• International journal of advanced smart convergence
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• 제12권3호
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• pp.13-18
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• 2023

This paper aims to investigate data features for neighbor path selection (NPS) in computer network with regional failures. It is necessary to find an available alternate communication path in advance when regional failures due to earthquakes or forest fires occur simultaneously. We describe previous general heuristics and simulation heuristic to solve the NPS problem in the regional fault network. The data features of general heuristics using proximity and sharing factor and the data features of simulation heuristic using machine learning are explained through examples. Simulation heuristic may be better than general heuristics in terms of communication success. However, additional data features are necessary in order to apply the simulation heuristic to the real environment. We propose novel data features for NPS in computer network with regional failures and Keras modeling for computing the communication success probability of candidate neighbor path.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2002년도 학술발표회 논문집(II)
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• pp.1187-1194
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• 2002

In this study, we have simulated regional climate over East Asia using dynamical downscaling For dynamic downscaling experiments for regional climate simulation, MM5method. with 27 km horizontal resolution and 18 layers of sigma-coordinate in vertical is nested within global-scale NCEP reanalysis data with 2.5。${\times}$2.5。 resolution in longitude and latitude. In regional simulation, January and July, 1979 monthly mean features have been obtained by both continuous integration and daily restart integration driven by updating the lateral boundary forcing at 6-hr intervals from the NCEP reanalysis data using a nudging scheme with the updating design of initial and boundary conditions in both continuous and restart integrations. In result, we may successfully generated regional detail features which might be forced by topography, lake, coastlines and land use distribution from a regional climate. There is no significant difference in monthly mean features either integrate continuously or integrate with daily restart. For climatologically long integration, the initial condition may not be significantly important. Accordingly, MM5 can be integrated for a long period without restart frequently, if a proper lateral boundary forcing is given.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권1호
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• pp.272-287
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• 2016

Recently, various and effective contrast based salient object detection models to focus on a single target have been proposed. However, there is a lack of research on detection of multiple objects, and also it is a more challenging task than single target process. In the multiple target problem, we are confronted by new difficulties caused by distinct difference between properties of objects. The characteristic of existing models depending on the global maximum distribution of data point would become a drawback for detection of multiple objects. In this paper, by analyzing limitations of the existing methods, we have devised three main processes to detect multiple salient objects. In the first stage, regional features are extracted from over-segmented regions. In the second stage, the regional features are categorized into homogeneous cluster using the mean-shift algorithm with the kernel function having various sizes. In the final stage, we compute saliency scores of the categorized regions using only spatial features without the contrast features, and then all scores are integrated for the final salient regions. In the experimental results, the scheme achieved superior detection accuracy for the SED2 and MSRA-ASD benchmarks with both a higher precision and better recall than state-of-the-art approaches. Especially, given multiple objects having different properties, our model significantly outperforms all existing models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권3호
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• pp.894-907
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• 2022

The features extracted by convolutional neural networks are more descriptive of images than traditional features, and their convolutional layers are more suitable for retrieving images than are fully connected layers. The convolutional layer features will consume considerable time and memory if used directly to match an image. Therefore, this paper proposes a feature weighting and region integration method for convolutional layer features to form global feature vectors and subsequently use them for image matching. First, the 3D feature of the last convolutional layer is extracted, and the convolutional feature is subsequently weighted again to highlight the edge information and position information of the image. Next, we integrate several regional eigenvectors that are processed by sliding windows into a global eigenvector. Finally, the initial ranking of the retrieval is obtained by measuring the similarity of the query image and the test image using the cosine distance, and the final mean Average Precision (mAP) is obtained by using the extended query method for rearrangement. We conduct experiments using the Oxford5k and Paris6k datasets and their extended datasets, Paris106k and Oxford105k. These experimental results indicate that the global feature extracted by the new method can better describe an image.

• ETRI Journal
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• 제44권2호
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• pp.208-219
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• 2022

Crime amount prediction is crucial for optimizing the police patrols' arrangement in each region of a city. First, we analyzed spatiotemporal correlations of the crime data and the relationships between crime and related auxiliary data, including points-of-interest (POI), public service complaints, and demographics. Then, we proposed a crime amount prediction model based on 2D convolution and long short-term memory neural network (2DCONV-LSTM). The proposed model captures the spatiotemporal correlations in the crime data, and the crime-related auxiliary data are used to enhance the regional spatial features. Extensive experiments on real-world datasets are conducted. Results demonstrated that capturing both temporal and spatial correlations in crime data and using auxiliary data to extract regional spatial features improve the prediction performance. In the best case scenario, the proposed model reduces the prediction error by at least 17.8% and 8.2% compared with support vector regression (SVR) and LSTM, respectively. Moreover, excessive auxiliary data reduce model performance because of the presence of redundant information.

• 기술혁신연구
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• 제9권2호
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• pp.34-55
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• 2001

With increasing globalization, a proper policy for global inter-city networking strongly required, which presumes the study on the heterogeneity among regional innovation systems. While surveying the research results of the Evolutionary Economics, 1 stress that regional systems of innovation differ in technological capacities, industrial structures, institutional arrangements and socio-cultural features. 1 make the empirical investigation based on the data of Busan and Hamburg, making clear the regional specificities among regional innovation systems. The results show that regional systems of innovation in Busan and Hamburg are quitely asymmetric. They suggest also the limitation of neo-classical assumption on the ‘homogeneous production function’ and that policy-makers in regions should make the policy for ‘global inter-cities networking’, based on perspectives of regional heterogeneity.

