• International conference on construction engineering and project management
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• 2024.07a
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• pp.487-493
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• 2024

Presently, prefabricated concrete panels are extensively employed in diverse construction projects across the globe due to their exceptional quality. To maintain the overall quality of these construction projects, it is crucial to ensure that the dimensions of precast concrete panels align with their designated design specifications. Therefore, it is essential to develop a methodology capable of quickly and accurately measuring the dimensions of precast concrete panels. Currently, there are many advanced technologies used to examine the dimensions of prefabricated concrete panels such as terrestrial laser scanning, which is prone to time consuming and cost inefficiencies. To address these limitations, this study suggests a computer vision-based approach that utilizes April Tag markers and images taken from a mobile phone to measure and evaluate the dimensions and quality of precast concrete panels. The proposed algorithm operates as follows: Initially, the RGB image coordinates are converted to the world coordinate systems using April tag markers. Following, the masks of the precast concrete components are extracted using the state-of-the-art Segment Anything Model (SAM). Finally, an algorithm based on image processing technique is developed to estimate the dimensional properties of precast concrete panels. The effectiveness of the proposed method is validated through preliminary experiments conducted in the field-scale precast slabs, and the result is evaluated by comparing to the manual measurement result.

• Proceedings of the KIPE Conference
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• 2007.11a
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• pp.103-107
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• 2007

In doubly fed induction generators (DFIGs), control of rotor currents allows for adjustable speed operation, active, and reactive power control. This paper presents a DFIG control strategy that enhances the active and reactive power control with controllers that can compensate for the errors caused by current measurement path in the DFIG system. The errors can be divided into two categories: offset and scaling errors. These can induce the speed, active, and reactive power pulsations, which are one and two times the fundamental slip frequency in the DFIG. And these undesirable ripples can do the DFIG harm. In this paper, a new compensation algorithm is proposed. Therefore, the proposed algorithm has several advantages: to implement is easy; it require less computation time; it is robust with regard to the variation of the induction generator parameters. In this paper, a new algorithm is introduced by using the integral of phase currents to measure the current ripples of rotor-side converterin the DFIG system. The experiment results are shown the effectiveness of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1689-1696
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• 2017

In this paper, the voltage measurement-based coordinated Voltage/VAR control (VMCVVC) algorithm for conservation voltage reduction(CVR) is proposed. The proposed algorithm has the purpose of enhancing the CVR effect through coordinated control of the voltage control devices such as the distributed energy resources and the load tap changer(LTC) transformers. It calculates the references of the voltage control devices such that the bus voltages are maintained at as close to the lower operation limit as possible. For this purpose, firstly, the distribution system is divided into LTC transformer control zones through topological search. Secondly, the reactive power references of the reactive power control devices are determined such that the voltage profile of the section is flattened. Finally, the tap references of the LTC transformers are calculated to lower the voltage profile. The effectiveness of the proposed algorithm is demonstrated through case studies using IEEE test network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1817-1839
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• 2024

To solve the problem of insufficient recognition of key nodes in the existing software defect detection process, this paper proposes a key node recognition algorithm based on node expansion degree and improved K-shell position, shortened as SDD_KNR. Firstly, the calculation formula of node expansion degree is designed to improve the degree that can measure the local defect propagation capability of nodes in the software network. Secondly, the concept of improved K-shell position of node is proposed to obtain the improved K-shell position of each node. Finally, the measurement of node defect propagation capability is defined, and the key node recognition algorithm is designed to identify the key function nodes with large defect impact range in the process of software defect detection. Using real software systems such as Nano, Cflow and Tar to design three sets of experiments. The corresponding directed weighted software function invoke networks are built to simulate intentional attack and defect source infection. The proposed SDD_KNR algorithm is compared with the BC algorithm, K-shell algorithm, KNMWSG algorithm and NMNC algorithm. The changing trend of network efficiency and the strength of node propagation force are analyzed to verify the effectiveness of the proposed SDD_KNR algorithm.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.597-599
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• 2006

