• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.491-501
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• 2019

In side-channel analysis, which exploit physical leakage from a cryptographic device, deep learning based attack has been significantly interested in recent years. However, most of the state-of-the-art methods have been focused on classifying side-channel information in a profiled scenario where attackers can obtain label of training data. In this paper, we propose a new method based on deep learning to improve non-profiling side-channel attack such as Differential Power Analysis and Correlation Power Analysis. The proposed method is a signal preprocessing technique that reduces the noise in a trace by modifying Auto-Encoder framework to the context of side-channel analysis. Previous work on Denoising Auto-Encoder was trained through randomly added noise by an attacker. In this paper, the proposed model trains Auto-Encoder through the noise from real data using the noise-reduced-label. Also, the proposed method permits to perform non-profiled attack by training only a single neural network. We validate the performance of the noise reduction of the proposed method on real traces collected from ChipWhisperer board. We demonstrate that the proposed method outperforms classic preprocessing methods such as Principal Component Analysis and Linear Discriminant Analysis.

• Journal of the Korea Computer Graphics Society
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• v.24 no.5
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• pp.31-39
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• 2018

In this paper, we propose an automated method for the ground-glass nodule(GGN) classification using GGN-Net based on intensity, texture, and shape-enhanced images in chest CT images. First, we propose the utilization of image that enhances the intensity, texture, and shape information so that the input image includes the presence and size information of the solid component in GGN. Second, we propose GGN-Net which integrates and trains feature maps obtained from various input images through multiple convolution modules on the internal network. To evaluate the classification accuracy of the proposed method, we used 90 pure GGNs, 38 part-solid GGNs less than 5mm with solid component, and 23 part-solid GGNs larger than 5mm with solid component. To evaluate the effect of input image, various input image set is composed and classification results were compared. The results showed that the proposed method using the composition of intensity, texture and shape-enhanced images showed the best result with 82.75% accuracy.

• Journal of Korea Port Economic Association
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• v.35 no.2
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• pp.109-126
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• 2019

The Panmunjom Declaration adopted by the leaders of South and North Korea on April 27, 2018, has created an environment conducive for peace and cooperation in the Korean Peninsula. In the June of last year, South Korea has joined the Organization for Cooperation between Railways (OSJD). The membership of OSJD has established a solid foundation for restoring a multimodal logistics system that connects the Korean peninsula to Eurasia countries, including China and Russia. In this paper, a questionnaire survey targeting working-level experts was conducted to find the barriers in constructing multimodal logistics that efficiently connect the port-continental railways of the Korean peninsula and the Eurasian nations. Survey items were divided into five categories-border crossing procedures, technology, facilities, operation, and government support. As a result, among the most important problems of international multimodal logistics in Eurasia that need to be solved on priority include improving transshipment facilities, eliminating inspection carried out at every country for transit, simplifying documents for customs clearance, and minimizing the changes in freight rates. In conclusion, for vitalizing the connection between the Korean peninsula and the continental railways, it is necessary to develop a transshipment system to facilitate the changes in tracks at the borders by making a joint effort with the international community. Second, railway and operational systems in South Korea, North Korea, China, and Russia should be standardized. Third, international cooperation among South Korea, North Korea, China, and Russia is essential for simplifying customs clearance at borders, priority departure of domestic cargo, sharing information about the changes in freight rates, and so on. Finally, the government should come up with measures to secure the quantity of cargo required to form block trains, while developing new business models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.887-895
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• 2018

This paper presents a braking model that can be used to design the safety distance of a train control system and a train braking system to increase the volume of traffic. For the braking model, a train set (electric multiple unit composed 6 cars) was tested. The factors that can affect the braking characteristics include the friction coefficient, braking pressure, and regenerative braking. The braking pressure was classified into service and emergency braking and reflected the characteristics of the vehicle. The external force acting on the running railway car was tested in accordance with KS R 9217, and the running resistance of the train is presented in the form of a polynomial. The dynamic behavior of the train running on a straight flat line was simulated using UM 8.3. The results were validated with experimental data, and the results were reasonable. With the validated model, a stopping distance was determined according to the initial braking speed and compared with the deceleration braking model. In addition, a safety distance for the train control system could be changed according to the frictional coefficient limits. These results are expected to be useful for analyzing the dynamic behavior of trains, and for analyzing various railway environments and improving the braking performance.

• Journal of The Korean Society For Urban Railway
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• v.6 no.4
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• pp.319-326
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• 2018

The safety interval to prevent collision between trains in a train control system is based on the braking distance according to the emergency braking of the train. The evaluation of the braking performance is based on the longitudinal train dynamics or the commissioning test in the test track, but since the conditions such as the weakening of the adhesion coefficient between the wheel and rail can not all be considered, these conventional methods are not sufficient to design of the train control systems. Therefore, in this study, the Monte Carlo Method (MCM) which can consider various environments is used to analyze braking performance and limitations. The braking model is based on the air braking used in the emergency braking and is modeled to take into account the braking pressure, efficiency, friction coefficient, adhesion condition, and vehicle mass distribution. It is confirmed that braking performance can be improved by controlling the quality of braking device. In addition, the change of the braking performance was confirmed according to the vehicle constituting the train. The results of this study are expected to be used as basic information for designing safety clearance for the train control systems and as a basis for improving the braking performance of railway vehicles.

• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.3
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• pp.303-310
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• 2021

Cracks in bridges are important factors that indicate the condition of bridges and should be monitored periodically. However, a visual inspection conducted by a human expert has problems in cost, time, and reliability. Therefore, in recent years, researches to apply a deep learning model are started to be conducted. Deep learning requires sufficient data on the situations to be predicted, but bridge crack data is relatively difficult to obtain. In particular, it is difficult to collect a large amount of crack data in a specific situation because the shape of bridge cracks may vary depending on the bridge's design, location, and construction method. This study developed a crack detection model that generates and trains insufficient crack data through a Generative Adversarial Network. GAN successfully generated data statistically similar to the given crack data, and accordingly, crack detection was possible with about 3% higher accuracy when using the generated image than when the generated image was not used. This approach is expected to effectively improve the performance of the detection model as it is applied when crack detection on bridges is required, though there is not enough data, also when there is relatively little or much data f or one class.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1291-1300
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• 2020

Recently, a deep learning-based non-profiling side-channel analysis was proposed. The deep learning-based non-profiling analysis is a technique that trains a neural network model for all guessed keys and then finds the correct secret key through the difference in the training metrics. As the performance of non-profiling analysis varies greatly depending on the neural network training model design, a correct model design criterion is required. This paper describes the two types of loss functions and eight labeling methods used in the training model design. It predicts the analysis performance of each labeling method in terms of non-profiling analysis and power consumption model. Considering the characteristics of non-profiling analysis and the HW (Hamming Weight) power consumption model is assumed, we predict that the learning model applying the HW label without One-hot encoding and the Correlation Optimization (CO) loss will have the best analysis performance. And we performed actual analysis on three data sets that are Subbytes operation part of AES-128 1 round. We verified our prediction by non-profiling analyzing two data sets with a total 16 of MLP-based model, which we describe.

• Journal of the Korea Computer Graphics Society
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• v.27 no.1
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• pp.9-17
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• 2021

In this paper, we present a simple and fast supervised learning framework based on model predictive control so as to learn motion controllers for a physic-based character to track given example motions. The proposed framework is composed of two components: training data generation and offline learning. Given an example motion, the former component stochastically controls the character motion with an optimal controller while repeatedly updating the controller for tracking the example motion through model predictive control over a time window from the current state of the character to a near future state. The repeated update of the optimal controller and the stochastic control make it possible to effectively explore various states that the character may have while mimicking the example motion and collect useful training data for supervised learning. Once all the training data is generated, the latter component normalizes the data to remove the disparity for magnitude and units inherent in the data and trains an artificial neural network with a simple architecture for a controller. The experimental results for walking and running motions demonstrate how effectively and fast the proposed framework produces physics-based motion controllers.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1172-1179
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• 2020

With the establishment of the railway integrated radio network (LTE-R) environment, radio-based train control transmission and reception and various forms of service are provided. The smooth delivery of these services requires improved performance in a highly reliable and available wireless environment. This paper measured the LTE-R radio communication environment to improve radio communication performance of railway integrated wireless network reliability and availability, analyzed the results, and established the wireless environment model. Based on the built-up model, we also proposed an improved radio-access algorithm to control trains for improved reliability, suggesting a way to improve stability for handover that occur during open-air operation, and proposed an algorithm for frequency auto-heating to improve availability. For simulation, data were collected from the Korea Rail Network Authority (Daejeon), Manjong-Gangneung KTX route, which can measure the actual data of LTE-R wireless environment, and the results of the simulation show performance improvement through algorithm.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.2
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• pp.9-17
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• 2022

To take into account of the increasing speed of next generation high-speed trains, a new design code for the traffic safety of railway bridges is required. To solve dynamic responses of the bridge, this research offers a numerical analyses of PSC (Pre-stressed Concrete) box girder bridge, which is most representative of all the bridges on Gyungbu high-speed train line. This model takes into account of the inertial mass forces by the 38-degree-of-freedom and interaction forces as well as track irregularities. Our numerical analyses analyze the maximum vertical deflection and DAF (Dynamic Amplification Factor) between simple span and two-span continuous bridges to show the dynamic stability of the bridge. The third-order polynomial regression equations we use predict the maximum vertical deflections depending on varying running speeds of the train. We also compare the vertical deflections at several cross-sectional positions to check the influence of running speeds and the maximum irregularity at a longitudinal level. Moreover, our model analyzes the influence lines of vertical deflection accelerations of the bridge to evaluate traffic safety.