• IEMEK Journal of Embedded Systems and Applications
• /
• v.5 no.4
• /
• pp.206-216
• /
• 2010

Wireless sensor networks (WSNs) are used to collect various data in environment monitoring applications. A spatial clustering may reduce energy consumption of data collection by partitioning the WSN into a set of spatial clusters with similar sensing data. For each cluster, only a few sensor nodes (samplers) report their sensing data to a base station (BS). The BS may predict the missed data of non-samplers using the spatial correlations between sensor nodes. ASAP is a representative data collection algorithm using the spatial clustering. It periodically reconstructs the entire network into new clusters to accommodate to the change of spatial correlations, which results in high message overhead. In this paper, we propose a new data collection algorithm, name EPDC (Energy-efficient Periodic Data Collection). Unlike ASAP, EPDC identifies a specific cluster consisting of many dissimilar sensor nodes. Then it reconstructs only the cluster into subclusters each of which includes strongly correlated sensor nodes. EPDC also tries to reduce the message overhead by incorporating a judicious probabilistic model transfer method. We evaluate the performance of EPDC and ASAP using a simulation model. The experiment results show that the performance improvement of EPDC is up to 84% compared to ASAP.

• Proceedings of the Korea Water Resources Association Conference
• /
• 1998.05b
• /
• pp.7-13
• /
• 1998

A methodology to analyze and quantify regional meteorological drough based on annual precipitation data has been introduced in this paper In this study, based on posterior probability estimator and Bayesian classifier in Spatial Analysis Neural Network ISANN), point drought probabilities categorized as extreme, severe, mild, and non drought events has been defined, and a Bayesian Drought Severity Index (BPSI) has been introduced to classify the region of interest into four drought serverities. For example, the proposed methodology has been applied to analyze the regional drought of South Korea. This is a new method to classify and quantify the spatial or regional drought based on neural network pattern recognition technique and the results show that it may be apprepriate and valuable to analyze the spatial drought.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.4
• /
• pp.627-635
• /
• 2008

Using directional antenna in wireless network can offer many advantages including significant decrease of interference, increase of spatial reuse and possibility of improving network capacity. However, existing 802.11 MAC is designed for use of omni-directional antenna then those advantages can not be shown in that MAC protocol when it uses directional antenna. In this paper, we present a MAC protocol specifically designed for directional antenna to achieve spatial reuse and improve capacity of MAC protocol. Simulation result shows the advantages of our proposal in comparison with existing MAC in terms of end-to-end delay and network throughput.

• Health Policy and Management
• /
• v.27 no.3
• /
• pp.229-240
• /
• 2017

Background: This study purposed to analyze the spatial accessibility of women in childbearing age to the healthcare organizations (HCOs) providing delivery services in Gangwon-do. Methods: Network analysis was applied to assess the spatial accessibility based on the travel time and road travel distance. Travel time and travel distance were measured between the location of HCOs and the centroid of the smallest administrative areas, eup, myeon, and dong in Gangwon-do. Korean Transport Database Center provided road network GIS (Geographic Information System) Database in 2015 and it was used to build the network dataset. Two types of network analysis, service area analysis and origin-destination (OD)-cost matrix analysis, applied to the created network dataset. Service area analysis defined all-accessible areas that are within a specified time, and OD-cost matrix analysis measured the least-cost paths from the HCOs to the centroids. The visualization of the number of the HCOs and the number of women in childbearing age on the Ganwon-do map and network analysis were performed with ArcGIS ver. 10.0 (ESRI, Redlands, CA, USA). Results: Twenty HCOs were providing delivery services in Gangwon-do in 2016. Over 50% of the women in childbearing age were aged more than 35 years. Service area analysis found that 89.56% of Gangwon-do area took less than 60 minutes to reach any types of HCOs. For tertiary hospitals, about 74.37% of Gangwon-do area took more than 60 minutes. Except Wonju-si and Hoengseong-gun, other regions took more than 60 minutes to reach the tertiary hospital. Especially, Goseong-gun, Donghae-si, Samcheok-si, Sokcho-si, Yanggu-gun, Cheorwon-gun, and Taebaek-si took more than 100 minutes to the tertiary hospital. Conclusion: This study provided that the accessibility toward the tertiary hospital was limited and it may cause problems in high-risk delivery patients such as over 35 years. Health policy makers will need to handle the obstetric accessibility issues in Gangwon-do.

