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2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

作者： Qin, Huafeng El Yacoubi, Mounîm A. Chongqing Engineering Laboratory of Detection Control and Integrated System Chongqing Technology and Business University Chongqing China Telecom SudParis Institut Polytechnique de Paris Courcouronnes France

ISBN: (纸本)9783030598297

Palm-vein recognition has received increasing researchers’ attention in recent years. However, palm-vein recognition faces various challenges in practical applications, one of which is the lack of robustness against image quality degradation, resulting in reduction of the verification accuracy. To address this problem, this paper proposes an end-to-end convolutional neural network to automatically extract vein network features, thus without resorting to any hand-crafted features. Firstly, we label the palm-vein pixels based on several handcraft-based segmentation methods and reconstruct a training set accordingly. Secondly, an end-to-end vein segmentation model is proposed based on a generative adversarial network. After training, this model outputs a map where each value is the probability that the corresponding pixel belongs to a vein pattern. The resulting map is then subject to binarization by thresholding and stored in a binary image, used subsequently for verification matching. The experimental results on the public CASIA palm-vein dataset demonstrate the effectiveness of our proposed method. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Plasmonic and photonic enhancement of chiral near fields

arXiv

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arXiv 2022年

作者： Hu, Li Sun, Zhiguang Nie, Yingdong Huang, Yingzhou Fang, Yurui Chongqing Engineering Laboratory for Detection Control and Integrated System School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing400067 China School of Physics Dalian University of Technology Dalian116024 China State Key Laboratory of Coal Mine Disaster Dynamics and Control Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials College of Physics Chongqing University Chongqing400044 China

A chiral near field with a highly contorted electromagnetic field builds a bridge to match the chiral molecules and light wavelengths with large size differences. It significantly enhances the circular dichroism of chiral molecules and has great prospects in chirality sensing, detection, trapping, and other chirality-related applications. Surface plasmons feature outstanding light-trapping and electromagnetic-field-concentrating abilities. Plasmonic chiral nanostructures facilitate light manipulation to generate superchiral near fields. Meanwhile, the nanophotonic structures have attracted significant interest to obtain strong chiral fields due to their unique electromagnetic resonant properties. During the interaction of light and chiral materials, the chiral near field not only bridges the light and chiral molecules but is also responsible for the optical activities. This paper reviews state-of-the-art studies on chiral near field enhancement using plasmonic and photonic nanostructures. We review the principle of chiral electromagnetic fields and the development of plasmonic and photonic nanostructures for near field enhancement. The properties and applications of enhanced chiral near fields for chiral molecule detection, spin-orbit angular interaction, and the generation of the chiral optical force are examined. Finally, we discuss current challenges and provide a brief outlook of this field. Copyright © 2022, The Authors. All rights reserved.

关键词： Dichroism

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An Automatic Ridge Estimation Method for Tacholess Order Tracking of Wind Turbine Gearbox Under Varying-Speed Operation

SSRN

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SSRN 2023年

作者： Liu, Changliang Liu, Shaokang Wu, Yingjie Liu, Weiliang Liu, Shuai Control and Computer Engineering Department University of North China Electric Power Beijing102200 China Baoding key Laboratory of State Detection and Optimization Regulation for Integrated Energy System Baoding071000 China Automation Engineering Department University of Northeast Electric Power Jinlin132000 China

The instantaneous shaft speed estimation method of wind turbine gearbox based on time-frequency ridge estimation is the most widely used method, but it requires parameter adjustment and expert knowledge to ensure the accuracy of ridge estimation, which significantly reduces the effectiveness of such methods in practice. In this paper, an end-to-end automated ridge estimating (ARE) method is proposed. ARE uses the inherent harmonic relationship in the vibration signal of wind turbine (WT) gearbox to construct a selection index for automatically selecting the target ridge with clear physical meaning. Then, a sub-region division strategy is proposed to avoid the interference of other components and improve computational efficiency. Finally, a parameter-free objective function considering both the amplitude and smoothness of the ridge is proposed to achieve target ridge estimation. The vibration signals of different WT gearboxes collected on-site were applied to verify the performance of ARE. The result shows that ARE realizes the automatic estimation of a ridge. It outperforms other methods in terms of accuracy and computational efficiency. © 2023, The Authors. All rights reserved.

