• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.267-268
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• 2023

Unmanned patrol robots are currently being developed for autonomous data survey in construction sites. As the amount of data acquired by robots increases, it is important to utilize proper metadata and system to manage data flow. In this study, we developed three materials, metadata design, robot system and web system, in the purpose of automating construction site data survey using unmanned patrol robots. The metadata was mainly designed to represent when and where raw data was acquired. To identify the location of data acquired, localization data from SLAM algorithm was converted to suit the construction drawings. The robot system and web system were developed to generate, store and parse the raw data and metadata automatically. The materials developed in this study was adopted to Boston Dynamics SPOT, a quadruped robot. Autonomous data survey of 360-picture and environment sensor was tested in two construction sites and the robot worked as intended. As a further study, development on the autonomous data survey to improve the convenience and productivity will be continued.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.595-602
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• 2013

WiFi fingerprinting is well known as an effective localization technique used for indoor environments. However, this technique requires a large amount of pre-built fingerprint maps over the entire space. Moreover, due to environmental changes, these maps have to be newly built or updated periodically by experts. As a way to avoid this problem, crowd-sourced fingerprint mapping attracts many interests from researchers. This approach supports many volunteer users to share their WiFi fingerprints collected at a specific environment. Therefore, crowd-sourced fingerprinting can automatically update fingerprint maps up-to-date. In most previous systems, however, individual users were asked to enter their positions manually to build their local fingerprint maps. Moreover, the systems do not have any principled mechanism to keep fingerprint maps clean by detecting and filtering out erroneous fingerprints collected from multiple users. In this paper, we present the design of a crowd-sourced fingerprint mapping and localization(CMAL) system. The proposed system can not only automatically build and/or update WiFi fingerprint maps from fingerprint collections provided by multiple smartphone users, but also simultaneously track their positions using the up-to-date maps. The CMAL system consists of multiple clients to work on individual smartphones to collect fingerprints and a central server to maintain a database of fingerprint maps. Each client contains a particle filter-based WiFi SLAM engine, tracking the smartphone user's position and building each local fingerprint map. The server of our system adopts a Gaussian interpolation-based error filtering algorithm to maintain the integrity of fingerprint maps. Through various experiments, we show the high performance of our system.

• The Journal of the Acoustical Society of Korea
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• v.32 no.6
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• pp.548-555
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• 2013

The localization of sources has a numerous number of applications. To estimate the position of sources, the relative delay between two or more received signals for the direct signal must be determined. Although the generalized cross-correlation method is the most popular technique, an approach based on eigenvalue decomposition (EVD) is also popular one, which utilizes an eigenvector of the minimum eigenvalue. The performance of the eigenvalue decomposition (EVD) based method degrades in the low SNR and the correlated environments, because it is difficult to select a single eigenvector for the minimum eigenvalue. In this paper, we propose a new adaptive algorithm based on Canonical Correlation Analysis (CCA) in order to extend the operation range to the lower SNR and the correlation environments. The proposed algorithm uses the eigenvector corresponding to the maximum eigenvalue in the generalized eigenvalue decomposition (GEVD). The estimated eigenvector contains all the information that we need for time delay estimation. We have performed simulations with uncorrelated and correlated noise for several SNRs, showing that the CCA based algorithm can estimate the time delays more accurately than the adaptive EVD algorithm.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.2
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• pp.370-376
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• 2022

Smart ships related studies are being conducted in various fields owing to the development of technology, and an engine room patrol robot that can patrol the unmanned engine room is one such study. A patrol robot moves around the engine room based on the information learned through artificial intelligence and checks the machine normality and occurrence of abnormalities such as water leakage, oil leakage, and fire. Study on engine room patrol robots is mainly conducted on machine detection using artificial intelligence, however study on movement and control is insufficient. This causes a problem in that even if a patrol robot detects an object, there is no way to move to the detected object. To secure maneuverability to quickly identify the presence of abnormality in the engine room, this study experimented with whether a patrol robot can determine the shortest path by applying the A* algorithm. Data were obtained by driving a small car equipped with LiDAR in the ship engine room and creating a map by mapping the obtained data with SLAM(Simultaneous Localization And Mapping). The starting point and arrival point of the patrol robot were set on the map, and the A* algorithm was applied to determine whether the shortest path from the starting point to the arrival point was found. Simulation confirmed that the shortest route was well searched while avoiding obstacles from the starting point to the arrival point on the map. Applying this to the engine room patrol robot is believed to help improve ship safety.

