• Proceedings of the KSME Conference
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• 2001.11a
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• pp.49-54
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• 2001

Measurement of welding residual stresses is one of important tasks to assess the structural integrity of welded structures. For operating components, especially, nondestructive techniques are required. By now, authors have proposed a new residual stress evaluation method, the bead flush method, where residual stresses are calculated from eigenstrain distribution determined by surface removal of reinforcement. In this paper, a brief description of the bead flush method is given and its utility and problem to be overcome are discussed. And also stabilization method of solution set of eigenstrain in inverse analysis is proposed.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.37-39
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• 2016

최근 Convolutional neural networks(CNN) 기반의 초해상화 기법인 Super-Resolution Convolutional Neural Networks (SRCNN) 이 좋은 PSNR 성능을 발휘하는 것으로 보고되었다 [1]. 하지만 많은 제안 방법들이 고주파 성분을 복원하는데 한계를 드러내는 것처럼, SRCNN 도 고주파 성분 복원에 한계점을 지니고 있다. 또한 SRCNN 의 네트워크 층을 깊게 만들면 좋은 PSNR 성능을 발휘하는 것으로 널리 알려져 있지만, 네트워크의 층을 깊게 하는 것은 네트워크 파라미터 학습을 어렵게 하는 경향이 있다. 네트워크의 층을 깊게 할 경우, gradient 값이 아래(역방향) 층으로 갈수록 발산하거나 0 으로 수렴하여, 네트워크 파라미터 학습이 제대로 되지 않는 현상이 발생하기 때문이다. 따라서 본 논문에서는 네트워크 층을 깊게 하는 대신에, 입력을 다중 채널로 구성하여, 네트워크에 고주파 성분에 관한 추가적인 정보를 주는 방법을 제안하였다. 많은 초해상화 기법들이 고주파 성분의 복원 능력이 부족하다는 점에 착안하여, 우리는 네트워크가 고주파 성분에 관한 많은 정보를 필요로 한다는 것을 가정하였다. 따라서 우리는 네트워크의 입력을 고주파 성분이 여러 가지 강도로 입력되도록 저해상도 입력 영상들을 구성하였다. 또한 잔차신호 네트워크(residual networks)를 도입하여, 네트워크 파라미터를 학습할 때 고주파 성분의 복원에 집중할 수 있도록 하였다. 본 논문의 효율성을 검증하기 위하여 set5 데이터와 set14 데이터에 관하여 실험을 진행하였고, SRCNN 과 비교하여 set5 데이터에서는 2, 3, 4 배에 관하여 각각 평균 0.29, 0.35, 0.17dB 의 PSNR 성능 향상이 있었으며, set14 데이터에서는 3 배의 관하여 평균 0.20dB 의 PSNR 성능 향상이 있었다.

• Journal of the Korean Mathematical Society
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• v.54 no.1
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• pp.87-99
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• 2017

To indicate the statistical complexity of dynamical systems, we introduce the notions of higher order irregular set and higher order maximal Birkhoff average oscillation in this paper. We prove that, in the setting of topologically mixing Markov chain, the set consisting of those points having maximal k-order Birkhoff average oscillation for all positive integers k is as large as the whole space from the topological point of view. As applications, we discuss the corresponding results on a repeller.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.130-130
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• 2000

This paper presents a robust fault diagnosis and fault tolerant control lot the actuator and sensor faults in the closed-loop systems affected by unknown inputs or disturbances. The fault diagnostic scheme is based on the residual set generation by using robust Parity space approach. Residual set is evaluated through the threshold test and then fault is isolated according to the decision logic table. Once the fault diagnosis module indicates which actuator or sensor is faulty, the fault magnitude is estimated by using the disturbance-decoupled optimal state estimation and a new additive control law is added to the nominal one to override the fault effect on the system. Simulation results show that the method has definite fault diagnosis and fault tolerant control ability against actuator and sensor faults.

• Journal of the Korean Society of Safety
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• v.8 no.1
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• pp.47-53
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• 1993

Residual Deformation Analysis(RDA) is a new method for ratings of the continuous bridges. The RDA makes it possible to expand the inelastic steel girder bridge design method set forth in the American Association of State Highway Officals'(AASHTO) Guide Specifications for Alternate Load Factor Design Procedures for Steel Beam Bridges Using Braced Compact Sections(1986) into an inelastic rating method. It is a method to assess the residual moments and deformations that are set up in a beam that has been loaded into the post-elastic range This method combines classical elastic conjugate beam theory with linear moment-rotation relationships for midspan inelastic positive moment. The limit state is inelastic serviceability limit. which is defined as the ratio of the span to midspan inelastic deflection(C=L/D).

