• Nuclear Engineering and Technology
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• v.52 no.4
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• pp.846-855
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• 2020

A loose part monitoring system is used to detect unexpected loose parts in a reactor coolant system in a nuclear power plant. It is still necessary to develop a new methodology for the localization and mass estimation of loose parts owing to the high estimation error of conventional methods. In addition, model-based diagnostics recently emphasized the importance of a model describing the behavior of a mechanical system or component. The purpose of this study is to propose a new localization and mass-estimation method based on finite element analysis (FEA) and optimization technique. First, an FEA model to simulate the propagation behavior of the bending wave generated by a metal sphere impact is validated by performing an impact test and a corresponding FEA and optimization for a downsized steam-generator structure. Second, a novel methodology based on FEA and optimization technique was proposed to estimate the impact location and mass of a loose part at the same time. The usefulness of the methodology was then validated through a series of FEAs and some blind tests. A new feature vector, the cross-correlation function, was also proposed to predict the impact location and mass of a loose part, and its usefulness was then validated. It is expected that the proposed methodology can be utilized in model-based diagnostics for the estimation of impact parameters such as the mass, velocity, and impact location of a loose part. In addition, the FEA-based model can be used to optimize the sensor position to improve the collected data quality in the site of nuclear power plants.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.84-91
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• 2008

This paper suggests the robust localization system in the application of ambient display with multiple ultrasonic range sensors. The ambient display provides the interactive image and video to improve the quality of life, especially for low mobility elders. Due to the limitation of indoor localization, this paper employs linear prediction algorithm to recover the missing information based on AR(Autoregressive) model by using Yule-Walker method. Numerous speculations from prediction error and computation load are considered to decide the optimal length of referred data and order. The results of these analyses demonstrate that the linear prediction algorithm with the 16th order and 50 reference data can improve reliability of the system in average 74.39% up to 97.97% to meet the performance of interactive system.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.6
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• pp.17-24
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• 2013

The localization accuracy in wireless sensor networks using ranging-based localization algorithms is greatly influenced by the ranging accuracy. Software implementation of HAL(Hardware Abstraction Layer) and MAC(Medium Access Layer) should seamlessly deliver the raw performance of ranging-based localization provided by hardware capability fully to the applications without degrading the raw performance. This paper presents the design and implementation of the software stack for IEEE 802.15.4a which supports normal ranging mode of the Nanotron's NA5TR1 RF chip. The experiment results shows that average ranging error rate with our implementation is 24.5% for the normal mode of the SDS-TWR ranging scheme.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.261-267
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• 2016

In this paper, we studied the range-free localization algorithm between sensor nodes based on the Radical Line for sensor networks. Routing in wireless sensor networks should reduce the overall energy consumption of the sensor network, or induce equivalent energy consumption of all the sensor nodes. In particular, when the amount of data to send more data, the energy consumption becomes worse. New methods have been proposed to address this. So as to allow evenly control the overall energy consumption. For this, the paper covers designing a localization algorithm that can obtain the location information of the peripheral nodes with fewer operations. For the operation of the algorithm is applicable Radical Line. The experimental environment is windows 7, the Visual C ++ 2010, MSSQL 2008. The experimental results could be localized to perform an error rate of 0.1837.

• Journal of Digital Convergence
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• v.13 no.7
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• pp.207-212
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• 2015

The sensor network that is component of the Internet of Things require a lot of research to select the best route to send information to the anchor node, to collect a number of environment and cost efficient for communication between the sensor life. On the sensor network in one of the components of IOT's environment, sensor nodes are an extension device with low power low capacity. For routing method for data transmission between the sensor nodes, the connection between the anchor and the node must be accurate with in adjacent areas relatively. Localization CA (Centroid Algorithm) is often used although an error frequently occurs. In this paper, we propose a range-free localization method between sensor nodes based on the Radical Line in order to solve this problem.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.1
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• pp.1-6
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• 2011

In this study, we investigated the effect of the simplification for high frequency region in Head-Related Transfer Function(HRTF) on the sound localization. For this purpose, HRTF was measured and analyzed. The result in the HRTF frequency characteristic of the back sound source showed that the decrease revel of high frequency was smaller than that of low frequency region, which means the possibility of simplification in the high frequency region. Simplification was performed by flattening of the high frequency amplitude characteristics with the insertion of the low-pass filter, whose cutoff frequency is given by boundary frequency. Auditory experiments were performed to evaluate the simplified HRTF. The result showed that direction perception was not influenced by the simplification of the frequency characteristics of HRTF for the error of sound localization. The rate of confusion for the front and back was not affected by the simplification of the frequency characteristics over 8kHz of HRTF. Finally, we made it clear that the sound localization was not affected by the simplification of frequency characteristics of HRTF over 8kHz.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1131-1142
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• 2018

The autonomous vehicles are being developed and operated widely because of the advantages of reducing the traffic accident and saving time and cost for driving. The vehicle localization is an essential component for autonomous vehicle operation. In this paper, localization algorithm based on sensor fusion is developed for cost-effective localization using in-vehicle sensors, GNSS, an image sensor and reference images that made in advance. Information of the reference images can overcome the limitation of the low positioning accuracy that occurs when only the sensor information is used. And it also can acquire estimated result of stable position even if the car is located in the satellite signal blockage area. The particle filter is used for sensor fusion that can reflect various probability density distributions of individual sensors. For evaluating the performance of the algorithm, a data acquisition system was built and the driving data and the reference image data were acquired. Finally, we can verify that the vehicle positioning can be performed with an accuracy of about 0.7 m when the route image and the reference image information are integrated with the route path having a relatively large error by the satellite sensor.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.419-422
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• 1995

In this paper, we propose two methods for AE source location on the material with unknown AE wave velocity. By this method, we can apply this method to arbitrary material of which properties are not known exactly. Also, in this paper, the mechanism of error generation in both methods are discussed and performances are compared by using computer simulation and experiments which uses a lead break as the AE source on the aluminum plate.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.584-586
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• 2005

In this paper, low cost inertial sensor and compass were used instead of encoder for localization of mobile robot. Movements by encoder, movements by inertial sensor and movements by complementary filter with inertial sensor and compass were analyzed. Movement by complementary filter was worse than by only inertial sensor because of imperfection of compass. For the complementary filter to show best movements, compass need to be compensated for position error.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.4
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• pp.256-262
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• 2004

A key component of an autonomous mobile robot is to localize itself and build a map of the environment simultaneously. In this paper, we propose a vision-based localization and mapping algorithm of mobile robot using fisheye lens. To acquire high-level features with scale invariance, a camera with fisheye lens facing toward to ceiling is attached to the robot. These features are used in mP building and localization. As a preprocessing, input image from fisheye lens is calibrated to remove radial distortion and then labeling and convex hull techniques are used to segment ceiling and wall region for the calibrated image. At the initial map building process, features we calculated for each segmented region and stored in map database. Features are continuously calculated for sequential input images and matched to the map. n some features are not matched, those features are added to the map. This map matching and updating process is continued until map building process is finished, Localization is used in map building process and searching the location of the robot on the map. The calculated features at the position of the robot are matched to the existing map to estimate the real position of the robot, and map building database is updated at the same time. By the proposed method, the elapsed time for map building is within 2 minutes for 50㎡ region, the positioning accuracy is ±13cm and the error about the positioning angle of the robot is ±3 degree for localization.