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4th International Conference on Cloud Computing and Security, ICCCS 2018

作者： Xie, Xiaolan Zheng, Qiangqing Guo, Zhihong Wang, Qi Li, Xinrong College of Information Science and Engineering Guilin University of Technology GuilinGuangxi Zhuang Autonomous Region China Guangxi Universities Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin China College of Mechanical and Control Engineering Guilin University of Technology GuilinGuangxi Zhuang Autonomous Region China

ISBN: (纸本)9783030000059

With the development of economy and technology, blockchains has become the preferred technology to solve the credit problems of both parties. Taking the sensitivity of transaction data into account, the use of blockchains technology in the B2B trading system is very appropriate. The credit problem of the B2B trading platform are solved in the article by introducing consortium blockchains technology, and a container-based cloud platform based on docker and kubernetes is built to improve the performance in the application of the consortium blockchains. First, it solves the problem of node performance bottleneck caused by the sudden increase of nodes, high-frequency transactions, and overclocking transactions. Second, it solves the problem of rapid iterative upgrade of node system versions with the rapid development of consortium blockchains technology, and securely stores consortium blockchains data and enterprise’s own sensitive data. The platform, to a certain extent, solved the credit problems of the transactions between enterprises, and at the same time reduced the time cost and the economic cost of the company. © Springer Nature Switzerland AG 2018.

关键词： Blockchain

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A Pneumonia Detection Method Based on Improved Convolutional Neural Network

A Pneumonia Detection Method Based on Improved Convolutional...

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IEEE Information technology, Networking, Electronic and Automation Control Conference

作者： Xin Li Fan Chen Haijiang Hao Mengting Li Guangxi key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin China

ISBN: (数字)9781728143903

ISBN: (纸本)9781728143910

For traditional machine learning and image processing, it is difficult to extract features, the quality of feature extraction will directly affect the classification accuracy, another problem is that the large difference between the original datasets and the target datasets in the transfer learning, lead to unsatisfactory results. In addition, the original convolution model network is too shallow, recognition rate is not high; Therefore, this paper presents an improved convolutional neural network method for pneumonia detection based on deep learning model. First, the image size in the original datasets are fixed and the appropriate batch size are used as the input of the network, Then the convolution layers and pooling layers are added on the basis of lenet-5 model, Secondly, a feature integration layer is added to construct an optimal model for accurate classification. The proposed method not only avoids the complex feature extraction process but also has fewer parameters than other classical convolutional neural networks. By using two public datasets, the second which provided by the RSNA® joint Kaggle medical image pneumonia recognition competition, after a series of experiments of single dataset and fusion of two datasets, the final accuracy rate reached 98.83% and 98.44% respectively, the test accuracy also reached 97.26% and 91.41% respectively. Compared with existing transfer learning, GoogleNet Inception V3+Data Augmentation(GIV+DA), GIV3+RF models, this model has many improvements.

关键词： Convolution Feature extraction Lung Diseases Convolutional neural networks Kernel Training

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Design of Five-Axis Camera Stabilizer Based on Quaternion Untracked Kalman Filtering Algorithm 5th

Design of Five-Axis Camera Stabilizer Based on Quaternion Un...

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5th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2019

作者： Cheng, Xiaohui Zhang, Yu Liu, Dezhi College of Information Science and Engineering Guilin University of Technology Jiangan Road No.12 Guilin541000 China Guangxi Key Laboratory of Embedded Technology and Intelligent System Jiangan Road No.12 Guilin541000 China

ISBN: (纸本)9789811501203

A five-axis camera stabilizer based on quaternion unscented Kalman filter algorithm is designed. It combined the unscented Kalman filter algorithm with the quaternion attitude solution and was solved by attitude sensor. By attitude algorithm, the motor in three directions of pitch, heading and roll in the stabilizer was accurately adjusted to control the movement of the three electronic arms. In order to improve the three-axis hand-held camera stabilizer’s performance, and to solve the jitter problem of up-and-down movement not being eliminated, two mechanical anti-shake arms were loaded under the stabilizer to balance the camera’s picture in pitch, roll, heading, and above and below five directions. Movement can maintain a stable effect. The simulation results show that the algorithm can effectively suppress the attitude angle divergence and improve the attitude calculation accuracy. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Kalman filters

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Detection of abnormal Electricity Consumption Behavior of Users Based on K-means Clustering Algorithm Fusion and Improved SVM 22

Detection of abnormal Electricity Consumption Behavior of Us...