• East Asian Economic Review
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• 제16권2호
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• pp.117-137
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• 2012

The objective of this article is to examine the characteristic features of contemporary policy-led regionalism in Asia. It identifies the positive and negative features associated with the free trade agreements that have proliferated in Asia during the first decade of the $21^{st}$ century. There has been a marked transformation in Asia's regional architecture in a short span of a decade-and-a-half. The mode and conduct of multilateral trade has been significantly transformed during recent years and Asia could not possibly remain immune to this transformation. The importance of regionalism in multilateral trade has increased steadily. In addition, the trade-investment-services nexus has developed and grown increasingly important. As business firms now manufacture parts of their products across the border, bilateral trade agreements (BTAs), regional trade agreements (RTAs) and free trade agreements (FTAs) of the contemporary period need to take into account the new kind of trade barriers that have been created due to the changing mode of trade. The contemporary regional agreements need to be designed to facilitate the new modes of conducting business and trade. It was understood rather late in Asia that the 'WTO-Plus' FTAs are more functional and result-oriented than their predecessors.

• 대한교통학회지
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• 제32권6호
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• pp.638-650
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• 2014

본 연구의 목적은 서울 도심지역의 행정동별 지역적 요소와 계획적 요소가 목적지로의 통행수단으로서 보행을 결정하는 것에 미치는 영향의 정도를 파악하고, 이에 대한 분석 결과 및 보행의 공간적 특성을 통해 지역별 특성을 파악하고자 함에 있다. 보행 결정에 영향을 미치는 요소들의 영향 정도를 파악하기 위해 본 연구에서는 전역적 차원의 회귀분석 모형인 최소자승모형을 사용하였고, 이와 더불어 다양한 공간통계 분석모형 중 지역별 보행특성을 기반으로 공간적 이질성을 고려하는 지리가중회귀모형의 적용을 통해 지역별 특성을 파악하고자 하였다. 전역적 차원의 회귀분석 결과 목적지로의 보행 선택에 영향을 주는 요소로는 지역적 요소 중 교통시설 지역 및 상업지역, 대학교 면적이, 계획적 요소 중에서는 교육.연구시설 및 계획시설 면적이 보행 선택에 정의 영향을 주었다. 마지막으로 지리가중회귀모형의 분석 결과를 통해 서울 도심지역 중 교통중심지 및 취약지, 상업 업무 중심지, 대학가 중심지, 연구시설 밀집지를 파악할 수 있었다. 본 연구의 결과를 통해 지역 및 공간적 이질성에 대한 이해 없이 진행되었던 기존의 계획 및 정책들에 지역적 특성이라는 정보를 제공함으로 이에 대한 반영의 여지를 주어 보다 지역발전 차원의 계획 수립에 기여할 수 있을 것이라는 점에 본 연구의 의의가 있을 것이라 여겨진다.

• 한국제4기학회:학술대회논문집
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• 한국제4기학회 2004년도 하계학술대회
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• pp.24-25
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• 2004

It is well known that there is an inverse relationship between the strength of Indian summer monsoon Rainfall (ISMR) and extent of Eurasian snow cover/depth in the preceding season. Tibetan snow cover/depth also affects the Asian monsoon rainy season largely. The positive correlation between Tibetan sensible heat flux and southeast Asian rainfall suggest an inverse relationship between Tibetan snow cover and southeast Asian rainfall. Developments in Regional Climate Models suggest that the effect of Tibetan snow on the ISMR can be well studied by Limited Area Models (LAMs). LAMs are used for regional climate studies and operational weather forecast of several hours to 3 days in future. The Eta model developed by the National Center for Environmental Prediction (NCEP), the Fifth-Generation NCAR/Penn State Mesoscale Model (MM5) and Regional Climate Model (RegCM) have been used for weather prediction as well as for the study of present-day climate and variability over different parts of the world. Regional Climate Model (RegCM3) has been widely . used for various mesoscale studies. However, it has not been tested to study the characteristics of circulation features and associated rainfall over India so far. In the present study, Regional Climate Model (RegCM-3) has been integrated from 1 st April to 30th September for the years 1993-1996 and monthly mean monsoon circulation features and rainfall simulated by the model at 55km resolution have been studied for the Indian summer monsoon season. Characteristics of wind at 850hPa and 200hPa, temperature at 500hPa, surface pressure and rainfall simulated by the model have been examined for two convective schemes such as Kuo and Grell with Arakawa-Schubert as the closure scheme, Model simulated monsoon circulation features have been compared with those of NCEP/NCAR reanalyzed fields and the rainfall with those of India Meteorological Department (IMD) observational rainfall datasets, Comparisons of wind and temperature fields show that Grell scheme is closer to the NCEP/NCAR reanalysis, The influence of Tibetan snowdepth in spring season on the summer monsoon circulation features and subsequent rainfall over India have been examined. For such sensitivity experiment, NIMBUS-7 SMMR snowdepth data have been used as a boundary condition in the RegCM3, Model simulation indicates that ISMR is reduced by 30% when 10cm of snow has been introduced over Tibetan region in the month of previous April. The existence of Tibetan snow in RegCM3 also indicates weak lower level monsoon westerlies and upper level easterlies.

• 한국정보시스템학회지:정보시스템연구
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• 제17권3호
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• pp.135-152
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• 2008

Basically within a special regional area, a regional cluster seems to be based on core competencies of individual intra-cluster companies and collaboration among them. Information infrastructure has been emphasized as on one of competitive factors of a regional cluster, and it can be organized using collaboration system architecture integrated with each company's internal systems for efficient supply chain operation. As one of technical methods to prepare the system infrastructure supporting the collaboration of companies in a regional cluster, the Web Services can be effectively used. In this paper, a collaborative information system infrastructure for a regional cluster is suggested within the scope of supply chain management. And the efficiency of the proposed alternative is appraised with the features of a regional cluster.