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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Jianlong Chen Xiwei Liu Sifeng Jing Gang Xiong Xiuqin Shang Changjian Cheng The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Chemical Engineering and the Environment Beijing Institute of Technology Beijing China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Engineering Research Center for Intelligent Systems and Technology Chinese Academy of Science Beijing China

ISBN: (纸本)9781509029280

Hazard and operability (HAZOP) methodology is an important Process Hazard Analysis (PHA) technique for ensuring safety production in chemical industry. Due to the lacks of accuracy and reliability in manual HAZOP analysis, the quantitative HAZOP analysis has gained more and more attention. In this work, a process simulation-based HAZOP analysis method and its flow chart are proposed. And the proposed method is applied to a steam generation subsystem in an ethylene pyrolysis unit on a dynamic process simulation platform. All deviation parameters of system can be calculated and described quantitatively. The case study shows the proposed method is useful for the effective risk prevention and control measures in a chemical process.

关键词： Analytical models Hazards Manuals Process control Boilers

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Mechanics and Thermodynamics of Multivalent-Binding Induced Shrinkage of Hydrogels

SSRN

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SSRN 2024年

作者： Rajendran, Barathan Chen, Xiao Wang, Xin Wu, Tao Goh, Kek Boon Department of Mechanical Engineering School of Engineering Monash University Malaysia Jalan Lagoon Selatan Bandar Sunway Selangor 47500 Malaysia Institute of Biomechanics and Medical Engineering Department of Engineering Mechanics Tsinghua University Beijing100084 China School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University Xi’an710072 China MIIT Key Laboratory of Dynamics and Control of Complex Systems Xi’an710072 China

Understanding the fundamental mechanism of shrink hydrogel sensors necessitates a complete comprehension of analyte-centered multivalent binding that occurs within their salt-rich microenvironments. However, the mechanics and thermodynamics governing this phenomenon remain insufficiently understood. Here, we derive a theoretical framework that examines the formation of temporary cross–links between the polymeric chains induced by the specific binding interaction between the fixed receptors and the multivalent analytes. As a highlight of our theory, we use statistical thermodynamics to characterize the analyte transport and these multivalent complexations, allowing us to subsequently determine the mechanics of the shrinkage experienced by these hydrogel sensors. Consequently, our theory unveils the valency of the bindings, setting an upper bound for the performance of these shrink sensors;i.e., tuning the receptor density allows us to further modulate their performance. In summary, our theory tightly correlates the microscopic properties of the analyte and hydrogel to the macroscopic shrinking properties of these sensors, hence facilitating a structured design and optimization regime for advanced biomedical applications. © 2024, The Authors. All rights reserved.

关键词： Shrinkage

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End-to-End Driving Activities and Secondary Tasks Recognition Using Deep Convolutional Neural Network and Transfer Learning

End-to-End Driving Activities and Secondary Tasks Recognitio...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Xing, Yang Tang, Jianlin Liu, Hong Lv, Chen Cao, Dongpu Velenis, Efstathios Wang, Fei-Yue Advanced Vehicle Engineering Centre Cranfield University BedfordMK43 0AL United Kingdom Vehicle Intelligence Pioneers Ltd Qingdao China Jiangsu XCMG Construction Machinery Research Institute Ltd. No. 26 Tuolanshan Road Economic Development Zone Xuzhou Jiangsu221004 China School of Mechanical and Mechatronics Engineering University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781538644522

Drivers' decision and their corresponding behaviors are important aspects that can affect the driving safety, and it is necessary to understand the driver behaviors in real-time. In this study, an end-to-end driving-related tasks recognition system is proposed. Specifically, seven common driving activities are identified, which are normal driving, right mirror checking, rear mirror checking, left mirror checking, using in-vehicle video device, texting, and answering mobile phone. Among these, the first four activities are regarded as normal driving tasks, while the rest three are divided into distraction group. The images are collected using a consumer range camera, namely, Kinect. In total, five drivers are involved in the naturalistic data collection. Before training the identification model, the raw images are first segmented using a Gaussian mixture model (GMM) to extract the driver region from the background. Then, a pre-trained deep convolutional neural network (CNN) model is trained to classify the behaviors, which directly takes the processed RGB images as the input and outputs the identified label. In this work, the AIexNet is selected as the pre-trained CNN model. Then, to reduce the training cost, the transfer learning mechanism is applied to the CNN model. An average of 79\% detection accuracy is achieved for the seven driving tasks. The proposed integration model can be used as a low-cost driver distraction and dangerous tasks recognition modeL. © 2018 IEEE.

关键词： Neural network models

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Optimization of the Landslide Susceptibility Model based on Deep Forest and Twice-Sample

Optimization of the Landslide Susceptibility Model based on ...

