• Journal of Korea Society of Digital Industry and Information Management
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• v.4 no.3
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• pp.85-90
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• 2008

Recently, with the sensor technology, research about the electronic nose system which imitated the olfactory organ are being pushed actively. But, in case of general electronic nose system, an aroma is measured at the laboratory space where blocked external environment and is analyzed a part of measured data. In this paper, we propose the system which can measure and recognize an aroma in natural environment. We propose the Entropy algorithm which can detect the sensor reaction section among the continuous detection processing about an aroma. And we implement the aroma recognition system using the PCA(Principal Components Analysis) and K-NN(K-Nearest Neighbor) about the detected aroma. In order to evaluate the performance, we measured the aroma pattern, about 9 aroma oil, 50 times respectively. And we experimented the aroma detection and recognition using this. There was an error of 0.2s in the aroma detection and we get 84.3% recognition rate of the aroma recognition.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.247-248
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• 2020

호스트가 숙소 가격을 정할 때, 기존 숙박 플랫폼들이 제공하는 최적화된 가격을 참고하기 위해선 숙소의 유형, 편의 시설 제공 여부 등 많은 단계를 거쳐야하므로 불편하다. 본 논문은 호스트가 보다 편리하게 자신의 숙소에 최적화된 가격을 알 수 있도록 하는 '숙소의 적정 가격 결정을 위한 웹 사이트'를 개발하였다. 이 웹을 통해 호스트는 더 간편하게 자신의 숙소에 대한 적정 가격을 알고 가격 산정 시 참고할 수 있다.

• Journal of Korea Spatial Information System Society
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• v.11 no.3
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• pp.55-62
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• 2009

Location based Services in the ubiquitous computing environment, namely u-LBS, use very large and skewed spatial objects that are closely related to locational information. It is especially essential to achieve fast search, which is looking for POI(Point of Interest) related to the location of users. This paper examines how to search large and skewed POI efficiently in the u-LBS environment. We propose the Dynamic-level Grid based R-Tree(DGR-Tree), which is an index for point data that can reduce the cost of stationary POI search. DGR-Tree uses both R-Tree as a primary index and Dynamic-level Grid as a secondary index. DGR-Tree is optimized to be suitable for point data and solves the overlapping problem among leaf nodes. Dynamic-level Grid of DGR-Tree is created dynamically according to the density of POI. Each cell in Dynamic-level Grid has a leaf node pointer for direct access with the leaf node of the primary index. Therefore, the index access performance is improved greatly by accessing the leaf node directly through Dynamic-level Grid. We also propose a K-Nearest Neighbor(KNN) algorithm for DGR-Tree, which utilizes Dynamic-level Grid for fast access to candidate cells. The KNN algorithm for DGR-Tree provides the mechanism, which can access directly to cells enclosing given query point and adjacent cells without tree traversal. The KNN algorithm minimizes sorting cost about candidate lists with minimum distance and provides NEB(Non Extensible Boundary), which need not consider the extension of candidate nodes for KNN search.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.4
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• pp.617-624
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• 2014

Smart phone usage has increased exponentially and open source based Android OS occupy significant market share. However, various malicious applications that use the characteristic of Android threaten users. In this paper, we construct an efficient malicious application detector by using the principle component analysis and the incremental k nearest neighbor algorithm, which consider an required permission, of Android applications. The cross validation is exploited in order to find a critical parameter of the algorithm. For the performance evaluation of our approach, we simulate a real data set of Contagio Mobile.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.178-184
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• 2003

Case-based reasoning (CBR) often shows significant promise for improving effectiveness of complex and unstructured decision making. Consequently, it has been applied to various problem-solving areas including manufacturing, finance and marketing. However, the design of appropriate case indexing and retrieval mechanisms to improve the performance of CBR is still challenging issue. Most of previous studies to improve the effectiveness for CBR have focused on the similarity function or optimization of case features and their weights. However, according to some of prior researches, finding the optimal k parameter for k-nearest neighbor (k-NN) is also crucial to improve the performance of CBR system. Nonetheless, there have been few attempts which have tried to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. In this study, we introduce a genetic algorithm (GA) to optimize the number of neighbors to combine. This study applies the new model to the real-world case provided by an online shopping mall in Korea. Experimental results show that a GA-optimized k-NN approach outperforms other AI techniques for purchasing behavior forecasting.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6145-6158
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• 2019

