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An adaptive fault detection strategy for Cz crystal growth process

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Mu, Lingxia Feng, Nan Zhang, Zhenhua Zhang, Youmin Zhang, Jian Xian University of Technology Crystal Growth Equipment and Systems Integration National Engineering Research Center Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xian710048 China University of Science and Technology Beijing School of Intelligence Science and Technology Beijing100083 China Concordia University Department of Mechanical Industrial and Aerospace Engineering MontrealQCH3G 1M8 Canada

The monitoring and fault detection for the Czochralski (Cz) crystal growth process is important to ensure the quality of the produced mono-crystalline silicon. The Cz process is a typical multi-modes industrial process, which makes the detection difficult in distinguish the change caused by the fault or mode switching. In this paper, an adaptive fault detection strategy for Cz crystal growth process is proposed. Firstly, quality-related variables are selected from numerous process variables, based on which the training dataset is constructed. Then, a fault detection model is built based on slowly-varying feature and statics indicator. A model switching strategy and updating algorithm are designed to handle the abnormal situation caused by the mode switching or fault occurrence. Three representative experiments are conducted to verify the performance of the proposed strategy, including numerical cases, a publicly available dataset, and Cz crystal growth process from our laboratory. © 1963-2012 IEEE.

关键词： Crystal growth from melt

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Online Gyro Bias Estimation from Single Vector Measurements Using Regression Models

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IFAC-PapersOnLine 2023年第1期56卷 252-257页

作者： Bowen Yi Lei Wang Weidong Zhang Australian Centre for Field Robotics The University of Sydney NSW 2006 Australia State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou 310027 China School of Information and Communication Engineering Hainan University Haikou 570228 China Department of Automation Shanghai Jiao Tong University Shanghai 200240 China

This paper addresses the problem of on-line consistent estimation of gyro bias using the measurements of a single vector and the biased angular velocity - both in the body-fixed frame. We propose two globally convergent gyro bias observers using new regression models, which are capable to deal with the cases of constant and time-varying reference vectors, respectively. Indeed, there are quite a few works discussing the latter case. To address this, we derive a nonlinear regression model, based on which a convexified gradient descent observer is designed, providing globally asymptotically convergent estimates to the gyro bias under some sufficient excitation conditions. The proposed schemes are illustrated by some numerical simulations.

关键词： Nonlinear systems observer parameter identification robotics

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SC-NeRF: Self-Correcting Neural Radiance Field with Sparse Views

arXiv

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arXiv 2023年

作者： Song, Liang Wang, Guangming Liu, Jiuming Fu, Zhenyang Miao, Yanzi Wang, Hesheng Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

In recent studies, the generalization of neural radiance fields for novel view synthesis task has been widely explored. However, existing methods are limited to objects and indoor scenes. In this work, we extend the generalization task to outdoor scenes, trained only on object-level datasets. This approach presents two challenges. Firstly, the significant distributional shift between training and testing scenes leads to black artifacts in rendering results. Secondly, viewpoint changes in outdoor scenes cause ghosting or missing regions in rendered images. To address these challenges, we propose a geometric correction module and an appearance correction module based on multi-head attention mechanisms. We normalize rendered depth and combine it with light direction as query in the attention mechanism. Our network effectively corrects varying scene structures and geometric features in outdoor scenes, generalizing well from object-level to unseen outdoor scenes. Additionally, we use appearance correction module to correct appearance features, preventing rendering artifacts like blank borders and ghosting due to viewpoint changes. By combining these modules, our approach successfully tackles the challenges of outdoor scene generalization, producing high-quality rendering results. When evaluated on four datasets (Blender, DTU, LLFF, Spaces), our network outperforms previous methods. Notably, compared to MVSNeRF, our network improves average PSNR from 19.369 to 25.989, SSIM from 0.838 to 0.889, and reduces LPIPS from 0.265 to 0.224 on Spaces outdoor scenes. © 2023, CC BY.

关键词： Rendering (computer graphics)

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GCBF: Grouped Cross-Band Fusion Network for Multispectral Scene Classification

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Li, Jin Liu, Yu Zhao, Wenda Jiang, Zhizhuo Wang, Xueqian Zheng, Bolun Tsinghua University Shenzhen International Graduate School Shenzhen518055 China Tsinghua University Department of Electronic Engineering Beijing100084 China Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education School of Information and Communication Engineering Dalian116024 China Hangzhou Dianzi University School of Automation Hangzhou310018 China