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Chinese Automation Congress (CAC)

作者： Yuelin Yang Wanke Yu Zepeng Liu Lingran Zhao Chengda Lu Min Wu Ruiqing Niu 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 Institute of Geophysics and Geomatics China University of Geosciences Wuhan China

In the analysis of regional landslide susceptibility, the geological data used usually have the characteristics of multiplicity and nonlinearity. At the same time, in the past researches, the selection of non-landslide units in the training set is usually random or subjective, and there is no guarantee sample accuracy. In this paper, the deep forest algorithm combined with the twice-sample method is used to evaluate the landslide susceptibility in the Zigui Badong area of the Three Gorges Reservoir area. First, 11 landslide impact factors were selected according to the geological data to initially construct a sample set, and the cleaned data set was obtained by twice-sample. After dividing the training set and the test set, the deep forest model was used to predict the landslide susceptibility and evaluate the accuracy. The results show that the AUC of the susceptibility prediction model built using the deep forest is 1%-10% higher than that of GBDT, MLP and other methods, while the Twice-Sample method improves the AUC by 2%.

关键词： Training Geology Predictive models Feature extraction Reservoirs Prediction algorithms Terrain factors

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A Model-Predictive-Enabled Equivalent-Input-Disturbance Approach for Disturbance Rejection

A Model-Predictive-Enabled Equivalent-Input-Disturbance Appr...

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Industrial Cyber-Physical systems (ICPS)

作者： Yujian Zhou Jinhua She Feng Wang Makoto Iwasaki 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 School of Engineering Tokyo University of Technology Tokyo Japan Department of Electrical and Mechanical Engineering Nagoya Institute of Technology Nagoya Japan

This paper presents a model-predictive-enabled equivalent-input-disturbance (MPEID) method for disturbance rejection. An EID estimator with a state observer estimates the effect of disturbances in a system. The disturbance estimation will not be directly added to the input channel for disturbance rejection. A cost function that considers the disturbance estimation is designed. An MPC controller calculates an optimal control input by minimizing the cost function. The stability condition of the closed-loop system is analyzed based on the Lyapunov stability theory. Simulation results show that our method has better disturbance-rejection performance than an MPC method when the control input is similar.

关键词： Simulation Estimation Optimal control Predictive models Observers Cyber-physical systems Cost function

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Stability and Neimark-Sacker bifurcation analysis for single gene discrete system with delay

Stability and Neimark-Sacker bifurcation analysis for single...

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Chinese control Conference

作者： Feng Liu Qicheng Mei Fenglan Sun Xinmei Wang Hua O. Wang Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Automation China University of Geosciences Wuhan China Research Center of System Theory and Application Chongqing University of Posts and Telecommunications Chongqing China Department of Mechanical Engineering Boston University Boston MA USA

The network structure and functional properties of gene regulatory system and their relationships arc an important research field in systems biology. In this paper, a new improved model is proposed based on the study of gene expression process. The model takes into account the effect of protein concentration on the gene expression, so as to obtain a new bifurcation point and improve the performance of the system. The validity of the model is verified by theoretical analysis and data simulation.

关键词： Mathematical model Proteins Stability analysis Bifurcation Biological system modeling Delays RNA

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Effect of leakage delay on Hopf bifurcation in a fractional BAM neural network

arXiv

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arXiv 2018年

作者： Lin, Jiazhe Xu, Rui Li, Liangchen Tian, Xiaohong Institute of Applied Mathematics Army Engineering University ShijiazhuangHebei050003 China Complex Systems Research Center Shanxi University Taiyuan Shanxi030006 China Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention Shanxi University Taiyuan Shanxi030006 China

Recently, the influence of leakage delay on the dynamics of integer-order neural networks has been investigated extensively. It has been confirmed that fractional calculus can depict the memory and hereditary attributes of neural networks more accurately. In this paper, we study the existence of Hopf bifurcation in a six-neuron fractional bidirectional associative memory (BAM) neural network with leakage delay. By selecting two appropriate bifurcation parameters and analyzing corresponding characteristic equations, it is verified that the delayed fractional neural network generates a Hopf bifurcation when the bifurcation parameters pass through some critical values. In order to measure how much impact of leakage delay on Hopf bifurcation, sensitivity analysis methods, such as scatter plots and partial rank correlation coefficients (PRCCs), are introduced to assess the sensitivity of bifurcation amplitudes to leakage delay. Numerical examples are carried out to illustrate the theoretical results and help us gain an insight into the effect of leakage delay vividly. Copyright © 2018, The Authors. All rights reserved.

