• 한국압력기기공학회 논문집
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• 제11권2호
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• pp.61-67
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• 2015

3D FEM modeling based on 3D CAD data has been performed to evaluate the efficiency of CP system in a real operating nuclear power plant. The results of it successfully produced sophisticated profiles of electrolytic potential and current distributions in the soil of an interested area. This technology is expected to be a breakthrough for detection technology of damages on buried pipes when it comes into combining with a brand of area potential earth current (APEC) and ground penetrated radar (GPR) technologies. 2D current distribution and 2D current vectors on the earth surface from the APEC survey will be used as boundary conditions with exact 3D geometry data resulting in visualization of locations and extents of corrosion damages on the buried pipes in nuclear power plants.

• 한국철도학회논문집
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• 제10권1호
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• pp.1-6
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• 2007

Railway wheels in service are regularly checked by ultrasonic testing, acoustic emission and eddy current testing method and so on. However, ultrasonic testing is sometimes inadequate for sensitively detecting the cracks in railway wheel which is mainly because of the fact of crack closure. Recently, many researchers have actively fried to improve precision for defect detection of railway wheel. The development of a nondestructive measurement tool for wheel defects and its use for the maintenance of railway wheels would be useful to prevent wheel failure. The induced current focusing potential drop(ICFPD) technique is a new non-destructive tasting technique that can detect defects in railway wheels by applying on electro-magnetic field and potential drops variation. In the present paper, the ICFPD technique is applied to the detection of surface and internal defects for railway wheels. To defect the defects for railway wheels, the sensor for ICFPD is optimized and the tests are carried out with respect to 4 surface defects and 6 internal defects each other. The results show that the surface crack depth of 0.5 mm and internal crack depth of 0.7 mm in wheel tread could be detected by using this method. The ICFPB method is useful to detect the defect that initiated in the tread of railway wheels

• 한국정보통신학회논문지
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• 제24권3호
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• pp.378-383
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• 2020

Viola & Jones의 객체 검출 알고리즘은 얼굴 요소 검출을 위한 매우 우수한 알고리즘이지만 변수 설정에 따른 중복 검출, 오 검출, 미 검출 같은 문제들이 여전히 존재한다. 본 논문은 Viola & Jones의 객체 검출 알고리즘에 미 검출을 줄이기 위한 가변 변수와 중복 검출과 오 검출을 줄이기 위한 검증을 적용한 개선된 얼굴 요소 검출 알고리즘을 제안한다. 제안된 알고리즘은 잠재적 유효 얼굴 요소들을 검출할 때까지 Viola & Jones의 객체 검출의 변수 값을 변화시켜 미 검출을 줄이고, 검출된 얼굴 요소의 크기, 위치, 유일성을 평가하는 검증을 이용해 중복 검출과 오 검출들을 제거시켜 준다. 시뮬레이션 결과들은 제안된 알고리즘이 검출된 객체들에 유효 얼굴 요소들을 포함시키고 나서 무효 얼굴 요소들을 제거하여 유효 얼굴 요소들만을 검출하는 것을 보여준다.

• Smart Structures and Systems
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• 제21권5호
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• pp.687-694
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• 2018

The problem of wheel tread defects has become a major challenge for the health management of high-speed rail as a wheel defect with small radius deviation may suffice to give rise to severe damage on both the train bogie components and the track structure when a train runs at high speeds. It is thus highly desirable to detect the defects soon after their occurrences and then conduct wheel turning for the defective wheelsets. Online wheel condition monitoring using wheel impact load detector (WILD) can be an effective solution, since it can assess the wheel condition and detect potential defects during train passage. This study aims to develop an FBG-based track-side wheel condition monitoring method for the detection of wheel tread defects. The track-side sensing system uses two FBG strain gauge arrays mounted on the rail foot, measuring the dynamic strains of the paired rails excited by passing wheelsets. Each FBG array has a length of about 3 m, slightly longer than the wheel circumference to ensure a full coverage for the detection of any potential defect on the tread. A defect detection algorithm is developed for using the online-monitored rail responses to identify the potential wheel tread defects. This algorithm consists of three steps: 1) strain data pre-processing by using a data smoothing technique to remove the trends; 2) diagnosis of novel responses by outlier analysis for the normalized data; and 3) local defect identification by a refined analysis on the novel responses extracted in Step 2. To verify the proposed method, a field test was conducted using a test train incorporating defective wheels. The train ran at different speeds on an instrumented track with the purpose of wheel condition monitoring. By using the proposed method to process the monitoring data, all the defects were identified and the results agreed well with those from the static inspection of the wheelsets in the depot. A comparison is also drawn for the detection accuracy under different running speeds of the test train, and the results show that the proposed method can achieve a satisfactory accuracy in wheel defect detection when the train runs at a speed higher than 30 kph. Some minor defects with a depth of 0.05 mm~0.06 mm are also successfully detected.

