• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.755-761
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• 2014

Heterogeneous components have different component models, which prevents such components from sharing the functionalities of other components based on the different models. As one of methods for linking heterogeneous components, this paper suggests a proxy component to construct a bridge between heterogeneous components of OPRoS (Open Platform for Robotic Service) and RTM (Robot Technology Middleware). The proxy component consists of two types of components called Adaptor and Interceptor, via which the heterogeneous components can exchange data and services easily. The proposed method enables adaptor and interceptor components to directly invoke the services of the latter and the former, respectively, in order to exchange data and services on a real-time basis. The proxy component can be implemented for OPRoS and RT (Robot Technology) component models to connect with RT and OPRoS ones, respectively. It is shown through a simple experiment that the proposed method works well for real-time control.

• Journal of Korean Institute of Industrial Engineers
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• v.18 no.1
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• pp.105-120
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• 1992

An expert system was developed as a framework of integrating diverse and multifactored ergonomic knowledge to investigate its effectiveness in ergonomic workplace design and evolution. Although numerous computer-assisted approaches have been made to overcome the lack of integrated design principles, those models being used require very specific information of various design activities that may not be available in the design stage. On the other hand, an expert system would be an effective design aid that is capable of guiding the designer to solve a problem. However, most expert systems lack detailed evaluation capabilities due to a qualitative nature of inference mechanisms. Furthermore, those approaches were independently developed, focusing mostly on a single aspect such as biomechanics, physiology, etc. In this paper, a design framework was developed which takes advantage of expert system metholologies, a relational data base and existing ergonomic models. The pattern-directed, rule-based expert system allows the designer to gradually formulate and subsequently evaluate workplace design. A comprehensive and modularized knowledge base was built incorporating biomechanics, physiology and psychophysics, which is, in turn, capable of accessing not only qualitative knowledge but complex analytic evaluation models and massive information in the data base through an interface. A conflict resolution strategy using multiple criteria decision-making schemes was also employed to reconcile multiple design alternatives.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.516-525
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• 2005

Case-based reasoning (CBR) has been widely used in various areas due to its convenience and strength in complex problem solving. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. Most prior studies have tried to optimize the weights of the features or selection process of appropriate instances. But, these approaches have been performed independently until now. Simultaneous optimization of these components may lead to better performance than in naive models. In particular, there have been few attempts to simultaneously optimize the weight of the features and selection of the instances for CBR. Here we suggest a simultaneous optimization model of these components using a genetic algorithm (GA). We apply it to a customer classification model which utilizes demographic characteristics of customers as inputs to predict their buying behavior for a specific product. Experimental results show that simultaneously optimized CBR may improve the classification accuracy and outperform various optimized models of CBR as well as other classification models including logistic regression, multiple discriminant analysis, artificial neural networks and support vector machines.

• International Journal of Highway Engineering
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• v.12 no.4
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• pp.1-9
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• 2010

Predictive information on the freeway incident impacts can be a critical criterion in selecting travel options for users and in operating transportation system for operators. Provided properly, users can select time-effective route and operators can effectively run the system efficiently. In this study, a model is proposed to predict freeway incident impacts. The predictive model for incident impacts is based on short-term prediction. The proposed models are examined using MARE. The analysis results suggest that the models are accurate enough to be deployed in a real-world. The development of microscopic models to predict incident effects is expected to help minimize traffic delay and mitigate related social costs.

• Journal of Veterinary Clinics
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• v.37 no.4
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• pp.180-184
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• 2020

The purpose of this study was to report the reliability and validity of oblique single-cut rotation osteotomy (OSCRO) in 3D-reconstructed canine bone models with femoral varus and torsional deformities. A healthy adult male beagle was recruited to create a 3D bone model, and this bone model was modified by using a 3D program. Fifteen bone models were constructed for this study. OSCRO simulation was performed in accordance with the plan after printing using a 3D printing machine. The anatomical lateral distal femoral angle (aLDFA), anteversion angle (AA), anatomical caudo-distal femoral angle (aCdDFA), mechanical caudo-distal femoral angle (mCdDFA) and pre- and postoperative bone length were calculated. There were no significant differences between the target values and postoperative values. In addition, the difference between pre- and postoperative bone length was small (p = 0.001). Our findings suggest that OSCRO could be an effective surgical option for MPL with bone deformities in small-breed dogs that often undergo conventional distal femoral osteotomy.

