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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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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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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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Accelerometer optimization based on real-time measurement of Hole Deviation INS with While-Drilling

Accelerometer optimization based on real-time measurement of...

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2021 China automation Congress, CAC 2021

作者： Liu, Yuan-Qiu Jin, Fang Song, Jun-Lei Dong, Kai-Feng Mo, Wen-Qin Hui, Ya-Juan School of Automation China University of Geosciences Wuhan420074 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

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. © 2021 IEEE

关键词： Accelerometers

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A Fast-trained and Generalized Spiking Neural Network for Robotic Gas Source Localization

A Fast-trained and Generalized Spiking Neural Network for Ro...

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International Conference on advanced Robotics and Mechatronics (ICARM)

作者： Chiyang Liu Xinxing Chen Jian Huang Hubei Key Laboratory of Brain-inspired Intelligent Systems and the Key Laboratory for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Mobile robots for Gas Source Localization (GSL) tasks are a safer alternative than human and animal rescuers in hazardous scenarios. Existing research primarily concentrates on rule-based algorithms or conventional artificial neural networks (ANNs). However, these approaches are either inapplicable in cluttered environments or undeployable due to their high energy consumption and the demand for substantial computational resources. This paper introduces the application of energy-efficient spiking neural networks (SNNs) to address robotic GSL tasks. A pipeline is proposed to train SNNs with deep Q learning and a pretrain-finetune paradigm. To facilitate the training process, a small dataset of gas dispersion is generated utilizing openFoam and GADEN, a high-fidelity simulator for gas dispersion. Data from a simplified plume model are leveraged to pretrain an ANN, the activation function of which gradually transitions from a bounded rectified linear unit (bReLU) to a step function. Subsequently, an SNN initialized with the ANN parameters undergoes finetuning on the GADEN-based dataset. The training pipeline significantly reduces training time compared to direct training of SNNs. The trained SNN is validated within the GADEN simulation environment and compared to three different models, demonstrating promising performance and superior generalization despite limited training data.

关键词： Training Location awareness Q-learning Mechatronics Pipelines Training data Spiking neural networks Data models Mobile robots Dispersion

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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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A tuning strategy for microwave filter using variable universe adaptive fuzzy logic system

A tuning strategy for microwave filter using variable univer...

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2021 China automation Congress, CAC 2021

作者： Peng, Sai Cao, Weihua Bi, Leyu Yuan, Yan 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

Microwave filters are the core frequency selection device in 5G base station, which play an important role in the field of communication. The errors of design and processing of microwave filter make it difficult to meet the specific requirements of frequency selection, thus the tuning process before delivery is significant. However, the tuning process relies heavily on skilled workers, resulting in low tuning efficiency and high cost. The automatic tuning method based on fuzzy logic does not depend on the model and has strong applicability. It has great practical value to the production trend of multi-variety, small batch and customization of microwave filters. In this paper, a variable universe adaptive fuzzy tuning method is proposed. First, intelligent optimal contraction-expansion factors based on fuzzy inference are proposed according to expert knowledge. Then, the input universe and output universe are multiplied, and the initial rule is transformed into more effective new rules through the transformation which is adaptive to the variable universe. Last, the effectiveness of the proposed tuning method is verified by simulations. © 2021 IEEE

关键词： Microwave filters

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A fault diagnosis method based on multi-feature fusion and broad learning system

A fault diagnosis method based on multi-feature fusion and b...

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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021

作者： Wang, Yan Hu, Wenkai Cao, Weihua Wu, Min 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)9781665401159

Reliable fault diagnosis is of practical significance for process safety in modern industrial facilities. This article proposes a data-driven fault diagnosis method based on multi-feature fusion and broad learning. The contributions are twofold: First, a systematic multi-feature extraction approach is presented to extract long-term trend features, short-term trend features, and binary-valued alarm signals;second, a broad learning system based fault diagnosis method is proposed to diagnose faults based on multi-feature fusion and to update the diagnosis model in an incremental manner. The proposed method has a strong nonlinear modeling capability and can also update the diagnosis model incrementally when unseen fault types are detected. The efficacy of the proposed method is demonstrated by simulations with the benchmark Tennessee Eastman (TE) process. © 2021 IEEE.

关键词： Learning 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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Partially Occluded Face Expression Recognition with CBAM-Based Residual Network for Teaching Scene

Partially Occluded Face Expression Recognition with CBAM-Bas...

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2023 China automation Congress, CAC 2023

作者： Bai, Yanxing Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China School of Automation Ministry of Education 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 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 Department of Computer Engineering Sariyer Istanbul34396 Turkey Tokyo Institute of Technology Yokohama226-8502 Japan

ISBN: (纸本)9798350303759

In this paper, a deep residual network based on convolutional block attention module (CBAM) is proposed, which is utilized for feature extraction of partially occluded face expression data. The proposed method overcomes the problem of localized occlusion face feature extraction by focusing on the regions and channels containing important information in the occluded face data through CBAM. Multi-task cascaded convolutional networks (MTCNN) are firstly utilized to localize the key regions of face emotion, and then deep emotion features are extracted by CBAM-ResNet network. The final emotion labels are generated. The effectiveness of this paper's method is verified on the RAF-DB dataset and the occluded CK+ dataset. The experimental accuracy in the RAF-DB dataset is 76.3%, which is 3.74% and 1.64% higher than the accuracy produced by the method of RGBT, and the WLS-RF, respectively. Application experiments are carried out in the real teaching scenario, which verifies the applicability of the algorithm in the real teaching scene. © 2023 IEEE.

关键词： Emotion Recognition

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