• 한국도로학회논문집
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• 제17권5호
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• pp.25-31
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• 2015

PURPOSES: The objective of this study is to analyze the relationship between the FWD back-calculated modulus and dynamic modulus of asphalt layers for existing asphalt pavements. METHODS: To evaluate the dynamic modulus of the asphalt mixture in the existing and new asphalt layers, the uniaxial direct tension test was conducted on small asphalt specimens obtained from the existing asphalt-covered pavements. A dynamic modulus master curve was estimated by using the uniaxial direct tension test for each asphalt layer. The falling weight deflectometer (FWD) testing was conducted on the test sections, and the modulus values of pavement layers were back-calculated using the genetic algorithm and the finite element method based back-calculation program. The relationship between measured and back-calculated asphalt layer moduli was examined in this study. The normalized dynamic modulus was adopted to predict the stiffness characteristics of asphalt layers more accurately. RESULTS: From this study, we can conclude that there is no close relationship between dynamic modulus of first layer and back-calculated asphalt modulus. The dynamic moduli of second and third asphalt layers have some relation with asphalt stiffness. Test results also showed that the normalized dynamic modulus of the asphalt mixture is closely related to the FWD back-calculated modulus with 0.73 of R square value. CONCLUSIONS: The back-calculated modulus of asphalt layer can be used as an indicator of the stiffness characteristics of asphalt layers in the asphalt-covered pavements.

• 한국해양공학회지
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• 제22권6호
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• pp.75-82
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• 2008

Flame bending has been extensively used in the shipbuilding industry for hull plate forming In flame bending it is difficult to obtain the desired shape because the residual deformation dependson the complex temperature distribution and the thermal plastic strain. Mechanical bending such as reconfigurable press forming multi-point press forming or die-less forming has been found to improve the automation of hull plateforming because it can more accurately control the desired shape than line heating. Multi-point forming is a process in which external forces are used to form metal work-pieces. Therefore it can be a flexible and efficient forming technique. This paper presents an optimal approach to determining the press-stroke for multi-point press forming of curved shapes. An integrated configuration of Finite element analysis (FEA) and spring-back compensation algorithm is developed to calculate the strokes of the multi-point press. Not only spring-back is modeled by elastic plastic shell elements but also an iterative algorithm to compensate the spring-back is applied to adjust the amount of pressing stroke. An iterative displacement adjustment (IDA) method is applied by integration of the FEA procedure and the spring-back compensation work. Shape deviation between the desired surface and deform￡d plate is minimized by the IDA algorithm.

• Korean Journal of Radiology
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• 제23권7호
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• pp.752-762
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• 2022

Objective: To compare a deep learning-based reconstruction (DLR) algorithm for pediatric abdominopelvic computed tomography (CT) with filtered back projection (FBP) and iterative reconstruction (IR) algorithms. Materials and Methods: Post-contrast abdominopelvic CT scans obtained from 120 pediatric patients (mean age ± standard deviation, 8.7 ± 5.2 years; 60 males) between May 2020 and October 2020 were evaluated in this retrospective study. Images were reconstructed using FBP, a hybrid IR algorithm (ASiR-V) with blending factors of 50% and 100% (AV50 and AV100, respectively), and a DLR algorithm (TrueFidelity) with three strength levels (low, medium, and high). Noise power spectrum (NPS) and edge rise distance (ERD) were used to evaluate noise characteristics and spatial resolution, respectively. Image noise, edge definition, overall image quality, lesion detectability and conspicuity, and artifacts were qualitatively scored by two pediatric radiologists, and the scores of the two reviewers were averaged. A repeated-measures analysis of variance followed by the Bonferroni post-hoc test was used to compare NPS and ERD among the six reconstruction methods. The Friedman rank sum test followed by the Nemenyi-Wilcoxon-Wilcox all-pairs test was used to compare the results of the qualitative visual analysis among the six reconstruction methods. Results: The NPS noise magnitude of AV100 was significantly lower than that of the DLR, whereas the NPS peak of AV100 was significantly higher than that of the high- and medium-strength DLR (p < 0.001). The NPS average spatial frequencies were higher for DLR than for ASiR-V (p < 0.001). ERD was shorter with DLR than with ASiR-V and FBP (p < 0.001). Qualitative visual analysis revealed better overall image quality with high-strength DLR than with ASiR-V (p < 0.001). Conclusion: For pediatric abdominopelvic CT, the DLR algorithm may provide improved noise characteristics and better spatial resolution than the hybrid IR algorithm.

