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检索条件"任意字段=4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021"

共 515 条记录，以下是11-20 订阅

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EAPNet: Enhanced Attention-Guided Network for Fine-Grained Semantic Segmentation 7

EAPNet: Enhanced Attention-Guided Network for Fine-Grained S...

引用

7th international conference on robotics, control and automation engineering, rcae 2024

作者： Huang, Minyu Hou, Beiping Pan, Feng Dong, Jianwei Zhejiang University of Science and Technology School of Automation and Electrical Engineering Hangzhou China China Telecom Corporation Ltd. Quzhou Hangzhou China

ISBN: (纸本)9798350355642

Ground-based cloud observation is essential for meteorological research and climate monitoring. Existing methods for cloud detection and classification in whole-sky images (WSIs) often focus solely on binary segmentation (sky and cloud) or image-level classification, which perform poorly in tasks requiring the classification and segmentation of different cloud types. this paper presents an enhanced attention-guided pyramid network (EAPNet) designed to enhance the accuracy and robustness of fine-grained cloud classification in whole-sky images through deep learning techniques. the key innovations include: 1) the utilization of the pyramid spatial pooling (PSP) module to capture multi-scale contextual information;2) the introduction of the convolutional block attention module (CBAM), which leverages an attention mechanism to enhance feature representation;and 3) an improved decoder module that enhances localization accuracy. Experimental results demonstrate that EAPNet outperforms existing mainstream methods in both cloud classification and segmentation tasks. © 2024 IEEE.

关键词： Semantic Segmentation

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ES-MSCNN: An Echo State Multi-Scale Convolutional Neural Network for Multi-Step Prediction of Wastewater Quality Variables 7

ES-MSCNN: An Echo State Multi-Scale Convolutional Neural Net...

引用

7th international conference on robotics, control and automation engineering, rcae 2024

作者： Sun, Yue Liu, Yiqi South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science & Engineering Guangzhou510640 China

ISBN: (纸本)9798350355642

Given the limitations of hardware sensors together with strong nonlinearities and nexus between variables in wastewater treatment processes (WWTPs), the measurement of key quality variables is usually inaccurate or even impossible. Moreover, due to the short data sampling period, the single-output single-step prediction model cannot meet the actual demand. therefore, this paper proposes a new soft sensor, termed as the Echo State Multi-Scale Convolutional Neural Network (ES-MSCNN), designed for accurate multivariate multi-step prediction. ES-MSCNN first generates an echo state representation (ESR) containing temporal information through an echo state network (ESN). Subsequently, a convolutional neural network (CNN) is employed to avoid the shallowness of ESN and further extract high-dimensional feature information from the data. Besides, a multi-scale learning strategy is used in the convolutional module to capture multi-timescale features and improve the multivariate prediction accuracy. Validation on the benchmark simulation model 2 platform and comparison with several models indicate that the proposed ES-MSCNN has higher accuracy and stronger robustness in multi-step prediction tasks with multi-input and multi-output. © 2024 IEEE.

关键词： Convolutional neural networks

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Applications of Magnetorheological Dampers in Automobile Semi-Active Suspension: A Review 6

Applications of Magnetorheological Dampers in Automobile Sem...

引用

6th international conference on robotics, control and automation engineering, rcae 2023

作者： Liang, Shumao Lu, Sheng Li, Chuan Luo, Jiufei Shou, Mengjie School of Automation Chongqing University of Posts and Telecommunications Chongqing China School of Advanced Manufacturing Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Mechanical Engineering Dongguan University of Technology Dongguan China

ISBN: (纸本)9798350315301

Magnetorheological dampers are intelligent shock absorbers that utilize the magneto rheological effect to achieve adaptable damping, which can effectively enhance vehicle riding comfort and handling stability. An initial introduction to magnetorheological fluids and the magnetorheological effect is provided in this paper, followed by an in-depth exploration of the operational principles and structural characteristics of magnetorheological dampers. the control methods for magnetorheological semi-active suspensions, including skyhook control, groundhook control, hybrid skyhook-groundhook control, PID control, fuzzy control, sliding mode control, and neural network control, are then analyzed. Furthermore, the challenges of applying magneto rheological semi-active suspension technology are identified and summarized, while prospective directions for future development and research focus are outlined. © 2023 IEEE.

关键词： Magnetorheological fluids

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Cheese Production Based on Industry 4.0 automation and Simulation in the Company "vakilact" 7

Cheese Production Based on Industry 4.0 automation and Simul...

