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International Automatic Control Conference

作者： Xunfang Tao Bo Sun Ji Li Xiaonan Luo Guangxi Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin University of Electronic Technology Guilin China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781728171982

ISBN: (纸本)9781728171999

This paper proposes a palm vein recognition system based on compressed sensing theory to solve the problem of blurred vein image recognition, and establishes a more accurate palm vein recognition system. This method is mainly composed of two parts: First, the compressed sensing is utilized to reconstruct the palm vein image extracted by the infrared sensor to obtain a clearer image than the original blurred image. Second, geometry-based method for processing palm vein images to extract image feature information easier. This paper solve the problems of blurred veins, difficult segmentation and feature information extraction in palm vein images. The accurate recognition experiment of palm vein images is conducted on the polyu database which processed by gaussian noise show that our method has advantages in accuracy compared to traditional palm vein recognition systems.

关键词： Image recognition Databases Veins Feature extraction Security Compressed sensing Image reconstruction

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Multi-Scale Visual Semantic Enhanced for Multi-Modal Ner

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SSRN 2023年

作者： Wang, Hairong Xu, Xi Wang, Tong Sun, Chong School of Computer Science and Engineering North Minzu University Ningxia750021 China The Key Laboratory of Images Graphics Intelligent Processing of State Ethnic Affairs Commission Ningxia750021 China

A series of methods using image semantics to assist in extracting named entity recognition have been proposed recently. However, in these multi-modal named entity recognition methods, there may be problems of visual semantic missing and weak semantic constraints in multi-modal representations. For this reason, we propose a multi-scale visual semantic enhanced for multi-modal named entity recognition method. This method uses a variety of visual features for collaborative representation to obtain more complete visual semantics, integrates semantic information in multi-scale visual features through the multi-modal feature fusion module, to generate multi-scale visual semantic features, and transmits its semantics to text features to obtain semantically enhanced multi-modal text representations. The visual entity perceiver is used to semantically constrain the multi-scale visual semantic features to learn consistent visual semantics. The multi-task label decoder is called to jointly mine the fine-grained entity semantics of the multi-modal text representation and text features, to enhance the semantic accuracy of the prediction features, and solve the problem of semantic bias, thereby further improving the accuracy of named entity recognition. In the two multi-modal named entity recognition datasets (Twitter-2015, Twitter-2017), compared with 10 methods such as HvpNet, MNER-QG, RGCN, etc., the average F1 value of this method increased by 0.85% and 1.45% respectively. In addition, visualization and comparative analysis were conducted on the recognition results, visual features, and text features of methods such as MSVSE, UMT, MAF, and HvpNet in different multimodal data scenarios with varying entity types and quantities. This further validated the effectiveness of the text-based methods. © 2023, The Authors. All rights reserved.

关键词： Semantics

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PFMRC: A Unified MRC Framework for Chinese Named Entity Recognition via Semantic Feature Fusion

PFMRC: A Unified MRC Framework for Chinese Named Entity Reco...

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Artificial Intelligence, Robotics, and Communication (ICAIRC), IEEE International Conference on

作者： Zhenrong Deng Jinglin Zhang Yannan Xiao Shiwei Wei Guangxi Key Laboratory of Images and Graphics Intelligent Processing Guilin University of Electronic Technology Guilin China Nanning Research Institute Guilin University of Electronic Technology Guilin China School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

The landscape of natural language processing (NLP) has been revolutionized by advancements that enable the upstream training of knowledge-rich models for downstream tasks, particularly named entity recognition (NER). Traditional NER approaches, treating it as a sequence labeling problem, often fail to deeply understand the whole text and to capture syntactic nuances of text. To address these issues, we introduce PFMRC, a novel machine reading comprehension (MRC) framework that leverages the strengths of both contextual semantics and syntactic information for enhanced NER performance. PFMRC employs a dual-pronged feature extraction strategy, utilizing RoBERTa and BiGRU to encode text features for contextual semantic information and representing them as node features of the graph structure, and a weighted embedding representation to translate syntactic dependencies into graph-structured edge features. Our model further incorporates Graph Attention Networks (GAT) to harmonize node and edge features, facilitating a nuanced understanding of text. The integration of prior knowledge and the modeling of intricate syntactic relationships through Graph Attention Networks (GAT) not only improves entity recognition accuracy but also enriches the feature representation, leading to more robust NER outcomes. Extensive experiments on three Chinese datasets demonstrate PFMRC's superior performance over state-of-the-art models.

