• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.458-459
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• 2021

Recently, researches using deep learning technology based on Wi-Fi fingerprints have been conducted for accurate services in indoor location-based services. Among the deep learning models, an RNN model that can store information from the past can store continuous movements in indoor positioning, thereby reducing positioning errors. At this time, continuous sequential data is required as training data. However, since Wi-Fi fingerprint data is generally managed only with signals for a specific location, it is inappropriate to use it as training data for an RNN model. This paper proposes a path generation method through prediction of a moving path based on Wi-Fi fingerprint data extended to region data through clustering to generate sequential input data of the RNN model.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2000.11a
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• pp.302-305
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• 2000

최근에 ITU-R에서 30～3,000MHz 주파수 범위에서 사용할 수 있는 포괄적인 전계 강도 커브를 제시하고 있다. ITU-R Question 중의 하나인 의제 210/3에서 요구하는 내용은 '30MHz～3GHz 대역의 육상이동과 지상방송 업무를 위한 전파전파 예측절차'이다. 이러한 의제를 WP SG3에서 연구하여 부속서 3K/TEMP/4에 정리하였다. 본 논문에서는 위 부속서 3K/TEMP/4에 나와있는 전송손실 예측 그래프의 적용범위와 적용환경을 알아보기 위해 일반적인 실험적 경로손실 예측식인 Okumura-Hata 모델 그리고 Cost231-Hata 모델과 비교 분석하였다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.277-286
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• 2002

전통적인 고객관계관리 연구는 특정 시점에서 고객관계관리에 중점을 두어 연구되었다. 정적인 고객관계관리와 고객 행동에 관한 지식은 마케팅 관리자가 제한된 마케팅 자원을 이익의 극대화를 위해 사용할 수 있게 해주었다. 그러나 시간이 경과하게 되면 이러한 정적인 지식은 쓸모가 없어지게 된다. 그러므로 고객관계관리는 고객의 동적 특성을 반영해야 한다. 과거 고객의 구매 행위를 관찰하여 현재 또는 미래 시장의 고객을 세분화하여 구분된 고객 군집에 대해 서로 다른 마케팅 전략을 사용할 수 있다. 고객의 구매행동을 근간으로 한 고객관계관리는 수십 년 전부터 연구되어왔지만 동적인 고객관계관리에 대한 연구는 최근에 들어 활발하게 진행되고 있다. 본 논문은 인터넷 상점의 고객 데이터로부터 추출된 지식과 시간 경과에 따른 고객 행동 패턴의 분석을 위해 데이터마이닝과 모니터링 에이전트 시스템(MAS)을 이용하며, 이를 통해 동적인 고객관계관리 모델을 제시한다. 이 모델은 고객 이력 경로에 대한 예측과 고객에게 나타나는 집단 이력경로의, 분석, 그리고 시간 경과에 따른 고객 군집의 변화에 대한 분석, 그에 따른 마케팅 전략 도출을 포함한다. 이 모델의 제안은 많은 온라인 소매상이 직면할 수 있는 경영상의 문제를 해결하는데 유용할 것이다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.8
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• pp.437-444
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• 2014

In this paper, we propose a path loss model with the multiple antennas and doppler shift for high speed railroad communication. Path loss model is very important in order to design consider diverse characteristic in high-speed train communication. Currently wireless communication systems use the multiple antennas in order to improve the channel capacity or diversity gain. However, until recently, many researches on path loss model only consider geographical environment between the transmitter and the receiver. There is no study about path loss model considering diversity effect and doppler shift. In order to make average residuals considering doppler shift we use tuned free space path loss model which is utilized for measurement results at high speed railroad. The environment of high speed rail is mostly at viaduct and flatland over than 50 percent. And in order to make average residuals considering multiple antenna we use theoretical estimation of diversity gain with MRC scheme. proposed model predict loss of received signal by estimating average residuals between diversity effect and doppler shift.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.12A
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• pp.1974-1984
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• 1999

