• Journal of Electrical Engineering and Technology
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• v.3 no.3
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• pp.320-330
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• 2008

This paper presents an improved dynamic programming (IDP) approach to solve the economic power dispatch problem including transmission losses in power systems. A detailed mathematical derivation of recursive dynamic programming approach for the economic power dispatch problem with transmission losses is presented. The transmission losses are augmented with the objective function using price factor. The generalized expression for optimal scheduling of thermal generating units derived in this article can be implemented for the solution of the economic power dispatch problem of a large-scale system. Six-unit, fifteen-unit, and forty-unit sample systems with non-linear characteristics of the generator, such as ramp-rate limits and prohibited operating zones are considered to illustrate the effectiveness of the proposed method. The proposed method results have been compared with the results of genetic algorithm and particle swarm optimization methods reported in the literature. Test results show that the proposed IDP approach can obtain a higher quality solution with better performance.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.8
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• pp.972-979
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• 1999

QFT(Quantitative Feedback Theory) is a very practical design technique that emphasizes the use of feedback for achieving the desired system performance tolerances in despite of plant uncertainty and disturbance. The fundamental concept of QFT is a loop shaping procedure that a suitable controller can be found by shaping a nominal loop transfer function. The loop shaping synthesis involves the identification of a structure and the specialization of parameter optimization of a desired system. This paper presents an improved loop shaping approach of QFT with model validation using GA(Genetic Algorithm). The method presented in this paper removes the problems of iterative operation, transformation error, and model validation in the conventional methods without consideration of frequency domain.

• Journal of Electrical Engineering and Technology
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• v.5 no.1
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• pp.45-53
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• 2010

This paper presents an improved parallel particle swarm optimization approach (IPPSO) based decomposed network for economic power dispatch with discontinuous fuel cost functions. The range of partial power demand corresponding to the partial output powers near the global optimal solution is determined by a flexible decomposed network strategy and then the final optimal solution is obtained by parallel Particle Swarm Optimization. The proposed approach tested on 6 generating units with smooth cost function, and to 26-bus (6 generating units) with consideration of prohibited zone effect, the simulation results compared with recent global optimization methods (Bee-OPF, GA, MTS, SA, PSO). From the different case studies, it is observed that the proposed approach provides qualitative solution with less computational time compared to various methods available in the literature survey.

• Journal of the Korean Society of Systems Engineering
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• v.17 no.2
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• pp.1-8
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• 2021

This study proposes a systems engineering approach for the development of an advanced planning & scheduling (APS) system for a cosmetic case manufacturing factory. The APS system makes production plans and schedules based on the injection process, which consists of 27 plastic injection machines in parallel to control recommended inventory of products. The system uses machine operation/failure information and defective product/work-in-process tracking information to support intelligent scheduling. Furthermore, a genetic algorithm model is applied to handle the complexity of heuristic rules and machine/quality constraints in this process. As a result of the development, the recommended inventory compliance rate is improved by scheduling the 30-day production plan for 15 main products.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.252-253
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• 2023

Container terminal automation might offer many potential benefits, such as increased productivity, reduced cost, and improved safety. Autonomous trucks can lead to more efficient container transport. A robust lane detection method is proposed using score-based generative modeling through stochastic differential equations for image-to-image translation. Image processing techniques are combined with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Genetic Algorithm (GA) to ensure lane positioning robustness. The proposed method is validated by a dataset collected from the port terminals under different environmental conditions and tested the robustness of the lane detection method with stochastic noise.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.19 no.3 s.73
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• pp.313-322
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• 2006

In the genetic algoricm (GA) based damage detection methods using vibration of structures, the selection of modal properties is important to improve the accuracy of damage detection. The objective of this study is to improve the accuracy of damage detection using natural frequency and modal strain energy, The following approaches are used to achieve the goal. First, modal strain energy is formulated and a new GA-based damage detection technique using natural frequency and modal strain energy is proposed. Next, to verify the efficiency of proposed technique, damage scenarios for free-free beam are designed and vibration modal tests of the target structure are conducted. Finally, the feasibility of the proposed technique is verified in comparison with other GA-based damage detection technique using natural frequency and mode shape.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.955-963
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• 2019

Currently, the establishment and management of civil defense shelters are under the initiative of the government and local governments to protect the lives of citizens. In the future, there is a need for efficient civil defense shelters operation through the expansion of general shelters, including designated dedicated shelters. Therefore, it is more efficient to consider the distribution of residents and the location of access to shelters, not the quantitative operation considering only the number of residents. This study uses genetic algorithms and Huff gravity model based on census output data, building data, and road network information to understand the distribution of inhabitants more precisely than existing administrative district data. In addition, the spatial- database was used for efficient data management and fast processing, and if this study is improved, it can be used as a basis for the selection and improvement of general shelters positioning for a wider area.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.16 no.2
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• pp.64-74
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• 2004

In order to accelerate the rate of consolidation settlement, to reduce settlement, and to increase bearing capacity for soft ground under quay wall, sand compaction pile method (SCP) has widely been applied. Improved ground is composite ground which is consisted of the sand pile-surrounding clayey soil. As caisson and upper structures are installed on SCP composite ground, the settlement is compositively occurred by elastic compression of sand compaction piles and also consolidation of the surrounding clay ground. In this study, the combined settlement model is proposed to predict the settlement of SCP composite ground in basis of elastic theory for sand compaction pile and consolidation theory for marine soft clay. Optimization technique was performed based on back-analysis so that real coded genetic algorithm was applied to estimate the parameters of the proposed settlement model. Case analysis was carried out for a domestic SCP composite ground to examine the applicability of the proposed prediction technique.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.4
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• pp.698-707
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• 2017

This paper deals with predictive control methods of mobile robots for reference trajectory tracking control. Predictive control methods using predictive model are known as effective schemes that minimize the future errors between the reference trajectories and system states; however, the amount of real-time computation for the predictive control are huge so that their applications were limited to slow dynamic systems such as chemical processing plants. Lately with high computing power due to advanced computer technologies, the predictive control methods have been applied to fast systems such as mobile robots. These predictive controllers have some control parameters related to control performance. But these parameters have not been optimized. In this paper we employed the genetic algorithm to optimize the control parameters of the predictive controller for mobile robots. The improved performances of the proposed control method are demonstrated by the computer simulation studies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.3
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• pp.91-95
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• 2010

Using artificial neural network to use a search preference based on the user's information, the ranking of search results that will enable flexible searches can be improved. After trained in several different queries by other users in the past, the actual search results in order to better reflect the use of artificial neural networks to neural network learning. In order to change the weights constantly moving backward in the network to change weights of backpropagation algorithm. In this study, however, the initial training, performance data, look for increasing the number of lessons that can be overfitted. In this paper, we have optimized a lot of objects that have a strong advantage to apply genetic algorithms to the relevant page of the search rankings flexible as an object to the URL list on a random selection method is proposed for the study.