• Journal of the Korean Geophysical Society
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• v.9 no.3
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• pp.181-188
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• 2006

The data, methodology, and the resulting accurate gravimetric geoid model for the Korean Peninsula (latitude from 32˚ N to 40˚ N and longitude from 124˚ E to 131˚ E) are presented in this study. The types of used data were a high degree geopotential model (the EGM96 spherical harmonic coefficient set), a set of 12,615 land gravity observations, 1,056,075 shipborne gravity observations, and KMS2002 gravity anomalies from satellite altimetry. The remove-restore technique was successfully applied to combining the above mentioned data sets using up to degree and order 112 of the EGM96 coefficient. The residual geoid was calculated with residual Free-Air anomaly values using the spherical Stokes' formula with a 37-km integration cap radius. The geoid model was referred to WGS84 geodetic system and was tested using a set of GPS/levelling geoid undulations. The absolute accuracy is 0.132 m and some improvement compared to the PNU95 geoid model was found.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1115-1126
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• 2021

Plant disease is one of the most irritating problems for agriculture growers. Thus, timely detection of plant diseases is of high importance to practical value, and corresponding measures can be taken at the early stage of plant diseases. Therefore, numerous researchers have made unremitting efforts in plant disease identification. However, this problem was not solved effectively until the development of artificial intelligence and big data technologies, especially the wide application of deep learning models in different fields. Since the symptoms of plant diseases mainly appear visually on leaves, computer vision and machine learning technologies are effective and rapid methods for identifying various kinds of plant diseases. As one of the fruits with the highest nutritional value, apple production directly affects the quality of life, and it is important to prevent disease intrusion in advance for yield and taste. In this study, an improved deep residual network is proposed for apple leaf disease identification in a novel way, a global residual connection is added to the original residual network, and the local residual connection architecture is optimized. Including that 1,977 apple leaf disease images with three categories that are collected in this study, experimental results show that the proposed method has achieved 98.74% top-1 accuracy on the test set, outperforming the existing state-of-the-art models in apple leaf disease identification tasks, and proving the effectiveness of the proposed method.

• Journal of Korean Society of Water and Wastewater
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• v.21 no.5
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• pp.601-607
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• 2007

In order to maintain constant residual chlorine in sedimentation basin, It is necessary to develop real time prediction model of residual chlorine considering water treatment plant data such as water qualities, weather, and plant operation conditions. Based on the operation data acquired from K water treatment plant, prediction models of residual chlorine in sediment basin were accomplished. The input parameters applied in the models were water temperature, turbidity, pH, conductivity, flow rate, alkalinity and pre-chlorination dosage. The multiple regression models were established with linear and non-linear model with 5,448 data set. The corelation coefficient (R) for the linear and non-linear model were 0.39 and 0.374, respectively. It shows low correlation coefficient, that is, these multiple regression models can not represent the residual chlorine with the input parameters which varies independently with time changes related to weather condition. Artificial neural network models are applied with three different conditions. Input parameters are consisted of water quality data observed in water treatment process based on the structure of auto-regressive model type, considering a time lag. The artificial neural network models have better ability to predict residual chlorine at sediment basin than conventional linear and nonlinear multi-regression models. The determination coefficients of each model in verification process were shown as 0.742, 0.754, and 0.869, respectively. Consequently, comparing the results of each model, neural network can simulate the residual chlorine in sedimentation basin better than mathematical regression models in terms of prediction performance. This results are expected to contribute into automation control of water treatment processes.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2008.04a
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• pp.331-336
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• 2008

An self-centering energy-dissipative (SCED) bracing system has recently been developed as a new seismic force resistant bracing system. The advantage of the SCED brace system is that, unlike other comparable advanced bracing systems that dissipate energy, such as the buckling restrained brace system, it has a self-centering capability that reduces or eliminates residual building deformations after major seismic events. In this study seismic performance of SCED braced frames is evaluated for a set of 20 design level earthquake records. According to analysis results the SCED systems showed more uniform interstory drift demand for buildings with 8 story or fewer. The residual deformation in SCED buildings turned out to be much less than that of moment-resisting frames.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.305-307
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• 2016

The purpose of this paper is to determine the optimal values of the gain parameters used in the tracking module for a highly dynamic warship. The algorithm of the tracking module uses the ${\alpha}-{\beta}-{\gamma}$ filter to compute accurate estimates and update the state variables, that is, positions, velocity and acceleration. The filtering coefficients ${\alpha}$, ${\beta}$ and ${\gamma}$ are determined from set values of the damping parameter, ${\xi}$. Optimization is achieved by plotting a range of the damping parameter ${\xi}$ against the corresponding residual error and then selecting the best value of ${\xi}$ with the minimum residual error. Optimal values of the smoothing coefficients are subsequently computed from the selected damping parameter, ${\xi}$.