• Proceedings of the KIEE Conference
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• 2001.05a
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• pp.112-115
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• 2001

This paper illustrates a new nodal effective load model for nodal probabilistic production cost simulation of the load point in a composite power system. The new effective load model includes capacities and uncertainties of generators as well as transmission lines. The CMELDC based on the new effective load model at HLII has been developed also. The CMELDC can be obtain from convolution integral processing of the outage capacity probabilistic distribution function of the fictitious generator and the original load duration curve given at the load point. It is expected that the new model for the CMELDC proposed. In this study will provide some solutions to many problems based on nodal and decentralized operation and control of an electric power systems under competition environment in future. The CMELDC based on the new model at HLII will extend the application areas of nodal probabilistic production cost simulation, outage cost assessment and reliability evaluation etc. at load points. The characteristics and effectiveness of this new model are illustrated by a case study of a test system.

• Proceedings of the KIEE Conference
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• 1998.11a
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• pp.239-241
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• 1998

The probabilistic production simulation of power system generally has been used to formulate a reasonable power production plan or generation planning. It integrates the convolution process of a generating unit's random outage(FOR) with equivalent load duration curve(ELDC), and provides the reliability indices of power system. This paper presents the reliability characteristics of power system reflected on demand side management and proposes the modified ELDC representation technique due to the high-efficient end-use diffusion among the customers. Load reductions are simulated from the multi-state deconvolution process with the saved capacity of end-use. Case study shows the computed reliability from the power system production simulation incorporated with DSM planning scheme.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.246-246
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• 2021

본 연구는 강우 발생시 유량을 추정하는 것에 목적이 있다. 이를 위해 본 연구는 선행연구의 모형 개발방법론에서 벗어나 딥러닝 알고리즘 중 하나인 합성곱 신경망 (convolution neural network)과 수문학적 이미지 (hydrological image)를 이용하여 강우 발생시 유량을 추정하였다. 합성곱 신경망은 일반적으로 분류 문제 (classification)을 해결하기 위한 목적으로 개발되었기 때문에 불특정 연속변수인 유량을 모의하기에는 적합하지 않다. 이를 위해 본 연구에서는 합성곱 신경망의 완전 연결층 (Fully connected layer)를 개선하여 연속변수를 모의할 수 있도록 개선하였다. 대부분 합성곱 신경망은 RGB (red, green, blue) 사진 (photograph)을 이용하여 해당 사진이 나타내는 것을 예측하는 목적으로 사용하지만, 본 연구의 경우 일반 RGB 사진을 이용하여 유출량을 예측하는 것은 경험적 모형의 전제(독립변수와 종속변수의 관계)를 무너뜨리는 결과를 초래할 수 있다. 이를 위해 본 연구에서는 임의의 유역에 대해 2차원 공간에서 무차원의 수문학적 속성을 갖는 grid의 집합으로 정의되는 수문학적 이미지는 입력자료로 활용했다. 합성곱 신경망의 구조는 Convolution Layer와 Pulling Layer가 5회 반복하는 구조로 설정하고, 이후 Flatten Layer, 2개의 Dense Layer, 1개의 Batch Normalization Layer를 배열하고, 다시 1개의 Dense Layer가 이어지는 구조로 설계하였다. 마지막 Dense Layer의 활성화 함수는 분류모형에 이용되는 softmax 또는 sigmoid 함수를 대신하여 회귀모형에서 자주 사용되는 Linear 함수로 설정하였다. 이와 함께 각 층의 활성화 함수는 정규화 선형함수 (ReLu)를 이용하였으며, 모형의 학습 평가 및 검정을 판단하기 위해 MSE 및 MAE를 사용했다. 또한, 모형평가는 NSE와 RMSE를 이용하였다. 그 결과, 모형의 학습 평가에 대한 MSE는 11.629.8 m3/s에서 118.6 m3/s로, MAE는 25.4 m3/s에서 4.7 m3/s로 감소하였으며, 모형의 검정에 대한 MSE는 1,997.9 m3/s에서 527.9 m3/s로, MAE는 21.5 m3/s에서 9.4 m3/s로 감소한 것으로 나타났다. 또한, 모형평가를 위한 NSE는 0.7, RMSE는 27.0 m3/s로 나타나, 본 연구의 모형은 양호(moderate)한 것으로 판단하였다. 이에, 본 연구를 통해 제시된 방법론에 기반을 두어 CNN 모형 구조의 확장과 수문학적 이미지의 개선 또는 새로운 이미지 개발 등을 추진할 경우 모형의 예측 성능이 향상될 수 있는 여지가 있으며, 원격탐사 분야나, 위성 영상을 이용한 전 지구적 또는 광역 단위의 실시간 유량 모의 분야 등으로의 응용이 가능할 것으로 기대된다.

