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Graph dynamical networks for forecasting collective behavior of active matter

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Chinese Physics B 2022年第11期31卷 438-444页

作者：刘彦君王瑞赵偲郑文 Institute of Public-Safety and big data College of Data ScienceTaiyuan University of TechnologyTaiyuan 030024China center of information Management and Development Taiyuan University of TechnologyTaiyuan 030024China center for Healthy big data Changzhi Medical CollegeChangzhi 046000China

After decades of theoretical studies,the rich phase states of active matter and cluster kinetic processes are still of research interest.How to efficiently calculate the dynamical processes under their complex conditions becomes an open problem.Recently,machine learning methods have been proposed to predict the degree of coherence of active matter systems.In this way,the phase transition process of the system is quantified and studied.In this paper,we use graph network as a powerful model to determine the evolution of active matter with variable individual velocities solely based on the initial position and state of the particles.The graph network accurately predicts the order parameters of the system in different scale models with different individual velocities,noise and density to effectively evaluate the effect of diverse condition.Compared with the classical physical deduction method,we demonstrate that graph network prediction is excellent,which could save significantly computing resources and time.In addition to active matter,our method can be applied widely to other large-scale physical systems.

关键词： active matter graph network improvement of Vicsek collective motion

Privacy-preserving deep learning techniques for wearable sensor-based big data applications

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Virtual Reality & Intelligent Hardware 2022年第3期4卷 210-222页

作者： Rafik HAMZA Minh-Son DAO Integrated big data Research center|NICT-National Institute of information and Communications Technology TokyoJapan

Wearable technologies have the potential to become a valuable influence on human daily life where they may enable observing the world in new ways,including,for example,using augmented reality(AR)applications.Wearable technology uses electronic devices that may be carried as accessories,clothes,or even embedded in the user's body.Although the potential benefits of smart wearables are numerous,their extensive and continual usage creates several privacy concerns and tricky information security challenges.In this paper,we present a comprehensive survey of recent privacy-preserving big data analytics applications based on wearable sensors.We highlight the fundamental features of security and privacy for wearable device applications.Then,we examine the utilization of deep learning algorithms with cryptography and determine their usability for wearable sensors.We also present a case study on privacy-preserving machine learning techniques.Herein,we theoretically and empirically evaluate the privacy-preserving deep learning framework's performance.We explain the implementation details of a case study of a secure prediction service using the convolutional neural network(CNN)model and the Cheon-Kim-Kim-Song(CHKS)homomorphic encryption algorithm.Finally,we explore the obstacles and gaps in the deployment of practical real-world applications.Following a comprehensive overview,we identify the most important obstacles that must be overcome and discuss some interesting future research directions.

关键词： Wearable technology Augmented reality Privacy-preserving Deep learning big data Secure prediction service

A Correlation Visual Analytics System for Air Quality

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Chinese Journal of Electronics 2018年第5期27卷 920-926页

作者： DU Yi Abish Malik ZHOU Lianke ZHOU Yuanchun Department of big data Technology and Application Development Computer Network Information CenterChinese Academy of Sciences

A visual analytics system is proposed to reveal the lead/lag correlation when air pollution is detected. In this system, an Overview + Detail approach is utilized for analyzing the correlation of air quality under both the spatial and temporal dimensions and different spatial-temporal scales. An annular container is proposed to preserve the context spatial information while the zoom level of the map changes. Based on the annular container, several analysis techniques such as STL decomposition view and correlation algorithm are integrated.

关键词： Spatial-temporal visualization Time series visualization Correlation analysis Air pollution

big data in support of the Sustainable Development Goals:a celebration of the establishment of the International Research center of big data for Sustainable Development Goals(CBAS)

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big Earth data 2021年第3期5卷 259-262页

作者： Huadong Guo Heide Hackmann Ke Gong International Research center of big data for SDGs International Science Council World Federation of Engineering Organizations

In the last century the impacts of human activity on natural processes that sustain the Earth’s biosphere,atmosphere,hydrosphere and lithosphere and that provide the bed-rock of human life support systems,have grown to the extent that they pose a credible existential threat to humanity.Today,the biggest challenge for science,technology and innovation(STI)is to contribute to the pursuit of global sustainability as exemplified in the Sustainable Development Goals(SDGs)that were adopted by the United Nations(UN)in 2015.

关键词： Sustainable pursuit humanity

big data in support of the sustainable development goals(continued):a celebration of the establishment of the International Research center of big data for Sustainable Development Goals(CBAS)

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big Earth data 2021年第4期5卷 443-444页

作者： Huadong Guo Heide Hackmann Ke Gong International Research center of big data for Sustainable Development Goals World Federation of Engineering Organizations

The rapid development of analytics and concepts in big data enables us to diversify our efforts and enhance opportunities to implement the Sustainable Development Goals(SDGs).big data improves the extent to which scientific evidence and innova-tive technological solutions can be adopted to meet these goals.However,the tools and methods of big data are still somewhat of a novelty in this respect.

