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2021 China automation Congress, CAC 2021

作者： Qian, Kun Cai, Yeyun Xu, Kun Deng, Fang Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781665426473

Heat dissipation is extremely important for electronic component such as CPU or GPU of the computer. Now most researchers only design optimal heat sink for CPU for better heat dissipation, while few of them consider waste heat recycling of CPU. The CPU can be used as the heat source, while the original system has fan and heat sink as the cooling system. Thus, the original CPU working condition is very suitable for thermoelectric power generation and will bring great benefit to system itself. However, adding thermoelectric generator will increase the temperature of CPU, which will decrease the performance of the computer. It is necessary to reduce the hot side temperature while generating energy as much as possible. To achieve this, this paper designed a kind of intelligent waste heat generation system based on thermoelectric power generator. Three different shape heat sinks are designed, contrasted, and tested. By building experimental platform and establishing simulation model, the power of thermoelectric power generation and temperature change of the three heat sink structures are compared. The fin shaped heat sink is considered has the best output performance while keeping the hot side temperature within the acceptance range. The maximum output power is 10.67mW, and the maximum hot side temperature is 72◦C during the experiment. © 2021 IEEE

关键词： Waste heat

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A Multi-modal Panoramic Speaker Localization Method

A Multi-modal Panoramic Speaker Localization Method

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2021 China automation Congress, CAC 2021

作者： Long, Y.Z. Jian, Y.W. 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

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. © 2021 IEEE

关键词： Face recognition

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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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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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Anomaly Detection Based on Principle of Justifiable Granularity and Probability Density Estimation

Anomaly Detection Based on Principle of Justifiable Granular...

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2023 China automation Congress, CAC 2023

作者： Du, Sheng Ma, Xian Li, Xiang Wu, Min Cao, Weihua Pedrycz, Witold 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 University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul Sariyer Turkey

ISBN: (纸本)9798350303759

Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes. © 2023 IEEE.

关键词： Time series

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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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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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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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A Preliminary Study on the Functional Coupling between Nerve and Blood Microcirculation for Applications in Rehabilitation Robots

A Preliminary Study on the Functional Coupling between Nerve...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Li, Qingge Dong, Yuanzhe Zhang, Yuxiang Wang, Xin Jiang, Naifu Huang, Jianping Cui, Han Tian, Lan Zheng, Yue Li, Xiangxin Wang, Lin Li, Guanglin Liang, Wenyuan Peng, Liang Fang, Peng Shenzhen Institute of Advanced Technology Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China National Research Center for Rehabilitation Technical Aids Beijing100176 China Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing100190 China

ISBN: (纸本)9798350325706

Rehabilitation robots play an important role in the motor function rehabilitation for stroke survivors with hemiplegia. However, the rehabilitation effect of current robots is still limited partly because a single training of motor function can be strongly affected by the decreased blood supply function of the bedridden patients. This work proposed an approach to study the coupling relationship between the motor and blood supply functions by combining the synchronously recorded EEG and cerebral blood oxygen information, where the cerebrations in different movement paradigms were analyzed from an aspect of "functional coupling". The results show that the information of oxyhemoglobin concentration change (ΔHbO) can effectively indicate the cortex activation, and a stronger blood supply is needed in cortexes to perform body movements. The correlations within motor cortexes are significantly stronger than the ones between motor and prefrontal cortexes, and a higher resistance level of extremity training will cause stronger correlations. Calculation of transfer entropy (TE) shows that there exists a bidirectional information transmission between the electrophysiological and blood supply signals, and more information is transmitted always in the direction from ΔHbO to EEG than in the opposite direction. The information transmission or the coupling relationship can be significantly enhanced by extremity movements, and large TE values are achieved in the Theta, Beta and Gamma frequency bands of EEG that correspond to motor functions. This work has demonstrated the functional coupling between nerve and blood microcirculation, which would provide a technical guidance to improve the rehabilitation effect for current robot systems and have great application potentials. © 2023 IEEE.

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Multi-agent coverage control for enhanced geohazard monitoring: a brief review

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control Theory and Technology 2021年第3期19卷 418-420页

作者： Chao Zhai Zhaoxu Wang Jie Dou School of Automation China University of GeosciencesWuhan430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems WuhanChina Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhanChina Three Gorges Research Center for Geo-Hazards Ministry of EducationChina University of GeosciencesWuhan430074China

In recent years,cooperative coverage control of multi-agent system(MAS)has attracted plenty of researchers in various fields[1,2].Different from multi-agent consensus or synchronization,multi-agent coverage control cares about how to coordinate a team of agents for effectively monitoring or covering a given terrain,which inevitably gives rise to the interaction between individual dynamics and external ***,environmental uncertainties that include static uncertainties and dynamic uncertainties and limited sensing capabilities of a single agent make it a great challenge to design control algorithms of MAS for achieving the desired coverage performance.

关键词： agent plenty coverage

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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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