• Smart Structures and Systems
• /
• 제18권6호
• /
• pp.1233-1250
• /
• 2016

Cracks in plate-like structures are some of the main reasons for destruction of the entire structure. In this study, a novel two-stage methodology is proposed for damage detection of flexural plates using an optimized artificial neural network. In the first stage, location of damages in plates is investigated using curvature-moment and curvature-moment derivative concepts. After detecting the damaged areas, the equations for damage severity detection are solved via Bat Algorithm (BA). In the second stage, in order to efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, multiple damage location assurance criterion index based on the frequency change vector of structures are evaluated using properly trained cascade feed-forward neural network (CFNN) as a surrogate model. In order to achieve the most generalized neural network as a surrogate model, its structure is optimized using binary version of BA. To validate this proposed solution method, two examples are presented. The results indicate that after determining the damage location based on curvature-moment derivative concept, the proposed solution method for damage severity detection leads to significant reduction of computational time compared with direct finite element method. Furthermore, integrating BA with the efficient approximation mechanism of finite element model, maintains the acceptable accuracy of damage severity detection.

• 대한설비공학회:학술대회논문집
• /
• 대한설비공학회 2008년도 동계학술발표대회 논문집
• /
• pp.119-124
• /
• 2008

Recently, focus has been drawn on natural refrigerants due to increasing concern on global warming. As a consequence, CO2 systems such as a heat pump water heater using CO2 as a refrigerant are rapidly growing on the market. Currently, rolling piston rotary compressors are widely used for CO2 heating and/or refrigeration systems. There are several ways of realizing gas compression structure. They are single stage compression with single cylinder, single stage compression with two cylinders, and two stage compression with two cylinders. In this paper, computer simulation program which was validated for a single stage rotary compressor with one cylinder has been extended for a single stage, two cylinder rotary type. Numerical investigation has been made on optimal design for the cylinder configuration using the extended simulation program. For a single stage two cylinder rotary compressor having a displacement volume of 4 cc for each cylinder, compressor efficiency has been found to be maximum when the cylinder radius and height are 31mm and 10mm, respectively.

• 한국전자파학회논문지
• /
• 제27권1호
• /
• pp.20-26
• /
• 2016

본 논문에서는 GaN-HEMT를 이용하여 X-대역에서 동작하는 이단으로 구성된 전력증폭기를 설계 및 제작하였다. 높은 전력 이득을 얻기 위해 간단한 구조의 중간 단 정합 네트워크를 통해 이단으로 구성하였다. 3D EM 시뮬레이션을 통하여 본드와이어 인덕턴스와 기생 캐패시턴스를 예측하였다. 본드와이어 인덕턴스를 줄임으로써 정합 네트워크의 Q(quality-factor)를 최소화하여 대역 특성을 향상시켰다. 제작된 전력증폭기는 40 V의 동작 전압을 인가하였으며, 8.1~8.5 GHz에서 16 dB 이상의 전력 이득, 42.5 dBm 이상의 출력 전력, 35 % 이상의 효율 특성을 나타냈다.

• 한국수자원학회논문집
• /
• 제36권2호
• /
• pp.143-152
• /
• 2003

본 논문은 3개의 강우관측망과 최적 강우관측망을 토대로 호우의 면적 평균강우량 산정용 섬진유역 강우관측망의 개량안을 제안했다. 강우관측망 설계문제는 면적평균강우량추정분산으로 나타내지는 정확도와 자료 수집비로 구성되는 목적함수를 최소화하는 것이다. 익히 알려진 분산경감법으로는 최소화 알고리즘인 SATS기법이 채용되었다. 첫 단계에서, 비용에 부과된 2개의 교환계수값에 따라 최적 관측망과 대안관측망이 얻어졌다. 다음 단계에서, 최적으로 선정된 우량국에 기존우량국이 인접해 있을 경우 그 기존국이 포함되는 준 최적관측망과 준 대안관측망을 제안했다.

• International Journal of Internet, Broadcasting and Communication
• /
• 제14권1호
• /
• pp.53-63
• /
• 2022

In the paper, we propose a TSCH-based scheduling scheme for IEEE 802.15.4e, which is able to perform the scheduling of its own network by avoiding collision from interference network cluster (INC). Firstly, we model a bipartite graph structure for presenting the slot-frame (channel-slot assignment) of TSCH. Then, based on the bipartite graph edge weight, we utilize the Hungarian assignment algorithm to implement a scheduling scheme. We have employed two features (maximization and minimization) of the Hungarian-based assignment algorithm, which can perform the assignment in terms of minimizing the throughput of INC and maximizing the throughput of own network. Further, in this work, we called the scheme "dual-stage Hungarian-based assignment algorithm". Furthermore, we also propose deep learning (DL) based deep neural network (DNN)scheme, where the data were generated by the dual-stage Hungarian-based assignment algorithm. The performance of the DNN scheme is evaluated by simulations. The simulation results prove that the proposed DNN scheme providessimilar performance to the dual-stage Hungarian-based assignment algorithm while providing a low execution time.

