检索结果-内蒙古大学图书馆

A traffic monitoring stream-based real-time vehicular offence detection approach

JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS 2018年第1期22卷 53-64页

作者： Liu, Ying Ou, Guoyu Univ Chinese Acad Sci Sch Comp & Control Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management 80 Zhongguancun East Rd Beijing 100190 Peoples R China

Traffic offences are becoming increasingly serious as traffic volume increases rapidly in large cities, causing serious property damage and threatening public safety. Existing traffic monitoring systems lack the capability of detecting various types of offences in real-time. This paper proposes a novel monitoring stream-based vehicular offence detection algorithm, which discovers various types of offences from high-throughput traffic monitoring stream in real-time. An offence detecting and monitoring system is also designed and implemented. In order to achieve real-time detection, parallel computing techniques are utilized. An optimized data structure, a one producer-multiple consumer model and a re-hash strategy are proposed to reduce the synchronization cost incurred by multiple threads in the parallel implementation. Both real-world data and synthetic data are applied in the experiments. Experimental results demonstrate that the proposed algorithm is able to discover three types of offences from high-throughput traffic monitoring stream in real-time. Scalability is also observed. The experimental results indicate that the proposed system is sufficiently efficient to provide real-time offence detection for major metropolises.

关键词： intelligent traffic system parallel processing traffic surveillance traffic stream traffic offence

来源：评论

学校读者我要写书评

暂无评论

A novel self-adaptive density-based clustering algorithm for radar signal sorting

A novel self-adaptive density-based clustering algorithm for...

引用

IET International Radar Conference 2015

作者： Liu, Ying Cui, Hongyuan School of Computer and Control University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781849199940

Radar signal sorting, which de-interleaves pulse sequences from different radar emitters, is one of the key techniques in Electronic Support Measures (ESM) system of modern warfare. As the electromagnetic environment in modern electronic wars is complex and dense, conventional multi-parameter de-interleaving techniques cannot achieve satisfactory accuracy. Although clustering algorithms have shown promising results in signal sorting, they all have their own weaknesses. In this paper, we propose a novel densitybased clustering algorithm targeting radar signal sorting problem. Our clustering algorithm determines the number of clusters and the density thresholds by the distribution of the data itself rather than depending on user experience as in K-Means or other existing density-based algorithms. Multiple density thresholds may be generated when necessary. We ran our algorithm on a set of simulation radar data. It outperforms DBSCAN-based signal sorting algorithm a lot in terms of accuracy, missing sorting rate and the error sorting in each set of parameters. In addition, it is observed that our algorithm is insensitive to the parameters, ratio factor, uniformity, and quantile. Thus, our proposed self-adaptive density-based clustering algorithm is promising in solving radar signal sorting problem in real electromagnetic environment.

关键词： Sorting

来源：评论

学校读者我要写书评

暂无评论

Pedestrian detection based on the privileged information

引用

NEURAL COMPUTING & APPLICATIONS 2018年第12期29卷 1485-1494页

作者： Meng, Fan Qi, Zhiquan Tian, Yingjie Niu, Lingfeng Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China 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

The pedestrian detection is always a challenging issue in the computer vision. Unlike the object recognition problem, the detection's speed is a critical factor. In order to accelerate detection speed while maintaining competitive accuracy, in this paper we introduce a new model: twin support vector machine based on privileged information (called TSVMPI, in this paper) (Qi et al. in Neurocomputing 129:146-152, 2014) to detect pedestrian. TSVMPI uses two nonparallel hyperplane classifiers to decide the label of an unknown sample and is superior to the standard SVM, especially in the linear kernel case, resulting in a significant advantage to deal with the special task. All experimental results demonstrate our strategy's effectiveness and show that the privileged information indeed offers a significant improvement for the pedestrian detection.

