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Mapping homogeneous regions for flash floods using machine learning: A case study in Jiangxi province, China

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2022年 108卷

作者： Zhang, Ruojing Chen, Yuehong Zhang, Xiaoxiang Ma, Qiang Ren, Liliang Hohai Univ Coll Hydrol & Water Resources Nanjing 210098 Peoples R China China Inst Water Resources & Hydropower Res Res Ctr Flood & Drought Disaster Reduct Beijing 100038 Peoples R China

Regionalization of flash floods aims to partition a geographical space into homogeneous regions in which flash floods have similar generation mechanism. In this paper, we present a flash flood regionalization approach using machine learning algorithms to generate the flash flood regionalization map. First, the random forest algorithm is used to identify thirteen key factors of flash floods from a series of rainfall and topographic factors that have great potential to drive the occurrence of flash floods. Second, the two-stage hybrid self-organizing-map-based clustering algorithm is built to delineate the homogeneous regions of flash floods according to the identified thirteen key factors. Third, the best clustering result is selected by the clustering validity indices and it is regarded as the initial regionalization map. Last, the post-processing is implemented to obtain the final regionalization map of flash floods. A case study in the Jiangxi province of China was conducted to generate the flash flood regionalization map with eighteen homogeneous regions. The regionalization map clearly divides the historical flash flood events with different densities into different regions and it is visually satisfied. The quantitative evaluation method further confirmed that the flash flood regionalization map can provide the determinant power of 77.31% for the spatial distribution of historical flash flood events. Hence, it is a valuable option to perform the flash flood regionalization of watersheds and it is beneficial for local government in the Jiangxi province of China in future flash flood mitigation and prevention.

关键词： Regionalization Flash floods clustering algorithm clustering validity indices Jiangxi province China

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Data Mining Study of Chinese Herbal Fumigation and Washing in the Treatment of Perianal Eczema

International Core Journal of Engineering

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International Core Journal of Engineering 2022年第8期8卷 867-876页

作者： Jiyun Wu Yaping Cui Qiangqiang Gao Lu Lin Yan Ou Yanrong Zhan

Objective To explore the law of Chinese herbal fumigation and washing in the treatment of perianal eczema through data mining. Methods China National Knowledge Network (CNKI), VIP Chinese Journal Server (VIP), Wanfang Statistical Information Public Service network platform (Wanfang Statistics), National Biomedical Literature Research Information Service Network Management System (SinoMed) were retrieved from the database construction to May 2020 On the research paper on the treatment of perianal eczema by traditional Chinese medicine fumigating and washing. According to the prescriptions recorded in the collected articles, Microsoft Excel or WPS was used to establish a database to analyze the drug frequency, sexual taste and meridian return, etc. IBM SPSS Statistics 18.0 statistical software was used to analyze the data of association rules based on association rules and Apriori *** 286 articles were included, including 283 prescriptions, including 155 traditional Chinese medicines, 22 of which were more frequently used than 35. Sophora flavescens was the most frequently used drug. The medicinal taste of 155 is mainly bitter cold and simmering temperature, and mainly goes to the liver meridian. The efficacy classification is mainly heat-clearing medicine. In the association rule, the core drug pair of Sophora flavescens and Phellodendron phellodendri was the association rule (support = 73.85).Conclusion TCM fumigating and washing prescription drugs for perianal eczema are mainly heat-clearing drugs, accompanied by poison, insecticide and antipruritic drugs, antipruritic drugs, etc. Sophora flavescens, Phellodendron phellodendri, Cnidium fruit, Difuzi fruit, Fructus diorrhizae, rhizoma dioscoreae, parsnip and so on are frequently used.

关键词： Perianal Eczema Fumigation of Traditional Chinese Medicine Data Mining clustering algorithm

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Improved Artificial Bee Colony algorithm and Its Application to Fundus Retinal Blood Vessel Image Binarization

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IEEE ACCESS 2020年 8卷 123726-123734页

作者： Pan, Xiuqin Zhang, Qinrui Pan, Haichuan Minzu Univ China Sch Informat Engn Beijing 100081 Peoples R China

The content of this work is based on the characteristics of standard artificial bee colony(ABC) algorithm with weak local search ability and slow convergence speed. Then, an improved algorithm named KD-ABC is proposed. For improving the diversity and quality of the solution, it changes the generation method of honey source. In the initialization phase, it uses the cluster center generated by the K-MEANS method as the initial honey source instead of the initialization in the standard method. For improving the local optimization ability and the convergence speed without reducing the global search, we proposed a dynamic neighborhood search mechanism based on the number of iterations in terms of ABC search strategy and neighborhood selection stage. In order to find a suitable threshold to divide the grayscale image into blood vessels and background parts, we applied the characteristics of the KD-ABC algorithm to the binary processing stage of the fundus retinal blood vessel image, which lays the foundation for future image recognition.

