• Journal of Auto-vehicle Safety Association
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• v.8 no.2
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• pp.17-23
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• 2016

Construction of our machine "Digital Tacho Graph" toghether with development and intergrated management system information is attached under Traffic Safety Law in article submission. This machine "Digital Tach Graph" has been mandated on a priority basis. The entire machine is constructed with specialised components to collect data that can retrive basic information. To obtain the components to perfect the information to optimize the device and system.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.185-207
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• 2022

Recently, with the development of deep learning technology, researches to apply a deep learning algorithm to analyze unstructured data such as text and images are being actively conducted. Text classification has been studied for a long time in academia and industry, and various attempts are being performed to utilize data characteristics to improve classification performance. In particular, a hierarchical relationship of labels has been utilized for hierarchical classification. However, the top-down approach mainly used for hierarchical classification has a limitation that misclassification at a higher level blocks the opportunity for correct classification at a lower level. Therefore, in this study, we propose a methodology for classifying hierarchical data using the autoencoder-based deeply supervised network that high-level classification does not block the low-level classification while considering the hierarchical relationship of labels. The proposed methodology adds a main classifier that predicts a low-level label to the autoencoder's latent variable and an auxiliary classifier that predicts a high-level label to the hidden layer of the autoencoder. As a result of experiments on 22,512 academic papers to evaluate the performance of the proposed methodology, it was confirmed that the proposed model showed superior classification accuracy and F1-score compared to the traditional supervised autoencoder and DNN model.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2892-2894
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• 2000

인터넷을 이용한 원격 제어에 있어서는 통신 프로토콜은 TCP/IP를 기반으로 이루어지고 있는데 대개의 경우는 소켓을 이용한 PC기반에서 해석되어 지고 처리되어 진다. 본 논문에서는 PC기반의 원격제어 시스템을 임베디드화함으로써 시스템의 안정성과 속도면에서 우수한 시스템을 구현하려고 한다. 이를 위하여 임베디드 시스템은 HD860-R3보드를 이용하였고 실시간 운영체제로 리눅스를 사용하였다 실질적인 제어물의 제어를 위해서 제어기를 설계 HD860-R3보드와 시리얼을 통하여 통신하였다. 나아가 이 시스템을 PC기반의 시스템에서 탈피한 시스템을 홈 오토메이션의 제어 시스템에 적용시켜 보려 한다.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.483-490
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• 2019

In the Network Intrusion Detection System (NIDS), the function of classification is very important, and detection performance depends on various features. Recently, a lot of research has been carried out on deep learning, but network intrusion detection system experience slowing down problems due to the large volume of traffic and a high dimensional features. Therefore, we do not use deep learning as a classification, but as a preprocessing process for feature extraction and propose a research method from which classifications can be made based on extracted features. A stacked AutoEncoder, which is a representative unsupervised learning of deep learning, is used to extract features and classifications using the Random Forest classification algorithm. Using the data collected in the IOT environment, the performance was more than 99% when normal and attack traffic are classified into multiclass, and the performance and detection rate were superior even when compared with other models such as AE-RF and Single-RF.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.889-891
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• 2005

본 논문에서는 주거생활의 안전을 위해 암호화 코드를 포함한 홈 서버를 설계하고, 블루투스 통신을 이용하여 암호화 코드를 송수신하는 시스템을 구현하였다. 제안한 홈 시큐리티 시스템은 적외선 통신이나 무선랜 등 다양한 인터페이스를 활용할 수 있다는 종점을 가진다. 이와 더불어 제안된 보안 시스템은 건물이나 공장, 자동차 등에서도 적용가능하다. 본 논문에서는 홈 오토메이션을 위한 DES 암호화 알고리즘 기반의 디지털 도어 락 시스템을 제안한다. 암호화 코드를 이용하는 보안 시스템을 구성하면 높은 수준의 보안성을 얻을 수 있고, 키의 분배가 용이하다. 제안하는 보안 시스템은 스마트 폰을 키로 이용함으로써 사용자 편이성을 증대시켰다.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.560-569
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• 2011

