• KIISE Transactions on Computing Practices
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• v.21 no.8
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• pp.555-560
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• 2015

In this paper, we analyzed the existing control methods of the legacy wearable-based CE devices and identified the requirements for improvements. In the conventional system, users waste their time configuring the initial network and registering their devices with the management server. To overcome these hurdles, we implemented the Easy-Setup framework for smart phones to personalized cloud devices.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.179-182
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• 2016

안드로이드 스마트폰의 효율적인 메모리 관리 연구를 위해서 기존 정책을 정확하게 분석해야 한다. 현재 안드로이드는 메모리가 부족하면 프로세스 우선순위(ADJ)가 낮은 것부터 제거하여 공간을 확보한다. 본 논문은 프로세스의 상태에 따라 어떤 ADJ 값을 가지는지 실행 시나리오를 통해 자세히 설명한다. 우리는 프로세스의 상태 분류를 위해 안드로이드 프레임워크 코드를 분석하였다.

• Journal of KIISE
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• v.42 no.3
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• pp.295-306
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• 2015

MOnCa2 is a framework for building intelligent smartphone applications based on smartphone sensors and ontology reasoning. In previous studies, MOnCa determined and inferred user situations based on sensor values represented by ontology instances. When this approach is applied, recognizing user space information or objects in user surroundings is possible, whereas determining the user's physical context (travel behavior, travel destination) is impossible. In this paper, MOnCa2 is used to build recognition models for travel behavior and routes using smartphone sensors to analyze the user's physical context, infer basic context regarding the user's travel behavior and routes by adapting these models, and generate high-level context by applying ontology reasoning to the basic context for creating intelligent applications. This paper is focused on approaches that are able to recognize the user's travel behavior using smartphone accelerometers, predict personal routes and destinations using GPS signals, and infer high-level context by applying realization.

• The Journal of the Korea Contents Association
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• v.9 no.11
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• pp.80-89
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• 2009

The importance of personalized services has been attracted in smart home environments according to the development of ubiquitous computering. In this paper, we proposed the smart home personalized service system based on context information using the speech recognition. The proposed service consists of an OSGi framework based service mobile manager, service manager, voice recognition manager, and location manager. Also, this study defines the smart home space and configures the commands of units, sensor information, and user information that are largely used in the defined space as context information. In particular, this service identifies users who exist in the same space that shows a difficulty in the identification using RFID through the training model and pattern matching in voice recognition and supports the personalized service of smart home applications. In the results of the experiment, it was verified that the OSGi based automated and personalized service can be achieved through verifying users in the same space.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.157-171
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• 2021

The purpose of this study is to assess the smart city for major cities in Korea. The assessment indicators are based on the STIM structure (Service, Technology, Infrastructure, and Management Layer Architecture) of the Multi-Layered Smart City Model. Assessment indicators are established through smart city concepts, case analysis, big data analysis, as well as weighted through expert AHP survey. For the assessment, seven major metropolitan cities are selected, including Seoul, and their data such as KOSIS, KISDISTAT from 2017 to 2019 is utilized for the smart city level assessment. The smart city level results show that the service, technology, infrastructure, and management levels were relatively high in Seoul and Incheon, which are metropolitan areas. Whereas, Busan, Daegu, and Ulsan, the Gyeongsang provinces are relatively moderate, while Daejeon and Gwangju, the South Chungcheong region and the Jeolla provinces, were relatively low. The overall STIM ranking shows a similar pattern, as the Seoul metropolitan area smart city level outperforms the rest of the analyzed areas with a large difference. Accordingly, balanced development strategies are needed to reduce gaps in the level of smart cities in South Korea, and respective smart city plans are needed considering the characteristics of each region. This paper will follow the literature review, assessment index establishment, weight analysis of assessment index, major cities assessment and result in analysis, and conclusion.