关键词： Artificial bee colony algorithm clustering algorithms Particle swarm optimization Blood vessels Biomedical imaging Heuristic algorithms Retina Initialization clustering algorithm dynamic neighborhood search binarization

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Distributed multi-sensor multi-view fusion based on generalized covariance intersection

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SIGNAL PROCESSING 2020年 166卷

作者： Li, Guchong Battistelli, Giorgio Yi, Wei Kong, Lingjiang Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Sichuan Peoples R China Univ Firenze Dipartimento Ingn Informaz Florence Italy

Distributed multi-target tracking (DMTT) is addressed for sensors having different fields of view (FoVs). The proposed approach is based on the idea of fusing the posterior Probability Hypotheses Densities (PFIDs) generated by the sensors on the basis of the local measurements. An efficient and robust distributed fusion algorithm combining the Generalized Covariance Intersection (GCI) rule with a suitable clustering algorithm (CA) is proposed, named CA-GCI fusion algorithm. The CA is used to decompose each posterior PHD into well-separated components (clusters). For the commonly detected targets, an efficient parallelized GCI fusion strategy is proposed and analyzed in terms of L-1 error. For the remaining targets, a suitable compensation strategy is adopted so as to counteract the GCI sensitivity to independent detections while reducing the occurrence of false targets. Detailed implementation steps using a Gaussian Mixture (GM) representation of the PHDs are provided. Numerical experiments clearly confirm the effectiveness of the proposed CA-GCI fusion algorithm. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Multi-target tracking Multi-view sensors Fields-of-view GCI fusion clustering algorithm GM-PHD filter

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MMiRNA-Viewer², a bioinformatics tool for visualizing functional annotation for MiRNA and MRNA pairs in a network

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BMC BIOINFORMATICS 2020年第4期21卷 1-10页

作者： Bai, Yongsheng Baker, Steve Exoo, Kevin Dai, Xingqin Ding, Lizhong Khattak, Naureen Aslam Li, Hongtao Liu, Hannah Liu, Xiaoming Indiana State Univ Dept Biol Terre Haute IN 47809 USA Univ Michigan Dept Internal Med Ann Arbor MI 48109 USA Indiana State Univ Dept Math & Comp Sci Terre Haute IN 47809 USA St Mary Of The Woods Coll Dept Sci & Math St Mary Of The Woods IN 47876 USA Carmel High Sch 520 E Main St Carmel IN 46032 USA Univ S Florida Coll Publ Hlth USF Genom Tampa FL 33620 USA

Background Although there are many studies on the characteristics of miRNA-mRNA interactions using miRNA and mRNA sequencing data, the complexity of the change of the correlation coefficients and expression values of the miRNA-mRNA pairs between tumor and normal samples is still not resolved, and this hinders the potential clinical applications. There is an urgent need to develop innovative methodologies and tools that can characterize and visualize functional consequences of cancer risk gene and miRNA pairs while analyzing the tumor and normal samples simultaneously. Results We developed an innovative bioinformatics tool for visualizing functional annotation of miRNA-mRNA pairs in a network, known asMMiRNA-Viewer(2). The tool takes mRNA and miRNA interaction pairs and visualizes mRNA and miRNA regulation network. Moreover, ourMMiRNA-Viewer(2)web server integrates and displays the mRNA and miRNA gene annotation information, signaling cascade pathways and direct cancer association between miRNAs and mRNAs. Functional annotation and gene regulatory information can be directly retrieved from our web server, which can help users quickly identify significant interaction sub-network and report possible disease or cancer association. The tool can identify pivotal miRNAs or mRNAs that contribute to the complexity of cancer, while engaging modern next-generation sequencing technology to analyze the tumor and normal samples concurrently. We compared our tools with other visualization tools. Conclusion OurMMiRNA-Viewer(2)serves as a multitasking platform in which users can identify significant interaction clusters and retrieve functional and cancer-associated information for miRNA-mRNA pairs between tumor and normal samples. Our tool is applicable across a range of diseases and cancers and has advantages over existing tools.