It is a challenge for the current security industry to respond to a large number of malicious codes distributed indiscriminately as well as intelligent APT attacks. As a result, studies using machine learning algorithms are being conducted as proactive prevention rather than post processing. The k-NN algorithm is widely used because it is intuitive and suitable for handling malicious code as unstructured data. In addition, in the malicious code analysis domain, the k-NN algorithm is easy to classify malicious codes based on previously analyzed malicious codes. For example, it is possible to classify malicious code families or analyze malicious code variants through similarity analysis with existing malicious codes. However, the main disadvantage of the k-NN algorithm is that the search time increases as the learning data increases. We propose a fast k-NN algorithm which improves the computation speed problem while taking the value of the k-NN algorithm. In the test environment, the k-NN algorithm was able to perform with only the comparison of the average of similarity of 19.71 times for 6.25 million malicious codes. Considering the way the algorithm works, Fast k-NN algorithm can also be used to search all data that can be vectorized as well as malware and SSDEEP. In the future, it is expected that if the k-NN approach is needed, and the central node can be effectively selected for clustering of large amount of data in various environments, it will be possible to design a sophisticated machine learning based system.

• Journal of KIISE:Computer Systems and Theory
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• v.28 no.3
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• pp.155-160
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• 2001

본 논문은 영역별 근접 그래프 (geographic nearest neighbor graph)와 레인지 트리 (range tree)를 이용하여 평면 위의 n 개의 점에 대한 L$_{\infty}$ (L$_1$) 거리 (metric) 상의 디루니 삼각분할 (Delaunay triangulation)을 구축하는 방법을 소개한다. 이 방법은 L$_{\infty}$ (L$_1$) 거리 상에서 디루니 삼각분할에 있는 각 삼각형의 최소한 한 선분이 영역별 근접 그래프에 포함됨을 이용하여 레인지 트리 방법으로 디루니 삼각분할을 구축한다. 본 방법은 0(nlogn)의 순차계산 시간에 L$_{\infty}$ (L$_1$) 디루니 삼각분할을 구축하며, CREW-PRAM (Concurrent Read Exclusive Write Parallel Random Access Machine)에서 0(n)의 프로세서로 0(logn)의 병렬처리 시간에 L$_{\infty}$ (L$_1$) 디루니 삼각분할을 구축한다. 또한, 이 방법은 직선간의 교차점 계산 대신 거리비교를 하기 때문에 수치오차가 적고 구현이 용이하다.

• Journal of IKEEE
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• v.28 no.3
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• pp.412-418
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• 2024

This paper proposes an effective method for detecting hacking attacks in automotive CAN bus using the RANSAC (Random Sample Consensus) algorithm. Conventional deep learning-based detection techniques are difficult to be applied to resource-constrained environments such as vehicles. In this paper, the attack detection performance in vehicular CAN communication has been improved by utilizing the lightweight nature and efficiency of the RANSAC algorithm. The RANSAC algorithm can perform effective detection with minimal computational resources, providing a practical hacking detection solution for vehicles.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.7 no.2
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• pp.69-74
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• 2013

In this paper, we propose the pattern classification algorithm of recognizing wrist movements based on electromyogram(EMG) to raise the recognition rate. We consider 30 characteristics of EMG signals wirh the root mean square(RMS) and the difference absolute standard deviation value(DASDV) for the extraction of precise features from EMG signals. To get the groups of each wrist movement, we estimated 2-dimension features. On this basis, we divide each group into two parts with mean to compare and promote the recognition rate of pattern classification effectively. For the motion classification based on EMG, the k-nearest neighbor(k-NN) is used. In this paper, the recognition rate is 92.59% and 0.84% higher than the study before.

• Journal of Korea Multimedia Society
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• v.9 no.3
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• pp.279-286
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• 2006

Classification method used in this paper is applied for the first time to Korean traditional music. Among the frequency distribution vectors, average peak value is suggested and proved effective comparing to previous classification success rate. Mean, variance, spectral centroid, average peak value and ZCR are used to classify Korean traditional musical instruments. To achieve Korean traditional instruments automatic classification, Spectral analysis is used. For the spectral domain, Various functions are introduced to extract features from the data files. k-NN classification algorithm is applied to experiments. Taegum, gayagum and violin are classified in accuracy of 94.44% which is higher than previous success rate 87%.