• 한국멀티미디어학회논문지
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• 제18권12호
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• pp.1432-1438
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• 2015

We present a methodology to detect moving shadows in video sequences, which is considered as a challenging and critical problem in the most visual surveillance systems since 1980s. While most previous moving shadow detection methods used hand-crafted features such as chromaticity, physical properties, geometry, or combination thereof, our method can automatically learn features to classify whether image segments are shadow or foreground by using a deep learning architecture. Furthermore, applying Markov Random Field enables our system to refine our shadow detection results to improve its performance. Our algorithm is applied to five different challenging datasets of moving shadow detection, and its performance is comparable to that of state-of-the-art approaches.

• Journal of Magnetics
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• 제21권3호
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• pp.322-329
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• 2016

In this paper, we propose an array sensing detection method for smelting of submerged arc furnaces (SAF) based on electromagnetic radiation. AC magnetic field generated by electrode currents and molten currents in the furnace is reflected outside of the furnace. According to the spatial distribution of electromagnetic field a radiation model of SAF is built. We design a 3D magnetic field sensing array system in order to collect the magnetic field information. Through the collected information, the current distribution characteristics of SAF are described and the key parameters of smelting are obtained. Theoretical simulation and field test show that the curves acquired by the sensing array can accurately reflect the information of the relative displacement when the relative displacement between the array and electrode is 10 cm. Compared with the detection method of 3D single point, the proposed array sensing method of magnetic field obtains better results in terms of real-time and accuracy, and has good practical value for industrial measurement.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2013년도 춘계학술대회 논문집
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• pp.585-586
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• 2013

• 한국전자파학회:학술대회논문집
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• 한국전자파학회 2005년도 종합학술발표회 논문집 Vol.15 No.1
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• pp.33-38
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• 2005

In this paper, a simulator for radar performance evaluation in a far-field test range is proposed, which can forecast maximum detection range, minimum detection range, number of test trials, resolution (range, azimuth, elevation) with input parameters before radar performance test and process results after. The proposed simulator is designed by Microsoft Foundation Class (MFC) of VC++ 6.0.

• 한국환경생태학회지
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• 제37권5호
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• pp.327-336
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• 2023

본 연구는 야생조류 생물음향 탐지와 현장 조사의 결과 비교를 통해 효과적인 야생조류 조사 방법을 제시하는데 목적이 있다. 연구대상지는 제주 동백동산과 1100고지 습지였다. 생물음향 탐지 기간은 2020년 12개월간이었다. 생물음향 탐지는 시간당 1분씩, .wav, 44,100Hz 포맷으로 생물음향 측정장비(Song meter SM4)를 각 연구대상지에 설치하여 녹음하였다. 현장 조사는 Banjade et al. (2019)의 「제주 동백동산과 1100고지 습지의 조류군집 장기간 동향」 결과를 인용하였다. 연구결과는 다음과 같다. 첫 번째, 생물음향 탐지 기법을 통해 확인된 조류상은 동백동산에서 29과 46종, 1100고지 습지에서 16과 25종이었다. 두 번째, 울음 빈도를 통해 나타난 종 구성은 동백동산에서 직박구리(33.62%), 섬휘파람새(12.13%), 동박새(9.77%) 순으로 나타났고, 1100고지 습지에서 큰부리까마귀(27.34%), 섬휘파람새(19.43%), 직박구리(16.56%) 순으로 나타났다. 세 번째, 동백동산에서 실시한 현장 조사의 경우 2009년 39종, 2012년 51종, 2015년 35종, 2018년 45종이 조사되었고, 생물음향 탐지를 통해 46종이 확인되었다. 1100고지 습지에서 실시한 현장 조사의 경우 2009년 37종, 2012년 42종, 2015년 34종, 2018년 38종이 조사되었고, 생물음향 탐지를 통해 25종이 확인되었다. 종합적으로 현장 조사를 통해 동백동산에서 확인된 78종 중 43.6%인 34종, 1100고지 습지에서 확인된 47종 중 38.3%인 18종이 생물음향 탐지로 확인되었다. 네 번째, 현장 조사에서 확인되지 않았지만, 생물음향 탐지를 통해 확인된 종은 동백동산에서 9과 12종, 1100고지 습지에서 3과 7종으로 대표적으로 소쩍새, 솔부엉이와 같은 야행성 조류, 쇠유리새, 울새와 같은 나그네새, 도래하는 개체수가 상대적으로 적은 황여새, 솔잣새와 같은 겨울철새가 있다. 다섯 번째, 현장 조사에서 확인된 종 중에서 생물음향 탐지를 통해 확인되지 않은 종은 동백동산에서 18과 48종, 1100고지 습지에서 14과 27종으로 대표적으로 백로과, 수리과, 솔딱새과가 있다. 본 연구는 국내에서 생물음향 장비 운용을 통한 조류 연구가 활성화되고 있는 가운데 현장 조사와 생물음향 탐지를 병행했을 때 효과적인 조사를 실시할 수 있다는 기초 자료를 작성했다는 점에서 의미가 있다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2017년도 추계학술대회
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• pp.699-701
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• 2017

