• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.1
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• pp.21-30
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• 2022

The restart of scramjet engine is almost impossible in case its unstart happens during engine operation. Therefore, it is essential to prognosticate the scramjet engine unstart phenomena. A numerical simulation of a scramjet engine is conducted to investigate the unstart process of the scramjet engine by adjusting the backpressure at the isolator outlet to the engine analysis environment. The start and unstart of the engine are identified by applying a support vector machine (SVM) through the data measured by wall pressure so that the locations of the pressure sensors most suitable for the unstart detection are selected. And the operation conditions in which the engine is avoid to be unstarted are quantified to perceive the safety margin.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1895-1896
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• 2008

지능형 빌딩 환경 모니터링 시스템과 같이 실내에서 센서 네트워크를 이용하여 환경 데이터를 수집하는 네트워크가 점점 확산되고 있다. 이와 같은 건축물 내에서의 무선 센서 네트워크는 랜덤하게 센서 노드들이 뿌려지는 것이 아니라, 사람의 의지대로 배치가 된다. 따라서 위치정보를 모르는 상황의 무선 센서 네트워크들이 가지는 라우팅 방법을 사용하는 것이 아니라 더 간결하면서 강한 네트워크 유지 능력을 가지는 라우팅 방법이 사용되게 된다. 본 논문에서는 트리 라우팅을 이용한 건물 환경 모니터링 시스템에 에너지 효율을 높이기 위하여 네트워크의 상황을 고려한 SVM을 이용한 동적 라우터 선택기법을 포함한 동적 트리 라우팅 기법에 대한 연구와 이의 구현을 보이고 있다.

• Economic and Environmental Geology
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• v.53 no.4
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• pp.479-489
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• 2020

A fault is a geological structure that can be a migration path or a cap rock of hydrocarbon such as oil and gas, formed from source rock. The fault is one of the main targets of seismic exploration to find reservoirs in which hydrocarbon have accumulated. However, conventional fault detection methods using lateral discontinuity in seismic data such as semblance, coherence, variance, gradient magnitude and fault likelihood, have problem that professional interpreters have to invest lots of time and computational costs. Therefore, many researchers are conducting various studies to save computational costs and time for fault interpretation, and machine learning technologies attracted attention recently. Among various machine learning technologies, many researchers are conducting fault interpretation studies using the support vector machine, multi-layer perceptron, deep neural networks and convolutional neural networks algorithms. Especially, researchers use not only their own convolution networks but also proven networks in image processing to predict fault locations and fault information such as strike and dip. In this paper, by investigating and analyzing these studies, we found that the convolutional neural networks based on the U-Net from image processing is the most effective one for fault detection and interpretation. Further studies can expect better results from fault detection and interpretation using the convolutional neural networks along with transfer learning and data augmentation.

• 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%.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.4
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• pp.345-354
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• 2014

In this paper, we propose a real-time algorithm for estimating the relative position and velocity of a person with respect to a robot using a Kinect sensor and an extended Kalman filter (EKF). Additionally, we propose an algorithm for controlling the robot in the proximity of a person in a variety of modes. The algorithm detects the head and shoulder regions of the person using a histogram of oriented gradients (HOG) and a support vector machine (SVM). The EKF algorithm estimates the relative positions and velocities of the person with respect to the robot using data acquired by a Kinect sensor. We tested the various modes of proximity movement for a human in indoor situations. The accuracy of the algorithm was verified using a motion capture system.

• Explosives and Blasting
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• v.36 no.4
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• pp.48-61
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• 2018

This case study is concerned with the project of the explosive demolition for the KOGAS office building located in Bundang district in Seongnam city. Since the office building was a kind of long-span beam structures, a mechanical demolition method using jacking support systems was considered in the beginning of the project. With consideration of the excessive reinforcement cost, uncertainty of safety, and prolonged construction period, however, the original plan was later changed to use an explosive demolition method. For the purpose of protecting nearby buildings and facilities during the collapse process, the explosive initiation sequence was elaborately designed to bring down the building structure towards its front left corner. A total of over 550 electronic detonators (Unitronic 600) was used to sequentially initiate the explosives installed at appropriate columns in the first, second, and fifth floors. To diminish dust production, water bags of small and large sizes were respectively installed at each column and on the floors to be blasted. As such, every effort was exercised to mitigate overall noise, dust, and shock vibrations that could be generated during the explosive demolition process for the office building.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1855-1862
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• 2022

Various indoor location-based services can be provided through indoor presence determination and indoor positioning technology using Beacon. However, since the BLE signal advertised by the beacon has an unstable RSSI due to problems such as multi-path fading, it is difficult to guarantee the accuracy of indoor presence determination. In this paper, data were collected while the classroom door was open to ensure accuracy in various situations. Based on the collected data, we propose an indoor presence determination method considering the characteristics of the signal. The proposed method uses support vector machine, showed about 10% accuracy improvement compared to the results using raw RSSI only. This method has the advantage of being able to accurately determine indoor presence with only one receiver. It is expected that the proposed method can implement a low-cost system for determining indoor presence with high accuracy.

• Fire Science and Engineering
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• v.34 no.2
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• pp.86-93
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• 2020

This study aims to maintain the safety of an outdoor storage tank through the fundamental case analysis of explosion and fire accidents in the storage tank. We consider an accident caused by the explosion of fire inside the tank, as a result of the gradual spreading of the residual fire generated by wind lamps flying off a workplace in the storage tank yard. To determine the cause of the accident, atmospheric diffusion conditions were derived through CCTV image analysis, and the wind direction was analyzed using computational fluid dynamics. Additionally, the amount of oil vapor inside the tank when the floating roof was at the lowest position, and the behavior of the vapor inside the tank when the floating roof was at the highest position were investigated. If the cause of the explosion in the storage tank is identified and the level of the storage tank is maintained below the internal floating roof, dangerous liquid fills the storage tank, and the vapor in the space may stagnate on the internal floating roof. We intend to improve the operation procedure such that the level of the storage tank is not under the Pontoon support, as well as provide measures to prevent flames from entering the storage tank by installing a flame arrester in the open vent of the tank.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.33-47
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• 2018

City gas pipelines are buried underground. Because of this, pipeline is hard to manage, and can be easily damaged. This research proposes a real time prediction system that helps experts can make decision about pressure anomalies. The gas pipline pressure data of Jungbu City Gas Company, which is one of the domestic city gas suppliers, time variables and environment variables are analysed. In this research, regression models that predicts pipeline pressure in minutes are proposed. Random forest, support vector regression (SVR), long-short term memory (LSTM) algorithms are used to build pressure prediction models. A comparison of pressure prediction models' preformances shows that the LSTM model was the best. LSTM model for Asan-si have root mean square error (RMSE) 0.011, mean absolute percentage error (MAPE) 0.494. LSTM model for Cheonan-si have RMSE 0.015, MAPE 0.668.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.870-878
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• 2013

In this paper, we propose a novel fingerprint classification method to enhance the accuracy and efficiency of the fingerprint identification system, one of biometrics systems. According to the previous researches, fingerprints can be categorized into the several patterns based on their pattern of ridges and valleys. After construction of fingerprint database based on their patters, fingerprint classification approach can help to accelerate the fingerprint recognition. The reason is that classification methods reduce the size of the search space to the fingerprints of the same category before matching. First, we suggest a method to extract region of interest (ROI) which have real information about fingerprint from the image. And then we propose a feature extraction method which combines gray level co-occurrence matrix (GLCM) and wavelet features. Finally, we compare the performance of our proposed method with the existing method which use only GLCM as the feature of fingerprint by using the multi-layer perceptron and support vector machine.