Remote sensing scene classification is a crucial task for remote sensing image interpretation. Existing multispectral scene classification methods have overlooked the interrelationships between different spectral bands, which limits the mining of complementary information within the images. Addressing this issue, we propose a grouped cross-band fusion (GCBF) network for remote sensing multispectral scene classification to take full advantage of complementary information between various spectral bands. Firstly, we separate the various bands of the given multispectral image into different groups to better capture the characteristics of each spectral band. Then, we use the existing UniFormer as a feature extractor to learn the representations of red, green, and blue (RGB) bands. For the spectral bands other than RGB, we propose a new network called multi-stage grouped spectral feature extraction (MGSFE) network to learn discriminative representations. We also draw inspiration from the band combination in the field of remote sensing and introduce a cross-band attention fusion (CBAF) module designed to adaptively merge features from both the RGB bands and other spectral bands. Extensive experiments on three widely used remote sensing multispectral scene classification datasets of BigEarthNet, SEN12MS, and EuroSAT demonstrate the superiority of our proposed method compared with several state-of-the-art (SOTA) methods. © 1980-2012 IEEE.

关键词： Photointerpretation

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CMAC-Based Sliding-Mode control for Large-Scale Fuzzy Descriptor Systems

CMAC-Based Sliding-Mode Control for Large-Scale Fuzzy Descri...

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IEEE International Conference on Robotics and Biomimetics

作者： Zhixiong Zhong Zhenghua Shao Zhijie Huan Xingyi Wang Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China

ISBN: (纸本)9781665481106

This paper addresses the problem of sliding-mode control for large-scale fuzzy descriptor systems subject to unknown uncertainties. First, a CMAC neural network is used to approximate the unknown uncertainties, and the CMAC-based sliding-mode controller is proposed. Then, a compensation technique is employed to eliminate the influence on the approximation error stemmed from CMAC model. We use the gradient-descent method to online tune the parameters of CMAC and the Lyapunov function is introduced to ensure the stability of the closed-loop control system and the CMAC's convergence. Finally, the effectiveness and correctness of the obtained theories are verified by the tracking control of multi-autonomous underwater vehicles (AUVs).

关键词： Uncertainty Biomimetics Neural networks Approximation error Stability analysis Sliding mode control Underwater vehicles

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Extended State Observer Based Fault-Tolerant Predictive control for a Four-Mecanum Wheels Mobile Robot

Extended State Observer Based Fault-Tolerant Predictive Cont...

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IEEE International Conference on Cyber technology in automation, control, and Intelligent Systems

作者： Binghao Yang Dongliang Wang Ziling Wen Wu Wei Wenji Li Zhun Fan Key Lab of Digital Signal and Image Processing of Guangdong Province Shantou University Guangdong China School of Department of Electronic and Information Engineering Shantou University Guangdong China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangzhou Guangdong China School of Automation Science and Engineering South China University of Technology Guangzhou Guangdong China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

The application of unmanned vehicles in industrial cargo transportation is becoming increasingly widespread, particularly playing a significant role in transporting heavy-duty goods. However, when performing such tasks, whether in single-vehicle transportation or multi-vehicle collaborative transportation, the occurrence of wheel failures can potentially lead to serious safety accidents. Therefore, this paper proposes a fault-tolerant control scheme, establishing a kinematic model considering disturbances and failures, and adopts model predictive control for trajectory tracking. Additionally, an extended state observer is introduced to observe system states and counteract disturbances. To address potential failure issues, a controller reconstruction strategy is proposed. Finally, the effectiveness and superiority of the controller is validated through numerical simulation and comparative experiment.

关键词： Fault tolerance Trajectory tracking Fault tolerant systems Wheels Transportation Kinematics Predictive models Safety State estimation Predictive control

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Identification of Prognostic Signature in Esophageal Cancer Based on Network Analysis 15th

Identification of Prognostic Signature in Esophageal Cancer ...

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15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Ma, Jianfei Huang, Yabing Key Laboratory of Image Information Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Luoyu Road 1037 Wuhan430074 China Department of Pathology Renmin Hospital of Wuhan University Wuhan430060 China

ISBN: (纸本)9789811613531

Esophageal cancer is the 6th most common cancer in the world with a five-year survival among 15 % to 25 % patients. It’s of great significance to identify prognostic signature for precise prediction of patient survival. In this work, we use a network-based approach to identify the disease genes of esophageal cancer. We construct the co-expression networks of normal and cancer samples by interpreting the gene expression profile and further divide the networks into inactivated subnetwork and enhanced subnetwork. Functional enrichment analysis shows that phosphoprotein and acetylation are both enriched in the inactivated genes and enhanced genes. Furthermore, 5 kinesin family members, KIF11, KIF23, KIF18A, KIF18B, and KIF2A, are all found to be significantly enhanced, suggesting that kinesin is crucial in promoting the formation of esophageal cancer. The 11 genes both inactivated and enhanced in the process of tumor development are found to be prognosis-associated and perform well in evaluating the survival of esophageal cancer patients. This study provides us fundamental recognition about the functional dysregulation in esophageal cancer and help to identify the biomarker for the further development of therapeutic targets. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Diseases

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Adaptive Fault-Tolerant Boundary control of a Flexible Marine Riser-vessel System Under Actuator and Structural Failures

Adaptive Fault-Tolerant Boundary Control of a Flexible Marin...

