• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.2
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• pp.466-474
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• 2014

According to wireless network technology and mobile processors performance were improved, the small wireless mobile device such as smart phones has been widely utilized. The mobile devices can be used GPS information, thereby the services based on location information was increased. GPS was impossible to provide location information in indoor and signal shading environment, and the tracking systems based on short distance wireless communication are required infrastructure. The IEEE 802.11 based tracking system is possible estimation using APs, however the tracking device is exhausted battery power seriously. In this paper, we propose IEEE 802.11 based low-power tracking system. We reduced power consumption from channel scanning and network connection. For performance evaluation, we designed and implemented the tracking tag device, and measured power consumption of the device. As the simulation result, we confirmed that the power consumption was reduced 46% compare to the standard execution.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.93-102
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• 2023

Data processing through the cloud causes many problems, such as latency and increased communication costs in the communication process. Therefore, many researchers study edge computing in the IoT, and autonomous driving is a representative application. In indoor self-driving, unlike outdoor, GPS and traffic information cannot be used, so the surrounding environment must be recognized using sensors. An efficient autonomous driving system is required because it is a mobile environment with resource constraints. This paper proposes a machine-learning method using neural networks for autonomous driving in an indoor environment. The neural network model predicts the most appropriate driving command for the current location based on the distance data measured by the LiDAR sensor. We designed six learning models to evaluate according to the number of input data of the proposed neural networks. In addition, we made an autonomous vehicle based on Raspberry Pi for driving and learning and an indoor driving track produced for collecting data and evaluation. Finally, we compared six neural network models in terms of accuracy, response time, and battery consumption, and the effect of the number of input data on performance was confirmed.

• The Journal of the Acoustical Society of Korea
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• v.20 no.4
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• pp.22-28
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• 2001

This paper describes the implementation of a robust speaker position location system using the voice signal received by microphone array. To be robust to the reverberation which is the major factor of the performance degradation, low-frequency phase restoration algorithm which eliminates the influence of reverberations using the low-frequency information of the CPSP function is proposed. The implemented real-time system consists of a general purpose DSP (TMS320C31 of Texas instruments), analog part which contains amplifiers and filters, and digital part which is composed of the external memory and 12-bit A/D converter. In the real conference room environment, the implemented system that was constructed by the proposed algorithms showed better performance than the conventional system. The error of the TDOA estimation reduced more than 15 samples.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.259-264
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• 2014

This paper is research paper on the MPD(Magnet Position Device) for the outdoor MGV(Magnet /Magnet Gyro Guidance Vehicle). Usually, MGV is used in indoor environment because of a measurement height of the magnet position device. CMPD(Commercial magnet position device) has 30 mm measurement height, so this is suitable structure in indoor environment like to a flat surface. Outdoor environment is an uneven and irregular, So Outdoor MGV must has a suspension. But CMPD is unsuitable for outdoor environment because of a collision with a surface caused by suspension. Thus, measurement height of the outdoor MPD is positively necessary more than 100 mm. So, we suggest the outdoor MPD using analog magnet hall sensor, moving average filter and Characteristic(rate of the magnet hall sensor) function of the localization. Result of the experiments, the proposed Magnet Position Device for the outdoor MGV has localization accuracy 4.31 mm, measurement height 150 mm and width 150 mm and is efficient more than CMPD.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.11-17
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• 2022

In this paper, a model that can increase the efficiency of work in arranging interior furniture by applying augmented reality technology was studied. In the existing system to which augmented reality is currently applied, there is a problem in that information is limitedly provided depending on the size and nature of the company's product when outputting the image of furniture. To solve this problem, this paper presents an AR labeling algorithm. The AR labeling algorithm extracts feature points from the captured images and builds a database including indoor location information. A method of detecting and learning the location data of furniture in an indoor space was adopted using the CNN technique. Through the learned result, it is confirmed that the error between the indoor location and the location shown by learning can be significantly reduced. In addition, a study was conducted to allow users to easily place desired furniture through augmented reality by receiving detailed information about furniture along with accurate image extraction of furniture. As a result of the study, the accuracy and loss rate of the model were found to be 99% and 0.026, indicating the significance of this study by securing reliability. The results of this study are expected to satisfy consumers' satisfaction and purchase desires by accurately arranging desired furniture indoors through the design and implementation of AR labels.

