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Artificial Intelligence-based Fault Detection and Diagnosis: Towards Application in a Chemical Process

Artificial Intelligence-based Fault Detection and Diagnosis:...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Liu, Yiqi Li, Zhi Chen, Hongtian The School of Automation Science & Engineering Key Laboratory of Autonomous Systems and Networked Control Ministry of Education South China University of Technology Guangzhou510640 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China

ISBN: (纸本)9798350337754

As the chemical processes become more and more complicated, it is crucial but also difficult to detect and diagnose faults in the system than ever. With the development of machine learning algorithms and cloud computing, to monitor chemical processes completely with the help of big data become true. However, most machine learning methods suffer from unexplainable problem. By employing Explainable Artificial Intelligence(XAI) techniques, this paper applies two kinds of unsupervised machine learning methods, One-Class Support Vector Machine and Depth-based Isolation Forest Feature Importance, to detect and diagnosis faults in a chemical process. By combining two different fault explanation methodologies, accuracy and reliability of the fault diagnosis results can be achieved by the validation in a simulated continuous stirred tank reactor. © 2023 IEEE.

关键词： Support vector machines

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E-Nose-Based Domain-Generalization Recognition of Ignitable Liquids

E-Nose-Based Domain-Generalization Recognition of Ignitable ...

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第43届中国控制会议

作者： Tian-Shu Song Zhen-Peng Chen Hui-Rang Hou Hong-Yue Li Qing-Hao Meng Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Detecting ignitable liquids(ILs) at the scene of a fire is crucial for fire investigation. The electronic nose(e-nose) is crucial for detecting ILs due to its affordability and rapid response time. Process limitations and changes in environmental conditions can cause variations in the reactions of multiple e-noses with the same sensor array to the same input gas. This can significantly reduce the accuracy of identifying ILs when deploying a model trained on one e-nose to other e-noses. In order to tackle the issue of cross-platform identification of ILs using e-noses, we developed a feature mute gated network(FMG-Net).This network is capable of maintaining consistency in the shared components of different e-noses at the low-level feature level while effectively muting the distinct low-level features. FMG-Net is comprised of a main network and multiple branch networks that are specific to different domains. Initially, a subsidiary domain-specific convolutional neural network(SDS-CNN) is trained to learn the network parameters on various e-nose platforms with known labels. Additionally, the backbone network generates low-level features of the e-nose responses. Furthermore, the SDS-CNN incorporates random input of low-level features and generates a mask using the gradient. This mask is then used to explicitly remove the non-overlapping parts of the low-level features. At last, the low-level features are sent back to the backbone network for further training once the non-overlapping parts have been removed. Experiments were conducted to identify four types of ILs(diesel, gasoline, ethanol, and thinner) using five self-made e-nose platforms with identical sensor arrays. The results indicated that the proposed FMG-Net consistently outperforms other domain-generalization algorithms in terms of recognition accuracy and parameter sizes. FMG-Net has the ability to detect volatile gases in ILs using an e-nose, even without the need for a dataset.

关键词： Electronic Nose Fire Investigation Ignitable Liquids Convolutional Neural network Cross Platform

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Task Offloading Optimization for Mobile Edge Computing System: A CNO Approach 23

Task Offloading Optimization for Mobile Edge Computing Syste...

引用

23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Long, Changqing Meng, Wenchao Zhang, Yongmin College of Control Science and Engineering Zhejiang University Hangzhou310027 China The Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China School of Computer and Engineering Central South University Changsha410083 China

ISBN: (纸本)9798350325959

Mobile edge computing (MEC) can extend computing power at the edge of wireless networks by migrating compute-intensive tasks to MEC servers, which has the potential to enable compute-intensive applications in 5G networks. However, whether to offload tasks and how much edge computing resources to allocate to it is an NP-hard problem. In this paper, a MEC system enabled multi-user wireless network is proposed, in which the access point (AP) is equipped with a MEC server that provides computing resources for mobile users through task offloading. Then, the issue of task offloading and resource allocation is formulated as a mixed integer nonlinear program to minimize the overhead of the entire MEC system, represented by a weighted sum of delay and energy consumption. Furthermore, a collaborative neurodynamic optimization (CNO) with levy mutation operator (CNO-LMO) algorithm is proposed for searching global optimal solutions to the formulated problem. Finally, the experimental results are given to verify the superiority of the proposed CNO-LMO in reducing the overhead of the MEC system compared to some other algorithms. © 2023 IEEE.

