• 한국정보과학회논문지:정보통신
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• 제36권2호
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• pp.98-107
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• 2009

본 논문에서는 Wireless Sensor Networks에서 Max k-Cut Problem을 기반으로 위치 정보를 사용하지 않고 클러스터 헤드를 적절히 분산하여 선출함으로써 에너지 효율적인 클러스터링을 하는 중앙처리 방식의 새로운 알고리즘 "MCCA : Max k-Cut based Clustering Algorithm for Wireless Sensor Networks"을 제안한다. MCCA는 이웃 노드와의 상대적이고 근사적인 거리 정보만을 사용하여 효율적으로 클러스터링을 하고 에너지가 적은 노드는 클러스터 헤드 선출에서 일정 기간 제외되는 방법을 사용함으로써 LEACH, EECS보다 에너지 효율이 증대됨과 GPS를 사용한 BCDCP와 에너지 효율이 비슷함을 실험을 통하여 보인다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권10호
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• pp.3972-3988
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• 2020

Existing methods always rely on statistical features to extract local words for microblog user geolocation. There are many non-local words in extracted words, which makes geolocation accuracy lower. Considering the statistical and semantic features of local words, this paper proposes a microblog user geolocation method by extracting local words based on word clustering and wrapper feature selection. First, ordinary words without positional indications are initially filtered based on statistical features. Second, a word clustering algorithm based on word vectors is proposed. The remaining semantically similar words are clustered together based on the distance of word vectors with semantic meanings. Next, a wrapper feature selection algorithm based on sequential backward subset search is proposed. The cluster subset with the best geolocation effect is selected. Words in selected cluster subset are extracted as local words. Finally, the Naive Bayes classifier is trained based on local words to geolocate the microblog user. The proposed method is validated based on two different types of microblog data - Twitter and Weibo. The results show that the proposed method outperforms existing two typical methods based on statistical features in terms of accuracy, precision, recall, and F1-score.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권6호
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• pp.1586-1605
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• 2012

With the prevalence of multimedia applications and the potential commercial usage of Mobile Ad hoc Networks (MANETs) in group communications, Quality of Service (QoS) support became a key requirement. Recently, some researchers studied QoS multicast issues in MANETs. Most of the existing QoS multicast routing protocols are designed with flat topology and small networks in mind. In this paper, we investigate the scalability problem of these routing protocols. In particular, a Position-Based QoS Multicast Routing Protocol (PBQMRP) has been developed. PBQMRP builds a source multicast tree guided by the geographic information of the mobile nodes, which helps in achieving more efficient multicast delivery. This protocol depends on the location information of the multicast members which is obtained using a location service algorithm. A virtual backbone structure has been proposed to perform this location service with minimum overhead and this structure is utilized to provide efficient packet transmissions in a dynamic mobile Ad hoc network environment. The performance of PBQMRP is evaluated by performing both quantitative analysis and extensive simulations. The results show that the used virtual clustering is very useful in improving scalability and outperforms other clustering schemes. Compared to On-Demand Multicast Routing Protocol (ODMRP), PBQMRP achieves competing packet delivery ratio and significantly lower control overhead.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.456-457
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• 2021

최근, 실내 위치 기반 서비스를 보다 정확하게 제공하기 위해서 Wi-Fi 핑거프린트와 딥러닝을 이용한 기술이 연구되고 있다. 딥러닝 모델 중에서 과거의 정보를 기억할 수 있는 RNN 모델은 실내측위에서 연속된 움직임을 기억할 수 있어 측위 오차를 줄일 수 있다. 실내 측위에서 RNN 모델을 사용하는 경우 수집된 학습 데이터가 연속적인 순차 데이터이어야 한다. 그러나 특정 위치 정보를 판단하기 위해 수집된 Wi-Fi 핑거프린트 데이터는 특정 위치에 대한 RSSI만 기록되었기 때문에 RNN 모델의 학습 데이터로 사용이 불가능하다. 본 논문은 Wi-Fi 핑거프린트 데이터를 기반으로 RNN 모델의 순차적인 입력 데이터의 생성을 위한 영역 클러스터링 방법에 대해 제안한다.

• 한국공작기계학회논문집
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• 제15권5호
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• pp.104-113
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• 2006

The main objective of this study is to investigate the effect of compounded welding by using acoustic emission (AE) signals and doing a source location for weld heat affected zone (HAZ) through tensile testing. This study was carried out an SWS 490A high strength steel for electric shield metal arc welding, SMAW; $CO_2$ gas metal arc welding, GMAW($CO_2$); and gas tungsten arc welding, GTAW/TIG. Data displays are based on the measured parameters of the AE signals, along with environmental variables such as time and load. For instance, Gutenberg-Richter magnitude-frequency relationship (G-R MFR) offers useful b-value in data analysis. Namely event identification, source location gives the X- and Y-coordinates of the AE source. And K-means clustering analysis by Euclidean distance confirmed that was powerful to source location. Generally, strength of welded metal zone was stronger than strength of base metal. As the result, confirmed certainly that fracture is produced in HAZ instead of welded metal zone from source location.

