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2020 International Conference on Virtual Reality and intelligent Systems, ICVRIS 2020

作者： Zhan-Jiang, Ji Wei, Shi Wuzhou University School of Information and Electronic Engineering China Guangxi Colleges Universities Key Laboratory of Image Processing and Intelligent Information System Wuzhou University China Guangxi Colleges China Wuzhou Vocational College China

ISBN: (纸本)9781728196367

The accessibility and transitivity have an important significance in terms of theory and application. The concept of the accessibility and transitivity are introduced in this paper. We study their dynamical properties. The following result are obtained: (l)Let (K , G ) be the hyperspace of (X , G ). Then ( X , G ) is accessible if and only if (K , G ) is accessible;(2) Let (K , G) be the hyperspace of (X , G). Then (X , G ) is transitive if and only if (K , G ) is transitive. These results enriched the theory of the accessibility and transitivity of topological group. © 2020 IEEE.

关键词： Accessibility Topological group Transitivity

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Pedestrian partial occlusion detection method based on improved YOLO network structure

Pedestrian partial occlusion detection method based on impro...

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2019 International Conference on intelligent Computing, Automation and Systems, ICICAS 2019

作者： Chen, guangxi Zhan, Baoying Tang, Min Xie, Chen Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781728161068

The problem of pedestrian detection accuracy is low due to partial occlusion of pedestrians in practical application scenarios, a pedestrian occlusion detection method based on improved YOLOv3 network structure is proposed. By improving the activation function and clustering method to strengthen the feature output layer of the shallow layer of the network, which effectively make the accuracy of detection better. The experimental results show that the mAP of proposed method is improved by 2.63 percent in the INRIA data set, and the improved network has good robustness and generalization ability, and guarantees the real-time of detection. © 2019 IEEE.

关键词： Chemical activation

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Arbitrary shape text detection method based on improved PSE network structure 3

Arbitrary shape text detection method based on improved PSE ...

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3rd IEEE International Conference of Safe Production and Informatization, IICSPI 2020

作者： Xie, Chen Chen, guangxi Tang, Min Li, Qiaochu Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781728177380

The problem of low efficiency of text detection in natural scenes due to the Curved text or the multi-directional text, a arbitrary shape text detection method based on PSE network structure is proposed. By changing the backbone network of the original PSE and adopting a new lightweight network as backbone, and the post-processing method of the original network is optimized, which effectively improves the speed without losing the original accuracy. The experimental results show that the speed of the proposed method is 5.81 FPS/s compared with the original PSE on CTW1500 data set. The improved network has good robustness and generalization ability, and has better overall performance. © 2020 IEEE.

关键词： lightweight network PSEnet scene text text detection

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Research on neural network temperature compensation for embedded weighing systems 2

Research on neural network temperature compensation for embe...

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2nd International Conference on Electrical, Electronics, and Information Engineering, EEIE 2023

作者： Gan, Hui Lv, Meini Zhan, Liping Yan, Zelin Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Image Processing and Intelligent Information System Wuzhou University Wuzhou China Wuzhou Medical College Wuzhou China

ISBN: (纸本)9781510672840

The accuracy of weighing systems is easily affected by sensors, conversion circuits, temperature, and other factors. Aiming at the problem of significant temperature impact during the operation of the weighing system, which leads to a decrease in weighing accuracy. This article proposes a BP neural network method for temperature compensation of the weighing system to further improve the weighing accuracy. Firstly, a weighing system is built based on STM32 to collect system temperature and weighing data. Then, a BP neural network is built using MATLAB, and the calibration data is imported to train the optimal model of the BP neural network. Finally, the model parameters are imported into an embedded system to achieve real-time temperature compensation for the STM32 weighing system. The experimental results show that the accuracy and stability of the weighing system have been improved, and it has good application value. © 2024 SPIE.

