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3rd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2019

作者： Zhang, Tenglong Zhang, Jinbo Song, Jiajia Han, Xinxiu HoHai University Key Laboratory of Intelligent Technology and Special Robot College of Internet of Things Engineering Changzhou213022 China

ISBN: (纸本)9781728105130

New energy power generation relies on power electronic equipment, such as inverters. As a large amount of power electronic equipment is connected to the power grid, the power quality of the grid decreases, which causes voltage harmonics to exceed the standard. At present, the voltage harmonic measurement of the transmission line grid can only be indirectly measured in the substation by means of the Potential transformer (PT)/ Capacitor Voltage TransformerCVTcabinet. Due to the characteristics of PT/CVT and the wiring mode, the accuracy of harmonic voltage measurement is reduced, which seriously affects the accurate and true judgment of the voltage harmonics of the power grid. Therefore, we propose and design a new method based on field strength method for measuring voltage harmonic signals of high-voltage transmission lines. The method solves the problem of accurate measurement of 2-32 and 3-31 harmonics, and realizes direct measurement of voltage harmonic content of high-voltage transmission line through obtaining directly voltage harmonic signals by electric field coupling principle and applying fast Fourier transform (FFT), Hanning window algorithm to analyze and process harmonic measurement data. Besides, the actual test and measurement accuracy meets the national standard of 0.5 level requirement. The high-voltage line harmonic detector based on electric field coupling method provides a simple and fast method for harmonic detection of high-voltage transmission lines and has good practical value. © 2019 IEEE.

关键词： Fast Fourier transforms

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Research on Parameter Selection of Multi-layer Kernel Extreme Learning Machine Based on Differential Evolution 4

Research on Parameter Selection of Multi-layer Kernel Extrem...

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2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021

作者： Yang, Min Liu, Lizhi Deng, Kaiwei Liu, Jie Wuhan Institute of Technology School of Computer Science and Engineering Hubei Key Laboratory of Intelligent Robot Wuhan430073 China

ISBN: (纸本)9781665412704

The multi-layer kernel extreme learning machine (MKELM) is an improved multi-layer extreme learning machine(ML-ELM) based on kernel method, which has the advantages of no need to manually adjust the number of nodes in each hidden layer, no need to randomly initialize weights and offsets. However, due to the introduction of kernel method, it is necessary to select appropriate kernel parameters to obtain a model with better generalization performance, and manually adjust the parameters in kernel method according to prior knowledge, which is undoubtedly a very time-consuming process. In this paper, a self-adaptive differential evolution multi-layer kernel extreme learning machine (SaDE-MKELM) is proposed. The main idea of this algorithm is to use the self-adaptive differential evolution(SaDE) to automatically optimize the parameters in MKELM, take the parameter vector composed of the kernel parameter σ and penalty factor c of MKELM as an individual in the differential evolution algorithm, and build a MKELM model for each individual. The average root mean square error obtained by 10-fold cross-validation is used as the fitness of the corresponding individual, and the individuals in the population are selected by evolution within a certain optimization range and iteration times, and finally the optimal parameter combination suitable for the current model is obtained. The proposed algorithm is tested on several UCI simulation datasets. The experimental results show that the classification prediction accuracy of SaDE-MKELM is improved by at least 2% compared with MK-ELM and SaDE-ELM, and it has better stability. © 2021 IEEE.

关键词： Optimization

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Learning Multi-Scale Deep Features for Person Re-Identification

Learning Multi-Scale Deep Features for Person Re-Identificat...

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2021 International Conference on Signal Processing and Communication technology, SPCT 2021

作者： Guan, Wanting Zhang, Yanduo Hubei Key Laboratory of Intelligent Robot School of Computer Science and Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510653344

ISBN: (纸本)9781510653337

Person re-identification (Re-ID) aims at retrieving a person of interest across multiple cameras. With the advancement of deep network and increasing demand of intelligent video surveillance, it has gained significantly increased interest in the computer vision community. In this paper, we propose a simple yet effective Multi-Scale Horizontal (MSH) model for person Re-ID task. Firstly, the model consists of a novel Multi-branch network which adopted residual network ResNet50. There are two branches in our network: global branch and local branch. In local branch, the model slice a person into different parts in multi-scales. Secondly, we present a mix pooling method which considering both average and maximum pooling method. Finally, we employ triple loss and softmax loss as the loss function of the network. Experiments on two datasets (Market1501 and DukeMTMC-reID) demonstrate the advantage of the proposed model. © 2022 SPIE.

