• 인터넷정보학회논문지
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• 제22권3호
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• pp.53-58
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• 2021

보안관제 분야의 실제 업무활동에 대해서는 거의 연구가 없는 실정이다. 이에 본 논문에서는 보안관제의 위협정보 탐지 대응시간 모델링을 통해 적정 투입인력 규모 산정에 기여하고 최신 보안솔루션 투입시의 효과성 분석 등에 활용할 수 있는 실질적인 연구 방법론을 제시하고자 한다. 보안관제센터에서 수행하는 전체 위협정보 탐지대응시간은 TIDRT(Total Intelligence Detection & Response Time)로 정의한다. 전체 위협정보 탐지 대응시간(TIDRT)는 내부 위협정보 탐지대응시간(IIDRT, Internal Intelligence Detection & Response Time)과 외부 위협정보(EIDRT, External Intelligence Detection & Response Time)의 합으로 구성된다. 내부위협정보 탐지대응시간(IIDRT)는 다섯 단계의 소요시간의 합으로 계산할 수 있다. 본 연구의 궁극적인 목표는 보안관제센터의 주요한 업무활동들을 수식으로 모델링하여 보안관제센터의 사이버 위협정보 탐지대응시간 계산식을 산정하는데 있다. 2장에서는 선행연구를 살펴보고, 3장에서는 전체 위협정보 탐지대응시간의 계산식을 모델링한다. 4장에서 결론으로 끝을 맺는다.

• 반도체디스플레이기술학회지
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• 제20권3호
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• pp.125-130
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• 2021

Scaffold is used to produce bio sensor. Scaffold is required high dimensional accuracy. 3D printer is used to manufacture scaffold. 3D printer can't detect defect during printing. Defect detection is very important in scaffold printing. Real-time defect detection is very necessary on industry. In this paper, we proposed the method for real-time scaffold defect detection. Real-time defect detection model is produced using CNN(Convolution Neural Network) algorithm. Performance of the proposed model has been verified through evaluation. Real-time defect detection system are manufactured on hardware. Experiments were conducted to detect scaffold defects in real-time. As result of verification, the defect detection system detected scaffold defect well in real-time.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.17-18
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• 2006

In this paper, we propose a robust detection scheme of OSTBCs with channel estimation errors over time-selective fading channels. Channel estimation errors are inevitable over time-selective fading channels and even small channel estimation errors dramatically degrade the performance of space-time block coding schemes. Therefore, it is desired to investigate the effect of channel estimation errors on the performance of the proposed detection scheme compared with the existing detection scheme. The proposed detection scheme minimizes noise enhancement and impact of channel estimation errors which occur in an existing detection scheme. It is shown by simulations that the proposed detection scheme performs better than the existing detection scheme over time-selective fading channels.

• Journal of electromagnetic engineering and science
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• 제19권2호
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• pp.107-114
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• 2019

Alert-confirm detection is a highly efficient method to improve phased array radar search performance. It comprises sequential detection in two steps: alert detection, in which a target is detected at a low detection threshold, and confirm detection, which is triggered by alert detection with a longer dwell time to minimize false alarms. This paper provides a design method for applying the alert-confirm detection to multifunctional radars. We find optimum dwell times and false alarm probabilities for each alert detection and confirm detection under the dual constraints of total false alarm probability and maximum allowable dwell time per position. These optimum values are expressed as a function of the mean new target appearance rate. The proposed alert-confirm detection increases the maximum detection range even with a shorter frame time than that of uniform scanning.

• Journal of Multimedia Information System
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• 제8권3호
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• pp.159-166
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• 2021

COVID-19 has dramatically changed people's daily life. Wearing masks is considered as a simple but effective way to defend the spread of the epidemic. Hence, a real-time and accurate mask wearing detection system is important. In this paper, a deep learning-based mask wearing detection system is developed to help people defend against the terrible epidemic. The system consists of three important functions, which are image detection, video detection and real-time detection. To keep a high detection rate, a deep learning-based method is adopted to detect masks. Unfortunately, according to the suddenness of the epidemic, the mask wearing dataset is scarce, so a mask wearing dataset is collected in this paper. Besides, to reduce the computational cost and runtime, a simple online and real-time tracking method is adopted to achieve video detection and monitoring. Furthermore, a function is implemented to call the camera to real-time achieve mask wearing detection. The sufficient results have shown that the developed system can perform well in the mask wearing detection task. The precision, recall, mAP and F1 can achieve 86.6%, 96.7%, 96.2% and 91.4%, respectively.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.227-241
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• 2021