장거리 관측용 카메라의 일반적인 운용방법은 광각에서 표적에 대한 탐지를 하며, 망원으로 표적에 대한 인지/식별을 하게 된다. 탐지/인지거리 성능은 방산용 적외선카메라의 성능을 평가하는 중요한 항목이다. 탐지거리 성능이 증가하려면 카메라의 시야가 좁아져야 하고, 좁은 시야각으로 인하여 표적을 찾게 될 확률이 상대적으로 낮아지게 된다. 본 연구에서는 탐지거리 성능을 유지하면서 넓은 시야각을 제공하여 표적에 대한 탐색을 용이하게 할 수 있는 방안을 검토하였다. 그리고 M&S 및 최적화 설계를 통하여 시야 확장형 적외선카메라를 제작하고 시험한 결과를 정리하였다.

• 전기학회논문지
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• 제68권3호
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• pp.471-479
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• 2019

This research proposes a indoor magnetic map matching algorithm that improves the position accuracy by employing multiple magnetic sensors and probabilistic candidate weighting function. Since the magnetic field is easily distorted by the surrounding environment, the distorted magnetic field can be used for position mapping, and multiple sensor configuration is useful to improve mapping accuracy. Nevertheless, the position error is likely to increase because the external magnetic disturbances have repeated pattern in indoor environment and several points have similar magnetic field distortion characteristics. Those errors cause large position error, which reduces the accuracy of the position detection. In order to solve this problem, we propose a method to reduce the error using multiple sensors and likelihood boundaries that uses human walking characteristics. Also, to reduce the maximum position error, we propose an algorithm that weights according to their importance. We performed indoor walking tests to evaluate the performance of the algorithm and analyzed the position detection error rate and maximum distance error. From the results we can confirm that the accuracy of position detection is greatly improved.

• 산업경영시스템학회지
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• 제46권2호
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• pp.1-12
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• 2023

To make semiconductor chips, a number of complex semiconductor manufacturing processes are required. Semiconductor chips that have undergone complex processes are subjected to EDS(Electrical Die Sorting) tests to check product quality, and a wafer bin map reflecting the information about the normal and defective chips is created. Defective chips found in the wafer bin map form various patterns, which are called defective patterns, and the defective patterns are a very important clue in determining the cause of defects in the process and design of semiconductors. Therefore, it is desired to automatically and quickly detect defective patterns in the field, and various methods have been proposed to detect defective patterns. Existing methods have considered simple, complex, and new defect patterns, but they had the disadvantage of being unable to provide field engineers the evidence of classification results through deep learning. It is necessary to supplement this and provide detailed information on the size, location, and patterns of the defects. In this paper, we propose an anomaly detection framework that can be explained through FCDD(Fully Convolutional Data Description) trained only with normal data to provide field engineers with details such as detection results of abnormal defect patterns, defect size, and location of defect patterns on wafer bin map. The results are analyzed using open dataset, providing prominent results of the proposed anomaly detection framework.

• 한국전기전자재료학회논문지
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• 제30권4호
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• pp.253-257
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• 2017

Nowadays, it is increasingly important to detect whether cables are live for the operator's safety if there is a sudden power failure. It is especially hard to detect the electrical field of an underground line because of shielding. This paper on detection of live-line states in cables studied the detection characteristics of the change in the magnetic field and axis as the frequency, voltage, and distance at the same load are changed using 3 axes. A search coil type was used as a magnetic field sensor with non-contact. We found that magnetic fields decrease proportionally to the square of the distance and the decrease of rated voltage with load effected to magnetic field. The magnetic field was detected by 3-axis sensors given correct proximity, but appeared as noise components beyond a distance of 2 cm.

• 한국정밀공학회지
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• 제14권2호
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• pp.41-47
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• 1997

Fault diagnosis problem is currently a subject of extensive research in the control field. Although there are several works on the fault detection and isolation observers and the residual generators, those are con- cerned with only the detection of actuator failures or sensor failures. So, the perfect detection and isolation for the actuator and sensor failures is strongly required in the field of the practical applications. In this paper, a strategy of fault diagnosis using multiple proportional integral (PI) observers including the magnitude of actuator failures is provided. It is shown that actuator failures are detected and isolated perfectly by monitoring the integrated error between actual output and estimated output by a PI observer. Also in presence of complex actuator and sensor failures, these failures are detected and isolated by multiple PI observers.