In this paper, we introduce a highly efficient data structure that effectively captures the multipath phenomenon needed for accurate propagation modeling and fast propagation prediction. The proposed object representation procedure is called 'circular representation (CR)' of microwave masking objects such as buildings, to improve over the conventional vector representation (VR) form in fast ray tracing. The proposed CR encapsulates a building with a circle represented by a center point and radius. In this configuration, the CR essentially functions as the basic building block for higher geometric structures, enhancing the efficiency more than when VR is used alone. The simulation results indicate that the proposed CR scheme reduces the computational load proportionally to the number of potential scattering objects while its hierarchical structure achieves about 50% of computational load reduction in the hierarchical octree structure.

• Clean Technology
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• v.12 no.4
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• pp.224-231
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• 2006

Collection efficiencies and pressure drops for the removal of small particles from dilute liquid suspensions by granular bed filter were calculated using network model. The network model is composed of a number of nodes connected with cylindrical bond and particles are deposited on the bond surface. The collection efficiency of each cylindrical bond was predicted using unit cell model corresponding to the pore volume of cylindrical pore both at the initial and transient states. Deposited particles on the collector surface may act as additional collector and reduce the pore size of the collector. As a result, the collection efficiency was improved and pressure drop increased with deposition. Even though the stochastic nature of network requires a large number of simulation work, the model proposed in this study can be used in investigating collection efficiency and pressure drop.

• Journal of the Korea Concrete Institute
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• v.13 no.3
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• pp.251-260
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• 2001

It is required to evaluate the axial strain of reinforced concrete beams in order to predict the ductility of reinforced concrete beams subjected to reversed cyclic loading. A model was proposed to determine the axial strains In reinforced concrete beams by analysing the behavior of reinforced concrete sections and comparing with published test results. The proposed axial strain model inclusively reflected four kinds of paths : Path 1-steel bar in an elastic stage or a unloading region; Path 2-after flexural yielding; Path 3-a slip region; and Path 4-a reversing loading region. The equations to predict the axial strains of each path were proposed. The proposed equations took into account the effects of the loading program. Comparison of axial strains between experimental results and the results from proposed equations showed to be in a good agreement with experimental results.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1130-1135
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• 2022

In Seoul, about 75,000 CCTVs are installed in 25 district offices. Each ward office has built a control center for CCTV control and is performing 24-hour CCTV video control for the safety of citizens. Seoul Metropolitan Government is building a smart city integrated platform that is safe for citizens by providing CCTV images of the ward office to enable rapid response to emergency/emergency situations by signing an MOU with related organizations. In this paper, when an incident occurs at the Seoul Metropolitan Government Office, the escape route is predicted by discriminating people and vehicles using the AI DNN-based Template Matching technology, MLP algorithm and CNN-based YOLO SPP DNN model for CCTV images. In addition, it is designed to automatically disseminate image information and situation information to adjacent ward offices when vehicles and people escape from the competent ward office. The escape route prediction and tracking system using artificial intelligence can expand the smart city integrated platform nationwide.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.69-75
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• 2022

In this paper, a method for predicting the speed for each lane from the link speed using an artificial neural network is presented to increase the accuracy of predicting the required time of a driving route. The time required for passing through a link is observed differently depending on the direction of going straight, turning right, or turning left at the intersection of the end of the link. Therefore, it is necessary to predict the speed according to the vehicle's traveling direction. Data required for learning and verification were constructed by refining the data measured at the Gongpyeong intersection of Gukchaebosang-ro in Daegu Metropolitan City and four adjacent intersections around it. Five neural network models were used. In addition, error analysis of the prediction was performed to select a neural network experimentally suitable for the research purpose. Experimental results showed that the error in the estimation of the time required for each lane decreased by 17.4% for the straight lane, 4.4% for the right-turn lane, and 3.9% for the left-turn lane. This experiment is the result of analyzing only one link. If the entire pathway is tested, the effect is expected to be greater.