• Proceedings of the KSME Conference
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• 2000.04a
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• pp.725-730
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• 2000

Genetic algorithms based on the theory of natural selection, have been applied to many different fields, and have proven to be relatively robust means to search for global optimum and handle discontinuous or even discrete data. Genetic algorithms are widely used for unconstrained optimization problems. However, their application to constrained optimization problems remains unsettled. The most prevalent technique for coping with infeasible solutions is to penalize a population member for constraint violation. But, the weighting of a penalty for a particular problem constraint is usually determined in the heuristic way. Therefore this paper proposes, the effective technique for handling constraints, the ranking penalty method and hybrid genetic algorithms. And this paper proposes dynamic mutation tate to maintain the diversity in population. The effectiveness of the proposed algorithm is tested on several test problems and results are discussed.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.1
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• pp.72-84
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• 2000

To analyze the correlation between drivetrain systems and to optimize the vehicle design with satisfying of the initial design objects, the performance sensitivity analysis through the iterative design procedure must be carried out. In this study, effects of the design parameters for the main components of the parallel type hybrid drivetrain system are analyzed by using the developed method of the vehicle performance simulation, and the basis of the optimal selection of the design parameters from the relation of design constraints and required performances is suggested. In driving control of the parallel hybrid vehicle, power split ratio is the most important factor, and the improved drivetrain system can be constructed through the only change of the algorithm for determination of the power spilt ratio, which is strongly applicable to the driving patterns and the environments. Therefore, Various techniques, such as the change of the weighting factors and the range extended algorithm, are suggested and evaluated in this paper.

• Journal of the Optical Society of Korea
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• v.19 no.5
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• pp.531-536
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• 2015

This paper presents a new graphical method for selecting a pair of optical glass and housing materials to simultaneously achromatize and athermalize a multilens system composed of many elements. To take into account the lens spacing and housing, we quantify the lens power, chromatic power, and thermal power by weighting the ratio of the paraxial ray height at each lens to them. In addition, we introduce the equivalent single lens and the expanded athermal glass map including a housing material. Even though a lens system is composed of many elements, we can simply identify a pair of glass and housing materials that satisfies the athermal and achromatic conditions. Applying this method to design a black box camera lens equipped with a 1/4-inch image sensor having a pixel width of $2{\mu}m$, the chromatic and thermal defocusings are reduced to less than the depth of focus, over the specified ranges in temperature and frequency.

• Journal of Power Electronics
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• v.12 no.5
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• pp.830-841
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• 2012

Active power filters (APF) can be employed for harmonic compensation in power systems. In this paper, a fuzzy based method is proposed for identification of probable APF nodes of a radial distribution system. The modified adaptive particle swarm optimization (MAPSO) technique is used for final selection of the APFs size. A combination of Fuzzy-MAPSO method is implemented to determine the optimal allocation and size of APFs. New fuzzy membership functions are formulated where the harmonic current membership is an exponential function of the nodal injecting harmonic current. Harmonic voltage membership has been formulated as a function of the node harmonic voltage. The product operator shows better performance than the AND operator because all harmonics are considered in computing membership function. For evaluating the proposed method, it has been applied to the 5-bus and 18-bus test systems, respectively, which the results appear satisfactorily. The proposed membership functions are new at the APF placement problem so that weighting factors can be changed proportional to objective function.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.8 s.185
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• pp.89-99
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• 2006

In this paper a new shape reconstruction method that allows us to construct surface models from very large sets of points is presented. In this method the global domain of interest is divided into smaller domains where the problem can be solved locally. These local solutions of subdivided domains are blended together according to weighting coefficients to obtain a global solution using partition of unity function. The suggested approach gives us considerable flexibility in the choice of local shape functions which depend on the local shape complexity and desired accuracy. At each domain, a quadratic polynomial function is created that fits the points in the domain. If the approximation is not accurate enough, other higher order functions including cubic polynomial function and RBF(Radial Basis Function) are used. This adaptive selection of local shape functions offers robust and efficient solution to a great variety of shape reconstruction problems.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1231-1242
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• 2019