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7th international conference on robotics, control and automation engineering, rcae 2024

作者： Cruz Mendez, Jhans Miguel Katherine Melo Susanibar, Lady Marcalaya Laura, Daniela Jasmin Samuel Medina Jimenez, Carlos Universidad Continental Department of Industrial Engineering Huancayo Peru Universidad Continental Department of Industrial Enginnering Huancayo Peru

ISBN: (纸本)9798350355642

Cheese production in Peru has reached 145,765 tonnes in 2022, according to the report issued by the Agraria, on the other hand, cheese is the second most consumed dairy product, even so, in Peru much of the production is still maintained by artisanal processes which can result in fatigue and stress for workers. In addition, the lack of automation in production plants not only limits the ability to scale up production to meet growing demand, but can also affect the quality and consistency of the final product. In this context, the present research work proposes to analyse the cheese production processes in the company "Vakilact"with the aim of proposing a cheese production based on Industry 4. 0 of automation and simulation, for which an inspection of the company was carried out, to analyse the current processes, after which time was taken, to then establish the processes in sequence with the DAP, DOP, to establish the number of operations, transports, inspections, storage, distance, after which the tool of the Ishikawa diagram was used to determine the problems and causes that originate within the company, to be expressed by means of the Pareto diagram. Finally, the cheese production process was analysed, focusing on the application of advanced technologies and automated systems to ensure quality and efficiency at all stages. Key operations are detailed, from pH control and pasteurisation to pressing, ripening and storage of the cheese. Pneumatic presses, robotic arms and conveyor belts were considered, this would improve efficiency and accuracy allowing Vakilact to be more flexible and responsive to market demands, controlled from the HMI screen where we ensure quality control, resource optimisation and integration of advanced technologies to ensure excellence in cheese production. © 2024 IEEE.

关键词： Cheeses

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PCA-MD Planetary Gear Damage Identification Method Based on Acoustic Emission Signals 7

PCA-MD Planetary Gear Damage Identification Method Based on ...

引用

7th international conference on robotics, control and automation engineering, rcae 2024

作者： Qiao, Shuai Dong, Shishang Zhang, Hao You, Jun Li, Ming Guo, Li Cheng, Zian Yuan, Xuechao Anhui Polytechnic University School of Electrical Engineering Wuhu China Anhui Polytechnic University Anhui Institute of Future Engineering Wuhu China Anhui Polytechnic University Anhui Institute of Future Research Wuhu China Wuhu Coogao Automation Technology Co. Ltd Wuhu China

ISBN: (纸本)9798350355642

In order to study the damage identification of planetary gears in a reducer, a PCA-MD (Principal Component Analysis-Mahalanobis Distance) method for damage identification of planetary gears based on Acoustic Emission (AE) signals was proposed. In this paper, AE signals were collected using a DS5-16C Acoustic Emission instrument and RS-2A piezoelectric ceramic sensors, with a sampling frequency of 3MHz and a preamplifier gain of 40dB. First, a set of AE signals from healthy planetary gears was collected as a reference signal, and damage to the planetary gears was simulated on the experimental platform, collecting AE signals in the damaged state. then, noise interference in the signals was removed through Empirical Mode Decomposition (EMD), and 13 feature parameters of the denoised AE signals were calculated. through PCA, the 13-dimensional feature parameters were reduced to 2 dimensions, simplifying the complexity of data processing without losing the original valid information. Finally, the parameters obtained after dimensionality reduction were combined with Mahalanobis Distance (MD) to further establish a method for identifying damage in planetary gears of reducers. the damage condition of the planetary gears was judged by calculating the Mahalanobis distance between healthy and damaged signals. the experimental results showed that the PCA-MD method could identify early damage in planetary gears. © 2024 IEEE.

关键词： Epicyclic gears

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A Trajectory State Switching Mechanism for Nonholonomic Mobile Robot Trajectory Tracking 6

A Trajectory State Switching Mechanism for Nonholonomic Mobi...

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6th international conference on robotics, control and automation engineering, rcae 2023

作者： Jing, Changjiang Wang, Xiaomin Zheng, Zhou Sun, Zhendong Han, Jinshu Xue, Min College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9798350315301

this paper presents a switching controller that effectively exerts the excellencies of Model Predictive control (MPC) and Linear Quadratic Regulation (LQR) to handle the trajectory-variant tracking problem. the good balance between accuracy and computational cost is achieved by designing a novel trajectory state switching mechanism (TSSM) that considers both trajectory curvature and tracking error. the TSSM assigns computation-unexpensive LQR to address trajectory tracking when the trajectory exhibits smoothness and minimal lateral error. Conversely, LQR seamlessly transits to the performance-focused MPC by TSSM when substantial changes in curvature or pronounced lateral errors occur. the TSSM precludes the Zeno phenomenon, since the curvature and error will not change suddenly result from the continuity of the trajectory state. Furthermore, to mitigate potential oscillations during control algorithm transitions, a continuous control strategy is reported. In addition, the stability of the system is analyzed by constructing a common Rayapunov function. the experimental results demonstrate the performance of the proposed TSSM control in trajectory tracking. © 2023 IEEE.

关键词： Trajectories

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Multi-Objective Optimal control of Wastewater Treatment Processes Constrainted by Energy-Saving and Effluent Qualities 6

Multi-Objective Optimal Control of Wastewater Treatment Proc...