关键词： miRNA mRNA Gene regulation clustering algorithm The cancer genome atlas Target prediction Visualization MMiRNA-Viewer(2)

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Partial discharge direction-of-arrival estimation in air-insulated substation by UHF wireless array and RSSI maximum likelihood estimator

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第23期14卷 5491-5497页

作者： Han, Bei Luo, Lingen Sheng, Gehao Jiang, Xiuchen Shanghai Jiao Tong Univ Dept Elect Engn 800 Dongchuan Rd Shanghai Peoples R China

The quick detection and localisation of partial discharge (PD) in an air-insulated substation (AIS) based on ultrahigh-frequency (UHF) sensor arrays are efficient for power equipment monitoring. The adopted UHF PD time difference-of-arrival (DOA) methods mainly use the time difference of electromagnetic wave signals. Thus, the system requires both a high-sampling rate and time synchronisation accuracy, leading to a high cost and large size. In this study, the feasibility and accuracy of PD DOA in an AIS were investigated using an UHF wireless sensor array and the received signal strength indicator. First, the power pattern of the designed UHF wireless sensor array was obtained via an offline experiment. Then, a statistical approach to the PD DOA method based on the maximum likelihood estimator was employed to obtain the preliminary DOA result. Finally, interpolation and clustering algorithms were used to improve the accuracy of the DOA. A laboratory test was conducted. The average error of the PD DOA was <6 degrees, and the cost-effectiveness and portability were clearly improved.

关键词： wireless sensor networks direction-of-arrival estimation maximum likelihood estimation sensor arrays synchronisation gas insulated substations partial discharge measurement interpolation air-insulated substation UHF wireless array RSSI maximum likelihood estimator partial discharge detection AIS ultrahigh-frequency sensor arrays power equipment monitoring electromagnetic wave signals high-sampling rate time synchronisation accuracy received signal strength indicator power pattern UHF wireless sensor array PD DOA method preliminary DOA result partial discharge direction-of-arrival estimation partial discharge localisation PD time difference-of-arrival methods interpolation algorithm clustering algorithm

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algorithm-Switching-Based Last-Level Cache Structure with Hybrid Main Memory Architecture

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COMPUTER JOURNAL 2020年第1期63卷 123-136页

作者： Li, Xian-Shu Yoon, Su-Kyung Kim, Jeong-Geun Burgstaller, Bernd Kim, Shin-Dug Yonsei Univ Dept Comp Sci Seoul South Korea

In this research, we designed an algorithm-switching (AS)-based last-level cache (LLC) structure with DRAM-NAND Flash hybrid main memory architecture. In order to take full advantage of previous memory access patterns and achieve high performance in the upper level of memory hierarchy, an AS-based clustering engine that uses k-means, k-medoids and k-center clustering algorithms was applied to LLC. The proposed LLC consists of three major parts, namely a set-divisible cache, and victim and clustering buffers. The victim and clustering buffers efficiently managed the history of cache blocks evicted from the set-divisible cache through the AS-based engine mechanism. The experimental results that were evaluated using Redis application and YCSB benchmark show that compared with conventional LLC structure, the proposed AS-based LLC structure could reduce the total execution time by 19.50%, power consumption by 16.31%, and NAND-Flash memory write count by 8.6%.

关键词： last-level cache management clustering algorithm DRAM-NAND flash hybrid main memory

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Consensus clustering of single-cell RNA-seq data by enhancing network affinity

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BRIEFINGS IN BIOINFORMATICS 2021年第6期22卷 bbab236页

作者： Cui, Yaxuan Zhang, Shaoqiang Liang, Ying Wang, Xiangyun Ferraro, Thomas N. Chen, Yong Tianjin Normal Univ Coll Comp & Informat Engn Tianjin 300387 Peoples R China CMSRU Dept Biomed Sci Camden NJ USA Rowan Univ Dept Mol & Cellular Biosci Camden NJ 08028 USA

