• Proceedings of the KSR Conference
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• 2002.10a
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• pp.54-59
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• 2002

In general, the aluminum extrusions are used to the light construction of the high speed rail vehicle structures. However, the research works ok the crashworthy design of the high speed rail vehicle structures are not published sufficiently because the crash test of high speed rail vehicle structures costs high and is complicated. So, a method that can predict crash characteristics of a large size structure like a high speed tail vehicle should be suggested. In this study, the scale model studies are performed to predict the impact energy absorption characteristics of full scale model. In the first place, we verified the theory of scale law using FE-simulation from the crashworthiness point of view. Secondly, we performed the crush test using scale model, made of aluminum sub structure. As a result, we could predict the crash characteristics using scale model by 10∼20% error.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.464-469
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• 2023

As modern society develops, the number of diseases caused by bad smells is increasing. As it can harm people's health, it is important to predict in advance the extent to which bad smells may occur, inform the public about this, and take preventive measures. In this paper, we propose a hybrid neural network structure of CNN and LSTM that can be used to detect or predict the occurrence of odors, which are most required in manufacturing or real life, using odor complex sensors. In addition, the proposed learning model uses a complex odor sensor to receive four types of data, including hydrogen sulfide, ammonia, benzene, and toluene, in real time, and applies this data to the inference model to detect and predict the odor state. The proposed model evaluated the prediction accuracy of the training model through performance indicators based on accuracy, and the evaluation results showed an average performance of more than 94%.

• Structural Engineering and Mechanics
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• v.62 no.2
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• pp.225-236
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• 2017

The purpose of this work is to predict ductile fracture of structural steel under extremely low cyclic loading experienced in earthquake. A cumulative damage model is proposed on the basis of an existing damage model originally aiming to predict fracture under monotonic loading. The cumulative damage model assumes that damage does not grow when stress triaxiality is below a threshold and fracture occurs when accumulated damage reach unit. The model was implemented in ABAQUS software. The cumulative damage model parameters for steel base metal, weld metal and heat affected zone were calibrated, respectively, through testing and finite element analyses of notched coupon specimens. The damage evolution law in the notched coupon specimens under different loads was compared. Finally, in order to examine the engineering applicability of the proposed model, the fracture performance of beam-column welded joints reported by previous researches was analyzed based on the cumulative damage model. The analysis results show that the cumulative damage model is able to successfully predict the cracking location, fracture process, the crack initiation life, and the total fatigue life of the joints.

• Journal of the Korea Safety Management & Science
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• v.17 no.1
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• pp.169-177
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• 2015

We develop a model to predict typhoons in Korea. We collect data for typhoons and classify those depending on the severity level. Following a Bayesian approach, we develop a model that explains the relationship between different levels of typhoons. Through the analysis of the model, we can predict the rate of typhoons, the probability of approaching Korean peninsular, and the probability of striking Korean peninsular. We show that the uncertainty for the occurrence of various types of typhoons reduces dramatically by adaptively updating model parameters as we acquire data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.6
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• pp.433-439
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• 2022

Muscle fatigue models to predict maximum endurance time (MET) are broadly classified as either 'empirical' or 'theoretical'. Empirical models are based on fitting experimental data and theoretical models on mathematical representations of physiological process. This paper examines the effectiveness of dynamic muscle fatigue model as theoretical model to predict maximum endurance time during forearm isometric contraction. Forty volunteers (20 females, 20 males) are participated in this study. Empirical models (exponential model and power model) and theoretical model (dynamic muscle fatigue model) are used to compare. Mean absolute deviation (MAD), correlation coefficient (r) and intraclass correlation (ICC) are calculated between theoretical model and empirical models. MAD are below 3.5%p, r and ICC are above 0.93 and 0.87, respectively. This results demonstrate that dynamic muscle fatigue model as theoretical model is valid to predict MET.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.265-271
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• 2022

Social media is a form of communication based on the internet to share information through content and images. Their choice of profile images and type of image they post can be closely connected to their personality. The user posted images are designated as personality traits. The objective of this study is to predict five factor model personality dimensions from profile images by using deep learning and neural networks. Developed a deep learning framework-based neural network for personality prediction. The personality types of the Big Five Factor model can be quantified from user profile images. To measure the effectiveness, proposed two models using convolution Neural Networks to classify each personality of the user. Done performance analysis among two different models for efficiently predict personality traits from profile image. It was found that VGG-69 CNN models are best performing models for producing the classification accuracy of 91% to predict user personality traits.

• Journal of Mechanical Science and Technology
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• v.16 no.9
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• pp.1054-1064
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• 2002

Numerical prediction of welding-induced residual stresses using the finite element method has been a common practice in the development or refinement of welded product designs. Various researchers have studied several thermal models associated with the welding process. Among these thermal models, ramp heat input and double-ellipsoid moving source have been investigated. These heat-source models predict the temperature fields and history with or without accuracy. However, these models can predict the thermal characteristics of the welding process that influence the formation of the inherent plastic strains, which ultimately determines the final state of residual stresses in the weldment. The magnitude and distribution of residual stresses are compared. Although the two models predict similar magnitude of the longitudinal stress, the double-ellipsoid moving source model predicts wider tensile stress zones than the other one. And, both the ramp heating and moving source models predict the stress results in reasonable agreement with the experimental data.

• 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.

• Journal of Korean Society of Water and Wastewater
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• v.22 no.3
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• pp.389-396
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• 2008

The hybrid model can be used to predict the initial near field mixing and the far field transport of the buoyant jets, which are discharged from the submerged wastewater ocean outfall. In the near field, the jet integral model can be used for single port diffusers while the ${\sigma}$ transformed particle tracking model was used in the far field. In this study, the experimental study was performed to verify the developed hybrid model in the previous research. The developed hybrid model properly predict the surface and vertical concentration distribution of the single buoyant jets with various effluent and ambient conditions. The hybrid model can also simulate the surface concentration distribution of the rosette diffuser except for the parallel diffuser with the higher densimetric Froude number due to the assumption that dynamic effects of the effluent plumes are negligible in the far field. The application of the hybrid model to rosette diffusers can predict the concentration near the diffuser more accurately when the line-plume approximation is used.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.205-211
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• 2020

The method of predicting the future may be predicted by technical characteristics or technical performance. Therefore, technology prediction is used in the field of strategic research that can produce economic and social benefits. In this study, we predicted the future market through the study of how to predict the future with these technical characteristics. The future prediction method was studied through the prediction of the time when the market occupied according to the demand of special product. For forecasting market demand, we proposed the future forecasting model through comparison of representative quantitative analysis methods such as CAGR model, BASS model, Logistic model and Gompertz Growth Curve. This study combines Rogers' theory of innovation diffusion to predict when products will spread to the market. As a result of the research, we developed a methodology to predict when a particular product will mature in the future market through the spread of various factors for the special product to occupy the market. However, there are limitations in reducing errors in expert judgment to predict the market.