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Topology and size optimization of truss structures using an improved crow search algorithm

  • Mashayekhi, Mostafa;Yousefi, Roghayeh
    • Structural Engineering and Mechanics
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    • v.77 no.6
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    • pp.779-795
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    • 2021
  • In the recent decades, various optimization algorithms have been considered for the optimization of structures. In this research, a new enhanced algorithm is used for the size and topology optimization of truss structures. This algorithm, which is obtained from the combination of Crow Search Algorithm (CSA) and the Cellular Automata (CA) method, is called CA-CSA method. In the first iteration of the CA-CSA method, some of the best designs of the crow's memory are first selected and then located in the cells of CA. Then, a random cell is selected from CA, and the best design is chosen from the selected cell and its neighborhood; it is considered as a "local superior design" (LSD). In the optimization process, the LSD design is used to modify the CSA method. Numerical examples show that the CA-CSA method is more effective than CSA in the size and topology optimization of the truss structures.

Nonlinear optimization algorithm using monotonically increasing quantization resolution

  • Jinwuk Seok;Jeong-Si Kim
    • ETRI Journal
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    • v.45 no.1
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    • pp.119-130
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    • 2023
  • We propose a quantized gradient search algorithm that can achieve global optimization by monotonically reducing the quantization step with respect to time when quantization is composed of integer or fixed-point fractional values applied to an optimization algorithm. According to the white noise hypothesis states, a quantization step is sufficiently small and the quantization is well defined, the round-off error caused by quantization can be regarded as a random variable with identically independent distribution. Thus, we rewrite the searching equation based on a gradient descent as a stochastic differential equation and obtain the monotonically decreasing rate of the quantization step, enabling the global optimization by stochastic analysis for deriving an objective function. Consequently, when the search equation is quantized by a monotonically decreasing quantization step, which suitably reduces the round-off error, we can derive the searching algorithm evolving from an optimization algorithm. Numerical simulations indicate that due to the property of quantization-based global optimization, the proposed algorithm shows better optimization performance on a search space to each iteration than the conventional algorithm with a higher success rate and fewer iterations.

Approach toward footstep planning considering the walking period: Optimization-based fast footstep planning for humanoid robots

  • Lee, Woong-Ki;Kim, In-Seok;Hong, Young-Dae
    • ETRI Journal
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    • v.40 no.4
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    • pp.471-482
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    • 2018
  • This paper proposes the necessity of a walking period in footstep planning and details situations in which it should be considered. An optimization-based fast footstep planner that takes the walking period into consideration is also presented. This footstep planner comprises three stages. A binary search is first used to determine the walking period. The front stride, side stride, and walking direction are then determined using the modified rapidly-exploring random tree algorithm. Finally, particle swarm optimization (PSO) is performed to ensure feasibility without departing significantly from the results determined in the two stages. The parameters determined in the previous two stages are optimized together through the PSO. Fast footstep planning is essential for coping with dynamic obstacle environments; however, optimization techniques may require a large computation time. The two stages play an important role in limiting the search space in the PSO. This framework enables fast footstep planning without compromising on the benefits of a continuous optimization approach.

Effectiveness of Nutrition Intervention : Systematic Review & Meta-Analysis (임상영양서비스의 효과성 평가 : 체계적 검토와 메타분석을 통하여)

  • 이해영;김현아;양일선;남정모;박은철
    • Korean Journal of Community Nutrition
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    • v.9 no.1
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    • pp.81-89
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    • 2004
  • A systematic review and meta-analysis of the literatures was conducted to evaluate the effectiveness of nutrition intervention by dietitian. The 31 studies that were all randomized controlled trials, were identified from computerized search of published researches on MEDLINE, Embase database until January, 2003 and review of reference lists. The main search terms were the combination “dietitian”, “dietary intervention”, “nutrition intervention” or “nutritional intervention” and “effectiveness”. The subgroup analysis was performed by the publication year, quality score, type of disease, content of intervention, intervention provider, type of intervention (nutritional counseling and education/nutrilion counseling and education + diet modification), method of intervention (individual/group/individual + group) and follow-up period. Two reviewers independently selected trials for inclusion, assessed the quality and extracted the data. The meta-analysis of 31 studies based on the random effect model showed that the medical nutrition therapy was significantly effective in treating the diseases (effect size 0.1715 : 95% confidence interval 0.0938-0.2491). This study showed the clear evidence of the effectiveness of nutrition intervention through the meta-analysis. So the nutrition intervention by dietitian should be recommended and recognized as the effective therapy of treating the diseases. Also the nutrition intervention should be conducted regularly to maintain the effectiveness of the nutrition intervention. The nutrition intervention was significantly effective in treating the diseases on the random effect model (effect size 0.1715 : 95% confidence interval 0.0938-0.2491).