Elucidation of cell subpopulations at high resolution is a key and challenging goal of single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) data analysis. Although unsupervised clustering methods have been proposed for de novo identification of cell populations, their performance and robustness suffer from the high variability, low capture efficiency and high dropout rates which are characteristic of scRNA-seq experiments. Here, we present a novel unsupervised method for Single-cell clustering by Enhancing Network Affinity (SCENA), which mainly employed three strategies: selecting multiple gene sets, enhancing local affinity among cells and clustering of consensus matrices. Large-scale validations on 13 real scRNA-seq datasets show that SCENA has high accuracy in detecting cell populations and is robust against dropout noise. When we applied SCENA to large-scale scRNA-seq data of mouse brain cells, known cell types were successfully detected, and novel cell types of interneurons were identified with differential expression of gamma-aminobutyric acid receptor subunits and transporters. SCENA is equipped with CPU+GPU (Central Processing Units+Graphics Processing Units) heterogeneous parallel computing to achieve high running speed. The high performance and running speed of SCENA combine into a new and efficient platform for biological discoveries in clustering analysis of large and diverse scRNA-seq datasets.

关键词： single-cell RNA-seq clustering algorithm bioinformatics cell typing

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Modeling of the development of the fetus cognitive map from the sensorimotor system

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EGYPTIAN INFORMATICS JOURNAL 2020年第4期21卷 191-199页

作者： Ahmed, Eman El Khoribi, Reda A. Darwish, Gamal Muzy, Alexandre Bernot, Gilles Cairo Univ Fac Comp & Informat Giza Egypt Univ Cote dAzur CNRS Lab Informat Signaux & Syst Sophia Antipolis I3S UNS Nice France

The human cognitive map formation is still an open question. Based on biological facts, the cognitive map origin goes back to the age of the fetus human. In this paper, our aim is to provide a possible answer to that question. Accordingly, we present a theoretical model of the development of the cognitive map of a fetus human using its sensorimotor data. We define positions of the cognitive map as associations between high-level perceptions created from different sensory sources. We use a proposed method referred to as Frequency-based-means clustering algorithm to develop the perceptions that form the association map. Our proposed theoretical model is tested on simulated data. Results show that our model is a possible candidate for demonstrating how the cognitive map is formed. In addition, comparison with k-means clustering is presented and results show that the frequency-based-means clustering has a better performance than k-means clustering and is more suitable for this application. (C) 2020 Production and hosting by Elsevier B.V. on behalf of Faculty of Computers and Artificial Intelligence, Cairo University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： clustering algorithm k-means Cognitive map Fetus Brain Sensorimotor system

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Driving Conditions of New Energy Logistics Vehicles Using Big Data Technology

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IEEE ACCESS 2020年 8卷 123891-123903页

作者： Mao, Jia Hong, Dou Ren, Runwang Li, Xiangyu Wang, Ju Nasr, Emad S. Abouel Jilin Univ Sch Transportat Changchun 130025 Peoples R China Jilin Univ Coll Automot Engn Changchun 130025 Peoples R China Jilin Univ Coll New Energy & Environm Changchun 130012 Peoples R China King Saud Univ Ind Engn Dept Coll Engn Riyadh 12372 Saudi Arabia Helwan Univ Mech Engn Dept Fac Engn Cairo Egypt

With the development of e-commerce and trade, China's logistics transportation demand has increased significantly. To improve the operation efficiency of new energy trucks, logistics transportation companies need scientific management methods. They need to analyze a large number of real driving conditions for new energy trucks. Additionally, to reduce new energy trucks' energy consumption and pollutant emissions, automobile manufacturers have increased the research and development of new energy trucks, and the analysis of new energy truck driving conditions is the basis for the technical development and evaluation of new models. The research in this article is based on an actual project of an automobile manufacturing company, consulting a large amount of relevant domestic and foreign literature, summarizing the current status of driving conditions at home and abroad, explaining the principle of data collection for the driving conditions of new energy trucks, and developing in-vehicle data for driving conditions based on the principle transmission method and remote transmission method. Using the membership function and K-means clustering algorithm to determine the attribute characteristics of the new energy truck driving condition analysis, a truck driving condition analysis model is built, the software function of the model is designed, and a small amount of sample data is used to import the model instance to verify the model effectiveness. Finally, based on the constructed new energy truck driving condition analysis model, big data technology was used to perform a big data analysis experiment on the actual operation data of 200 trucks of an automobile manufacturing enterprise. The Spark big data calculation framework was used to perform stream calculation and offline analysis calculations on a large amount of data from the new energy trucks. The results show that the operating efficiency of the new energy truck driving condition analysis method using big data techn

关键词： New energy trucks driving conditions big data analysis clustering algorithm membership function

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