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Design and Development of FPGA-based High-Performance Radar data Stream mining System

Procedia Computer Science 2015年 55卷 876-885页

作者： Ying Liu Pengshan Ma Hongyuan Cui School of Computer and Control University of Chinese Academy of Sciences Beijing 101408 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

Radar signal sorting is a key technique in electronic reconnaissance systems and is currently an important research direction in radar signal processing. Clustering, one of data mining techniques, has been adopted to solve radar sorting problem. However, none of the existing clustering-based methods is able to perform real-time analysis on high speed radar stream. Therefore, in this paper, we propose, design and implement a high performance FPGA-based data stream mining system to perform real-time radar signal sorting on continuous data stream. Firstly, a density-based clustering algorithm is proposed for radar signal sorting; secondly, FPGA-based program of the clustering algorithm is designed and implemented; thirdly, the FPGA board is designed and implemented. Experiments are performed on the board we designed. The results show that the proposed system can achieve real-time radar signal sorting on FPGA, and the resource consumption on FPGA is very low. The clustering algorithm is efficient in terms of accuracy.

关键词： data mining clustering signal processing FPGA parallel computing

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A divide-and-combine method for large scale nonparallel support vector machines

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NEURAL NETWORKS 2016年 75卷 12-21页

作者： Tian, Yingjie Ju, Xuchan Shi, Yong Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 101408 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

Nonparallel Support Vector Machine (NPSVM) which is more flexible and has better generalization than typical SVM is widely used for classification. Although some methods and toolboxes like SMO and libsvm for NPSVM are used, NPSVM is hard to scale up when facing millions of samples. In this paper, we propose a divide-and-combine method for large scale nonparallel support vector machine (DCNPSVM). In the division step, DCNPSVM divide samples into smaller sub-samples aiming at solving smaller subproblems independently. We theoretically and experimentally prove that the objective function value, solutions, and support vectors solved by DCNPSVM are close to the objective function value, solutions, and support vectors of the whole NPSVM problem. In the combination step, the sub-solutions combined as initial iteration points are used to solve the whole problem by global coordinate descent which converges quickly. In order to balance the accuracy and efficiency, we adopt a multi-level structure which outperforms state-of-the-art methods. Moreover, our DCNPSVM can tackle unbalance problems efficiently by tuning the parameters. Experimental results on lots of large data sets show the effectiveness of our method in memory usage, classification accuracy and time consuming. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Support vector machine Nonparallel support vector machine Large scale Clustering Divide Combine

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A Survey on Feature Selection

A Survey on Feature Selection

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4th International Conference on Information Technology and Quantitative management (ITQM) - Promoting Business Analytics and Quantitative management of Technology

作者： Miao, Jianyu Niu, Lingfeng Univ Chinese Acad Sci Sch Math Sci Beijing 100019 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Feature selection, as a dimensionality reduction technique, aims to choosing a small subset of the relevant features from the original features by removing irrelevant, redundant or noisy features. Feature selection usually can lead to better learning performance, i.e., higher learning accuracy, lower computational cost, and better model interpretability. Recently, researchers from computer vision, text mining and so on have proposed a variety of feature selection algorithms and in terms of theory and experiment, show the effectiveness of their works. This paper is aimed at reviewing the state of the art on these techniques. Furthermore, a thorough experiment is conducted to check if the use of feature selection can improve the performance of learning, considering some of the approaches mentioned in the literature. The experimental results show that unsupervised feature selection algorithms benefits machine learning tasks improving the performance of clustering. (C) 2016 The Authors. Published by Elsevier B. V.

关键词： feature selection machine learning unsupervised clustering

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Graph classification based fault detection in nuclear power plants with graph formulation 10

Graph classification based fault detection in nuclear power ...

