• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.384-387
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• 2003

Most of the real world phenomena change over time. The ability to represent and to reason geographic data becomes crucial. A large amount of non-standard applications are dealing with data characterized by spatial, temporal and/or uncertainty features. Non-standard data like spatial and temporal data have an inner complex structure requiring sophisticated data representation, and their operations necessitate sophisticated and efficient algorithms. Current GIS technology is inefficient to model and to handle complex geographic phenomena, which involve space, time and uncertainty dimensions. This paper concentrates on developing a fuzzy spatio-temporal data model based on fuzzy set theory and relational data models. Fuzzy spatio-temporal operators are also provided to support dynamic query.

• ETRI Journal
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• v.44 no.2
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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.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.693-703
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• 2010

High spectral resolution of hyperspectral data enables analysis of complex natural phenomena that is reflected on the data nonlinearly. Although many manifold learning methods have been developed for such problems, most methods do not consider the spatial correlation between samples that is inherent and useful in remote sensing data. We propose a manifold learning method which directly combines the spatial proximity and the spectral similarity through kernel PCA framework. A gain factor caused by spatial proximity is first modelled with a heat kernel, and is added to the original similarity computed from the spectral values of a pair of samples. Parameters are tuned with intelligent grid search (IGS) method for the derived manifold coordinates to achieve optimal classification accuracies. Of particular interest is its performance with small training size, because labelled samples are usually scarce due to its high acquisition cost. The proposed spatial kernel PCA (KPCA) is compared with PCA in terms of classification accuracy with the nearest-neighbourhood classification method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.219-232
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• 2023

Mobility services need to change according to the regional characteristics of the target service area. Accordingly, analysis of mobility patterns and characteristics based on Origin-Destination (OD) data that reflect travel behaviors in the target service area is required. However, since conventional methods construct the OD data obtained from the administrative district-based zone system, it is hard to ensure spatial homogeneity. Hence, there are limitations in analyzing the inherent travel patterns of each mobility service, particularly for new mobility service like Demand Responsive Transit (DRT). Unlike the conventional approach, this study applies a data-driven clustering technique to conduct spatial analyses on OD travel patterns of regional mobility services based on reconstructed OD data derived from re-aggregation for original OD distributions. Based on the reconstructed OD data that contains information on the inherent feature vectors of the original OD data, the proposed method enables analysis of the spatial characteristics of regional mobility services, including public transit bus, taxi and DRT.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.99-115
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• 2003

In this paper, we present a knowledge-based object-oriented spatial database system called KOBOS. A knowledge-based approach is introduced to the object-oriented spatial database system for data modeling and approximate query answering. For handling the structure of spatial objects and the approximate spatial operators, we propose three levels of object-oriented data model: (1) a spatial shape model; (2) a spatial object model; (3) an internal description model. We use spatial type abstraction hierarchies(STAHs) to provide the range of the approximate spatial operators. We then propose SOQL, a spatial object-oriented query language. SOQL provides an integrated mechanism for the graphical display of spatial objects and the retrieval of spatial and aspatial objects. To support an efficient hybrid query evaluation, we use the top-down spatial query processing method.

• Journal of Distribution Science
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• v.21 no.3
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• pp.101-111
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• 2023

Purpose: The first purpose of this study is deriving in-store characteristics that affect the experience of customers using unmanned stores and reveals the value of major services that customers feel and experience. Also, an empirical analysis is conducted on the effect of intelligent consumption value after using unmanned stores on consumption emotions and continuous use intention, and the modulating effect of customers' untact tendency on environmental characteristics and the value of intelligent services is verified. Research design, data and methodology: Samples were taken from 186 people who visited the unmanned store from April to June 2022 to investigate the research model. Results: It was found that the environmental characteristics of unmanned stores had a positive effect on the intelligent service value. Also, the higher the value of intelligent service, the stronger the influence on consumption emotions, and the higher the value of the intelligence service and consumption emotions, the stronger the impact on the intention to use intention. The untact propensity played a role in controlling the relationship between ease of using technology and the intelligent service value and the relationship between spatial arrangement and functionality and intelligent service value. Conclusion: In order to improve unmanned store service in the trend of spreading unmanned stores, it is necessary to not only improve the technology using convenience in terms of store environmental characteristics but also create innovative consumption experiences in terms of space layout, function, and convenience of payment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2961-2975
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• 2022

