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检索条件"任意字段=8th International Workshop on Control Engineering and Advanced Algorithms, IWCEAA 2024"

共 143 条记录，以下是91-100 订阅

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Algorithm for Human Abnormal Behavior Recognition Based on Improved Spatial Temporal Graph Convolutional Networks 8th

Algorithm for Human Abnormal Behavior Recognition Based on I...

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8th international workshop on advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023

作者： Wu, Qi Zhao, Xiaoyan Zhang, Zhaohui Zhang, Tianyao Peng, Zexuan School of Automation and Electrical Engineering University of Science and Technology Beijing 30# Xueyuan Road Haidian District Beijing100083 China Beijing Engineering Research Center of Industrial Spectrum Imaging University of Science and Technology Beijing 30# Xueyuan Road Haidian District Beijing100083 China

ISBN: (纸本)9789819975921

With the increasing demand for public safety, the field of abnormal human behavior recognition has undergone significant development. In addressing the low accuracy issue of existing abnormal behavior recognition algorithms due to factors such as environmental influences, changes in viewpoint, and scale variations, this study proposed an improved Spatial temporal graph convolutional network. By incorporating spatial attention and channel attention mechanisms at relevant positions in the network, a dynamic optimization of the skeletal structure graph of the human body was achieved. this ensured that key nodes expressing motion information in the skeletal graph received greater weight values, ultimately improving the accuracy of abnormal behavior classification. To this end, an abnormal behavior dataset was constructed and transformed into skeletal information recognizable by the proposed algorithm using OpenPose. Extensive experiments were conducted on this dataset as well as the large-scale NTU RGB + D dataset using the improved algorithm. the results demonstrate that the algorithm has achieved an increase of approximately 5% in recognition accuracy compared to its pre-improvement state, placing it among the top-performing algorithms in various comparative evaluations. © 2024, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Large dataset

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A Mango Disease Prediction for Smart Agiculture using Machine Learning algorithms 8

A Mango Disease Prediction for Smart Agiculture using Machin...

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8th international Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Lavanya, K. Packialatha, A. Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Chennai Tamilnadu Pallavaram India

ISBN: (纸本)9798350376425

the country's economy relies heavily on agriculture, and the health of the crops is essential to its success. Crop diseases that go undetected can cost the agriculture industry. thus early detection and identification are crucial. It is possible to avoid crop diseases from destroying the harvest if they are accurately diagnosed and detected. Because healthy and diseased plants appear identical in their early stages, farmers cannot distinguish between the two by watching the crop leaf. India exports vast mangoes, making it an economically and environmentally significant fruit. About 1500 mango species are grown in India, with over 1000 commercial types. there are a lot of diseases that harm mangoes, affecting their look, taste, and economy. the "Prediction of Disease in Mango Fruit Crops"A complex warning system that uses machine learning and IoT. One of the main objectives is to develop a system that can forecast disease outbreaks on mango fruit harvests using historical weather information and crop yield Mango trees in India are plagued by a fungus called Anthracnose, which is the most frequent disease of its kind. Anthracnose, a highly contagious fungus, requires a quick and accurate method of diagnosis. As a result, an in-depth examination of the plants is essential before initiating any control measures. this study examines machine learning(ML) and deep learning (DL) strategies for detecting and classifying mango plant diseases. the performance of ML and DL-based classification models for mango crops and their datasets and feature extraction approachesare examined in this work. Finally, a variety of issues involved in plant disease identification are explored. © 2024 IEEE.

关键词： Smart agriculture

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advanced Hybrid control of Mobile Cable-Driven Parallel Robot with 8-Cables

Advanced Hybrid Control of Mobile Cable-Driven Parallel Robo...

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18th IEEE international Conference on advanced Motion control, AMC 2024

作者： Kim, Byeong-Geon Shin, Dong-Yeop Park, Kyoung-Su Gachon University Department of Mechanical Engineering Gyeonggi-do Korea Republic of

ISBN: (纸本)9798350342819

this paper introduces control algorithms aimed at improving the stability of a mobile cable-driven parallel robot (MCDPR), consisting of four mobile platforms and eight cables during motion. the discussed algorithms include cable length control (CLC), addressing target cable length calculation through inverse kinematics, considering pulley influence;the tension distribution algorithm (TDA) for cable tension calculation to maintain static equilibrium at the end-effector and cable length control based on tension errors;path curvature-based localization (CBL) that estimates robot positions using curved path predictions from robot velocities and angular velocities;and index error feedback (IEF), which sustains robot formation by providing feedback on robot positions. Experimental verification was conducted using a prototype MCDPR. Results indicated that all algorithms reduced both position and tension errors. Notably, algorithms directly affecting cable control, especially CLC and TDA, had a more pronounced impact on tension errors. Failure to apply CLC, in particular, led to extremely high tensions, resulting in slip and tipping in each robot and larger position errors. these findings contribute to the advancement of MCDPR technology, enhancing its stability and reliability for various applications. © 2024 IEEE.