• 한국방송∙미디어공학회:학술대회논문집
• /
• 한국방송∙미디어공학회 2020년도 추계학술대회
• /
• pp.113-117
• /
• 2020

Images were taken under various weather such as rain, haze, snow often show low visibility, which can dramatically decrease accuracy of some tasks in computer vision: object detection, segmentation. Besides, previous work to enhance image usually downsample the image to receive consistency features but have not yet good upsample algorithm to recover original size. So, in this research, we jointly implement removal streak in heavy rain image and super resolution using a deep network. We put forth a 2-stage network: a multi-model network followed by a refinement network. The first stage using rain formula in the single image and two operation layers (addition, multiplication) removes rain streak and noise to get clean image in low resolution. The second stage uses refinement network to recover damaged background information as well as upsample, and receive high resolution image. Our method improves visual quality image, gains accuracy in human action recognition task in datasets. Extensive experiments show that our network outperforms the state of the art (SoTA) methods.

• 경영과학
• /
• 제27권3호
• /
• pp.15-31
• /
• 2010

We consider a reverse logistics network design problem for recycling. The problem consists of three stages of transportation. In the first stage products are transported from retrieval centers to disassembly centers. In the second stage disassembled modules are transported from disassembly centers to processing centers. Finally, in the third stage modules are transported from either processing centers or a supplier to a manufacturer, a recycling site, or a disposal site. The objective is to design a network which minimizes the total transportation cost. We design a cooperative coevolutionary algorithm to solve the problem. First, the problem is decomposed into three subproblems each of which corresponds to a stage of transportation. For subproblems 1 and 2, a population of chromosomes is constructed. Each chromosome in the population is coded as a permutation of integers and an algorithm which decodes a chromosome is suggested. For subproblem 3, an heuristic algorithm is utilized. Then, a performance evaluation procedure is suggested which combines the chromosomes from each of two populations and the heuristic algorithm for subproblem 3. An experiment was carried out using test problems. The experiments showed that the cooperative coevolutionary algorithm generally tends to show better performances than the previous genetic algorithm as the problem size gets larger.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2000년도 하계학술대회 논문집 A
• /
• pp.519-521
• /
• 2000

Network reconfiguration is an operation task, and consists in the determination of the switching operations such to reach the minimum loss conditions of the distribution network. In this paper, an effective heuristic based switch scheme for loss minimization is given for the optimization of distribution loss reduction and a solution approach is presented. The solution algorithm for loss minimization has been developed based on the two stage solution methodology. The first stage of this solution algorithm sets up a decision tree which represent the various switching operations available, the second stage applies a proposed technique called cyclic best first search. Therefore, the solution algorithm of proposed method can determine on-off switch statuses for loss reduction, with a minimum computational effort.

• ETRI Journal
• /
• 제37권4호
• /
• pp.657-666
• /
• 2015

We propose a two-stage resource allocation algorithm for the high link utilization of an orthogonal frequency-division multiplexing passive optical network (OFDM-PON). An OFDM-PON is assumed to use a synchronous frame structure in supporting service differentiation. In distributing resources, the proposed algorithm first allocates a time window for each optical network unit (ONU), and then it arranges a subchannel, which is a group of subcarriers. This algorithm needs to satisfy two constraints. First, computations for the resource allocation should be done using a frame unit. Second, an ONU has to use a single subchannel to send upstream data for multiple services within a frame duration. We show through a computer simulation that the proposed algorithm improves the link utilization.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
• /
• 제11권3호
• /
• pp.99-104
• /
• 2022

대부분 첨단 동작 인식 컨볼루션 네트워크는 RGB 스트림과 광학 흐름 스트림, 양 스트림 아키텍처를 기반으로 하고 있다. RGB 프레임 스트림은 모양 특성을 나타내고 광학 흐름 스트림은 동작 특성을 해석한다. 그러나 광학 흐름은 계산 비용이 매우 높기 때문에 동작 인식 시간에 지연을 초래한다. 이에 양 스트림 네트워크와 교사-학생 아키텍처에서 영감을 받아 행동 인식을 위한 새로운 네트워크 디자인을 개발하였다. 제안 신경망은 두 개의 하위 네트워크로 구성되어있다. 즉, 교사 역할을 하는 광학 흐름 하위 네트워크와 학생 역할을 하는 RGB 프레임 하위 네트워크를 연결하였다. 훈련 단계에서 광학 흐름의 특징을 추출하고 교사 서브 네트워크를 훈련시킨 다음 그 특징을 학생 서브 네트워크를 훈련시키기 위한 기준선으로 지정하여 학생 서브 네트워크에 전송한다. 테스트 단계에서는 광학 흐름을 계산하지 않고 대기 시간이 줄어들도록 학생 네트워크만 사용한다. 제안 네트워크는 실험을 통하여 정확도 면에서 일반 이중 스트림 아키텍처에 비해 높은 정확도를 보여주는 것을 확인하였다.