• Bulletin of the Society of Naval Architects of Korea
• /
• v.25 no.1
• /
• pp.11-20
• /
• 1988

From the engineering point of view, a synthesis as well as an analysis technique is explored to search for the improved design of grillage which is common in ship structure. As an approximate analysis method for the grillage, an interaction reaction method is developed and compared with the finite element method. It is found that the discrepancy between these two methods is so negligible that the percent method could be used effectively for the grillage analysis. As an optimization technique, a feasible direction method could be used is combined with the intersection reaction method in order to design a minimum weight optimal grillage. The feasible direction method shows a good numerical performance although it requires more calculation times compared with the direct search method. Finally, the application of multiple objective optimization method to grillage is investigated in order to resolve conflicts existed between the multiple objectives which is a common characteristic of structure design problem. Goal programming method is extended to handle a nonlinear property of constraints and objective functions. It seems that the nonlinear goal programming could help not only to establish a relative importance of each objective, but also enable the designer to choose the best combination of design variables.

• Nuclear Engineering and Technology
• /
• v.55 no.9
• /
• pp.3277-3290
• /
• 2023

Nuclear power plant (NPP) operations with multiple objectives and devices are still performed manually by operators despite the potential for human error. These operations could be automated to reduce the burden on operators; however, classical approaches may not be suitable for these multi-objective tasks. An alternative approach is deep reinforcement learning (DRL), which has been successful in automating various complex tasks and has been applied in automation of certain operations in NPPs. But despite the recent progress, previous studies using DRL for NPP operations have limitations to handle complex multi-objective operations with multiple devices efficiently. This study proposes a novel DRL-based approach that addresses these limitations by employing a continuous action space and straightforward binary rewards supported by the adoption of a soft actor-critic and hindsight experience replay. The feasibility of the proposed approach was evaluated for controlling the pressure and volume of the reactor coolant while heating the coolant during NPP startup. The results show that the proposed approach can train the agent with a proper strategy for effectively achieving multiple objectives through the control of multiple devices. Moreover, hands-on testing results demonstrate that the trained agent is capable of handling untrained objectives, such as cooldown, with substantial success.

• Korean Management Science Review
• /
• v.16 no.1
• /
• pp.63-74
• /
• 1999

This paper presents a R&D investment model for the Information and telecommunications(l&T) technology using multiple objective linear programming(MOLP). The MOLP model involves the simultaneous maximization of three linear objective functions associated with three criteria, which are social, technological, and economic criterion. This model is different from the traditional one which only involves the maximization of economic criterion. It yields a suitable R&D investment ratio to each technology field. Its application to the National R&D Project in l&t Industry is also presented. In this application, the Analytic Hierarchy Process(AHP) is proposed to estimate the weights, which used as the coefficients in each objective function of the MOLP model. Then the problem is solved using the interactive method STEM. It is showed that with the aid of STEM, the MOLP model can be useful decision aid in formulation R&D investment plan in l&t industry. It is expected that the MOLP model works as the basis for planning R&D investment strategy in l&T industry.

• CELLMED
• /
• v.13 no.15
• /
• pp.58.1-58.6
• /
• 2023

Objective: Report on the case of improvement of multiple alopecia areata through implementation of Ortho-Cellular Nutrition Therapy (OCNT). Methods: A Korean female in her 50s suffering from multiple alopecia areata. Results: Multiple alopecia areata was improved following implementation of OCNT. Conclusion: Application of OCNT can be helpful to multiple alopecia areata.