关键词： Computational efficiency

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The Fast Fixed-Time Bipartite Synchronization of Coupled Delayed Neural Networks with Signed Graphs

Research Square

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Research Square 2021年

作者： Yang, Shiju Li, Chuandong Li, Yu Yang, Ting Li, Bo Chongqing Engineering Laboratory for Detection Control and Integrated System School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing400067 China College of Electronic and Information Engineering Southwest University Chongqing400715 China Army Logistics Academy of PLA Chongqing401311 China

In this paper, the fixed-time bipartite synchronization problem for coupled delayed neural networks with signed graphs is discussed. Different from traditional neural networks, the interactions between nodes of delayed neural networks can be either collaborative or antagonistic. Furthermore, compared with the initial-condition based finite-time synchronization, the settling time is bounded by a constant within fixed-time regardless of the initial condition. It is worth noting that the fixed-time stable network for bipartite synchronization in this paper achieves more faster convergence than most existing publications. By applying constructing comparison system method, Lyapunov stability theory and inequality techniques, some sufficient criteria for fixed-time bipartite synchronization are obtained. Finally, two numerical examples are granted to display the performance of the obtained results. © 2021, CC BY.

关键词： Synchronization

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Insulating materials for realising carbon neutrality:Opportunities,remaining issues and challenges

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High Voltage 2022年第4期7卷 610-632页

作者： Chuanyang Li Yang Yang Guoqiang Xu Yao Zhou Mengshuo Jia Shaolong Zhong Yu Gao Chanyeop Park Qiang Liu Yalin Wang Shakeel Akram Xiaoliang Zeng Yi Li Fangwei Liang Bin Cui Junpeng Fang Lingling Tang Yulin Zeng Xingtao Hu Jiachen Gao Giovanni Mazzanti Jinliang He Jianxiao Wang Davide Fabiani Gilbert Teyssedre Yang Cao Feipeng Wang Yunlong Zi The State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijingChina Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina Simpson Querrey Institute Northwestern UniversityEvanstonIllinoisUSA Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA Department of Information Technology and Electrical Engineering ETH ZürichZürichSwitzerland School of Electrical and Information Engineering Tianjin UniversityTianjinChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA Electrical and Electronic Engineering The University of ManchesterManchesterUK Department of Electrical Engineering School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina Key Laboratory of Control of Power Transmission and Conversion(SJTU) Ministry of EducationShanghaiChina College of Electrical Engineering Sichuan UniversityChengduChina Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ShenzhenShenzhenChina School of Electrical Engineering Wuhan UniversityWuhanChina School of Integrated Circuits Tsinghua UniversityBeijingChina Jiangsu Jinxin Electric Appliance Co.Ltd. YangzhouChina Tai'an Tiancheng Safety Evaluation Co. Ltd.Tai'anChina Department Electrical Electronic and Information EngineeringUniversity of BolognaBolognaItaly National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Laplace Paul Sabatier Universityand CNRSToulouseFrance Electrical and Computer Engineering University of ConnecticutStorrsConnecticutUSA State Key Laboratory of Power Transmission Equipment&System Security and New Technology School of Electrical EngineeringChongqing UniversityChongqingChina Thrust of Sustainable Energy and Environment The Hong Kong University of Science and Technology(Guangzhou)GuangzhouChina De

