• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.213-223
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• 2017

Recently, the air quality of South Korea has deteriorated and public interest has been increasing. Various observation means are used for the monitoring of the atmospheric environment, but it relies on the experience and judgment of the observer in the absence of spatial information on the emission facilities. The purpose of this study was to determine the availability of using drones for monitoring air pollutant emission facilities. A texture transformation method was applied to the drone ortho image to detect the small gas emission facility and the slope data calculated by the digital surface model (DSM) was used to reduce the false alarm ratio. As a result, it shows the possibility of using drones in the detection of small gas emission facilities by showing about 80% of positive detection ratio and 40% of false alarm ratio. In the future, various researches are required to the improve positive detection ratio and the reduction of the false alarm ratio. Based on these results, it is necessary to construct a database including 3D spatial information of air pollutant emission facilities.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.2
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• pp.601-606
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• 2015

Background: Prostate cancer is predominately a disease of older men, with a median age of diagnosis of 68 years and 71% of cancer deaths occurring in those over 75 years of age. While prostate cancer screening is not recommended for men >70 years, fit elderly men with controlled comorbidities may have a relatively long life expectancy. We compare the use of age related PSA with the detection of primary malignant circulating prostate cells mCPCs to detect clinically significant PC in this population. Materials and Methods: All men undergoing PC screening with a PSA >4.0ng/ml underwent TRUS 12 core prostate biopsy (PB). Age, PSA, PB results defined as cancer/no-cancer, Gleason, number of positive cores and percentage infiltration were registered. Men had an 8ml blood sample taken for mCPC detection; mononuclear cells were obtained using differential gel centrifugation and mCPCs were identified using immunocytochemistry with anti-PSA and anti-P504S. A mCPC was defined as a cell expressing PSA and P504S; a positive test as at least one mCPC detected/sample. Diagnostic yields for subgroups were calculated and the number of avoided PBs registered. Esptein criteria were used to define small grade tumours. Results: A total of 610 men underwent PB, 398 of whom were aged <70yrs. Men over 70 yrs had: a higher median PSA, 6.24ng/ml versus 5.59ng/ml (p=0.04); and a higher frequency of cancer detected 90/212 (43%) versus 134/398 (34%) (p=0.032). Some 34/134 cancers in men <70yrs versus 22/90 (24%) of men >70yrs complied with criteria for active surveillance. CPC detection: 154/398 (39%) men <70yrs were CPC (+), specificity for cancer 86%, sensitivity 88%, 14/16 with a false (-) result had a small low grade PC. In men >70 years, 88/212 (42%) were CPC (+); specificity 92%, sensitivity 87%, 10/12 with a false (-) had small low grade tumours. False (+) results were more common in younger men 36/154 versus 10/88 (p<0.02). With a PSA cutoff of 6.5ng/ml, in men <70yrs, 108 PB would be avoided, missing 56 cancers of which 48 were clinically significant. Using CPC detection, 124 biopsies would be avoided, missing only 2 clinically significant cancers. In men >70 yrs using a PSA >6.5ng/ml would have resulted in 108 PB with 34 PC detected, of which 14(41%) were small low grade tumours. Conclusions: The use of CPC detection in the fit elderly significantly decreases the number of PBs without missing clinically significant cancers, indicating superiority to the use of age-related PSA.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1153-1166
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• 2011

Detecting misbehavior in vehicular ad-hoc networks is very important problem with wide range of implications including safety related and congestion avoidance applications. Most misbehavior detection schemes are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners. Because of rational behavior, it is more important to detect false information than to identify misbehaving nodes. In this paper, we propose the variable precision rough sets based misbehavior detection scheme which detects false alert message and misbehaving nodes by observing their action after sending out the alert messages. In the proposed scheme, the alert information system, alert profile is constructed from valid actions of moving nodes in vehicular ad-hoc networks. Once a moving vehicle receives an alert message from another vehicle, it finds out the alert type from the alert message. When the vehicle later receives a beacon from alert raised vehicle after an elapse of time, then it computes the relative classification error by using variable precision rough sets from the alert information system. If the relative classification error is lager than the maximum allowable relative classification error of the alert type, the vehicle decides the message as false alert message. Th performance of the proposed scheme is evaluated as two metrics: correct ratio and incorrect ratio through a simulation.

