• 한국멀티미디어학회논문지
• /
• 제21권2호
• /
• pp.117-129
• /
• 2018

In this paper, we suggest a new system that detects abnormal region of skim. First, an illumination elimination algorithm which uses LAB color model is processed on input facial image to obtain robust facial image for illumination, and then gabor filter is processed to detect the reactivity of discontinuity. And last, the density-based spatial clustering of applications with noise(DBSCAN) algorithm is processed to classify areas of wrinkles, dots, and other skin diseases. This method allows the user to check the skin condition of the images taken in real life.

• Journal of Astronomy and Space Sciences
• /
• 제41권1호
• /
• pp.35-42
• /
• 2024

Sungrazing comets, known for their proximity to the Sun, are traditionally classified into broad groups like Kreutz, Marsden, Kracht, Meyer, and non-group comets. While existing methods successfully categorize these groups, finer distinctions within the Kreutz subgroup remain a challenge. In this study, we introduce an automated classification technique using the densitybased spatial clustering of applications with noise (DBSCAN) algorithm to categorize sungrazing comets. Our method extends traditional classifications by finely categorizing the Kreutz subgroup into four distinct subgroups based on a comprehensive range of orbital parameters, providing critical insights into the origins and dynamics of these comets. Corroborative analyses validate the accuracy and effectiveness of our method, offering a more efficient framework for understanding the categorization of sungrazing comets.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권9호
• /
• pp.2961-2975
• /
• 2022

The Location-Based Service (LBS) is one of the most well-known services on the Internet. Positioning is the primary association with LBS services. This study proposes an intelligent LoRa-based positioning system, called AI@LBS, to provide accurate location data. The fingerprint mechanism with the clustering algorithm in unsupervised learning filters out signal noise and improves computing stability and accuracy. In this study, data noise is filtered using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, increasing the positioning accuracy from 95.37% to 97.38%. The problem of data imbalance is addressed using the SMOTE (Synthetic Minority Over-sampling Technique) technique, increasing the positioning accuracy from 97.38% to 99.17%. A field test in the NTUST campus (www.ntust.edu.tw) revealed that AI@LBS system can reduce average distance error to 0.48m.

• 한국음향학회지
• /
• 제34권4호
• /
• pp.303-309
• /
• 2015

화자 분할은 사전에 분류되지 않은 데이터를 각각의 화자로 분류하는 연구이며 DBSCAN(Density-Based Spatial Clustering of Applications with Noise)은 간결함과 계산의 효율성으로 인해 화자분할 분야에 널리 사용되어 왔다. 그러나 클러스터의 데이터들이 공간적이지 않으며 서로 다른 클러스터가 근접하여 경계를 공유할 때 오버클러스터링 문제가 발생하여 DBSCAN의 성능이 하락한다. 본 논문에서는 DBSCAN과 문제점을 설명하고, 개체의 지역적 특성에 기반한 밀도 기반 클러스터링 알고리즘을 제안한다. 제안하는 알고리즘은 개체의 지역적 밀도와 분산의 정도에 따라 가변적인 판단 기준을 탐색에 이용한다. DBSCAN과 제안 기법의 실험을 통해 성능을 비교하고 제안 기법의 효용을 보인다. 실험 결과 제안한 방법은 오버클러스터링이 발생하지 않으며 DBSCAN에 비해 보다 높은 정확도를 보여 지역적 특성을 이용한 접근 방법이 효과적임을 증명한다.

• Journal of Information Processing Systems
• /
• 제16권4호
• /
• pp.750-759
• /
• 2020

The wide application of various integrated location-based services (LBS social) and tourism application (app) has generated a large amount of trajectory space data. The trajectory data are used to identify popular tourist attractions with high density of tourists, and they are of great significance to smart service and emergency management of scenic spots. A hot spot analysis method is proposed, based on spatial clustering of trajectory stop points. The DBSCAN algorithm is studied with fast clustering speed, noise processing and clustering of arbitrary shapes in space. The shortage of parameters is manually selected, and an improved method is proposed to adaptively determine parameters based on statistical distribution characteristics of data. DBSCAN clustering analysis and contrast experiments are carried out for three different datasets of artificial synthetic two-dimensional dataset, four-dimensional Iris real dataset and scenic track retention point. The experiment results show that the method can automatically generate reasonable clustering division, and it is superior to traditional algorithms such as DBSCAN and k-means. Finally, based on the spatial clustering results of the trajectory stay points, the Getis-Ord Gi* hotspot analysis and mapping are conducted in ArcGIS software. The hot spots of different tourist attractions are classified according to the analysis results, and the distribution of popular scenic spots is determined with the actual heat of the scenic spots.

• 디지털산업정보학회논문지
• /
• 제11권4호
• /
• pp.89-97
• /
• 2015

In this paper, a method of color image segmentation based on DBSCAN(Density Based Spatial Clustering of Applications with Noise) using compactness of superpixels and texture information is presented. The DBSCAN algorithm can generate clusters in large data sets by looking at the local density of data samples, using only two input parameters which called minimum number of data and distance of neighborhood data. Superpixel algorithms group pixels into perceptually meaningful atomic regions, which can be used to replace the rigid structure of the pixel grid. Each superpixel is consist of pixels with similar features such as luminance, color, textures etc. Superpixels are more efficient than pixels in case of large scale image processing. In this paper, superpixels are generated by SLIC(simple linear iterative clustering) as known popular. Superpixel characteristics are described by compactness, uniformity, boundary precision and recall. The compactness is important features to depict superpixel characteristics. Each superpixel is represented by Lab color spaces, compactness and texture information. DBSCAN clustering method applied to these feature spaces to segment a color image. To evaluate the performance of the proposed method, computer simulation is carried out to several outdoor images. The experimental results show that the proposed algorithm can provide good segmentation results on various images.

