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2021 China automation Congress, CAC 2021

作者： Cheng, Xin Hu, Wenkai Cao, Weihua Wu, Min 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

ISBN: (纸本)9781665426473

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. © 2021 IEEE

关键词： Timing circuits

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Temperature Rising Model of Tubular Permanent Magnet Linear Synchronous Motor Based on Transfer Learning-Deep Neural Network

Temperature Rising Model of Tubular Permanent Magnet Linear ...

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Chinese control Conference (CCC)

作者： YiFei Wang Tao Wu School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China System Integration and Industrial Robotics Laboratory Wuhan P. R. China

High temperature rise of permanent magnet linear synchronous motor can lead to irreversible demagnetization of the motor permanent magnet, which can negatively affect the motor performance. To address this problem, a thermal modeling analysis method based on Transfer learning-Deep neural network (TL-DNN) was proposed in this paper. Its specific implementation steps include (1) corresponding to different heat source inputs, the equivalent thermal circuit method and the finite element analysis was adopted based on the structure and main parameters of PMLSM including overall average temperature rise, coil temperature rise and permanent magnet temperature rise from the data sets of the motor; (2) TL-DNN was used to fit the functional relationship between the input source features and the output targets based on the characteristics of the sample data sets. In order to verify the accuracy of the prediction model with small sample data sets, this paper divided the proportion of the data set and compared the results with other classic nonparametric models (random forest, support vector machine, and deep neural network). The results show that the TL-DNN model outperforms other machine learning models and has better robustness and generalization ability when the training datasets are insufficient, and achieves an organic combination of physical field path model and data-driven model, which provides a feasible solution for PMLSM modeling in the case of small samples.

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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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2021 China automation Congress, CAC 2021

作者： Han, M.T. Liu, Z.T. 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

ISBN: (纸本)9781665426473

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 tri-channel Log-Mel spectrograms features include the raw of Log-Mel 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 non-local 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. © 2021 IEEE

关键词： Convolutional neural networks

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Finite-Time State Estimation for Discrete-Time Nonlinear Singularly Perturbed complex Networks under New Event-Triggered Mechanism† 40

Finite-Time State Estimation for Discrete-Time Nonlinear Sin...

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40th Chinese control Conference, CCC 2021

作者： Yang, Chao Wan, Xiongbo China University of Geosciences School of Automation 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

ISBN: (纸本)9789881563804

This paper investigates the finite-time state estimation problem for a class of discrete-time nonlinear singularly perturbed complex networks under a new dynamic event-triggered mechanism (DETM). This new DETM is devised to adjust the date packet transmissions flexibly with hope to save network resources. By constructing a new Lyapunov function dependent on the information of the singular perturbation parameter (SPP) and DETM, a sufficient condition is derived which ensures that the error dynamics of state estimation is finite-time stable. The parameters of the state estimator are given by means of the solutions to several matrix inequalities and the upper bound of the SPP can be evaluated simultaneously. The effectiveness of the designed state estimator is demonstrated by a numerical example. © 2021 Technical Committee on control Theory, Chinese Association of automation.

关键词： complex networks

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A Weighting Factors Autotuning Strategy for Model Predictive Torque control of Permanent Magnet Synchronous Motor Based on Adaptive Multi-Objective Black Hole Algorithm

A Weighting Factors Autotuning Strategy for Model Predictive...

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Chinese control Conference (CCC)

作者： Zixiang Zhou Tao Wu School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China System Integration and Industrial Robotics Laboratory Wuhan P. R. China

The rational design of weighting factors in the cost function for finite control set model predictive torque control (FCS-MPTC) has been a matter of great interest in power electronics and electrical drives. In order to solve this problem, a weighting factors autotuning strategy for FCS-MPTC of permanent magnet synchronous motor (PMSM) based on the adaptive multi-objective black hole algorithm (AMOBH) is proposed. In this paper, the design process of the FCS-MPTC algorithm is first analyzed in detail. Then, an AMOBH algorithm that can take into account both population convergence and population diversity is introduced, and based on this algorithm, the design problem of the weighting factors is successfully transformed into a multi-objective optimization problem by means of reconstructing the cost function and designing the motor operation information collected in real time as the objective functions of the multi-objective optimization algorithm. Simulation results show that the proposed method can find a set of weighting factor combinations suitable for different working condition requirements, and these weighting factors can effectively improve the operation performance of the PMSM system.

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Syllable Level Speech Emotion Recognition Based on Formant Attention 1st

Syllable Level Speech Emotion Recognition Based on Formant ...

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1st CAAI International Conference on Artificial Intelligence, CICAI 2021

作者： Rehman, Abdul Liu, Zhen-Tao Xu, Jin-Meng 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

ISBN: (纸本)9783030930486

The performance of speech emotion recognition (SER) systems can be significantly compromised by the sentence structure of words being spoken. Since the relation between affective content and the lexical content of speech is difficult to determine in a small training sample, the temporal sequence based pattern recognition methods fail to generalize over different sentences in the wild. In this paper, a method to recognize emotion for each syllable separately instead of using a pattern recognition for a whole utterance is proposed. The work emphasizes the preprocessing of the received audio samples where the skeleton structure of Mel-spectrum is extracted using formant attention method, then utterances are sliced into syllables based on the contextual changes in the formants. The proposed syllable onset detection and feature extraction method is validated on two databases for the accuracy of emotional class prediction. The suggested SER method achieves up to 67% and 55% unweighted accuracy on IEMOCAP and MSP-Improv datasets, respectively. The effectiveness of the method is proved by the experimentation results and compared to the state-of-the-art SER methods. © 2021, Springer Nature Switzerland AG.

