• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.147-154
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• 2018

This paper focuses on the development of a support system that can rapidly generate the design data of a hot-form die with cooling channels, commonly known as hot stamping technology. We propose a new process for designing hot-form dies based on our (automated) system, whose main features are derived from the analysis of the design requirements and design process in the current industry. Our design support system consists of two modules, which allow for the generation of a 3D geometry model and its 2D drawings. The module for 3D modeling automation is implemented as a type of CATIA template model based on CATIA V5 Knowledgeware. This module automatically creates a 3D model of a hot-form die, including the cooling channels, that depends on the shape of the forming surface and the number of STEELs (subsets of die product) and cooling channels. It also allows for both the editing of the positions and orientations of the cooling channels and testing for the purpose of satisfying the constraints on the distance between the forming surface and cooling channels. Another module for the auto-generation of the 2D drawings is being developed as a plug-in using CAA (CATIA SDK) and Visual C++. Our system was evaluated using the S/W test based on a user defined scenario. As a result, it was shown that it can generate a 3D model of a hot form die and its 2D drawings with hole tables about 29 times faster than the conventional manual method without any design errors.

• Journal of the Korean Society of Food Science and Nutrition
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• v.38 no.2
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• pp.235-243
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• 2009

This study was performed to find the optimum ratio of ingredients in the Chinese radish jam. The experiment was designed according to the RSM (response surface methodology), which included 18 experimental points with 4 replicates for three independent variables (sugar $45{\sim}70%$, pectin $0.5{\sim}2.0.%$, citric acid $0.2{\sim}0.5%$). The compositional and functional properties of the prepared products were measured, and these values were applied to the mathematical models. By use of F-test, sweetness, color values (L, a, b), and firmness were expressed by a linear model, while the sensory characteristics (color, smell, taste, texture and overall acceptance) were by a quadratic model. In the numeric optimization, the optimal ingredient amounts were 53.7% sugar, 1.0% pectin, and 0.3% citric acid. And in the graphical optimization, 53.9% sugar, 1.0% pectin, and 0.3% citric acid; these data were equivalent to 0.6985 desirability, indicating that the values were almost equivalent to the numerical optimization points. The above results demonstrate the feasibility of Chinese radish jam, and therefore, the commercialization of a Chinese radish jam marketed as a functional food is deemed possible.

• Journal of Food Hygiene and Safety
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• v.24 no.1
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• pp.63-68
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• 2009

The statistics probability approach for microbial risk assessment (MRA) has been recognized as an efficient method because this probability approach, which can be presented the diversity, variability, and uncertainty for the environmental factors of food processing, provide better realistic results than point estimate. This study was conducted to determine of probability statistics for the environmental factors of the pork-cutting processing i.e. the processing time, the pork meat temperature, and processing room temperature etc. As the input parameters for the MRA, triangular distribution and normal distribution were selected as an efficient probability distribution model, these distributions were analyzed by the simulation. The simulation results showed the processing time estimated 53 min as mean (5% - 22 min and 95% - 98 min), pork meat temperature estimated $4.83^{\circ}C$ as mean (5% - $2.25^{\circ}C$ and 95% - $7.12^{\circ}C$, 48.78% exceed $5^{\circ}C$), and processing room temperature estimated $17^{\circ}C$ as mean (5% - $10.92^{\circ}C$ and 95% - $22.56^{\circ}C$, 71.178% exceed $15^{\circ}C$).

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.5
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• pp.282-292
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• 2007

In this paper, we propose the active model based on the distributed object group framework which provides adaptive information sharing service to support it to various Healthcare home service. And we applied it to Healthcare home service. This proposed model provides information that collected from physical environment of the home network and sensors for healthcare service according to situation of the user. We uses information of the healthcare information database which was constructed previously, and we uses TMO scheme for to solve each other different periodic qualify. We uses information of the healthcare information database which was constructed previously, and we uses TMO scheme for to solve each other different periodic quality. Specialty, We wrote about interaction of various Healthcare Home services for adaptive information services, and wrote about u-healthcare framework based on DOGF. Finally, we apply active model to healthcare monitoring service, and we showed its result and performance evaluation.

