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An Improved Algorithm for Mining Correlation Item Pairs

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Computers, Materials & Continua 2020年第10期65卷 337-354页

作者： Tao Li Yongzhen ren Yongjun Ren Jinyue Xia College of Electronic and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China College of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China International Business Machines Corporation(IBM) New YorkUSA

Apriori algorithm is often used in traditional association rules mining,searching for the mode of higher frequency.Then the correlation rules are obtained by detected the correlation of the item sets,but this tends to ignore low-support high-correlation of association rules.In view of the above problems,some scholars put forward the positive correlation coefficient based on Phi correlation to avoid the embarrassment caused by Apriori algorithm.It can dig item sets with low-support but high-correlation.Although the algorithm has pruned the search space,it is not obvious that the performance of the running time based on the big data set is reduced,and the correlation pairs can be meaningless.This paper presents an improved mining algorithm with new association rules based on interestingness for correlation pairs,using an upper bound on interestingness of the supersets to prune the search space.It greatly reduces the running time,and filters the meaningless correlation pairs according to the constraints of the redundancy.Compared with the algorithm based on the Phi correlation coefficient,the new algorithm has been significantly improved in reducing the running time,the result has pruned the redundant correlation pairs.So it improves the mining efficiency and accuracy.

关键词： Interestingness item pairs positive correlation association rules redundancy

Dense Spatial-Temporal Graph Convolutional Network Based on Lightweight OpenPose for Detecting Falls

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Computers, Materials & Continua 2023年第10期77卷 47-61页

作者： Xiaorui Zhang Qijian Xie Wei Sun Yongjun Ren Mithun Mukherjee School of Computer Science Nanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing210044China

Fall behavior is closely related to high mortality in the elderly,so fall detection becomes an important and urgent research area.However,the existing fall detection methods are difficult to be applied in daily life due to a large amount of calculation and poor detection accuracy.To solve the above problems,this paper proposes a dense spatial-temporal graph convolutional network based on lightweight OpenPose.Lightweight OpenPose uses MobileNet as a feature extraction network,and the prediction layer uses bottleneck-asymmetric structure,thus reducing the amount of the network.The bottleneck-asymmetrical structure compresses the number of input channels of feature maps by 1×1 convolution and replaces the 7×7 convolution structure with the asymmetric structure of 1×7 convolution,7×1 convolution,and 7×7 convolution in parallel.The spatial-temporal graph convolutional network divides the multi-layer convolution into dense blocks,and the convolutional layers in each dense block are connected,thus improving the feature transitivity,enhancing the network’s ability to extract features,thus improving the detection accuracy.Two representative datasets,Multiple Cameras Fall dataset(MCF),and Nanyang Technological University Red Green Blue+Depth Action Recognition dataset(NTU RGB+D),are selected for our experiments,among which NTU RGB+D has two evaluation benchmarks.The results show that the proposed model is superior to the current fall detection models.The accuracy of this network on the MCF dataset is 96.3%,and the accuracies on the two evaluation benchmarks of the NTU RGB+D dataset are 85.6%and 93.5%,respectively.

关键词： Fall detection lightweight OpenPose spatial-temporal graph convolutional network dense blocks

A Sea Ice Recognition Algorithm in Bohai Based on Random Forest

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Computers, Materials & Continua 2022年第11期73卷 3721-3739页

作者： Tao Li Di Wu Rui Han Jinyue Xia Yongjun Ren School of Artificial Intelligence/School of Future Technology Nanjing University of Information Science and TechnologyNanjing210044China Unit 93117 of PLA PLAJiangsu210000China International Business Machines Corporation(IBM) NY100014USA School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China

As an important maritime hub,Bohai Sea Bay provides great convenience for shipping and suffers from sea ice disasters of different severity every winter,which greatly affects the socio-economic and development of the region.Therefore,this paper uses FY-4A(a weather satellite)data to study sea ice in the Bohai Sea.After processing the data for land removal and cloud detection,it combines multi-channel threshold method and adaptive threshold algorithm to realize the recognition of Bohai Sea ice under clear sky conditions.The random forests classification algorithm is introduced in sea ice identification,which can achieve a certain effect of sea ice classification recognition under cloud cover.Under non-clear sky conditions,the results of Bohai Sea ice identification based on random forests have been improved,and the algorithm can effectively identify Bohai Sea Ice and can improve the accuracy of sea ice identification,which lays a foundation for the accuracy and stability of sea ice identification.It realizes sea ice identification in the Bohai Sea and provides data support and algorithm support for marine climate forecasting related departments.

