• Proceedings of the KSR Conference
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• 2011.10a
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• pp.69-74
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• 2011

In the curving detection method by using an accelerometer, the ride comfort in the first car is worse than one in the others due to spend the time to calculate the tilting command and drive the tilting mechanism after entering in the curve. In order to enhance the ride comfort in the first car, the preditive curve detection method which predicts the distance from a train to the starting point of curve by using the GPS, Tachometer, Ground balise and position DB for track. In this study, we predicted and evaluated the ride comfort for predictive curve detection method in transient curves according to the shape and dimension of transient curve and the various driving speed. Also, we predicted the improvement of the ride comfort for predictive curve detection method by comparing with the result of the ride comfort for predictive curve detection method and for curve detection method using an accelerometer in the short transient curve.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.169-171
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• 2004

This paper propose a new detection method of curve lane using Catmull-Rom spline for recognition various shape of the curve lane. To improve the accracy of lane detection, binarization and thinning process are firstly performed on the input image. Next, features on the curve lane such as curvature and orientation are extracted, and the control points of Catmull-Rom spline are detected to recognize the curve lane. Finally, Computer simulation results are given using a natural test image to show the efficiency of the proposed scheme.

• Korean Journal of Food Science and Technology
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• v.32 no.3
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• pp.525-530
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• 2000

A study was carried out to establish the detection method of irradiated agricultural products cultivated in Korea by Thermoluminscece(TL). Samples were irradiated using Co-60 gamma rays at various doses(0.05, 0.1, 0.2, 0.3 and 0.5 kGy). After irradiation, separated minerals of the samples measured by TL. TL intensity increased with increasing doses and the irradiated samples were higher than the non-irradiated samples. TL first and second glow curves showed maximum TL temperature point at $176.16{\sim}190.08^{\circ}C$ and $143.84{\sim}146.56^{\circ}C$, respectively. All the irradiated samples can be classified correctly by the shape of the glow curve and the glow curve ratio. Correlation coefficients of ginger, carrot, potato and sweet potato were 0.9968, 0.8522, 0.9612 and 0.9071, respectively, that showed very high correlation between irradiation dose and TL intensity. Therefore, these results suggest that TL measurement is an useful detection method for irradiated agricultural products.

• Preventive Nutrition and Food Science
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• v.6 no.1
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• pp.23-28
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• 2001

A study was carried out to establish a detection method of irradiated shellfish through thermoluminescence (TL). The TL intensity of first glow curves for irradiated bloody, freshwater, and short-neck shellfish increased from control until 5 kGy and increased slightly room 5 kGy until 10 kGy. Maximum TL temperatures of all irradiated samples tested were below 23$0^{\circ}C$, within temperature interval of 150~25$0^{\circ}C$ recommended for evaluation. Since just in control, glow curve ratios of G3 and G4 calculated from re-irradiated (1 kGy) bloody, freshwater and shortneck were over 0.5, detection in control was possible. However, as glow curve ratios after three months were below 0.5, detection by glow curve ratios after three months was impossible. Gl, which calculated from unirradiated samples, exhibited below 0.1, they were classified as unirradiated. In all samples, all the irradiated shellfish could be classified correctly as irradiated by hemaximum TL temperatures and shape of the second glow curve because those were shown in a lower temperature region than those of the first glow curve.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5159-5178
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• 2018

Network Intrusion detection is a rapidly growing field of information security due to its importance for modern IT infrastructure. Many supervised and unsupervised learning techniques have been devised by researchers from discipline of machine learning and data mining to achieve reliable detection of anomalies. In this paper, a deep convolutional neural network (DCNN) based intrusion detection system (IDS) is proposed, implemented and analyzed. Deep CNN core of proposed IDS is fine-tuned using Randomized search over configuration space. Proposed system is trained and tested on NSLKDD training and testing datasets using GPU. Performance comparisons of proposed DCNN model are provided with other classifiers using well-known metrics including Receiver operating characteristics (RoC) curve, Area under RoC curve (AuC), accuracy, precision-recall curve and mean average precision (mAP). The experimental results of proposed DCNN based IDS shows promising results for real world application in anomaly detection systems.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.1577-1578
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• 2013

• Journal of KIISE:Software and Applications
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• v.36 no.11
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• pp.912-919
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• 2009

A novel algorithm using 2D tensor voting and edge-based approach is proposed for text detection in natural scene images. The tensor voting is used based on the fact that characters in a text line are usually close together on a smooth curve and therefore the tokens corresponding to centers of these characters have high curve saliency values. First, a suitable edge-based method is used to find all possible text regions. Since the false positive rate of text detection result generated from the edge-based method is high, 2D tensor voting is applied to remove false positives and find only text regions. The experimental results show that our method successfully detects text regions in many complex natural scene images.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.2
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• pp.130-136
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• 2015

In this paper, we propose a robust curved lane marking detection method. Several lane detection methods have been proposed, however most of them have considered only straight lanes. Compared to the number of straight lane detection researches, less number of curved-lane detection researches has been investigated. This paper proposes a new curved lane detection and tracking method which is robust to various illumination conditions. First, the proposed methods detect straight lanes using a robust road feature image. Using the geometric relation between a vehicle camera and the road plane, several circle models are generated, which are later projected as curved lane models on the camera images. On the top of the detected straight lanes, the curved lane models are superimposed to match with the road feature image. Then, each curve model is voted based on the distribution of road features. Finally, the curve model with highest votes is selected as the true curve model. The performance and efficiency of the proposed algorithm are shown in experimental results.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.263-267
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• 2005

Building classifiers for financial real-world classification problems is often plagued by severely overlapping and highly skewed class distribution. New performance measures such as receiver operating characteristic (ROC) curve and area under ROC curve (AUC) have been recently introduced in evaluating and building classifiers for those kind of problems. They are, however, in-effective to evaluation of classifier's discrimination performance in a particular class of the classification problems that interests lie in only a local operating range of the classifier, In this paper, a new method is proposed that enables us to directly improve classifier's discrimination performance at a desired local operating range by defining and optimizing a partial area under ROC curve or domain-specific curve, which is difficult to achieve with conventional classification accuracy based learning methods. The effectiveness of the proposed approach is demonstrated in terms of fraud detection capability in a real-world fraud detection problem compared with the MSE-based approach.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.253-259
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• 2016

Regarding the research on lane recognition, continuous studies have been in progress for vehicles to navigate autonomously and to prevent traffic accidents, and lane recognition and detection have remarkably developed as different algorithms have appeared recently. Those studies were based on vision system and the recognition rate was improved. However, in case of driving at night or in rain, the recognition rate has not met the level at which it is satisfactory. Improving the weakness of the vision system-based lane recognition and detection, applying sensor convergence technology for the response after accident happened, among studies on lane detection, the study on the curve road detection was conducted. It proceeded to study on the curve road detection among studies on the lane recognition. In terms of the road detection, not only a straight road but also a curve road should be detected and it can be used in investigation on traffic accidents. Setting the threshold value of curvature from 0.001 to 0.06 showing the degree of the curve, it presented that it is able to compute the curve road.