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6th IEEE information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023

作者： Xia, Haolin Sun, Sirui Liu, Xinyu Zhou, Ju Wang, Hanpu Chen, Tong School of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Non-Linear Circuit and Intelligent Information Processing of Chongqing Chongqing China Institute of Legal Psychology and Intelligent Computing Chongqing China

ISBN: (纸本)9781665460033

Stress is an emotional state that affects our lives, so it is vital to detect it accurately. Numerous studies have shown that tissue oxygen saturation (StO2) is an effective physiological signal for identifying different stress states. However, previous StO2 feature extraction methods relied too heavily on baselines to be realized in practice. In this paper, a two-stream network combining shallow and deep features, called CRNet, is proposed for baseline-independent StO2 stress classification. In CRNet, shallow features are extracted using a CNN network and deep features are extracted using a ResNet network. For the extracted high-dimensional deep features, MLP module is employed for dimensionality reduction, which can effectively combine the two levels of features and improve the feature extraction capability of the network. Experimental results show that CRNet can significantly improve the performance of StO2 stress classification to the state-of-the-art (SOTA) even when using only raw imbalanced data. © 2023 IEEE.

关键词： Feature extraction

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Edge computing based multi-objective equilibrium task unloading of Internet of Vehicles 2

Edge computing based multi-objective equilibrium task unload...

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2nd International Conference on Electronic information Technology, EIT 2023

作者： Wang, Lingjiao Tang, Chen Guo, Hua Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiang Tan University 411105 China School of Automation and Electronic Information Xiang Tan University 411105 China

ISBN: (数字)9781510666627

ISBN: (纸本)9781510666610

The requirements of the Internet of Vehicles on the quality of service (QoS) of computing task offloading are constantly improving. The processing mode of edge computing, which deploys computing, bandwidth and storage resources near the user side, effectively improves the user experience. However, the computing capacity of edge servers is limited. Task offloading requests are overloaded and inefficient due to uneven server loads. Traditional cloud computing consumes a lot of energy and time. To solve the above problem, a KDQN based edge computing balanced task unloading mechanism is proposed: Q learning and convolutional neural network are used to obtain the optimal unloading strategy according to the returns of each step and continuous iterative updates, which reduces the convergence time of task unloading;K-means clustering is used to distribute part of the load of the overloaded server to the server with the closest European distance to achieve server load balancing. The experimental results show that compared to the existing offload mechanism, the new mechanism can reduce the average server energy consumption by 12.8%, the average service delay by 8.2%, and the average overload rate by 29.8%. © 2023 SPIE.

关键词： Quality of service

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Camera Abnormal Movement and Foreign Object Invasion Detection Based on Cumulative Edge Distribution Probability Model

Camera Abnormal Movement and Foreign Object Invasion Detecti...

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2018 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2018

作者： Yu, Xiangru Cai, Fudong Dou, Yimin Li, Jinping Institute of Pattern Recognition and Intelligent System School of Information Science and Engineering University of Jinan Jinan China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China Shandong College University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-year University of Jinan Jinan China Shandong Senter Electronic Co. Ltd Zibo China

ISBN: (纸本)9781728105512

In practice, there often exist some occasions where video surveillance can only be realized by using rechargeable batteries due to the high cost of power supply. In order to extend the battery life, images can only be taken at a certain interval. Obviously, image difference is an important method to obtain various changes in this case. Among these changes, foreign invasion is one of the focuses. However, due to bolt shedding, abnormal weather and other factors, the camera may have abnormal movement which may seriously affect the detection effect of image difference method. Therefore, it is an urgent issue to find an effective way to detect the abnormal movement of camera and improve the detection accuracy of foreign invasion. Considering the characteristics of this kind of image sequences, we propose an effective algorithm to detect the camera abnormal movement and foreign object invasion based on a cumulative edge distribution probability model. Since sky region is relatively simple, we only discuss changes in sky region to detect camera abnormal movement. Our algorithm has 6 basic steps: firstly, segment the sky region;secondly, extract the edge information of the current image and the preceding adjacent image in the image sequence;thirdly, determine if the edge information of two adjacent images coincide. If consistent, then go to the next step, otherwise, it indicates that the camera has abnormal movement, then alarm;fourthly, calculate the cumulative edge probability distribution model in the sky region by using the historical image sequence;fifthly, by using adaptive Parzen window, determine if foreign object invasion exists by comparing the probability model of cumulative edge distribution with edge distribution of the current image;sixthly, update the cumulative edge distribution probability model in sky region. The algorithm achieves good results in practical applications. Through the test of thousands of images taken in the wild, the detection accuracy of

