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Chinese Automation Congress (CAC)

作者： Yuan-Qiu Liu Fang Jin Jun-Lei Song Kai-Feng Dong Wen-Qin Mo Ya-Juan Hui School of Automation China University of Geosciences Wuhan China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9781665426480

Well deviation measurement of While-Drilling(MWD) refers to the real-time measurement of well deviation and azimuth While-Drilling is the key for navigation in automatic drilling, real-time detection of drilling direction and automatic drilling control. In this paper, we solve the problem that the accelerometer could not accurately measure gravity because of the high-speed rotation caused by the eccentric installation of INS (Inertial Navigation System) and the acceleration of vibration line in the real-time measurement of borehole deviation and azimuth angle while drilling. Firstly, the dynamic physical model of accelerometer and gyroscope under eccentric installation is built, the non-gravity error of accelerometer measurement under eccentric motion is analyzed, and the algorithm optimization is made. Secondly, a calibration scheme for the compensation coefficients of centrifugal and tangential forces is given, which enables the carrier to make eccentric pure rotational motion on the rotating platform to determine the compensation calibration coefficient matrix. Finally, the first-order complementary filter algorithm is used to fuse the gyroscope angular velocity with the corrected acceleration for attitude calculation, and the algorithm is experimented. In this study, MPU6050 is selected as the chip of INS, and the attitude algorithm is realized by STM32. The experimental results show that the high dynamic error of the accelerometer measurement caused by eccentric motion is effectively reduced by the algorithm compensation.

关键词： Accelerometers Drilling Semiconductor device measurement Heuristic algorithms measurement uncertainty Vibration measurement Real-time systems

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A Generalized Probabilistic Monitoring Model with Both Randomand Sequential Data

arXiv

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arXiv 2022年

作者： Yu, Wanke Wu, Min Huang, Biao Lu, Chengda The School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China The Department of Chemical and Materials Engineering University of Alberta EdmontonABT6G 2G6 Canada

Many multivariate statistical analysis methods and their corresponding probabilistic counterparts have been adopted to develop process monitoring models in recent decades. However, the insightful connections between them have rarely been studied. In this study, a generalized probabilistic monitoring model (GPMM) is developed with both random and sequential data. Since GPMM can be reduced to various probabilistic linear models under specific restrictions, it is adopted to analyze the connections between different monitoring methods. Using expectation maximization (EM) algorithm, the parameters of GPMM are estimated for both random and sequential cases. Based on the obtained model parameters, statistics are designed for monitoring different aspects of the process system. Besides, the distributions of these statistics are rigorously derived and proved, so that the control limits can be calculated accordingly. After that, contribution analysis methods are presented for identifying faulty variables once the process anomalies are detected. Finally, the equivalence between monitoring models based on classical multivariate methods and their corresponding probabilistic graphic models is further investigated. The conclusions of this study are verified using a numerical example and the Tennessee Eastman (TE) process. Experimental results illustrate that the proposed monitoring statistics are subject to their corresponding distributions, and they are equivalent to statistics in classical deterministic models under specific restrictions. Copyright © 2022, The Authors. All rights reserved.

关键词： Process monitoring

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A Multi-modal Panoramic Speaker Localization Method

A Multi-modal Panoramic Speaker Localization Method

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Chinese Automation Congress (CAC)

作者： Y. Z. Long Y. W. Jian Z. T. Liu School of Automation China University of Geosciences WuHan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems WuHan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9781665426480

As a supporting technology in the field of human-computer interaction, speaker localization method has been a research hotspot in recent years. However, the existing single-mode speaker location methods cannot meet the requirements of stability and rapidity. For improve the accuracy and effective positioning range of speaker localization system, a multi-modal panoramic speaker localization method is proposed in this paper. Firstly, the coarse position of the speaker was preliminarily obtained by sound source localization. Then, according to the initial positioning of the speaker, whether the image used for face recognition need to be stitched is decided. What’s more, the position of speaker is calculated by face detection and calibration. Finally, the results of voice and image localization are fused by coordinate transformation. The proposal is tested on self-built experimental system, and the experimental results prove the effectiveness of the proposed method.

关键词： Location awareness Image resolution Image edge detection Face recognition Standardization Cameras Robustness

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A Novel E-Nose Chamber Design for VOCs Detection in Automobiles

A Novel E-Nose Chamber Design for VOCs Detection in Automobi...