关键词： Inverse kinematics

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Sequence generation of discrete event system and logic controller and its applicability to fault detection

Sequence generation of discrete event system and logic contr...

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international workshop on advanced Motion control (AMC)

作者： T. Zanma T. Miyabayashi M. Ishida Faculty of Electrical and Electronic Engineering Mie University Japan

In this paper, a sequential control system controlled by a logic controller is considered as an example. Incompleteness of the system model is given by nondeterministic representation which is modeled by a set-valued function. therefore, the plant model has more than one transition in each state of it. We propose algorithms to construct the state space of the closed loop system of the plant model and the logic controller. Applying the proposed algorithms to an illustrative example, we show applicability to fault detection when an unpredicted state is generated.

关键词： Discrete event systems Logic control systems Fault detection State-space methods Programmable control Electrical fault detection Closed loop systems control system synthesis Resilience

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the fuzzy state space controller for buck converter

The fuzzy state space controller for buck converter

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international workshop on advanced Motion control (AMC)

作者： D. Gleich M. Milanovic S. Uran F. Mihalic Faculty of Electrical Engineering and Computer Science University of Maribor Slovenia Faculty of Electrical Engineering and Computer Science University of Maribor

In this paper digitally controlled step down converter using fuzzy state space control law algorithms is studied. the state controller is designed to eliminate the start-up overshoot and reduce maximal dynamic error of load change response, but the state controller is not capable to eliminate the steady-state error under the load change condition. In order to improve this property two control algorithm were investigated. the first one was the state controller with constant gains supported by additional decomposed fuzzy PID controller and the second one was fuzzy state space controller. Both of control algorithms are performed at a continuous current mode of operation. the experimental results are presented in the paper. the control algorithms were implemented on 16-bit micro-computer.

关键词： Fuzzy control State-space methods Buck converters Signal processing algorithms Pulse width modulation Error correction Sliding mode control Digital control Steady-state three-term control

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Research on algorithms of Lateral Face Recognition Based on Data Generation 8th

Research on Algorithms of Lateral Face Recognition Based on ...

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8th international workshop on advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023

作者： Zhang, Zimin Zhang, Zhaohui Zhao, Xiaoyan Zhang, Tianyao School of Automation and Electrical Engineering University of Science and Technology Beijing 30# Xueyuan Road Haidian District Beijing100083 China Beijing Engineering Research Center of Industrial Spectrum Imaging University of Science and Technology Beijing 30# Xueyuan Road Haidian District Beijing100083 China

ISBN: (纸本)9789819975921

With the rapid progress of artificial intelligence, the methods of face recognition have also achieved considerable progress. However, when multiple head poses are present, the accuracy of face recognition decreases due to angular deviation. therefore, how to realize the enhancement of accurate precision in two-dimensional multiple head poses is still a worthy research topic. In this paper, based on the ResNet50 residual network structure, data enhancement and attention mechanism are adopted, and the Public Figures Face Database public face database is used to partition the training and test sets. this hybrid approach effectively improved the accuracy of face recognition in lateral. However, the hybrid method has low accuracy for large angle deflection face recognition. therefore, based on the hybrid method, this article proposes a side face recognition algorithm based on data generation, incorporating a data generation algorithm. the recognition accuracy of the front face of this algorithm is as high as 96.3%. this article presents a data-generated lateral face recognition algorithm that further improves the accuracy of large angle deflection face recognition. © 2024, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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Use of dynamic emulation of mechanical loads in the design of adjustable speed applications

Use of dynamic emulation of mechanical loads in the design o...

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international workshop on advanced Motion control (AMC)

作者： M. Rodic K. Jezernik M. Trlep Faculty of Electrical Engineering and Computer Science University of Maribor Slovenia Faculty of Electrical Engineering and Computer Science University of Maribor

A rapid prototyping procedure is presented in the paper. An approach to the dynamic emulation of mechanical loads, the "hardware-in-the-loop" procedure where the applied prototype is reused for many different applications using the same drive, is explained and analyzed. Instead of the actual load, a torque driven machine is introduced. the load torque, which it produces, is calculated in such a manner, that the input/output behavior of the experimental system resembles one of the actual (emulated) one. A simple feedforward scheme, combined with a feedback controller, is used fore the calculation. In contrast to the existing references the method is simple and requires no torque measurement. the method is used for the evaluation of the control algorithms, regardless the control and estimation methods used. algorithms are developed with the use of Matlab/Simulink and experiments are executed with the use of the dSPACE system.