关键词： Machine learning Support vector machine Privileged information Pedestrian detection

来源：评论

学校读者我要写书评

暂无评论

Analyzing Multiple Phenotypes Based on Principal Component Analysis

引用

Acta Mathematicae Applicatae Sinica 2022年第4期38卷 843-860页

作者： De-liang BU San-guo ZHANG Na LI School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100049China LSC NCMISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China

Joint analysis of multiple phenotypes can have better interpretation of complex diseases and increase statistical power to detect more significant single nucleotide polymorphisms(SNPs)compare to traditional single phenotype analysis in genome-wide association *** component analysis(PCA),as a popular dimension reduction method,has been broadly used in the analysis of multiple *** PCA transforms the original phenotypes into principal components(PCs),it is natural to think that by analyzing these PCs,we can combine information across *** PCA-based methods can be divided into two categories,either selecting one particular PC manually or combining information from all *** this paper,we propose an adaptive principle component test(APCT)which selects and combines the PCs adaptively by using Cauchy combination *** proposed method can be seen as a generalization of traditional PCA based method since it contains two existing methods as special *** simulation shows that our method is robust and can generate powerful result in various *** real data analysis of stock mice data also demonstrate that our proposed APCT can identify significant SNPs that are missed by traditional methods.

关键词： multiple phenotypes principal component analysis cauchy combination method

来源：评论

学校读者我要写书评

暂无评论

Delivery Efficiency and Supplier Performance Evaluation in China's E-retailing Industry

引用

Journal of Systems Science & Complexity 2017年第2期30卷 392-410页

作者： SHI Yong YANG Zhuofan YAN Hong TIAN Xin Research Center on Fictitious Economy&Data Science Chinese Academy of SciencesBeijing 100190China School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Research Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Department of Logistics and Maritime Studies Faculty of Business The Hong Kong Polytechnic UniversityHong KongChina

This paper focuses on overall and sub-process supply chain efficiency evaluation using a network slacks-based measure model and an undesirable directional distance model. Based on a case analysis of a leading Chinese B2 C firm W, a two-stage supply chain structure covering procurementstock and inventory-sale management is constructed. The research shows overall supply chain inefficiency is attributable to procurement-stock conversion inefficiency. From a view of operations model,the third-party platform model is more efficient than a "shop in shop" self-operated model. However,the self-operated mode performs better in product categories such as computer & Office & digital, food& drink and healthy products due to these products' delivery characteristics and consumers' shopping habits. In the logistics selection, most e-retail players are inclined to choose the hybrid model of 3 PL and self-operated logistics with the product category extension from vertical model to all-category model. These findings may help managers improve supplier-buyer relationship and strengthen supply chain management. This research offers a new explanation regarding the failure of e-retail supply chain.

关键词： Efficiency e-retail performance evaluation supply chain virtual business

来源：评论

学校读者我要写书评

暂无评论

Sentiment contagion analysis of interacting investors: Evidence from China's stock forum

引用

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2019年 523卷 246-259页

作者： Shi, Yong Tang, Ye-ran 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

This paper uses an agent-based model to study the sentiment contagion of interacting investors in China. We use text mining techniques to identify investor sentiment in stock forum, and then estimate the parameters of the sentiment contagion model to analyze sentiment formation from individual perspective. The empirical results suggest that the intensities of sentiment contagion in the periods of rising and falling are higher than that of the storming period. Intensities of sentiment contagion in emerging industries are higher than basic industries. A favorable macroeconomic environment will promote investors' optimism of the stock market, and high degree of investors' attention and discussions will accelerate contagion of such optimism. Once the stock market tends to be overheated, it may cause investors to worry about the market bubble, which will restrain further rise of investor sentiment. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Investor sentiment Sentiment contagion Text mining Stock market

来源：评论

学校读者我要写书评

暂无评论

Robust deep auto-encoding network for real-time anomaly detection at nuclear power plants

引用

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2022年 163卷 438-452页

作者： Yong, Shi Linzi, Zhang Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100049 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China

