• 대한의용생체공학회:의공학회지
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• 제16권2호
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• pp.175-182
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• 1995

태아 심음을 측정하기 위한 기존의 자기상관 함수 법은 처리과정이 간편한 반면에 많은 문제점을 가지고 있다. 초음파 도플러 신호가 열악할 경우 고전적인 자기상관 함수 법은 문턱 값의 선정과 창 함수 크기에 매우 민감하다. 특히 데이터 손실이 생길 때 정확한 태아 심박동 수를 찾기가 어렵다. 이들 문제점들을 보완하기 위하여 초음파 도플러 신호로부터 정확한 태아 심박동 수를 찾는 고해상 피치검출 알고리듬이 제안되었다. 이 알고리듬은 자기상관 함수법 보다 정확하고, 잡음에 강하며, 높은 신뢰성을 갖으나 계산량이 많아 실시간 처리가 어렵다. 본 논문에서는 실시간 처리에 적합한 새로운 태아 심음 추출 알고리듬을 제안하고, 제안된 알고리듬을 적용한 실시간 태아 심음 감시시스템에 관하여 연구하였다.

• Journal of Microbiology and Biotechnology
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• 제16권11호
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• pp.1778-1783
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• 2006

Event-specific PCR detection methods are the primary trend in genetically modified (GM) plant detection owing to their high specificity based on the flanking sequence of the exogenous integrant. Therefore, this study describes a real-time PCR system for event-specific GM canola GT73, consisting of a set of primers, TaqMan probe, and single target standard plasmid. For the specific detection of GT73 canola, the 3'-integration junction sequence between the host plant DNA and the integrated specific border was targeted. To validate the proposed method, test samples of 0, 1, 3, 5, and 10% GT73 canola were quantified. The method was also assayed with 15 different plants, and no amplification signal was observed in a real-time PCR assay with any of the species tested, other than GT73 canola.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권12호
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• pp.2355-2373
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• 2011

Vision-based vehicle detector systems are becoming increasingly important in ITS applications. Real-time operation, robustness, precision, accurate estimation of traffic parameters, and ease of setup are important features to be considered in developing such systems. Further, accurate vehicle detection is difficult in varied complex traffic environments. These environments include changes in weather as well as challenging traffic conditions, such as shadow effects and jams. To meet real-time requirements, the proposed system first applies a color background to extract moving objects, which are then tracked by considering their relative distances and directions. To achieve robustness and precision, the color background is regularly updated by the proposed algorithm to overcome luminance variations. This paper also proposes a scheme of feedback compensation to resolve background convergence errors, which occur when vehicles temporarily park on the roadside while the background image is being converged. Next, vehicle occlusion is resolved using the proposed prior split approach and through reasoning for rule-based tracking. This approach can automatically detect straight lanes. Following this step, trajectories are applied to derive traffic parameters; finally, to facilitate easy setup, we propose a means to automate the setting of the system parameters. Experimental results show that the system can operate well under various complex traffic conditions in real time.

• Food Science and Biotechnology
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• 제18권5호
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• pp.1118-1123
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• 2009

Event-specific real-time polymerase chain reaction (PCR) detection method for genetically modified (GM) maize MIR604 was developed based on integration junction sequences between the host plant genome and the integrated transgene. In this study, 2 primer pairs and probes were designed for specific amplification of 100 and 111 bp DNA fragments from the zSSIIb gene (the maize endogenous reference gene) and MIR604. The quantitative method was validated using 3 certified reference materials (CRMs) with levels of 0.1, 1, and 10% MIR604. The method was also assayed with 14 different plants and other GM maize. No amplification signal was observed in real-time PCR assays with any of the species tested other than MIR604 maize. As a result, the bias from the true value and the relative deviation for MIR604 was within the range from 0 to 9%. Precision, expressed as relative standard deviation (RSD), varied from 2.7 to 10% for MIR604. Limits of detections (LODs) of qualitative and quantitative methods were all 0.1%. These results indicated that the event-specific quantitative PCR detection system for MIR604 is accurate and useful.

• 대한전자공학회논문지SP
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• 제37권3호
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• pp.57-69
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• 2000

본 논문에서는 주행차량에 장착된 CCD 카메라를 통하여 획득되어진 영상으로부터 moving window를 사용하여 차선을 인식하고 장애물을 감지하는 방법을 제안한다 입력되는 동영상을 실시간에 처리하기 위해서는 하드웨어적으로 상당히 많은 제약을 초래한다. 이러한 문제점을 극복하고 영상을 사용하여 실시간에 차선 인식 및 장애물을 감지하기 위해, 도로조건과 차량상태에 바탕을 둔 최적의 window 크기를 결정하고 그 window 영상만을 처리하여 차선 인식 및 장애물 감지를 실시간에 가능하게 하는 기법을 제안한다 영상의 각 프레임에 대하여 moving window는 칼만필터에 의해 정확성이 향상된 예측방향으로 옮겨진다. 제안된 알고리즘의 효용성을 고속도로 주행영상을 사용한 실험을 통해 보여준다

