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2022 Chinese automation Congress, CAC 2022

作者： Peng, Zhi-Kun Liu, Zhen-Tao Han, Meng-Ting 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)9781665465335

Speech emotion recognition (SER) is a key technology to achieve natural human-computer interaction. The development of SER is significantly influenced by the scale of the sample. In recent years, the study of SER has been intensified by introducing data augmentation methods. However, most of these methods directly augment the sample data scale, neglecting the rational analysis and utilization of the feature distribution of samples. In this paper, we propose a new framework for SER based on clustering assistance, which can utilize the feature distribution information of the sample data directly and effectively. It considers the sample proportion of each emotion category in the clusters obtained by clustering, converts it into a probability score, which is called the clustering emotion probability score, and fuses it with the emotion probability score from the simple classification model according to different fusion weighting factors. We evaluated the proposed method with the baseline model on the IEMOCAP dataset. Experimental results show that our method achieves better results than the baseline model in terms of both weighted and unweighted accuracy. © 2022 IEEE.

关键词： K-means clustering

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The applied principles of EEG analysis methods in neuroscience and clinical neurology

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Military Medical Research 2024年第6期11卷 907-941页

作者： Hao Zhang Qing-Qi Zhou He Chen Xiao-Qing Hu Wei-Guang Li Yang Bai Jun-Xia Han Yao Wang Zhen-Hu Liang Dan Chen Feng-Yu Cong Jia-Qing Yan Xiao-Li Li School of Systems Science Beijing Normal UniversityBeijing 100875China College of Electrical and Control Engineering North China University of TechnologyBeijing 100041China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China Department of Psychology the State Key Laboratory of Brain and Cognitive Sciencesthe University of Hong KongHong Kong SAR 999077China HKU-Shenzhen Institute of Research and Innovation Shenzhen 518057GuangdongChina Department of Health Technology and Informatics the Hong Kong Polytechnic UniversityHong Kong SAR 999077China Department of Rehabilitation Medicine the First Afliated Hospital of Nanchang UniversityNanchang 330006China Rehabilitation Medicine Clinical Research Center of Jiangxi Province Nanchang 330006China Beijing Key Laboratory of Learning and Cognition School of PsychologyCapital Normal UniversityBeijing 100048China School of Communication Science Beijing Language and Culture UniversityBeijing 100083China Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004HebeiChina School of Computer Science Wuhan UniversityWuhan 430072China School of Biomedical Engineering Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalian 116081LiaoningChina Guangdong Artifcial Intelligence and Digital Economy Laboratory(Guangzhou) Guangzhou 510335China.

Electroencephalography(EEG)is a non-invasive measurement method for brain *** to its safety,high resolution,and hypersensitivity to dynamic changes in brain neural signals,EEG has aroused much interest in scientific research and medical *** article reviews the types of EEG signals,multiple EEG signal analysis methods,and the application of relevant methods in the neuroscience feld and for diagnosing neurological ***,3 types of EEG signals,including time-invariant EEG,accurate event-related EEG,and random event-related EEG,are ***,5 main directions for the methods of EEG analysis,including power spectrum analysis,time-frequency analysis,connectivity analysis,source localization methods,and machine learning methods,are described in the main section,along with diferent sub-methods and effect evaluations for solving the same ***,the application scenarios of different EEG analysis methods are emphasized,and the advantages and disadvantages of similar methods are *** article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives,provide references for subsequent research,and summarize current issues and prospects for the future.

关键词： Electroencephalogram analysis methods Applied principles Neuroscience Diagnosis Neurological diseases

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Multi-Feature Fusion Transformer for Natural Language Inference

Multi-Feature Fusion Transformer for Natural Language Infere...

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Robotics, Intelligent control and Artificial Intelligence (RICAI), International Conference on

作者： Lei Sun Xiangyang Li Hengxin Yan Tianj in Key Laboratory for Control Theory and Complicated Industry Systems School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China

Natural Language Inference (NLI) is a branch of Natural Language Processing (NLP) whose main task is to determine the relationship between two sentences. Such tasks essentially use pre-trained models to ensure accuracy, and many applications are found in human-computer dialog and question-answering systems. However, in some cases where small devices are deployed offline, lightweight model implementations are often required to save computational resources. To address this problem and ensure more efficient inference capabilities, this paper describes how to improve the preexisting Transformer and proposes a Multi-Feature Fusion Transformer Network for NLI processing. The model integrates sequence features and introduces a priori information to enhance local features. It also achieves a comprehensive fusion of sequence information, local features, and non-local features, thus compensating for many potential shortcomings of Transformer. At the same time, it maintains the lightweight feature of the model and facilitates the implementation of downstream tasks. We verified the above point by experiments, our model has better inference performance compared to previous models used for the same task. The accuracy in the SNLI dataset reached 89.0%, while the matched and mismatched versions of MultiNLI reached 79.3% and 78.7%.

