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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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An intelligent Action Decision Algorithm for Exoskeleton in Edge Networks Environment based on Deep Reinforcement learning 11

An Intelligent Action Decision Algorithm for Exoskeleton in ...

引用

11th international conference on Advanced Cloud and Big data, CBD 2023

作者： Xu, Ben Sun, Jiacheng Wang, Yunzhe Fu, Qiming Lu, You Suzhou University of Science and Technology School of Electronic and Information Engineering Suzhou China

ISBN: (纸本)9798350345346

As wearable devices, ankle exoskeletons can be connected to edge computing networks, this connectivity enables real-time data processing and decision-making at the edge, reducing latency and enhancing responsiveness. the quality of their action decision algorithms significantly influences their effectiveness. Most current algorithms commonly face these issues, including inflexible trajectory tracking and less accurate on-demand assistance, and they did not take into account the noise present in edge networks. To address these challenges, this paper introduces a novel control algorithm named 'SD3-DAE' (Softmax Deep Double Deterministic Policy Gradients Based On Denoising AutoEncoder). Firstly, the ankle exoskeleton control problem is modeled as a Markovian decision process. then the SD3 method is introduced to implement the framework of the ankle exoskeleton action decision algorithm for the exoskeleton control problem. Finally, this paper incorporates a Denoising AutoEncoder (DAE) to enhance the SD3 method, improving its robustness and adaptability in the presence of external environmental factors, such as noise. Experimental results demonstrate that our method outperforms other existing model-free algorithms in the realm of ankle exoskeleton control. © 2023 IEEE.

关键词： Reinforcement learning

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TEMPORAL COHERENT TEST-TIME OPTIMIZATION FOR ROBUST VIDEO CLASSIFICATION 11

TEMPORAL COHERENT TEST-TIME OPTIMIZATION FOR ROBUST VIDEO CL...

引用

11th international conference on learning Representations, ICLR 2023

作者： Yi, Chenyu Yang, Siyuan Wang, Yufei Li, Haoliang Tan, Yap-Peng Kot, Alex C. School of Electrical and Electronic Engineering Nanyang Technological University Singapore Interdisciplinary Graduate Programme Nanyang Technological University Singapore Department of Electrical Engineering City University of Hong Kong Hong Kong

Deep neural networks are likely to fail when the test data is corrupted in real-world deployment (e.g., blur, weather, etc.). Test-time optimization is an effective way that adapts models to generalize to corrupted data during testing, which has been shown in the image domain. However, the techniques for improving video classification corruption robustness remain few. In this work, we propose a Temporal Coherent Test-time Optimization framework (TeCo) to utilize spatio-temporal information in test-time optimization for robust video classification. To exploit information in video with self-supervised learning, TeCo minimizes the entropy of the prediction based on the global content from video clips. Meanwhile, it also feeds local content to regularize the temporal coherence at the feature level. TeCo retains the generalization ability of various video classification models and achieves significant improvements in corruption robustness across Mini Kinetics-C and Mini SSV2-C. Furthermore, TeCo sets a new baseline in video classification corruption robustness via test-time optimization. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Classification (of information)

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GROUNDING GRAPH NETWORK SIMULATORS USING PHYSICAL SENSOR OBSERVATIONS 11

GROUNDING GRAPH NETWORK SIMULATORS USING PHYSICAL SENSOR OBS...

引用

11th international conference on learning Representations, ICLR 2023

作者： Linkerhägner, Jonas Freymuth, Niklas Scheikl, Paul Maria Mathis-Ullrich, Franziska Neumann, Gerhard Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-University Erlangen-Nürnberg Erlangen Germany

Physical simulations that accurately model reality are crucial for many engineering disciplines such as mechanical engineering and robotic motion planning. In recent years, learned Graph Network Simulators produced accurate mesh-based simulations while requiring only a fraction of the computational cost of traditional simulators. Yet, the resulting predictors are confined to learning from data generated by existing mesh-based simulators and thus cannot include real world sensory information such as point cloud data. As these predictors have to simulate complex physical systems from only an initial state, they exhibit a high error accumulation for long-term predictions. In this work, we integrate sensory information to ground Graph Network Simulators on real world observations. In particular, we predict the mesh state of deformable objects by utilizing point cloud data. the resulting model allows for accurate predictions over longer time horizons, even under uncertainties in the simulation, such as unknown material properties. Since point clouds are usually not available for every time step, especially in online settings, we employ an imputation-based model. the model can make use of such additional information only when provided, and resorts to a standard Graph Network Simulator, otherwise. We experimentally validate our approach on a suite of prediction tasks for mesh-based interactions between soft and rigid bodies. Our method results in utilization of additional point cloud information to accurately predict stable simulations where existing Graph Network Simulators fail. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Forecasting

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Research on Fault Diagnosis of Electrical Equipment Based on Deep learning and Infrared Imaging Technology 11

Research on Fault Diagnosis of Electrical Equipment Based on...

