• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.3
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• pp.556-571
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• 2020

This study predicts consumer preference for social clothing at work, excluding uniforms using the self-product congruence theory that also establishes a model to predict the preference for recommended products that match the consumer's own image. A total of 490 Korean male office workers participated in this study. Participants' self-image and the product images of 20 apparel items were measured using nine adjective semantic scales (namely elegant, stable, sincere, refined, intense, luxury, bold, conspicuous, and polite). A model was then constructed to predict the consumer preferences using a neural network with Python and TensorFlow. The resulting Predict Preference Model using Product Image (PPMPI) was trained using product image and the preference of each product. Current research confirms that product preference can be predicted by the self-image instead of by entering the product image. The prediction accuracy rate of the PPMPI was over 80%. We used 490 items of test data consisting of self-images to predict the consumer preferences for using the PPMPI. The test of the PPMPI showed that the prediction rate differed depending on product attributes. The prediction rate of work apparel with normative images was over 70% and higher than for other forms of apparel.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.133-134
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• 2022

Recently, the criteria for assessing industrial accidents have been replaced by the mortality rate. It was found that the number of deaths from excavation work was the highest among construction machinery. The risk assessment is being conducted, however the industrial accident mortality rate has not decreased. Accordingly, this study aims to provide the basic for the create of a risk assessment model specialized in construction work at excavator. It provides absolute value from the risk model which is capable of delivery the probability of a disaster. In addition, we provide a relative risk model that compares the risk through scores between detailed works. The relative risk model is combined by likelihood and severity; the likelihood indicates the frequency of accidents and the severity indicates seriousness of fatal accidents. A variable that reflects the conditions of the construction site was added to the risk assessment model based on past disaster cases. And using the concepts of probability and average, the risk assessment process was quantified and used as an objective indicator. Therefore, the model is expected to reduce disasters by raising the awareness of disasters.

• International Journal of Highway Engineering
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• v.13 no.3
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• pp.195-202
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• 2011

The purpose of this study is to estimate the capacity of two-lane freeway work zone blocking one lane using traffic flow models of each vehicle-type. Firstly, three traffic flow models of three different vehicle-types were developed using the data collected from each at the beginning and the ending point of the work zone. For each model, the maximum flow rate of three vehicle-types were calculated respectively. Maximum flow rate at the work zone was recalculated using passenger car equivalent value and percentage of vehicle-type. Secondly, traffic flow model using passenger car equivalent volume data was developed using the data collected from each at the beginning and the ending point of the work zone. Maximum flow rate for the work zone was calculated along. Two values of maximum flow rates through the work zone were compared and evaluated as the capacity of the work zone. This study found that the maximum flow rate of the work zone at the beginning point was less than that at the ending point because of impedance such as lane changing behaviors before entering the work zone. The capacity of two-lane freeway work zone blocking one lane was estimated 1,800pcphpl.

• Journal of ILASS-Korea
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• v.25 no.1
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• pp.8-14
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• 2020

Recently, 1-D model-based engine development using virtual engine system is getting more attention than experimental-based engine development due to the advantages in time and cost. Injection rate profile is the one of the main parameters that determine the start and end of combustion. Therefore, it is essential to set up a sophisticated model to accurately predict the injection rate as starting point of virtual engine system. In this research, procedure of 1-D model setup based on AMESim is introduced to predict the dynamic behavior and injection rate of diesel injector. As a first step, detailed 3D cross-sectional drawing of the injector was achieved, which can be done with help of precision measurement system. Then an approximate AMESim model was provided based on the 3D drawing, which is composed of three part such as solenoid part, control chamber part and needle and nozzle orifice part. However, validation results in terms of total injection quantity showed some errors over the acceptable level. Therefore, experimental work including needle movement visualization, solenoid part analysis and flow characteristics of injector part was performed together to provide more accuracy of 1-D model. Finally, 1-D model with the accuracy of less than 10% of error compared with experimental result in terms of injection quantity and injection rate shape under normal temperature and single injection condition was established. Further work considering fuel temperature and multiple injection will be performed.

• Journal of Biosystems Engineering
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• v.38 no.1
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• pp.33-40
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• 2013

Purpose: The goal of this study was to build an accurate digital factory that evaluates the performance of a factory using computer simulation. To achieve this goal, we evaluated the effect of worker-related variables on production in a simulation model using comparative analysis of two cases. Methods: The overall work process and worker-related variables were determined and used to build a simulation model. Siemens PLM Software's Plant Simulation was used to build a simulation model. Also, two simulation models were built, where the only difference was the use of the worker-related variable, and the total daily production analyzed and compared in terms of the individual process. Additionally, worker efficiency was evaluated based on worker analysis. Results: When the daily production of the two models were compared, a 0.16% error rate was observed for the model where the worker-related variables were applied and error rate was approximately 5.35% for the model where the worker-related variables were not applied. In addition, the production in the individual processes showed lower error rate in the model that included the worker-related variables than the model where the worker-related variables were not used. Also, among the total of 22 workers, only three workers satisfied the IFRS (International Financial Reporting Standards) suggested worker capacity rate (90%). Conclusions: In the daily total production and individual process production, the model that included the worker-related variables produced results that were closer to the real production values. This result indicates the importance of worker elements as input variables, in regards to building accurate simulation models. Also, as suggested in this study, the model that included the worker-related variables can be utilized to analyze in more detail actual production. The results from this study are expected to be utilized to improve the work process and worker efficiency.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1995.10a
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• pp.60-63
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• 1995

