• 지식경영연구
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• 제13권3호
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• pp.55-77
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• 2012

Selecting promising technology is becoming more and more difficult due to the increased number and complexity. In this study, we propose hybrid AHP/DEA-AR method and hybrid AHP/DEA-AR-G method to evaluate efficiency of technology alternatives based on ordinal rating data collected through survey to technology experts in a certain field and select efficient technology alternative as promising technology. The proposed method normalizes rating data and uses AHP to derive weights to improve the credibility of analysis, then in order to avoid basic DEA models' problems, use DEA-AR and DEA-AR-G to evaluate efficiency of technology alternatives. In this study, we applied the proposed methods to clean technology and compared with the basic DEA models. According to the result of the comparison, we can find that the both proposed methods are excellent in confirming most efficient technology, and hybrid AHP/DEA-AR method is much easier to use in the process of technology selection.

• International Journal of Precision Engineering and Manufacturing
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• 제1권2호
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• pp.124-131
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• 2000

A shop floor can be considered as and importand level to develop a Computer Integrated Manufacturing system (CIMs). The shop foor is a dynamic environment where unexpected events contrinuously occur, and impose changes to planned activities. The shop floor should adopt an appropriate control system that is responsible for scheduling coordination and moving the manufacturing material and information flow. In this paper, the architecture of the hybrid control model identifies three levels; i.e., the shop floor controller (SFC), the cell controller(CC) and the equipment controller (EC). The methodology for developing these controller is employ an object-oriented approach for static models and IDEF0 for function models for dispatching a job. SFC and CC are coordinated by employing a multi-factors bidding and an adapted Analytic Hierarchy Process(AHP) prove applicability of the suggested method. Test experiment has been conducted by with the shopfloor, consisting of six manufacturing cells.

• Journal of Information Technology Applications and Management
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• 제23권1호
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• pp.45-59
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• 2016

An ensemble classifier is a method that combines output of multiple classifiers. It has been widely accepted that ensemble classifiers can improve the prediction accuracy. Recently, ensemble techniques have been successfully applied to the bankruptcy prediction. Bagging and random subspace are the most popular ensemble techniques. Bagging and random subspace have proved to be very effective in improving the generalization ability respectively. However, there are few studies which have focused on the integration of bagging and random subspace. In this study, we proposed a new hybrid ensemble model to integrate bagging and random subspace method using genetic algorithm for improving the performance of the model. The proposed model is applied to the bankruptcy prediction for Korean companies and compared with other models in this study. The experimental results showed that the proposed model performs better than the other models such as the single classifier, the original ensemble model and the simple hybrid model.

• Journal of Electrical Engineering and Technology
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• 제8권4호
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• pp.930-937
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• 2013

In this paper, a motor control algorithm for performing a mode change without an integrated starter generator (ISG) is suggested for the automatic transmission-based hybrid electric vehicle (HEV). Dynamic models of the HEV powertrains such as engine, motor, and mode clutch are derived for the transient state during the mode change, and the HEV performance simulator is developed. Using the HEV performance bench tester, the characteristics of the mode clutch torque are measured and the motor torque required for the mode clutch synchronization is determined. Based on the dynamic models and the mode clutch torque, a motor torque control algorithm is presented for mode changes, and motor control without the ISG is investigated and compared with the existing ISG control.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2008년도 추계학술대회 논문집
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• pp.358-363
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• 2008

Through this study, 2D multibody dynamics models for analysis of train collisions have been developed to evaluate the crashworthiness requirements of the TSI regulation. The crashworthiness regulation requires some performance requirements for two heavy collision accident scenarios; a train-to-train collision at the relative speed of 36 kph, and a collision against a standard deformable obstacle of 15 ton at 110 kph. The complete train set will be composed of hybrid model with 2D and 1D model. Using numerical analysis of the hybrid model, some crashworthy design were evaluated in terms of mean crush forces and energy absorptions for main crushable structures and devices. especially, 2D model can evaluate overriding effect in train collisions. It is shown from the simulation results that the suggested hybrid model can easily evaluate the crashworthiness requirements.

• Structural Engineering and Mechanics
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• 제80권5호
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• pp.539-551
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• 2021

With the continuous increase of computational capacity, more and more complex nonlinear elastoplastic constitutive models were developed to study the mechanical behavior of elastoplastic materials. These constitutive models generally contain a large amount of physical and phenomenological parameters, which often require a large amount of computational costs to determine. In this paper, an inverse parameter determination method is proposed to identify the constitutive parameters of elastoplastic materials, with the consideration of both strain rate effect and temperature effect. To carry out an efficient design, a hybrid optimization algorithm that combines the genetic algorithm and the Nelder-Mead simplex algorithm is proposed and developed. The proposed inverse method was employed to determine the parameters for an elasto-viscoplastic constitutive model and Johnson-cook model, which demonstrates the capability of this method in considering strain rate and temperature effect, simultaneously. This hybrid optimization algorithm shows a better accuracy and efficiency than using a single algorithm. Finally, the predictability analysis using partial experimental data is completed to further demonstrate the feasibility of the proposed method.