• 한국지구물리탐사학회:학술대회논문집
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• 한국지구물리탐사학회 2002년도 정기총회 및 제4회 특별심포지움
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• pp.179-195
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• 2002

본 문은 수리시설물의 누수탐지를 위한 물리탐사의 적용성에 대해서 검토했다. 저수지 및 방조제의 누수탐지를 위해서 전기비저항탐사 및 자연전위 (SP)탐사가 실시되고 있으며, 많은 사례축적으로부터 그 적용성이 입증되고 있다. 그러나, 향후 수리시설물을 보다 정확하게 누수지점을 탐지하고, 효율적인 유지관리를 위해서는 다음과 같은 사항이 요구된다. 1) 단일 탐사보다는 전기비저항탐사와 자연전위탐사를 병용 실시하여 종합적으로 해석함으로서 보다 신뢰성을 높일 수 있다. 2) 전기비저항탐사 결과로부터 누수취약구간을 정확하게 파악하기 위해서는 제체의 전기비저항을 좌우하는 토질의 물성을 잘 파악하는 것이 무엇보다도 중요하다. 3) 측정치의 보다 높은 신뢰성을 얻기 위해서는 3차원 탐사의 도입이 시급하다. 4) 수리시설물을 효율적이고 경제적으로 유지관리하기 위해서는 모니터링 계측시스템의 필요성이 요구된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권4호
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• pp.1799-1818
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• 2018

Malware detections continue to be a challenging task as attackers may be aware of the rules used in malware detection mechanisms and constantly generate new breeds of malware to evade the current malware detection mechanisms. Consequently, novel and innovated malware detection techniques need to be investigated to deal with this circumstance. In this paper, we propose a new secure malware detection system in which API call fragments are used to recognize potential malware instances, and these API call fragments together with the homomorphic encryption technique are used to construct a privacy-preserving Naive Bayes classifier (PP-NBC). Experimental results demonstrate that the proposed PP-NBC can successfully classify instances of malware with a hit-rate as high as 94.93%.

• 대한의용생체공학회:의공학회지
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• 제30권5호
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• pp.412-417
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• 2009

The purpose of this study was to assess the ability of this device during clinically important episodes of extravasation. The extravasation detection accessory (EDA) system was based of infrared ray with detection sensor, an amplifier, alarm device, receiver, cable and a computer based system. This study was a prospective, observational study in which the EDA system was used to monitor the automated mechanical injection of contrast media. Three hundred patients referred for contrast media enhanced body computed tomography studied in a prospective, observation study in which the EDA system was used to identify and interrupt any injection associated with clinically important extravasation. There were 8 true-positive cases, 276 true-negative cases, 15 false-positive cases and 1 false-negative cases. The EDA system had a sensitivity of 88.8% and a specificity of 94.8% for the detection of clinically important extravasation. The EDA system had good sensitivity for the detection of clinically important extravasation and the EDA system has the clinical potential for the early detection of extravasation of the contrast medium that is administered with power injectors.

• 한국자동차공학회논문집
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• 제16권6호
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• pp.134-140
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• 2008

Preceding vehicle detection is a crucial issue for driver assistance system as well as for autonomous vehicle guidance function and it has to be performed with high reliability to avoid any potential collision. The vision-based preceded vehicle detection systems are regarded promising for this purpose because they require little infrastructure on a highway. However, the feasibility of these systems in passenger car requires accurate and robust sensing performance. In this paper, an preceded vehicle detection system is developed using stereo vision sensors. This system utilizes feature matching, epipoplar constraint and feature aggregation in order to robustly detect the initial corresponding pairs. After the initial detection, the system executes the tracking algorithm for the preceded vehicles including a leading vehicle. Then, the position parameters of the preceded vehicles or leading vehicles can be obtained. The proposed preceded vehicle detection system is implemented on a passenger car and its performances is verified experimentally.

• 한국멀티미디어학회논문지
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• 제24권3호
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• pp.416-422
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• 2021

Due to the large differences in human activity within classes, the large similarity between classes, and the problems of visual angle and occlusion, it is difficult to extract features manually, and the detection rate of human behavior is low. In order to better solve these problems, an improved Faster R-CNN-based detection algorithm is proposed in this paper. It achieves multi-object recognition and localization through a second-order detection network, and replaces the original feature extraction module with Dense-Net, which can fuse multi-level feature information, increase network depth and avoid disappearance of network gradients. Meanwhile, the proposal merging strategy is improved with Soft-NMS, where an attenuation function is designed to replace the conventional NMS algorithm, thereby avoiding missed detection of adjacent or overlapping objects, and enhancing the network detection accuracy under multiple objects. During the experiment, the improved Faster R-CNN method in this article has 84.7% target detection result, which is improved compared to other methods, which proves that the target recognition method has significant advantages and potential.

• 로봇학회논문지
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• 제19권1호
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• pp.130-138
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• 2024

High-precision 3D Object Detection is a crucial component within autonomous driving systems, with far-reaching implications for subsequent tasks like multi-object tracking and path planning. In this paper, we propose a novel approach designed to enhance the performance of 3D Object Detection, especially in heading angle estimation by employing a moving object segmentation technique. Our method starts with extracting point-wise moving labels via a process of moving object segmentation. Subsequently, these labels are integrated into the LiDAR Pointcloud data and integrated data is used as inputs for 3D Object Detection. We conducted an extensive evaluation of our approach using the KITTI-road dataset and achieved notably superior performance, particularly in terms of AOS, a pivotal metric for assessing the precision of 3D Object Detection. Our findings not only underscore the positive impact of our proposed method on the advancement of detection performance in lidar-based 3D Object Detection methods, but also suggest substantial potential in augmenting the overall perception task capabilities of autonomous driving systems.