A Study on the Prediction of Optimized Injection Molding Condition using Artificial Neural Network (ANN) (인공신경망을 활용한 최적 사출성형조건 예측에 관한 연구)

  • Yang, D.C.;Lee, J.H.;Yoon, K.H.;Kim, J.S.
    • Transactions of Materials Processing
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    • v.29 no.4
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    • pp.218-228
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    • 2020
  • The prediction of final mass and optimized process conditions of injection molded products using Artificial Neural Network (ANN) were demonstrated. The ANN was modeled with 10 input parameters and one output parameter (mass). The input parameters, i.e.; melt temperature, mold temperature, injection speed, packing pressure, packing time, cooling time, back pressure, plastification speed, V/P switchover, and suck back were selected. To generate training data for the ANN model, 77 experiments based on the combination of orthogonal sampling and random sampling were performed. The collected training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. Grid search and random search method were used to find the optimized hyper-parameter of the ANN model. After the training of ANN model, optimized process conditions that satisfied the target mass of 41.14 g were predicted. The predicted process conditions were verified through actual injection molding experiments. Through the verification, it was found that the average deviation in the optimized conditions was 0.15±0.07 g. This value confirms that our proposed procedure can successfully predict the optimized process conditions for the target mass of injection molded products.

Optimization of inlet concentration condition for uniform film growth in a cylindrical CVD chamber (원통형 화학증착로에서 균일한 박막형성을 위한 입구 농도분포의 최적화)

  • Jo, Won-Guk;Choe, Do-Hyeong;Kim, Mun-Eon
    • Transactions of the Korean Society of Mechanical Engineers B
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    • v.22 no.2
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    • pp.173-183
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    • 1998
  • An optimization procedure to find the inlet concentration profile that yields the most uniform deposition rate in a cylindrical CVD chamber has been developed. Assuming that the chemical reaction time is negligibly small, a SIMPLE based finite-volume method is adopted to solve the fully elliptic equations for momentum, temperature, and concentration. The inlet concentration profile is expressed by a linear combination of Chebyshev polynomials and the coefficients of which are determined by the local random search technique. It is shown that the present method is very effective in improving the uniformity of the deposition rate, especially when Re is high and/or the wafer is placed close to the inlet. The optimal profiles have been obtained for various Re, Gr, and geometry combinations.

Accurate dam inflow predictions using SWLSTM (정확한 댐유입량 예측을 위한 SWLSTM 개발)

