• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.6
• /
• pp.2060-2073
• /
• 2022

Cloud Computing permits users to access vast amounts of services of computing power in a virtualized environment. Providing secure services is essential. There are several problems to real-world optimization that are dynamic which means they tend to change over time. For these types of issues, the goal is not always to identify one optimum but to keep continuously adapting to the solution according to the change in the environment. The problem of scheduling in Cloud where new tasks keep coming over time is unique in terms of dynamic optimization problems. Until now, there has been a large majority of research made on the application of various Evolutionary Algorithms (EAs) to address the issues of dynamic optimization, with the focus on the maintenance of population diversity to ensure the flexibility for adapting to the changes in the environment. Generally, trust refers to the confidence or assurance in a set of entities that assure the security of data. In this work, a dynamic optimization technique is proposed to find an optimal trust weights in cloud during scheduling.

• Knowledge Management Research
• /
• v.21 no.1
• /
• pp.27-40
• /
• 2020

Response surface methodology (RSM) empirically studies the relationship between a response variable and input variables in the product or process development phase. The ultimate goal of RSM is to find an optimal condition of the input variables that optimizes (maximizes or minimizes) the response variable. RSM can be seen as a knowledge management tool in terms of creating and utilizing data, information, and knowledge about a product production and service operations. In the field of product or process development, most real-world problems often involve a simultaneous consideration of multiple response variables. This is called a multiple response surface (MRS) problem. Various approaches have been proposed for MRS optimization, which can be classified into loss function approach, priority-based approach, desirability function approach, process capability approach, and probability-based approach. In particular, the loss function approach is divided into univariate and multivariate approaches at large. This paper focuses on the univariate approach. The univariate approach first obtains the mean square error (MSE) for individual response variables. Then, it aggregates the MSE's into a single objective function. It is common to employ the weighted sum or the Tchebycheff metric for aggregation. Finally, it finds an optimal condition of the input variables that minimizes the objective function. When aggregating, the relative weights on the MSE's should be taken into account. However, there are few studies on how to determine the weights systematically. In this study, we propose an interactive procedure to determine the weights through considering a decision maker's preference. The proposed method is illustrated by the 'colloidal gas aphrons' problem, which is a typical MRS problem. We also discuss the extension of the proposed method to the weighted MSE (WMSE).

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.47 no.11
• /
• pp.7-13
• /
• 2010

For swiftness and economic feasibility of parcel delivery, optimal location of mail exchanging centers considering not only section delivery distance but also some weights like the number of delivery vehicles are necessary. In this paper, a mechanism with section delivery distance and vehicle number for locating of mail distribution centers which connect some mail centers of major cities in Republic Korea by applying GOSST theory is proposed. This mechanism for locating mail exchange centers can make the total delivery distance of postal matters less than not only present method which assigns Daejun as mail exchange center, but also Steiner tree method which does not consider weights like number of delivery vehicles. The mechanism is good for protecting the environment as well as rapid and economic delivery. In experiment, the proposed mechanism could curtail total delivery distance by 2.52 percentages compared with Steiner tree method and by 6.66 percentages compared with present method. The mechanism may be used in electric power transmission routing, distribution line topology design, power relay station locating where various voltages and other weights should be considered.

• Journal of Korean Society for Quality Management
• /
• v.50 no.4
• /
• pp.793-810
• /
• 2022

Purpose: As improving the search performance of aviation security equipment is considered essential, this study proposes the need for research on how to find an optimized test sequence that can reduce test time and operator power during the search function test of explosive detection systems. We derive the weights and work difficulty adjustment factor required to find the optimized test order. Methods: First, after setting the test factors, the time of each test and the difficulty scale determined by the worker who performed the test directly were used to derive weights. Second, the work difficulty adjustment coefficient was determined by combining the basic weight adjustment factor and corresponding to the body part used by the test using objective rating. Then the final standard time was derived by calculating the additional weights for the changeability of the test factors. Results: The order in which the final standard time is minimized when 50 tests are performed was defined as the optimized order. 50 tests should be conducted without duplication and the optimal order of tests was obtained when compared to previously numbered tests. As a result of minimizing the total standard time by using Excel's solver parameters, it was reduced by 379.14 seconds, about 6.32 minutes. Conclusion: We tried to express it in mathematical formulas to propose a method for setting an optimized test sequence even when testing is performed on other aviation security equipment. As a result, the optimal test order was derived from the operator's point of view, and it was demonstrated by minimizing the total standard time.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.18 no.5
• /
• pp.217-223
• /
• 2023