• ETRI Journal
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• v.37 no.2
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• pp.251-261
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• 2015

We present a novel method for real-time automatic license plate detection in high-resolution videos. Although there have been extensive studies of license plate detection since the 1970s, the suggested approaches resulting from such studies have difficulties in processing high-resolution imagery in real-time. Herein, we propose a novel cascade structure, the fastest classifier available, by rejecting false positives most efficiently. Furthermore, we train the classifier using the core patterns of various types of license plates, improving both the computation load and the accuracy of license plate detection. To show its superiority, our approach is compared with other state-of-the-art approaches. In addition, we collected 20,000 images including license plates from real traffic scenes for comprehensive experiments. The results show that our proposed approach significantly reduces the computational load in comparison to the other state-of-the-art approaches, with comparable performance accuracy.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.1
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• pp.1-12
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• 2010

Recently, large amount of information in IDS(Intrusion Detection System) can be un manageable and also be mixed with false prediction error. In this paper, we propose a data mining methodology for IDS, which contains uncertainty based on training process and post-processing analysis additionally. Our system is trained to classify the existing attack for misuse detection, to detect the new attack pattern for anomaly detection, and to define border patter between attack and normal pattern. In experimental results show that our approach improve the performance against existing attacks and new attacks,from 0.62 to 0.84 about 35%.

• Journal of Multimedia Information System
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• v.7 no.3
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• pp.205-214
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• 2020

Crime has become one of the major problems in modern society. Even though visual surveillances through closed-circuit television (CCTV) is extensively used for solving crime, the number of crimes has not decreased. This is because there is insufficient workforce for performing 24-hour surveillance. In addition, CCTV surveillance by humans is not efficient for detecting dangerous situations owing to accuracy issues. In this paper, we propose the autonomous detection of dangerous situations in CCTV scenes using a deep learning model with relational inference. The main feature of the proposed method is that it can simultaneously perform object detection and relational inference to determine the danger of the situations captured by CCTV. This enables us to efficiently classify dangerous situations by inferring the relationship between detected objects (i.e., distance and position). Experimental results demonstrate that the proposed method outperforms existing methods in terms of the accuracy of image classification and the false alarm rate even when object detection accuracy is low.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.127-130
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• 2009

We propose a method for detecting DDoS (Distributed Denial of Service) traffic in real-time inside the backbone network. For this purpose, we borrow the concepts of MACD (Moving Average Convergence Divergence) and RoC (Rate of Change), which are used for technical analysis in the stock market Due to the fact that the method is based on a quantitative, rather than a heuristic, detection level, DDoS traffic can be detected with greater accuracy (by reducing the false alarm ratio). Through simulation results, we show how the detection level is determined and demonstrate how much the accuracy of detection is enhanced.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.1
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• pp.66-74
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• 2008

This paper presents a new small target detection method using scale invariant feature. Detecting small targets whose sizes are varying is very important to automatic target detection. Scale invariant feature using the Laplacian scale-space can detect different sizes of targets robustly compared to the conventional spatial filtering methods with fixed kernel size. Additionally, scale-reflected adaptive thresholding can reduce many false alarms. Experimental results with real IR images show the robustness of the proposed target detection in real world.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.928-931
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• 2002

Network based intrusion detection system is a computer network security tool. In this paper, we present an intrusion detection system based on Self-Organizing Maps (SOM) and Resilient Propagation Neural Network (RPROP) for visualizing and classifying intrusion and normal patterns. We introduce a cluster matching equation for finding principal associated components in component planes. We apply data from The Third International Knowledge Discovery and Data Mining Tools Competition (KDD cup'99) for training and testing our prototype. From our experimental results with different network data, our scheme archives more than 90 percent detection rate, and less than 5 percent false alarm rate in one SYN flooding and two port scanning attack types.

• Proceedings of the IEEK Conference
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• 2003.07a
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• pp.242-245
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• 2003

This paper introduces a new algorithm of pulse generation and detection for UWB communication system. The existing UWB systems using Gaussian pulse have some difficulties to cope with bandwidth limitation and frequency transition. Moreover. the system sensitivity to channel noise has made the processes of acquisition and tacking difficult. in this paper, we introduce a new pulse generation method which is able to control the bandwidth and center frequency applying modulation method. thus could improve the detection performance of receiving algorithm. Also, we made a system to search maximum perk by applying the proposed algorithm and consequently could guarantee the correct detection. By the result of simulation, when accumulate 10 times at every 2dB band shifting from 0 to 18dB on AWGN channel, we could confirm the proposed method has 97.4% PDR(Pulse Detection Rate) and 1.868% FAR(False Alarm Rate) performance at 4dB SNR and 15% transmission power threshold level.