In this paper, we propose an artificial intelligence method to compensate the nonlinearity error which occurs in the heterodyne laser interferometer. Some superior properties such as long measurement range, ultra-precise resolution and various system set-up lead the laser interferometer to be a practical displacement measurement apparatus in various industry and research area. In ultra-precise measurement such as nanometer or subnanometer scale, however, the accuracy is limited by the nonlinearity error caused by the optical parts. The feedforward neural network trained by back-propagation with a capacitive sensor as a reference signal minimizes the nonlinearity error and we demonstrate the effectiveness of our proppsed algorithm through some experimental results.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.9 s.90
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• pp.819-826
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• 2004

A multi-step optimum strategy for the selection of the locations and directions of strain gauges is proposed in this paper to capture at best the modal response of blade in a series of modes on fan blades. It is consist of three steps including two pass reduction step, genetic algorithm and fine optimization to find the locations-directions of strain gauges. The optimization is based upon the maximum signal-to-noise ratio(SNR) of measured strain values with respect to the inherent system measurement noise, the mispositioning of the gauge in location and gauge failure. Optimal gauge positions for a fan blade is analyzed to prove the effectiveness of the multi-step optimum methodology and to investigate the effects of the considering parameters such as the mispositioning level, the probability of gauge failure, and the number of gauges on the optimal strain gauge position.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.3
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• pp.192-197
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• 2003

The ‘AEIPR’(Accelerometer Established Inertial Profiling Reference) method has been applied to measure the road profile. The dynamic road profiling method using AEIPR has the advantages of cost effectiveness, measuring speed and relatively high reliability. However, it is required to improve the double integration algorithm to get the measurement results with the accuracy of hither level. In the first part of this paper, the effective double integration algorithm is suggested and the ‘Road Profiler’ software is developed on the basis of the algorithm. Road profiling tests are performed using the developed ‘Road Profiler’ system on the specially designed tracks for the durability tests and the various types of pubic roads. Test results are shown and evaluated by the international road evaluation indicies and classification.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.768-776
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• 2011

This paper considers a data fitting problem for rational function models using the LM (Levenberg-Marquardt) optimization method. Rational function models have various merits on representing a wide range of shapes and modeling complicated structures by polynomials of low degrees in both the numerator and denominator. However, rational functions are nonlinear in the parameter vector, thereby requiring nonlinear optimization methods to solve the fitting problem. In this paper, we propose a data fitting method for rational function models based on the LM algorithm which is renowned as an effective nonlinear optimization technique. Simulations show that the fitting results are robust against the measurement noises and uncertainties. The effectiveness of the proposed method is further demonstrated by the real application to a 3D depth camera calibration problem.

• 한국공간정보시스템학회:학술대회논문집
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• 2004.05a
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• pp.29-36
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• 2004

In this paper, a study of damage detection for 2-Dimensional Truss Structures using the parametric projection filter theory is presented. Many researchers are interested in inverse problem and one of solution procedures for inverse problems that are very effective is the approach using the filtering algorithm in conjunction with numerical solution methods. In filtering algorithm, the Kalman filtering algorithm is well known and have been applied to many kind of inverse problems. In this paper, the Parametric projection filtering in conjunction with structural analysis is applied to the identification of damages in 2-D truss structures. The natural frequency and modes of damaged truss model are adopted as the measurement data. The effectiveness of proposed method is verified through the numerical examples.

• ETRI Journal
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• v.45 no.3
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• pp.492-504
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• 2023

Researchers have recently shown an increased interest in estimating the direction-of-arrival (DOA) of wideband noncircular sources, but existing studies have been restricted to subspace-based methods. An off-grid sparse recovery-based algorithm is proposed in this paper to improve the accuracy of existing algorithms in low signal-to-noise ratio situations. The covariance and pseudo covariance matrices can be jointly represented subject to block sparsity constraints by taking advantage of the joint sparsity between signal components and bias. Furthermore, the estimation problem is transformed into a single measurement vector problem utilizing the focused operation, resulting in a significant reduction in computational complexity. The proposed algorithm's error threshold and the Cramer-Rao bound for wideband noncircular DOA estimation are deduced in detail. The proposed algorithm's effectiveness and feasibility are demonstrated by simulation results.