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10th International Conference on Information Technology and Quantitative management, ITQM 2023

作者： Qu, Yi Xue, Xiaodong Shi, Yong Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska OmahaNE68182 United States

In the routine production and operation of nuclear power plants (NPPs), safety management is extremely important, and consequently, a fault detection system yielding high accuracy could provide effective decision support and prevent the accidents to the maximum extent. Most of existing studies are based on the analysis of time-series data, while researches oriented to the implementation of graph data mining are barely seen. In this paper, we propose an improved fault detection method to address this task from the perspective of graph representation learning. By calculating the similarity between indicators, the multivariate temporal sequences of the running characteristics of NPPs have been formulated as a group of graphs with corresponding labels, on which the graph convolutional network (GCN) has been introduced to detect fault samples in the way of graph classification. Compared to conventional supervised learning classifiers, our proposed methodology performs more superior identification of major faults, and the sensitivity regarding significant parameter or settings in the model design has been analyzed to investigate their optimal arrangements. The findings in this article not only present the promising prospects of introducing graph learning techniques into safety management, also indicate that various kinds of digital signals could be reformulated into graph dataset based on which the original task could be executed more effectively in the way of graph representation learning. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information Technology and Quantitative management.

关键词： Nuclear power plants

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Value of High-Quality Distribution in Front Warehouse Mode Retailing 8

Value of High-Quality Distribution in Front Warehouse Mode R...

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8th International Conference on Information Technology and Quantitative management (ITQM) - Developing Global Digital Economy after COVID-19

作者： Zhu, Jiayi Tian, Xin Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Front warehouse mode retailing originates from the e-commerce logistics industry, pre-positioning the warehouse and placing customer orders close to the community service station, so that goods can be delivered quickly. Consumers have put forward higher requirements for the timeliness of logistics distribution and the convenience of online purchases in the front warehouse mode. Using panel data of 37,362 transactions from 159 front warehouses in the online market, we study how consumers' satisfaction on products and logistics, as well as the punctuality of sorting and delivery affect sales performance. Our results show that negative review of products and logistics will significantly reduce retail sales performance. Interestingly, we find that punctual sorting and delivery increase the number of daily orders while reduce the average sales amount per order. When the punctuality of delivery service is guaranteed, consumers are willing to place orders more frequently even for some low-priced daily goods. This paper emphasizes the importance of the quality of delivery service in the front warehouse mode, and inspires retailers to give full play to the role of the front warehouse in meeting the daily needs of consumers for high-frequency and fast-moving consumer goods. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： front warehouse delivery service customer satisfaction sales performance retailing

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Improving operational efficiency through blockchain: evidence from a field experiment in cross-border trade

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PRODUCTION PLANNING & CONTROL 2024年第9期35卷 1009-1024页

作者： Tian, Xin Zhu, Jiayi Zhao, Xuan Wu, Junfeng Univ Chinese Acad Sci Sch Econ & Management Beijing Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Wilfrid Laurier Univ Lazaridis Sch Business & Econ Waterloo ON Canada Shanghai WinJoin Informat Technol Co Ltd Shanghai Peoples R China

Based on the practical needs of cross-border trade between Yunnan Province, China, and Southeast Asian countries, this paper studies the impact of blockchain technology usage on operational performance. Owing to immutability and traceability, the implementation of a blockchain-based platform solves several existing problems of cross-border trade. We conducted a field experiment to verify the effect of blockchain technology on the operational efficiency of cross-border trade companies. The results show that operational, agency, and time costs significantly decrease. Blockchain usage reduces the intermediary agencies of cross-border trade and settlement payment for traders, also improves the timeliness of business processing and the efficiency of cross-departmental business collaboration. This study provides new insights on the interface of blockchain technology and supply-chain operation management.

关键词： Blockchain technology operational efficiency cross-border trade field experiment

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Scale, congestion, efficiency and effectiveness in e-commerce firms

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ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS 2016年 20卷 171-182页

作者： Yang, Zhuofan Shi, Yong Yan, Hong Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Res Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Hong Kong Polytech Univ Dept Logist & Maritime Studies Fac Business Hong Kong Hong Kong Peoples R China

This research examines what affects operations efficiency and explains the paradox of high efficiency and low profits using a two-stage analytic framework. It provides a brief overview of efficiency evaluation research for e-commerce, and establishes a set of efficiency evaluation criteria, with the application of data envelopment analysis (DEA). The results suggest that e-commerce firms suffer from input congestion. Also, scale and technology issues, as well as low economic effectiveness, lead to the overall operational inefficiency. Business-to-business (B2B) e-commerce was more efficient and effectiveness in 2014, while online-to-offline (O2O) commerce had steadier and faster growth. In general, the performance of e-commerce firms is becoming better over time. The findings of this research can help managers to improve their firms' efficiency levels by resizing their operational scale, strengthening their technology infrastructure and applications, and improving their discretionary allocation of resources. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Congestion data envelopment analysis (DEA) E-commerce Efficiency Effectiveness Math programming Production economics Returns to scale