The 2050 carbon-neutral vision spawns a novel energy structure revolution,and the construction of the future energy structure is based on equipment *** material,as the core of electrical power equipment and electrified transportation asset,faces unprecedented challenges and *** goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first *** engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are *** materials,including intelligent insulating material,high thermal conductivity insulating material,high energy storage density insulating material,extreme environment resistant insulating material,and environmental-friendly insulating material,are cat-egorised with their scientific issues,opportunities and challenges under the goal of carbon neutrality being *** the context of carbon neutrality,not only improves the understanding of the insulation problems from a macro level,that is,electrical power equipment and electrified transportation asset,but also offers opportunities,remaining issues and challenges from the insulating material *** is hoped that this paper en-visions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality *** authors also hope that this paper can be helpful in future development and research of novel insulating materials,which promote the realisation of the carbon-neutral vision.

关键词： neutral insulation extreme

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Palm-vein verification based on U-Net

Palm-vein verification based on U-Net

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2019人工智能与大数据应用研讨会

作者： Peng Wang Huafeng Qin National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing Engineering Laboratory of Detection Control and Integrated System Chongqing Technology and Business University

As one of the biometric characteristics, palm-vein have received more and more attention in recent years. However, in practical applications, the vein image capturing is affected by various factors, so many low quality images are produced in recognition system. Generally, the vein networks extracted from such low quality images contains many noises, which degrades the recognition accuracy. To address these problems, this paper proposes an end-to-end convolutional neural network to extract vein feature for verification. Firstly, we label the palmvein pixel by the combination of some handcraft-based palm-vein image segmentation methods and build a training dataset. Secondly, a U-Net network is trained based on the resulting dataset and its outputs are the probability of pixels to belong to vein pattern. Thirdly, we propose a scheme to encode the outputs of U-Net to obtain the vein network patterns. The experiment results on the public CASIA palm-vein dataset implies the effectiveness of our proposed method.

关键词： image Palm-vein verification based on U-Net

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An approach for monitoring sand mining based on sound feature

An approach for monitoring sand mining based on sound featur...

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2019人工智能与大数据应用研讨会

作者： Qingmei Zhou Xiping He National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University School of Artificial Intelligence Chongqing Technology and Business University Chongqing Engineering Laboratory for Detection Control and Integrated SystemChongqing Technology and Business University

In order to strengthen the management of sand excavation in river courses and prevent the occurrence of illegal sand mining activities, this paper proposes an approach for monitoring sand mining based on sound. Firstly, Mel Frequency Cepstral Coefficients(MFCCs) abstractor and Autoencoder are combined to extract features of every frame of a sound sequence, and then each frame of the sound sequence is classified by a specific classifier. Finally a voting strategy is used among the frames to determine the final category of the sound sequence. Experiments show that whether the classifier is SVM, KNN, or BP neural network, the result of combined features is better than the result of features extracted by the MFCC abstractor. Therefore, it is feasible to use the artificial intelligence method based on sound features extracted through MFCC abstractor and Autoencoder to monitor sand mining.

关键词： An approach for monitoring sand mining based on sound feature

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Kinematic feature-based evaluation method for elderly balance ability by using factor analysis 2nd

Kinematic feature-based evaluation method for elderly balanc...

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2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019

作者： Ming, Rui Xing-Rong, Fan Xu, Guoliang Chongqing Engineering Laboratory for Detection Control and Integrated System Chongqing Technology and Business University Chongqing400067 China College of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Electronic Information and Networking Research Institute Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9783030317256

Modeling and assessing balance ability for elderly people is an important and realistic task with a view to assisting them in mobility status, correcting postures and preventing accidental falling. The aim of this study was to develop a novel kinematic feature-based evaluation method for elderly balance ability by using factor analysis. Based on the kinematics, twenty-five feature indicators were first extracted from walking gait data, which were collected by deploying twenty-four monitoring points on the body of the elderly subjects. Then, two main factors were identified by using factor analysis that affect the walking balance ability of the elderly, and the comprehensive evaluation scoring model of the elderly balance ability was constructed. Finally, real data from all the elderly subjects in free walking state were used to validate our method. The results of empirical analysis confirm the validity and usefulness of the proposed method. © Springer Nature Switzerland AG 2019.