关键词： collaborative neurodynamic optimization (CNO) mixed integer nonlinear program (MINLP) mobile edge computing (MEC) resource allocation task offloading

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A Depression Level Classification Model Based on EEG and GCN network with Domain Generalization

A Depression Level Classification Model Based on EEG and GCN...

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第43届中国控制会议

作者： Zhongyi Zhang Chenyang Xu Licheng Jin Huirang Hou Qinghao Meng Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Electroencephalography(EEG) contains a wealth of information, including neuron activity, allowing for a partial understanding of the brain's state. As a reliable tool, EEG, combined with deep neural networks, has been increasingly utilized to assist in the detection of depression and has achieved satisfactory results. However, there are still some limitations in the current models. Firstly, it is worth noting that many existing models are binary classification models. These models can determine whether a subject is depressed or not, but they lack the capability to differentiate the severity of depression. Besides, the variations in EEG signals among various individuals have not been thoroughly taken into account, leading to subpar performance when applying the trained model to new subjects. To address the aforementioned issues, we suggest implementing a multi-classification model that utilizes a graph convolutional neural network(GCN) to identify levels of depression. Additionally,to mitigate the problem of imbalanced samples in multi-classification tasks, we introduce a penalty coefficient for the smaller categories. At the same time, the issue of signal discrepancies among subjects was taken into account, leading to the introduction of the domain generalization module. Finally, the depression level identification task on the MODMA and PRED+CT dataset achieved an accuracy of 75.47% and 77.97% respectively, surpassing the performance of the state-of-the-art(SOTA) model. In addition, numerous ablation experiments were conducted to validate the efficacy of each module.

关键词： Depression Level EEG Graph Convolution network Domain Generalization

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AMG: A Depression Detection Model with Autoencoder and Multi-Head Graph Convolutional network 42

AMG: A Depression Detection Model with Autoencoder and Multi...

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42nd Chinese control Conference, CCC 2023

作者： Wang, Han-Guang Meng, Qing-Hao Jin, Li-Cheng Wang, Jian-Bang Hou, Hui-Rang Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China China Southern Power Grid Company Limited Guangzhou510623 China

ISBN: (纸本)9789887581543

Depression is a common chronic mental disorder characterized by high prevalence, recurrence, suicide rate, disability rate and heavy disease burden. Depression assessment based on questionnaires is subjective, therefore, a novel framework to is hereby designed to detect depression automatically and obj ectively. The framework first utilizes a two-way autoencoder to extract the hidden features from the original differential entropy features. The new features are used to generate the channel connectivity graph, using the multi-head attention mechanism. Next, the new features and graphs are input into the graph convolutional network (GCN) to aggregate spatial information. Finally, the output from GCN is passed to a fully connected layer and a soft layer to obtain the predicted label. Experiments are performed on Multimodal Open Dataset for Mental-disorder Analysis dataset. A final accuracy of 88.68% and a cross entropy loss of 2.4532 are obtained, when using ten-fold cross validation, outperforming other traditional machine learning methods including SVM, KNN, decision tree. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Decision trees

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Design of Yaw controller for a Small Unmanned Helicopter Based on ADRC

Design of Yaw Controller for a Small Unmanned Helicopter Bas...

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International Conference on Guidance, Navigation and control, ICGNC 2020

作者： Huang, Linjie Pei, Hailong Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong South China University of Technology Guangzhou510640 China

ISBN: (纸本)9789811581540

For ensuring the stabilities of unmanned helicopters under various working conditions, this paper introduces a yaw controller based on ADRC. Because the effect of PI or PID controller is not enough in some severe environments such as working in a situation with strong wind, so ADRC is wildly used in the area of UAV. However, it takes a lot of time to adjust parameters, which is not conductive to piratical application. What this article makes is to enhance and to simplify traditional ADRC through practical experiments and theory analysis. This paper uses gradient descent to identify the actuator gain$$b:control$$, and summarizes the practical tuning experience during the tuning process. Compared with the traditional PID method, the experimental analysis claim that the method proposed in this article has significant advantages in both dynamic performance and anti-interference performance. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： controllers

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Dynamic control Allocation for a Twin Ducted Fan UAV

Dynamic Control Allocation for a Twin Ducted Fan UAV

引用

International Conference on Guidance, Navigation and control, ICGNC 2020

作者： Meng, Chaoheng Pei, Hailong Cheng, Zihuan Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong South China University of Technology Guangzhou510640 China

ISBN: (纸本)9789811581540

Twin ducted fan UAV is a novel aircraft with redundant control effectors and the interactions between actuators and control allocators can not be ignored. In this paper, dynamic control allocation is used to deal with the control allocation problem of Twin ducted fan UAV. First, the actuator rate is penalized in the optimization function of the control allocation based on the quadratic programming method. Second, a post-compensation method is applied to compensate for command increment attenuation. The simulation results show that the dynamic control allocation method with actuator dynamic compensation can prevent the high bandwidth actuator from saturating in advance, and the virtual control command is effectively allocated to actuators with different bandwidths. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Actuators

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A method based on rotated object detection to detect defects regarding distance in strain clamps 3

A method based on rotated object detection to detect defects...

