• Journal of Advanced Marine Engineering and Technology
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• v.38 no.2
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• pp.188-194
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• 2014

Recently, indoor localization systems based on wireless sensor networks have received a great deal of attention because they help achieve high accuracy in position determination by using various algorithms. In order to minimize the error in the estimated azimuth that can occur owing to sensor drift and recursive calculation in these algorithms, we propose a novel relative azimuth estimation algorithm. The advantages of the proposed technique in an indoor environment are that an improved weight average filter is used to effectively reduce impulse noise from the raw data acquired from nodes with inherent errors and a rotational displacement algorithm is applied to obtain a precise relative azimuth without using additional sensors, which can be affected by electromagnetic noise. Results from simulations show that the proposed filter reduces the impulse noise, and the acquired estimation error does not accumulate with time by using proposed algorithm.

• The Journal of Korea Robotics Society
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• v.11 no.4
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• pp.205-216
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• 2016

The localization of the robot is one of the most important factors of navigating mobile robots. The use of featured information of landmarks is one approach to estimate the location of the robot. This approach can be classified into two categories: the natural-landmark-based and artificial-landmark-based approach. Natural landmarks are suitable for any environment, but they may not be sufficient for localization in the less featured or dynamic environment. On the other hand, artificial landmarks may generate shaded areas due to space constraints. In order to improve these disadvantages, this paper presents a novel development of the localization system by using artificial and natural-landmarks-based approach on a topological map. The proposed localization system can recognize far or near landmarks without any distortion by using landmark tracking system based on top-view image transform. The camera is rotated by distance of landmark. The experiment shows a result of performing position recognition without shading section by applying the proposed system with a small number of artificial landmarks in the mobile robot.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.703-722
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• 2016

Passive wireless devices have increasing civilian and military applications, especially in the scenario with wearable devices and Internet of Things. In this paper, we study indoor localization of a target equipped with radio-frequency identification (RFID) device in ultra-wideband (UWB) wireless networks. With known room layout, deterministic multipath components, including the line-of-sight (LOS) signal and the reflected signals via multipath propagation, are employed to locate the target with one transmitter and a single inaccurate receiver. A factor graph corresponding to the joint posterior position distribution of target and receiver is constructed. However, due to the mixed distribution in the factor node of likelihood function, the expressions of messages are intractable by directly applying belief propagation on factor graph. To this end, we approximate the messages by Gaussian distribution via minimizing the Kullback-Leibler divergence (KLD) between them. Accordingly, a parametric message passing algorithm for indoor passive localization is derived, in which only the means and variances of Gaussian distributions have to be updated. Performance of the proposed algorithm and the impact of critical parameters are evaluated by Monte Carlo simulations, which demonstrate the superior performance in localization accuracy and the robustness to the statistics of multipath channels.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.643-644
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• 2018

Recently indoor and outdoor location tracking systems are operated in different ways. The indoor positioning method uses WiFi and BLE beacon positioning, and the outdoor positioning uses GPS and PDR. In this paper, it is a device to measure position by using it. It is used to check whether it is indoors or outdoors when measuring based on a smart phone, A automatic conversion method is needed. When using GPS in the room, it is difficult to distinguish the floor or space. We propose a method to solve this problem.

• Aerospace Engineering and Technology
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• v.12 no.1
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• pp.128-136
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• 2013

Recently, the localization recognition system research has been studied using various sensors according to increased interest in autonomous navigation flight. In case of indoor environment which cannot support GPS information, we have to look for another way to recognize current position. The Image-based localization recognition system has been interested although there are lots of way to know current pose. In this paper, we explain the localization recognition system based on mark and implementation of autonomous navigation flight. In order to apply to real environment which cannot support marks, localization based on real-time 3D map building is discussed.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.12 no.1
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• pp.75-82
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• 2000

This paper presents the predicted results by CFD technique of air flow and contaminant distribution in a full-scale kitchen opened to a living room, ventilated by a exhaust hood. To analyze the characteristics of the indoor environment, the concept of contaminant index was defined. In this study, the locations of the gas range and the window were chosen as the parameters to investigate the indoor environment. The values of the contaminant index for several layout of the gas range and the window were calculated and compared. When the gas range is installed along the wall with specified window location, its position in relation to the wall has unnoticed effect on contaminant infer. Once the location of the gas range is fixed, the indoor air quality may deteriorate by the proximity of the window to the gas range. This is due to the shorter distance that external fresh air must travel within the kitchen before it reaches the exhaust fan.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.05a
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• pp.159-160
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• 2019

It would be very important to localize people at risk as soon as possible in order to minimize the damage. Generally the infrastructure should be deployed additionally for indoor positioning system. In this paper, we proposed an indoor localization system for people at risk using the existing fire alarm networks. The system detects the signal of smart devices of people in danger immediately and let the main alarm controller ring all alarms in vessel and display the position. Thus, the proposed system can make the burden much less to deploy additional network and infrastructure.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.319-322
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• 2019

There are many techniques for locating users in an indoor spot. Among them, WiFi fingerprinting technique which is widely used is phased into a data collection step and a positioning step. In the data collection step, all surrounding Wi-Fi signals are collected and managed as a list. The more data collected, the better the accuracy of the indoor position based on Wi-Fi fingerprint. Existing high-quality data collection and management methods are time consuming and costly, and many operations are required to extract and generate data necessary for machine learning. Therefore, we research how to collect and manage large amount of data in limited resources. This paper presents efficient data collection methods and data generation for learning.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.640-642
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• 2021

In order to collect a lot of data clearly and efficiently, it is essential to know the locations of the current facilities and analyze the movement data. The current location collection technology can collect data using a GPS (Global Positioning System) sensor, but in the case of GPS, it has strong straightness and low diffraction and reflectivity, making it difficult to position indoors. It is impossible to measure the distance between the server and the client because the signal sensitivity cannot be received. This paper implements an indoor positioning system using beacons and scanners in Raspberry Pi 3 B+. It controls Advertise Mode and Connection Mode at the same time using the scanner algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.467-468
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• 2021

In order to improve the performance of the indoor positioning system using BLE beacons, a receiver that measures the angle of arrival among the direction finding technologies supported by BLE5.1 was manufactured and analyzed by machine learning to measure the optimal position. For the creation and testing of machine learning models, k-nearest neighbor classification and regression, logistic regression, support vector machines, decision tree artificial neural networks, and deep neural networks were used to learn and test. As a result, when the test set 4 produced in the study was used, the accuracy was up to 99%.