• The Journal of the Acoustical Society of Korea
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• v.29 no.1
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• pp.10-17
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• 2010

In a passive sonar, the improvement of detection performance by using noise cancellation is usually a important problem. In this paper, we have analyzed the own-ship noise cancellation in the two operation modes which are used in the HMS system. In the operator mode, an adaptive line enhancer(ALE) is applied to improve the tonal detection by using broadband noise cancellation and the normalized least mean square(NLMS) algorithm is applied to the design of an adaptive filter. The reference input that is correlated with a primary input can be used to remove the noise incident on the observation directionin the automatic mode. Computer simulations with real sea that data show that the proposed adaptive noise canceller has good performance in passive detection under HMS operation.

• Journal of Korean Society of Transportation
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• v.29 no.5
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• pp.121-138
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• 2011

More affordable and available cutting-edge technologies (e.g., wireless vehicle communication) are regarded as a possible alternative to the fixed infrastructure-based traffic information system requiring the expensive infrastructure investments and mostly implemented in the uninterrupted freeway network with limited spatial system expansion. This paper develops an advanced decentralized traveler information System (ATIS) using vehicle-to-vehicle (V2V) communication system whose performance (drivers' travel time savings) are enhanced by three complementary functions (autonomous automatic incident detection algorithm, reliable sample size function, and driver behavior model) and evaluates it in the typical $6{\times}6$ urban grid network with non-recurrent traffic state (traffic incident) with the varying key parameters (traffic flow, communication radio range, and penetration ratio), employing the off-the-shelf microscopic simulation model (VISSIM) under the ideal vehicle communication environment. Simulation outputs indicate that as the three key parameters are increased more participating vehicles are involved for traffic data propagation in the less communication groups at the faster data dissemination speed. Also, participating vehicles saved their travel time by dynamically updating the up-to-date traffic states and searching for the new route. Focusing on the travel time difference of (instant) re-routing vehicles, lower traffic flow cases saved more time than higher traffic flow ones. This is because a relatively small number of vehicles in 300vph case re-route during the most system-efficient time period (the early time of the traffic incident) but more vehicles in 514vph case re-route during less system-efficient time period, even after the incident is resolved. Also, normally re-routings on the network-entering links saved more travel time than any other places inside the network except the case where the direct effect of traffic incident triggers vehicle re-routings during the effective incident time period and the location and direction of the incident link determines the spatial distribution of re-routing vehicles.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.1
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• pp.95-107
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• 2017

In this study, a preliminary study was undertaken for development of a tunnel incident automatic detection system based on a machine learning algorithm which is to detect a number of incidents taking place in tunnel in real time and also to be able to identify the type of incident. Two road sites where CCTVs are operating have been selected and a part of CCTV images are treated to produce sets of training data. The data sets are composed of position and time information of moving objects on CCTV screen which are extracted by initially detecting and tracking of incoming objects into CCTV screen by using a conventional image processing technique available in this study. And the data sets are matched with 6 categories of events such as lane change, stoping, etc which are also involved in the training data sets. The training data are learnt by a resilience neural network where two hidden layers are applied and 9 architectural models are set up for parametric studies, from which the architectural model, 300(first hidden layer)-150(second hidden layer) is found to be optimum in highest accuracy with respect to training data as well as testing data not used for training. From this study, it was shown that the highly variable and complex traffic and incident features could be well identified without any definition of feature regulation by using a concept of machine learning. In addition, detection capability and accuracy of the machine learning based system will be automatically enhanced as much as big data of CCTV images in tunnel becomes rich.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.22 no.4
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• pp.307-313
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• 2009

Quantitative analysis of optical scattering intensities from a Au nanoparticle with a diameter of 100 nm, which is effected by the localized surface plasmon resonance (LSPR), were numerically carried out by using a dark-field detection scheme on prism basal plane for two different beam incident modes of reflectance (R-mode) and transmittance (T-mode). Two-dimensional finite difference time domain (FDTD) algorithm was adopted, and its applicabilibility was verified by comparing the simulation results with the theoretical ones. Simulation results of the scattered light intensities from a Au nanoparticle revealed that the scattered intensity of the T-mode was much stronger than that of R-mode. Comparison of the calculated results with the theoretical intensity distribution on the prism showed that the scattered intensity is marimized when the evanescent field, which is generated from the interface of prism and air at TIR angle, is coupled with Au nanoparticle.