关键词： Security systems

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GAN-based Fusion of Infrared and Visible Images in Dark Light Environments

GAN-based Fusion of Infrared and Visible Images in Dark Ligh...

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2024 China Automation Congress, CAC 2024

作者： Shi, Jiaoxing Chen, Yanfei Li, Haiming Yang, Lei Liu, Yangkai Yue, Tong Hubei Key Laboratory of Intelligent Robot School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (纸本)9798350368604

In low light conditions, the fused image generated by fusion of infrared and visible images is dark and lacks detailed texture information, resulting in low target detection accuracy, based on which a target detection network for fusion of infrared and visible images in low light conditions is proposed. Firstly, the dark light perception network is designed to enhance the brightness of the dark light image;secondly, the feature extraction module of the image fusion network introduces the gradient residual dense block to strengthen the ability of the image fusion network to extract the depth features and fine-grained detail features of the source image;subsequently, the loss function incorporates the intensity loss and the structural similarity loss to enhance the ability of the image fusion network to extract the pixel intensities and the structural texture details of the source image;Finally, the optimal solution of the fusion network for target detection is achieved by joint training of the image fusion network and the target detection network. The experimental results show that the fusion network in the paper improves the mutual information, average gradient, visual fidelity, gradient-based fusion performance, and structural similarity by 0.142, 0.135, 0.043, 0.067, and 0.082 compared to the other network models, and improves the average precision mean and accuracy by 2.6% and 2.5%, respectively, in the YOLOV8 detection network. © 2024 IEEE.

关键词： Image fusion

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Image Classification Based On Pcanet And Salient Feature Fusion

Image Classification Based On Pcanet And Salient Feature Fus...

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2022 International Conference on Artificial Intelligence and Computer Information technology, AICIT 2022

作者： Chen, Yanfei Huang, Yuliang Yan, Zhangchen Wang, Gang Huang, Tiange Hu, Jinhu Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot School of Electrical Information Engineering Wuhan430073 China

ISBN: (数字)9781665450874

ISBN: (纸本)9781665450874

Aiming at the shortcomings of traditional image classification model in extracting features, we propose an improved color contrast algorithm to extract higher quality saliency map. We first analyze the feature extraction ability of HC saliency algorithm in image classification and improve it by adding the location information, then we propose a novel features fusion module to combine the saliency map with the output features from PCANet to enhance the feature expression, contributing to classification capability of the model. The accuracy on Caltech101 and Pascal VOC2007 can achieve excellent performance by using our method. © 2022 IEEE.

关键词： Image classification

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Text Classification Based on A New Joint Network 5

Text Classification Based on A New Joint Network

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5th International Conference on Control, robotics and Cybernetics, CRC 2020

作者： Chen, Ming He, Chengwan Lei, Li Li, Duojiao Wuhan Institute of Technology School of Computer Science and Engineering Hubei Key Laboratory of Intelligent Robot Wuhan430205 China

ISBN: (纸本)9781728186245

In order to solve the problem that single convoluti- onal neural network (CNN) lacks the ability to make use of text context information and that simple recurrent neural network (RNN) cannot solve the problem of long-time dependence, this paper proposes the joint network (BL-CN) combine bidirectional long short term memory network (BiLSTM) with capsule network (CapsNet) for text classification. CapsNet is an improved version of CNN, which can retain more text features, increase the diversity of features, and capture the local pluralistic features of sentences. Combined with BiLSTM which has ability to extract context-related features. It can get not only the local phrase features with multiple information, but also the sequence structure features of text and the bidirectional semantic dependence of sentence, which can more comprehensively express the text features. Experiments were carried out on the data sets of IMDB and THUCNews to compare the accuracy and loss of each single network and the combined network respectively. The results show that the model proposed in this paper can improve the accuracy of text classification. © 2020 IEEE.

关键词： Convolutional neural networks

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Determination of Protein Content of Wheat Using Partial Least Squares Regression Based on Near-Infrared Spectroscopy Preprocessing 4

Determination of Protein Content of Wheat Using Partial Leas...