引用

2022 3rd International Conference on Electrical Technology and Automatic control, ICETAC 2022

作者： Du, Qiliang Zhou, Huangze Qu, Fengrui Tian, Lianfang School of Automation Science and Engineering South China University of Technology Guangdong Guangzhou China Sino-Singapore International Joint Research Institute Guangdong Guangzhou China Key Laboratory of Autonomous Systems and Network Control of Ministry of Education China Research Institute of Modern Industrial Innovation South China University of Technology Guangdong Guangzhou China

Strain clamps in the transmission line have a vital role. With X-ray inspection in the transmission line, X-ray images to check defects of the tensioning line clip method have already become the mainstream method. In this paper, we concentrate on the strain clamps' steel anchor end and strand distance defects. We propose a method based on the rotated object detection model RoI Transformer with KFIoU loss to extract the diameter of the steel anchor tube as a reference to determine whether the distance is too long. We first obtain the rotated bounding boxes of the steel anchor and the strand, then calculate the distance between the steel anchor end and the strand and the diameter of the steel anchor by rotated bounding boxes, and finally determine whether there is a defect. Through the study of this essay, the defect detection of strain clamps realizes with high accuracy which has an essential reference for the quality inspection of tension clamp crimping in engineering applications. © Published under licence by IOP Publishing Ltd.

关键词： Defects

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Efficient and High-quality Neural Implicit RGB-D Surface Reconstruction with Volume Feature Rendering

Efficient and High-quality Neural Implicit RGB-D Surface Rec...

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Robotics, control and Automation (ICRCA), International Conference on

作者： Jintao Wan Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Key Laboratory of Collaborative sensing and autonomous unmanned systems of Zhejiang Province Hangzhou China

ISBN: (数字)9798350344721

ISBN: (纸本)9798350344738

Acquiring high-quality 3D surfaces of indoor scenes is essential for applications in VR/ AR and robotics. Recent advancements in neural implicit surface learning and differ-entiable volume rendering methods have significantly improved the accuracy and completeness of geometry. In our work, we propose a volumn feature rendering method for fast and detailed surface reconstruction from muti-view RGB-D frames. We adopt a hybrid scene representation utilizing muti-resolution hash encoding and two shallow MLPs for fast convergence. We suggest improvements by replacing standard volume rendering with vol-ume feature rendering to optimize speed. Additionally, we employ an adaptive sampling strategy guided by dynamic occupancy grids to accelerate training. We introduce a curvature term to promote surface smoothness and hole filling in untexture areas. Experiments on standard benchmarks show that our method can process room-scale scenes in approximately 10 minutes, achieving a 2x speedup compared to Go-Surf [14] in a memory-efficient way, while maintaining comparable or superior reconstruction quality.

关键词： Training Geometry Surface reconstruction Three-dimensional displays Accuracy Rendering (computer graphics) Surface treatment

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Adaptive Sliding Mode control for Trajectory Tracking in Four-Wheeled Mobile Robots Under Uncertainties and External Disturbances

Adaptive Sliding Mode Control for Trajectory Tracking in Fou...

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Chinese control and Decision Conference, CCDC

作者： Naeem Ajilforoushan Saba Ghorbani Barzegar Yongliang Lin Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Hangzhou China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

In this paper, we propose an adaptive sliding mode control (SMC) design technique to track the desired trajectory of the skid-steering mobile robots in the presence of model uncertainties and perturbations. To enhance the stability of the robot and ensure accurate trajectory tracking, adaptive rules have been employed to estimate the upper bound of perturbations. This estimation, in turn, serves to stabilize the robot and guarantee the desired trajectory. Moreover, we analyze and calculate the convergence time of the sliding surface, ensuring its convergence within finite time. Additionally, efforts have been made to minimize the chattering phenomenon by smoothing the discontinuity of the sliding surface. Simulation results validate the efficacy of the proposed controller, demonstrating its ability to deliver acceptable performance in diverse scenarios.

关键词： Uncertainty Upper bound Trajectory tracking Perturbation methods Estimation Stability analysis Trajectory

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