• Computers and Concrete
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• 제33권6호
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• pp.739-754
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• 2024

This study presents an innovative AI-driven approach to assess the ultimate axial load in Double-Skinned Profiled Steel sheet Composite Walls (DPSCWs). Utilizing a dataset of 80 entries, seven input parameters were employed, and various AI techniques, including Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Decision Tree with AdaBoost Regression, Random Forest Regression, Gradient Boost Regression Tree, Elastic Net Regression, Ridge Regression, and LASSO Regression, were evaluated. Decision Tree Regression and Random Forest Regression emerged as the most accurate models. The top three performing models were integrated into a hybrid approach, excelling in accurately estimating DPSCWs' ultimate axial load. This adaptable hybrid model outperforms traditional methods, reducing errors in complex scenarios. The validated Artificial Neural Network (ANN) model showcases less than 1% error, enhancing reliability. Correlation analysis highlights robust predictions, emphasizing the importance of steel sheet thickness. The study contributes insights for predicting DPSCW strength in civil engineering, suggesting optimization and database expansion. The research advances precise load capacity estimation, empowering engineers to enhance construction safety and explore further machine learning applications in structural engineering.

• 한국소음진동공학회논문집
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• 제24권8호
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• pp.606-612
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• 2014

Vehicle simulation models for noise and vibration prediction have been developed so far generally in two schemes. One is FE models generally used for problems below 200 Hz such as booming noise, and the other is SEA models for high frequencies of more than 1 kHz, representatively related to sound packages. There have been many researches to develop a simulation model for 200~1000 Hz, so-called mid-frequency region, and this paper shows one practical result that covers the trimmed body of a sedan vehicle. The simulation model is developed based on an FE model, and then FE elements at some areas are substituted with SEA elements to reduce DOFs. SEA panels are described by modal density, radiation efficiency, stiffness and damping characteristics that are found from some numerical assessments. Sound packages are modeled similarly as a conventional SEA model. The results obtained from the hybrid model were compared to experimental results. Predicted pressure and vibrational velocity generally show a good agreement. The developed simulation model and related technology are successfully being used in vehicle development process.

• Smart Structures and Systems
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• 제26권1호
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• pp.63-75
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• 2020

Substructure pseudo-dynamic hybrid simulation (SPDHS) combining the advantages of physical experiments and numerical simulation has become an important testing method for evaluating the dynamic responses of structures. Various parameter identification methods have been proposed for online model updating. However, if there is large model gap between the assumed numerical models and the real models, the parameter identification methods will cause large prediction errors. This study presents an ANN (artificial neural network) method based on forgetting factor. During the SPDHS of model updating, a dynamic sample window is formed in each loading step with forgetting factor to keep balance between the new samples and historical ones. The effectiveness and anti-noise ability of this method are evaluated by numerical analysis of a six-story frame structure with BRBs (Buckling Restrained Brace). One BRB is simulated in OpenFresco as the experimental substructure, while the rest is modeled in MATLAB. The results show that ANN is able to present more hysteresis behaviors that do not exist in the initial assumed numerical models. It is demonstrated that the proposed method has good adaptability and prediction accuracy of restoring force even under different loading histories.

• Advances in Computational Design
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• 제5권4호
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• pp.445-465
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• 2020

This paper presents a study conducted with the aim of developing a model of tendering based on a technique of artificial intelligence by managing and controlling the factors of success or failure of construction projects through the evaluation of the process of invitation to tender. Aiming to solve this problem, analysis of the current environment based on SWOT (Strengths, Weaknesses, Opportunities, and Threats) is first carried out. Analysis was evaluated through a case study of the construction projects in Algeria, to bring about the internal and external factors which affect the process of invitation to tender related to the construction projects. This paper aims to develop a mean to identify threats-opportunities and strength-weaknesses related to the environment of various national construction projects, leading to the decision on whether to continue the project or not. Following a SWOT analysis, novel artificial intelligence models in forecasting the project status are proposed. The basic principal consists in interconnecting the different factors to model this phenomenon. An artificial neural network model is first proposed, followed by a model based on fuzzy logic. A third model resulting from the combination of the two previous ones is developed as a hybrid model. A simulation study is carried out to assess performance of the three models showing that the hybrid model is better suited in forecasting the construction project status than RNN (recurrent neural network) and FL (fuzzy logic) models.