http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
A Scalable Feature Based Clustering Algorithm for Sequences with Many Distinct Items
Sangheum Hwang,Dohyun Kim 한국지능시스템학회 2018 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.18 No.4
Various sequence data have grown explosively in recent years. As more and more of such data become available, clustering is needed to understand the structure of sequence data. However, the existing clustering algorithms for sequence data are computationally demanding. To avoid such a problem, a feature-based clustering algorithm has been proposed. Notwithstanding that, the algorithm uses only a subset of all possible frequent sequential patterns as features, which may result in the distortion of similarities between sequences in practice, especially when dealing with sequence data with a large number of distinct items such as customer transaction data. Developed in this article is a feature-based clustering algorithm using a complete set of frequent sequential patterns as features for sequences of sets of items as well as sequences of single items which consist of many distinct items. The proposed algorithm projects sequence data into feature space whose dimension consists of a complete set of frequent sequential patterns, and then, employs K-means clustering algorithm. Experimental results show that the proposed algorithm generates more meaningful clusters than the compared algorithms regardless of the dataset and parameters such as the minimum support value of frequent sequential patterns and the number of clusters considered. Moreover, the proposed algorithm can be applied to a large sequence database since it is linearly scalable to the number of sequence data.
Jeong, Young-Seon,Hwang, Sangheum,Ko, Young-Don Korean Institute of Industrial Engineers 2015 Industrial Engineeering & Management Systems Vol.14 No.4
Monitoring of plasma etch processes for fault detection is one of the hallmark procedures in semiconductor manufacturing. Optical emission spectroscopy (OES) has been considered as a gold standard for modeling plasma etching processes for on-line diagnosis and monitoring. However, statistical quantitative methods for processing the OES data are still lacking. There is an urgent need for a statistical quantitative method to deal with high-dimensional OES data for improving the quality of etched wafers. Therefore, we propose a robust relevance vector machine (RRVM) for regression with statistical quantitative features for modeling etch rate and uniformity in plasma etch processes by using OES data. For effectively dealing with the OES data complexity, we identify seven statistical features for extraction from raw OES data by reducing the data dimensionality. The experimental results demonstrate that the proposed approach is more suitable for high-accuracy monitoring of plasma etch responses obtained from OES.
Quantitative Analysis for Plasma Etch Modeling Using Optical Emission Spectroscopy
Young-Seon Jeong,Sangheum Hwang,Young-Don Ko 대한산업공학회 2015 Industrial Engineeering & Management Systems Vol.14 No.4
Monitoring of plasma etch processes for fault detection is one of the hallmark procedures in semiconductor manufacturing. Optical emission spectroscopy (OES) has been considered as a gold standard for modeling plasma etching processes for on-line diagnosis and monitoring. However, statistical quantitative methods for processing the OES data are still lacking. There is an urgent need for a statistical quantitative method to deal with high-dimensional OES data for improving the quality of etched wafers. Therefore, we propose a robust relevance vector machine (RRVM) for regression with statistical quantitative features for modeling etch rate and uniformity in plasma etch processes by using OES data. For effectively dealing with the OES data complexity, we identify seven statistical features for extraction from raw OES data by reducing the data dimensionality. The experimental results demonstrate that the proposed approach is more suitable for high-accuracy monitoring of plasma etch responses obtained from OES.
Similarity based Deep Neural Networks
Seungyeon Lee,Eunji Jo,Sangheum Hwang,Gyeong Bok Jung,Dohyun Kim 한국지능시스템학회 2021 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.21 No.3
Deep neural networks (DNNs) have recently attracted attention in various areas. Their hierarchical architecture is used to model complex nonlinear relationships in high-dimensional data. DNNs generally require large numbers of data to train millions of parameters. However, the training of a DNN with a small number of high-dimensional data can result in an overfitting. To alleviate this problem, we propose a similarity-based DNN that can effectively reduce the dimensionality of the data. The proposed method utilizes a kernel function to calculate pairwise similarities of observations as input, and the nonlinearity based on the similarities is then explored using a DNN. Experiment results show that the proposed method performs effectively regardless of the dataset used, implying that it can be applied as an alternative when learning a small number of high-dimensional data.