关键词： Training Accuracy Translation Semantics Refining Named entity recognition Syntactics Feature extraction Solids Robots

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Suspicious face detection based on key frame recognition under surveillance video

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2016年 9712 LNCS卷 645-652页

作者： Zheng, Xiaohui Ning, Yi Chen, Xianjun Zhan, Yongsong Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology GuilinGuangxi541004 China Guangxi Experiment Center of Information Science Guilin University of Electronic Technology GuilinGuangxi541004 China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology GuilinGuangxi541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology GuilinGuangxi541004 China

ISBN: (纸本)9783319409993

Surveillance video is characterized by large amount of data and redundancy, which makes the suspicious face detection to be a problem. To solve the problem above we proposed suspicious face detection based on key frame. Surveillance video has the type of fixed background, so this paper used the frame difference method with low computational complexity and small computation to extract the key frame. We proposed a new method combined DPM with skin color detection to detect the suspicious face in the key frames. To solve the speed bottleneck of the traditional DPM, we proposed to use the fast HOG LUT feature extraction and the near optimal cost sensitive decision making improving the traditional method. Meanwhile we used YCrCb + otsu skin color segmentation. Since the otsu is easily affected by the illumination, we proposed an improved skin color detection. Experiment results show that the proposed algorithms are robust and accurate for real-time. © Springer International Publishing Switzerland 2016.

关键词： Face recognition

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Combining permuted language model and adversarial training for Chinese machine reading comprehension

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Journal of intelligent and Fuzzy Systems 2024年第4期46卷 10059-10073页

作者： Liu, Jianping Chu, Xintao Wang, Jian Wang, Meng Wang, Yingfei College of Computer Science and Engineering North Minzu University Yinchuan China The Key Laboratory of Images Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu University Yinchuan China Agricultural Information Institute Chinese Academy of Agricultural Sciences Beijing China

Due to the polysemy and complexity of the Chinese language, Chinese machine reading comprehension has always been a challenging task. To improve the semantic understanding and robustness of Chinese machine reading comprehension models, we propose a model that utilizes adversarial training algorithms and Permuted Language Model (PERT). Firstly, we employ the PERT pre-training model to embed paragraphs and questions into vector space to obtain corresponding sequential representations. Secondly, we use a multi-head self-attention mechanism to extract key textual information from the sequence and employ a Bi-GRU network to semantically fuse the output feature vectors, aiming to learn deep semantic representations in the text. Finally, we introduce perturbations into the model training process. We achieve this by utilizing adversarial training algorithms such as Fast Gradient Method (FGM) and Projected Gradient Descent (PGD). These algorithms generate adversarial samples to enhance the model’s robustness and stability when facing diverse inputs. We conducted comparative experiments on the publicly available Chinese reading comprehension datasets CMRC2018 and DRCD. The experimental results show that our proposed model has achieved significant improvements in both EM and F1-Score compared to the baseline model. To validate the model’s generalization and robustness, we utilized ChatGPT to construct a scientific dataset that includes a large number of domain-specific terms, sentences with mixed Chinese and English, and complex comprehension tasks. Our model also performed remarkably well on the self-built dataset. In conclusion, the proposed model not only effectively enhances the understanding of semantic information in Chinese text but also demonstrates a certain level of generalization capability. © 2024 – IOS Press. All rights reserved.

关键词： Vector spaces

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Corrections to "Low-Forward-Voltage-Drop 4H-SiC BJTs Without Base Contact Implantation"

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IEEE Transactions on Electron Devices 2008年第9期55卷 2531-2531页

作者： Hyung-Seok Lee Martin Domeij Carl-Mikael Zetterling Mikael Ostling Guangxi Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin University of Electronic Technology Guilin China

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Multi-Wavelength Narrow Linewidth Random Fiber Laser Based on Fiber Bragg Grating Fabry-Perot Filter

Multi-Wavelength Narrow Linewidth Random Fiber Laser Based o...

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International Conference on Information Science and Technology (ICIST)

作者： Sokliep Pheng Luo Xiaonan Zhongshuai Wang Yijie Zhu Zetao Jiang School of Information and Communication Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781728155586

ISBN: (纸本)9781728155593

Recently, multi-wavelength narrow linewidth random fiber laser has very interested for every researcher in this field, because of their useful advantages application, such as high-resolution spectroscopy and fiber optic sensing. In this paper, the standard single-mode fiber is used to form a half-opened cavity structure for generating the narrow linewidth random fiber laser and used the FBG-FP as a filter to form the narrow linewidth RFL into multi-wavelength. Firstly, we used the Rayleigh scattering that processed as well as in a standard single-mode fiber to provide random distribution feedback at the same time while using erbium-doped fiber (EDF) to provide the gain or amplification for achieving a broadband random laser output. Then, FBG-FP is added to the half-open cavity random laser structure. The multi-wavelength and narrow linewidth RFL can be achieved when the broadband RFL goes through the FBG-FP. In this paper we have generated the multi-wavelength narrow linewidth random fiber laser which has more than 10 wavelengths and the 3dB bandwidth is less than 0.01 nm and the mode separation of each wavelength is 0.04nm.