关键词： Emulation Nonlinear dynamical systems Application software Prototypes Inverse problems Computer science Adaptive control Torque measurement Open loop systems Variable speed drives

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Helmet Detection Algorithm of Electric Bicycle Riders Based on YOLOv5 with CBAM Attention Mechanism Integration 8th

Helmet Detection Algorithm of Electric Bicycle Riders Based ...

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8th international workshop on advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023

作者： Fu, Si-Yue Wei, Dong Zhou, Liu-Ying School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing Municipal Science and Technology Commission Beijing100044 China

ISBN: (纸本)9789819975921

Object detection algorithms can assist in detecting the helmet-wearing status of electric bicycle riders, thereby saving regulatory manpower costs. However, there is currently a lack of standardized and publicly available datasets. Additionally, the basic YOLOv5s object detection algorithm, due to its limited feature extraction capabilities, may lead to numerous instances of both false negative and false positive. To enhance the model’s focus on critical information within the feature maps, this paper introduces the CBAM attention mechanism module into the Backbone section of YOLOv5s. this module sequentially infers attention maps from the input feature maps along both channel and spatial dimensions independently, and then multiplies these attention maps with the input feature maps to achieve adaptive feature optimization. this paper have established self-built dataset for experimental research, and the results indicate that compared to the original YOLOv5s model, the proposed method has improved the model’s overall mAP score by 1.89%. © 2024, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Structural Health Monitoring of Similar Gantry Crane Based on Federated Learning Algorithm 8th

Structural Health Monitoring of Similar Gantry Crane Based o...

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8th international workshop on advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023

作者： Peng, Zexuan Zhang, Zhaohui Zhao, Xiaoyan Zhang, Tianyao Wu, Qi School of Automation and Electrical Engineering University of Science and Technology Beijing 30# Xueyuan Road Haidian District Beijing100083 China Beijing Engineering Research Center of Industrial Spectrum Imaging University of Science and Technology Beijing 30# Xueyuan Road Haidian District Beijing100083 China

ISBN: (纸本)9789819975891

When using Internet of things (IoT) technology for health monitoring of similar batches of identical gantry cranes, uploading all their structural data to a cloud server would result in significant bandwidth waste and reduced real-time performance. Additionally, due to different usage scenarios and habits among similar batches of identical gantry cranes, existing algorithms can only monitor specific gantry cranes after training is completed. For the health monitoring of identical gantry cranes, this study employed the federated averaging algorithm (FedAvg) and XGBoost algorithm in an edge gateway to detect damage types in gantry crane structures. A small-scale gantry crane was used to simulate various connection damages. A IoT gateway was designed to monitor the structural condition of facilities. Upon receiving structural data, the gateway locally performed anomaly detection. When the edge gateway detected anomalies in the time series, it utilized the XGBoost algorithm for anomaly type classification. Additionally, the edge gateway employed the FedAvg to mitigate the impact of non-independent and imbalanced data caused by different environments and usage patterns. Ultimately, a unified anomaly detection model was obtained to facilitate subsequent usage of identical gantry cranes. © 2024, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection

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3D representation of Architectural Heritage: a comparative analysis of NeRF, Gaussian Splatting, and SfM-MVS reconstructions using low-cost sensors 8

3D representation of Architectural Heritage: a comparative a...

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8th international ISPRS workshop LowCost 3D - Sensors, algorithms, Applications

作者： Clini, Paolo Nespeca, Romina Angeloni, Renato Coppetta, Laura Dept. of Civil and Building Engineering and Architecture Università Politecnica della Marche Ancona Italy

this study evaluates the performance of three image-based 3D reconstruction methods-Structure-from-Motion with Multi-View Stereo (SfM-MVS), Neural Radiance Fields (NeRF), and Gaussian Splatting (GS)-for documenting Architectural Heritage (AH). Data acquisition was conducted using low-cost sensors: a drone to capture the stone portal of the external façade and a 360° camera to document the interior spaces. NeRF and GS significantly outperformed SfM-MVS in processing time, with NeRF excelling in reconstruction completeness. However, GS faced challenges with point number control, and NeRF reconstructions exhibited artifacts and noise, particularly on flat surfaces. Accuracy assessments, using TLS point clouds as a benchmark, revealed that SfM-MVS provided the highest geometric precision for the external façade, despite minor gaps in reconstruction. In contrast, NeRF and GS fell short of the accuracy required for precise geometric documentation, with NeRF exhibiting prominent artifacts in flat or poorly detailed regions. Interior reconstructions were further limited by the higher Ground Sampling Distance (GSD) caused by the technical constraints of the 360° camera and the increased capture distance for elevated areas. In conclusion, we can affirm that while NeRF and GS demonstrate strong potential for visualization due to their rendering quality and efficiency, SfM-MVS remains the most reliable method for achieving accurate geometric documentation of AH. Copyright: © 2024 Paolo Clini et al.

关键词： Stereo image processing

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