关键词： FY-4A random forests Bohai Sea Ice sea ice identification adaptive threshold

Privacy-Preserving Recommendation Based on Kernel Method in Cloud Computing

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Computers, Materials & Continua 2021年第1期66卷 779-791页

作者： Tao Li Qi Qian Yongjun Ren Yongzhen ren Jinyue Xia College of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China College of Electronic and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China College of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China International Business Machines Corporation(IBM) NYUSA

The application field of the Internet of Things(IoT)involves all aspects,and its application in the fields of industry,agriculture,environment,transportation,logistics,security and other infrastructure has effectively promoted the intelligent development of these aspects.Although the IoT has gradually grown in recent years,there are still many problems that need to be overcome in terms of technology,management,cost,policy,and security.We need to constantly weigh the benefits of trusting IoT products and the risk of leaking private data.To avoid the leakage and loss of various user data,this paper developed a hybrid algorithm of kernel function and random perturbation method based on the algorithm of non-negative matrix factorization,which realizes personalized recommendation and solves the problem of user privacy data protection in the process of personalized recommendation.Compared to non-negative matrix factorization privacy-preserving algorithm,the new algorithm does not need to know the detailed information of the data,only need to know the connection between each data;and the new algorithm can process the data points with negative characteristics.Experiments show that the new algorithm can produce recommendation results with certain accuracy under the premise of preserving users’personal privacy.

关键词： IoT kernel method privacy-preserving personalized recommendation random perturbation

Secure Provenance of Electronic Records Based on Blockchain

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Computers, Materials & Continua 2020年第11期65卷 1753-1769页

作者： Qirun Wang Fujian Zhu Sai Ji Yongjun Ren School of Engineering and Technology University of HertfordshireHertfordUK School of Computer and Software Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologyNanjing University of Information Science&TechnologyNanjing210044China

At present,the provenance of electronic records is stored centrally.The centralized way of information storage has huge risks.Whether the database itself is destroyed or the communication between the central database and the external interruption occurs,the provenance information of the stored electronic records will not play its role.At the same time,uncertainties such as fires and earthquakes will also pose a potential threat to centralized databases.Moreover,the existing security provenance model is not specifically designed for electronic records.In this paper,a security provenance model of electronic records is constructed based on PREMIS and METS.Firstly,this paper analyses the security requirements of the provenance information of electronic records.Then,based on the characteristics of blockchain decentralization,and combined with coding theory,a distributed secure provenance guarantees technology of electronic records is constructed,which ensures the authenticity,integrity,confidentiality and reliability of the provenance information.

关键词： Secure provenance electronic records blockchain

Ground Nephogram Recognition Algorithm Based on Selective Neural Network Ensemble

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Computers, Materials & Continua 2020年第5期63卷 621-631页

作者： Tao Li Xiang Li Yongjun Ren Jinyue Xia College of Electronic and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China College of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China International Business Machines Corporation(IBM) New YorkUSA

In view of the low accuracy of traditional ground nephogram recognition model,the authors put forward a k-means algorithm-acquired neural network ensemble method,which takes BP neural network ensemble model as the basis,uses k-means algorithm to choose the individual neural networks with partial diversities for integration,and builds the cloud form classification model.Through simulation experiments on ground nephogram samples,the results show that the algorithm proposed in the article can effectively improve the Classification accuracy of ground nephogram recognition in comparison with applying single BP neural network and traditional BP AdaBoost ensemble algorithm on classification of ground nephogram.