• Journal of information and communication convergence engineering
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• 제17권4호
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• pp.239-245
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• 2019

Recently, effort to obtain various information from the vast amount of social network services (SNS) big data generated in daily life has expanded. SNS big data comprise sentences classified as unstructured data, which complicates data processing. As the amount of processing increases, a rapid processing technique is required to extract valuable information from SNS big data. We herein propose a system that can extract human sentiment information from vast amounts of SNS unstructured big data using the naïve Bayes algorithm and natural language processing (NLP). Furthermore, we analyze the effectiveness of the proposed method through various experiments. Based on sentiment accuracy analysis, experimental results showed that the machine learning method using the naïve Bayes algorithm afforded a 63.5% accuracy, which was lower than that yielded by the NLP method. However, based on data processing speed analysis, the machine learning method by the naïve Bayes algorithm demonstrated a processing performance that was approximately 5.4 times higher than that by the NLP method.

• 대한토목학회논문집
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• 제10권2호
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• pp.11-22
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• 1990

본(本) 논문(論文)에서는 보존력(保存力) 및 비보존력(非保存力)을 받는 구조물(構造物)의 비선형(非線形) 유한요소해석(有限要素解析)을 수행(遂行)하기 위하여 기존의 하중증분법(荷重增分法)과 변위증분법(變位增分法)을 효율적(效率的)으로 결합(結合)시킨 수치적(數値的)인 해석(解析)알고리즘을 제시(提示)하였다. 제안(提案)한 알고리즘은 하중증분(荷重增分)과 변위증분(變位增分)이 자동(自動) 생성되도록 하므로써 Snap-Through, Turning-Back과 같은 비선형(非線形) 거동(擧動)을 포함(包含)하는 다양(多樣)한 평형경로(平衡經路)들을 추적(追跡)할 수 있었다.

• Journal of information and communication convergence engineering
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• 제2권3호
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• pp.177-181
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• 2004

In this paper, we suggest an Iris recognition system with an excellent recognition rate and confidence as an alternative biometric recognition technique that solves the limit in an existing individual discrimination. For its implementation, we extracted coefficients feature values with the wavelet transformation mainly used in the signal processing, and we used neural network to see a recognition rate. However, Scale Conjugate Gradient of nonlinear optimum method mainly used in neural network is not suitable to solve the optimum problem for its slow velocity of convergence. So we intended to enhance the recognition rate by using Levenberg-Marquardt Back-propagation which supplements existing Scale Conjugate Gradient for an implementation of the iris recognition system. We improved convergence velocity, efficiency, and stability by changing properly the size according to both convergence rate of solution and variation rate of variable vector with the implementation of an applied algorithm.

• 한국전기전자재료학회논문지
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• 제28권5호
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• pp.320-325
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• 2015

This paper dealt with a defect identification algorithm which is based on single partial discharge (PD) pulse analysis in gas insulated structure. Four types of electrode systems such as a needle-plane, a plane-needle, a free particle and a crack inside spacer were fabricated to simulate defects in gas insulated switchgear (GIS). We measured single PD pulse by an oscilloscope with a sampling rate of 5 GS/s and a frequency bandwidth of 1 GHz. Data aquisition and signal processing were controlled by a LabVIEW program. Physical shapes of PD pulses were compared with kurtosis, skewness and time-based parameters as rising time, falling time and pulse-width. These parameters were analysed by an algorithm with a back propagation algorithm (BPA). By applying the algorithm, the identification rate was 97% for the needle-plane electrode, 96% for the plane-needle electrode, 91% for the free particle and 93% for the crack inside spacer. The results verified that the algorithm could identify the type of defects in GIS.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.215-218
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• 1997

A dynamic back-propagation neural network is addressed for adaptive neural control system to approximate non-linear control system rather than static networks. It has the capability to represent the approximation of nonlinear system without mathematical analysis and to carry out the on-line learning algorithm for real time application. The simulated results show fast tracking capability and adaptive response by using dynamic back-propagation neurons.

• 한국통신학회논문지
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• 제21권8호
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• pp.1874-1884
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• 1996

In this paper, we proposed automatic searching algorithm of thresholding value using multilevel thresholding for face segmentation from input bust image effectively. The proposed algorithm extracted the thresholding value of brightness that is formed background region, face region and hair region without illumination, background and face size from input image. The statistical projection analysis project the brightness of multilevel thresholding image into horizontal and vertical direction and decide the thresholding value of face. And the algorithm extracted elliptical type block of face from input image in order to reduce the back ground region and hair region efficiently. The proposed algorithm can reduce searching area of feature extraction and processing time for face recognication.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권5호
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• pp.361-367
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• 2004

This paper describes design of an algorithm for analyzing human activity using body-fixed 3-axis accelerometer in the small of the back. In the first step, we distinguish static and dynamic activity period using AC signal analysis. Then five postures were classified by applying the threshold in DC signal corresponding to the static activity period. Also, after comparison of average power and taking negative peak signal in the dynamic activity period, the four dynamic activities were classified by adaptive threshold method. To evaluate the performance of the proposed algorithm, the measured signals obtained from six subjects were applied to the proposed algorithm and the results were compared with the simultaneously measured video data. As a result, the activity classification rate of 95.7％ on average was obtained. Overall results show that the proposed classification algorithm has a possibility to be used to analyze the static and dynamic physical activity.