• Journal of Advanced Navigation Technology
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• v.19 no.6
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• pp.524-533
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• 2015

In this paper, results for an installation and operation of a GPS jammer localization system were analyzed. The jammer localization system was developed by Korea Aerospace Research Institute and it consists of 4 Receiver Stations, a Central Tracking Station, and a Monitoring Station. The system was installed at Incheon International Airport in November 2014; each Receiver Stations were installed at rooftop of buildings apart from 4km, and the Central Tracking Station and a Monitoring Station were installed at indoor. Results of the operation can be monitored through web-browser in real-time, Korea Aerospace Research Institute and Incheon International Airport Corporation are continuously monitoring them. So far, there is no jamming signal which affects GPS receivers around the airport, however, some abnormal signals were frequently received at Receiver Stations. Therefore, the characteristics of those signals were also analyzed in this paper.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.706-711
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• 2008

This paper is presented an accurate localization scheme for mobile robots based on the fusion of ultrasonic satellite (U-SAT) with inertial navigation system (INS), i.e., sensor fusion. Our aim is to achieve enough accuracy less than 100 mm. The INS consist of a yaw gyro, two wheel-encoders. And the U-SAT consist of four transmitters, a receiver. Besides the localization method in this paper fuse these in an extended Kalman filter. The performance of the localization is verified by simulation and two actual data(straight, curve) gathered from about 0.5 m/s of driving actual driving data. localization methods used are general sensor fusion and sensor fusion through Kalman filter using data from INS. Through the simulation and actual data studies, the experiment show the effectiveness of the proposed method for autonomous mobile robots.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.50-57
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• 2007

사용자가 현재 위치해 있는 장소를 알아내는 것은 상황인식 분야에서 활발히 연구되고 있는 분야중 하나로, 이를 위해서 사용자의 몸에 다양한 센서를 장착하고 그 센서로부터 추출되는 데이터를 분석하여 사용자의 위치를 인식하는 연구가 많이 이루어져왔다. 본 논문에서는, 사용자의 몸에 장착된 카메라로부터 얻어진 영상을 이용하여 사용자의 현재 장소를 인식하는 장소 인식 시스템을 제안한다. 기존의 방법론들에 비해서 높은 성능을 보이기 위해서 본 논문에서는 두 가지 방법을 제안하였다. 먼저 한 방향만의 영상으로는 인식이 어려운 장소에서도 좋은 인식 성능을 보일 수 있도록 하기 위해, 여러 대의 카메라를 동시에 사용하여 여러 방향의 영상을 얻어내는 방법을 제안하였다. 또한 이전 시간의 장소 인식 결과로부터 현재 시간의 장소를 추론하는 데에 있어서, 각 장소들에 대해 알고 있는 사전지식을 보다 많이 적용할 수 있는 인식 모델을 제안하였다. 실제 대학 실내 환경에서의 실험을 통하여, 제안한 방법을 이용한 장소 인식기법이 좋은 성능을 보임을 확인할 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.142-145
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• 2016

본 논문은 기존의 RFID 감지 시스템에서 더 나아가 다수의 RFID 클러스터의 협업 및 RFID가 부착된 객체들 사이의 관계를 통하여 객체 추적이 가능한 시스템을 제안한다. 서버는 다수 클러스터와의 통신으로 모든 객체의 데이터를 관리하며 클러스터는 객체의 RFID가 감지되는 경우에 객체의 데이터를 얻어 서버에 전송한다. 이러한 서버와 클러스터의 상호작용을 통해 감지된 클러스터의 위치를 파악하여 객체 추적이 가능하다. 만약 RFID를 감지하는 데 있어 문제가 발생할 경우, 객체 관계를 활용하여 해결한다. 얻은 데이터의 신뢰도가 낮더라도 RFID가 감지된 상황에 따라 가진 데이터를 기반으로 객체는 싱글과 그룹 관계로 결정되며 시간이 지남에 따라 그룹 관계를 싱글 관계로 바꾸어 모든 객체를 분별할 수 있도록 한다. 실제 제한된 실내 공간을 선정해 이를 기반으로 시뮬레이션 프로그램을 구현하여 이 시스템의 효율성을 확인하였다.

• Journal of KIISE
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• v.42 no.2
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• pp.264-271
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• 2015

The general way to use a triangulation method is based on PTMP communication between an object and wireless modules in an environment, which is established by more than three wireless modules, to recognize the location of an object. Thus, this method has a problem that the PTMP-based system can only be applied in an environment where the wireless infra is already established. In order to solve this problem, the PTP communication schemes have been proposed but they are insufficient to generalize because they lack specific verification. In this paper, problems of an existed location estimation algorithm based on PTP communication are analyzed, and we propose a location estimation algorithm of a fixed object that satisfyies the condition of a single receiver being substituted to multiple receivers. A location estimation system we designed and implemented using CSS wireless communication modules to evaluate the proposed algorithm. We verify, by experimental results, that the optimum moving interval for the location estimation is 3m in indoor environment of $10m{\times}16m{\times}1m$.