In this paper, we propose a new model for the conditional generation of music, considering key and rhythm, fundamental elements of music. MIDI sheet music is converted into a WAV format, which is then transformed into a Mel Spectrogram using the Short-Time Fourier Transform (STFT). Using this information, key and rhythm details are classified by passing through two Convolutional Neural Networks (CNNs), and this information is again fed into the Music Transformer. The key and rhythm details are combined by differentially multiplying the weights and the embedding vectors of the MIDI events. Several experiments are conducted, including a process for determining the optimal weights. This research represents a new effort to integrate essential elements into music generation and explains the detailed structure and operating principles of the model, verifying its effects and potentials through experiments. In this study, the accuracy for rhythm classification reached 94.7%, the accuracy for key classification reached 92.1%, and the Negative Likelihood based on the weights of the embedding vector resulted in 3.01.

• Proceedings of the IEEK Conference
• /
• 2002.07a
• /
• pp.486-489
• /
• 2002

This paper proposes a tabu search (TS) algorithm to optimal weight selection in design of robust H$_{\infty}$ power system stabilize. (PSS), In H$_{\infty}$ control design, the weight selection and the representation of system uncertainties are the major difficulties. To cope with these problems, TS is employed to automatically search for the optimal weights. On the other hand, the normalized coprime factorization (NCF) is used. The H$_{\infty}$ controller can be directly developed without ${\gamma}$-iteration. Also, the pole-zero cancellation phenomena are prevented. The performance and robustness of the proposed PSS under different loading conditions are investigated in comparison with a robust tuned PSS by examining the case of a single machine infinite bus (SMIB) system. The simulation results illustrate the effectiveness and robustness of the proposed PSS.

• The Journal of the Korea Contents Association
• /
• v.4 no.3
• /
• pp.99-105
• /
• 2004

This paper proposes optimal learning rates in the gradient descent training of multilayer perceptrons, which are a separate learning rate for weights associated with each neuron and a separate one for assigning virtual hidden targets associated with each training pattern Effectiveness of the proposed error function was demonstrated for a handwritten digit recognition and an isolated-word recognition tasks and very fast learning convergence was obtained.

• Structural Engineering and Mechanics
• /
• v.88 no.6
• /
• pp.535-549
• /
• 2023

Deep extreme learning machine (DELM) and multi-verse optimization algorithms (MVO) are hybridized for designing an optimal and adaptive control framework for uncertain buildings. In this approach, first, a robust model predictive control (RMPC) scheme is developed to handle the problem uncertainty. The optimality and adaptivity of the proposed controller are provided by the optimal determination of the tunning weights of the linear programming (LP) cost function for clustered external loads using the MVO. The final control policy is achieved by collecting the clustered data and training them by DELM. The efficiency of the introduced control scheme is demonstrated by the numerical simulation of a ten-story benchmark building subjected to earthquake excitations. The results represent the capability of the proposed framework compared to robust MPC (RMPC), conventional MPC (CMPC), and conventional DELM algorithms in structural motion control.

• Journal of the Korea Society for Simulation
• /
• v.32 no.3
• /
• pp.67-74
• /
• 2023

A semiconductor fabrication facility(FAB) is one of the most capital-intensive and large-scale manufacturing systems which operate under complex and uncertain constraints through hundreds of fabrication steps. To improve fab performance with intuitive scheduling, practitioners have used weighted-sum scheduling. Since the determination of weights in the scheduling significantly affects fab performance, they often rely on simulation-based decision making for obtaining optimal weights. However, a large-scale and high-fidelity simulation generally is time-intensive to evaluate with an exhaustive search. In this study, we investigated three sampling methods (i.e., Optimal latin hypercube sampling(OLHS), Genetic algorithm(GA), and Decision tree based sequential search(DSS)) for the optimization. Our simulation experiments demonstrate that: (1) three methods outperform greedy heuristics in performance metrics; (2) GA and DSS can be promising tools to accelerate the decision-making process.

• Journal of information and communication convergence engineering
• /
• v.12 no.4
• /
• pp.215-220
• /
• 2014

We consider quantization optimized for distributed estimation, where a set of sensors at different sites collect measurements on the parameter of interest, quantize them, and transmit the quantized data to a fusion node, which then estimates the parameter. Here, we propose an iterative quantizer design algorithm with a weighted distance rule that allows us to reduce a system-wide metric such as the estimation error by constructing quantization partitions with their optimal weights. We show that the search for the weights, the most expensive computational step in the algorithm, can be conducted in a sequential manner without deviating from convergence, leading to a significant reduction in design complexity. Our experments demonstrate that the proposed algorithm achieves improved performance over traditional quantizer designs. The benefit of the proposed technique is further illustrated by the experiments providing similar estimation performance with much lower complexity as compared to the recently published novel algorithms.