Yu Jiang^{1, 2}, Dengwen Yu¹, Mingzhao Zhao^{1, 2}, Hongtao Bai^{1, 2}, Chong Wang^{1, 2, 3}, Lili He^{1, 2, *}

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 207-216, 2020, DOI:10.32604/cmc.2020.09861

Abstract Semi-supervised clustering improves learning performance as long as it uses a small number of labeled samples to assist un-tagged samples for learning. This paper implements and compares unsupervised and semi-supervised clustering analysis of BOAArgo ocean text data. Unsupervised K-Means and Affinity Propagation (AP) are two classical clustering algorithms. The Election-AP algorithm is proposed to handle the final cluster number in AP clustering as it has proved to be difficult to control in a suitable range. Semi-supervised samples thermocline data in the BOA-Argo dataset according to the thermocline standard definition, and use this data for semi-supervised cluster analysis. Several semi-supervised clustering… More >

Haiwen Chen¹, Guang Yu¹, Fang Liu², Zhiping Cai^{1, *}, Anfeng Liu³, Shuhui Chen¹, Hongbin Huang¹, Chak Fong Cheang⁴

CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 917-927, 2020, DOI:10.32604/cmc.2020.05981

Abstract For many Internet companies, a huge amount of KPIs (e.g., server CPU usage, network usage, business monitoring data) will be generated every day. How to closely monitor various KPIs, and then quickly and accurately detect anomalies in such huge data for troubleshooting and recovering business is a great challenge, especially for unlabeled data. The generated KPIs can be detected by supervised learning with labeled data, but the current problem is that most KPIs are unlabeled. That is a time-consuming and laborious work to label anomaly for company engineers. Build an unsupervised model to detect unlabeled data is an urgent need… More >

S.Mohanty¹, A. Chattopadhyay², J. Wei³, P. Peralta⁴

Structural Durability & Health Monitoring, Vol.5, No.3, pp. 227-250, 2009, DOI:10.3970/sdhm.2009.005.227

Abstract This paper proposes unsupervised system identification based methods to estimate time-series fatigue damage states in real-time. Ultrasound broadband input is used for active damage interrogation. Novel damage index estimation techniques based on dual sensor signals are proposed. The dual sensor configuration is used to remove electrical noise, as well as to improve spatial resolution in damage state estimation. The scalar damage index at any particular damage condition is evaluated using nonparametric system identification techniques, which includes an empirical transfer function estimation approach and a correlation analysis approach. In addition, the effectiveness of two sensor configurations (configuration 1: sensors placed near… More >

Chang Kook Oh¹, Hoon Sohn¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.8, No.3, pp. 91-100, 2008, DOI:10.3970/icces.2008.008.091

Abstract Structural Health Monitoring (SHM) is concerned with identifying damage based on measurements obtained from structures being monitored. For the civil structures exposed to time-varying environmental and operational conditions, it is inevitable that environmental and operational variability produces an adverse effect on the dynamic behaviors of the structures. Since the signals are measured under the influence of these varying conditions, normalizing the data to distinguish the effects of damage from those caused by the environmental and operational variations is important in order to achieve successful structural health monitoring goals. In this paper, kernel principal component analysis (kernel PCA) using unsupervised support… More >

A.Iwasaki¹, K.Yuguchi², A.Todoroki³, Y.Shimamura⁴

The International Conference on Computational & Experimental Engineering and Sciences, Vol.6, No.3, pp. 183-188, 2008, DOI:10.3970/icces.2008.006.183

Abstract The present study is about study on the diagnostic accuracy of the unsupervised damage diagnosis method named SI-F method. For the health monitoring of existing structures, modeling of entire structure or obtaining data sets after creating damage for training is almost impossible. This raises significant demand for development of a low-cost diagnostic method that does not require modeling of entire structure or data on damaged structure. Therefore, the present study proposes a low-cost unsupervised statistical diagnostic method for structural damage detection. The proposed method statistically diagnoses structural condition by means of investigating the change of a response surface which conducts… More >

Zhe Liu^1,2,*, Bao Xiang^1,3, Yuqing Song¹, Hu Lu¹, Qingfeng Liu¹

CMC-Computers, Materials & Continua, Vol.58, No.2, pp. 451-461, 2019, DOI:10.32604/cmc.2019.04069

Abstract Most image segmentation methods based on clustering algorithms use single-objective function to implement image segmentation. To avoid the defect, this paper proposes a new image segmentation method based on a multi-objective particle swarm optimization (PSO) clustering algorithm. This unsupervised algorithm not only offers a new similarity computing approach based on electromagnetic forces, but also obtains the proper number of clusters which is determined by scale-space theory. It is experimentally demonstrated that the applicability and effectiveness of the proposed multi-objective PSO clustering algorithm. More >