• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.744-748
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• 2007

In this paper, a new localization method of indoor mobile robot using multi IR(infrared) range sensors is proposed. Each IR range sensor detects the edge of obstacles and wall using the acquired range data. The environment map is built by the merging process of the detected edge data of each sensor. The performance of proposed system is verified by the comparison of the real environment and the detected map in experiments.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.1
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• pp.35-39
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• 2008

In this paper, we have developed a propagation loss prediction software with GUI (Graphic User Interface) functions, based on the geometrical ray optics model, which can predict radio parameters for the deployment of wireless indoor network. The program has two numerical modules consisted with electrical image and ray launching methods to implement UTD theory. The simulated results are compared with reported data measured in the foreign building environments for office and '一' type corridor, and measured and simulated results for the propagation loss agree with each other quite well. Simulation results for '一' type corridor and 'T' type corridor propagation environment are shown for reference.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.2
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• pp.285-297
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• 2015

In this paper, Ulta-WideBand (UWB) channel measurement and modeling based on compressive sampling (CS) are proposed. The sparsity of the channel impulse response (CIR) of the UWB signal in frequency domain enables the proposed channel measurement to have a low-complexity and to provide a comparable performance compared with the existing approaches especially used for the indoor geo-localization purpose. Furthermore, to improve the performance under noisy situation, the soft thresholding method is also investigated in solving the optimization problem for signal recovery of CS. Via numerical results, the proposed channel measurement and modeling are evaluated with the real measured data in terms of location estimation error, bandwidth, and compression ratio for indoor geo-localization using UWB system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.499-503
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• 2005

The objective of this thesis is to measure the limit of the real space where wireless Local Area Network(LAN) is used, and to apply this result as reference to set up a real Wireless LAN environment. The organized circumference was composed of Wireless LAN based on IEEE 802.11b of the American standard, and the examination is carried out on the campus. On the real condition of office environment and open space, the ratio of available distance to a signal at each distance was measured and this result was studied as data to embody wireless LAN on the campus. The measurement under an indoor environment was executed under circumstance having two wall and open space, however, was executed respectively under three circumstances : Where no obstacle, where one wall exists, and where two or more walls exist.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.5
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• pp.76-84
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• 2007

In this paper, we measured the propagation characteristics of I-UWB channel in time domain and in frequency domain. The measurements carried out in apartment environment while furniture and household electric appliance. Typical indoor scenarios, including line-of-sight(LOS), non-line-of-sight(NLOS) in room-to-room, within-the-room. A transmitting and receiving antenna is a biconical antenna. Results for indoor propagation measurements are presented for power delay profiles(PDP). We extracted the channel characteristic parameters from a power delay profile. Finally, we designed I-UWB channel model in indoor environment, and I-UWB simulator. The results of simulation shows BER characteristics below $10^{-5}$ for SNR over 20[dB].

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.4
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• pp.84-92
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• 2009

In this paper, using the received signal strength of radio beacon such as Wi-Fi, Bluetooth, CDMA and GPS signal from the satellite, we propose the system of positioning which considered indoor and outdoor based on the Place Lab. Conventional Place Lab utilize the various positioning parameters to estimate the indoor location. However, this conventional system has limitations with respect to the range and efficiency of usage. Therefore, we defined the converged model of multisensor data and re-organized the Place Lab to overcome the limitation of a conventional system. Proposed system uses the radio beacon signal and GPS signal together to estimate the location. Furthermore, it provides the seamless PNS service with many mobile devices because this system realized by the OSGi bundle. This proposed system has evaluated the performance with SAMSUNG T*OMNIA SCH-M490 smart phone and the result shows the system is able to support the PNS service.

• Spatial Information Research
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• v.21 no.1
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• pp.87-100
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• 2013

Recently, the use of locational information is growing rapidly. GPS technology has been adopted generally for obtaining locational information in outdoor spaces. In the other hand, the researches on indoor positioning have been carried out applying WLAN, RFID or Bluetooth technology because of the multi-path interference of GPS signal caused by the physical obstacles such as walls or columns in buildings. However, such technologies for indoor positioning cost too much to build sensing infrastructure and compute-intensive processes are involved. Furthermore, the accuracy of location estimation is variable caused by interior structures in buildings. In this study, to make up for the limitations, descriptive data such as phone number, unique room numbers, or business names readily available in mixed-use buildings is used for extracting location information. Furthermore, during the process, a geocoding method using character matching is applied to this study enabling prompt location estimation and sublating the fluctuation of accuracy caused by interior structures. Based on the proposed method in this study, an architecture is designed, and three-dimensional viewer program is developed for the implementation of this study. Also, this research is quantitatively analyzed through match rate and processing time of proposed method.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.127-133
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• 2020

As the usages of artificial intelligence is increased, the demand to algorithms for small portable devices increases. Also as the embedded system becomes high-performance, it is possible to implement algorithms for high-speed computation and machine learning as well as operating systems. As the machine learning algorithms process repetitive calculations, it depend on the cloud environment by network connection. For an stand alone system, low power consumption and fast execution by optimized algorithm are required. In this study, for the purpose of smart control, an energy measurement sensor is connected to an embedded system, and a real-time monitoring system is implemented to store measurement information as a database. Continuously measured and stored data is applied to a learning algorithm, which can be utilized for optimal power control, and a system interfacing various sensors required for energy measurement was constructed.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.361-365
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• 2007

위치정보는 유비쿼터스 컴퓨팅의 가장 중요한 항목 중 하나이다. 일반적인 위치 인식 시스템은 GPS가 대표적이지만, 실내에서 사용할 수 없고 건물내부와 같은 좁은 지역에서의 위치 인식이 어렵다는 단점이 있다. 특히 핸드폰, PDA와 같은 개인용 장비 에서는 더욱 정교한 위치 인식 기술이 필요한데, 무선랜을 기반으로 하는 위치 인식 기술은 그러한 목적을 달성하기에 적절하다. AP (Access Point)로부터 수집된 무선 신호의 세기는 모바일 기기의 위치를 측정하는데 필요한 지도로써 사용할 수 있지만, 건물의 벽, 사물, 사람 등과 같은 장애물의 간섭으로 변화가 심해 쉽게 사용할 수 없다. 본 논문에서는 이러한 문제점을 극복하기 위하여 신경망 모델을 이용한 무선랜 환경에서의 위치 인식 시스템을 제안한다. 아울러 신경망 학습에 사용될 학습데이터의 오차를 보정하고, 중복을 제거하기 위하여 칼만 필터를 사용하였다.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.1065-1071
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• 2023

In this paper discuss with research developing an LSTM model for classifying the behavior of occupants within a residence. The multi-sensor consists of an IAQ (Indoor Air Quality) sensor that measures indoor air quality, a UWB radar that tracks occupancy detection and location, and a Piezo sensor to measure occupants' biometric information, and collects occupant behavior data such as going out, staying, cooking, cleaning, exercise, and sleep by constructed an experimental environment similar to the actual residential environment. After the data with removed outliers and missing, the LSTM model is used to calculate accuracy, sensitivity, specificity of the occupant behavior classification model, T1 score.