• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.514-518
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• 1990

This paper describes an experimental Knowledge-Based Control System, named KBCS, for manufacturing and assembly. The KBCS of five parts and function : data-base, knowledge acquisition, optimization, and graphic monitoring. The KBCS is utilized for a FMS which is of five machine centers and automatic assembly lines. Each machine can perform almost all manufacturing functions which some difference in efficiency. Buffers store temporarily the incoming components and the outing components. Parts arrive at assembly lines after many steps of manufacturing, and the transfer path and time are determined by procedural knowledge of control systems. Nine different incoming components are set up. The total control system is expected to perform four algorithms, timing algorithm ,sequencing algorithm, penalty algorithm, and cart algorithm. The construction of controller require basic components of manufacturing systems in which knowledges are formulated on the base of the results and the repeated simulation of KBCS with graphic monitoring system. Simulation results by KBCS are compared with those by the other rules of manufacturing.

• Journal of the Korean Society of Safety
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• v.13 no.1
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• pp.40-46
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• 1998

This paper is presented that theoretical optimization design method in order to consider mass reduction for the structural safety In this paper, it described methods for reducing vibration in structural safety by the determination of the optimum sizes and locations of tunning masses through formal mathematical optimization techniques. The optimization procedure which employs the tunning masses and corresponding locations is developed. Design variables are systematically changed to achieve low values of shear without a large mass penalty. Three optimization methods ire developed and tested. The first is based on minimizing the modal shaping parameter which indirectly reduce the modal shear amplitudes corresponding to each harmonic of airload. The second method reduces these amplitudes directly and the third method reduces the shear as a function of time during a revolution of the blade. The first method works well for reducing the shear for one mode responding to a single harmonic of the airload but has been found in some bases to be ineffective for more than one mode.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1996.04a
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• pp.54-61
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• 1996

This paper deals with finite element analysis for incompressible viscous flow problem By formulating the governing equation based on the streamline upwind concept , the wiggle phenomenon of fluid flow is minimized in spite of a few number of finite element used. The penalty function method which can reduce the number of independent variables is adopted for the purpose of computational efficiency and the selected reduced integral is carried out for the convection and pressure terms to reserve the stability of solution. In time-history analysis of fluid flow, the accuracy and reliability of an obtained solution are established by using the predictor-corrector method. Finally, correlation studies between analytical and experimental results are conducted wi th the object ive to establish the validity of the proposed numerical approach.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.69-78
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• 2007

In real world, the project managers handle conflicting goals that govern the use of resources within the stipulated time and budget with required quality and safety. These conflicting goals are required to be optimized simultaneously by the project managers in the framework of fuzzy aspiration levels. The fuzzy linear programming model proposed herein helps project managers to minimize total project costs, completion time, and crashing costs considering indirect costs, contractual penalty costs etc by practically charging them in terms of direct cost of the project. A case study of bituminous pavement under construction is considered to demonstrate the feasibility of applying the proposed model for optimization of project parameters. Consequently, the proposed model yields an efficient compromise solution and the decision maker's overall degree of satisfaction with multiple fuzzy goal values. Additionally, the proposed model provides a systematic decision-making framework, enabling decision maker to interactively modify the fuzzy data and model parameters until a satisfactory solution is obtained. The significant characteristics that differentiate the proposed model with other models include, flexible decision-making process, multiple objective functions, and wide-ranging decision information.

• Journal of Power Electronics
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• v.17 no.5
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• pp.1288-1297
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• 2017

The estimation of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is important for intelligent battery management system (BMS). Data mining technology is becoming increasingly mature, and the RUL estimation of Li-ion batteries based on data-driven prognostics is more accurate with the arrival of the era of big data. However, the support vector machine (SVM), which is applied to predict the RUL of Li-ion batteries, uses the traditional single-radial basis kernel function. This type of classifier has weak generalization ability, and it easily shows the problem of data migration, which results in inaccurate prediction of the RUL of Li-ion batteries. In this study, a novel multi-kernel SVM (MSVM) based on polynomial kernel and radial basis kernel function is proposed. Moreover, the particle swarm optimization algorithm is used to search the kernel parameters, penalty factor, and weight coefficient of the MSVM model. Finally, this paper utilizes the NASA battery dataset to form the observed data sequence for regression prediction. Results show that the improved algorithm not only has better prediction accuracy and stronger generalization ability but also decreases training time and computational complexity.