关键词： Kinematics

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A two-stage Deep Convolutional Generative Adversarial Network-based data augmentation scheme for agriculture image regression tasks

A two-stage Deep Convolutional Generative Adversarial Networ...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Hajar Hammouch Mounim El-Yacoubi Huafeng Qin Aissam Berrahou Hassan Berbia Mohamed Chikhaoui SAMOVAR Institut Polytechnique de Paris Palaiseau France SSLab National School For Computer Science Mohammed V University in Rabat Rabat Maroc SAMOVAR CNRS Institut Polytechnique de Paris Palaiseau France Chongqing Engineering Laboratory of detection control and integrated system School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China National School For Computer Science Mohammed V University in Rabat Rabat Maroc Soil and Water Management Laboratory Institute of Agronomy & Veterinary Medicine Rabat Maroc

ISBN: (纸本)9781665426220

Recently, Deep Learning models, such as Deep Convolutional Neural Networks (CNNs), have shown remarkable performance on various Computer Vision tasks. Unfortunately, many application domains, such as agriculture image analysis, do not have access to large datasets. In this study, we are interested in the prediction of soil moisture dissipation rates from aerial images using a CNN model. This entails a regression task where the CNN, given an aerial input image, has to predict a vector of regression values associated with the dissipation rates at different locations of the agricultural field. CNNs, however, require large datasets to successfully train the deep network. To this end, we present a Deep Convolutional Generative Adversarial Network (DCGAN) to generate fake agriculture images, that are realistic enough with the objective to augment the original dataset, for better generalization capabilities and more accurate predictions by the trained CNN. Unlike existing GAN-based augmentation approaches used for segmentation or classification, where only images have to be generated to augment the training set, the regression task requires both an image and its associated regression vector. In our approach, we use a two-stage scheme where the baseline CNN trained on the original dataset is harnessed to predict, for each image generated by the DCGAN, the regression vector that will serve as its ground truth in the augmented dataset. The results show that the CNN trained on the augmented dataset in this way allows a relative reduction of the Mean Absolute Error by a percentage of 20.8 w.r.t to the CNN trained on the original dataset. We believe that our approach can be generalized to any image augmentation task involving a regression task.

关键词： Training Deep learning Computational modeling Soil moisture Predictive models Agriculture Social intelligence

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Robust temporal alignment for multivariate time series forecasting

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Expert systems with Applications 2025年 289卷

作者： Xuguang Wang Mi Zhang Jie Su School of Control and Computer Engineering North China Electric Power University 619 Yonghuabei Street Lianchi District Baoding 071003 PR China Baoding Key Laboratory of State Detection and Optimization Regulation for Integrated Energy System 619 Yonghuabei Street Lianchi District Baoding 071003 PR China

Temporal alignment of time series is crucial in the task of multivariate time series forecasting, as temporal misalignment can introduce randomness into the learned quantitative relationships between the target and related factor time series, resulting in unreliable forecasting outcomes. Nevertheless, anomalous information gain (AIG) and data outliers present significant challenges to the temporal alignment of time series. To address the issues of AIG and data outliers in this study, a time-delay-related ellipsoid is utilized to construct the alignment deviation metric, an information gain-based regularization term is designed to reinforce this metric by penalizing the occurrence of AIG, and an equiprobable ellipsoid-based outlier filtering process is introduced. Finally, a time-delay estimation (TDE) method robust to AIG and data outliers is proposed, upon which the time series alignment is performed. The robustness of the proposed method against AIG and data outliers is validated through simulation and real-data experiments. The wind speed forecast errors of the forecasting models are significantly reduced after aligning the related time series using our method; for instance, the mean squared error (MSE) achieved by traditional machine learning models and deep learning models is reduced to at least 74 % and 95 %, respectively.

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