• International Journal of Highway Engineering
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• v.10 no.3
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• pp.19-31
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• 2008

Video image processing systems(VIPS) offer numerous benefits to transportation models and applications, due to their ability to monitor traffic in real time. VIPS based on wide-area detection, i.e., multi-lane surveillance algorithm provide traffic parameters with single camera such as flow and velocity, as well as occupancy and density. However, most current commercial VIPS utilize a tripwire detection algorithm that examines image intensity changes in the detection regions to indicate vehicle presence and passage, i.e., they do not identify individual vehicles as unique targets. If VIPS are developed to track individual vehicles and thus trace vehicle trajectories, many existing transportation models will benefit from more detailed information of individual vehicles. Furthermore, additional information obtained from the vehicle trajectories will improve incident detection by identifying lane change maneuvers and acceleration/deceleration patterns. The objective of this research was to relate traffic safety to VIPS tracking and this paper has developed a computer vision system of monitoring individual vehicle trajectories based on image processing, and offer the detailed information, for example, volumes, speed, and occupancy rate as well as traffic information via tripwire image detectors. Also the developed system has been verified by comparing with commercial VIP detectors.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.1 s.4
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• pp.67-74
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• 2004

Next generation telematics technology, offering new and diverse multimedia service by connecting car and wireless access network, has been presented with promising industry. Telematics technology was simply to monitor and control the in-vehicle devices and to navigate the road in the early days but nowadays, telematics technology has provided the mobile internet access service, LBS service, agent rescue service and multimedia service to us using connection of wireless access network. In this paper, telematics mobile platform architecture is proposed for service promotion and efficiency.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.341-344
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• 2006

유고란 도로상에서 교통량의 주기적인 집중에 의한 혼잡과는 구별되는 개념으로 교통사고, 도로보수 그리고 자연재해와 같은 비 반복적인 정체의 상황을 일컫는다. 이러한 유고는 막대한 통행시간이 추가로 발생하고 연료소모, 환경피해 등의 문제가 발생하므로 이러한 교통손실을 최소화하기 위하여 자동유고감지 알고리즘의 개발이 필수적이다. 이를 위하여 현재 다양한 검지기에서 수집된 교통 데이터를 바탕으로 유고를 감지하는 연구가 많이 진행되고 있다. 본 논문에서는 각종 유고 상황을 인지하여 제2의 사고를 예방할 수 있는 효율적인 유고감지 알고리즘을 개발하기 위하여 퍼지논리와 패턴을 함께 사용하였다. 먼저 퍼지논리와 패턴에 사용되는 데이터는 루프 검지기에서 5분 마다 수집된 교통정보(교통량, 점유율, 속도)를 이용하였다. 교통정보를 이용하여 구축된 요일 및 시간대별 패턴과 함께 퍼지논리를 이용하여 도출된 유고 소속도를 가지고 유고를 감지하였다.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.603-605
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• 2005

In this paper, we deal with a calibration method for low cost motion capture sensor using PSD (Position Sensitive Detection). The PSD sensor is employed to measure the direction of incident light from moving markers attached to motion body. To calibrate the PSD optical module, a conventional camera calibration algorithm introduced by Tsai. The 3-dimensional positions of the markers are measured by using stereo camera geometry. From the experimental results, the low cost motion capture sensor can be used in a real time system.

• Journal of Preventive Medicine and Public Health
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• v.43 no.3
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• pp.257-264
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• 2010

Objectives: An accurate estimation of cancer patients is the basis of epidemiological studies and health services. However in Korea, cancer patients visiting out-patient clinics are usually ruled out of such studies and so these studies are suspected of underestimating the cancer patient population. The purpose of this study is to construct a more complete, hospital-based cancer patient registry using multiple sources of medical information. Methods: We constructed a cancer patient detection algorithm using records from various sources that were obtained from both the in-patients and out-patients seen at Asan Medical Center (AMC) for any reason. The medical data from the potentially incident cancer patients was reviewed four months after first being detected by the algorithm to determine whether these patients actually did or did not have cancer. Results: Besides the traditional practice of reviewing the charts of in-patients upon their discharge, five more sources of information were added for this algorithm, i.e., pathology reports, the national severe disease registry, the reason for treatment, prescriptions of chemotherapeutic agents and radiation therapy reports. The constructed algorithm was observed to have a PPV of 87.04%. Compared to the results of traditional practice, 36.8% of registry failures were avoided using the AMC algorithm. Conclusions: To minimize loss in the cancer registry, various data sources should be utilized, and the AMC algorithm can be a successful model for this. Further research will be required in order to apply novel and innovative technology to the electronic medical records system in order to generate new signals from data that has not been previously used.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.1
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• pp.25-38
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• 2022

With the diversification of payment methods and games, related financial accidents are causing serious problems for users and game companies. Recently, game companies have introduced an Fraud Detection System (FDS) for game payment systems to prevent financial incident. However, FDS is ineffective and cannot provide major evidence based on judgment results, as it requires constant change of detection patterns. In this paper, we analyze abnormal transactions among payment log data of real game companies to generate related features. One of the unsupervised learning models, Autoencoder, was used to build a model to detect abnormal transactions, which resulted in over 85% accuracy. Using X-FDS (Explainable FDS) with XAI-SHAP, we could understand that the variables with the highest explanation for anomaly detection were the amount of transaction, transaction medium, and the age of users. Based on X-FDS, we derive an improved detection model with an accuracy of 94% was finally derived by fine-tuning the importance of features that adversely affect the proposed model.