• Journal of Korea Water Resources Association
• /
• v.45 no.1
• /
• pp.15-27
• /
• 2012

SWMM (Storm Water Management Model) has been widely used in the world as a typical model for flood runoff analysis of urban areas. However, the calibration of the model is difficult, which is an obstacle to easy application. The purpose of the study is to develop an automatic calibration module of the SWMM linked with SCE-UA (Shuffled Complex Evolution-University of Arizona) algorithm. Generally, various objective functions may produce different optimization results for an optimization problem. Thus, five single objective functions were applied and the most appropriate one was selected. In addition to the objective function, another objective function was used to reduce peak flow error in flood simulation. They form a multiple objective function, and the optimization problem was solved by determination of Pareto optima. The automatic calibration module was applied to the flood simulation on the catchment of the Guro 1 detention reservoir and pump station. The automatic calibration results by the multiple objective function were more excellent than the results by the single objective function for model assessment criteria including error of peak flow and ratio of volume between observed and calculated flow. Also, the verification results of the model calibrated by the multiple objective function were reliable. The program could be used in various flood runoff analysis in urban areas.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.110.4-110
• /
• 2002

In practical control area, there are many examples with multiple objectives which may conflict or compete with each other like overhead crane control, automatic train operation, and refuse incinerator plant control, etc. These kinds of control problems are called multiobjective control problems, where it is difficult to provide the desired performance with control strategies based on single-objective optimization. Because the conventional control theories usually treat the control problem as the single objective optimization problem , the methods are not adequate to treat the multiobjective control problems. Particularly, in case of large scale systems or ill-defined systems, the multiple obj..

• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 1997.10a
• /
• pp.153-157
• /
• 1997

The investigation deals with the process design in cold rolling mill of electric plant. In this study, multiple objective optimization is conducted by a genetic algorithm, where the fitness values are evaluated on the basis of one - dimensional model of flat rolling. The approach is applied to the determination of the process conditions which are optimal with regard to minimization of roll power and maximization of productivity.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.7 no.2
• /
• pp.31-39
• /
• 1982

This paper presents an efficient algorithm both in computation speed and storage requirement by exploring the special structure of problems involving multiple objective goals. The algorithm developed here is limited to the problems with multiple, compensatory objectives, however it can be extended to‘traditional’preemptive priority goat programming problems. Computational results are included.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.21 no.8
• /
• pp.1195-1208
• /
• 1997

Bellows is a component in piping systems which absorbs mechanical deformation with flexibility. Its geometry is an axial symmetric shell which consists of two toroidal shells and one annular plate or conical shell. In order to analyze bellows, this study presents the finite element analysis using a conical frustum shell element. A finite element analysis is developed to analyze various bellows. The validity of the developed program is verified by the experimental results for axial and lateral stiffness. The formula for calculating the natural frequency of bellows is made by the simple beam theory. The formula for fatigue life is also derived by experiments. The shape optimal design problem is formulated using multiple objective optimization. The multiple objective functions are transformed to a scalar function by weighting factors. The stiffness, strength and specified stiffness are considered as the multiple objective function. The formulation has inequality constraints imposed on the fatigue limit, the natural frequencies, and the manufacturing conditions. Geometric parameters of bellows are the design variables. The recursive quadratic programming algorithm is selected to solve the problem. The results are compared to existing bellows, and the characteristics of bellows is investigated through optimal design process. The optimized shape of bellows is expected to give quite a good guideline to practical design.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.2
• /
• pp.442-460
• /
• 2021

Nowadays, recommendation systems (RSs) are applied to all aspects of online life. In order to overcome the problem that individuals who do not meet the constraints need to be regenerated when the many-objective evolutionary algorithm (MaOEA) solves the hybrid recommendation model, this paper proposes a many-objective particle swarm optimization algorithm based on multiple criteria (MaPSO-MC). A generation-based fitness evaluation strategy with diversity enhancement (GBFE-DE) and ISDE⁺ are coupled to comprehensively evaluate individual performance. At the same time, according to the characteristics of the model, the regional optimization has an impact on the individual update, and a many-objective evolutionary strategy based on bacterial foraging (MaBF) is used to improve the algorithm search speed. Experimental results prove that this algorithm has excellent convergence and diversity, and can produce accurate, diverse, novel and high coverage recommendations when solving recommendation models.