• Proceedings of the KIPE Conference
• /
• 2019.07a
• /
• pp.418-419
• /
• 2019

본 논문에서는 Dual-Bridge LLC 공진형 컨버터를 이용한 고효율 PV 스트링 Power Optimizer를 제안한다. 제안하는 Power Optimizer는 LLC 공진형 컨버터를 이용하여 모든 스위치의 ZVS 턴온 및 다이오드의 ZCS 턴오프를 성취하여 고효율을 달성할 수 있다. 또한 컨버터 토폴로지는 하프브릿지 회로와 풀브릿지 회로가 통합된 Dual-Bridge 구조를 적용하여 PV 스트링의 넓은 입력전압 범위(300V-900V) 조건에서도 정전압 출력이 가능하다. 본 논문에서는 6.25kW급 시작품 실험을 통하여 제안하는 Power Optimizer의 타당성을 검증하였다.

• Proceedings of the KIPE Conference
• /
• 2019.11a
• /
• pp.184-185
• /
• 2019

PV 계통연계형 시스템은 N대의 PV 스트링 Power optimizer와 1대의 인버터로 구성되며 초기 운전 시 인버터 보조전원에 전력을 공급하기 위한 Power optimizer의 DC링크 전압제어 운전 모드가 요구된다. 본 논문에서는 DC링크의 전압제어가 가능한 스트링 Power Optimizer의 start-up 제어기법을 제안한다. 제안하는 제어기법은 추가회로 없이 초기구동 시에 DC-Link의 전압을 서서히 원하는 전압으로 제어할 수 있는 장점을 갖는다. 또한 제안하는 start-up 시퀀스로 제안하는 제어기법을 검증하기 위한 6.25kW 시작품의 시험결과로 타당성을 검증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.3
• /
• pp.816-839
• /
• 2023

In a random vector functional link (RVFL) network, shortcomings such as local optimal stagnation and decreased convergence performance cause a reduction in the accuracy of illumination correction by only inputting the weights and biases of hidden neurons. In this study, we proposed an improved regularized random vector functional link (RRVFL) network algorithm with an optimized grey wolf optimizer (GWO). Herein, we first proposed the moth-flame optimization (MFO) algorithm to provide a set of excellent initial populations to improve the convergence rate of GWO. Thereafter, the MFO-GWO algorithm simultaneously optimized the input feature, input weight, hidden node and bias of RRVFL, thereby avoiding local optimal stagnation. Finally, the MFO-GWO-RRVFL algorithm was applied to ameliorate the performance of illumination correction of various test images. The experimental results revealed that the MFO-GWO-RRVFL algorithm was stable, compatible, and exhibited a fast convergence rate.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
• /
• 2003.05a
• /
• pp.69-71
• /
• 2003

This paper introduces TRIZ. TRIZ is the theory of inventive problem solving which haws started by G.Altschuler since 1946, Russia. TRIZ is applicable for not only mechanical engineering, but also science, economic and pedagogy fields. Characteristics of some kind of softwares for TRIZ method are briefly reviewed Especially Catia with IMC TechOptimizer is studied in detail and it is expected to be applicable well for the Ocean Engineering fields, if applicably applied for IM-Principles, IM-Predictions and IM-Effects, even though it will need much efforts and time to study. As an application example of TRIZ, Self-expandable anchor which is pending for patent is presented briefly.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1329-1341
• /
• 2022

Destroying the marine environment and marine ecosystem and causing marine accidents, marine debris is generated every year, and among them, submerged marine debris is difficult to identify and collect because it is on the seabed. Therefore, deep-learning-based semantic segmentation was experimented on waste fish nets and waste ropes using underwater images to identify efficient collection and distribution. For segmentation, a high-resolution network (HRNet), a state-of-the-art deep learning technique, was used, and the performance of each optimizer was compared. In the segmentation result fish net, F1 score=(86.46%, 86.20%, 85.29%), IoU=(76.15%, 75.74%, 74.36%), For the rope F1 score=(80.49%, 80.48%, 77.86%), IoU=(67.35%, 67.33%, 63.75%) in the order of adaptive moment estimation (Adam), Momentum, and stochastic gradient descent (SGD). Adam's results were the highest in both fish net and rope. Through the research results, the evaluation of segmentation performance for each optimizer and the possibility of segmentation of marine debris in the latest deep learning technique were confirmed. Accordingly, it is judged that by applying the latest deep learning technique to the identification of submerged marine debris through underwater images, it will be helpful in estimating the distribution of marine sedimentation debris through more accurate and efficient identification than identification through the naked eye.

• Advances in aircraft and spacecraft science
• /
• v.4 no.4
• /
• pp.467-486
• /
• 2017

A method for decoupling reliability based design optimization problem into a set of deterministic optimization and performing a reliability analysis is described. The inner reliability analysis and the outer optimization are performed separately in a sequential manner. Since the outer optimizer must perform a large number of iterations to find the optimized shape and size of structure, the computational cost is very high. Therefore, during the course of this research, new multilevel reliability optimization methods are developed that divide the design domain into two sub-spaces to be employed in an iterative procedure: one of the shape design variables, and the other of the size design variables. In each iteration, the probability constraints are converted into equivalent deterministic constraints using reliability analysis and then implemented in the deterministic optimization problem. The framework is first tested on a short column with cross-sectional properties as design variables, the applied loads and the yield stress as random variables. In addition, two cases of curvilinearly stiffened panels subjected to uniform shear and compression in-plane loads, and two cases of curvilinearly stiffened panels subjected to shear and compression loads that vary in linear and quadratic manner are presented.