3DACN: 3D Augmented convolutional network for time series data

Pei, Songwen; Shen, Tianma; Wang, Xianrong; Gu, Chunhua; Zhong, Ning; Ye, Xiaochun; Xiong, Naixue

doi:10.1016/j.ins.2019.11.040

Cited by 38 publications

(22 citation statements)

References 20 publications

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“…Our future work is to develop a new version of the proposed method by using fast clustering [28]- [30] and CNN [31] based time series data mining to deal with complex consultation. Also, we would optimize the depth of the decision tree, and prevent the tree building from overfitting.…”

Section: Discussionmentioning

confidence: 99%

Automatic Dialogue System of Marriage Law Based on the Parallel C4.5 Decision Tree

Wang

Chen

et al. 2020

IEEE Access

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In order to realize real-time marriage legal consultation automatically, a marriage legal dialogue system based on the parallel C4.5 decision tree was designed in this paper. Firstly, the legal consultation problem is transformed into a classification task. Secondly, a legal consultation classification prediction model based on the parallel C4.5 decision tree algorithm was trained with MapReduce by collected data. Finally, a model based on the SVM algorithm, which has a strategy that was designed to provide automatic interaction for users, was designed to extract attribute value from the user's input. When a new user comes to consult, an automatic legal dialogue is launched to respond to user intelligently. The proposed system works well in some real applications, such as the divorce problem, and the experimental results show that it is outperforming the SVM and NB algorithm and more applicable than the other two algorithms. Moreover, the system can return consultation results with fewer questions asked to the user than some automatic legal consultation websites, which improves the efficiency of consultation. INDEX TERMS Automatic dialogue system, parallel C4.5 decision tree, SVM, attribute value extraction, marriage law.

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Section: Discussionmentioning

confidence: 99%

Automatic Dialogue System of Marriage Law Based on the Parallel C4.5 Decision Tree

Wang

Chen

et al. 2020

IEEE Access

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“…A recommended future work focuses on clustering algorithm to deal with large scale data. Though some relevant researches for large scale data [34]- [36] and time series data [37] have been carried out, fast clustering algorithm applied to distributed cloud manufacturing system should be paid more attention. This is helpful to improve practicality of resource usage.…”

Section: Discussionmentioning

confidence: 99%

Composition of Resource-Service Chain Based on Evolutionary Algorithm in Distributed Cloud Manufacturing Systems

Weng

Tong

et al. 2020

IEEE Access

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In distributed cloud manufacturing (CMfg) systems, multi-resource service can complete more complex manufacturing tasks than single resource service. Especially in business process, all the resource services are invoked in a certain sequence, which is called the Resource-Service Chain (RSC). The RSC, as a sequential composition of resource services, expresses the scheduling and the flow of servicing to a distributed business process. A perfect composition can improve utilization ratio and efficient matching availability of resource services greatly. However, most of the existing methods for resource service composition paid no attention to the temporal relationship between resource services. Moreover, the methods strongly depend on relevant element to be considered. Inspired by biological evolution, a Resource-Service Chain Composition Evolutionary (RSCCE) algorithm is proposed. Specifically, RSCCE tries to find multiple optimal solutions, namely all RSCs in a workflow with given constraints. To begin, initial sets of composite resource service are resolved by calculating the degree of dependency between resource services, so as to obtain initial RSCs by workflow. Then, RSCCE algorithm applies genetic algorithm to search for the extended of each initial RSC, a longer chain composing of it, to improve the reuse of RSC. Under this approach, gene and chromosome represent resource service and the entire RSC respectively. If the propagated chromosomes violate the sequence of resource service, as constraint in RSCCE algorithm, they will be repaired to obtain a valid solution. Finally, we take a multi-enterprise collaborative business process as an example to simulate our approach. Experimental results confirm the effectiveness of the approach.

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“…In recent years, the machine learning has been used to extract statistical or dynamical characteristics for prediction. Several artificial neural networks (ANNs) [22], [23], multi-input-multi-output network [24], [25], and deep learning method [26] have been used to extract the hidden information in time series data. Wang proposed a doublelayer recurrent neural network to predict the PM2.5 value [27].…”

Section: Related Workmentioning

confidence: 99%

The System Identification and Prediction of the Social Earthquakes Burst in Human Society

et al. 2020

Self Cite

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The internal mechanism of the social "earthquakes"-the wars burst in human society, is investigated by nonlinear theory. We explore the scaling behavior in the human activity and the historical evolution of ancient China. Based on the war chronology and history chronology from B.C.2000 to A.D.1840, using statistical analysis together with the detrended fluctuation analysis, we find that the social evolution system shows self-organized critical behavior and there exists self-similar scaling behavior during the evolution system. Based on time-delay reconstructed phase space, a modified radial basis function neural network (modified RBF-NN) is used to predict the number of wars and the transformation of the historical stage. It shows that the prediction result matches quite well with the real value for the following two historical stages. Furthermore, by singular value decomposition and the sparse identification algorithm in time-delay coordinates, the coefficients of the candidate functions in identified system can be solved from a sparse regression problem. We extracted the governing equations of big events burst in social system. The identified system fits well with the real data, and it can capture the same topology with the original system of wars. In this paper, we investigated the wars burst in social system by statistic analysis, neural network as well as sparse identification. It sheds light on the study of wars or conflicts from perspective of data science and dynamical theory. INDEX TERMS Scaling behavior, Self-organized criticality, Neural networks, System identification.

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3DACN: 3D Augmented convolutional network for time series data

Cited by 38 publications

References 20 publications

Automatic Dialogue System of Marriage Law Based on the Parallel C4.5 Decision Tree

Automatic Dialogue System of Marriage Law Based on the Parallel C4.5 Decision Tree

Composition of Resource-Service Chain Based on Evolutionary Algorithm in Distributed Cloud Manufacturing Systems

The System Identification and Prediction of the Social Earthquakes Burst in Human Society

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