• Journal of Microbiology and Biotechnology
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• 제16권5호
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• pp.748-756
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• 2006

We propose a genome-scale clustering approach to identify whole genome relationships using the functional groups given by the Kyoto Encyclopedia of Genes and Genomes Orthology (KO) database. The metabolic capabilities of each organism were defined by the number of genes in each functional category. The archaeal, bacterial, and eukaryotic genomes were compared by simultaneously applying a two-step clustering method, comprised of a self-organizing tree algorithm followed by unsupervised hierarchical clustering. The clustering results were consistent with various phenotypic characteristics of the organisms analyzed and, additionally, showed a different aspect of the relationship between genomes that have previously been established through rRNA-based comparisons. The proposed approach to collect and cluster the metabolic functional capabilities of organisms should make it a useful tool in predicting relationships among organisms.

• Journal of Computing Science and Engineering
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• 제9권4호
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• pp.163-176
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• 2015

Self-organization of distributed wireless sensor nodes is a critical issue in wireless sensor networks (WSNs), since each sensor node has limited energy, bandwidth, and scalability. These issues prevent sensor nodes from actively collaborating with the other types of sensor nodes deployed in a typical heterogeneous and somewhat hostile environment. The automated self-organization of a WSN becomes more challenging as the number of sensor nodes increases in the network. In this paper, we propose a dynamic self-organized architecture that combines tree topology with a drawn-grid algorithm to automate the self-organization process for WSNs. In order to make our proposed architecture scalable, we assume that all participating active sensor nodes are unaware of their primary locations. In particular, this paper presents two algorithms called active-tree and drawn-grid. The proposed active-tree algorithm uses a tree topology to assign node IDs and define different roles to each participating sensor node. On the other hand, the drawn-grid algorithm divides the sensor nodes into cells with respect to the radio coverage area and the specific roles assigned by the active-tree algorithm. Thus, both proposed algorithms collaborate with each other to automate the self-organizing process for WSNs. The numerical and simulation results demonstrate that the proposed dynamic architecture performs much better than a static architecture in terms of the self-organization of wireless sensor nodes and energy consumption.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1039-1043
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• 2005

Despite the many significant advances made in robot architecture, the basic approaches are deliberative and reactive methods. They are quite different in recognizing outer environment and inner operating mechanism. For this reason, they have almost opposite characteristics. Later, researchers integrate these two approaches into hybrid architecture. In such architecture, Reactive module also called low-level motion control module have advantage in real-time reacting and sensing outer environment; Deliberative module also called high-level task planning module is good at planning task using world knowledge, reasoning and intelligent computing. This paper presents a framework of the integrated planning and control for mobile robot navigation. Unlike the existing hybrid architecture, it learns topological map from the world map by using MST (Minimum Spanning Tree)-based SOFM (Self-Organizing Feature Map) algorithm. High-level planning module plans simple tasks to low-level control module and low-level control module feedbacks the environment information to high-level planning module. This method allows for a tight integration between high-level and low-level modules, which provide real-time performance and strong adaptability and reactivity to outer environment and its unforeseen changes. This proposed framework is verified by simulation.

• 지능정보연구
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• 제22권1호
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• pp.187-204
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• 2016

소셜 미디어를 이용하는 사용자들이 직접 작성한 의견 혹은 리뷰를 이용하여 상호간의 교류 및 정보를 공유하게 되었다. 이를 통해 고객리뷰를 이용하는 오피니언마이닝, 웹마이닝 및 감성분석 등 다양한 연구분야에서의 연구가 진행되기 시작하였다. 특히, 감성분석은 어떠한 토픽(주제)를 기준으로 직접적으로 글을 작성한 사람들의 태도, 입장 및 감성을 알아내는데 목적을 두고 있다. 고객의 의견을 내포하고 있는 정보 혹은 데이터는 감성분석을 위한 핵심 데이터가 되기 때문에 토픽을 통한 고객들의 의견을 분석하는데 효율적이며, 기업에서는 소비자들의 니즈에 맞는 마케팅 혹은 투자자들의 시장동향에 따른 많은 투자가 이루어지고 있다. 본 연구에서는 중국의 온라인 시나 주식 포럼에서 사용자들이 직접 작성한 포스팅(글)을 이용하여 기존에 제시된 토픽들로부터 핫토픽을 선정하고 탐지하고자 한다. 기존에 사용된 감성 사전을 활용하여 토픽들에 대한 감성값과 극성을 분류하고, 군집분석을 통해 핫토픽을 선정하였다. 핫토픽을 선정하기 위해 k-means 알고리즘을 이용하였으며, 추가로 인공지능기법인 SOM을 적용하여 핫토픽 선정하는 절차를 제시하였다. 또한, 로짓, 의사결정나무, SVM 등의 데이터마이닝 기법을 이용하여 핫토픽 사전 탐지를 하는 감성분석을 위한 모형을 개발하여 관심지수를 통해 선정된 핫토픽과 탐지된 핫토픽을 비교하였다. 본 연구를 통해 핫토픽에 대한 정보 제공함으로써 최신 동향에 대한 흐름을 알 수 있게 되고, 주식 포럼에 대한 핫토픽은 주식 시장에서의 투자자들에게 유용한 정보를 제공하게 될 뿐만 아니라 소비자들의 니즈를 충족시킬 수 있을 것이라 기대된다.