• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.163-172
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• 2022

Typical anti-jamming technologies based on array antennas, Space Time Adaptive Process (STAP) & Space Frequency Adaptive Process (SFAP), are very effective algorithms to perform nulling and beamforming. However, it does not perform equally well for all types of jamming signals. If the anti-jamming algorithm is not optimized for each signal type, anti-jamming performance deteriorates and the operation stability of the system become worse by unnecessary computation. Therefore, jamming classification technique is required to obtain optimal anti-jamming performance. Machine learning, which has recently been in the spotlight, can be considered to classify jamming signal. In general, performing supervised learning for classification requires a huge amount of data and new learning for unfamiliar signal. In the case of jamming signal classification, it is difficult to obtain large amount of data because outdoor jamming signal reception environment is difficult to configure and the signal type of attacker is unknown. Therefore, this paper proposes few-shot jamming signal classification technique using meta-learning and transfer-learning to train the model using a small amount of data. A training dataset is constructed by anti-jamming algorithm input data within the GNSS receiver when jamming signals are applied. For meta-learning, Model-Agnostic Meta-Learning (MAML) algorithm with a general Convolution Neural Networks (CNN) model is used, and the same CNN model is used for transfer-learning. They are trained through episodic training using training datasets on developed our Python-based simulator. The results show both algorithms can be trained with less data and immediately respond to new signal types. Also, the performances of two algorithms are compared to determine which algorithm is more suitable for classifying jamming signals.

• 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.

• Convergence Security Journal
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• v.23 no.1
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• pp.117-123
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• 2023

To detect advanced ransomware attacks with machine learning-based models, the classification model must train learning data with high-dimensional feature space. And in this case, a 'curse of dimension' phenomenon is likely to occur. Therefore, dimensionality reduction of features must be preceded in order to increase the accuracy of the learning model and improve the execution speed while avoiding the 'curse of dimension' phenomenon. In this paper, we conducted classification of ransomware by applying three machine learning models and two feature extraction techniques to two datasets with extremely different dimensions of feature space. As a result of the experiment, the feature dimensionality reduction techniques did not significantly affect the performance improvement in binary classification, and it was the same even when the dimension of featurespace was small in multi-class clasification. However, when the dataset had high-dimensional feature space, LDA(Linear Discriminant Analysis) showed quite excellent performance.

• Journal of the Korean Geotechnical Society
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• v.24 no.2
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• pp.45-57
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• 2008

The improved turnout system is developed to speed-up the pre-existing railroad. The research has been actively carried out far the improved turnout system and the impact factor is estimated using the data sets achieved from the dynamic wheel-load field tests in both the conventional and the improved turnout system. In this study, the track performance and roadbed behavior are examined for the conventional and improved turnout system using the estimated impact factor. Dynamic wheel load and rail pressure are evaluated to assess the track performance. Roadbed stress and settlements are estimated using numerical analysis. Additionally, the stability of roadbed is estimated in soft roadbed condition influenced by the weather effects and cyclic train loading. The results show that dynamic wheel load, rail pressure, roadbed stress, and roadbed settlements in the improved turnout system substantially decrease compared with those in the conventional turnout system.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.27-34
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• 2023

This paper proposed an Inter-Carrier-Interference (ICI) Canceling Orthogonal Frequency Division Multiplexing (OFDM) receiver for 5G mobile system to support 500 km/h linear motor high speed terrestrial transportation service. A receiver in such high-speed train sees the transmission channel which is composed of multiple Doppler-shifted propagation paths. Then, a loss of sub-carrier orthogonality due to Doppler-spread channels causes ICI. The ICI Canceler is realized by the following three steps. First, using the Demodulation Reference Symbol (DMRS) pilot signals, it analyzes three parameters such as attenuation, relative delay, and Doppler-shift of each multi-path component. Secondly, based on the sets of three parameters, Channel Transfer Function (CTF) of sender sub-carrier number 𝒏 to receiver sub-carrier number 𝒍 is generated. In case of 𝒏≠𝒍, the CTF corresponds to ICI factor. Thirdly, since ICI factor is obtained, by applying ICI reverse operation by Multi-Tap Equalizer, ICI canceling can be realized. ICI canceling performance has been simulated assuming severe channel condition such as 500 km/h, 2 path reverse Doppler Shift for QPSK, 16QAM, 64QAM and 256QAM modulations. In particular, for modulation schemes below 16QAM, we confirmed that the difference between BER in a 2 path reverse Doppler shift environment and stationary environment at a moving speed of 500 km/h was very small when the number of taps in the multi-tap equalizer was set to 31 taps or more. We also confirmed that the BER performance in high-speed mobile communications for multi-level modulation schemes above 64QAM is dramatically improved by the use of a multi-tap equalizer.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.537-542
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• 2023

Recently, deformation of operating railway structures has occurred due to adjacent excavation works such as new structures and utility tunnel expansion concentrated around downtown areas. However, most of them are focused on structural review, repair and reinforcement of structures. A review of the Track is insufficient. In particular, in the case of the gravel track on the earthwork subgrade, the subgrade and the ballast are not solidified. A slight level of deformation can cause ballast relaxation. Sleeper support conditions may lead to unstable conditions. Sufficient safety must be ensured. In addition, it is a track type with a high risk of train derailment due to unstable support conditions. In this study, the correlation between the deformation characteristics of gravel tracks and track support performance according to subgrade deformation is experimentally and analytically verified. In addition, an evaluation technique that can evaluate the condition of the gravel track and the track support stiffness is presented.