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Proceedings of the 7th International Conference on intelligent Information Processing

作者： Haibin Huang Xiaolan Xie Yarong Liu Guilin University of Technology China Guangxi Key Laboratory of Embedded Technology and Intelligent System China

ISBN: (纸本)9781450396714

As the energy internet continues to advance, the degree of informatization of the power system has been continuously improved. However, finding out users' abnormal electricity consumption behavior is particularly important when there is a large amount of data about electricity consumption. As a solution to the above problems, this paper proposes a model for detecting abnormal consumption patterns of power users using k-means clustering algorithm and improved SVM. As a result of the experimental results, the model is able to detect abnormal consumption patterns of electricity by users very well.

关键词： Abnormal Power Consumption by Users

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Generalized Lesion Detector Based on Convolutional Neural Network 19

Generalized Lesion Detector Based on Convolutional Neural Ne...

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Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence

作者： Wu Hao Deng Jian-Zhi Guangxi key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin China

ISBN: (纸本)9781450372619

In current computer-aided diagnostic systems, existing detection functions are usually only for a specific type of lesion, such as skin lesions, pulmonary nodule lesions and liver lesions. However, in actual clinical diagnosis, many lesions are actually related. For example, pulmonary nodular lesions may metastasize and spread to lymph node areas or other body parts. The detection of a single site is not conducive to the doctor's comprehensive diagnosis of the condition. Multi-site lesion detection can detect lesion metastasis and treat it earlier, and can also explore the relationship between different lesions. In response to this situation, this thesis uses the Deeplesion dataset to establish a general lesion detection framework that can detect possible lesions through CT images of different parts of the body. Compared with the existing single-path computer-aided diagnosis system, this thesis studies and implements a general lesion detection system to explore the relationship between different lesions. This will help doctors to make a comprehensive clinical diagnosis and visualize the results. This thesis based on the Faster R-CNN network model. First it denoises and enhances the CT images. Then, the VGG16 network is used for feature extraction, and the feature map is obtained through the RPN network to obtain candidate suggestion regions. In view of the misdetection of missed detection, this thesis introduces a Gaussian weighted penalty function to improve the non-maximum suppression. Finally, Tkinter is used to create a GUI visualization interface for doctors to compare clinical diagnosis.

关键词： Faster R-CNN non-maximum suppression Tkinter CT image RPN

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A Detection Method of Abnormal Event in Crowds Based on Image Entropy 19

A Detection Method of Abnormal Event in Crowds Based on Imag...

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Proceedings of the 4th International Conference on intelligent Information Processing

作者： Haijiang Hao Xin Li Mengting Li Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin China

ISBN: (纸本)9781450361910

The occurrence of group abnormal events will pose a harm to social public safety. In order to improve the detection efficiency of abnormal events in dense populations. This paper proposes an algorithm for crowd abnormal event detection based on image entropy. The algorithm firstly extracts the amplitude of the optical flow of each frame in the video by Farneback optical flow method, and then constructs the image representation of the amplitude of the optical flow. The difference of the amplitude of the optical flow of two consecutive frames will provide us with the characteristic map; A population anomaly event is identified by comparing the entropy difference between the feature maps to a particular threshold. The experimental results show that the algorithm has high detection efficiency and good real-time performance.