关键词： Cameras

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Android Oriented Image Visualization Exploratory Search 5th

Android Oriented Image Visualization Exploratory Search

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

作者： Ouyang, Jianquan He, Hao Chu, Minnan Chen, Dong Tang, Huanrong College of Information Engineering Xiangtan University Xiangtan411105 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education College of Information Engineering Xiangtan University Xiangtan411105 China

ISBN: (纸本)9789811501203

When using traditional image search engines, smartphone users often complain about their poor user interface including poor user experience, and weak interaction. Moreover, users are unable to find a desired picture partly due to the unclear key words. This paper proposes the word-bag co-occurrence scheme by defining the correlation between images. Through exploratory search, the search range can be expanded and help users refine retrieval of the expected images. Firstly, the proposed scheme applied the bag of visual words (BoVW) vector by processing images on Hadoop. Secondly, similarity matrix was constructed to organize the image data. Finally, the images in which users were interested was visually displayed on the android mobile phone via exploratory search. Comparing the proposed method to current methods by testing with image data sets on ImageNet, the experimental results show that the former is superior to the latter on visual representation, and the proposed scheme can provide a better user experience. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Visualization

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Design and practice of virtual simulation of industrial Internet 4

Design and practice of virtual simulation of industrial Inte...

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4th International Conference on Advances in Computer Technology, information Science and Communications, CTISC 2022

作者： Wu, Qingjin Zeng, Taisheng Zhu, Daxin Cai, Danlin Quanzhou Normal University Laboratory and Equipment Management Office Quanzhou China Quanzhou Normal University School of Mathematics and Computer Science Fujian Provincial Key Laboratory of Data Intensive Computing Quanzhou China Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Quanzhou China Quanzhou Normal University School of Mathematics and Computer Science Key Laboratory of Intelligent Computing and Information Quanzhou China

ISBN: (数字)9781665458726

ISBN: (纸本)9781665458726

In the cultivating new engineering talents of information discipline, people tend to adopt virtual simulation to simulate production line scenes of enterprises' intelligent manufacturing so as to solve the problem that students fail to go deep into the production lines of industrial fields for scene practice and that they can not collect, analyze and optimize data in the real intelligent manufacturing production lines. The innovative talent training methods which combine regional industries can be used to improve students' ability to analyze, design, debug, operate, maintain and optimize the intelligent manufacturing big data and to master methods and ideas for solving complex engineering problems with big data technology. They can also provide beneficial experience references for the training of new engineering talents of information discipline. © 2022 IEEE.

关键词： Big data

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Bayesian localization microscopy based on intensity distribution of fluorophores

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Protein & Cell 2015年第3期6卷 211-220页

作者： Fan Xu Mingshu Zhang Zhiyong Liu Pingyong Xu Fa Zhang Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Laboratory of Non Coding RNA Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China

Super-resolution microscopy techniques have overcome the limit of optical diffraction. Recently, the Bayesian analysis of Bleaching and Blinking data （3B） method has emerged as an important tool to obtain super-resolution fluorescence images. 3B uses the change in information caused by adding or removing fiuorophores in the cell to fit the date. When adding a new fluorophore, 3B selects a random initial position, optimizes this position and then determines its reliability. However, the fluorophores are not evenly distributed in the entire image region, and the fluorescence intensilty at a given position positively correlates with the probability of observing a fluorophore at this position. In this paper, we present a Bayesian analysis of Bleaching and Blinking microscopy method based on fluorescence intensity distribution （FID3B）. We utilize the intensity distribution to select more reliable positions as the initial positions of fluorophores. This approach can improve the reconstruction results and significantly reduce the computational time. We validate the perfor. mance of our method using both simulated date and experimental date from cellular structures. The results confirm the effectiveness of our method.