Detecting anomaly conditions in nuclear reactor is a critical issue in safety management of Nuclear Power Plants (NPPs). Conventionally, the operating status are monitored in transient data with pre-designed labels by human operators or basic diagnosis systems. Nowadays, continuous time series data from multi-sensors are increasingly collected and emerging unlabeled abnormal status are monitored during the operation, making it challenging to capture both spatial and temporal dependency at each time steps without supervised labels. In this paper, a robust unsupervised Multi-Variate Convolutional GRU Encoder-Dncoder (MVCGED) method is proposed to perform anomaly detection and fault diagnosis in multi-sensor operation time series data. Specifically, MVCGED first construct each time steps into signature matrices to maintain both spatial and temporal features via sliding windows with inner-correlation and forget mechanism. Subsequently, A CNN feature extraction network, CNN-based GRU encoding network and CNN decoding network are implemented successively to capture and reconstruct the hidden patterns of the signature matrices. Finally, the reconstruction loss are further utilized to detect anomalies and diagnose faults. Extensive empirical studies based on PCTRAN nuclear power plant operation data demonstrate that MVCGED outperforms commonly-used baseline methods.(c) 2022 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.

关键词： Nuclear Power Plant Anomaly detection Multi-sensor Time series ConvGRU

来源：评论

学校读者我要写书评

暂无评论

Deep learning-based feature engineering for stock price movement prediction

引用

knowledge-BASED SYSTEMS 2019年 164卷 163-173页

作者： Long, Wen Lu, Zhichen Cui, Lingxiao 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

Stock price modeling and prediction have been challenging objectives for researchers and speculators because of noisy and non-stationary characteristics of samples. With the growth in deep learning, the task of feature learning can be performed more effectively by purposely designed network. in this paper, we propose a novel end-to-end model named multi-filters neural network (MFNN) specifically for feature extraction on financial time series samples and price movement prediction task. Both convolutional and recurrent neurons are integrated to build the multi-filters structure, so that the information from different feature spaces and market views can be obtained. We apply our MFNN for extreme market prediction and signal-based trading simulation tasks on Chinese stock market index CSI 300. Experimental results show that our network outperforms traditional machine learning models, statistical models, and single-structure(convolutional, recurrent, and LSTM) networks in terms of the accuracy, profitability, and stability. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Stock price prediction Feature engineering Deep learning

来源：评论

学校读者我要写书评

暂无评论

Investor sentiment identification based on the universum SVM

引用

NEURAL COMPUTING & APPLICATIONS 2018年第2期30卷 661-670页

作者： Long, Wen Tang, Ye-ran Tian, Ying-jie 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

Universum refers to additional samples which contain priori knowledge for classification but belonging to none of the class. It has been proved that universum positioned "in between" the two classes obtain better results. Since opinions on stock market defined as investor sentiment involve quite a number of neutral views, these neutral views can be used as universum samples to better identify investor sentiment. With universum samples, this paper uses support vector machine (SVM) to classify posts on stock forum. We define bullish views as positive samples, define bearish views as negative samples, and also further discuss the situation of a 3-class problem with neutral views. Compared with standard SVM, the empirical studies with universum samples in this paper show better performance for both 2- and 3-class classifications.

关键词： Universum SVM Investor sentiment Text mining Classification

来源：评论

学校读者我要写书评

暂无评论

Analysis of slump and surge phenomenon in Chinese stock market based on sequence alignment method

引用

SOFT COMPUTING 2020年第23期24卷 18185-18202页

作者： Long, Wen Song, Linqiu Tian, Yingjie Yang, Wenning 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

The method of sequence alignment in bioinformatics is introduced into financial time series analysis, which can capture the large-scale features of the variables, suppress the noise and reveal the implicit mode of the system from different angles without strict assumptions. Based on existing methods of sequence alignment, we propose a new scoring matrix construction method for financial sequence alignment, a purpose-oriented matrix. This method can be used to extract the feature segments of the sequence. An empirical study of the stocks of Shanghai Stock Exchange and Shenzhen Stock Exchange is implemented with this new method. The results reflect the relationship between the two stock markets under the conditions of boom or bust and confirm the feasibility and effectiveness of introducing the method into the financial analysis.

关键词： Sequence alignment Stock market Financial time series Correlation Scoring matrix

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：