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Glide Ratio Optimization for Wind Turbine Airfoils Based on Genetic Algorithms 24th

Glide Ratio Optimization for Wind Turbine Airfoils Based on ...

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24th International Conference on Intelligent Data engineering and Automated Learning, IDEAL 2023

作者： Radi, Jinane Djebli, Abdelouahed Sierra-Garcia, Jesús Enrique Santos, Matilde Energetic Laboratory Department of Physics Faculty of Sciences University of Abdelmalek Saadi Tetouan Tétouan Morocco Automation and Systems Engineering University of Burgos Burgos09006 Spain Institute of Knowledge Technology University Complutense of Madrid Madrid28040 Spain

ISBN: (纸本)9783031482311

The main objective in the design of wind turbine blades is the use of suitable airfoils to increase the aerodynamic performance and decrease the cost of energy. The objective of this research is to employ numerical optimization techniques to develop airfoils for wind turbines. To achieve this objective, a mathematical model is formulated by combining genetic algorithms with the XFOIL flow solver. Three different airfoils with different characteristics are created using the genetic algorithm. Throughout the optimization procedure, XFOIL software is used to determine the lift and drag coefficients. This study confirms the feasibility and effectiveness of the innovative design approach. Furthermore, it provides a valuable design concept that can be effectively applied to the airfoils of medium-thickness wind turbines. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Airfoils

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Equilibrium-Independent control of Continuous-Time Nonlinear systems via the LPV Framework - Extended Version

arXiv

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arXiv 2023年

作者： Koelewijn, Patrick J.W. Weiland, Siep Tóth, Roland The Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands The Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

In this paper, we consider the analysis and control of continuous-time nonlinear systems to ensure universal shifted stability and performance, i.e., stability and performance w.r.t. each forced equilibrium point of the system. This "equilibrium-free" concept is especially beneficial for control problems that require the tracking of setpoints and rejection of persistent disturbances, such as input loads. In this paper, we show how the velocity form, i.e., the time-differentiated dynamics of the system, plays a crucial role in characterizing these properties and how the analysis of it can be solved by the application of Linear Parameter-Varying (LPV) methods in a computationally efficient manner. Furthermore, by leveraging the properties of the velocity form and the LPV framework, a novel controller synthesis method is presented which ensures closed-loop universal shifted stability and performance. The proposed controller design is verified in a simulation study and also experimentally on a real system. Additionally, we compare the proposed method to a standard LPV control design, demonstrating the improved stability and performance guarantees of the new approach. Copyright © 2023, The Authors. All rights reserved.

关键词： controllers

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Fabrication and Path Following control of a Magnetic Soft Microrobot 8

Fabrication and Path Following Control of a Magnetic Soft Mi...

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8th International Conference on control Science and systems engineering, ICCSSE 2022

作者： Tang, Jianhui Zhang, Pan Wu, Jundong Wang, Yawu 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)9781665484138

Magnetically-controlled soft microrobots have great application prospects because of small size, flexible motion and wireless control. In this paper, a magnetically-controlled soft microrobot is designed and fabricated. In order to achieve its path-following control, two open-loop control strategies and a closed-loop one are developed. Especially, for the closed-loop control strategy, we present the image processing method and the pure pursuit algorithm, where the former is used to obtain the position and posture of the microrobot required for the latter. Finally, the developed open-loop and closed-loop control strategies are verified by carrying out the experiments. These methods are promising in numerous applications because of the ease of implementation. © 2022 IEEE.

关键词： Image processing

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Subspace Deep Learning of Independent Component Regression Model for Ensemble Spectroscopic Calibration

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IEEE Sensors Journal 2025年第12期25卷 22317-22325页

作者： Zheng, Junhua Zhou, Hansong Liu, Xinyu Lyu, Yuting Yang, Zeyu Ge, Zhiqiang Zhejiang University of Science and Technology School of Automation and Electrical Engineering Hangzhou China Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou313000 China Southeast University School of Mathematics Nanjing210096 China

Independent component regression (ICR) has become as an important spectroscopic calibration modeling method, due to its advantages in extracting non-Gaussian and high-order statistic features. While the calibration performance of ICR was recently improved by its deep learning form, the result is unstable which is subjected to different random seeds in the model initialization stage. This, however, will make the model less trustworthy in practical industrial applications. In this paper, a subspace deep learning framework is formulated to combine the ideas of both deep learning and ensemble learning, in order to improve the prediction stability of the ICR model. Meanwhile, with an appropriate design of the ensemble learning strategy in the subspace modeling framework, the calibration performance can be further enhanced. For performance evaluation, a detailed benchmark case study on the Corn dataset is carried out. Compared to ordinary and deep residual ICR models, both of the calibration performance and prediction stability have been significantly improved by the subspace deep learning approach. © 2001-2012 IEEE.