• 로봇학회논문지
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• 제14권4호
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• pp.301-310
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• 2019

A new algorithm has been propose to detect the reflected light region as disturbances in a real-time vision system. There have been several attempts to detect existing reflected light region. The conventional mathematical approach requires a lot of complex processes so that it is not suitable for a real-time vision system. On the other hand, when a simple detection process has been applied, the reflected light region can not be detected accurately. Therefore, in order to detect reflected light region for a real-time vision system, the detection process requires a new algorithm that is as simple and accurate as possible. In order to extract the reflected light, the proposed algorithm has been adopted several filter equations and clustering processes in the HSI (Hue Saturation Intensity) color space. Also the proposed algorithm used the pre-defined reflected light data generated through the clustering processes to make the algorithm simple. To demonstrate the effectiveness of the proposed algorithm, several images with the reflected region have been used and the reflected regions are detected successfully.

• 한국BIM학회 논문집
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• 제11권2호
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• pp.24-32
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• 2021

Recently, starting with smart construction research, interest in technology that automates construction site management using artificial intelligence technology is increasing. In order to automate construction site management, it is necessary to recognize objects such as construction equipment or workers, and automatically analyze the relationship between them. For example, if the relationship between workers and construction equipment at a construction site can be known, various use cases of site management such as work productivity, equipment operation status monitoring, and safety management can be implemented. This study derives a real-time object detection platform architecture that is required when performing construction site management using deep learning technology, which has recently been increasingly used. To this end, deep learning models that support real-time object detection are investigated and analyzed. Based on this, a deep learning model development process required for real-time construction site object detection is defined. Based on the defined process, a prototype that learns and detects construction site objects is developed, and then platform development considerations and architecture are derived from the results.

• 제어로봇시스템학회논문지
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• 제17권11호
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• pp.1106-1116
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• 2011

LINS (Laser Inertial Navigation System) consists of RLG (Ring Laser Gyroscopes)/accelerometers and provides real-time navigation information to the target system. Therefore it is very important to make a decision in the real time whether LINS is in the normal operation or not. That is called a fault detection method. In this paper, we propose the fault detection method of LINS based on the overlapping model. We also show that the fault detection probability is increased through overlapping the hardware model and the software model. It is verified through the long-term operation and RAM (Reliability Availability Maintainability) analysis of LINS that the fault detection method proposed in this paper is able to detect about 97% of probable system failures.

• 한국군사과학기술학회지
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• 제11권1호
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• pp.75-84
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• 2008

A new automatic small target detection and tracking algorithm for the real-time IR surveillance system is presented. The automatic target detection and tracking algorithm of the real-time systems, requires low complexity and robust tracking performance in the cluttered environment. Linear-array and parallel-scan IR systems usually suffer from severe scan noise caused by the detector non-uniformity. After the spatial filtering and thresholding, this scan noise still remains as high amplitude clutter which degrades the target detection rate and tracking performance. In this paper, we propose a new feature which consists of area and validity information of a measurement. By adopting this feature to the measurements selection and track confirmation, we can increase the target detection rate and reduce both the track loss rate and false track rate. From the experimental results, we can validate the feasibility of the proposed method in the noisy IR images.

• 미생물학회지
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• 제43권1호
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• pp.23-30
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• 2007

조류 인플루엔자바이러스(AIV) H5N1 아형을 Real-time PCR법을 이용하여 가장 빠르게 진단할 수 있는 방법을 개발하였다. 검색 대상의 염기서열은 AIV H5N1 아형의 hemagglutinin 유전자 중 가장 상동성이 높은 387 bp의 부위를 선택하였고, 실험의 안전을 위하여 인공합섬의 방법으로 제작하였다. Microchip을 기반으로 한Real-time PCR법을 사용하였으며, 총PCR 반응액의 양을 $1{\mu}l$로, PCR 과정 중 각 단계, 즉 해리, 접합, 신장의 시간을 각1초, 1초, 3초로 하여 총 실험시간을 단축하였다. 진단을 위한 실험과정에서 PCR 및 융점분식에 소요된 최단 시간은 12분28초였으며, 민감도측정에서 최소2.4개의 hemaggutinin 유전자를 기질로 하여 목적한 특이 189 bp의 PCR 산물을 증폭할 수 있었기에, 본 연구에서는 이런 초고속 PCR 실험방식을 Quick Real-time PCR이라 명명하였다. 이 결과들은 가금류 및 사람에게 전파된 AIV H5N1아형의 진단에 적용될 수 있을 뿐 아니라, PCR이 사용되는 다른 신속검색법에도 널리 적용 될 수 있을 것으로 기대한다.