High-speed Train Control System Big Data Analysis Based on the Fuzzy RDF model and Uncertain Reasoning

Zhang, Dalin

doi:10.15837/ijccc.2017.4.2914

Cited by 60 publications

(37 citation statements)

References 17 publications

(18 reference statements)

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“…Gaining great significance in multiple research fields of natural science, scale effect describes the phenomenon through which the properties or characteristics of the observed object change once with its observation scale, especially the spatial and temporal scale (Zhang, 2017;Sethi & Karnawat, 2018). For example, (Ye, 2018) studies the scale effect of soil characteristics, and finds that the evolution law on the structural damage of yellow soil in freezing-thawing environment under different observation scales.…”

Section: Scale Effectmentioning

confidence: 99%

Computer Supported Data-driven Decisions for Service Personalization: A Variable-Scale Clustering Method

Ai¹,

Gao²,

Tang³

2020

STUD INFORM CONTROL

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The aim of this paper is to solve the object segmentation problem designed for service personalization in the context of individual athletic events. Focusing on certain personalized characteristics of the marathon contestants, the research puts forward a discovery method based on the variable-scale clustering (PCD-VSC). This method could be employed in order to obtain object segmentation based on scale similarity measurement. A case study is created based on a real dataset related to 59 marathon events which took place in several cities between 2017 and 2018, with a total number of 14,160 contestants. The numerical experimental results show that the PCD-VSC algorithm divides marathon runners into seventeen qualified clusters based on clear competitive and preference characteristics. Hence, this method could support the managers of marathon competitions in designing and implementing a personalized service scheme for the marathon contestants. Also, in comparison with the traditional VSC, the proposed method improves the overall accuracy and efficiency in analyzing categorical dataset with duplicate attribute values.

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Section: Scale Effectmentioning

confidence: 99%

Computer Supported Data-driven Decisions for Service Personalization: A Variable-Scale Clustering Method

Ai¹,

Gao²,

Tang³

2020

STUD INFORM CONTROL

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“…Reference [20] presents a framework to handle such problems and apply it to the problem of semantic scene classification. Reference [21] uses concept approximation between fuzzy ontologies based on instances to solve the heterogeneity problems. Reference [22] proposed a new version of a Probabilistic Neural Network (PNN) to tackle these kind of problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Label Classification Based on the Improved Probabilistic Neural Networ

Fan

Qin

2019

IJEM

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This paper aims to overcome the defects of the existing multi-label classification methods, such as the insufficient use of label correlation and class information. For this purpose, an improved probabilistic neural network for multi-label classification (ML-IPNN) was developed through the following steps. Firstly, the traditional PNN was structurally improved to fit in with multi-label data. Then secondly, a weight matrix was introduced to represent the label correlation and synthetize the information between classes, and the ML-IPNN was trained with the backpropagation mechanism. Finally, the classification results of the ML-IPNN on three common datasets were compared with those of the seven most popular multi-label classification algorithms. The results show that the ML-IPNN outperformed all contrastive algorithms. The research findings brought new light on multi-label classification and the application of artificial neural networks (ANNs).

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“…Parts of the important components learned by deep learning model are indicated in Fig. 6, which are the partial detection images of the second convolutional layer, such that sufficient visual clues could be acquired from the input image [23][24]. …”

Section: Generation Of Partial Characteristic Detection Imagementioning

confidence: 99%

Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent Video

Qin

Zhao

2018

Teh. vjesn.

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Abstract:In order to improve the accuracy and real-time performance of abnormal behaviour identification in massive video monitoring data, the authors design intelligent video technology based on convolutional neural network deep learning and apply it to the smart city on the basis of summarizing video development technology. First, the technical framework of intelligent video monitoring algorithm is divided into bottom (object detection), middle (object identification) and high (behaviour analysis) layers. The object detection based on background modelling is applied to routine real-time detection and early warning. The object detection based on object modelling is applied to after-event data query and retrieval. The related optical flow algorithms are used to achieve the identification and detection of abnormal behaviours. In order to improve the accuracy, effectiveness and intelligence of identification, the deep learning technology based on convolutional neural network is applied to enhance the learning and identification ability of learning machine and realize the real-time upgrade of intelligence video's "brain". This research has a good popularization value in the application field of intelligent video technology.

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High-speed Train Control System Big Data Analysis Based on the Fuzzy RDF model and Uncertain Reasoning

Cited by 60 publications

References 17 publications

Computer Supported Data-driven Decisions for Service Personalization: A Variable-Scale Clustering Method

Computer Supported Data-driven Decisions for Service Personalization: A Variable-Scale Clustering Method

Multi-Label Classification Based on the Improved Probabilistic Neural Networ

Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent Video

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