• Structural Engineering and Mechanics
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• v.89 no.6
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• pp.579-588
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• 2024

The present paper investigates the curvature ductility of confined reinforced concrete (RC) beams with normal (NSC) and high strength concrete (HSC). For the purpose of predicting the curvature ductility factor, an analytical model was developed based on the equilibrium of internal forces of confined concrete and reinforcement. In this context, the curvatures were calculated at first yielding of tension reinforcement and at ultimate when the confined concrete strain reaches the ultimate value. To best simulate the situation of confined RC beams in flexure, a modified version of an ancient confined concrete model was adopted for this study. In order to show the accuracy of the proposed model, an experimental database was collected from the literature. The statistical comparison between experimental and predicted results showed that the proposed model has a good performance. Then, the data generated from the validated theoretical model were used to train the artificial neural network (ANN) prediction model. The R² values for theoretical and experimental results are equal to 0.98 and 0.95, respectively which proves the high performance of the ANN model. Finally, a parametric study was implemented to analyze the effect of different parameters on the curvature ductility factor using theoretical and ANN models. The results are similar to those extracted from experiments, where the concrete strength, the compression reinforcement ratio, the yield strength, and the volumetric ratio of transverse reinforcement have a positive effect. In contrast, the ratio and the yield strength of tension reinforcement have a negative effect.

• Smart Media Journal
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• v.13 no.7
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• pp.9-17
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• 2024

Due to the increase in energy consumption, and eco-friendly policies, there is a need for efficient energy consumption in buildings. Anomaly power detection based on deep learning are being used. Because of the difficulty in collecting anomaly data, anomaly detection is performed using reconstruction error with a Recurrent Neural Network(RNN) based autoencoder. However, there are some limitations such as the long time required to fully learn temporal features and its sensitivity to noise in the train data. To overcome these limitations, this paper proposes the TCN-USAD, combined with Temporal Convolution Network(TCN) and UnSupervised Anomaly Detection for multivariate data(USAD). The proposed model using TCN-based autoencoder and the USAD structure, which uses two decoders and adversarial training, to quickly learn temporal features and enable robust anomaly detection. To validate the performance of TCN-USAD, comparative experiments were performed using two building energy datasets. The results showed that the TCN-based autoencoder can perform faster and better reconstruction than RNN-based autoencoder. Furthermore, TCN-USAD achieved 20% improved F1-Score over other anomaly detection models, demonstrating excellent anomaly detection performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.170-177
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• 2020

This study examined the clamping force control method and the braking performance test results of an electromechanical brake (EMB) using braking test equipment. Most of the studies related to EMBs have been carried out in the automotive field, dealing mainly with the static test results for various control methods. On the other hand, this study performed a dynamic performance evaluation. The three-phase interior permanent magnet synchronous motor (IPMSM) was applied to drive the actuator of the EMB, and the analysis was verified by JMAG(Ver. 18.0), which is finite element method (FEM) software. The current control, speed control, and position control were used for clamping force control of the EMB, and the maximum torque per ampere (MTPA) control was applied to the current controller for efficient control. The EMB's emergency braking deceleration performance was tested in the same way as conventional pneumatic brake systems when the wheel of a train rotates at 110 km/h, 230 km/h, and 300 km/h. The emergency braking time, with the wheel stopped completely at the maximum rotational speed, was approximately 73 seconds. The similarity of the braking time and deceleration pattern was verified through a comparison with the performance test results of the pneumatic brake system applied to the next generation high-speed railway vehicle (HEMU-430X).

• Journal of the Korean Society for Railway
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• v.19 no.3
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• pp.288-296
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• 2016

A 400km/h simple catenary system was constructed as a test line in Korea. In the design stage of this system, the pre-sag was one of the engineering issues most focused on. It is known that the pre-sag improves the current collection performance in a certain band of high speed. However, the effect of pre-sag at 400km/h has not yet been established. To grasp a better pre-sag in the 400km/h catenary, we transacted the dynamic performance prediction simulation between catenary and pantograph under conditions of 0 and 1/3000 pre-sag. The level of 0 pre-sag was adapted for the 400km/h catenary design after reviewing predictions. We constituted the 1/3000 pre-sag sample section (about 1km) while constructing the 400km/h catenary test-bed (28km) of 0 pre-sag. With a HEMU-430X train, the contact forces were measured in the test-bed including the pre-sag sample section. In this paper, the predicted and measured dynamic performance values (contact forces) for 0 and 1/3000 pre-sag are described and compared. It is conclusively confirmed by analytical and experimental examination that the non pre-sag showed better dynamic (current collection) performance than that of the 1/3000 pre-sag for the 400km/h catenary system.