• 한국정밀공학회지
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• 제23권3호
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• pp.94-101
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• 2006

A global path planning method using self-organizing feature map which is a method among a number of neural network is presented. The self-organizing feature map uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are moved toward the input vector On the other hand, the modified method in this research uses a predetermined initial weight vectors of 1-dimensional string and 2-dimensional mesh, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are moved toward the input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2006년도 학술발표회 논문집
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• pp.28-35
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• 2006

Global Flood Alert System (GFAS) is an attempt to make the best use of satellite rainfall data in flood forecasting. The project of GFAS is promoted both by Ministry of Land, Infrastructure and Transport-Japan (MLIT) and Japan Aerospace Exploration Agency (JAXA), under which Infrastructure Development Institute-Japan (IDI) has been working on the development of Internet-based information system and just launched trial run of GFAS in April 2006 on International Flood Network (IFNet) website. The function of GFAS is to connect space agencies and hydrological services/river authorities in charge of flood forecasting and warning by providing global rainfall information in maps, text data e-mails and so on which is produced from binary global rainfall data downloaded from National Aeronautics and Space Administration (NASA) website. Although the effectiveness of satellite rainfall data in flood forecasting and warning has yet to be verified, satellite rainfall is expected to play an important role to strengthen existing flood forecasting systems by diversifying hydrological data source.

• Journal of Ecology and Environment
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• 제47권4호
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• pp.228-240
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• 2023

Growing complexity in ecosystem structure and functions, under impacts of climate and land-use changes, requires interdisciplinary understandings of processes and the whole-system, and accurate estimates of the changing functions. In the last three decades, observation networks for biodiversity, ecosystems, and ecosystem functions under climate change, have been developed by interested scientists, research institutions and universities. In this paper we will review (1) the development and on-going activities of those observation networks, (2) some outcomes from forest carbon cycle studies at our super-site "Takayama site" in Japan, and (3) a few ideas how we connect in-situ and satellite observations as well as fill observation gaps in the Asia-Oceania region. There have been many intensive research and networking efforts to promote investigations for ecosystem change and functions (e.g., Long-Term Ecological Research Network), measurements of greenhouse gas, heat, and water fluxes (flux network), and biodiversity from genetic to ecosystem level (Biodiversity Observation Network). Combining those in-situ field research data with modeling analysis and satellite remote sensing allows the research communities to up-scale spatially from local to global, and temporally from the past to future. These observation networks oftern use different methodologies and target different scientific disciplines. However growing needs for comprehensive observations to understand the response of biodiversity and ecosystem functions to climate and societal changes at local, national, regional, and global scales are providing opportunities and expectations to network these networks. Among the challenges to produce and share integrated knowledge on climate, ecosystem functions and biodiversity, filling scale-gaps in space and time among the phenomena is crucial. To showcase such efforts, interdisciplinary research at 'Takayama super-site' was reviewed by focusing on studies on forest carbon cycle and phenology. A key approach to respond to multidisciplinary questions is to integrate in-situ field research, ecosystem modeling, and satellite remote sensing by developing cross-scale methodologies at long-term observation field sites called "super-sites". The research approach at 'Takayama site' in Japan showcases this response to the needs of multidisciplinary questions and further development of terrestrial ecosystem research to address environmental change issues from local to national, regional and global scales.

• 지식경영연구
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• 제13권1호
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• pp.13-24
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• 2012

Unlike the traditional belief that knowledge flows along the formal reporting procedures, recent social network research has reported the importance of informal social networks which may play a critical role as the real knowledge conduits. In fact, as a complementary approach of utilizing knowledge management systems (KMSs), many firms have focused on managing informal knowledge flow through which to acquire and transfer valuable knowledge in a fast and effective way. In a case of global companies that have newly developed foreign branches or subsidiaries, due to cultural or institutional differences and lack of understanding of knowledge management and its benefits, they often have difficulties in activating knowledge sharing in local branches. In these situations, diagnosing organizational knowledge flow through SNA can be a first step to solve the problems. Therefore, in this paper, I report on the result of case study on a foreign branch of "A" global company by identifying organizational knowledge paths. Based on the results of the diagnosis, some implications and insights for building knowledge management (KM) strategy specified for a newly developed foreign branch will also be discussed.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1998년도 추계학술대회 논문집
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• pp.87-87
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• 1998

• 한국지구물리탐사학회:학술대회논문집
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• 한국지구물리탐사학회 2007년도 공동학술대회 논문집
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• pp.163-166
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• 2007

KMA established short period seismometer, accelerometer, and ocean bottom seismometer network to build the detail earthquake monitoring system and Tsunami monitoring system. KMA also replaced borehole seismometer and wave height meter monitoring system. The purposes of this study are to record the ambient seismic noise levels of short period seismometer and accelerometer installed in 2006 and 2007, and compare their characteristics to present the standard of site selection criteria.