• Journal of Communications and Networks
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• 제17권6호
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• pp.568-581
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• 2015

Predicting locations of users with portable devices such as IP phones, smart-phones, iPads and iPods in public wireless local area networks (WLANs) plays a crucial role in location management and network resource allocation. Many techniques in machine learning and data mining, such as sequential pattern mining and clustering, have been widely used. However, these approaches have two deficiencies. First, because they are based on profiles of individual mobility behaviors, a sequential pattern technique may fail to predict new users or users with movement on novel paths. Second, using similar mobility behaviors in a cluster for predicting the movement of users may cause significant degradation in accuracy owing to indistinguishable regular movement and random movement. In this paper, we propose a novel fusion technique that utilizes mobility rules discovered from multiple similar users by combining clustering and sequential pattern mining. The proposed technique with two algorithms, named the clustering-based-sequential-pattern-mining (CSPM) and sequential-pattern-mining-based-clustering (SPMC), can deal with the lack of information in a personal profile and avoid some noise due to random movements by users. Experimental results show that our approach outperforms existing approaches in terms of efficiency and prediction accuracy.

• 전기학회논문지
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• 제59권7호
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• pp.1320-1326
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• 2010

In this paper, we develop a design methodology of granular-based neurocomputing networks realized with the aid of the clustering techniques. The objective of this paper is modeling and evaluation of approximation and generalization capability of the Linear-Type Superconducting Power Supply (LTSPS). In contrast with the plethora of existing approaches, here we promote a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The underlying design tool guiding the development of the granular-based neurocomputing networks revolves around the Fuzzy C-Means (FCM) clustering method and the Radial Basis Function (RBF) neural network. In contrast to "standard" Radial Basis Function neural networks, the output neuron of the network exhibits a certain functional nature as its connections are realized as local linear whose location is determined by the membership values of the input space with the aid of FCM clustering. To modeling and evaluation of performance of the linear-type superconducting power supply using the proposed network, we describe a detailed characteristic of the proposed model using a well-known NASA software project data.

• 융합보안논문지
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• 제20권3호
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• pp.13-20
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• 2020

접근이 어렵거나 지속적인 모니터링이 필요한 서비스에 적용 가능한 WSN(Wireless Sensor Network) 기술은 최근 그 응용 분야의 확대 및 효율성의 증가로 인해 연구 개발의 필요성이 점증하고 있는 분야이다. 특히, 국방 분야에서는 경계 및 감시 정찰 등 인원 투입 시 발생 가능한 위험 요소를 회피하고 효율적인 인원 활용을 위한 대안으로 센서 네트워크를 포함한 최신 IT 기술에 대한 연구가 활발히 진행되는 상황이다. 본 논문에서는 클러스터링(Clustering) 및 위치 기반 기법을 적용하여 센서 네트워크를 구성하는 노드들의 수명을 늘리기 위한 조건을 분석한 후 수명 연장을 위한 요인들을 도출하였다. 도출된 요인들은 CH(Cluster Head) 선출 방안, 각각의 CH에서 BS(Base Station)까지의 경로 선정 방안 등이 포함되며 실험을 통해 검증된 최적의 결과 및 방안을 적용하여 클러스터링 기반의 WSN 구성 방안을 제안하였다.

• 한국멀티미디어학회논문지
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• 제13권6호
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• pp.833-840
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• 2010

In this paper a method for detecting and extracting the face from the image in YCbCr spaceis proposed. The face region is obtained from the complex original image by using the difference method and the face color information is taken from the reduced face region throughthe Ellipse clustering method. The experimental results showed that the proposed method can efficiently detect and extract the face from the original image under the general light intensity except for low luminance.

• 지능정보연구
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• 제8권2호
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• pp.177-187
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• 2002

공간 데이터 마이닝이란 공간 데이터베이스 내에 함축적으로 존재하는 흥미 있는 관계와 특징을 발견하는 과정이다. 많은 공간 클러스터링 알고리즘이 개발 되었으나, 공간 속성을 기준으로 클러스터링을 수행하면서 동시에 오브젝트의 비 공간적 속성에 대하여 가중치를 부여하는 방법에 대한 연구는 부족하였다. 본 논문은 새로운 공간 클러스터링 알고리즘인 DBSCAN-W를 제안하였다. DBSCAN-W는 밀도 기반 클러스터링 알고리즘인 DBSCAN을 확장한 알고리즘이다. 기존의 DBSCAN에서는 클러스터링을 위해 오브젝트의 위치 속성만을 고려한 반면, DBSCAN-W는 오브젝트의 위치 속성 뿐 아니라 주어진 응용과 관련된 오브젝트의 비 공간 속성들을 함께 고려한다. DBSCAN-W에서 각 오브젝트들은 다양한 크기의 원으로 표현되는 영역을 갖는다. 이때 원의 반지름은 해당 응용 시스템에서 오브젝트가 갖는 중요도를 반영한다 또한 실험을 통하여 DBSCAN-W알고리즘이 사용자의 의도를 반영한 다양한 클러스터를 효과적으로 생성하는 결과를 보였다.