• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.11
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• pp.3008-3025
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• 2012

In this paper, the downlink resource allocation of OFDMA system with decode-and-forward (DF) relaying is investigated. A non-convex optimization problem maximizing system throughput with users' satisfaction constraints is formulated with joint relay selection, subcarrier assignment and power allocation. We first transform it to a standard convex problem and then solve it by dual decomposition. In particular, an Optimal resource allocation scheme With Time-sharing (OWT) is proposed with combination of relay selection, subcarrier allocation and power control. Due to its poor adaption to the fast-varying environment, an improved version with subcarrier Monopolization (OWM) is put forward, whose performance promotes about 20% compared with that of OWT in the fast-varying vehicular environment. In fact, OWM is the special case of OWT with binary time-sharing factor and OWT can be seen as the tight upper bound of the OWM. To the best of our knowledge, such algorithms and their relation have not been accurately investigated in cooperative OFDMA networks in the literature. Simulation results show that both the system throughput and the users' satisfaction of the proposed algorithms outperform the traditional ones.

• Journal of the Korea Computer Graphics Society
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• v.20 no.3
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• pp.9-17
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• 2014

This paper presents a method to retarget a source motion to the rig-space parameter for a target character that can be equipped with a complex rig structure as used in traditional animation pipelines. Our solution allows the animators to edit the retargeted motion easily and intuitively as they can work with the same rig parameters that have been used for keyframe animation. To acheive this, we analyze the correspondence between the source motion space and the target rig-space, followed by performing non-linear optimization for the motion retargeting to target rig-space. We observed the general workflow practiced by animators and apply this process to the optimization step.

• Journal of Mechanical Science and Technology
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• v.14 no.11
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• pp.1206-1215
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• 2000

This paper introduces the application of evolutionary fuzzy modeling (EFM) in constructing global function approximations to subsequent use in non-gradient based optimizations strategies. The fuzzy logic is employed for express the relationship between input training pattern in form of linguistic fuzzy rules. EFM is used to determine the optimal values of membership function parameters by adapting fuzzy rules available. In the study, genetic algorithms (GA's) treat a set of membership function parameters as design variables and evolve them until the mean square error between defuzzified outputs and actual target values are minimized. We also discuss the enhanced accuracy of function approximations, comparing with traditional response surface methods by using polynomial interpolation and back propagation neural networks in its ability to handle the typical benchmark problems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.43
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• pp.139-144
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• 1997

The objective of this study is to establish an actual optimization strategy for the traditional multi-stage distribution system which consists of factory warehouse, central distribution warehouse, and regional distribution warehouse under the basic assumption of distribution system. A minimization algorithm of total operating cost in a multi-stage distribution system was developed by expanding the previously existing algorithm through consideration of additional transportation environment. Alternative non-linear transportation costs for the same travel distance can be applied for the multi-stage distribution system by estimating the corresponding characteristic values through the collection of the actual data representing the change of transportation circumstances.

• Structural Monitoring and Maintenance
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• v.10 no.2
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• pp.175-190
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• 2023

This article aims to quantitatively predict the snowmelt in extreme cold regions, considering a combination of grayscale and neural models. The traditional non-equidistant GM(1,1) prediction model is optimized by adjusting the time-distance weight matrix, optimizing the background value of the differential equation and optimizing the initial value of the model, and using the BP neural network for the first. The adjusted ice forecast model has an accuracy of 0.984 and posterior variance and the average forecast error value is 1.46%. Compared with the GM(1,1) and BP network models, the accuracy of the prediction results has been significantly improved, and the quantitative prediction of the ice sheet is more accurate. The monitoring and maintenance of the structure by quantitative prediction model by gray models was clearly demonstrated in the model.

• Journal of Distribution Science
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• v.16 no.7
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• pp.35-45
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• 2018

Purpose - The study investigates the impact of consignment on the economic performance in the supply chain with three stages. Through the analysis on distinct forms of consignment application, this study intends to answer to the question of how the consignment should be used in the multi-stage supply chain. Research design, data, and methodology - The proposed mathematical model represents the supply chain system with a manufacturer, a wholesaler, and a retailer. Three different forms of consignment application are considered depending on which stages adapt the consignment, and their system profits are compared with the traditional non-consignment system in numerical examples. Results - The numerical examples show that the serial consignment application performs better than any other forms of consignment as well as the non-consignment system. The additional analysis indicates that the system profit is significantly sensitive to the consignment rate. Conclusions - The outcome of this study implies the potential of consignment to improve the system performance even in the multi-stage supply chain system. Meanwhile, each supply chain member's preference to the specific form of consignment application could be different depending on which stage he has. All the supply chain members should jointly determine the appropriate consignment rates to obtain the best system performance.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.306-317
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• 2021