• International Journal of Knowledge Content Development & Technology
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• 제10권4호
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• pp.29-46
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• 2020

The paper describes the quantitative and qualitative dimensions of artificial neural networks (ANN) in India in the global context. The study is based on research publications data (8260) as covered in the Scopus database during 1999-2018. ANN research in India registered 24.52% growth, averaged 11.95 citations per paper, and contributed 9.77% share to the global ANN research. ANN research is skewed as the top 10 countries account for 75.15% of global output. India ranks as the third most productive country in the world. The distribution of research by type of ANN networks reveals that Feed Forward Neural Network type accounted for the highest share (10.18% share), followed by Adaptive Weight Neural Network (5.38% share), Feed Backward Neural Network (2.54% share), etc. ANN research applications across subjects were the largest in medical science and environmental science (11.82% and 10.84% share respectively), followed by materials science, energy, chemical engineering and water resources (from 6.36% to 9.12%), etc. The Indian Institute of Technology, Kharagpur and the Indian Institute of Technology, Roorkee lead the country as the most productive organizations (with 289 and 264 papers). Besides, the Indian Institute of Technology, Kanpur (33.04 and 2.76) and Indian Institute of Technology, Madras (24.26 and 2.03) lead the country as the most impactful organizations in terms of citation per paper and relative citation index. P. Samui and T.N. Singh have been the most productive authors and G.P.S.Raghava (86.21 and 7.21) and K.P. Sudheer (84.88 and 7.1) have been the most impactful authors. Neurocomputing, International Journal of Applied Engineering Research and Applied Soft Computing topped the list of most productive journals.

• 융합정보논문지
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• 제9권2호
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• pp.7-16
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• 2019

본 논문의 목적은 1999년에서 2018년 사이 발간된 디자인 관련 연구 논문들을 취합하고, 이들 논문들을 대상으로 키워드 네트워크 분석을 시행함으로써, 최근 20년간의 디자인분야 글로벌 연구의 특성과 동향을 파악하는 것이다. 이를 위해 스코퍼스에 등재된 디자인 분야의 22개 학술지로 부터 3,569개 연구 논문들을 취합하였으며, 저자 키워드와 인덱스 키워드를 활용하여 키워드 네트워크 모델을 설정하였다. 이들로부터 자주 사용되는 키워드들을 분석하였으며, 최근 20년을 4개 기간으로 나누어 각 기간 별로 중심성(연결, 매개) 지표를 산정, 키워드 네트워크 분석을 실시하였다. 연구결과, 디자인관련 주요 연구들과 디자인 관련 융합연구들의 특성과 동향이 키워드 네트워크 분석을 활용하여 정량적으로 설명될 수 있음을 보여주었다. 본 연구의 결과는 기술발전이 가져올 디자인 여건의 변화를 감지하고 적시성 있는 디자인 관련 연구를 추진하기 위한 자료로 활용 가능할 것으로 기대된다.

• 대한의용생체공학회:의공학회지
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• 제16권1호
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• pp.33-42
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• 1995

A new neural network architecture for the recognition of patterns from images is proposed, which is partially based on the results of physiological studies. The proposed network is composed of multi-layers and the nerve cells in each layer are connected by spatial filters which approximate receptive fields in optic nerve fields. In the proposed method, patterns recognition for complicated images is carried out using global features as well as local features such as lines and end-points. A new generating method of matched filters representing global features is proposed in this network.

• 한국항해항만학회지
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• 제45권4호
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• pp.181-185
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• 2021

본 연구에서는 어구의 정상 또는 유실 등의 상태 여부를 판단하기 위한 어구정보를 IoT 기반의 통신망을 이용하여 수신하고 분석하여, 어구의 현재 상태를 관리할 수 있고, 어구에 이상 상태 발생 시 이를 확인하고, 신속한 어구 회수 등의 관리를 수행할 수 있도록 지원하는 어구 부이 관리시스템을 개발하는 것에 관한 내용을 다룬다. IoT 기반의 통신망을 사용한 어구 관리 체계 및 통합 관리 구조 설계를 수행하였으며 이를 이용한 어구 부이 통합 관리 시스템을 개발하여 이에 대한 시스템 시험 및 검증을 수행하여 실효성을 확인하였다.