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第三十九届中国控制会议

作者： Lu Cheng Yin-Bo Liu Qing-Hao Meng Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and ControlSchool of Electrical and Information EngineeringTianjin University

To detect parts per billion（ppb）-level volatile organic compounds（VOCs） in automobiles, a pre-concentrator is generally needed for electronic noses（e-noses）, which can enrich ppb-level concentration to parts per million（ppm） level. However, the current e-nose chamber structure could dilute the pre-concentrated gases and then the detection performance is reduced. To solve this problem, a novel chamber design was proposed in this paper. The core design idea was to reduce the space for the gas flow in the chamber, taking into account the shape and size of the gas sensors. Specially, for the pin-type gas sensors, a series of cylindrical structure design was adopted, and for the micro-electro-mechanical system（MEMS） patch-type sensors, the headspace was compressed. The results of computational fluid dynamics（CFD） based simulation showed that the concentration of pre-concentrated gases in the novel chamber was higher than that of the traditional chamber. The designed chambers were manufactured using polylactic acid（PLA） material by 3D printing. Experimental gases were prepared from polyurethane（PU） leather and polyvinyl chloride（PVC） leather to verify the performance of the chambers, and the results demonstrated that the proposed new e-nose chamber was superior to the traditional one in terms of the pre-concentration effect, repeatability and classification performance.

关键词： e-nose chamber pre-concentrator parts per billion（ppb） volatile organic compounds（VOCs） repeatability classification

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Speech Emotion Recognition Based on Deep Learning Network with Non-local Networks

Speech Emotion Recognition Based on Deep Learning Network wi...

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Chinese Automation Congress (CAC)

作者： M.T. Han Z.T. Liu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9781665426480

Convolutional Neural Networks (CNN) based Speech Emotion Recognition (SER) has the problem of learning bias towards stronger emotions while giving less weight to relatively faint emotions. This lack of subtleness causes the misclassification of emotions that have similar arousal such as anger and joy. To solve this problem, a novel speaker-independent SER methods consist of CNN with non-local networks and attention-based Bi-directional Long Short-Term Memory (BiLSTM) is proposed. The non-local networks can capture the dependence between any two points globally to enhance the correlation of emotional features at the utterance level. The extracted trichannel Log-Mel spectrograms features include the raw of LogMel spectrums, the Log-Mel spectrums’ deltas and delta-deltas respectively. Finally long-term contextual dependencies, local information and non-local information which is obtained by nonlocal networks is contained in the extracted feature. It was tested on the Interactive Emotional Dyadic Motion Capture Database (IEMOCAP) dataset. The experimental results show that the WAR of SER is 66.85% and the UAR of SER is 61.01%. Compared with several newly proposed SER methods, the effectiveness of the proposed model is demonstrated.

关键词： Deep learning Emotion recognition Databases Speech recognition Feature extraction Data models Convolutional neural networks

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Identification of root cause alarms by detecting correlations and time delays between alarm variables

Identification of root cause alarms by detecting correlation...

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Chinese Automation Congress (CAC)

作者： Xin Cheng Wenkai Hu Weihua Cao Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9781665426480

Under an abnormal situation in process operations, it is important to find out the root cause, so as to bring the system back to normal timely. In a typical modern industrial facility, there are configured a large number of alarms. Usually, the presence and propagation of a fault may lead to a series of alarms, which hold certain correlations. Therefore, analyzing alarm correlations can help to track the prorogation of faults among alarm variables and thus find out the root cause. This paper proposes a systematic alarm root cause analysis method based on the detection of correlations and time delays between alarm variables. First, two types of alarm correlations based on time stamps of alarm events are defined and the calculation methods are provided. Then, alarms are determined to be correlated or not by comparing with designed thresholds. Last, a causal matrix is formed to present the causal relations between alarms based on the directions of time delays. The effectiveness of the proposed method is demonstrated by both a numerical example and the benchmark Tennessee Eastman model.

关键词： Root cause analysis Correlation Systematics Automation Delay effects Benchmark testing Industrial facilities

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Statistical solution to SDEs with α -stable Lévy noise via deep neural network

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International Journal of Dynamics and control 2020年第4期8卷 1129-1140页

作者： Zhang, Hao Xu, Yong Li, Yongge Kurths, Jürgen Department of Applied Mathematics Northwestern Polytechnical University Xian710072 China Department of Engineering Mechanics Northwestern Polytechnical University Xian710072 China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xian710072 China Potsdam Institute for Climate Impact Research Potsdam14412 Germany Department of Physics Humboldt University Berlin Berlin12489 Germany

The probability density function of the stochastic differential equations with α-stable Lévy noise is determined by the deterministic fractional FokkerPlanck (FFP) equation with the Riesz fractional derivative. In this paper, to solve the FFP equation, a new algorithm which is an efficient numerical approach using the deep neural network is proposed in view of the mesh-dependence phenomenon in the traditional discrete scheme and the nonsmooth solution of the Monte Carlo method. Under the framework of the DL-FP algorithm, the "fractional centered derivative" approach is applied to approximate the Riesz fractional derivative of the output in the neural network, which is the major novelty of this approach. Numerical results are presented to demonstrate the accuracy of the approach. Comparing with the traditional discrete scheme and the Monte Carlo method, the proposed algorithm is mesh-less and smoother. Furthermore, the proposed technique performs well in dealing with the heavy-tail case through comparing with the analytical solution. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Deep neural networks

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Multi view 3D reconstruction method for weak texture objects based on "eye-in-hand" model

Multi view 3D reconstruction method for weak texture objects...