• Spatial Information Research
• /
• v.14 no.2 s.37
• /
• pp.245-260
• /
• 2006

A practical use of the location information and user requirement are increased in a mobile environment which supports the portability. And Various service which GIS is related with a Spatial DB have been processed. Generally, logical relation of a traffic network which organizes the Road DB uses a basic node-link structure. In this way, Designed structure can not be flexible at various model apply and are not efficient with a database retrieval in a maintenance management side. In this research, We supplement with the problem of a existing network model and the limitation of the building through the design of a network model which uses dynamic segmentation. And we tried to implement efficient hierarchy model at the retrieval of the network and presentation. Designed model supports a stage presentation of various level and a hierarchy entity relation and We are expected to supplement a network spatial modelling function which the GIS has.

• Journal of Korean Society for Geospatial Information Science
• /
• v.21 no.4
• /
• pp.143-150
• /
• 2013

The purpose of this study is to suggest the methodology to develop variable-scale network model, which is able to induce large-scale road network in detailed level corresponding to small-scale linear objects with various abstraction in higher level. For this purpose, the definition of terms, the benefits and the specific procedures related with a variable-scale model were examined. Second, representation level and the components of layer to design the variable-scale map were presented. In addition, rule-based data generating method and indexing structure for higher LoD were defined. Finally, the implementation and verification of the model were performed to road network in study area (Jeju -do) so that the proposed algorithm can be practical. That is, generated variable scale road network were saved and managed in spatial database (Oracle Spatial) and performance analysis were carried out for the effectiveness and feasibility of the model.

• Journal of Environmental Policy
• /
• v.13 no.2
• /
• pp.39-63
• /
• 2014

This article aims to explore the connectivity and centrality between environment law and spatial law, where make implications in environmental planning. To achieve it, we used Network Analysis(NA) as a tool for analyzing the connectivities. 52 environmental Laws and 29 Spatial Laws are selected and used for this analysis. This study also attempts to explain the phenomenon through quantitative study rather than qualitative research. This paper is methodologically the first attempt to Environmental Law study, which will help to understand the structure of complex Environmental Law. The result of the network analysis for connectivity between Spatial Law and Environmental Law demonstrates that two laws are in less mutual relationship with each other. It also supports that Environmental Law and Spatial Law need to be closely connected with each other for effective environmental management.

• ETRI Journal
• /
• v.44 no.2
• /
• pp.208-219
• /
• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.1
• /
• pp.60-79
• /
• 2022

The U-Net architecture-based segmentation models attained remarkable performance in numerous medical image segmentation missions like skin lesion segmentation. Nevertheless, the resolution gradually decreases and the loss of spatial information increases with deeper network. The fusion of adjacent layers is not enough to make up for the lost spatial information, thus resulting in errors of segmentation boundary so as to decline the accuracy of segmentation. To tackle the issue, we propose a new deep learning-based segmentation model. In the decoding stage, the feature channels of each decoding unit are concatenated with all the feature channels of the upper coding unit. Which is done in order to ensure the segmentation effect by integrating spatial and semantic information, and promotes the robustness and generalization of our model by combining the atrous spatial pyramid pooling (ASPP) module and channel attention module (CAM). Extensive experiments on ISIC2016 and ISIC2017 common datasets proved that our model implements well and outperforms compared segmentation models for skin lesion segmentation.

• Journal of Korean Association for Spatial Structures
• /
• v.12 no.1
• /
• pp.14-19
• /
• 2012