关键词： Embedded systems

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Performance Analysis of NOMA-Based Cooperative Networks with Relay Selection

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China Communications 2020年第11期17卷 111-119页

作者： Ronglan Huang Dehuan Wan Fei Ji Hua Qing Jie Li Hua Yu Fangjiong Chen Guangxi Colleges and Universities Key Laboratory of Image Processing and Intelligent Information System Wuzhou UniversityWuzhou 543002China Guangdong University of Finance Guangzhou 510521China School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510641China Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural ResourcesGuangzhou510300China

Non-orthogonal multiple access(NOMA)technique is an expert on channel differences *** this paper,a dual-hop NOMA-based cooperative relaying network where a best relay is selected as an active node to accomplish the communication between a source and a destination is *** assume that both decode-and-forward(DF)and amplify-and-forward(AF)protocols are applied to the selected *** metrics that ergodic sum-rate and outage probability are investigated,and the closed-form expressions of the latter for DF and AF protocols are *** and simulation results are conducted to verify the validity of the theoretical analysis,in which we can see that the NOMA based DF relaying is better than the NOMA based AF relaying and other existing NOMA-based cooperative communication schemes.

关键词： non-orthogonal multiple access(NOMA) decode-and-forward(DF) amplify-and-forward(AF) relay selection ergodic sum-rate outage probability

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Portrait image segmentation based on improved grabcut algorithm 6th

Portrait image segmentation based on improved grabcut algori...

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6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the 2nd BRICS Congress on Computational Intelligence, CCI 2015

作者： Li, Shuai Zheng, Xiaohui Chen, Xianjun Zhan, Yongsong Key Laboratory of Cloud Computing and Complex System Guilin University of Electronic Technology Guilin Guangxi541004 China Guangxi Experiment Center of Information Science Guilin University of Electronic Technology Guilin Guangxi541004 China Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin Guangxi541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin Guangxi541004 China Information Engineering School Haikou College of Economics Haikou Hainan571127

ISBN: (纸本)9783319204680

Traditional computer portrait caricature system mainly take the method that exaggerate and deform real images directly, that lead the facial image background also been deformed when exaggerate facial image. If in pretreatment stage, we segmented the characters and background of the input image, and then do the subsequent processing, the problem may be solved. But for better portrait caricature effects, we need an excellent segmentation algorithm. So, we propose an improved Grabcut image segmentation algorithm and use it to extract the prospect character image for exaggeration and deformation. In practical application, we separate deform and exaggerate the foreground characters image with TPS method, then fuse it with the original or new background picture, get the final image. Application proves, the method solves the background deformation problem well, and improves the quality and rate of image segmentation, caricature synthesis effect reality and natural. © Springer International Publishing Switzerland 2015.

关键词： image segmentation

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HDMacBERT-FGM-CRF Model-Based Chinese Event Element Extraction 14th

HDMacBERT-FGM-CRF Model-Based Chinese Event Element Extracti...

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14th International Conference on Genetic and Evolutionary Computing, ICGEC 2021

作者： Chen, Peng Qiang, Baohua Wang, Yufeng Wang, Chen Chen, Ruidong Li, Baolian Peng, Bo Zhou, Xiangyu Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guangxi Guilin541004 China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang050002 China

ISBN: (纸本)9789811684296

Because of the inaccuracy of event element extraction caused by long text, the errors of pre-training and fine-tuning stages, and the single sample of semantic features in the current Chinese event element extraction model, Hierarchical Decomposition MacBERT and Fast Gradient Method-based Chinese event element extraction algorithm by jointing Conditional Random Field (HDMacBERT-FGM-CRF) is proposed. This model extends absolute location encoding based on hierarchical decomposition so that the HDMacBERT model can directly process more than 512 Token texts. The HDMacBERT model based on the Mask strategy of similar words is used to mitigate errors. The FGM-based approach implements adversarial networks and increases the diversity of semantic feature samples. The P, R and F 1 of the HDMacBERT-FGM-CRF model proposed in this paper are 2.52%, 4.03% and 3.26% higher than those of the classical Chinese event element extraction model BERT-CRF on the DuEE dataset. The experimental results show that the proposed model can learn more comprehensive deep-level semantic information of text and improve Chinese event element extraction. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Extraction

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Semi-supervised Classification on Data Streams with Recurring Concept Drift Based on Conformal Prediction 30th

Semi-supervised Classification on Data Streams with Recurr...