• Progress in Medical Physics
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• v.15 no.2
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• pp.63-69
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• 2004

HDR brachytherapy administers a large dose of radiation in a short time compare with LDR, and its optimization for treatment is related to several complex factors, such as physical, radiation and optimization algorithms, so there is a need for these to be verified for accurate dose delivery. In our approach, a previous study concerning the phantom for dose verification has been modified, and a new pelvic phantom fabricated for the purpose of localization, including a structure enabling the use of a CT or MRI system. In addition, a comparison study was performed to verify an orthogonal method that is commonly used for brachytherapy localization by comparing target coordinates from a CT system. Since the developed phantom was designed to simulate the clinical setups of cervix cancer, it included an air-filled bladder and a rectum structure shaped sphere and cylinder An N-shaped localizer was used to obtain precision coordinates from both CT and films. Moreover, the IDL 5.5 software program for Windows was used to perform coordinates analysis based on an orthogonal algorithm. The film results showed differences within 1.0 mm of the selected target points compare with the CT coordinates. For these results, a Plato planning system (Nucletron, Netherlands) could be independently verified using this phantom and software. Furthermore, the new phantom and software will be efficient and powerful qualify assurance (QA) tools in the field of brachytherapy QA.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.76-84
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• 2013

In the passive emitter localization using instantaneous TDOA (time difference of arrival) and FDOA (frequency difference of arrival) measurements, the estimation accuracy can be improved by collecting additional measurements. To achieve this goal, it is required to increase the number of the sensors. However, in electronic warfare environment, a large number of sensors cause the loss of military strength due to high probability of intercept. Also, the additional processes should be considered such as the data link and the clock synchronization between the sensors. Hence, in this paper, the passive localization of a stationary emitter is presented by using the successive TDOA and FDOA measurements from two moving sensors. In this case, since an independent pair of sensors is added in the data set at every instant of measurement, each pair of sensors does not share the common reference sensor. Therefore, the QCLS (quadratic correction least squares) methods cannot be applied, in which all pairs of sensor should include the common reference sensor. For this reason, a Gauss-Newton algorithm is adopted to solve the non-linear least square problem. In addition, to show the performance of the proposed method, we compare the RMSE (root mean square error) of the estimates with CRLB (Cramer-Rao lower bound) and derived the CEP (circular error probable) planes to analyze the expected estimation performance on the 2-dimensional space.

• Korean Journal of Digital Imaging in Medicine
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• v.9 no.2
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• pp.31-38
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• 2007

To evaluate the accuracy and extent in the localization of cerebral motor coutex activation using a gradient- echo echo planar imaging(GE-EPI) compared to spin-echo echo planar iimaging(SE-EPI) on 3T MR imaging. Functional MR imaging of cerebral motor cortex activation was examined in GE-EPI and SE-EPI in five healthy male volunteers. A right finger movement was accomplished with a paradigm of 6 task and rest, periods and the cross-correlation was used for a statistical mapping algorithm. We evaluated any sorts of differenced of the time seried and the signal intensity changes between the rest and task periods obtained with two technoques. The qualitative analysis was distributed with activation sites of large veins and small veins by using two techniques and was found that both the techniques were clinically uesful for delineating large veins and small veins in fMRL Signal intensity charge of the rest and activation periods provided simmilar activations in both methods(GE-EPI : 0.93$\pm$0.11, SE-EPI : 0.80$\pm$.015) but the signal intensity in GE-EPI(133.95$\pm$15.76) was larger than in SE-EPI(74.5$\pm$18.90). The average SNRs of EPI raw data were higher at SMA in SE-EPI(48.54$\pm$12.37) than GE-EPI(41.4$\pm$12.54) and at M1 in SE-EPI(43.24$\pm$11.77) than GE-EPI(38.27$\pm$6.53). The localization of activation voxels of the GE-EPI showed a larger vein but the SE-EPI generally showed small vein. Then the analysis results of the two techniques were used for a statistacal paired student t-test. SE-EPI was found clinically useful for localizing the cerebral moter cortex cativation on 3.0T, but showed a little different activation patterns comparad to GE-EPI. In conclusion, SE-EPI may be feasible and can detect true cortical activation from capillaries and GE-EPI can obtain the large veins in the motor cortex activation on 3T MR imaging.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.1
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• pp.64-73
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• 2012