• Journal of Welding and Joining
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• v.25 no.6
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• pp.64-70
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• 2007

Recently, several models to control weld quality, productivity and weld properties in arc welding process have been developed and applied. Also, the applied model to make effective use of the robotic GMA(Gas Metal Arc) welding process should be given a high degree of confidence in predicting the bead dimensions to accomplish the desired mechanical properties of the weldment. In this study, a development of the on-line learning neural network models that investigate interrelationships between welding parameters and bead width as well as apply for the on-line quality control system for the robotic GMA welding process has been carried out. The developed models showed an excellent predicted results comparing with the predicted ability using off-line learning neural network. Also, the system will extend to other welding process and the rule-based expert system which can be incorporated with integration of an optimized system for the robotic welding system.

• Journal of Intelligence and Information Systems
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• v.6 no.1
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• pp.35-50
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• 2000

Diverse requirements of users on web-based model management force a system agent to develop user-adaptive building a model in reality and providing an adequate solution method of the model. The relationship between models is important knowledge for the agent to effectively build a new model to adaptively adjust an existing model under a problem and to efficiently connect the new model into an adequate solution method. Since the generating process of the inter-model relationship is more difficult than the building a new model however the process mostly depends on the knowledge of operation research experts. Without the adequate scheme of the inter-model relationship the burden of the management for the agent increases rapidly and the quality of the services may worsen. This study shows that meta-knowledge generated from relationship between models is important for the user to build a model in reality and to acquire the solver appropriate to the model. The relationship that consists of common and exclusive objects between models can be represented by frames. The system under development to implement the idea includes user-adaptive ability which identifies a model through forward chaining method and searches the solver appropriate to the model by using the meta knowledge. We illustrate the meta knowledge with an applied delivery system in supply chain management.

• Geomechanics and Engineering
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• v.19 no.2
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• pp.127-139
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• 2019

Available methods to determine the ultimate bearing capacity of shallow foundations may not be accurate enough owing to the complicated failure mechanism and diversity of the underlying soils. Accordingly, applying new methods of artificial intelligence can improve the prediction of the ultimate bearing capacity. The M5' model tree and the genetic programming are two robust artificial intelligence methods used for prediction purposes. The model tree is able to categorize the data and present linear models while genetic programming can give nonlinear models. In this study, a combination of these methods, called the M5'-GP approach, is employed to predict the ultimate bearing capacity of the shallow foundations, so that the advantages of both methods are exploited, simultaneously. Factors governing the bearing capacity of the shallow foundations, including width of the foundation (B), embedment depth of the foundation (D), length of the foundation (L), effective unit weight of the soil (${\gamma}$) and internal friction angle of the soil (${\varphi}$) are considered for modeling. To develop the new model, experimental data of large and small-scale tests were collected from the literature. Evaluation of the new model by statistical indices reveals its better performance in contrast to both traditional and recent approaches. Moreover, sensitivity analysis of the proposed model indicates the significance of various predictors. Additionally, it is inferred that the new model compares favorably with different models presented by various researchers based on a comprehensive ranking system.

• Advances in biomechanics and applications
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• v.1 no.1
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• pp.67-76
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• 2014

Bone properties are one of the key components when constructing models that can simulate the mechanical behavior of a mandible. Due to the complexity of the structure, the tooth, ligaments, different bones etc., some simplifications are often considered and bone properties are one of them. The objective of this study is to understand if a simplification of the problem is possible and assess its influence on mandible behavior. A cadaveric toothless mandible was used to build three computational models from CT scan information: a full cortical bone model; a cortical and cancellous bone model, and a model where the Young's modulus was obtained as function of the pixel value in a CT scan. Twelve muscle forces were applied on the mandible. Results showed that although all the models presented the same type of global behavior and proximity in some locations, the influence of cancellous bone can be seen in strain distribution. The different Young's modulus defined by the CT scan gray scale influenced the maximum and minimum strains. For modeling general behavior, a full cortical bone model can be effective. However, when cancellous bone is included, maximum values in thin regions increase the strain distribution. Results revealed that when properties are assigned to the gray scale some peaks could occur which did not represent the real situation.

• Nuclear Engineering and Technology
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• v.51 no.5
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• pp.1333-1344
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• 2019

An integrated approach of model simplification for high burnup spent nuclear fuel is proposed based on material calibration using optimization. The spent fuel rods are simplified into a beam with a homogenous isotropic material. The proposed approach of model simplification is applied to fuel rods with two kinds of interfacial configurations between the fuel pellets and cladding. The differences among the generated models and the effects of interfacial bonding efficiency are discussed. The strategy of model simplification adopted in this work is to force the simplified beam model of spent fuel rods to possess the same compliance and failure characteristics under critical loads as those that result in the failure of detailed fuel rod models. It is envisioned that the simplified model would enable the assessment of fuel rod failure through an assembly-level analysis, without resorting to a refined model for an individual fuel rod. The effective material properties of the simplified beam model were successfully identified using the integrated optimization process. The feasibility of using the developed simplified beam models in dynamic impact simulations for a horizontal drop condition is examined, and discussions are provided.