关键词： Cloud shape k-means BP neural network adaboost

Outlier Detection of Mixed Data Based on Neighborhood Combinatorial Entropy

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Computers, Materials & Continua 2021年第11期69卷 1765-1781页

作者： Lina Wang Qixiang Zhang Xiling Niu Yongjun Ren Jinyue Xia School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai519080China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China International Business Machines Corporation(IBM) New York10504USA

Outlier detection is a key research area in data mining technologies,as outlier detection can identify data inconsistent within a data set.Outlier detection aims to find an abnormal data size from a large data size and has been applied in many fields including fraud detection,network intrusion detection,disaster prediction,medical diagnosis,public security,and image processing.While outlier detection has been widely applied in real systems,its effectiveness is challenged by higher dimensions and redundant data attributes,leading to detection errors and complicated calculations.The prevalence of mixed data is a current issue for outlier detection algorithms.An outlier detection method of mixed data based on neighborhood combinatorial entropy is studied to improve outlier detection performance by reducing data dimension using an attribute reduction algorithm.The significance of attributes is determined,and fewer influencing attributes are removed based on neighborhood combinatorial entropy.Outlier detection is conducted using the algorithm of local outlier factor.The proposed outlier detection method can be applied effectively in numerical and mixed multidimensional data using neighborhood combinatorial entropy.In the experimental part of this paper,we give a comparison on outlier detection before and after attribute reduction.In a comparative analysis,we give results of the enhanced outlier detection accuracy by removing the fewer influencing attributes in numerical and mixed multidimensional data.

关键词： Neighborhood combinatorial entropy attribute reduction mixed data outlier detection

A novel coordinated control for NZEB clusters to minimize their connected grid overvoltage risks

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Building Simulation 2022年第10期15卷 1831-1848页

作者： Yelin Zhang Norman Chung Fai Tse Haoshan ren yongjun Sun Division of Building Science and Technology City University of Hong KongKowloonHong KongChina

The increasing applications of net-zero energy buildings (NZEBs) will lead to more frequent and larger energy interactions with the connected power grid, thereby being able to result in severe grid overvoltage risks. Control optimization has been proven effective to reduce such risks. Existing controls have oversimplified the overvoltage quantification by simply using the aggregated power exchanges to represent the connected grid overvoltages. Ignoring the complex voltage influences among the grid nodes, such oversimplification can easily result in low-accuracy impact evaluations of the NZEB-grid energy interactions, thereby causing non-optimal/unsatisfying overvoltage mitigations. Therefore, this study proposes a novel coordinated control method in which a power-distribution-network model has been adopted for more accurate overvoltage quantification. Meanwhile, the battery operations of individual NZEBs are iteratively coordinated using a sequential optimization approach for achieving the global optimum with substantially reduced computation complexity. For verifications, the proposed coordinated control has been systematically compared with an uncoordinated control and a conventional coordinated control in grid overvoltage minimization. The study results show that the overvoltage improvements can reach 23.5% and 12.3% compared with the uncoordinated control and the conventional coordinated control, respectively. The reasons behind the improvements have also been analyzed in detail. The proposed coordinated control can be used in practice to improve NZEB-clusters’ grid friendliness.

关键词： net-zero energy building coordinated control optimization,genetic algorithm overvoltage quantification grid friendliness

Digital Continuity Guarantee Approach of Electronic Record Based on Data Quality Theory

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Computers, Materials & Continua 2020年第6期63卷 1471-1483页

作者： Yongjun Ren Jian Qi Yaping Cheng Jin Wang Osama Alfarraj School of Computer and Software Engineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia

Since the British National Archive put forward the concept of the digital continuity in 2007,several developed countries have worked out their digital continuity action plan.However,the technologies of the digital continuity guarantee are still lacked.At first,this paper analyzes the requirements of digital continuity guarantee for electronic record based on data quality theory,then points out the necessity of data quality guarantee for electronic record.Moreover,we convert the digital continuity guarantee of electronic record to ensure the consistency,completeness and timeliness of electronic record,and construct the first technology framework of the digital continuity guarantee for electronic record.Finally,the temporal functional dependencies technology is utilized to build the first integration method to insure the consistency,completeness and timeliness of electronic record.

关键词： Electronic record digital continuity data quality