• Journal of electromagnetic engineering and science
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• v.10 no.1
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• pp.18-24
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• 2010

A Ka-band microstrip array antenna for wide-range detection of moving targets is analyzed through a photonicassisted reactive-near-field characterization technique. The antenna array employs a 3-dB-tapered feed network to suppress the sidelobe level while retaining a wide azimuth beamwidth for a wide detection range. The relative nearelectric field patterns of the array and its 3-dB-tapered feed lines have been measured using an electro-optic fieldmapping technique for minimally invasive millimeter-wave sensing. A number of typical limitations on the technique, involving bandwidth, low signal-modulation depth, and high laser-induced noise in high-frequency applications, have been overcome by suppressing the carrier portion of the optical interrogation beam.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.8
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• pp.99-105
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• 2011

This paper is about the design of 3-axis magnetic sensor module which detects parking and moving vehicle. For the sensor module, MR Sensor from Honeywell of which maximum measurement range is ${\pm}2$[G] is used. It also consisted of amplifier and sensor filter and fabricated $30{\times}50$[mm] PCB. Fabricated sensor module produced helmholtz coil of which the length is 1.2[m] of 3-axis to know the performance. It installed sensor module at the center and measured the detected magnetic field. In result, 3-axis were detected as 0.2~0.3[mG] and the drift of the fluctuation of magnetic field was stabilized at 0.03[mG] unit. For the performance evaluation of the vehicle detection, after the entry and parking of the vehicle, variation of magnetic field was measured as 0.323~0.695[G] which the average 0.5[G] of the earth magnetic field was the center and the range of variation was confirmed as 0.37[G]. Therefore, the designed magnetic sensor can be used as the vehicle detection sensor module.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.371-393
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• 2023

Crack detection in structures plays a vital role in ensuring their safety, durability, and reliability. Traditional crack detection methods sometimes need significant manual inspections, which are laborious, expensive, and prone to error by humans. Deep learning algorithms, which can learn intricate features from large-scale datasets, have emerged as a viable option for automated crack detection recently. This study presents an in-depth review of crack detection methods used till now, like image processing, traditional machine learning, and deep learning methods. Specifically, it will provide a comparative analysis of crack detection methods using deep learning, aiming to provide insights into the advancements, challenges, and future directions in this field. To facilitate comparative analysis, this study surveys publicly available crack detection datasets and benchmarks commonly used in deep learning research. Evaluation metrics employed to check the performance of different models are discussed, with emphasis on accuracy, precision, recall, and F1-score. Moreover, this study provides an in-depth analysis of recent studies and highlights key findings, including state-of-the-art techniques, novel architectures, and innovative approaches to address the shortcomings of the existing methods. Finally, this study provides a summary of the key insights gained from the comparative analysis, highlighting the potential of deep learning in revolutionizing methodologies for crack detection. The findings of this research will serve as a valuable resource for researchers in the field, aiding them in selecting appropriate methods for crack detection and inspiring further advancements in this domain.

• The Korean Journal of Pesticide Science
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• v.15 no.2
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• pp.134-139
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• 2011

To investigate an amount of pesticide residue in rice paddy field soils and greenhouse soil, this monitoring was carried out pesticide detection frequency and concentrations collected samples from 150 rice paddy field soils and 152 greenhouse soils of nationwide in the year of 2007, and 2008, respectively. The detection limit of pesticides of this experiment were ranged 0.001~0.005 ppm. In 2007, One hundred fifty samples were collected from rice paddy field soils in April and monitored for 120 wide-used pesticides. A total of 11 pesticides were detected four fungicides, four insecticides and three herbicides in paddy field soils. The highest concentration levels of pesticide detected were 0.84 ppm as herbicide oxadiazon, 0.81 ppm as fungicide isoprothiolane and 0.50 ppm as insecticide buprofezin. The detection frequencies range were 0~19.3%, and the frequency was 2.7% as isoprothiolane and 19.3% as oxadiazon in paddy field soils. In 2008, One hundred fifty two samples were collected from greenhouse soils in April and monitored for 120 wide-used pesticides. A total of 29 pesticides were detected six fungicides, sixteen insecticides and seven herbicides in greenhouse soils. high concentration levels of pesticide detected levels were 5.09 ppm as insecticide chlorfenapyr, 2.57 ppm as fungicide chlorothalonil and 0.72 ppm as herbicide oxadiazon. The detection frequencies range were 0~38.8%, and high frequencies were 38.8% as insecticide endosulfan, 13.2% as oxadiazone, 10.5% as fungicide hexaconazole and 7.2% as isoprothiolane in greenhouse soils, Total endosulfan and oxadiazon were showed high detection frequency of 38.8% and 13.2%, respectively.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.10a
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• pp.69-70
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• 2006