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Linear Twin SVM for Learning from label Proportions

Linear Twin SVM for Learning from Label Proportions

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IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Wang, Bo Chen, Zhensong Qi, Zhiquan Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Res Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Management Beijing 100190 Peoples R China

ISBN: (纸本)9781467396189

In this paper, we study the problem of learning from label proportions in which label information of data is provided in bag level. In this kind of problem, training data is grouped into various bags and only the proportions of positive instances is known. Inspired by proportion-SVM, we propose a new classification model based on twin SVM, which is also in a large-margin framework and only needs to solve two smaller problems. Avoiding making restrictive assumptions of the data, our model can learn the labels of every single instance based on group proportions information. In order to solve the non-convex problem in our new model, we propose an alternative algorithm to obtain the optimal solution efficiently. Also, we prove the effectiveness of our method in theoretical and experimental way.

关键词： Learning from label proportions Twin SVM Alternative algorithm Large-margin

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A New Fuzzy Set and Nonkernel SVM Approach for Mislabeled Binary Classification With Applications

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2017年第6期25卷 1536-1545页

作者： Tian, Ye Sun, Miao Deng, Zhibin Luo, Jian Li, Yueqing Southwest Univ Finance & Econ Sch Business Adm Chengdu 611130 Sichuan Peoples R China Southwest Univ Finance & Econ Res Ctr Big Data Chengdu 611130 Sichuan Peoples R China Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Dongbei Univ Finance & Econ Sch Management Sci & Engn Dalian 116025 Peoples R China Lamar Univ Dept Ind Engn Beaumont TX 77710 USA

This paper proposes a new approach based on the kernel-free quadratic surface support vector machine model to handle a binary classification problem with mislabeled information. Unlike the traditional fuzzy and robust support vector machine models that reduce the weights of suspectable mislabeled points or even discard them, our new method first adopts the intuitionistic fuzzy set method to detect those suspectable mislabeled points, then deletes their labels, and indiscriminately utilizes their full position information to build a semisupervised model. In this way, we can not only eliminate the negative effect of mislabeled information but also avoid the difficult task of searching proper kernel functions in classical SVM models. Besides, to improve the efficiency and accuracy, a branch-and-bound algorithm is designed to accelerate the solving process. After that, we conduct some numerical tests with both artificial and real-world datasets to verify the superior performance of our proposed method among several benchmark methods. Furthermore, the proposed method is applied to brain-computer interface and credit risk assessment. The promising results strongly demonstrate the effectiveness of our method and show its big potential in some real applications.

关键词： Binary classification intuitionistic fuzzy set (IFS) mislabeled information real applications semisupervised quadratic surface support vector machine (SSQSSVM)

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Price change prediction of Ultra high frequency financial data based on temporal convolutional network 8

Price change prediction of Ultra high frequency financial da...

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8th International Conference on Information Technology and Quantitative management (ITQM) - Developing Global Digital Economy after COVID-19

作者： Dai, Wei An, Yuan Long, Wen Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Through in-depth analysis of Ultra high frequency (UHF) stock price change data, more reasonable discrete dynamic distribution models are proposed in this paper. Firstly, we classify the price changes into several categories. Then, temporal convolutional network (TCN) is utilized to predict the conditional probability for each category. Furthermore, attention mechanism is added into the TCN architecture to model the time-varying distribution for stock price change data. Empirical research on constituent stocks of Chinese Shenzhen Stock Exchange 100 Index (SZSE 100) found that the TCN framework model and the TCN (attention) framework have a better overall performance than GARCH family models and the long short-term memory (LSTM) framework model for the description of the dynamic process of the UHF stock price change sequence. In addition, the scale of the dataset reached nearly 10 million, to the best of our knowledge, there has been no previous attempt to apply TCN to such a large-scale UHF transaction price dataset in Chinese stock market. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Temporal convolutional network Ultra high frequency Financial prediction

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