• Magazine of the Korean Society of Agricultural Engineers
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• v.36 no.2
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• pp.111-122
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• 1994

In this study, an algorithm which can be applied to the optimum design of simple steel plate girders was developed, and efficient optimization strategies for the solution of algorithm were found out. The optimum design algorithm consists of 3-levels of optimization. In the first and second levels of optimization, the absolute maximum bending moment and shearing force are extracted and in the third level of optimization, the optimum cross section of steel plate girder is determined. For the optimum design of cross section, the objective function is formulated as the total area of cross section and constraints are derived in consideration of the various stresses and the minimum dimension of flange and web based on the part of steel bridge in the Korea standard code of road bridge. Sequential unconstrained minimization technique using the exterior penalty function method(SUMT-EP), sequential linear programming(SLP) and sequential quadratic programming (SQP) are proved to be efficient and robust strategies for the optimum design of simple plate girder cross section. From the reliable point of view, SLP is the most efficient and robust strategy and SQP is the most efficient one from the viewpoint of converguency and computing time.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.2
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• pp.21-31
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• 1999

This paper presents a new possibility of calculating optimal control for large scale which consist of non-linear dynamic sub-systems using two level hierarchical structures method. And the proposed method is based on the idea of block pulse transformation to simplify the algorithm and its calculation. This algorithm used an expansion around the equilibrium point of the system to fix the second and higher order terms. These terms are compensated for iteratively at the second level by providing a prediction for the states and controls which form of a part of the higher order terms. In this new approach the quadratic penalty terms are not used in the cost function. This allows convergence over a longer time horizon and also provides faster convergence. And the method is applied to the problem of optimization of the synchronous machine. Results show that the new approach is superior to conventional numerical method or other previous algorithm.

• Journal of Computing Science and Engineering
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• v.4 no.3
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• pp.189-206
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• 2010

It is generally accepted that dynamic voltage scaling (DVS) is one of the most effective techniques of energy minimization for real-time applications in embedded system design. The effectiveness comes from the fact that the amount of energy consumption is quadractically proportional to the voltage applied to the processor. The penalty is the execution delay, which is linearly and inversely proportional to the voltage. According to the granularity of tasks to which voltage scaling is applied, the DVS problem is divided into two subproblems: inter-task DVS problem, in which the determination of the voltage is carried out on a task-by-task basis and the voltage assigned to the task is unchanged during the whole execution of the task, and intra-task DVS problem, in which the operating voltage of a task is dynamically adjusted according to the execution behavior to reflect the changes of the required number of cycles to finish the task before the deadline. Frequent voltage transitions may cause an adverse effect on energy minimization due to the increase of the overhead of transition time and energy. In addition, DVS needs to be carefully applied so that the dynamically varying chip temperature should not exceed a certain threshold because a drastic increase of chip temperature is highly likely to cause system function failure. This paper reviews representative works on the theoretical solutions to DVS problems regarding inter-task DVS, intra-task DVS, voltage transition, and thermal-aware DVS.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.1
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• pp.31-38
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• 2008

This paper deals with the nonlinear optimal control approach to helicopter maneuver problems using the indirect method. We apply a penalty function to the integral deviation from a prescribed trajectory to convert the system optimality to an unconstrained optimal control problem. The resultant two-point boundary value problem has been solved by using a multiple-shooting method. This paper focuses on the model selection strategies to resolve the problem of numerical instability and high wait time when a high fidelity model with rotor dynamics is applied. Four different types of helicopter models are identified, two of which are linear models with or without rotor models, as well as two models which include the nonlinear mathematical model for rotor in its formulation. The relative computation time and the number of function calls for each model are compared in order to provide a guideline for the selection of helicopter model.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.642-652
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• 2007

Many contracting firms and project managers in the construction industry have started to utilize multi objective optimization methods to handle multiple conflicting goals for completing the project within the stipulated time and budget with required quality and safety. These optimization methods have increased the pressure on decision makers to search for an optimal resources utilization plan that optimizes simultaneously the total project cost, completion time, and crashing cost by considering indirect cost, contractual penalty cost etc., practically charging them in terms of direct cost of the project which is fuzzy in nature. This paper presents a multiple fuzzy goal programming model (MFGP) that supports decision makers in performing the challenging task. The model incorporates the fuzziness which stems from the imprecise aspiration levels attained by the decision maker to these objectives that are quantified through fuzzy linear membership function. The membership values of these objectives are then maximized which forms the fuzzy decision. The problem is solved using LINGO 8 optimization solver and the best compromise solution is identified. Comparison between solutions of MFGP, fuzzy multi objective linear programming (FMOLP) and multiple goal programming (MGP) are also presented. Additionally, an interactive decision making process is developed to enable the decision maker to interact with the system in modifying the fuzzy data and model parameters until a satisfactory solution is obtained. A case study is considered to demonstrate the feasibility of the proposed model for optimization of project network parameters in the construction industry.