引用

11th Joint international Information Technology and Artificial Intelligence conference, ITAIC 2023

作者： Yang, Jingjing Ma, Jinliang Li, Mingming Wang, Feifei State Grid Shandong Electric Power Company Weifang Power Supply Company Shandong China

ISBN: (纸本)9798350333664

the increasing number of unstructured data in electric power big data presents new challenges for the traditional processing method based on artificial diagnosis. As a typical unstructured data, infrared fault image is vital in researching electric power big data. Based on studying the fault diagnosis method of electrical equipment based on deep learning, this paper proposes image recognition technology based on deep learning for the intelligent diagnosis of infrared images of electrical equipment in the substation. On this basis, a model that can accurately and quickly diagnose most equipment faults in the substation is established to meet the requirements of the highly intelligent substation in constructing a smart grid. © 2023 IEEE.

关键词： deep learning electrical equipment fault diagnosis infrared imaging intelligence

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Implementation of Smart Solar Power System Monitoring and automated Cleaning Mechanism with Python based Embedded Technology 4

Implementation of Smart Solar Power System Monitoring and Au...

引用

4th international conference on intelligent Technologies, CONIT 2024

作者： Dhal, P.K. Reddy, C.Jaswanth Kumar, R S Aswin Sabareesh, S. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350349900

the research paper focuses on the design and implementation of a 55 Watts solar power monitoring and automated cleaning system integrated with Python-based embedded technology. the system aims to maximize the efficiency of solar power generation by monitoring the output of the solar panel and automatically cleaning it when necessary to maintain optimal performance. the system consists of voltage and current sensors to monitor the solar panel output, a DHT11 sensor to measure temperature and humidity. the LDR sensor is to detect ambient light levels. the data from the sensors are processed by an Arduino Uno board and sent to a Node MCU board which communicates with a server to log and display the data on the LCD screen. the Python program running on the Node MCU board analyses the sensors and triggers an automatic cleaning mechanism using an L293 driver to move cleaning brush across the surface of the solar panel. this ensures the solar panel remains free from dust and dirt. © 2024 IEEE.

关键词： Python

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Vibration Anomaly Detection of Transformer in Operation Based on Dynamic Response Analysis 11

Vibration Anomaly Detection of Transformer in Operation Base...

引用

11th China international conference on Electricity Distribution, CICED 2024

作者： Wu, Xuefeng Wu, Xuyang Chen, Wentong Liu, Guiquan Li, Zhong Jinhua Power Supply Company State grid Zhejiang electric power co. ltd Zhejiang China School of Electrical and Electronic Engineering North China Electric Power University Baoding China

ISBN: (数字)9798350368345

ISBN: (纸本)9798350368345

In this paper, the abnormal vibration detection method of transformer in operation based on dynamic response analysis is proposed, and the identification model of dynamic response relationship between transformer working condition and surface vibration is established, and its vibration health state is judged according to the identification accuracy analysis. For the imbalance problem of vibration samples under different working conditions, a two-stage learning framework is proposed by combining decoupling learning and conditional generation confrontation network. In the characterization learning stage, the encoder is trained based on the frequency domain characteristics of the original data to fully perceive and obtain the vibration characteristics of different working conditions. In the classification learning stage, the conditional generation confrontation network is used to enhance the small sample vibration data, and the retraining strategy is adopted to train the classifier and fine-tune and optimize the classification boundary. the simulation analysis of the measured vibration signal of the transformer in operation shows that the proposed method can detect the vibration anomaly based on whether the dynamic response relationship of the transformer in operation changes or not. © 2024 IEEE.

关键词： Vibration analysis

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Revolutionizing Blueberry Mosaic Disease Severity Classification with CNN-AdaBoost Hybrid Model 11

Revolutionizing Blueberry Mosaic Disease Severity Classifica...

引用

11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Kumar, Amresh Singla, Parveen Lamba, Shweta Chandigarh Engineering College Mohali India Chandigarh Engineering College Research Electronics and Communication Engineering Department Punjab Mohali140307 India

ISBN: (纸本)9798350350357

the proposed study introduces a new way of measuring how bad Blueberry Mosaic Disease (BMD) is using an advanced learning method that combines Convolutional Neural Networks (CNN) with AdaBoost. A carefully selected collection of 12,000 blueberry leaf pictures showing different levels of BMD damage is used as the base. Experts in the field carefully prepared and labeled data to help teach from examples. the mix of Deep learning shows very good results, getting an all-around rightness of 98.20/0 in sorting out how risky BMD is. In Table I, performance measures such as precision rate, recall percentage, and F1 score tell how good the model is at telling different levels of seriousness apart. the Confusion Matrix helps to see what the model does well and where it needs improvement. this model's success in being right about the levels of BMD shows it might be a very important tool for treating diseases exactly in blueberry farms. It gives farmers and crop experts a quick, machine-driven way to find problems early. this lets them come up with special plans for dealing with these issues. the study's effects are not just about BMD. It shows how fancy technology can change the way of looking at diseases in farming too. this study builds a strong base for future improvements, aiming to improve the model's accuracy, size, and real-world use in making sure blueberry crops grow healthy. © 2024 IEEE.