• 정보보호학회논문지
• /
• 제25권5호
• /
• pp.1131-1141
• /
• 2015

본 논문에서는 MMORPG에서 각 캐릭터의 소지금 증가/감소 이벤트 로그 데이터를 위주로 플레이어의 액션 로그 데이터를 조사하여 게임봇을 탐지하는 기계 학습 기반의 새로운 접근 방법을 제안한다. 게임봇 계정과 일반 계정을 구분하는 주요 피쳐를 추출하기 위해 밀도 기반 군집화 알고리즘의 하나인 DBSCAN (Density Based Spatial Clustering of Application with Noise)를 이용하였다. DBSCAN 알고리즘을 통해 각 플레이어의 소지금 증가/감소 위치 좌표를 클러스터링하고, 그 결과 생성된 클러스터의 수, 코어 포인트의 비율, 멤버 포인트의 비율, 노이즈 포인트의 비율과 같은 공간적 특성을 나타내는 값들을 추출하였다. 해당 피쳐들을 사용하면 게임봇 개발자들이 게임봇 탐지 시스템의 원리를 알더라도 넓은 지역을 돌아다니며 사냥을 하도록 게임봇 프로그램을 제작하는 것은 매우 비효율적이기 때문에 탐지 시스템을 우회하기 어렵게 된다. 결과적으로, 게임봇은 소지금 변동 좌표 데이터로부터 추출한 공간적 특성에서 일반유저와 명확한 차이를 보였다. 예를 들면, DBSCAN 클러스터링 결과 중 노이즈 포인트의 비율에서 게임봇은 5% 이하의 낮은 값을 가지는 반면에 일반 유저들은 대부분 높은 값을 갖는다. 실제 MMORPG의 액션 로그 데이터를 이용한 게임봇 탐지에서, 본 논문에서 제안된 시스템은 높은 탐지율의 우수한 성능을 보였다.

• Journal of the Korean Data Analysis Society
• /
• 제20권6호
• /
• pp.2917-2932
• /
• 2018

우리나라 식생활에 밀접한 관련을 가지고 있는 채소인 양파의 수급불균형 해결을 위한 생산량 예측 모형 개발의 노력이 많은 연구를 통해 이뤄지고 있다. 하지만 양파의 수확기와 저장 가능성을 고려해 봤을 때 생산량 예측만으로는 수급불균형 해결이 어렵다. 따라서 본 논문에서는 양파의 생산량 정보와 가격의 다양한 요인이 포함되어 있으며 일상에서 쉽게 접할 수 있는 인터넷 기사를 이용하여 가격 예측을 위한 감성사전을 구축하고자 한다. 양파 기사는 2012년부터 2016년까지의 데이터를 사용하였고 도매시장 가격을 통한 문서구분을 통해 4가지 TF-IDF를 비교하여 적합한 TF-IDF를 사용하였다. 분석을 위하여 분할적 군집분석 중 k-means 군집, 밀도기반군집(DBSCAN; density based spatial cluster applications with noise), 가우시안혼합분포군집(GMM; Gaussian mixture model) 군집을 통하여 가격에 대한 긍정/부정 단어를 구분한 결과 GMM 군집이 의미 있는 긍정, 부정, 무정의 3개의 사전으로 구성되었다. 구축된 사전의 합리성을 비교하기 위하여 가격 상승 기사와 가격 하락 기사의 분류에 로지스틱 회귀분석을 적용한 결과 85.7%의 정확도로 구축된 사전의 합리성을 확인할 수 있었다.

• 전력전자학회논문지
• /
• 제27권3호
• /
• pp.265-274
• /
• 2022

This paper proposes a machine learning-based screening algorithm to build the retired battery pack of the energy storage system. The proposed algorithm creates the dataset of various performance parameters of the retired battery, and this dataset is preprocessed through a principal component analysis to reduce the overfitting problem. The retried batteries with a large deviation are excluded in the dataset through a density-based spatial clustering of applications with noise, and the K-means clustering method is formulated to select the group of the retired batteries to satisfy the deviation requirement conditions. The performance of the proposed algorithm is verified based on NASA and Oxford datasets.

• Communications for Statistical Applications and Methods
• /
• 제31권3호
• /
• pp.349-363
• /
• 2024

Compositional data refers to data where the sum of the values of the components is a constant, hence the sample space is defined as a simplex making it impossible to apply statistical methods developed in the usual Euclidean vector space. A natural approach to overcome this restriction is to consider an appropriate transformation which moves the sample space onto the Euclidean space, and log-ratio typed transformations, such as the additive log-ratio (ALR), the centered log-ratio (CLR) and the isometric log-ratio (ILR) transformations, have been mostly conducted. However, in scenarios with sparsity, where certain components take on exact zero values, these log-ratio type transformations may not be effective. In this work, we mainly suggest an alternative transformation, that is the square-root transformation which moves the original sample space onto the directional space. We compare the square-root transformation with the log-ratio typed transformation by the simulation study and the real data example. In the real data example, we applied both types of transformations to the USG% data obtained from NBA, and used a density based clustering method, DBSCAN (density-based spatial clustering of applications with noise), to show the result.