• Proceedings of the KSR Conference
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• 1998.11a
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• pp.545-552
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• 1998

A study on collision analysis of TGV-K using a 2-dimensional model is described to evaluate its crashworthiness. Two-dimensional analysis gives good information on overriding behaviour and impact forces applied to interconnecting devices such as side buffers, ball & socket joints, hooks, pins, and fingers. Since the headstock of TGV-K is not designed in a crashworthy point of view, its conceptual design fur KHST(Korean High Speed Train), under development, is suggested to improve crashworthiness. The suggested design, which adopts an energy absorber and a crashworthy headstock, is compared with the conventional headstock on dynamic behaviour to the vertical direction under the accident scenario of SNCF (collision at 110km/h against a movable rigid mass of 15 ton). It is concluded that the design modification make little difference in vertical motion. To evaluate validation of the 2-dimensional model, the results fur longitudinal motion is compared with those of 1-dimemsional one. It is found that the two results are in good agreements.

• ETRI Journal
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• v.41 no.4
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• pp.483-493
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• 2019

Considering the high dimensions of video sequences, it is often challenging to acquire a sufficient dataset to train the tracking models. From this perspective, we propose to revisit the idea of hand-crafted feature learning to avoid such a requirement from a dataset. The proposed tracking approach is composed of two phases, detection and tracking, according to how severely the appearance of a target changes. The detection phase addresses severe and rapid variations by learning a new appearance model that classifies the pixels into foreground (or target) and background. We further combine the raw pixel features of the color intensity and spatial location with convolutional feature activations for robust target representation. The tracking phase tracks a target by searching for frame regions where the best pixel-level agreement to the model learned from the detection phase is achieved. Our two-phase approach results in efficient and accurate tracking, outperforming recent methods in various challenging cases of target appearance changes.

• Journal of the Korean Society of Safety
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• v.33 no.6
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• pp.42-49
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• 2018

In this study, we have developed a prediction model for expansion and contraction behaviors of expansion joint in Gwangan Bridge using machine learning techniques and bridge monitoring data. In the development of the prediction model, two famous machine learning techniques, multiple regression analysis (MRA) and artificial neural network (ANN), were employed. Structural monitoring data obtained from bridge monitoring system of Gwangan Bridge were used to train and validate the developed models. From the results, it was found that the expansion and contraction behaviors predicted by the developed models are matched well with actual expansion and contraction behaviors of Gwangan Bridge. Therefore, it can be concluded that both MRA and ANN models can be used to predict the expansion and contraction behaviors of Gwangan Bridge without actual measurements of those behaviors.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.77-88
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• 2021

Heart Failure represents a critical pathological case that is challenging to predict and discover at an early age, with a notable increase in morbidity and mortality. Machine Learning and Neural Network techniques play a crucial role in predicting heart attacks, diseases and more. These techniques give valuable perspectives for clinicians who may then adjust their diagnosis for each individual patient. This paper evaluated neural network models for heart attacks predictions. Several online learning methods were investigated to automatically and accurately predict heart attacks. The UCI dataset was used in this work to train and evaluate First Order and Second Order Online Learning methods; namely Backpropagation, Delta bar Delta, Levenberg Marquardt and QuickProp learning methods. An optimizer technique was also used to minimize the random noise in the database. A regularization concept was employed to further improve the generalization of the model. Results show that a three layers' NN model with a Backpropagation algorithm and Nadam optimizer achieved a promising accuracy for the heart attach prediction tasks.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.1-6
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• 2021

Recently, pre-trained convolutional neural network CNNs have been widely used and applied for medical image classification. These models can utilised in three different ways, for feature extraction, to use the architecture of the pre-trained model and to train some layers while freezing others. In this study, the ResNet18 pre-trained CNNs model is used for feature extraction, followed by the support vector machine for multiple classes to classify medical images from multi-classes, which is used as the main classifier. Our proposed classification method was implemented on Kvasir and PH2 medical image datasets. The overall accuracy was 93.38% and 91.67% for Kvasir and PH2 datasets, respectively. The classification results and performance of our proposed method outperformed some of the related similar methods in this area of study.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.127-130
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• 2022

The number of published journals, conferences, and research papers in computer science is increasing rapidly, which has led to a challenge in coming up with new and unique ideas for research. To alleviate the issue, this paper uses artificial neural networks (ANNs) to generate new computer science research ideas. It does so by using a dataset collected from IEEE published journals and conferences to train an ANN model. The results reveal that the model has a 14% success rate in generating usable ideas. The outcome of this paper has implications for helping both new and experienced researchers come up with novel research topics.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.67-74
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• 2022

One type of network security breach is the availability breach, which deprives legitimate users of their right to access services. The Denial of Service (DoS) attack is one way to have this breach, whereas using the Intrusion Detection System (IDS) is the trending way to detect a DoS attack. However, building IDS has two challenges: reducing the false alert and picking up the right dataset to train the IDS model. The survey concluded, in the end, that using a real dataset such as MAWILab or some tools like ID2T that give the researcher the ability to create a custom dataset may enhance the IDS model to handle the network threats, including DoS attacks. In addition to minimizing the rate of the false alert.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.3
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• pp.123-131
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• 1996

This paper presents the development of an algorithm for the reliability and life of a hypoid gear axle system (located in the last section of power train) in heavy vehicles. The algorithm is developed about expecting the reliability and service-life for applied loads on each component in the system using the Weibull's probabilistic distribution and the extended Palmgren's model. The probabilistic method is used to results. Also this model is involved in predicting the failure which is related to the number of load cycles with the approaching load. Then the precious evaluation of the reliability and life in the axle system can be effectively carried out. Thus the general procedures of a reliability and life design, including the mathematical formulation and numerical examples, are illustrated for a hypoid gear axle system.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.229-230
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• 2022

With the development of digital technology, construction companies at home and abroad are in the process of computerizing work and site information for the purpose of improving work efficiency. To this end, various technologies such as BIM, digital twin, and AI-based safety management have been developed, but the accuracy and completeness of the related technologies are insufficient to be applied to the field. In this paper, the learning data that has undergone a pre-processing process optimized for recognition of construction information based on structural members is trained on an existing artificial intelligence model to improve recognition accuracy and evaluate its effectiveness. The artificial intelligence model optimized for the structural member created through this study will be used as a base technology for the technology that needs to confirm the safety of the structure in the future.

• Annual Conference of KIPS
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• 2023.11a
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• pp.624-627
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• 2023

Unmanned aerial vehicles are gaining in popularity with the development of science and technology, and are being used for a wide range of purposes, including surveillance, rescue, delivery of goods, and data collection. In particular, the ability to avoid obstacles during navigation without human oversight is one of the essential capabilities that a drone must possess. Many works currently have solved this problem by implementing deep reinforcement learning (DRL) model. The essential core of a DRL model is reward function. Therefore, this paper proposes a new reward function with appropriate action space and employs dueling double deep Q-Networks to train a drone to navigate in indoor environment without collision.