关键词： Linear regression

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Photonic crystal fiber pressure sensing based on birefringence effect 8

Photonic crystal fiber pressure sensing based on birefringen...

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8th Symposium on Novel Photoelectronic Detection technology and Applications

作者： Meng, Fanlong Wang, Hong Fang, Di 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: (数字)9781510653122

ISBN: (纸本)9781510653115

In order to achieve high sensitivity of pressure magnitude and direction measurement, the elasto-optical effect of photonic crystal fiber is studied by the full vector finite element method. First of all, the birefringence characteristics of the fundamental mode of the edge-pass high birefringence photonic crystal fiber under lateral pressure and static liquid pressure are studied. The results show that the effective refractive index of the two polarization fundamental modes changes obviously under the static liquid pressure, the phase mode birefringence changes linearly with the pressure. The second, the influence of fiber structure on birefringence is discussed, the results show that the size of birefringence can be influenced by changing the shape, position and size of the air holes in the fiber. The structure of photonic crystal fiber with high birefringence is obtained by optimizing the structure. The result show that, when free space wavelength is 1.55µm and pressure is 120MPa, the sensitivity of its phase modal birefringence up to 1.498×10-5 MPa-1 and the maximum measurable hydrostatic pressure value is 353MPa. The research is meaningful to realize the pressure sensor with multi-angle and high sensitivity. © 2022 SPIE

关键词： Birefringence

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An IoMT Based Epileptic Seizure Predictor Using Dual-Path Temporal-Spectral Encoded Spatial Dynamic Graph Convolution Network

An IoMT Based Epileptic Seizure Predictor Using Dual-Path Te...

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Industrial automation, Robotics and control engineering (IARCE), International Conference on

作者： Weidong Yan Mingyi Sun Yang Li Department of Automation Science and Electrical Engineering Beihang University Beijing China Department of Automation Science and Electrical Engineering Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

Epilepsy is a neurological disorder characterized by recurrent and uncontrolled seizures, severely impacting the safety of patients' lives. It is a challenging task to accurately predict seizures before its occurrence, and timely alarm patients to take appropriate measures to reduce the consequence of seizures. This work proposes an accurate and smart seizure warning predictor, named Seiz-Predictor, which is designed on the basis of the Internet of Medical Thing (IoMT) framework and constructed with a seizure prediction algorithm coupled with a mobile warning application. The Seiz-Predictor employs a dual-path temporal-spectral encoded spatial dynamic graph convolution network (DPTSE-SDGCNet) to enhance seizure prediction accuracy. Additionally, a compatible seizure warning mobile application is developed for seizure alarming, location recording, and interacting with cloud-based databases for further reference by doctors. The designed Seiz-Predictor offers several key benefits, including (1) We propose a novel lightweight network structure more suitable for epileptic seizure warning predictors, achieving more accurate predicting and shorter identification time in the public dataset. (2) A seizure warning mobile application is developed for cloud-based database connectivity, enabling computation of patient epileptic states for doctoral reference. Experimental results on the public CHB-MIT dataset demonstrate that the sensitivity of 96.1% and a false prediction rate of 0.086/h, outperforming the most recent investigations.

关键词： Cloud computing Accuracy Sensitivity Convolution Databases Service robots Epilepsy Internet of Medical Things Mobile applications Safety

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Group Consensus of Multi-agent systems with Additive Noises and Multiplicative Noises

Group Consensus of Multi-agent Systems with Additive Noises ...

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2022 Chinese automation Congress, CAC 2022

作者： Qin, Haoxiang Li, Chuanjian Zong, Xiaofeng School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China

ISBN: (纸本)9781665465335

In this paper, we study the group consensus problem of multi-agent systems with additive noises and multiplicative noises. The multi-agent systems are composed of two sub networks affected by additive noises and multiplicative noises, respectively. A consensus protocol is designed to guarantee the group consensus. Based on this protocol, the mean square weak consensus condition and mean square strong consensus condition are obtained by algebraic graph theory and stochastic stability theory. Finally, the numerical simulations show the effectiveness of the group consensus protocol in this paper. © 2022 IEEE.

关键词： Additive noise

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