The behavior of the work materials in the chip-tool interface in extremely high strain rates and temperatures is more that of viscous liquids than that of normal solid metals. In these circumstances the principles of fluid mechanics can be invoked to describe the metal flow in the neighborhood of the cutting edge. In the present paper an Eulerian finite element model is presented that simulates metal flow in the vicinity of the cutting edge when machining a low carbon steel with carbide cutting tool. The work material is assumed to obey visco-plastic (Bingham solid) constitutive law and Von Mises criterion. Heat generation is included in the model, assuming adiabatic conditions within each element. the mechanical and thermal properties of the work material are accepted to vary with the temperature. The model is based on the virtual work-stream function formulation, emphasis is given on analyzing the formation of the stagnant metal zone ahead of the cutting edge. The model predicts flow field characteristics such as material velocity effective stress and strain-rate distributions as well as built-up layer configuration

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.10
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• pp.4618-4625
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• 2011

This paper suggests a specific model that could efficiently improve the interaction and the interface between MES(Manufacturing Execution System) server and POP(Point Of Production) Terminal through RFID(Radio Frequency Identification) system in Automative Module Assembly Line. The proposed model shows that the new method by RFID can more efficiently perform to receive work order informations and transmit work performances, compared with the current approach by proximity sensor. As a result of the certain test among the MES server, RFID system, PLC(Programmable Logic controller) and POP terminal, it is noted in case of the automatic control by RFID that the effects of proposed model are as follows; (a) While the processing time per truck for carrying by the current method was 10 minutes, the processing time by the new method was 1 minutes. (b) While the error rate by the current method was 20 %, the error rate by the new method was 1 %.

• Speech Sciences
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• v.14 no.1
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• pp.151-162
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• 2007

This study focuses on the classification of pathological voice using GMM (Gaussian Mixture Model) and compares the results to the previous work which was done by ANN (Artificial Neural Network). Speech data from normal people and patients were collected, then diagnosed and classified into two different categories. Six characteristic parameters (Jitter, Shimmer, NHR, SPI, APQ and RAP) were chosen. Then the classification method based on the artificial neural network and Gaussian mixture method was employed to discriminate the data into normal and pathological speech. The GMM method attained 98.4% average correct classification rate with training data and 95.2% average correct classification rate with test data. The different mixture number (3 to 15) of GMM was used in order to obtain an optimal condition for classification. We also compared the average classification rate based on GMM, ANN and HMM. The proper number of mixtures on Gaussian model needs to be investigated in our future work.

• Korean Journal of Construction Engineering and Management
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• v.21 no.3
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• pp.48-56
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• 2020

Prediction of construction cost in the planning stage that provides basic information for feasibility study, budgeting, and planning is an important factor for successful project execution. In this study, a prediction model was developed for the purpose of improving the accuracy of estimating the construction cost of Hanok in the planning stage. The cost of this model is estimated by two methods. First, the cost of wood work, which accounts for the largest portion of the total construction cost, is estimated by calculating the approximate quantity under various conditions (structure type, roof type, plane type, etc.). Second, the cost of the rest work sections except the wood work is estimated by using the unit cost model. The predictive model was verified by two case projects, and the error rate of total construction cost was -4%(case 1) and -6%(case 2). These results showed an error rate in the range that can be applied to practice in the planning stage.

• Nuclear Engineering and Technology
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• v.56 no.7
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• pp.2881-2892
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• 2024

Annular flow refers to a special type of two-phase flow pattern in which liquid flows as a thin film at the periphery of a pipe, tube, or conduit, and gas with relatively high velocity flows at the center of the flow section. This gas also includes dispersed liquid droplets. The liquid film flow rate continuously changes inside the tube due to two processes-entrainment and deposition. To determine the liquid holdup, pressure drop, the onset of dryout, and heat transfer characteristics in annular flow, it is important to have proper knowledge of flow characteristics. Especially a better understanding of entrainment fraction is important for the heat transfer and safe operation of two-phase flow systems operating in an annular two-phase flow regime. Therefore, the objective of this work is to develop a computational model for the simulation of the annular two-phase flow regime and assess the various existing models for the entrainment rate. In this work, Computational Fluid Dynamics (CFD) in ANSYS FLUENT has been applied to determine annular flow characteristics such as liquid film thickness, film velocity, entrainment rate, deposition rate, and entrainment fraction for various gas-liquid flow conditions in a vertical upward tube. The gas core with droplets was simulated using the Discrete Phase Model (DPM) which is based on the Eulerian-Lagrangian approach. The Eulerian Wall Film (EWF) model was utilized to simulate liquid film on the tube wall. Three different models of Entrainment rate were implemented and assessed through user-defined functions (UDF) in ANSYS. Finally, entrainment for fully developed flow was determined and compared with the experimental data available in the literature. From the simulations, it was obtained that the Bertodano correlation performed best in predicting entrainment fraction and the results were within the ±30 % limit when compared to experimental data.