  • Kim, Jongho;Tran, Trung Duc
    • Proceedings of the Korea Water Resources Association Conference
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    • 2021.06a
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    • pp.292-292
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    • 2021
  • 최근 데이터 과학의 획기적인 발전으로 딥러닝(Deep Learning) 알고리즘이 개발되어 다양한 분야에 널리 적용되고 있다. 본 연구에서는 인공신경망 중 하나인 LSTM(Long-Short Term Memory) 네트워크를 기반으로 정확한 댐유입량 예측을 수행하는 SWLSTM 모델을 제안하였다. SWLSM은 모델의 정확도를 개선하기 위해 세 가지 주요 아이디어를 채택하였다. (1) 통계적 속성 (PACF) 및 교차 상관 함수(CCF)를 사용하여 적절한 입력 변수와 시퀀스 길이를 결정하였다. (2) 선택된 입력 예측 변수 시계열을 웨이블릿 변환(WT)을 사용하여 하위 시계열로 분해한다. (3) k-folds cross validation 및 random search 기법을 사용하여 LSTM의 하이퍼 매개변수들을 효율적으로 최적화하고 검증한다. 제안된 SWLSTM의 효과는 한강 유역 5개 댐의 시단위/일단위/월단위 유입량을 예측하고 과거 자료와 비교함으로써 검증하였다. 모델의 정확도는 다양한 평가 메트릭(R2, NSE, MAE, PE)이 사용하였으며, SWLSTM은 모든 경우에서 LSTM 모델을 능가하였다. (평가 지표는 약 30 ~ 80 % 더 나은 성능을 보여줌). 본 연구의 결과로부터, 올바른 입력 변수와 시퀀스 길이의 선택이 모델 학습의 효율성을 높이고 노이즈를 줄이는 데 효과적임을 확인하였다. WT는 홍수 첨두와 같은 극단적인 값을 예측하는 데 도움이 된다. k-folds cross validation 및 random search 기법을 사용하면 모델의 하이퍼 매개변수를 효율적으로 설정할 수 있다. 본 연구로부터 댐 유입량을 정확하게 예측한다면 정책 입안자와 운영자가 저수지 운영, 계획 및 관리에 도움이 될 것이다.

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Idle Channel Search Scheme for Cognitive Radio Systems Based on Probability Estimation of Channel Idleness (채널 유휴 확률 추정을 이용한 인지 라디오 시스템의 유휴채널 탐색 기법)

  • Son, Min-Sung;Shin, Oh-Soon
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.36 no.5A
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    • pp.450-456
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    • 2011
  • In this paper, idle channel search schemes based on spectrum sensing are proposed for cognitive radio systems with multiple channels. Specifically, we propose a scheme for determining the order of sensing for multiple channels, for which the probability of each channel being idle is estimated every search interval. By performing sensing in the descending order of the probabilities, the time required for searching idle channels is expected to decrease. In addition, we combine the proposed scheme with a user grouping scheme to further improve the sensing performance. Simulation results show that the user grouping reduces the search time, although it degrades the reliability of detection. The proposed search scheme based on probability estimation of channel idleness is found to reduce the search time significantly as compared to the conventional random search scheme. We apply both the proposed search scheme and user grouping scheme to a cognitive radio system to validate the overall performance.

Design Optimization for Loop Heat Pipe Using Tabu Search (Tabu Search를 이용한 Loop Heat Pipe의 최적설계에 관한 연구)

  • Park, Yong-Jin;Yun, Su-Hwan;Ku, Yo-Cheun;Lee, Dong-Ho
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.37 no.8
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    • pp.737-743
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    • 2009
  • Design optimization process and results of Loop Heat Pipe(LHP) using Tabu Search have been presented in this study. An objective of optimization is to reduce a mass of the LHP with satisfying operating temperature of a Lithium Ion battery onboard an aircraft. The battery is assumed to be used as power supply of air borne high energy laser system because of its high specific energy. The analytical models are based on a steady state mathematical model and the design optimization is performed using a Meta Model and Tabu Search. As an optimization results, the Tabu search algorithm guarantees global optimum with small computation time. Due to searching by random numbers, initial value is dominant factor to search global optimum. The optimization process could reduce the mass of the LHP which express the same performance as an published LHP.

Differential Evolution Algorithm based on Random Key Representation for Traveling Salesman Problems (외판원 문제를 위한 난수 키 표현법 기반 차분 진화 알고리즘)

  • Lee, Sangwook
    • The Journal of the Korea Contents Association
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    • v.20 no.11
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    • pp.636-643
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    • 2020
  • The differential evolution algorithm is one of the meta-heuristic techniques developed to solve the real optimization problem, which is a continuous problem space. In this study, in order to use the differential evolution algorithm to solve the traveling salesman problem, which is a discontinuous problem space, a random key representation method is applied to the differential evolution algorithm. The differential evolution algorithm searches for a real space and uses the order of the indexes of the solutions sorted in ascending order as the order of city visits to find the fitness. As a result of experimentation by applying it to the benchmark traveling salesman problems which are provided in TSPLIB, it was confirmed that the proposed differential evolution algorithm based on the random key representation method has the potential to solve the traveling salesman problems.