In this paper, a new land preview system using laser range finder based on heave estimation algorithm is proposed. The proposed land preview system is an equipment which measures the shape of forward topography for autonomous vehicle. To implement this land preview system, the laser range finder is generally used because of its wide measuring range and robustness under various environmental condition. Then the current location of the vehicle has to be known to generate the shape of forward topography and sensors based on acceleration such as IMU and accelerometer are generally utilized to measure heave motion in the conventional land preview system. However the drawback to these sensors is that they are too expensive for low-cost vehicle such as mobile robot and their measurement error is increased for mobile robot with abrupt acceleration. In order to overcome this drawback, an algorithm that estimates heave motion using the information of odometer and previously measured topography is proposed in this paper. The proposed land preview system based on the heave estimation algorithm is verified through simulation and experiments for various terrain using a simulator and a real system.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.9
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• pp.836-841
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• 2016

An indoor positioning system using Wi-Fi is essential to produce a radio map that combines the indoor space of two or more dimensions, the information of node positions, and etc. in processing for constructing the radio map, the measurement of the received signal strength indicator(RSSI) and the confirmation of node placement information counsume substantial time. Especially, when the installed wireless environment is changed or a new space is created, easy installation of the node and fast indoor radio mapping are needed to provide indoor location-based services. In this paper, to reduce the time consumption, we propose an algorithm to distinguish the straight and curve lines of a corridor section by RSSI visualization and Sobel filter-based edge detection that enable accurate node deployment and space analysis using cable-type Wi-Fi node installed at a 3 m interval. Because the cable type Wi-Fi is connected by a same power line, it has an advantage that the installation order of nodes at regular intervals could be confirmed accurately. To be able to analyze specific sections in space based on this advantage, the distribution of the signal was confirmed and analyzed by Sobel filter based edge detection and total RSSI distribution(TRD) computation through a visualization process based on the measured RSSI. As a result to compare the raw data with the performance of the proposed algorithm, the signal intensity of proposed algorithm is improved by 13.73 % in the curve section. Besides, the characteristics of the straight and the curve line were enhanced as the signal intensity of the straight line decreased by an average of 34.16 %.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.4
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• pp.37-45
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• 2015

In modern electronic warfare systems, a demand on the more accurate estimation method based on TDOA and FDOA has been increased. TDOA/FDOA localization consists of two-stage procedures: the extraction of information from signals and the estimation of emitter location. Various algorithms based on CAF(complex ambiguity function), which is known as a basic method, has been presented in the area of extractions. When we extract TDOA and FDOA information using a conventional method based on the CAF algorithm from communication signals, considerably long integration time is required for the accurate position estimation of an unknown emitter far from sensors more than 300 km. Such long integration time yields huge amount of transmission data from sensors to a central processing unit, resulting in heavy computiational complexity. Therefore, we theoretically analyze the integration time for TDOA/FDOA information using CRLB and propose a two-stage global optimization algorithm which can minimize the transmission time and a computational complexity. The proposed method is compared with the conventional CAF-based algorithms in terms of a computational complexity and the CRLB to verify the estimation performance.