• Geophysics and Geophysical Exploration
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• v.16 no.3
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• pp.139-144
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• 2013

A computer program has been developed to estimate time-domain electromagnetic (EM) responses for a onedimensional model with multiple source and receiver dipoles that are finite in length. The time-domain solution can be obtained by applying an inverse fast Fourier transform (FFT) to frequency-domain fields for efficiency. Frequency-domain responses are first obtained for 10 logarithmically equidistant frequencies per decade, and then cubic spline interpolated to get the FFT input. In the case of phases, the phase curve must be made to be continuous prior to the spline interpolation. The spline interpolated data are convolved with a source current waveform prior to FFT. In this paper, only a step-off waveform is considered. This time-domain code is verified with an analytic solution and EM responses for a marine hydrocarbon reservoir model. Through these comparisons, we can confirm that the accuracy of the developed program is fairly high.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.52-57
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• 2014

This paper presents an implementation of a smooth path planning method considering physical limits on a real time operating system for a two-wheel mobile robot. A Bezier curve is utilized to make a smooth path considering a robot's position and direction angle through the defined path. A convolution operator is used to generate the center velocity trajectory to travel the distance of the planned path while satisfying the physical limits. The joint space velocity is computed to drive the two-wheel mobile robot from the center velocity. Trajectory planning, velocity command according to the planned trajectory, and monitoring of encoder data are implemented with a multi-tasking system. And the synchronization of tasks is performed with a real-time mechanism of Event Flag. A real time system with multi-tasks is implemented and the result is compared with a non-real-time system in terms of path tracking to the designed path. The result shows the usefulness of a real-time multi-tasking system to the control system which requires real-time features.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.31-40
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• 2024

In the realm of domestic online fashion platform industry the manual registration of product information by individual business owners leads to inconvenience and reliability issues, especially when dealing with simultaneous registrations of numerous product groups. Moreover, bias is significantly heightened due to the low quality of product images and an imbalance in data quantity. Therefore, this study proposes a ResNet50 model aimed at minimizing data bias through oversampling techniques and conducting multiple classifications for 13 fashion categories. Transfer learning is employed to optimize resource utilization and reduce prolonged learning times. The results indicate improved discrimination of up to 33.4% for data augmentation in classes with insufficient data compared to the basic convolution neural network (CNN) model. The reliability of all outcomes is underscored by precision and affirmed by the recall curve. This study is suggested to advance the development of the domestic online fashion platform industry to a higher echelon.

• The Journal of Korean Society for Radiation Therapy
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• v.24 no.2
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• pp.137-147
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• 2012

Purpose: Analyze the Effectiveness of Radiation Treatment Planning by dose calculation and optimization algorithm, apply consideration of actual treatment planning, and then suggest the best way to treatment planning protocol. Materials and Methods: The treatment planning system use Eclipse 10.0. (Varian, USA). PBC (Pencil Beam Convolution) and AAA (Anisotropic Analytical Algorithm) Apply to Dose calculation, DVO (Dose Volume Optimizer 10.0.28) used for optimized algorithm of Intensity Modulated Radiation Therapy (IMRT), PRO II (Progressive Resolution Optimizer V 8.9.17) and PRO III (Progressive Resolution Optimizer V 10.0.28) used for optimized algorithm of VAMT. A phantom for experiment virtually created at treatment planning system, $30{\times}30{\times}30$ cm sized, homogeneous density (HU: 0) and heterogeneous density that inserted air assumed material (HU: -1,000). Apply to clinical treatment planning on the basis of general treatment planning feature analyzed with Phantom planning. Results: In homogeneous density phantom, PBC and AAA show 65.2% PDD (6 MV, 10 cm) both, In heterogeneous density phantom, also show similar PDD value before meet with low density material, but they show different dose curve in air territory, PDD 10 cm showed 75%, 73% each after penetrate phantom. 3D treatment plan in same MU, AAA treatment planning shows low dose at Lung included area. 2D POP treatment plan with 15 MV of cervical vertebral region include trachea and lung area, Conformity Index (ICRU 62) is 0.95 in PBC calculation and 0.93 in AAA. DVO DVH and Dose calculation DVH are showed equal value in IMRT treatment plan. But AAA calculation shows lack of dose compared with DVO result which is satisfactory condition. Optimizing VMAT treatment plans using PRO II obtained results were satisfactory, but lower density area showed lack of dose in dose calculations. PRO III, but optimizing the dose calculation results were similar with optimized the same conditions once more. Conclusion: In this study, do not judge the rightness of the dose calculation algorithm. However, analyzing the characteristics of the dose distribution represented by each algorithm, especially, a method for the optimal treatment plan can be presented when make a treatment plan. by considering optimized algorithm factors of the IMRT or VMAT that needs to optimization make a treatment plan.

• Progress in Medical Physics
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• v.23 no.1
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• pp.48-53
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• 2012

The pencil beam convolution (PBC) algorithms in radiation treatment planning system have been widely used to calculate the radiation dose. A new photon dose calculation algorithm, referred to as the anisotropic analytical algorithm (AAA), was released for use by the Varian medical system. The aim of this paper was to investigate the difference in dose calculation between the AAA and PBC algorithm using the intensity modulated radiation therapy (IMRT) plan for lung cancer cases that were inhomogeneous in the low density. We quantitatively analyzed the differences in dose using the eclipse planning system (Varian Medical System, Palo Alto, CA) and I'mRT matirxx (IBA, Schwarzenbruck, Germany) equipment to compare the gamma evaluation. 11 patients with lung cancer at various sites were used in this study. We also used the TLD-100 (LiF) to measure the differences in dose between the calculated dose and measured dose in the Alderson Rando phantom. The maximum, mean, minimum dose for the normal tissue did not change significantly. But the volume of the PTV covered by the 95% isodose curve was decreased by 6% in the lung due to the difference in the algorithms. The difference dose between the calculated dose by the PBC algorithms and AAA algorithms and the measured dose with TLD-100 (LiF) in the Alderson Rando phantom was -4.6% and -2.7% respectively. Based on the results of this study, the treatment plan calculated using the AAA algorithms is more accurate in lung sites with a low density when compared to the treatment plan calculated using the PBC algorithms.