• Journal of the Korean Society for Precision Engineering
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• v.17 no.8
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• pp.46-52
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• 2000

3D Welding and Milling is a solid freeform fabrication process which is based on the combination of welding as additive and conventional milling as subtractive technique. This hybrid approach enables direct building of metallic parts with high accuracy and surface finish. Although it needs further improvements it shows an application potential in rapid tooling of injection mold inserts as the investigation results show. To optimize the process for higher surface quality and accuracy effectively Taguchi method is applied to the experimental investigation. in this way relationships between process parameters and final product qualities such as tensile strength and surface hardness are found with minimal efforts.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.5
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• pp.531-536
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• 2021

As artificial intelligence technology advances, it is being applied to various application fields. Artificial intelligence is performing well in the field of image recognition and classification. Chip design specialized in this field is also actively being studied. Artificial intelligence-specific chips are designed to provide optimal performance for the applications. At the design task, memory component optimization is becoming an important issue. In this study, the optimal algorithm for the memory size exploration is presented, and the optimal memory size is becoming as a important factor in providing a proper design that meets the requirements of performance, cost, and power consumption.

• Nonlinear Functional Analysis and Applications
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• v.27 no.1
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• pp.59-82
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• 2022

In this paper, we present a modified (improved) generalized M-iteration with the inertial technique for three quasi-nonexpansive multivalued mappings in a real Hilbert space. In addition, we obtain a weak convergence result under suitable conditions and the strong convergence result is achieved using the hybrid projection method with our modified generalized M-iteration. Finally, we apply our convergence results to certain optimization problem, and present some numerical experiments to show the efficiency and applicability of the proposed method in comparison with other improved iterative methods (modified SP-iterative scheme) in the literature. The results obtained in this paper extend, generalize and improve several results in this direction.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.12
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• pp.2062-2069
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• 2001

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses synthetic artificial intelligent technique, that is, Adaptive-Network-based Fuzzy Inference System(ANFIS) solved via hybrid learning algorithm(the back-propagation gradient descent and the least-squares method) are used to learn the input(the location and depth of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this ANFIS and a continuous evolutionary algorithm(CEA), it is possible to formulate the inverse problem. CEAs based on genetic algorithms work efficiently for continuous search space optimization problems like a parameter identification problem. With this ANFIS, CEAs are used to identify the crack location and depth minimizing the difference from the measured frequencies. We have tried this new idea on a simple beam structure and the results are promising.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.182-188
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• 2001

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses synthetic artificial intelligent technique, that is, Adaptive-Network-based Fuzzy Inference System(ANFIS) solved via hybrid learning algorithm(the back-propagation gradient descent and the least-squares method) are used to learn the input(the location and depth of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this ANFIS and a continuous evolutionary algorithm(CEA), it is possible to formulate the inverse problem. CEAs based on genetic algorithms work efficiently for continuous search space optimization problems like a parameter identification problem. With this ANFIS, CEAs are used to identify the crack location and depth minimizing the difference from the measured frequencies. We have tried this new idea on a simple beam structure and the results are promising.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.8
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• pp.890-895
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• 2016

As the wireless communication technique is developing rapidly, the use of smart devices is increasing. Due to gradually increasing data traffic, a new area, more than 6GHz of bandwidth to increase capacity of the network, has been studied. Millimeter Wave(MmWave) communications utilizes the bandwidth above 6GHz, which makes it possible to achieve one gigabit per second data rate. To overcome the path loss due to the smaller wavelength, the mass of the antenna arrangement is used. This paper presents an algorithm that maximizes the spectral efficiency of the system in the pre-coding process using a hybrid beamforming. Also it is suggested with the optimization of the number of beams that maximizes the spectral efficiency was maximized by the propose method.

• Steel and Composite Structures
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• v.51 no.6
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• pp.679-695
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• 2024

This paper presents a novel technique that combines machine learning (ML) with moth-flame optimization (MFO) methods to predict the axial compressive strength (ACS) of concrete filled double skin steel tubes (CFDST) columns. The proposed model is trained and tested with a dataset containing 125 tests of the CFDST column subjected to compressive loading. Five ML models, including extreme gradient boosting (XGBoost), gradient tree boosting (GBT), categorical gradient boosting (CAT), support vector machines (SVM), and decision tree (DT) algorithms, are utilized in this work. The MFO algorithm is applied to find optimal hyperparameters of these ML models and to determine the most effective model in predicting the ACS of CFDST columns. Predictive results given by some performance metrics reveal that the MFO-CAT model provides superior accuracy compared to other considered models. The accuracy of the MFO-CAT model is validated by comparing its predictive results with existing design codes and formulae. Moreover, the significance and contribution of each feature in the dataset are examined by employing the SHapley Additive exPlanations (SHAP) method. A comprehensive uncertainty quantification on probabilistic characteristics of the ACS of CFDST columns is conducted for the first time to examine the models' responses to variations of input variables in the stochastic environments. Finally, a web-based application is developed to predict ACS of the CFDST column, enabling rapid practical utilization without requesting any programing or machine learning expertise.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.6
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• pp.470-477
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• 2015

This paper presents a new technique for generating an optimum synchronizable test sequence that can be applied in the distributed test architecture where both external synchronization and input/output operation costs are taken into consideration. The method defines a set of phases that constructs a tester-related digraph from a given finite state machine representation of a protocol specification such that a minimum cost tour of the digraph with intrinsically synchronizable transfer sequences can be used to generate an optimum synchronizable test sequence using synchronizable state identification sequences as the state recognition sequence for each state of the given finite state machine. This hybrid approach with a heuristic and optimization technique provides a simple and elegant solution to the synchronization problem that arises during the application of a predetermined test sequence in some protocol test architectures that utilize remote testers.

• Journal of the Society of Naval Architects of Korea
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• v.29 no.2
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• pp.115-122
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• 1992

A sandwich element is a special Hybrid structural form of the composite construction, which is consisted of three main parts : thin, stiff and relatively high density faces separated by a thick, light, and weaker core material. In a sandwich construction, the shear deformation of the faces. Therefore, in the calculation of the bending stiffness, the shear effect should be included. In this paper, the minimum weight is selected as an object function, as the weight critical structures are usually composed of these kind of construction. To obtain the minimum weight of sandwich panel, the principle of minimum potential energy is used and as for the design constraints, the allowable bending stress of face material, the allowable shear stress of core material, the allowable value of panel deflection and the wrinkling stress of faces are adopted, as well as the different boundary conditions. For the engineering purpose of sandwich panel design, the results are tabulated, which are calculated by using the nonlinear optimization technique SUMT.

• Journal of the Korean Magnetics Society
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• v.23 no.3
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• pp.110-116
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• 2013

This paper proposes a sampling-based sensitivity method for dealing with electromagnetic coupled design problems effectively. The black-box modeling technique is basically applied to obtain an optimum regardless of how strong the electromagnetic, thermal and structural analyses are coupled with each other. To achieve this, Kriging surrogate models are produced in a hyper-cubic local window with the center of a current design point. Then design sensitivity values are extracted from the differentiation of basis functions which consist of the models. The proposed method falls under a hybrid optimization method which takes advantages of the sampling-based and the sensitivity-based methods. Owing to the aforementioned feature, the method can be applied even to electromagnetic problems of which the material properties are strongly coupled with thermal or structural outputs. To examine the accuracy and validity of the proposed method, a strongly nonlinear mathematical example and a coil design problem for local induction heating are tested.