In this paper, we propose a text processing method for Hindi characters, Devanagari scripts, in the Android. The key points of the text processing are to device automata, which define the combining rules of alphabets into a set of syllables, and to implement a font rendering engine, which retrieves and displays the glyph images corresponding to specific characters. In general, an automaton depends on the type and the number of characters. For the soft-keyboard, we designed the automata with 14 consonants and 34 vowels based on Unicode. Finally, a combined syllable is converted into a glyph index using the mapping table, used as a handle to load its glyph image. According to the multi-lingual framework of Freetype font engine, Dvanagari scripts can be supported in the system level by appending the implementation of our method to the font engine as the Hindi module. The proposed method is verified through a simple message system.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.3
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• pp.118-125
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• 2004

The purpose of this study is to decide an appropriate neighborhood and a transition rule of cellular automata by analyzing the past growth process of urban areas in Gimhae. With cellular automata which can manage the change based on the dynamic model and time, this study analyzes the urban growth of Gimhae from 1987 to 2001. Also, through the simulation of different types for neighborhood and transition rules, we can find the appropriate neighborhood and the transition rule for Gimhae. In conclusion, the forecast of physical urban growth pattern is more accurate under conditions when the number of matrixes for the neighborhood is small, the shape of the neighborhood is rectangular, "${\alpha}$" value, which control the pace of urban growth, is low and the transition possibility ($P_{ij}$) is high.

• The Journal of the Acoustical Society of Korea
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• v.41 no.4
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• pp.461-467
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• 2022

Sonobuoy is a disposable device that collects underwater acoustic information and is designed to transmit signals collected in a particular area to nearby aircraft or ships and sink to the seabed upon completion of its mission. In a conventional sonobouy signal transmission and reception system, collected signals are modulated and transmitted using techniques such as frequency division modulation or Gaussian frequency shift keying, and received and demodulated by an aircraft or a ship. However, this method has the disadvantage of the large amount of information to be transmitted and low security due to relatively simple modulation and demodulation methods. Therefore, in this paper, we propose a method that uses an autoencoder to encode a transmission signal into a low-dimensional latent vector to transmit the latent vector to an aircraft or ship and decode the received latent vector to improve signal security and to reduce the amount of transmission information by approximately a factor of a hundred compared to the conventional method. As a result of confirming the sample spectrogram reconstructed by the proposed method through simulation, it was confirmed that the original signal could be restored from a low-dimensional latent vector.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1087-1098
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• 2023

The rapid advancement of artificial intelligence (AI) technology has led to its proactive utilization across various fields. However, this widespread adoption of AI-based systems has raised concerns about the increasing threat of attacks on these systems. In particular, deep neural networks, commonly used in deep learning, have been found vulnerable to adversarial attacks that intentionally manipulate input data to induce model errors. In this study, we propose a method to protect image classification models from visually imperceptible One-Pixel attacks, where only a single pixel is altered in an image. The proposed defense technique utilizes an autoencoder model to remove potential threat elements from input images before forwarding them to the classification model. Experimental results, using the CIFAR-10 dataset, demonstrate that the autoencoder-based defense approach significantly improves the robustness of pretrained image classification models against One-Pixel attacks, with an average defense rate enhancement of 81.2%, all without the need for modifications to the existing models.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1998.04a
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• pp.64-74
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• 1998

본 연구에서는 선박 자동조타시스템의 비선형 요소가 추진 에너지 손실량에 미치는 영향을 정량적으로 평가, 해석하였다. 대양을 자동조타로써 보침 항해중인 선박의 자동조타시스템은 크게 선형 및 비선형요소로 나눌 수있는데, 각 요소들중, 특히 시스템 내부에 인위적으로 또는 불가피하게 설치되어 있는 비선형요소들의 영향은 매우 크다. 본 연구에서는 오토파일럿의 비선형 제어 상수가 전체 시스템의 안정성 또는 추진 에너지 손실의 증감에 미치는 영향을 조사하였다. 본 연구의 수치계산에 이용한 선박은 소형 어선 1척과 대형 광석운반석 1척이며 평균, 풍속 10m/sec 의 대표적 해상상태의 풍·파랑 외란 조건하에서 추지 에너지 손실의 지표인 성능평가지수를 계산하였다. 그리고 수치계산 결과에 대해서도 논하였다.