关键词： Fiber gratings Erbium-doped fiber lasers Optical fiber couplers Optical fiber amplifiers

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Photovoltaic Array Fault Detection Based on Manifold Learning and Neural Network

Photovoltaic Array Fault Detection Based on Manifold Learnin...

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computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Kai Wang Yanru Zhong Xiaonan Luo Guangxi Key Laboratory of Intelligent Processing of computer Images and Graphic Guilin University of Electronic Science and Technology Guilin China Navigation Positioning and Location Services National and Local Joint Engineering Research Center Guilin University of Electronic Science and Technology Satellite Guilin China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

This paper endeavors to devise an ingenious methodology rooted in artificial intelligence, which, in turn, can be effectively employed to identify sundry categories of faults that are closely associated with photovoltaic arrays. This novel approach amalgamates the manifold learning algorithm with a formidable multilayer perceptron neural network (MLP). Intriguingly, it substantiates that the initial reduction in data dimensionality is achieved through the remarkable Isomap algorithm. Consequently, this compressed data is seamlessly channeled into the MLP, thereby yielding remarkable outcomes concerning fault detection. To validate and scrutinize the aforementioned proposition, a quintessential circuit representing a photovoltaic array is initially simulated utilizing the venerable Matlab/Simulink tool. During this simulation, an extensive corpus of both normal and faulty data is meticulously generated. Subsequently, the manifold learning algorithm is artfully applied to drastically diminish the dimensionality of this extensive dataset The resultant compressed data is then deftly fed into the MLP, allowing it to discern the salient characteristics pertaining to the sundry fault types. Through incessant iterations of intensive training, these faults can be astutely detected and discerned with remarkable accuracy. The results conclusively demonstrate the efficacy of combining manifold-based learning and multilayer perceptron neural network in detecting faults in photovoltaic arrays. The proposed method achieves high precision and effectiveness, surpassing the traditional approach by 2.52%.

关键词： Photovoltaic systems Training Accuracy Fault detection Computational modeling Multilayer perceptrons Mathematical models

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Quantity-Simulation-Analysis Method based Novel RSA Timing Attack Algorithm for Single-Chip Microcomputer Platform

Quantity-Simulation-Analysis Method based Novel RSA Timing A...

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IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Cong Li Qiang Han Tao Zhang Bingbing Lei Yu He School of Computer Science and Engineering North Minzu University Yinchuan Ningxia China The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu University (IGIPLab) Yinchuan Ningxia China School of Computer Science and Engineering Macau University of Science and Technology Macao SAR China

There are limitations in storage and computational capacity on the single-chip microcomputer platform under the secure edge computing paradigm. A higher success rate is possible via collecting sensitive information on the time side channel by multivariate statistical analysis to crack the RSA private key when attackers decrypt ciphertexts. We proposed a quantity-simulation-analysis (QSA) method to construct Markov model for RSA timing attack tasks, which firstly quantizes the decrypt process to obtain the time-consuming characteristics, then simulates the machine instruction cycles through parallel computing to analyze Markov model with more precise state transition matrix. On this basis, a novel timing attack algorithm with fuzzy clustering state transition probability matrix of the higher order Markov model on different step sizes is proposed, compared with some algorithms from other literatures taking an exhaustive search attack algorithm as a benchmark. Experimental results show that the algorithm achieves better results in terms of success rate.

关键词： Analytical models Correlation Computational modeling Hidden Markov models Clustering algorithms Markov processes Microcomputers

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Image Encryption Using 2D Sine-Piecewise Linear Chaotic Map

Image Encryption Using 2D Sine-Piecewise Linear Chaotic Map

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International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

作者： Yan-Ru Zhong Hua-Yi Liu Xi-Yan Sun Ru-Shi Lan Xiao-Nan Luo School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China School of Information and Communication Guilin University of Electronic Technology Guilin China Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin China

In this paper, a new image encryption algorithm is proposed by integrating Sine and piecewise linear chaotic maps. In order to realize the effect of encrypted image, the security key and 2D Sine-piecewise linear chaotic map (SPLCM) are used to encrypt the image by using random sequence and random matrix, and then using the replacement operation and the diffusion operation of the original image. The proposed image encryption algorithm is simple and practical, and the simulation results show that this algorithm is able to encrypt different types of digital images into unidentifiable random images. The security analysis also shows that this algorithm has a higher security level.

关键词： Encryption Two dimensional displays Digital images Chaotic communication

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