• Journal of Animal Science and Technology
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• 제61권6호
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• pp.366-370
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• 2019

Overweight and obesity induce serious health problems that exert negative effects on dog's welfare. Body condition score (BCS) is a common method to evaluate the body fat mass in animals. By palpating and observing fats under the skin it is possible to predict animal's body fat accumulation condition. BCS is also a useful tool to estimate body fat composition in dogs. However, BCS can be subjective when it was performed by non-professionals like pet's owners. To develop a method to avoid the misevaluation of BCS twenty-four Beagles were enrolled and performed BCS evaluation. In addition, the length of chest and abdominal girths were measured. In correlation analysis, the sizes of chest and abdominal girth were significantly correlated with BCS. Especially, the difference and ratio of the chest and abdominal length were highly correlated with the BCS. With that, we suggested that this simple measurement of chest and abdominal girths by a measuring tape would be an effective method to estimate BCS scores in dogs that helps non-professionals to manage their own dog's nutritional condition by monitoring body fat accumulation condition.

• 비파괴검사학회지
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• 제14권1호
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• pp.16-22
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• 1994

일반적으로 구름 베어링 시스템에 비해 발전용 터어빈이나 내연기관 엔진과 같은 저어널 베어링을 가진 시스템은 상대적으로 대형 설비이거나 더 가혹한 운전조건에서 가동되는 경우가 많다. 이런 회전기계류에서의 베어링의 파손은 설비의 운전 중단 및 관련 설비의 파손까지도 초래할 수 있게 된다. 따라서 이로 인한 보수에 소비되는 시간 및 경제적인 손실등을 피하기 위해서는 저어널 베어링의 조기파손 감지의,역할은 매우 중요하게 된다. 본 연구에서는 음향방출 기술을 이용하여 베어링에서 발생할 수 있는 파손의 조기검출을 위해 실험실용으로 직접 제작한 저어널 베어링 시스템을 이용하여 여러 형태의 비정상 조건을 만들어 가며 실험을 행하였다. 베어링 손상 및 피로의 주 요인으로서는 윤활유 부족, 윤활층에의 이물질의 혼입, 조립 불량 등이 대표적인 원인으로서 알려져 있으며 이에 근거하여 실험 조건을 윤활유에의 이물질 혼입, 윤활유 부족, 그리고 축과 베어링간의 금속간 접촉등의 인위적인 형태로 구성하여 실험하였다. 그 결과로서 음향방출 기술이 저어널 베어링의 조기파손 감지에 매우 효과적인 도구라는 것을 입증하였다.

• Computers and Concrete
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• 제33권4호
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• pp.341-348
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• 2024

Ultra-high-performance concrete (UHPC) has received remarkable attentions in civil infrastructure due to its unique mechanical characteristics and durability. UHPC gains increasingly dominant in essential structural elements, while its unique properties pose challenges for traditional inspection methods, as damage may not always manifest visibly on the surface. As such, the need for robust inspection techniques for detecting cracks in UHPC members has become imperative as traditional methods often fall short in providing comprehensive and timely evaluations. In the era of artificial intelligence, computer vision has gained considerable interest as a powerful tool to enhance infrastructure condition assessment with image and video data collected from sensors, cameras, and unmanned aerial vehicles. This paper presents a computer vision-based approach employing deep learning to detect cracks in UHPC beams, with the aim of addressing the inherent limitations of traditional inspection methods. This work leverages computer vision to discern intricate patterns and anomalies. Particularly, a convolutional neural network architecture employing transfer learning is adopted to identify the presence of cracks in the beams. The proposed approach is evaluated with image data collected from full-scale experiments conducted on UHPC beams subjected to flexural and shear loadings. The results of this study indicate the applicability of computer vision and deep learning as intelligent methods to detect major and minor cracks and recognize various damage mechanisms in UHPC members with better efficiency compared to conventional monitoring methods. Findings from this work pave the way for the development of autonomous infrastructure health monitoring and condition assessment, ensuring early detection in response to evolving structural challenges. By leveraging computer vision, this paper contributes to usher in a new era of effectiveness in autonomous crack detection, enhancing the resilience and sustainability of UHPC civil infrastructure.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2005년도 춘계학술대회논문집
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• pp.941-944
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• 2005

Asymmetry of rotor systems is an important factor for identification of dynamic characteristics including the stability and response of rotors and for condition monitoring. In this work, asymmetry of rotors is identified by applying curve-fitting method to the directional frequency response functions (dFRFs), which are known as a powerful tool for detecting the presence and degree of asymmetry. This method minimizes least square error between analytical and measured dFRFs by iteratively updating physical parameters associated with rotor asymmetry. The effectiveness of the identification method is demonstrated by experiments with a laboratory test rotor.

