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RISS 인기검색어
- 검색키워드
- 저자 : Kyeongtaek Park (검색결과 4 건)
- 무료
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Operational modal analysis of reinforced concrete bridges using autoregressive model
Kyeongtaek Park,Sehwan Kim,Marco Torbol 국제구조공학회 2016 Smart Structures and Systems, An International Jou Vol.17 No.6
This study focuses on the system identification of reinforced concrete bridges using vector autoregressive model (VAR). First, the time series output response from a bridge establishes the autoregressive (AR) models. AR models are one of the most accurate methods for stationary time series. Burg\'s algorithm estimates the autoregressive coefficients (ARCs) at p-lag by reducing the sum of the forward and the backward errors. The computed ARCs are assembled in the state system matrix and the eigen-system realization algorithm (ERA) computes: the eigenvector matrix that contains the vectors of the mode shapes, and the eigenvalue matrix that contains the associated natural frequencies. By taking advantage of the characteristic of the AR model with ERA (ARMERA), civil engineering can address problems related to damage detection. Operational modal analysis using ARMERA is applied to three experiments. One experiment is coupled with an artificial neural network algorithm and it can detect damage locations and extension. The neural network uses a specific number of ARCs as input and multiple submatrix scaling factors of the structural stiffness matrix as output to represent the damage.
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An Application of Deep Clustering forAbnormal Vessel Trajectory Detection
Heon-Jei Park,Ji Hoon Kyung,Kyeongtaek Kim,Jae Joon Suh 한국산업경영시스템학회 2021 한국산업경영시스템학회 학술대회 Vol.2021 No.추계
Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation.
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박헌제(Heon-Jei Park),이준우(Jun Woo Lee),경지훈(Ji Hoon Kyung),김경택(Kyeongtaek Kim) 한국산업경영시스템학회 2021 한국산업경영시스템학회지 Vol.44 No.4
Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation. We first generate gridded trajectory images by mapping the raw vessel trajectories into two dimensional matrix. Based on the gridded image input, we test the proposed model along with the other deep autoencoder-based models for the abnormal trajectory data generated through rotation and speed variation from normal trajectories. We show that the proposed model improves detection accuracy for the generated abnormal trajectories compared to the other models.
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개방형 구조(OA)를 이용한 함정체계통합 구축 방법론 : 통합함정컴퓨팅환경(TSCE)기반 아키텍처 구축 및 검증을 중심으로
박강수(Gang-Soo Park),유병천(Byeong-Chun Yoo),김경택(Kyeongtaek Kim),최봉완(Bong-Wan Choi) 한국산업경영시스템학회 2020 한국산업경영시스템학회지 Vol.43 No.3
In a series of recent launch tests, North Korea has been improving the firepower of its missiles that can target South Korea. North Korea’s missiles and submarines are capable of threatening targets in South Korea and are likely faster and more covert than the systems previously seen in North Korea. The advanced threats require that ROK Navy should not only detect them earlier than ever but also response quicker than ever. In addition to increasing threats, the number of young man that can be enlisted for military service has been dramatically decreasing. To deal with these difficulty, ROK navy has been making various efforts to acquire a SMART warship having enhanced defense capability with fewer human resources. For quick response time with fewer operators, ROK Navy should improve the efficiency of systems and control tower mounted on the ship by promoting the Ship System Integration. Total Ship Computing Environment (TSCE) is a method of providing single computing environment for all ship systems. Though several years have passed since the first proposal of TSCE, limited information has been provided and domestic research on the TSCE is still in its infancy. In this paper, we apply TSCE with open architecture (OA) to solve the problems that ROK Navy is facing in order to meet the requirements for the SMART ship. We first review the level of Ship System Integration of both domestic and foreign ships. Then, based on analyses of integration demands for SMART warship, we apply real time OA to design architecture for TSCE from functional view and physical view. Simulation result shows that the proposed architecture has faster response time than the response time of the existing architecture and satisfies its design requirements.
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