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3rd IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2020

作者： Cheng, Xiaohui Feng, Xianfeng Li, Wei Guilin University of Technology College of Information Science and Engineering Guilin China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin China College of Environmental Science and Engineering Guilin University of Technology Guilin China

ISBN: (纸本)9781728198279

Image feature extraction is a key step in medical image processing, and it also plays an important role in the recognition and classification of medical images. A feature extraction method of fundus images using deep learning technology is proposed to recognize and classify fundus images. Firstly, the fundus image in the data set needs to be preprocessed. Then, the fundus image is extracted in the VGG16 network for the optic cup and disc region. Finally, the fundus feature image is input into the improved Softmax classifier for recognition and classification. The experimental results show that the fundus image feature extraction method used in the fundus images of glaucoma patients and normal people has a higher classification accuracy rate and the classification effect is better. © 2020 IEEE.

关键词： Feature extraction

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A Framework of Online Learning and Experiment system Based on Affective Computing 3

A Framework of Online Learning and Experiment System Based o...

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3rd International Conference on E-Business, Information Management and Computer Science, EBIMCS 2020

作者： Zhou, Yinghui Tao, Xiaomei School of Information Science and Engineering Guilin University of Technology Guilin Guangxi Zhuang Autonomous Region China School of Information Science and Engineering Guilin University of Technology Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin Guangxi Zhuang Autonomous Region China

ISBN: (纸本)9781450389099

With the development of online education, the problem of emotional deficiency has gradually emerged. In recent years, the research about emotional interaction in online education, and the application of Affective Computing in online education came into being. This paper introduces the impact of Affective Computing on online education, including the use of expressions, speech, eye movement, and physiological signals, as well as text and questionnaires and other modals, or multi-modal to infer students' emotional state and learning state in the learning process, and then to realize personalized learning support. The emerging technologies such as Artificial Intelligence, Big Data and Brain Computer Interface, can be applied to online education combined with Affective Computing technologies to improve the interaction between learners and the on-line learning system. This paper proposes a framework of online learning and experiment system based on Affective Computing supported by Artificial Intelligence, Big Data and Brain Computer Interface. The system can provide personalized education for students and help them to carry out experiments in a virtual reality environment. The framework can reduce the impact on students in the process of data collection, so that it can obtain more real data, and the framework uses emerging technologies to provide support and reference for emotion analysis and recognition, which will make the analysis and response more timely and emotion recognition more accurate. © 2020 ACM.

关键词： Students

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Research on authentication of user information in cloud manufacturing

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Recent Patents on Engineering 2019年第4期13卷 410-415页

作者： Xie, Xiaolan Zhou, Xiao Liu, Yarong College of Information Science and Engineering Guilin University of Technology GuilinGuangxi Zhuang Autonomous Region541004 China Guangxi Universities Key Laboratory Fund of Embedded Technology and Intelligent Information Processing Guilin University of Technology Guilin541004 China College of Mechanical and Control Engineering Guilin University of Technology GuilinGuangxi Zhuang Autonomous Region541004 China

Background: Aiming at the problem of user information security authentication in cloud manufacturing, a user information security authentication model is proposed. The model is introduced in detail. Methods: In the security authentication model, the point set topology group fractal transformation algorithm is combined with the biometric identification technology to acquire the user biometric information to generate the secret key. Combined with the AES (Advanced Encryption Standard) encryption algorithm, the user biometric information is encrypted. Result: It provides a stronger guarantee for the user's security certification. We revised all patents relating to pharmaceutical formulations of applicability in authentication of user information in cloud manufacturing. Conclusion: Experiments show that the research has credibility and practicability, which improves the overall security performance of cloud manufacturing user information authentication and access. © 2019 Bentham Science Publishers.

关键词： Biometrics

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Dual Flow Framework on Bimodality Emotion Recognition Based on Facial Expression and Eye Movement

Dual Flow Framework on Bimodality Emotion Recognition Based ...