• Proceedings of the SAREK Conference
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• 2008.06a
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• pp.103-108
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• 2008

Automatic control makes the air-handling unit go into operation and determines the functions of high-efficient and energy-saving machines. Yamatake, an automatic control system manufacturer, have expanded fault detection and diagnosis, and data volumes so as to achieve higher technology in control by developing a sensor which makes field data visible, an actuator and Intelligent Conponent. This study, thus, focuses on applications for saving energy with Intelligent Conponent and goes in for easing global warming by creating future field data-based applications.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.383-384
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• 2014

본 논문에서 제시된 배전 선로용 자계 검출 센서는 비접촉형이며, 페라이트 코어로 구성되어 있다. 시편 코어의 권선수 및 길이의 변화에 따른 특성을 조사한 결과 탐색 코일의 권선수가 5,000턴이고 이격 거리가 6[cm] 이내에서 활선 여부를 검출할 수 있었으며, 전선과의 이격 거리에 대해서 자계의 세기가 제곱에 반비례로 감소하는 것을 확인할 수 있었다.

• Journal of Soil and Groundwater Environment
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• v.14 no.2
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• pp.45-53
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• 2009

A quick and simple detection method of explosive compounds in environmental matrix (soil and water) can provide a screening step which reduces the number of unnecessary samples and the cost of expensive laboratory analysis at a site investigation. A commercially available EXPRAY$^{(R)}$Explosives Field Detection Kit (EXPRAY) was used to determine the minimum detection concentration and to test the possibility of semi-quantitative analysis of 14 explosive compounds using standard solutions. The results showed that EXPRAY could detect 5 explosive compounds, TNT, RDX, HMX, Tetryl, and TNB, out of 14 US EPA designated explosives. The minimum detection limit of the nitramine explosives was 14 ng/$^2$ for HMX and RDX. EXPRAY was more sensitive to nitroaromatics than the nitramines and the minimum detection limits per unit area (mm$^2$) for Tetryl, TNB, and TNT, were 3 ng, 3 ng, and 0.3 ng, respectively. The semi-quantification of 5 explosive compounds in an order ofmagnitude could be achieved by the intensity of developed color only when EXPRAY was applied on the standard solutions under controlled laboratory conditions. With contaminated soil samples, however, only the presence and type of explosive compounds was identified. Therefore, EXPRAY is an economic and sensitive method that can be used in a screening step for the identification of explosives in the field samples.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.3
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• pp.296-301
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• 2012

This paper describes an analytic method for infrared thermography to detect surface cracks in thin plates. Traditional thermographic method uses the spatial contrast of a thermal field, which is often corrupted by noise in the experiment induced mainly by emissivity variations of target surfaces. This study developed a robust analytic approach to crack detection for thermography using the holomorphic function of a temperature field in thin plate under steady-state thermal conditions. The holomorphic function of a simple temperature field was derived for 2-D heat flow in the plate from Cauchy-Riemann conditions, and applied to define a contour integral that varies depending on the existence and strength of singularity in the domain of integration. It was found that the contour integral at each point of thermal image reduced the noise and temperature variation due to heat conduction, so that it provided a clearer image of the singularity such as cracks.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.672-679
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• 2005

Damage detection is a very active research field, in which significant efforts have been invested in recent years. In this paper, analysis using structural stiffness estimation for damage detection is presented and compared to other methodologies. By using a cantilever analytical beam model, it is shown here that not only location but also the amount of damage in structure can be predicted from the ratio of change in stiffness. Damage detection experiment in real beam specimen on is also peformed and the results are compared.