• 한국정보시스템학회지:정보시스템연구
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• 제29권1호
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• pp.159-179
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• 2020

Purpose: We present a scientometric review of the literature on platform government to serve three primary purposes: First, to cluster researches on platform government based on the research issues; second, to identify the major papers, authors, and keywords in the domain; and third, to explore the promising research areas of platform government. Design/methodology/approach: We collected the platform government research from Web of Science, and analyzed 1,536 articles that was published during time span of 1998-2019. Next, co-citation networks are constructed and analyzed by using CiteSpace to visualize the domain clusters and dynamic research trends in the platform government domain. Findings: We identified 13 sub areas of the platform government research: global investigation, consumer product quality, digital agora, civic crowd funding, and open data use etc. We also visualize the top 20 references with the strongest citation bursts, co-authors network, co-occurring keyword network, and timeline of co-citation clusters.

• 대한환경공학회지
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• 제33권7호
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• pp.501-510
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• 2011

한강의 주요 14개 지류하천 유역의 수질오염원을 평가하고, 2007. 1~2009. 12의 하천 수질자료(14 data set)로 SPSS-17.0을 이용하여 하천별 수질 특성을 평가하였다. 시 공간변화에 대한 군집 분석을 실시한 결과 공간변화에 따라 4그룹으로 평가되었으며, 유역의 오염원 종류 및 밀도가 군집분류에 가장 큰 영향을 미치는 것으로 나타났다. 시간변화에 따라 여름에서 가을까지(7~10월)와 겨울에서 초여름까지(11~6월)의 2그룹으로 분류되어 강우와 기온 그리고 부영양화 현상이 군집화에 기여하는 것으로 평가되었다. 조사대상 하천의 수질오염 요인은 유기물질 영양염류 세균오염요인과 하천 내 물질대사요인으로(71~90%) 설명되었고, 계절에 따라 주요인(수질오염물질)은 변화하는 것으로 나타났다. 각 하천의 수질특성은 요인과 유역 오염원을 같이 평가하였을 때 유용한 결과를 얻을 수 있었다.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2002년도 춘계학술대회 논문집
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• pp.393-398
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• 2002

Neural networks are used in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD (an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. forthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• 산업기술연구
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• 제29권B호
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• pp.9-15
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• 2009

In the case of traditional binary encoding technique, it takes long time to converge the optimal solutions and brings about complexity of the systems due to encoding and decoding procedures. However, the ROGAs (real-coded genetic algorithms) do not require these procedures, and the k-means clustering algorithm can avoid global searching space. Thus, this paper proposes a new approach by using their advantages. The proposed method constructs the multiple predictors using the optimal differences that can reveal the patterns better and properties concealed in non-stationary time series where the k-means clustering algorithm is used for data classification to each predictor, then selects the best predictor. After selecting the best predictor, the cluster centers of the predictor are tuned finely via RCGKA in secondary tuning procedure. Therefore, performance of the predictor can be more enhanced. Finally, we verifies the prediction performance of the proposed system via simulating typical time series examples.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2001년도 춘계학술대회 논문집(한국공작기계학회)
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• pp.239-243
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• 2001

Neural networks are used in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD (an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2000년도 춘계학술대회논문집 - 한국공작기계학회
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• pp.171-175
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• 2000

Neural networks are in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD (an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2000년도 추계학술대회논문집 - 한국공작기계학회
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• pp.108-112
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• 2000

Neural networks are used in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD (an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• Journal of Information Processing Systems
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• 제17권1호
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• pp.163-177
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• 2021

Many methods of discovering social networking communities or clustering of features are based on the network structure or the content network. This paper proposes a community discovery method based on topic models using a time factor and an unsupervised clustering method. Online community discovery enables organizations and businesses to thoroughly understand the trend in users' interests in their products and services. In addition, an insight into customer experience on social networks is a tremendous competitive advantage in this era of ecommerce and Internet development. The objective of this work is to find clusters (communities) such that each cluster's nodes contain topics and individuals having similarities in the attribute space. In terms of social media analytics, the method seeks communities whose members have similar features. The method is experimented with and evaluated using a Vietnamese corpus of comments and messages collected on social networks and ecommerce sites in various sectors from 2016 to 2019. The experimental results demonstrate the effectiveness of the proposed method over other methods.

• 대한전자공학회논문지SD
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• 제37권2호
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• pp.55-64
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• 2000

본 논문에서는 트랙 밀도를 최소화할 수 있는 효과적인 데이터패스 배치 알고리즘을 제안한다. 주어진 n개의 데이터패스 element 각각을 한 개의 클러스터라 놓고 이들 클러스터 중 가장 강하게 연결된 두 개를 선택하고 병합하는 과정을 한 개의 클러스터만 남을 때까지 반복한다. 병합될 두 클러스터내의 element들은 이미 각각 선형배열되어 있으므로 병합 시 이 두 선형배열을 연결하면 되며, 최종적으로 남은 클러스터의 선형배열의 처음과 끝을 연결하면 회전선형배열을 만들 수 있다. 이 회전선형배열에서 인접한 두 element 사이를 절단하면 서로 다른 n개의 선형배열을 만들 수 있으며 제안된 알고리즘에서는 이들 중 트랙밀도가 가장 낮은 선형배열을 선택한다. 본 논문에서는 스펙트럴방법을 이용해 d차원에 사상시킨 벡터의 내적이 최대가 되면 대응되는 두 클러스터가 강하게 연결되었음을 보였으며, 이를 이용해 병합될 두 클러스터를 찾는다. 기존 GA/SA/sup ［2］/방법과 비교하여 제안된 방법은 트랙밀도 면에서 유사한 성능을 내지만 수행시간 면에서 상당히 향상되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권4호
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• pp.1442-1462
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• 2014

Packet classification is a key technology of the Internet for routers to classify the arriving packets into different flows according to the predefined rulesets. Previous packet classification algorithms have mainly focused on search speed and memory usage, while overlooking update performance. In this paper, we propose PreCuts, which can drastically improve the update speed. According to the characteristics of IP field, we implement three heuristics to build a 3-layer decision tree. In the first layer, we group the rules with the same highest byte of source and destination IP addresses. For the second layer, we cluster the rules which share the same IP prefix length. Finally, we use the heuristic of information entropy-based bit partition to choose some specific bits of IP prefix to split the ruleset into subsets. The heuristics of PreCuts will not introduce rule duplication and incremental update will not reduce the time and space performance. Using ClassBench, it is shown that compared with BRPS and EffiCuts, the proposed algorithm not only improves the time and space performance, but also greatly increases the update speed.