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2020 International Conference on Artificial Intelligence and Education, ICAIE 2020

作者： Tao, Xiaomei Liu, Shengxi Chen, Xinyi Guilin University of Technology College of Information Science and Engineering Guangxi Key Laboratory of Embedded Technology and Intelligent Systems Guilin Guangxi China Guilin University of Technology College of Information Science and Engineering Guilin Guangxi China

ISBN: (纸本)9781728166599

Emotion affects many aspects of our daily life. Lack of emotional communication in online learning results in poor learning effect and learning experience. This paper discusses the feasibility of recognizing emotions by using eye movements and facial expressions data together. A framework for identifying the learner's emotional state based on expression and eye movement bimodality recognition is proposed based on the complementarity of eye movement and facial expression data. The video stream sequence is used to obtain the learner's facial expression and eye movement information as dual-channel data, which is input into the data stream framework, and the machine learning method is used to predict the learner's emotional state. Dual-channel data flow framework proposed in this paper can be used not only to discover learners' emotional states in learning environments, but also can be applied to the area of identifying mental disorder and human-computer interaction. © 2020 IEEE.

关键词： Emotion Recognition

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Adsorption and Sensing Characteristics of Transition Metal (AU, Fe, Pt, Rh and Cu) Doped Ws2 on Dissolved Gases in Transformer Oil: A Dft Perspective

SSRN

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SSRN 2025年

作者： Jiang, Wenwen Zeng, Qingbin Zhang, Yiyi Wang, Guishan Sun, Kuoteng Xu, Min Chen, Dachang Jia, Pengfei School of Electrical Engineering Guangxi University Nanning China School of Artificial Intelligence Guangxi University Nanning China Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Guangxi University Nanning China China Southern Power Grid Extra High Voltage Power Transmission Company Liuzhou Branch Liuzhou China College of Food Science and Bioengineering Xihua University Chengdu China School of Electrical and Electronic Engineering Wuhan Polytechnic University Wuhan China Guangxi Key Laboratory of Power System Optimization and Energy Technology China

In this work, the sensing characteristics of transition metal (Au, Pt, Fe, Rh, Cu) doped WS2 for faulty decomposition gases (CO, CH4, C2H2, C2H4) in oil-immersed transformers were studied using density functional theory (DFT). The analysis showed that pristine WS2 had limited adsorption capacity for these gases. After comparing two doping sites, the more stable TM-WS2 structure was selected. Adsorption structures were optimized, and interactions between the gas molecules and TM-WS2 were analyzed through binding energies, charge transfer, band gaps, and density of states. Results showed that doped WS2 exhibited enhanced gas adsorption compared to pristine WS2. Pt-WS2 showed excellent performance for CH4, Rh-WS2 for C2H4, and Cu-WS2 for C2H2, with short response times and high detection efficiency. This work provides insights into the design of gas-sensing materials based on TM-WS2 © 2025, The Authors. All rights reserved.

关键词： Chemical sensors

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Sound Event Recognition Based in Feature Combination with Low SNR

Sound Event Recognition Based in Feature Combination with Lo...

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2019 International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2019

作者： Yan, Guolin Wang, Mei Liu, X. Song, Xiyu Guilin University of Technology College of Information Science and Engineering Guilin China Guilin University of Technology Guangxi Key Laboratory Fund of Embedded Technology and Intelligent System Guilin China Guilin University of Electronic Technology Key Laboratory of Cognitive Radio and Information Processing Guilin China

ISBN: (纸本)9781728146911

In the sound environment of instability noise, the recognition rate of sound events would be reduced if using a single sound feature to train public sound data. To solve this problem, we propose a new method of sound event recognition based in feature combination with low SNR in this paper. It improves the recognition of sound events in real scenes by adding noise to public sound data sets. On the other hand, in order to overcome the inaccuracy of single features in noise recognition and significantly improved the recognition rate, we propose a new combination method which combining multiple features and drawing on the advantages of image recognition. Actual test shows that the public sound data set with noise has good generalization ability to the actual scene. The designed combined features can further improve the classification recognition performance, and the recognition rate of real environmental acoustic events can reach 75%, meeting the requirements of the actual system. © 2019 IEEE.

关键词： Signal to noise ratio

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The Configuration Design of Electronic Products Based on Improved NSGA-III with Information Feedback Models

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Journal of Computers (Taiwan) 2022年第4期33卷 81-94页

作者： Chen, Yi-Hui Ye, Heng-Zhou Huang, Feng-Yi Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology China

The configuration of electronic products is an important means to meet the diverse and personalized needs of users and achieve mass customization, and one of its goals is to recommend an excellent bill of material to users according to users’ individualized needs and preferences. Current research describes the configuration of electronic products as a single-objective optimization model, which suffers from the problems of single recommended configuration and difficulty in meeting the dynamic adjustment of user preferences. Therefore, we describe it as a multi-objective optimization model and propose an NSGA-III-FR algorithm to solve the model. In order to balance the convergence and diversity of the algorithm, NSGA-III-FR has made two improvements on the basis of NSGA-III with information feedback models: introducing adaptive parameters to balance NSGA-III-F1 and NSGA-III-R1;and using Angle Penalized Distance (APD) to improve the niche technology. The experimental results show that our modified method can achieve better performance compared with the other three algorithms. © 2022 Authors. All rights reserved.

