuanglong Geng, Haijun School of Automation and Software Shanxi University Shanxi Taiyuan 030006 China Shanxi Qingzhong Technology Co. Ltd. Shanxi Taiyuan 030006 China

ISBN: (纸本)9781510689312

Aiming at the issues of incomplete feature information extraction and low detection accuracy in current network anomaly traffic detection methods, a Network Anomaly Traffic Detection Method Based on Ensemble Learning and Feature Fusion (NATDBELFF) is proposed. NATDBELFF integrates the spatial features extracted by Convolutional Neural Network (CNN) and Transformer, respectively, and utilizes Bidirectional Long Short-Term Memory (BiLSTM) to capture temporal features. It then ensembles the Decision Tree (DT) and Extreme Gradient Boosting (XGBoost) models to complete the recognition and classification of feature information. In this study, NATDBELFF is validated on the KDDCUP99, UNSW-NBV15, and NSL-KDD datasets. Experiments demonstrate that the features extracted by different models in the proposed method are distinctive and highly complementary. The fusion of these features results in a more complete representation. Furthermore, the integration of learning significantly enhances the overall classification performance. © The Authors.

关键词： anomaly detection DT Ensemble learning feature fusion XGBoost

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19th international conference on Computer Aided Systems Theory, EUROCAST 2024

19th International Conference on Computer Aided Systems Theo...

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19th international conference on Computer Aided Systems Theory, EUROCAST 2024

ISBN: (纸本)9783031838873

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Design, Modeling, and Bionic Applications of a Rigid-soft Integrated Soft Actuator

Design, Modeling, and Bionic Applications of a Rigid-soft In...

引用

robotics, Control and automation (ICRCA), international conference on

作者： Dong Zhang Sheng Liu Dazhong Wang Mengyao Shi Yu Cai School of Mechanical and Automotive Engineering Shanghai University of Engineering Science Shanghai China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

Fiber-reinforced soft actuators are particularly promising candidates in the field of soft robotics. To minimize circumferential expansion, the cross-sectional shapes of the fiber-reinforced soft actuators are generally designed as circles, squares, and semicircles. The specific requirement for the cross-sectional shape limits their further development. Therefore, we propose a novel rigid-soft integrated soft actuator that primarily consists of soft inflatable chambers and rigid frames. For comparison, the change in the cross-sectional shape of fiber-reinforced soft actuators under pressurization is investigated in theory. Then, a predictive model for the behavior of the rigid-soft integrated soft actuators is established by using the virtual work principles and integrating the Yeoh model, and it is also verified through experiments and finite element simulations. The results show that rigid frames can provide geometric constraints, minimize circumferential expansion, and ensure that noncircular cross-sectional shapes remain unchanged during inflation. Furthermore, the potential applications of the proposed rigid-soft integrated design are demonstrated with two examples: a flexible gripper and a biomimetic fishtail. The proposed soft actuators, which combine hyper-elastic materials with the properties of geometric constraints, expand the design scope of the soft pneumatic actuators and exploit more possibilities for practical application.

关键词： Pneumatic actuators Actuators Shape Soft robotics Predictive models Pneumatic systems Integrated design Optical fiber theory Finite element analysis Grippers

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