The Location-Based Service (LBS) is one of the most well-known services on the Internet. Positioning is the primary association with LBS services. This study proposes an intelligent LoRa-based positioning system, called AI@LBS, to provide accurate location data. The fingerprint mechanism with the clustering algorithm in unsupervised learning filters out signal noise and improves computing stability and accuracy. In this study, data noise is filtered using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, increasing the positioning accuracy from 95.37% to 97.38%. The problem of data imbalance is addressed using the SMOTE (Synthetic Minority Over-sampling Technique) technique, increasing the positioning accuracy from 97.38% to 99.17%. A field test in the NTUST campus (www.ntust.edu.tw) revealed that AI@LBS system can reduce average distance error to 0.48m.

• Journal of Korean Society of Rural Planning
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• v.23 no.3
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• pp.163-174
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• 2017

This study developed the prototype of the system and implemented its main functions, which is the intelligent integrated agricultural water management information system and service (IaWAMISS). The developed system was designed to be able to collect, process and analyze the agricultural water information of spatially dispersed reservoirs in whole country and spatial geographic information distributed in various systems of other organizations. The system, IaWAMISS, is also possible to provide the reproduced information services in each reservoir and space units, such as agricultural water demand and supply analysis and drought prediction, to the people, experts, and policy makers. This study defined the 6 step modules to develop the system, which are to design the components of intelligent integrated information system, to derive the utilization contents of existing systems, to design the new development elements for IaWAMISS, to design the reservoir information system can be used by managers of city and county, to designate the monitoring reservoirs managed by city and county, and finally to prepare the sharing system between organizations with the existing information systems. In order to implement the prototype of the system, this study shows the results for three important functions of the system: spatial integration of reservoirs' information, data link integration between the existing systems, and intelligent analysis program development to assist decision support for agricultural water management. For the spatial integration with the reservoir water information of the Korea Rural Community Corporation, this study get IaWAMISS to receive the real-time reservoir storage information from the measurement facility installed in the municipal management reservoir. The data link integration connecting databases of the existing systems, was implemented by integrating the meteorological information of the Korea Meteorological Administration with IaWAMISS, so that the rainfall forecast data could be derived and used. For the implementation of the intelligent analysis program, this study also showed the results of analysis and prediction of agricultural water demand and supply amount, estimation of Palmer drought index, analysis of flood risk area in typhoon course region, and analysis of the storage status of reservoirs related to each storm. This study confirmed the possibility and efficiency of an useful system development through the prototype design and implementation of IaWAMISS. By solving the preliminary 6 step modules presented in this study, it is possible not only to efficiently manage water by spatial unit, but also to provide the service of information and to enhance the relevant policy and national understanding to the people.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.54-69
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• 2021

Policy improvement such as the revision of the Road Traffic Act are proposed for personal mobility(especially e-scooter) usage. However, there is not enough discussion to solve the problem of using shared e-scooter. In this study, we analyze the influencing factors that amount of pick-up and drop-off of shared e-scooter by dividing the Seoul into a 200m grid. we develop spatial auotcorrelation model such as spatial lag model, spatial error model, spatial durbin model, and spatial durbin error model in order to consider the characteristics of the aggregated data based on a specific space, and the spatial durbin error model is selected as the final model. As a result, demographic factor, land use factor, and transport facility factors have statistically significant impacts on usage of shared e-scooter. The result of this study will be used as basic data for suggesting efficient operation strategies considering the characteristics of weekday and weekend.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.55 no.2
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• pp.103-109
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• 2006

In this paper, the detection scheme of the spatial coordinates based on stereo camera for a intelligent path planning of an automatic mobile robot is proposed. In the proposed system, face area of a moving person is detected from a left image among the stereo image pairs by using the YCbCr color model and its center coordinates are computed by using the centroid method and then using these data, the stereo camera embedded on the mobile robot can be controlled for tracking the moving target in real-time. Moreover, using the disparity mad obtained from the left and right images captured by the tracking-controlled stereo camera system and the perspective transformation between a 3-D scene. and an image plane, depth information can be detected. Finally, based-on the analysis of these calculated coordinates, a mobile robot system is derived as a intelligent path planning and a estimation.