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4th International Conference on robotics and Computer Vision, ICRCV 2022

作者： Liu, Ruirui Liu, Jun Liu, Chuang Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology School of Computer Science and Engineering Wuhan China

ISBN: (数字)9781665481700

ISBN: (纸本)9781665481700

When using near-infrared spectral modeling to determine the protein content of wheat, spectral instrument and background noise will reduce the prediction accuracy of the model. In order to make the prediction results not affected by the data itself, this paper study the effect of the data preprocessing method on modeling. The experiment shows that the best results can be obtained using the KS-MC-PLSR algorithm on this dataset, the R2 and RMSE of the experimental test set are 0.9902 and 0.1685, respectively. © 2022 IEEE.

关键词： Proteins

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An Image Defog Network Based on Multi-scale Feature Extraction and Weighting 4

An Image Defog Network Based on Multi-scale Feature Extracti...

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4th International Conference on Artificial Intelligence and Big Data, ICAIBD 2021

作者： Yu, Chen Qiong, Cai Huang, Qianqian Chen, GuoQing Fu, Xingbao Hubei Key Laboratory of Intelligent Robot School of Computer Science and Engineering Wuhan Institute of Technology Wuhan430073 China

ISBN: (纸本)9780738131702

The research on image defogging is a long-running topic. With the wave of artificial intelligence and deep learning sweeping the world, the research on image defogging based on deep learning has gradually reached a new height. However, the following problems are still emerging, such as severely distorted image color after defogging, image jagging, and image blurring and so on. In response to these problems, this paper proposes an improved image defogging network based on the traditional CycleGAN. Different from the single feature extraction of traditional CycleGAN, this paper adopts a multi-scale feature extraction method. Combining small-scale convolution and large-scale convolution with residual network for in-depth feature extraction, and at the same time, the extracted features are weighted and fused through the attention module to make the extracted network information more complete. Finally, a clear and fog-free image is reconstructed through upsampling. Compared with the traditional defogging network, the improved network can better avoid the problems of image color distortion and blurring. © 2021 IEEE.

关键词： Feature extraction

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Maximum ladle shell temperature prediction based on GABP neural network

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International Journal of Wireless and Mobile Computing 2023年第2期24卷 120-126页

作者： Sun, Ying Huang, Peng Tao, Bo Yun, Juntong Zhao, Guojun Liu, Xin Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China

intelligent manufacturing is the main development trend of today's manufacturing industry, and talents are the first resource. Through the analysis of the current situation of cultivating talents in mechanical engineering in colleges and universities, it is found that most students of this major have difficulty in involving in knowledge of other fields outside their specialties, and their knowledge structure is relatively single. In response to the above problems, this paper proposes the training mode of multidisciplinary cross-integration of talents in mechanical engineering, which is analysed through the study of maximum temperature prediction of steel ladle shell. The BP neural network based on the improved genetic algorithm is trained on the experimental data samples to achieve the maximum temperature prediction of ladle shell under different thickness combinations of insulation layer, safety layer and working layer. By learning the knowledge of target prediction, students' overall development is promoted. © 2023 Inderscience Enterprises Ltd.

关键词： Forecasting

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A tactile sensor based on photoelasticity

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International Journal of Wireless and Mobile Computing 2024年第3期27卷 281-289页

作者： Tao, Bo Huang, Zhili Zhu, Wenqiong Li, Gongfa Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China

In the new industrial revolution, the market demands integrated talents in new engineering fields. To meet the requirements of the new era for cultivating high-level talents in integrated disciplines, this paper takes the photoelastic tactile sensor as a carrier, developing a task-driven teaching method oriented to student achievement. Combining society’s need with professional knowledge learning and innovation ability cultivation of students, a practical and innovative platform is built to meet the needs of different stages of integrated graduate education for bachelor’s and master’s degrees. Achieving simultaneous enhancement of learning, scientific research and innovation capabilities, it proposes a multi-dimensional assessment mechanism for integrated graduate education, involving long-term assessment of students at different stages and levels. It implements a strict entry and exit policy and comprehensively improves the quality of talent cultivation. The results show very obvious effects, significantly improving the quality of graduates and promoting student development. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Tactile sensors

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