引用

Chinese control and Decision Conference, CCDC

作者： Mengfei Li Dong Zhao Wenjing Ren Darong Huang School of Artificial Intelligence Anhui University Hefei P. R. China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai P. R. China School of Electrical Engineering and Automation Hefei University of Technology Hefei P. R. China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

This paper develops an adaptive fault-tolerant boundary control scheme for riser-vessel systems to deal with actuator failure and structural failure, simultaneously. The marine riser-vessel system dynamics is modeled by a fourth-order partial-ordinary differential equation. An adaptive fault-tolerant boundary control framework is proposed, which together with the designed adaptive update law guarantees the uniformly ultimately boundedness of the closed-loop system. The simulation results show that the developed fault-tolerant control method can still make the system state converge in a small area of zero in the presence of actuator failure and structural failure.

关键词： Vibrations Fault tolerance Actuators Asymptotic stability System dynamics Simulation Fault tolerant systems

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Operator-Adapted Evolutionary Large-Scale Multiobjective Optimization for Voltage Transformer Ratio Error Estimation 11th

Operator-Adapted Evolutionary Large-Scale Multiobjective Opt...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Huang, Changwu Li, Lianghao He, Cheng Cheng, Ran Yao, Xin Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783030720612

Large-scale multiobjective optimization problems (LSMOPs) exist widely in real-world applications, and they are challenging for existing evolutionary algorithms due to their massive volume of search space. Despite that a number of large-scale multiobjective evolutionary algorithms (LSMOEAs) have been proposed in recent years, their effectiveness in solving LSMOPs remains unsatisfactory. One main reason is that most existing LSMOEAs may fail to balance convergence enhancement and diversity maintenance, especially for solving real-world problems. To address this issue, we propose to use a hybridized LSMOEA with adaptive operator selection (AOS) to handle real-world LSMOPs. Specifically, the proposed hybridized LSMOEA with AOS (AOS-LSMOEA) includes multiple different offspring generation and environmental selection strategies extracted from some existing LSMOEAs. Then it uses the AOS to adaptively determine the application rates of different offspring generation and environmental selection operators in an online manner. The proposed approach is capable of taking advantage of existing LSMOEAs, and the AOS enables the algorithm to choose suitable operators for solving different LSMOPs. In this study, the proposed algorithm is expected to solve the voltage transformer ratio error estimation (TREE) problems effectively. Experimental results show that AOS-LSMOEA achieves significant performance improvement due to the hybridization of different operators and the adoption of AOS method. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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A Multi-Point Two-Hop Neighbor Relay Based OLSR Routing Protocol Design Method for Self-organized Multi-agent Communication*

A Multi-Point Two-Hop Neighbor Relay Based OLSR Routing Prot...

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control Conference (ANZCC), Australian and New Zealand

作者： Zhenlei Wang Chunfeng Li Xiongjun Wu The 54th Research Institute of China Electronics Technology Group Corporation (CETC54) Hebei Province China The 802 Institute of Shanghai Academy of Space Flight Technology The Eighth Academy of China Aerospace Science and Technology Corporation National Defense Key Laboratory of Science and Technology on Electromagnetic Scattering Shanghai Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

The OLSR routing protocol is a proactive Linkstate routing protocol in Ad hoc networks. It uses MPR nodes to spread TC messages across the network, reducing the flooding of protocol control messages in the network. However, all MPR nodes used to propagate a TC message in the whole network are not optimal, and there are a lot of redundancy, which results in unnecessary forwarding of TC messages and increases the overhead of control protocol. To overcome this limitation, the concepts of multi-point one hop neighbor relay (MPOHNR) and multi-point two hop neighbor relay (MPTHNR) are proposed, and the node selection algorithms of MPOHNR and MPTHNR are given. By using MPOHNR and MPTHNR to forward TC messages alternately, the number of forwarding nodes is reduced, the control protocol overhead is reduced, the effective bandwidth of the link is greatly increased, and the end-to-end average delay is reduced. Numerical simulations by using the EXATA software verifies that, the improved OLSR routing protocol is a potential design method for optimizing OLSR routing protocol.

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