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6th international conference on robotics, control and automation engineering, rcae 2023

作者： Zhang, Jing Yan, Haoming Chen, Hongtian Xu, Shuiqing Liu, Yiqi School of Automation Science & Engineering South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangzhou510640 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China College of Electrical Engineering and Automation Hefei University of Technology Hefei230009 China

ISBN: (纸本)9798350315301

Considering the high energy consumption problem in the wastewater treatment process, this paper proposes a multi-objective optimization control scheme based on NSGA-II algorithm. the proposed scheme can not only ensures meet effluent quality requirements but also can achieves energy cost savings. this method takes the aeration energy consumption and pumping energy consumption of the system as the optimization goal with the effluent water quality being the constraint condition. At the same time, the adaptive fuzzy PID controller is designed to ensure the control accuracy and stability of the controlled system. the results of simulation experiments validated in benchmark simulation platform, BSM1, show that, compared with PI control and single-objective optimization control, the proposed optimization scheme in this paper can achieve the desired quality of the effluent water withmore energy-saving. © 2023 IEEE.

关键词： Energy utilization

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Real-Time SLAM with Point-Line Features Using EDLines in Unknown Aquatic Environments 6

Real-Time SLAM with Point-Line Features Using EDLines in Unk...

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6th international conference on robotics, control and automation engineering, rcae 2023

作者： Li, Yunfan Liu, Xiaocheng Liu, Xinze Han, Peng Zhang, Weidong Chen, Hongtian Shanghai Jiao Tong University Department of Automation Shanghai200240 China

ISBN: (纸本)9798350315301

Autonomous navigation of autonomous surface vessels (ASVs) in unknown aquatic environments is challenging. this paper proposes a real-Time point-line feature-based visual simultaneous localization and mapping (SLAM). To solve the problems of inaccurate localization and frequent tracking losses of ASV in poor textural environments, the proposed system introduces line features due to their ability to overcome the interference caused by textureless areas. Futhermore, the edge drawing lines (EDLines) algorithm is applied to extract more efficient line segments in less time. the proposed method is evaluated on both public EuRoc and real outdoor datasets. Experimental results show that the designed system can improve accuracy while ensuring real-Time performance. © 2023 IEEE.

关键词： Mapping

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Diffusion Actor with Behavior Critic Guidance Algorithm for Offline Reinforcement Learning 7

Diffusion Actor with Behavior Critic Guidance Algorithm for ...

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7th international conference on robotics, control and automation engineering, rcae 2024

作者： Dong, Botao Huang, Longyang Pang, Ning Liu, Ruonan Zhang, Weidong Chen, Hongtian Shanghai Jiao Tong University Department of Automation Shanghai China

ISBN: (纸本)9798350355642

To address the multimodal nature of offline dataset distributions and the overestimation problem associated with out- of-distribution (OOD) actions, this paper introduces the diffusion actor with behavior critic guidance (DABCG) algorithm. the proposed method first employs diffusion models to model the target policy, incorporating a Q-value guidance term into the actor loss to enhance the algorithm's expressiveness while achieving performance that surpasses the behavior policy. the introduction of Q-value guidance ensures that the learned actions are directed toward higher-value regions of the action space, thus facilitating enhanced policy optimization. Furthermore, by integrating behavior critic guidance into the policy evaluation process, the algorithm effectively constrains the overestimation of OOD actions, ensuring robust performance. Experimental results on the D4RL benchmark dataset provide strong empirical evidence supporting the efficacy of the DABCG algorithm. © 2024 IEEE.

关键词： Reinforcement learning

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Manufacturing Knowledge Graph-Based Disentangled Prediction Framework and Industrial Practices 6

Manufacturing Knowledge Graph-Based Disentangled Prediction ...

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6th international conference on robotics, control and automation engineering, rcae 2023

作者： Sui, Qingkai Wang, Yalin Liu, Chenliang Central South University School of Automation Changsha China

ISBN: (纸本)9798350315301

Quality prediction assumes a pivotal role in manufacturing processes, serving as a linchpin to uphold product uniformity while mitigating the incidence of defects. However, the wealth of prior knowledge inherent in the manufacturing process is often underutilized, leading to limited predictive performance. Simultaneously, the integration of fragmented knowledge in the manufacturing industry often overlooks the ubiquitous presence of time series data, resulting in incomplete knowledge. To address these issues, this paper proposes a manufacturing knowledge graph-based disentangled prediction framework (MFKG-DP Framework). First, a comprehensive manufacturing knowledge graph construction method is proposed, which completes graph relationships through Bayesian network analysis of time series. the second part is the knowledge graph-based disentangled prediction method, which aims to design disentangled mapping matrixes according to the variable relationship provided by the knowledge graph. this facilitates the segmentation and disentanglement of hidden layer features, contributing to enhanced prediction capabilities. Furthermore, this paper incorporates the proposed disentanglement method into deep state space models, creating a disentangled deep state space model called DisSSM. Finally, the effectiveness of the proposed MFKG-DP Framework and DisSSM is validated in industrial processes within manufacturing enterprises. © 2023 IEEE.

关键词： Deep learning

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