关键词： Multiobjective optimization

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An adaptive weight based particle swarm optimization scheduling algorithm on cloud computing system 3

An adaptive weight based particle swarm optimization schedul...

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3rd International Conference on E-Business, Information Management and Computer Science, EBIMCS 2020

作者： Pei, Fan Minggang, Dong Chao, Jing College of Information Science and Engineering Guilin University of Technology Guilin Guangxi541004 China College of Information Science and Engineering Guilin University of Technology Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin Guangxi541004 China College of Information Science and Engineering Guilin University of Technology Guangxi Key Laboratory of Trusted Software Guilin University of Electronic and Technology Guilin541004 China

ISBN: (纸本)9781450389099

The resource scheduling problem in cloud computing is one of the most concerned issues of researchers in related fields in recent years. With the aim of solving defects in particle swarm optimization algorithm (PSO) that it is easy to fall into local optimal solution and "premature", we propose a particle swarm optimization scheduling algorithm based on adaptive weights. By improving the inertia weight factor and using the number of iterations to control the size of the inertia weight, the algorithm convergence speed can be accelerated. Meanwhile, by updating the particle speed to update the weights of the cognitive and social coefficients in the number of iterations, the fitness function of the particles is improved to strengthen the adaptive ability of the particles and made the algorithm more in line with objective laws. A large number of simulation comparison experiments has been conducted on the Cloudsim platform. The experimental results show that the improved particle swarm optimization algorithm has better performance in adaptability, convergence speed, and capabilities of finding global optimums. © 2020 ACM.

关键词： Scheduling algorithms

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A Skip Residual Features Aggregation-Based Methodology for Classification of 3D Lung Nodules 12

A Skip Residual Features Aggregation-Based Methodology for C...

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12th International Conference on Information technology in Medicine and Education, ITME 2022

作者： Wu, Danhua Wu, Danhui Zhang, Panfeng Guilin University of Technology College of Information Science and Engineering Guilin China China Jiliang University College of Information Engineering Hangzhou China Guilin University of Technology Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin China

ISBN: (纸本)9798350310153

Lung cancer is one of the deadliest diseases caused by genetic and textural mutations leading to infinite proliferation of tissue cells, which can be diagnosed and prevented in advance by medical images, and deep learning provides an effective automatic diagnosis technique for this purpose. However, the diagnostic accuracy as well as the reduction of fault-diagnosis and misdiagnosis rates still need to be improved. To address this problem, a skip residual features aggregation-based methodology for classification of 3D Lung nodules is proposed. Through two pathways, residual fusion and dimensional stitching of features are achieved to improve the richness of deep feature information, suppress network degradation, and maintain feature continuity. Before training, the images are pre-processed with ROI region extraction, setting of window width window level, voxel normalization and data augmentation. Finally, the average precision in benign and malignant classification task is 86%, the recall reaches 87%, and the f1-score is 86%. The average precision in texture type of classification is 82%, the recall is 83%, and the f1-score is 82%. © 2022 IEEE.

关键词： Classification (of information)

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Large-scale video compression： recent advances and challenges

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Frontiers of Computer Science 2018年第5期12卷 825-839页

作者： Tao TIAN Hanli WANG Department of Computer Science and Technology Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Shanghai Engineering Research Center of Industrial Vision Perception & Intelligent Computing Shanghai 200092 China

The evolution of social network and multimedia technologies encourage more and more people to generate and upload visual information, which leads to the generation of large-scale video data. Therefore, preeminent compression technologies are highly desired to facilitate the storage and transmission of these tremendous video data for a wide variety of applications. In this paper, a systematic review of the recent advances for large-scale video compression （LSVC） is presented. Specifically, fast video coding algorithms and effective models to improve video compression efficiency are introduced in detail, since coding complexity and compression efficiency are two important factors to evaluate video coding approaches. Finally, the challenges and fu- ture research trends for LSVC are discussed.

关键词： large-scale video compression fast video coding compression efficiency

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