• Polymer(Korea)
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• v.24 no.1
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• pp.105-112
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• 2000

Using a commercial epoxy/carbon fiber composite prepreg (DMS 2224) as a model system, the cure kinetics of vitrifying thermoset system were analyzed by isothermal and dynamic-heating experiments. Focusing on the processing condition of high performance composite systems, a phenomenological kinetic model was developed by using differential scanning calorimetry (DSC) and reaction kinetics theories. The model system exhibited a limited degree of cure as a function of isothermal temperature seemingly due to the diffusion-controlled reaction rates. The diffusion-controlled cure reaction was incorporated in the development of the kinetic model, and the model parameters were determined from isothermal experiments. The first order reaction was confirmed from the characteristic shape of isothermal cure thermograms, and the activation energy wes 78.43 kJ/mol. Finally, the proposed model was used to predict a complex autoclave thermal condition, which was composed of several isothermal and dynamic-heating stages.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.302-313
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• 2023

This paper aims to develop a framework that can fully automate the quality management of training data used in large-scale Artificial Intelligence (AI) models built by the Ministry of Science and ICT (MSIT) in the 'AI Hub Data Dam' project, which has invested more than 1 trillion won since 2017. Autonomous driving technology using AI has achieved excellent performance through many studies, but it requires a large amount of high-quality data to train the model. Moreover, it is still difficult for humans to directly inspect the processed data and prove it is valid, and a model trained with erroneous data can cause fatal problems in real life. This paper presents a dataset reconstruction framework that removes abnormal data from the constructed dataset and introduces strategies to improve the performance of AI models by reconstructing them into a reliable dataset to increase the efficiency of model training. The framework's validity was verified through an experiment on the autonomous driving dataset published through the AI Hub of the National Information Society Agency (NIA). As a result, it was confirmed that it could be rebuilt as a reliable dataset from which abnormal data has been removed.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.47-55
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• 2023

This paper presents a method for predicting the threat index of combat systems using Gradient Boosting Regressors and Support Vector Regressors among machine learning models. Currently, combat systems are software that emphasizes safety and reliability, so the application of AI technology that is not guaranteed to be reliable is restricted by policy, and as a result, the electrified domestic combat systems are not equipped with AI technology. However, in order to respond to the policy direction of the Ministry of National Defense, which aims to electrify AI, we conducted a study to secure the basic technology required for the application of machine learning in combat systems. After collecting the data required for threat index evaluation, the study determined the prediction accuracy of the trained model by processing and refining the data, selecting the machine learning model, and selecting the optimal hyper-parameters. As a result, the model score for the test data was over 99 points, confirming the applicability of machine learning models to combat systems.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2007.05a
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• pp.229-232
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• 2007

In hot strip rolling, the work roll profile is one of the main factors in predicting and correcting the strip profile. Various studies concerning the wear profile and the thermal crown of work roll have been performed, and the results of these studies have shown that the work roll profile must be predicted accurately so as to efficiently control the strip qualities such as thickness, crown, flatness, and camber. Therefore, a precise prediction model of roll profile is called for in a perfect shape control system. In this paper, a genetic algorithm was applied to improve on the roll profile prediction model in hot strip rolling. In this approach, the optimal design problem is formulated on the basis of a numerical model so as to cover the diverse design variables and objective functions. A genetic algorithm was adopted for conducting design iteration for optimization to determine the coefficient of the numerical model for minimization of errors in the result of the calculated value and the measured data. A comparative analysis showed a satisfactory conformity between them..

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.3
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• pp.17-24
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• 2008

Rail clamps are mechanical components installed to fix the container crane to its lower members against wind blast or slip. According to rail clamps should be designed to survive harsh wind loading conditions. In this study, a jaw structure, which is a part of a wedge-typed rail clamp, is optimized with respect to its strength under a severe wind loading condition. According to the classification of structural optimization, the structural optimization of a jaw is included in the category of shape optimization. Conventional structural optimization methods have difficulties in defining complex shape design variables and preventing mesh distortions. To overcome the difficulties, the metamodel using Kriging interpolation method is introduced to replace the true response by an approximate one. This research presents the shape optimization of a jaw using iterative Kriging interpolation models and a simulated annealing algorithm. The new Kriging models are iteratively constructed by refining the former Kriging models. This process is continued until the convergence criteria are satisfied. The optimum results obtained by the suggested method are compared with those obtained by the DOE (design of experiments) and VT (variation technology) methods built in ANSYS WORKBENCH.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.3
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• pp.87-95
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• 2008

The purpose of this study is to provide a decision support to select an appropriate rapid prototyping(RP) machine that suits the application of a part. Selection factors include concept model, form/fit/functional model, pattern model for molding, material property, build time and part cost that greatly affect the performance of RP machines. However, the selection of a RP is not an easy decision because they are uncertain and vague. For this reason, the aim of this research is to propose hybrid multiple attribute decision making approaches to effectively evaluate RP machines. In addition, because subjective considerations are relevant to selection decision, a fuzzy logic approach is adopted. The proposed selection procedure consists of several steps. First, we identify RP machines that the users consider. After constructing the evaluation criteria, we calculate the weights of the criteria by applying the fuzzy Analytic Hierarchy Process(AHP) method. Finally, we construct the fuzzy Technique of Order Preference by Similarity to Ideal Solution(TOPSIS) method to achieve the ranking order of all machines providing the decision information for the selection of RP machines.