2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)最新文献_第2页

Fuzzy Quality Evaluation Algorithm for Higher Engineering Education Quality via Quasi-neural-network Framework 基于准神经网络框架的高等工程教育质量模糊评价算法

2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2019-12-01 DOI: 10.1109/SPAC49953.2019.237872

Ya-Xin Zhou, Shiyuan Han, Jin Zhou, Kang Yao

{"title":"Fuzzy Quality Evaluation Algorithm for Higher Engineering Education Quality via Quasi-neural-network Framework","authors":"Ya-Xin Zhou, Shiyuan Han, Jin Zhou, Kang Yao","doi":"10.1109/SPAC49953.2019.237872","DOIUrl":"https://doi.org/10.1109/SPAC49953.2019.237872","url":null,"abstract":"Quality evaluation for higher engineering education has important guiding significance and feedback role on cultivating engineering talents. Combining with the educational core concept of outcomes-based education (OBE) and the educational process data, a fuzzy quality evaluation algorithm is developed for engineering education deriving from a constructed Quasi-Neural-Network (QNN) framework. More specifically, considering the logical relationships among basic components in the whole process of engineering education, a four-layers QNN framework is designed first to underly and implement the educational concept of OBE reasonably, which includes the training objectives layer, requirement capability for graduation layer, requirement sub-capability for graduation layer, and course layer. After that, by employing the educational process data under the proposed QNN framework, a fuzzy comprehensive evaluation algorithm is designed to describe the achievement scale of target capability for engineering education. Finally, focusing on the research capability for computer science with related four courses, the experiments based on the process educational data sets show the superiority and efficiency of the proposed framework and algorithm.","PeriodicalId":410003,"journal":{"name":"2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114463628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-Intrusive Load Disaggregation Using Semi-Supervised Learning Method 基于半监督学习方法的非侵入式负载分解

2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2019-12-01 DOI: 10.1109/SPAC49953.2019.237865

Nan Miao, Shengjie Zhao, Qingjiang Shi, Rongqing Zhang

引用次数: 5

Random Feature Based Attribute-weighed Kernel Fuzzy Clustering for Non-linear Data 基于随机特征的非线性数据属性加权核模糊聚类

2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2019-12-01 DOI: 10.1109/SPAC49953.2019.237882

Qiushi Tian, Jin Zhou, Shiyuan Han, Lin Wang, Yuehui Chen

{"title":"Random Feature Based Attribute-weighed Kernel Fuzzy Clustering for Non-linear Data","authors":"Qiushi Tian, Jin Zhou, Shiyuan Han, Lin Wang, Yuehui Chen","doi":"10.1109/SPAC49953.2019.237882","DOIUrl":"https://doi.org/10.1109/SPAC49953.2019.237882","url":null,"abstract":"Traditional kernel clustering methods are useful in dealing with non-linear data, but the high-dimensional kernel space obtained by kernel mapping is an abstract concept, which is difficult to be determined. The kernel mapping between raw data space and kernel space needs high computational complexity which is burdensome for hardware. At the same time, due to the unknown nature of kernel space, traditional kernel clustering methods cannot process data with the consideration of different importance among dimensions, i.e., discover the hidden feature subset of high-dimensional sparse data. To overcome these limitations, we put forward a novel random Fourier feature based attribute-weighed kernel fuzzy c-means clustering algorithm (RFF-WKFCM). This method employs RFF map to generate low-rank random features, and performs fuzzy c-means clustering with attribute weight entropy regularization in this feature space, which greatly reduces the computational complexity. What is more, the adoption of the maximum entropy technique ensures the optimal distribution of attribute weights, which stimulate important dimensions play a greater role in the clustering process. The proposed method shows good performance on the experiments of ring data set compared with other fuzzy clutering methods.","PeriodicalId":410003,"journal":{"name":"2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114863847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Temporal Segmentation for Action Recognition 动作识别的自适应时间分割

2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2019-12-01 DOI: 10.1109/SPAC49953.2019.237869

Zhiyu Chen, Yangwei Gu, Chunhua Deng, Ziqi Zhu

引用次数: 0

Self-organized Clustering -Fission Swarm System Based on the Coupling Degree of Weighted Information 基于加权信息耦合度的自组织聚类-裂变群系统

2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2019-12-01 DOI: 10.1109/SPAC49953.2019.243776

Xuchen Wang, Yuxuan Huang, Dengxiu Yu, Mingyong Liu

引用次数: 0

Adaptive State Continuity-Based Sparse Inverse Covariance Clustering for Multivariate Time Series 基于自适应状态连续性的多元时间序列稀疏逆协方差聚类

2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2019-12-01 DOI: 10.1109/SPAC49953.2019.237883

Lei Li, Wei Li, Jianxing Liao, Xuegang Hu

{"title":"Adaptive State Continuity-Based Sparse Inverse Covariance Clustering for Multivariate Time Series","authors":"Lei Li, Wei Li, Jianxing Liao, Xuegang Hu","doi":"10.1109/SPAC49953.2019.237883","DOIUrl":"https://doi.org/10.1109/SPAC49953.2019.237883","url":null,"abstract":"Compared with univariate time series clustering, multivariate time series (MTS) clustering has become a challenging research topic on the data mining of time series. In this paper, a novel model-based approach Adaptive State Continuity-Based Sparse Inverse Covariance Clustering (ASCSICC) is proposed for MTS clustering. Specifically, the state continuity is introduced to make the traditional Gaussian mixture model (GMM) applicable to time series clustering. To prevent overfitting, the alternating direction method of multipliers (ADMM) is applied to optimize the parameter of GMM inverse covariance. In addition, the proposed approach simultaneously segments and clusters the time series. Technically, first, the adaptive state continuity is estimated based on the distance similarity of adjacent time series data. Then, a dynamic programming algorithm of cluster assignment by adaptive state continuity is taken as the E-step, and the ADMM for solving sparse inverse covariance is taken as the M-step. E-step and M-step are combined into an Expectation-Maximization (EM) algorithm to conduct the clustering process. Finally, we show the effectiveness of the proposed approach by comparing the ASC-SICC with several state-of-the-art approaches in experiments on two datasets from real applications.","PeriodicalId":410003,"journal":{"name":"2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127367252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Cement Texture Synthesis Based on Feedforward Neural Network 基于前馈神经网络的水泥肌理合成

2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2019-12-01 DOI: 10.1109/SPAC49953.2019.244103

J. Fan, Lin Wang, Chen Xiao, Bo Yang, Jin Zhou

引用次数: 1

Low-Rate Non-Intrusive Appliance Load Monitoring Based on Graph Signal Processing 基于图信号处理的低速率非侵入式电器负荷监测

2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2019-12-01 DOI: 10.1109/SPAC49953.2019.237866

Bing Zhang, Shengjie Zhao, Qingjiang Shi, Rongqing Zhang

引用次数: 2

A Binary I-Ching Divination Evolutionary Algorithm for Feature Selection 一种用于特征选择的二元易经占卜进化算法

2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2019-12-01 DOI: 10.1109/SPAC49953.2019.243772

Bianna Chen, Tong Zhang, Xue Jia, Jianxiu Jin, C. L. P. Chen, Xiangmin Xu

引用次数: 0

Traffic Flow Prediction Algorithm Based on Flexible Neural Tree 基于柔性神经树的交通流预测算法

2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2019-12-01 DOI: 10.1109/SPAC49953.2019.237874

Xiao-Yue Ma, Ya Fang, Shiyuan Han, Ya-Xin Zhou, Ke Yang, Jin Zhou, Kang Yao

引用次数: 0