Object detection techniques based on deep learning such as YOLO have high detection performance and precision in a single channel video stream. In order to expand to multiple channel object detection in real-time, however, high-performance hardware is required. In this paper, we propose a novel back-end server framework, a real-time AI vision platform (RAVIP), which can extend the object detection function from single channel to simultaneous multi-channels, which can work well even in low-end server hardware. RAVIP assembles appropriate component modules from the RODEM (real-time object detection module) Base to create per-channel instances for each channel, enabling efficient parallelization of object detection instances on limited hardware resources through continuous monitoring with respect to resource utilization. Through practical experiments, RAVIP shows that it is possible to optimize CPU, GPU, and memory utilization while performing object detection service in a multi-channel situation. In addition, it has been proven that RAVIP can provide object detection services with 25 FPS for all 16 channels at the same time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권9호
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• pp.2341-2350
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• 2012

Face detection is the first step of vision-based driver fatigue detection method. Traditional face detection methods have problems of high false-detection rates and long detection times. A space-time restrained Adaboost method is presented in this paper that resolves these problems. Firstly, the possible position of a driver's face in a video frame is measured relative to the previous frame. Secondly, a space-time restriction strategy is designed to restrain the detection window and scale of the Adaboost method to reduce time consumption and false-detection of face detection. Finally, a face knowledge restriction strategy is designed to confirm that the faces detected by this Adaboost method. Experiments compare the methods and confirm that a driver's face can be detected rapidly and precisely.

• 한국컴퓨터정보학회논문지
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• 제21권11호
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• pp.31-38
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• 2016

In this paper, we propose multi-scale infrared target detection algorithm with varied filter size using integral image. Filter based target detection is widely used for small target detection, but it doesn't suit for large target detection depending on the filter size. When there are multi-scale targets on the sky background, detection filter with small filter size can not detect the whole shape of the large targe. In contrast, detection filter with large filter size doesn't suit for small target detection, but also it requires a large amount of processing time. The proposed algorithm integrates the filtering results of varied filter size for the detection of small and large targets. The proposed algorithm has good performance for both small and large target detection. Furthermore, the proposed algorithm requires a less processing time, since it use the integral image to make the mean images with different filter sizes for subtraction between the original image and the respective mean image. In addition, we propose the implementation of real-time embedded system using FPGA.

• Journal of Information Processing Systems
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• 제1권1호
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• pp.9-13
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• 2005

Computer security has become a critical issue with the rapid development of business and other transaction systems over the Internet. The application of artificial intelligence, machine learning and data mining techniques to intrusion detection systems has been increasing recently. But most research is focused on improving the classification performance of a classifier. Selecting important features from input data leads to simplification of the problem, and faster and more accurate detection rates. Thus selecting important features is an important issue in intrusion detection. Another issue in intrusion detection is that most of the intrusion detection systems are performed by off-line and it is not a suitable method for a real-time intrusion detection system. In this paper, we develop the real-time intrusion detection system, which combines an on-line feature extraction method with the Least Squares Support Vector Machine classifier. Applying the proposed system to KDD CUP 99 data, experimental results show that it has a remarkable feature extraction and classification performance compared to existing off-line intrusion detection systems.

• 한국식품위생안전성학회지
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• 제34권3호
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• pp.290-295
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• 2019

최근 한국에서 발생한 Salmonella로 인한 식중독 사고는 2018년 9월 학교급식에서 제공된 초콜릿 무스 케이크가 원인이 되었다. 이 연구의 목적은 Salmonella Typhimurium이 인위적으로 접종된 무스케이크와 티라미수에서 3M Molecular Detection Assay 2 - Salmonella와 식품공전에 등재된 방법인 분리배지와 real-time PCR을 비교하는 것이었다. 무스케이크 2종과 티라미수 2종 25 g에 225 mL BPW를 넣고 $37^{\circ}C$에서 24시간 동안 증균 배양하였다. 배양 후, 3M Molecular Detection Assay 2 - Salmonella, 분리배지 그리고 real-time PCR로 분석하였다. 초콜릿 무스 케이크를 제외하고 3가지 방법은 유사한 결과를 보였다. 초콜릿 무스 케이크에서 분리배지와 3M Molecular Detection Assay 2 - Salmonella는 모든 접종수준에서 동일한 결과를 나타낸 반면 real-time PCR은 $10^4CFU/25g$ 수준에서 1번의 양성결과를 제외하고 모두 검출되지 않았다. 초콜릿 무스에 S. Typhimurium을 $10^2CFU/25g$ 수준으로 접종하였을때, real-time PCR를 이용한 검출은 15%에서는 부분적인 음성을 나타냈고, 20-100% 함량의 초콜릿 무스에서는 모두 음성이었다. Real-time PCR로는 chocolate이 15% 이상 함유된 식품에서의 Salmonella균 검출이 불가능하였지만, LMAP 기반의 3M Molecular Detection Assay 2으로는 chocolate 농도에 관계없이 검출이 가능하였다.