关键词： Feature extraction

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Analysis and circuit implementation of a new fourth-order multi-wing chaotic system based on memristors

Analysis and circuit implementation of a new fourth-order mu...

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2021 China automation Congress, CAC 2021

作者： Jia, Lei Jiang, Xiaowei Zhang, Xianhe Chi, Ming School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Geodetection Technology Ministry of Education Wuhan China School of Electrical Engineering and Automation Hubei Normal University Huangshi China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665426473

In this paper, a new fourth-orders memristor chaotic system is obtained by introducing a smooth memristor model as the feedback term based on the modified three-orders chaotic system. The system can generate double-wing, four-wing chaos or hyperchaos for different parameters and initial conditions. Through the analysis of Lyapunov exponent and other dynamics, we can see that the system has complex chaotic, hyperchaotic, and continuous chaotic. Other dynamical behaviors such as multistability and extreme multi-stability are found by comparing phase and bifurcation diagrams, and equilibrium point calculations. Finally, the chaotic behavior is successfully verified by circuit simulation. © 2021 IEEE

关键词： Chaotic systems

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Modeling and Analysis of Mtj Array with Shielding Layer

SSRN

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SSRN 2023年

作者： Qu, Ying-Hui Xiong, Jing-Ya Zuo, Chao Dong, Kai-Feng Song, Jun-Lei Mo, Wen-Qin Hui, Ya-Juan Jin, Fang School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Geological Exploration Intelligent Technology Engineering Research Center The Ministry of Education Wuhan Second Ship Design and Research Institute Hubei Province Wuhan China

In STT-MRAM, data is stored in a magnetic tunnel structure (MTJ) containing multiple ferromagnetic layers. Each ferromagnetic layer generates a stray field, which affects the critical switching current, switching time, thermal stability, and the density of the MTJ array. These factors have a direct impact on the performance and storage capacity of STT-MRAM. Previously, we have studied that hexagonal MTJ arrays can improve storage density. However, this improvement also leads to increased intracellular and intercellular stray fields, which in turn affects the device's performance. This paper aims to reduce the intercellular stray field by covering each MTJ unit with a soft magnetic shielding layer and investigates the performance of the MTJ array with a shielding layer. Firstly, a simulation model of a single MTJ stack is built using the simulation software COMSOL. Then, based on modeling the MTJ stack, square and hexagonal MTJ arrays are established. The intercellular magnetic coupling coefficient (Ψ) is quoted, and Ψ=2% is defined as the threshold below which the intercellular magnetic coupling can be considered negligible. The results indicate that the soft magnetic shielding layer can effectively shield the stray field, reducing its impact on MTJ performance. Specifically, a thicker shielding layer with higher magnetic permeability provides better shielding efficacy. However, comparing square and hexagonal MTJ array after magnetic shielding reveals a larger stray field between the hexagonal array. © 2023, The Authors. All rights reserved.

关键词： Ferromagnetic materials

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A Trajectory Generation Method Based on control Points and Improved Whale Algorithm Optimization Algorithm for Solving Unmanned Aerial Vehicle Path Planning Problems

SSRN

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SSRN 2023年

作者： Li, Bingyu Gan, Chao Zhu, Yifeng Cao, Wei-Hua Wuhan430074 China 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

Three-dimensional trajectory planning is crucial for improving the safety and operational efficiency of unmanned aerial vehicle(UAV). However, it is hard to plan a suitable path in complex real-world environments. To overcome this challenge, this paper introduces a novel trajectory planning method based on control point modelling and an advanced whale optimisation algorithm (WOA). Firstly, a trajectory modeling method based on control point interpolation algorithm was proposed, which constrains the one-dimensional dimensions of 3D points, reducing the complexity of the traditional modelling methods and increasing the efficiency of trajectory generation. After that, an advanced adaptive multi-population cooperative whale optimisation algorithm (AMCWOA) has been proposed to enhance the problem-solving capabilities of the algorithm in complex environments. Through a population evolution stage evaluation strategy (ESE), this algorithm dynamically divides the population size, thereby balancing the problem-solving breadth and precision of the algorithm. It increases algorithm accuracy and convergence speed in solving trajectory planning problems. It is imperative to emphasis that the evaluation strategy in the evolution stage purposefully addresses the blindness of multi-population evolution algorithms when dividing populations. Finally, multiple function sets are utilized to test the solving performance of the algorithm and the simulations in multiple environments are designed to demonstrate the performance in UAV path planning. The experimental comparison results demonstrate that the proposed population evolution stage evaluation strategy (ESE) and population information sharing strategy can effectively increase the search range and convergence accuracy of the algorithm in generating high-quality paths and optimising test functions. © 2023, The Authors. All rights reserved.

关键词： Efficiency

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Psychological Resistance Recognition Based on Acoustic Features Extraction

Psychological Resistance Recognition Based on Acoustic Featu...

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2021 China automation Congress, CAC 2021

作者： Lv, Z. Liu, Z.T. Han, M.T. Wu, B.H. 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

ISBN: (纸本)9781665426473

Psychological resistance can express the change of the client's psychological state, which is considered to be an important factor affecting psychological counseling. This paper proposed a method of psychological resistance recognition based on acoustic features extraction. The method includes three main steps, i.e., statistical acoustic feature extraction, feature selection, and classification. Our method uses statistical acoustic features to express psychological resistance, and analyzes the application value of high correlation features obtained from feature selection in the recognition of different psychological resistance. This method can be used to determine the high correlation features of psychological resistance adaptively, and avoid the deficiency of subjective judgment. A speech database of psychological resistance is built for research needs, in which three psychological consultants and four subjects are involved in psychological counseling with 1200 sentences in speech. The experimental results verified the effectiveness of our method. © 2021 IEEE

关键词： Extraction

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