• 한국생산제조학회지
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• 제5권2호
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• pp.38-45
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• 1996

This study was focused on the prediction of the approprite tool life by clarifying the correlation between progressive drill wear and AE signal. on drilling SM45C the following results have been obtained; RMSAE, AE CUM-CNTS had a tendency to increase slowly according to wear size, at 1000rpm, 150mm/min However, these increased suddenly in the range of 0.20~0.22mm wear, about 102 holes and had a tendency to go up and down until the drilling was impossible. The sudden increase of AE signals shows that something is wrong and it is closely connected with drill wear and chipping. It also makes the working surface bad From the above results, AE signals could be used to monitor the drill's condition and to determine the right time to change tools.

• Journal of Electrical Engineering and Technology
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• 제8권6호
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• pp.1503-1511
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• 2013

This paper deals with the analysis of leakage current characteristics of silicone rubber insulator in order to develop a new condition monitoring tool to identify the flashover of outdoor insulators. In this work, laboratory based pollution performance tests are carried out on silicone rubber insulator under ac voltage at different pollution levels and relative humidity conditions with sodium chloride (NaCl) as a contaminant. Min-Norm spectral analysis is adopted to calculate the higher order harmonics and Signal Noise Ratio (SNR). Choi-Williams Distribution (CWD) function is employed to understand the time frequency characteristics of the leakage current signal. Reported results on silicone rubber insulators show that the flashover development process of outdoor polymer insulators could be identified from the higher order harmonics and signal noise ratio values of leakage current signals.

• 한국기계가공학회지
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• 제9권6호
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• pp.43-50
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• 2010

This study aims to analyze the structural safety by comparing deformation and equivalent stress of door with a stiffener or no stiffener when the door crashes against something in case of overturn. Three types are classified on the basis of the no stiffener model in the vehicle door. One is the type which has a stiffener. Another is the type which has no stiffener and the other is the type which has a hole in the stiffener. These three types are compared with each other by analyzing. This side door of vehicle is the automotive part about the kind of vehicle as Mercedes Benz E-Klasse scaled down as 1/18 times as the real size. The study model of vehicle door is modelled by CATIA program and it is analyzed by ANSYS.

• 한국공작기계학회논문집
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• 제13권4호
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• pp.95-99
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• 2004

In this study, the leak detection technique of pressure vessel by using acoustic emission(AE) signal is suggested experimentally. The leak of pressure vessel is located at the welding line due to welding defects. we measured the AE signal using Rl5I sensor, and examined the AE parameters in leak condition. It is investigated that the mean value of AE signal is dependent on leak source location. So the absolute mean value of AE signal is adopted as dominant AE parameter. We proposed leak detection algorithm using AE signal mean value for monitoring the leak source location.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제55권11호
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• pp.572-576
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• 2006

As the increasing of capacity and technology of power facilities, rotating machines such as turbine generators and water turbines are getting higher at capacity but smaller in size. Thus the monitoring and diagnosis of generators for fault detection and protection has attracted intensive interest. Most of electrical faults of rotating machines appear in their windings. In case of an after-fault in high capacity rotating machines, the recovering cost is usually very expensive and additional time is necessary for returning in a normal situation. In this paper, the magnetic flux patterns in air-gap of a generator under various fault states as well as a normal state are simulated by a conventional FEM tool. These results are successfully applied to detection and diagnosis of the short-circuit condition in rotor windings of a high capacity generator.

• 한국공작기계학회논문집
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• 제15권4호
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• pp.22-28
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• 2006

Detection of exit burr is very important in manufacturing automation. In this paper, acoustic emission(AE) was used to detect the burr formation during milling. By using wavelet transformation, AE data was compressed without unnecessary details. Then the transformed data were used as selected features (inputs) of a back-propagation artificial neural net. In order to validate the proposed scheme, the wavelet based ANN results were compared with cutting condition(cutting speed, feed, depth of cut, etc.) based ANN results.