• The KIPS Transactions:PartB
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• v.19B no.3
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• pp.177-182
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• 2012

We propose effective motion and observation models for the position of a WiFi-equipped smartphone user in large indoor environments. Three component motion models provide better proposal distribution of the pedestrian's motion. Our Gaussian interpolation-based observation model can generate likelihoods at locations for which no calibration data is available. These models being incorporated into the particle filter framework, our WiFi fingerprint-based localization algorithm can track the position of a smartphone user accurately in large indoor environments. Experiments carried with an Android smartphone in a multi-story building illustrate the performance of our WiFi localization algorithm.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.73-76
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• 2022

본 논문에서는 메타버스 공간에서 패션 아이템 판매에 있어서 필수적이라 할 수 있는 온라인 가상 피팅 기술을 동작 인식 전용 디바이스가 아닌 일반 스마트폰 카메라를 활용하여 구현하는 기술을 제안한다. 가상 피팅 기술을 구현하기 위해서는 딥러닝 기법을 활용하여 입력 영상을 분석하고, 분석 결과를 토대로 인체의 전체 자세를 추정하며, 인체 사이즈의 근사값을 추출하는 과정들이 수행되어야 하는데, 현재의 스마트폰 컴퓨팅 환경은 이를 수행하기에 충분한 연산 성능을 가지지 못한다는 문제점을 가진다. 본 논문에서는 높은 비용이 요구되는 고부하 연산을 클라우드 서버를 통해 수행하는 서버 기반 프레임워크를 도입하여, 낮은 성능의 스마트폰으로도 고성능 연산이 가능한 서비스 구조를 확보하고 이를 통해 휴대성 높은 증강현실 기반의 가상 피팅 기술을 구현한다. 본 논문의 성과를 통해 메타버스 상거래의 활성화와 메타버스 본연의 의미에 충실한 가상 월드 구축에 기여할 것이라 기대한다.

• Spatial Information Research
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• v.19 no.4
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• pp.91-97
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• 2011

In this paper, we propose an architecture for geosemantic services. With the rapid progress of web services, wireless internet technologies and popularization of smart phone in recent years, a lot of applications based on geographic information are being developed. Moreover the search portals empowered by semantic web technologies are enabling general users to access on-line resources more easily. However, several studies in GIS domain have pointed out the practical limitation of existing service patterns, which are limited only to linking heterogenous spatial databases, insufficient for several important use cases. Hence we draw functional elements of geosemantic services from GIS and semantic web standards, and present the use cases and a new architecture for geosemantic services. This approach could set a foundation to implement geoemantic services.

• Spatial Information Research
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• v.22 no.2
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• pp.63-71
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• 2014

Ocean leisure sports have recently emerged as one of so-called blue ocean industries. They are sensitive to diverse environmental conditions such as current, temperature, and salinity, which can increase needs of forecasting data as well as in-situ observations for the ocean. In this context, a Web-based geovisualization system for coastal information produced by model forecasts was implemented for use in supporting various ocean activities. First, FVCOM(Finite Volume Coastal Ocean Model) was selected as a forecasting model, and its data was preprocessed by a spatial interpolation and sampling library. The interpolated raster data for water surface elevation, temperature, and salinity were stored in image files, and the vector data for currents including speed and direction were imported into a distributed DBMS(Database Management System). Web services in REST(Representational State Transfer) API(Application Programming Interface) were composed using Spring Framework and integrated with desktop and mobile applications developed on the basis of hybrid structure, which can realize a cross-platform environment for geovisualization.

• Smart Media Journal
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• v.1 no.2
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• pp.12-22
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• 2012

Today we are witnessing an increasingly widespread use of cameras in our lives for video surveillance, robot vision, and mobile phones. This has led to a renewed interest in computer vision in general and an on-going boom in human activity recognition in particular. Although not particularly fancy per se, human gait is inarguably the most common and frequent action. Early on this decade there has been a passing interest in human gait recognition, but it soon declined before we came up with a systematic analysis and understanding of walking motion. This paper presents a set of DBN-based models for the analysis of human gait in sequence of increasing complexity and modeling power. The discussion centers around HMM-based statistical methods capable of modeling the variability and incompleteness of input video signals. Finally a novel idea of extending the discrete state Markov chain with a continuous density function is proposed in order to better characterize the gait direction. The proposed modeling framework allows us to recognize pedestrian up to 91.67% and to elegantly decode out two independent gait components of direction and posture through a sequence of experiments.