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30th International Conference on Neural Information processing, ICONIP 2023

作者： Ma, ShiLun Kang, Wei Xue, Yun Wen, YiMin Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China School of Municipal and Surveying Engineering Hunan City University Yiyang413000 China

ISBN: (纸本)9789819981830

In this article, we consider the problem of semi-supervised data stream classification. The main difficulties of data stream semi-supervised classification include how to jointly utilize labeled and unlabeled samples to adress concept drift detection and how to use unlabeled to update trained classifier. Existing algorithms like the CPSSDS method constantly retrain a new classifier when concept drift is detected, it is very consuming and wasteful. In this paper, the algorithm of data stream semi-supervised classification with recurring concept drift named as CPSSDS-R is proposed. First, the labeled samples in the first data block are used to initialize a classifier, which is added into a pool and actived for classification. While a new data block arrives, concept drift is detected by computing conformal prediction results. If no concept drift is detected, the pseudo-labeled samples in the previous data block are added with the labeled samples in the current data block to incrementally train the active classifier. If a new concept is detected, a new classifier is trained on the labeled samples of the current data block and added into the pool and actived for classification, else if a recurring concept is detected, the pseudo-labeled samples and labeled samples in the current data block are used to incrementally update the classifier corresponding to the recurring concept in the pool and actived for classification. The proposed algorithm is tested on multiple synthetic and real datasets, and its cumulative accuracy and block accuracy at different labeling ratios demonstrate the effectiveness of the proposed algorithm. The code for the proposed algorithm is available on https://***/ymw12345/cpssds-r. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Sparse weighted naive bayes classifier for efficient classification of categorical data 3

Sparse weighted naive bayes classifier for efficient classif...

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3rd IEEE International Conference on Data Science in Cyberspace, DSC 2018

作者： Zheng, Zhuoyuan Cai, Yunpeng Yang, Yujie Li, Ye Guangxi Key Laboratory of Trusted Software Guangxi Colleges and Universities Key Lab. of Intelligent Processing of Computer Images and Graphics Guilin University of Electronic Technology Guilin541004 China Shenzhen Institutes of Advanced Technology Key Laboratory for Biomedical Informatics and Health Engineering Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781538642108

Feature selection has become a key challenge in machine learning with the rapid growth of data size in real-world applications. However, existing feature selection methods mainly focus on numeric data, which will lead to quality loss when handling classification problems involving categorical variables. In this paper, we proposed an improvement of Bayesian Classifier with the sparse regression technology. To the best of our knowledge, this is the first attempt to extend sparse regression for directly process of categorical variables. We implemented the idea for the case of weighted naive Bayes classifier. The introduction of L1 regularized learning ensures the algorithm to retain only a minimal subset of variables for model building, while at the same time achieves a near-optimal decision hyper-plane, which leads to excellent performance in case of high dimensional or small sample size situations. We carried out benchmark test on five UCI benchmark categorical data sets, which proved that the proposed algorithm have competitive performances over the original weighted naive bayes classifier and several state-of-the-art feature selection methods including L1 logistic regression and SVM-RFE. © 2018 IEEE.

关键词： Feature Selection

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Smart Power Safety Hazard Inspection System Based on YOLOv7 1st

Smart Power Safety Hazard Inspection System Based on YOLOv7

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1st International Conference on Applied Intelligence, ICAI 2023

作者： Liang, Yiheng Li, Xiaoming Deng, Zhenrong School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9789819708260

Power safety is closely related to people’s well-being. Electric power sectors regularly inspect and maintain power lines to guarantee people’s safe and stable use of electricity, and the current mainstream inspection method is to use UAVs for power inspection. Traditional inspection and detection methods have many shortcomings, such as high labor costs, slow inspection speed, low detection efficiency, and single detection targets. In this paper, a set of smart power inspection systems based on YOLOv7 was designed to check the common potential security risks of power lines such as bird nests, insulators, garbage, and hardware. After the detection, the detection report could be generated. The system adopted the YOLOv7 target detection algorithm. The accuracy rate of all detection targets was up to 93.9%, and the mAP @.5% reached 95.9%. Therefore, the algorithm could greatly improve the detection efficiency and greatly facilitate the electric power sectors to check power lines, so it has a high use value. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Inspection

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