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Chinese Automation Congress (CAC)

作者： Xuan Gong Xin Chen Xu Jian Wenpeng He School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration

ISBN: (纸本)9781665426480

Size measurement and defect detection of ceramics are required in the production process of ceramic products. Therefore, it is necessary to perform high-precision and fast three-dimensional reconstruction of such targets with smooth surfaces and weak textures. However, the reconstruction of a large number of point clouds from multiple perspectives is time-consuming due to the matching of adjacent feature points. Based on this, a fast point cloud registration strategy based on the "eye-in-hand" model is proposed, which can obtain RGB-D images at multi-view around weak-textured objects with RGB-D camera for rapid point cloud registration and reconstruction. Meanwhile, a 3D point cloud segmentation method based on voxel-grid and euclidean distance clustering is applied to divide the scene point and obtain an accurate 3D model. The experimental results indicate that the multi view 3D reconstruction speed of this method is faster and the accuracy of objects point cloud is within 2 mm. Hence, this method can be well applied in reconstructing the 3D point clouds model for ceramic products.

关键词： Point cloud compression Solid modeling Three-dimensional displays Ceramic products Euclidean distance Reconstruction algorithms Cameras

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Stability analysis of linear systems with two additive time-varying delays via the extended reciprocally convex matrix inequality

Stability analysis of linear systems with two additive time-...

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第40届中国控制会议

作者： Chen Yan Chuan-Ke Zhang Xi-Zi Zhou School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Art and Design Wuhan University of Technology

This paper investigates the stability of linear systems with two additive time-varying delays. Firstly, an augmented Lyapunov-Krasovskii functional(LKF) is constructed by introducing a novel augmented term which contains the derivative of state vector into integral terms. Then the extended reciprocally convex matrix inequality together with the Wirtinger integral inequality is used to estimate the derivative of the proposed LKF. As a result, a stability criterion with less conservatism is established. Finally, the advantage of the stability criterion is demonstrated through a numerical example and the application of the stability criterion to stability analysis of load frequency control is illustrated.

关键词： linear system additive time-varying delays stability analysis reciprocally convex matrix inequality

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A hybrid correction framework using disentangled seasonal-trend representations and MoE for NWP solar irradiance forecast

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Applied Energy 2025年 397卷

作者： Weijing Dou Kai Wang Shuo Shan Kanjian Zhang Haikun Wei Victor Sreeram State Grid Electric Power Research Institute (NARI Group Corporation) National Key Laboratory of Risk Defense Technology and Equipment for Power Grid Operation Nanjing 211106 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing 210096 China Southeast University Shenzhen Research Institute Shenzhen 518063 China Department of Electrical Electronics and Computer Engineering The University of Western Australia Perth Australia

Day-ahead solar irradiance forecast holds important value for optimizing energy utilization within the power system and ensuring stable grid scheduling. The forecast outputs of numerical weather prediction (NWP) are widely acknowledged as one of the indispensable data sources for day-ahead solar irradiance forecast tasks. In previous studies, post-processing methods have generally been employed as correction models to enhance the accuracy of NWP solar irradiance forecasts. However, irradiance sequences contain complex mixed patterns and exhibit various seasonal periodic differences. Based on the analysis of NWP global horizontal irradiance (GHI) error characteristics in this study, errors in NWP GHI forecasts also show obvious seasonal variations. Given these issues, it is challenging for a single correction model to achieve good correction performance and strong seasonal robustness. Therefore, this paper proposes a hybrid model comprising representation learning module, feature sparse activation module, and encoder-decoder-based correction module to address the aforementioned problems. A contrastive-learning-based representation learning module named CoST is introduced to learn disentangled seasonal features and trend features of irradiance sequences. A learnable mixture-of-experts (MoE) layer is adopted to sparsely activate the seasonal-trend features that contribute more to improving correction accuracy. The encoder-decoder-based correction module takes the sparsely activated seasonal-trend features as inputs, achieving the final corrected NWP GHI forecasts. The correction performance of the proposed method was validated on both publicly available datasets and actual field dataset. The results for various datasets show that our proposed CoST-MoELSTM model achieves the highest improvement for NWP forecasts, with increases of 29.82 %, 36.54 %, and 26.58 %. Additionally, we conducted a detailed analysis of the correction performance of CoST-MoELSTM across different

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