关键词： Sustainable continued enable

Attribute Reduction of Hybrid Decision information Systems Based on Fuzzy Conditional information Entropy

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Computers, Materials & Continua 2024年第5期79卷 2063-2083页

作者： Xiaoqin Ma JunWang Wenchang Yu Qinli Zhang School of big data and Artificial Intelligence Chizhou UniversityChizhou247000China Anhui Education big data Intelligent Perception and Application Engineering Research center Chizhou247000China

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that

关键词： Hybrid decision information systems fuzzy conditional information entropy attribute reduction fuzzy relationship rough set theory(RST)

A structural variation genotyping algorithm enhanced by CNV quantitative transfer

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Frontiers of Computer Science 2022年第6期16卷 159-165页

作者： Tian ZHENG Xinyang QIAN Jiayin WANG Department of Computer Science and Technology School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an710049China Institute of data Science and information Quality Shaanxi Engineering Research Center of Medical and Health Big DataXi’an Jiaotong UniversityXi’an710049China

Genotyping of structural variations considering copy number variations(CNVs)is an infancy and challenging problem.CNVs,a prevalent form of critical genetic variations that cause abnormal copy numbers of large genomic regions in cells,often affect transcription and contribute to a variety of diseases.The characteristics of CNVs often lead to the ambiguity and confusion of existing genotyping features and algorithms,which may cause heterozygous variations to be erroneously genotyped as homozygous variations and seriously affect the accuracy of downstream analysis.As the allelic copy number increases,the error rate of genotyping increases sharply.Some instances with different copy numbers play an auxiliary role in the genotyping classification problem,but some will seriously interfere with the accuracy of the model.Motivated by these,we propose a transfer learning-based method to genotype structural variations accurately considering CNVs.The method first divides the instances with different allelic copy numbers and trains the basic machine learning framework with different genotype datasets.It maximizes the weights of the instances that contribute to classification and minimizes the weights of the instances that hinder correct genotyping.By adjusting the weights of the instances with different allelic copy numbers,the contribution of all the instances to genotyping can be maximized,and the genotyping errors of heterozygote variations caused by CNVs can be minimized.We applied the proposed method to both the simulated and real datasets,and compared it to some popular algorithms including GATK,Facets and Gindel.The experimental results demonstrate that the proposed method outperforms the others in terms of accuracy,stability and efficiency.The source codes have been uploaded at github/TrinaZ/CNVtransfer for academic use only.

关键词： genotyping copy number variations transfer earning

Research on the mechanical fault diagnosis method based on sound signal and IEMD-DDCNN

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International Journal of Intelligent Computing and Cybernetics 2023年第3期16卷 629-646页

作者： Haoning Pu Zhan Wen Xiulan Sun Lemei Han Yanhe Na Hantao Liu Wenzao Li Chengdu University of information Technology ChengduChina Education Department of Sichuan Province Education Informatization and Big Data CenterChengduChina Network and data Security Key Laboratory of Sichuan Province UESTCChengduChina

Purpose–The purpose of this paper is to provide a shorter time cost,high-accuracy fault diagnosis method for water pumps.Water pumps are widely used in industrial equipment and their fault diagnosis is gaining increasing attention.Considering the time-consuming empirical mode decomposition(EMD)method and the more efficient classification provided by the convolutional neural network(CNN)method,a novel classification method based on incomplete empirical mode decomposition(IEMD)and dual-input dual-channel convolutional neural network(DDCNN)composite data is proposed and applied to the fault diagnosis of water pumps.Design/methodology/approach–This paper proposes a data preprocessing method using IEMD combined with mel-frequency cepstrum coefficient(MFCC)and a neural network model of DDCNN.First,the sound signal is decomposed by IEMD to get numerous intrinsic mode functions(IMFs)and a residual(RES).Several IMFs and one RES are then extracted by MFCC features.Ultimately,the obtained features are split into two channels(IMFs one channel;RES one channel)and input into DDCNN.Findings–The Sound dataset for Malfunctioning Industrial Machine Investigation and Inspection(MIMII dataset)is used to verify the practicability of the method.Experimental results show that decomposition into an IMF is optimal when taking into account the real-time and accuracy of the diagnosis.Compared with EMD,51.52% of data preprocessing time,67.25% of network training time and 63.7%of test time are saved and also improve accuracy.Research limitations/implications–This method can achieve higher accuracy in fault diagnosis with a shorter time cost.Therefore,the fault diagnosis of equipment based on the sound signal in the factory has certain feasibility and research importance.Originality/value–This method provides a feasible method for mechanical fault diagnosis based on sound signals in industrial applications.

关键词： Fault identification Neural nets DDCNN IEMD MFCC

Method for wheat ear counting based on frequency domain decomposition of MSVF-ISCT

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information Processing in Agriculture 2023年第2期10卷 240-255页

作者： Wenxia Bao Ze Lin Gensheng Hu Dong Liang Linsheng Huang Xin Zhang National Engineering Research center for Agro-ecological big data Analysis and Application Anhui UniversityHefei 230601China

Wheat ear counting is a prerequisite for the evaluation of wheat yield.A wheat ear counting method based on frequency domain decomposition is proposed in this study to improve the accuracy of wheat yield estimation.The frequency domain decomposition of wheat ear image is completed by multiscale support value filter(MSVF)combined with improved sampled contourlet transform(ISCT).Support Vector Machine(SVM)is the classic classification and regression algorithm of machine learning.MSVF based on this has strong frequency domain filtering and generalization ability,which can effectively remove the complex background,while the multi-direction characteristics of ISCT enable it to represent the contour and texture information of wheat ears.In order to improve the level of wheat yield prediction,MSVF-ISCT method is used to decompose the ear image in multiscale and multi direction in frequency domain,reduce the interference of irrelevant information,and generate the sub-band image with more abundant information components of ear feature information.Then,the ear feature is extracted by morphological operation and maximum entropy threshold segmentation,and the skeleton thinning and corner detection algorithms are used to count the results.The number of wheat ears in the image can be accurately counted.Experiments show that compared with the traditional algorithms based on spatial domain,this method significantly improves the accuracy of wheat ear counting,which can provide guidance and application for the field of agricultural precision yield estimation.

关键词： Wheat ear counting Frequency domain decomposition Multiscale support value filter Improved sampled contourlet transform Image segmentation Morphological processing