• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.617-629
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• 2020

In order to overcome the limitations of the rule-based intrusion detection system due to changes in Internet computing environments, the emergence of new services, and creativity of attackers, network anomaly detection (NAD) using machine learning and deep learning technologies has received much attention. Most of these existing machine learning and deep learning technologies for NAD use supervised learning methods to learn a set of training data set labeled 'normal' and 'attack'. This paper presents the feasibility of the unsupervised learning AutoEncoder(AE) to NAD from data sets collecting of secured network traffic without labeled responses. To verify the performance of the proposed AE mode, we present the experimental results in terms of accuracy, precision, recall, f1-score, and ROC AUC value on the NSL-KDD training and test data sets. In particular, we model a reference AE through the deep analysis of diverse AEs varying hyper-parameters such as the number of layers as well as considering the regularization and denoising effects. The reference model shows the f1-scores 90.4% and 89% of binary classification on the KDDTest+ and KDDTest-21 test data sets based on the threshold of the 82-th percentile of the AE reconstruction error of the training data set.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.29-32
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• 2000

We proposed a neural network based “left shoulder”detector. The auto-associative neural network was trained with the “left shoulder”patterns obtained from the Korea Composite Stock Price Index, and then tested out-of-sample with a reasonably good result. A hypothetical investment strategy based on the detector achieved a return of 132% in comparison with 39% return from a buy and hold strategy

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.1066-1070
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• 2002

We propose a neural network based up-trend detector. An auto-associative neural network was trained with 'up-trend' data obtained from the KOSPI 200 future price. It was then used to predict an up-trend Simple investment strategies based on the detector achieved a two year return of $19.8\%$ with no leverage.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.35.3-35
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• 2001

Developed is an optical auto-inspection system to detect some microscopic defects on the Inside surface of the hydraulic automobile brakes at the production line. A small cylindrical detection module with a solid laser source at its center has two rings of optical fibers to separately collect light reflected and scattered from the defects on the surface. The cylindrical brake part rotates with respect to the detection module that will move parallel to the rotational axis of the cylinder. Thus, the optical module can scan the whole inside surface area. The automatic detection of the defects is to compare the intensity distributions ...

• Journal of the Korea Society of Computer and Information
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• v.7 no.1
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• pp.80-87
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• 2002

In this papers, It was researched Automatic frequency detection which need using ultrasonic utmost effort. As far as ultrasonic Automatic frequency detection, It was analog method, In more dedicated field, to solve a limit. It was needed a new method detection instrument.

• Proceedings of the KIEE Conference
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• 2007.11c
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• pp.191-193
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• 2007

Pantograph sliding plate abrasion auto-detect system, one of the electric rail car auto-detecting devices, is a system that decides how much abrasion and when to replace without an inspector physically looking at the abrasion on the wet plate using machine vision, a cutting-edge technology. This paper covers the cause of deteriorating reliability that affects pantograph wet plate edge detection due to noise added to the video when it rains. In order to remove such noise, problems should be checked through Smoothing, Averaging mask and Median filter using filtering technique and stable edge detection without being affected by noise should be induced in video measurement used in machine vision technology.

• Journal of Auto-vehicle Safety Association
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• v.4 no.1
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• pp.27-32
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• 2012

Recently, in order to reduce traffic accident related fatalities, increasing number of studies are conducted regarding the vehicle safety enhancement devices. But very few studies about test procedures and requirements for vehicle safety systems are being carried out. Since BSD, as one of the most important safety features, is installed on a new vehicle, its performance test method has to be evaluated. Independent factors irrelevant to the device types including collision position, vehicle speed and closing speed are used to calculate test distance away from the current vehicle. Effect of roadway geometry as radius of curvature is introduced to propose possible misjudgement of following vehicle as adjacent one. The study results would be utilized to enhance the test procedure of BSD performance.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.385-388
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• 2006

비선형 주성분 분석은 기존에 널리 알려져 있는 주성분 분석기법과 유사한 다변수 데이터 분석을 위한 새로운 접근 방법이다. 비선형 주성분 분석은 AANN(Auto Associative Neural Network)으로 PCA와 마찬가지로 변수들 간에 존재하는 상관관계를 제거함으로써 고차의 다변수 데이터를 정보의 손실을 최소화하면서 최소 차원의 데이터로 변환하는 기법이다. AANN 기반 센서노드 고장검출 기법을 실제 센서 네트워크에 적용하여 봄으로써 센서 드리프트 등과 같은 센서 고장의 검출 및 유효한 센서 보정 성능을 확인하였다.

• Journal of Auto-vehicle Safety Association
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• v.12 no.3
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• pp.20-26
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• 2020

Many children have died (Heat stroke deaths) in the U.S. after being left alone in cars during hot weather, especially summer season. According to related report, more than 800 children have died of heat stroke from being trapped in a hot car since 1998. The regulation party, government has started to make not only technical regulation to prevent tragedy but also legislate to punish. However the 75% of accident has occurred unintended by their parents. So punishment is not the best solution for this case. So in this study, we analyze the trend of regulation and technology to save occupant who remained back seat. And finally we propose a countermeasure to prevent heat stroke deaths.

• Journal of Auto-vehicle Safety Association
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• v.10 no.4
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• pp.13-24
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• 2018

According to DEKRA (a Germany Certification and Inspection Agency)'s accident rate analysis by vehicle defect factor, as a result of analysis of the causes of accidents by flaws, it was found that braking devices accounted for 41%. Defects in the braking system are closely related to the accident, so it is very important to find faulty brking systems to ensure safety. The EU and USA uses ISO brake tester and the Korea is brake teater is first introduse in Japan for vehicle inspection and maintanance. KOTSA introduce the ISO brake tester in order to promote the advanced standardization of the inspection equipment and inspection tecnology, and examined the detection characteristics and applied it to the improvement direction of the brake tester to secure the driving safety.