关键词： Farneback optical flow method

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Research on Cloud Computing load forecasting based on LSTM-ARIMA combined model

Research on Cloud Computing load forecasting based on LSTM-A...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Xiaomin Liu Xiaolan Xie Qiang Guo Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin China

ISBN: (纸本)9798350309720

With the continuous development of cloud computing technology, the change of cloud computing resource load presents more and more complex characteristics, and efficient load prediction has become a key technology to solve the imbalance of cloud computing resource utilization. Aiming at the problem of low prediction performance of the current load prediction model, a combined prediction model LSAR based on long short-term memory network LSTM and autoregressive moving average model ARIMA is proposed by comprehensively considering the factors of prediction accuracy and prediction time. Compared with the traditional load prediction models ARIMA and LSTM, the open data set was used for the experiment. The experimental results show that the prediction accuracy of the cloud computing resource combination prediction model is significantly higher than that of other prediction models, and the real-time prediction error of resource load in the cloud environment is significantly reduced.

关键词： Cloud computing Computational modeling Time series analysis Predictive models Data models Real-time systems Resource management

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Research and Implementation of Data Storage Management system Based on Blockchain technology 19

Research and Implementation of Data Storage Management Syste...

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Proceedings of the 4th International Conference on intelligent Information Processing

作者： Deng Jian-zhi Wu Hao Guo Xuan Guangxi key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin China

ISBN: (纸本)9781450361910

This thesis develops blockchain data storage management system based on the relevant basic theory of blockchain. The system is based on an open source project of Hyperledger. Through the integration of various projects, this paper introduces a blockchain data transmission management system to provide users with a series of services related to data sharing and management across units. The system uses blockchain technology to solve the problems of encryption, disaster tolerance, sharing, and multi-unit coordination. This system uses smart contracts to ensure the security of the data used, without the involvement of third-party authorities or central proxy services, and without the need to set up a separate server. The system mainly reflects the characteristics of system decentralization, trust, secure transmission, collective maintenance, and trusted data management. The system uses the Java language, the Linux development environment, based on the fabric project, the league chain as the technology foundation, and the hierarchical structure design idea.

关键词： Blockchain

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Improved image classification algorithm based on convolutional neural network 20

Improved image classification algorithm based on convolution...

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Proceedings of the 2020 3rd International Conference on E-Business, Information Management and Computer Science

作者： Luyu Dong Fan Chen Xin Li Mengting Li Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin China

ISBN: (纸本)9781450389099

At present, convolutional neural networks are widely used in image classification, but the training effect of the network model with a relatively shallow number of layers is not good, and the model with a deeper network is prone to overfitting problems at the end. This article uses the cat and dog data set, and the selection is relatively mature The VGG16 model was improved. Add a dropout layer and a feature extraction layer to it, and perform L2 regularization on the loss function at the end to deepen the model depth and improve the fit of the entire model. The experimental results show that the improved model can greatly improve the detection accuracy.

关键词： Convolutional neural network

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Towards Reflected Object Detection: A Benchmark

arXiv

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arXiv 2024年

作者： Wang, Zhongtian Wu, You Zhou, Hui Li, Shuiwang Guilin University of Technology China Guangxi Key Laboratory of Embedded Technology and Intelligent System China

Object detection has greatly improved over the past decade thanks to advances in deep learning and large-scale datasets. However, detecting objects reflected in surfaces remains an underexplored area. Reflective surfaces are ubiquitous in daily life, appearing in homes, offices, public spaces, and natural environments. Accurate detection and interpretation of reflected objects are essential for various applications. This paper addresses this gap by introducing a extensive benchmark specifically designed for Reflected Object Detection. Our Reflected Object Detection Dataset (RODD) features a diverse collection of images showcasing reflected objects in various contexts, providing standard annotations for both real and reflected objects. This distinguishes it from traditional object detection benchmarks. RODD encompasses 10 categories and includes 21,059 images of real and reflected objects across different backgrounds, complete with standard bounding box annotations and the classification of objects as real or reflected. Additionally, we present baseline results by adapting five state-of-the-art object detection models to address this challenging task. Experimental results underscore the limitations of existing methods when applied to reflected object detection, highlighting the need for specialized approaches. By releasing RODD, we aim to support and advance future research on detecting reflected objects. Dataset and code are available at: https: //***/Tqybu-hans/RODD. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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