The unbalance between system ergodic sum rate and high fairness is one of the key issues affecting the performance of non-orthogonal multiple access (NOMA) system. To solve the problem, this paper proposes a power allocation algorithm to realize the ergodic sum rate maximization of NOMA system. The scheme is mainly achieved by the construction algorithm of fair model based on α fair utility function and the optimal solution algorithm based on the interior point method of penalty function. Aiming at the construction of fair model, the fair target is added to the traditional power allocation model to set the reasonable target function. Simultaneously, the problem of ergodic sum rate and fairness in power allocation is weighed by adjusting the value of α. Aiming at the optimal solution algorithm, the interior point method of penalty function is used to transform the fair objective function with unequal constraints into the unconstrained problem in the feasible domain. Then the optimal solution of the original constrained optimization problem is gradually approximated within the feasible domain. The simulation results show that, compared with NOMA and time division multiple address (TDMA) schemes, the proposed method has larger ergodic sum rate and lower Fairness Index (FI) values.

• Steel and Composite Structures
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• v.44 no.6
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• pp.867-882
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• 2022

Predicting the compressive strength of concrete (CSoC) is of high significance in civil engineering. The CSoC is a highly dependent and non-linear parameter that requires powerful models for its simulation. In this work, two novel optimization techniques, namely evaporation rate-based water cycle algorithm (ER-WCA) and equilibrium optimizer (EO) are employed for optimally finding the parameters of a multi-layer perceptron (MLP) neural processor. The efficiency of these techniques is examined by comparing the results of the ensembles to a conventionally trained MLP. It was observed that the ER-WCA and EO optimizers can enhance the training accuracy of the MLP by 11.18 and 3.12% (in terms of reducing the root mean square error), respectively. Also, the correlation of the testing results climbed from 78.80% to 82.59 and 80.71%. From there, it can be deduced that both ER-WCA-MLP and EO-MLP can be promising alternatives to the traditional approaches. Moreover, although the ER-WCA enjoys a larger accuracy, the EO was more efficient in terms of complexity, and consequently, time-effectiveness.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.22 no.12
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• pp.859-866
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• 2010

This study examined the economics of a solar water heating system for an office building using life cycle cost (LCC) optimization simulations. The numerical simulations were conducted with TRNSYS and GenOpt employing the Hooke-Jeeves algorithm. The solar collector area, slope, mass flow rate per collector area and storage tank volume were selected as the main design parameters of the solar water heating system. The LCC optimization simulations of the system were carried out for cases where water temperature was $60^{\circ}C$ and $50^{\circ}C$. The results showed that for water temperature at $60^{\circ}C$ and $50^{\circ}C$ the collector area could be decreased by 17% and 28%, storage tank volume could be decreased by 49% and 54%, and mass flow rate per collector area increased by 5% and 9% respectively compared to a non-optimized system. The LCC of the system was reduced by 4% for $60^{\circ}C$ and 7% for $50^{\circ}C$. The initial installation cost of the system was reduced by 24% for $60^{\circ}C$ and 34% for $50^{\circ}C$. However, the operating cost of the system increased by 16% for $60^{\circ}C$ and 36% for $50^{\circ}C$ compared to a traditional solar water heating system.

• Journal of Intelligence and Information Systems
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• v.3 no.2
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• pp.1-9
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• 1997

These papers propose an evolutionary algorithm for re-using output of waste fuel of light water reactor system in nuclear power plants. Evolutionary algorithm is useful for optimization of the large space problem. The wastes contain several re-useable elements, and they should be carefully selected and blended to satisfy requirements as input material to the heavy water nuclear reactor system. This problem belongs to a NP-hard like the 0/1 Knapsack problem. Two evolutionary strategies are used as a, pp.oximation algorithms in the highly constrained combinatorial optimization problem. One is the traditional strategy, using random operator with evaluation function, and the other is heuristic based search that uses the vector operator reducing between goal and current status. We also show the method, which performs the feasible teat and solution evaluation by using the vectorized data in problem. Finally, We compare the simulation results of using random operator and vector operator for such combinatorial optimization problems.