关键词： Convolutional neural networks

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Towards AI-controlled FES-restoration of arm movements: neuromechanics-based reinforcement learning for 3-D reaching 11

Towards AI-controlled FES-restoration of arm movements: neur...

引用

11th international IEEE EMBS conference on Neural engineering (IEEE/EMBS NER)

作者： Wannawas, Nat Faisal, A. Aldo Imperial Coll London Brain & Behav Lab London England Univ Bayreuth Chair Digital Hlth & Data Sci Bayreuth Germany

ISBN: (纸本)9781665462921

Reaching disabilities affect the quality of life. Functional Electrical Stimulation (FES) can restore lost motor functions. Yet, there remain challenges in controlling FES to induce desired movements. Neuromechanical models are valuable tools for developing FES control methods. However, focusing on the upper extremity areas, several existing models are either overly simplified or too computationally demanding for control purposes. Besides the model-related issues, finding a general method for governing the control rules for different tasks and subjects remains an engineering challenge. Here, we present our approach toward FES-based restoration of arm movements to address those fundamental issues in controlling FES. Firstly, we present our surface-FES-oriented neuromechanical models of human arms built using well-accepted, open-source software. the models are designed to capture significant dynamics in FES controls with minimal computational cost. Our models are customisable and can be used for testing different control methods. Secondly, we present the application of reinforcement learning (RL) as a general method for governing the control rules. In combination, our customisable models and RL-based control method open the possibility of delivering customised FES controls for different subjects and settings with minimal engineering intervention. We demonstrate our approach in planar and 3D settings.

关键词： Electrical Stimulation FES Neuromechanical Model Reinforcement learning Arm Movements

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data Center Energy Consumption Simulation and Prediction from the Perspective of Digital Twin 11

Data Center Energy Consumption Simulation and Prediction fro...

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11th China international conference on Electricity Distribution, CICED 2024

作者： Huang, Shuai Gao, Zhenyu Xiang, Xinyu Zhou, Jinya Hangzhou Electric Power Design Institute Co. Ltd. Hangzhou China State Grid Zhejiang Hangzhou Power Supply Company Hangzhou China

ISBN: (数字)9798350368345

ISBN: (纸本)9798350368345

In recent years, the Chinese data center industry has experienced rapid growth, yet it faces challenges of high energy consumption and low resource utilization. To achieve intelligent management, the digital twin technology has received widespread attention. this paper utilizes digital twin technology and leverages Unity3D to construct a simulation platform for the data center. this platform is capable of receiving temperature field information, displaying three-dimensional temperature cloud maps visually to evaluate the energy efficiency of data centers. Additionally, we design a deep neural network to predict the device energy consumption and calculate energy efficiency. Our research method helps management personnel gain comprehensive insights into the real-time status of data centers and make effective adjustments, thereby promoting the green and high-quality development of data centers. © 2024 IEEE.

关键词： Training data centers Energy consumption Temperature distribution Clouds Artificial neural networks Energy efficiency Real-time systems Digital twins Personnel

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Tech-Driven Agronomy: Federated learning CNN's for Aloe Vera Leaf Disease Diagnosis 11

Tech-Driven Agronomy: Federated Learning CNN's for Aloe Vera...

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Upadhyay, Deepak Verma, Aditya Kukreja, Vinay Mehta, Shiva Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350350357

the research paper discussed how integrating a federated learning framework using a Convolutional Neural Network (CNN) enables groundbreaking work in classifying aloe vera leaf diseases. the research is concerned with classifying leaf diseases into four grades, using datasets from four clients. By applying federated averaging methods, local data from each client is converted into one global unified model that preserves private information and capitalizes on collective wisdom. the result analysis offers essential findings concerning the model performance. Each client adds to the federated model uniquely, with different levels of accuracy and precision regarding disease categories. the macro, micro, and weighted averages are computed to assess the efficiency of the entire model for all customers and classes. the model for the client uj_1 has a macro average value of 78.80, a weighted average value equal to 79.25, and a micro-average of 79.36, indicating adequate initial performance. Micro, macro-averaged F1 scores of client uj_2 are 88.43 with a weighted average score at the same value. Client uj_3 refines the model's performance, scoring 90.62 in macro intermediate, 90.66 in the weighted average, and a micro according of 90. Finally, showing the exceptional performance levels is client uj_4, which attains a mean of 92.89 from macro-average, average weighted means to value at 92.0 and micro averages standing on values as high up as %. In the end, this study showed that federated learning with CNN can accurately classify aloe vera leaf diseases and establish a baseline for future research on agricultural disease management using state-of-the-art machine learning approaches. © 2024 IEEE.

关键词： Statistical methods

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