关键词： Sensitivity analysis

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A Cyclic Small Phase Theorem

arXiv

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arXiv 2023年

作者： Chen, Chao Chen, Wei Zhao, Di Chen, Jianqi Qiu, Li KU Leuven Leuven3001 Belgium The Department of Mechanics and Engineering Science State Key Laboratory for Turbulence and Complex Systems Peking University Beijing100871 China The Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

This paper introduces a brand-new phase definition called the segmental phase for multi-input multi-output linear time-invariant systems. The underpinning of the definition lies in the matrix segmental phase which, as its name implies, is graphically based on the smallest circular segment covering the matrix normalized numerical range in the unit disk. The matrix segmental phase has the crucial product eigen-phase bound, which makes itself stand out from several existing phase notions in the literature. The proposed bound paves the way for stability analysis of a cyclic feedback system consisting of multiple subsystems. A cyclic small phase theorem is then established as our main result, which requires the loop system phase to lie between −π and π. The proposed theorem complements a cyclic version of the celebrated small gain theorem. In addition, a generalization of the proposed theorem is made via the use of angular scaling techniques for reducing conservatism. Copyright © 2023, The Authors. All rights reserved.

关键词： Linear time-invariant system

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Capturing Spatial Information for sEMG-Based Gesture Recognition Using Graph Attention Networks

Capturing Spatial Information for sEMG-Based Gesture Recogni...

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Pervasive and Intelligent Computing (PICom), IEEE Conference on

作者： Tong Xia Feng Wang Seiichi Kawata Juan Zhao Jinhua She Daisuke Chugo 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 School of Engineering Tokyo University of Technology Hachioji Tokyo Japan School of Engineering Kwansei Gakuin University Sanda Hyogo Japan

ISBN: (数字)9798331522742

ISBN: (纸本)9798331522759

Surface electromyography (sEMG) signals, known for their rich encapsulation of motion intention, have emerged as a promising avenue for human-computer interaction. Despite the advances in gesture recognition through machine learning, the omission of spatial information in sEMG signals has been identified as a limitation, often resulting in diminished accuracy and robustness of recognition systems. In this paper, we introduce the application of graph attention networks (GATs) to harness the spatial dynamics within sEMG signals. Initially, the sEMG signals are transformed into a graph-based structure through a meticulous preprocessing phase, where each electrode is represented as a node, and the spatial interplay among them is meticulously captured. Subsequently, the GATs are employed to conduct gesture recognition on this structured graph, with the networks adeptly adjusting the significance of weights between channels through the attention mechanism. This adaptive approach effectively captures the intricate spatial and temporal correlations embedded within the sEMG signals. On the CapgMyo dataset DB-a, our model demonstrates a consistent recognition accuracy of approximately 88.8%, surpassing previous machine learning models such as support vector machines (SVMs) and random forests (RFs) in recognition performance.

关键词： Electrodes Support vector machines Adaptation models Accuracy Computational modeling Gesture recognition Graph neural networks Electromyography Data models Topology

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Reducing the Influence of Individual Variability in sEMG-Based Fatigue Detection Using Transfer Learning

Reducing the Influence of Individual Variability in sEMG-Bas...

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Pervasive and Intelligent Computing (PICom), IEEE Conference on

作者： Renwei Lin Feng Wang Seiichi Kawata Juan Zhao Jinhua She Daisuke Chugo 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 School of Engineering Tokyo University of Technology Hachioji Tokyo Japan School of Engineering Kwansei Gakuin University Sanda Hyogo Japa

ISBN: (数字)9798331522742

ISBN: (纸本)9798331522759

In the field of sEMG fatigue detection, deep learning algorithms have emerged as prominent tools due to their unparalleled capability to automatically learn discriminative features from extensive datasets. However, the practicality of fatigue detection in everyday life remains limited, as it demands a substantial effort from users to generate vast amounts of sEMG data. In this paper, we propose the hypothesis that by aggregating sEMG data from multiple users, it becomes feasible to extract general informative features from the resulting large dataset, thus enhancing the ability to detect fatigue. To achieve this, transfer learning is employed on the aggregated data from various users, leveraging the power of deep learning algorithms to learn discriminative features from large datasets. The approach involves initially training the model using one individual's sEMG data, followed by fine-tuning using minimal data from other individuals, allowing for efficient personalized adaptation with significantly less time compared to retraining from scratch. This study designs a transfer learning model using sEMG data from the Tokyo Institute of Technology and demonstrates noteworthy efficiency improvements, reducing the required time by an average of 74.31%, while achieving an average accuracy of 95.32%. These experimental results emphasize the potential of transfer learning to enhance the practicality and widespread applicability of sEMG fatigue detection in real-world applications.

关键词： Training Deep learning Adaptation models Accuracy Transfer learning Muscles Fatigue Feature extraction Data models Optimization

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