关键词： super-resolution fluorescence image 3B,intensity distribution

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Salient region detection and segmentation for general object recognition and image understanding

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Science China(information Sciences) 2011年第12期54卷 2481-2490页

作者： HUANG TieJun 1 , TIAN YongHong 1 , LI Jia 2 & YU HaoNan 11 National Engineering laboratory for Video Technology, School of Electrical Engineering and Computer Science, Peking University, Beijing 100871, China 2 key laboratory of intelligent information processing, Institute of computing Technology, Chinese Academy of Sciences, Beijing 100190, China 1. National Engineering Laboratory for Video Technology School of Electrical Engineering and Computer Science Peking University Beijing 100871 China2. Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

General object recognition and image understanding is recognized as a dramatic goal for computer vision and multimedia retrieval. In spite of the great efforts devoted in the last two decades, it still remains an open problem. In this paper, we propose a selective attention-driven model for general image understanding, named GORIUM (general object recognition and image understanding model). The key idea of our model is to discover recurring visual objects by selective attention modeling and pairwise local invariant features matching on a large image set in an unsupervised manner. Towards this end, it can be formulated as a four-layer bottom-up model, i.e., salient region detection, object segmentation, automatic object discovering and visual dictionary construction. By exploiting multi-task learning methods to model visual saliency simultaneously with the bottom-up and top-down factors, the lowest layer can effectively detect salient objects in an image. The second layer exploits a simple yet effective learning approach to generate two complementary maps from several raw saliency maps, which then can be utilized to segment the salient objects precisely from a complex scene. For the third layer, we have also implemented an unsupervised approach to automatically discover general objects from large image set by pairwise matching with local invariant features. Afterwards, visual dictionary construction can be implemented by using many state-of-the-art algorithms and tools available nowadays.

关键词： object recognition image understanding visual saliency salient object segmentation visual dictionary

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Parallel Feature Selection Using Positive Approximation Based on MapReduce

Parallel Feature Selection Using Positive Approximation Base...

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The 2014 10th International Conference on Natural Computation (ICNC 2014) and the 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2014)(第十届自然计算和第十一届模糊系统与知识发现国际会议)

作者： Qing He Zhongzhi Shi Fuzhen Zhuang Xiaohu Cheng The Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese A The Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese A

ISBN: (纸本)9781479951499

Over the last few decades,feature selection has been a hot research area in pattern recognition and machine learning,and many famous feature selection algorithms have been *** them,feature selection using positive approximation(FSPA)is an accelerator for traditional rough set based feature selection algorithms,which can significantly reduce the running ***,FSPA still cannot handle large scale and high dimension dataset due to the memory *** this paper,we propose a parallel implementation of FSPA using MapReduce framework,which is a programming model for processing large scale *** experimental results demonstrate that the proposed algorithm can process large scale and high dimension dataset efficiently on commodity computers.

关键词： Computers Approximation methods Approximation algorithms Algorithm design and analysis Arrays Machine learning algorithms Computational modeling

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FEATURE TRANSFORMATION FOR EFFICIENTLY IMPROVING PERFORMANCE OF HSC

FEATURE TRANSFORMATION FOR EFFICIENTLY IMPROVING PERFORMANCE...

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2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

作者： FU-ZHEN ZHUANG QING HE ZHONG-ZHI SHI The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese

Hyper Surface Classification (HSC) is a novel classification method based on hyper surface which is put forward by Qing He, etc. Experiments show that HSC can efficiently and accurately classify large-size data in two dimensional space and three-dimensional space. Actually, it is difficult to deal with high dimensional data for HSC. So the dimension reduction (data rearrangement) and ensemble methods (feature subspace) are proposed for HSC. But the method based on ensemble will produce many inconsistent and repetitious data in some density dataset, which influence the classification ability of HSC. To solve the problem, a simple and effective kind of data feature transformation method for enhancing performance of HSC is proposed in this paper. The experimental results show that this method can efficiently reduce the inconsistent and repetitious data, efficiently utilize the data information, and remarkably improve the classification performance of HSC.

关键词： Hyper Surface Classification Ensemble Classification Performance Feature Transformation.

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THE EFFECT OF SCALE TRANSFORMATION FOR HYPER SURFACE CLASSIFICATION METHOD

THE EFFECT OF SCALE TRANSFORMATION FOR HYPER SURFACE CLASSIF...

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2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

作者： QING HE ZHONG-ZHI SHI XU-DONG MA FU-ZHEN ZHUANG The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese

Hyper Surface Classification (HSC), which is based on Jordan Curve Theorem in Topology, has been proven to be a simple and effective method for classifying a large database in our previous work. In this paper, through theoretical analysis, we find that different scales may affect the training process of HSC, which influences its classification performance. To investigate the impact and find a suitable scale, the scale transformation of HSC is studied. The experimental results show that the accuracy increases with the shrinkage of the scale, but the effect is tiny. Furthermore, we find that some samples become inconsistent and repetitious when the scale is adequately small, because of the powerlessly providing enough precision by the data type of computer. Fortunately, HSC can get a high performance with common scales as experiments exhibit.

关键词： Hyper Surface Classification HSC Scale Transformation

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