• Journal of the Korea Safety Management & Science
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• v.11 no.1
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• pp.27-33
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• 2009

A study of fracture to material is getting interest in nuclear and aerospace industry as a viewpoint of safety. Acoustic emission (AE) is a non-destructive testing and new technology to evaluate safety on structures. In previous research continuously, all tensile tests on the pre-defected coupons were performed using the universal testing machine, which machine crosshead was move at a constant speed of 5mm/min. This study is to evaluate an AE source characterization of SM45C steel by using k-nearest neighbor classifier, k-NNC. For this, we used K-means clustering as an unsupervised learning method for obtained multi -variate AE main data sets, and we applied k-NNC as a supervised learning pattern recognition algorithm for obtained multi-variate AE working data sets. As a result, the criteria of Wilk's $\lambda$, D&B(Rij) & Tou are discussed.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.1093-1102
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• 2010

Even though the k-Nearest Neighbors Classification(KNNC) is one of the popular non-parametric classification methods, it does not consider the local features and class information for each observation. In order to overcome such limitations, several methods have been developed such as Adaptive Nearest Neighbors Classification(ANNC) and Modified k-Nearest Neighbors Classification(MKNNC). In this paper, we propose the Modified Adaptive Nearest Neighbors Classification(MANNC) that employs the advantages of both the ANNC and MKNNC. Through a real data analysis and a simulation study, we show that the proposed MANNC outperforms other methods in terms of classification accuracy.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.4
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• pp.88-96
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• 2007

This paper discusses the fast modulation classifiers capable of classifying both analog and digital modulation signals in wireless communications applications. A total of 7 statistical signal features are extracted and used to classify 9 modulated signals. In this paper, we investigate the performance of the two types of fast modulation classifiers (i.e. 2 nearest neighbor classifiers and 2 minimum distance classifiers) and compare the performance of these classifiers with that of the state of the art for the existing classification methods such as SVM Classifier. Computer simulations indicate good performance on an AWGN channel, even at low signal-to-noise ratios, in case of minimum distance classifiers (MDC for short) and k nearest neighbor classifiers (kNNC for short). Besides a good performance, these type classifiers are considered as ideal candidate to adapt real-time software radio because of their fast modulation classification capability.

• Journal of IKEEE
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• v.24 no.2
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• pp.403-408
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• 2020

Photovoltaic (PV) has both intermittent and uncertainty in nature, so it is difficult to accurately predict. Thus anomaly detection technology is important to diagnose real time PV generation. This paper identifies a correlation between various parameters and classifies the PV data applying k-nearest neighbor and dynamic time warpping. Results for the two classifications showed that an outlier detection by a fault of some facilities, and a temporary power loss by partial shading and overall shading occurring during the short period. Based on 100kW plant data, machine learning analysis and test results verified actual outliers and candidates of outlier.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.722-725
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• 2016

많은 화장품이 출시함에 따라 고객들이 자신의 피부 상태에 맞는 화장품을 고르는 것에 대한 어려움이 증가하게 되었다. 기존의 화장품 추천 프로그램들은 해당 프로그램의 DB에 존재하는 화장품들에 대한 추천 밖에 하지 못한다. 본 논문에서 제안하는 새로운 화장품 추천 방법은 화장품을 구입하기 전에 상자 표면에 적힌 성분을 사진으로 찍어 단어 별로 추출한다. 추출한 단어를 DB에 저장되어 있는 성분 이름과 비교하여 화장품 성분만 구분한 뒤 k-최근접 이웃 알고리즘과 내용 기반 기법을 이용하여 사용자에게 맞는 화장품인지 판단해준다. 화장품의 성분표를 통해 DB에 없는 화장품이라도 즉석에서 사용자의 정보와 비교하여 적정도를 분석할 수 있다. 또한 사용자의 화장품 구입 성향으로 화장품을 추천해주어 사용자의 화장품 선택에 도움을 준다.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.2
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• pp.170-177
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• 2009

The objective of this study is to evaluate an acoustic emission (AE) source characterization and fracture behavior of the SM45C steel by using back-propagation neural network (BPN). In previous research Ref. [8] about k-nearest neighbor classifier (k-NNC) continuity, we used K-means clustering method as an unsupervised learning method for obtaining multi-variate AE main data sets, such as AE counts, energy, amplitude, risetime, duration and counts to peak. Similarly, we applied k-NNC and BPN as a supervised learning method for obtaining multi-variate AE working data sets. According to the error of convergence for determinant criterion Wilk's ${\lambda}$, heuristic criteria D&B(Rij) and Tou values are discussed. As a result, in k-NNC before fracture signal is detected or when fracture signal is detected, showed that produce some empty classes in BPN. And we confirmed that could save trouble in AE signal processing if suitable error of convergence or acceptable encoding error give to BPN.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.561-568
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• 2015

In this study we predict apartment prices per unit in Daegu-Gyeongbuk areas by spatial lag and spatial error models, both of which belong to so-called spatial regression model. A spatial weight matrix is constructed by k-nearest neighbours method and then the models for the apartment prices in March, 2012 are fitted using the weight matrix. The apartment prices in March, 2013 are predicted by the fitted spatial regression models and then performances of two spatial regression models are compared by RMSE (root mean squared error), RRMSE (root relative mean squared error), MAE (mean absolute error).

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.472-474
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• 2005

최근에 P2P(Peer-to-Peer) 시스템에서 효율적인 자원 탐색 방법에 대해 많이 연구되고 있다. 대부분의 P2P 시스템은 overlay network를 형성하므로 노드와 노드 사이의 물리적인 거리를 고려하지 않는다. 그래서 서로 이웃한 노드라도 실제 물리적인 latency가 클 수 있다는 문제점을 가지고 있다. 이런 문제점을 해결하기 위해 Topology를 고려한 계층적 시스템을 설계하였다. 이 시스템을 TB-Chord(Topology-based Chord)라 부른다. TB-Chord는 자신의 subnet에 Global network에 있는 데이터의 사본(Replica)을 저장하기 때문에 저장 공간(Storage)의 낭비와 노드가 떠날(leave) 때 데이터의 이동에 따른 네트워크 부하가 생기는 문제가 있다. 이 논문은 효율적인 복제 기법을 이용하여 저장공간과 네트워크의 효율성을 높이고 노드 fail에 대한 rejoin 메카니즘을 사용하여 효율적으로 시스템을 회복하는 방법을 제안한다.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.6
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• pp.395-402
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• 2013

We propose an accelerometer-based gesture recognition method for smartphone users. In our method, similarities between a new time series accelerometer data and each gesture exemplar are computed with DTW algorithm, and then the best matching gesture is determined based on k-NN algorithm. In order to investigate the performance of our method, we implemented a gesture recognition program working on an Android smartphone and a gesture-based teleoperating robot system. Through a set of user-mixed and user-independent experiments, we showed that the proposed method and implementation have high performance and scalability.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.291-303
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• 2006

Data mining is the work to extract information from existing data file. So, the one of best important thing in data mining process is the quality of data to be used. In this thesis, we propose the data fusion technique using decision rule for data enrichment that one phase to improve data quality in KDD process. Simulations were performed to compare the proposed data fusion technique with the existing techniques. As a result, our data fusion technique using decision rule is characterized with low MSE or misclassification rate in fusion variables.