This paper presents a two-dimensional attention-based long short-memory (2D-ALSTM) model for stock index prediction, incorporating input attention and temporal attention mechanisms for weighting of important stocks and important time steps, respectively. The proposed model is designed to overcome the long-term dependency, stock selection, and stock volatility delay problems that negatively affect existing models. The 2D-ALSTM model is validated in a comparative experiment involving the two attention-based models multi-input LSTM (MI-LSTM) and dual-stage attention-based recurrent neural network (DARNN), with real stock data being used for training and evaluation. The model achieves superior performance compared to MI-LSTM and DARNN for stock index prediction on a KOSPI100 dataset.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.35-46
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• 2021

Small and Medium Enterprises (SMEs) are increasingly using ERP systems to connect and manage all their functions, whether internally between the different departments, or externally with customers in electronic commerce. However, the selection of the right ERP system is usually an issue, due to the complexities of identifying the criteria, weighting them, and selecting the best system and provider. Because cost is usually important for SMEs, ERP systems based on Cloud Software as a Service (SaaS) has been adopted by many SMEs. However, SMEs face an issue of selecting the right system. Therefore, this paper proposes a fuzziness ranking engine system in order to match the SMEs requirements with the most suitable service provider. The extensive experimental result shows that our approach has better result compared with traditional approaches.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.7
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• pp.339-349
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• 2001

This paper suggests an optimal identification method for complex and nonlinear system modeling that is based on Fuzzy-Neural Networks(FNN). The proposed Hybrid Identification Algorithm is based on Yamakawa's FNN and uses the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. In this paper, the FNN modeling implements parameter identification using HCM algorithm and hybrid structure combined with two types of optimization theories for nonlinear systems. We use a HCM(Hard C-Means) clustering algorithm to find initial apexes of membership function. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are adjusted using hybrid algorithm. The proposed hybrid identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregated objective function(performance index) with weighting factor is introduced to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity(distribution of I/O data), we show that it is available and effective to design an optimal FNN model structure with mutual balance and dependency between approximation and generalization abilities. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• The Journal of the Korea Contents Association
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• v.9 no.11
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• pp.54-62
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• 2009

To avoid the redundant investment on the project selection process, it is necessary to check whether the submitted research topics have been proposed or carried out at other institutions before. This is possible through the search engines adopted by the keyword matching algorithm which is based on boolean techniques in national-sized research results database. Even though the accuracy and speed of information retrieval have been improved, they still have fundamental limits caused by keyword matching. This paper examines implemented TFIDF-based algorithm, and shows an experiment in search engine to retrieve and give the order of priority for similar and redundant documents compared with research proposals, In addition to generic TFIDF algorithm, feature weighting and K-Nearest Neighbors classification methods are implemented in this algorithm. The documents are extracted from NDSL(National Digital Science Library) web directory service to test the algorithm.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.5
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• pp.39-47
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• 2016

Nowadays, several welding processes are generally used to join parts together, and the materials are generally steel, aluminum, copper, stainless steel, and other difficult-to-weld materials. If a proper welding process is chosen, it is helpful for welding parts. However, there is no desirable technique for appropriately deciding on the welding process in the industry. Therefore, an appropriate method of selecting a welding process is needed for the novice worker in the industry. In this sense, a new analytic network process (ANP) technique is used for effective decision making in welding. By considering several criteria in ANP, a selection method is suggested to decide on the proper welding process. In the study, several criteria were considered for the proper welding of parts. By considering a matrix of prior interdependence effects among various welding processes, a decision-making method based on an ANP is accomplished using a weighting matrix, which is supposed to select an appropriate welding process. In addition, for appropriate decision criteria of the welding process, several factors, such as material, shape, precision, economics, and equipment, are used to accomplish the ANP algorithm. Moreover, the final weighting matrix is calculated following its ANP strategy. Furthermore, this decision-making technique is applied to both stainless razor spot joining and thick steel pipe joining. The results show its reliability and practicality, and the novice engineer and manager can use this technique to determine the best welding process.