• Archives of Pharmacal Research
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• v.19 no.6
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• pp.562-565
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• 1996

The photoreactivity of twelve anthraquinone derivatives was examined to evaluate its usefulness as a photo-reagent for the analysis of ginsenosides using photoreduction fluorescence (PRF) detection method. Among the tested compounds, 2-tert-butylandthraquinone (TBAQ), 2-chloroanthraquinone (CAQ) and anthraquinone (AQ) showed good characteristics as photoreagents. The detection limits of ginsenoside $Rg_{1}$PRF-HPLC method using TBAQ, CAQ or AQ as a photo-reagent were found to be ca. 35 ng, 50 ng and 50 ng, respectively.

• Proceedings of the KIPE Conference
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• 2014.07a
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• pp.522-523
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• 2014

Analysis of faults in induction motors has become a major field of research due to importance of loss and damage reduction and maximum online performance of motors. There are several methods to analyze the faults in an induction motor from conventional Fourier transform to modern decision-making neural networks. Considering detectability of fault among all methods, a new fault detection solution has been proposed; it is called as frequency-domain Discrete Wavelet Transform (FD-DWT). In this method, the stator current is decomposed through series of low- and high-pass filters and consequently, the fault characteristics are more visible, because additional components have been reduced. The objective of this paper is early detection of input voltage unbalance in induction motor using wavelet transform in frequency domain. Experimental results show the effectiveness of the proposed method in early detection of faults.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.3
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• pp.270-279
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• 2003

Parametric studies have been conducted into the variability of the factors affecting the ultrasonic testing applied to weldments. The influence of ultrasonic equipment, transducer parameters, test technique, job parameters, defect type and characteristics on reliability far defect detection and sizing was investigated by experimentation. The investigation was able to build up substantial bank of information on the reliability of manual ultrasonic method for testing weldments. The major findings of the study separate into two parts, one dealing with correlation between ultrasonic techniques, equipment and defect parameters and inspection performance effectiveness and other with human factors. Defect detection abilities are dependent on the training, experience and proficiency of the UT operators, the equipment used, the effectiveness of procedures and techniques.

• International Journal of Contents
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• v.2 no.3
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• pp.27-32
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• 2006

We present an effective algorithm for automatic tracing of retinal vessel structure and vascular landmark extraction of bifurcations and ending points. In this paper we deal with vascular patterns from RGB images for personal identification. Vessel tracing algorithms are of interest in a variety of biometric and medical application such as personal identification, biometrics, and ophthalmic disorders like vessel change detection. However eye surface vasculature tracing in RGB images has many problems which are subject to improper illumination, glare, fade-out, shadow and artifacts arising from reflection, refraction, and dispersion. The proposed algorithm on vascular tracing employs multi-stage processing of ten-layers as followings: Image Acquisition, Image Enhancement by gray scale retinal image enhancement, reducing background artifact and illuminations and removing interlacing minute characteristics of vessels, Vascular Structure Extraction by connecting broken vessels, extracting vascular structure using eight directional information, and extracting retinal vascular structure, and Vascular Landmark Extraction by extracting bifurcations and ending points. The results of automatic retinal vessel extraction using jive different thresholds applied 34 eye images are presented. The results of vasculature tracing algorithm shows that the suggested algorithm can obtain not only robust and accurate vessel tracing but also vascular landmarks according to thresholds.

• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.2899-2908
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• 2021

The laser transport system of the high power laser facility is mainly composed of large-aperture laser transport mirrors (TMs). Obtaining the high-resolution online damage images during the operation, which is of great significance for operating safely of the mirrors and the facility. Based on wavefront coding, pan-tilt scanning and image stitching technologies, an online laser-damage images detection system is designed, and it can achieve high-precision detection of surface characteristics of large-aperture laser transport mirrors. The preliminary simulation proves that the system can solve the depth of field matching problem caused by pan-tilt tilt imaging and achieve higher resolution.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.1-7
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• 2018

In this paper, we propose soft dead pixels detection method by analysing different response at hot and cold images. Abnormal pixels are able to effect detecting a small target. It also makes confusing real target or not cause of changing target size. Almost exist abnormal pixels after image signal processing even if dead pixels are removed by dead pixel compensation are called soft dead pixels. They are showed defect in final image. So removing or compensating dead pixels are very important for detecting object. The key idea of this proposed method, detecting dead pixels, is that most of soft deads have different response characteristics between hot image and cold image. General infrared cameras do NUC to remove FPN. Working 2-reference NUC must be needed getting data, hot & cold images. The way which is proposed dead pixel detection is that we compare response, NUC gain, at each pixel about two different temperature images and find out dead pixels if the pixels exceed threshold about average gain of around pixels.

• Smart Structures and Systems
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• v.23 no.1
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• pp.45-60
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• 2019

This paper is devoted to proposing a new approach for damage detection of structures. In this technique, the biconjugate gradient method (BCG) is employed. To remedy the noise effects, a new preconditioning algorithm is applied. The proposed preconditioner matrix significantly reduces the condition number of the system. Moreover, based on the characteristics of the damage vector, a new direct search algorithm is employed to increase the efficiency of the suggested damage detection scheme by reducing the number of unknowns. To corroborate the high efficiency and capability of the presented strategy, it is applied for estimating the severity and location of damage in the well-known 31-member and 52-member trusses. For damage detection of these trusses, the time history responses are measured by a limited number of sensors. The results of numerical examples reveal high accuracy and robustness of the proposed method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.2
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• pp.28-35
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• 2019

Since guided missiles with the characteristics of the one-shot system remain stored throughout their entire life cycle, it is important to maintain their storage reliability until the launch. As part of maintaining storage reliability, period of preventive test is set up to perform preventive periodic test, in this case failure detection rate has a great effect on setting up period of preventive test to maintain storage reliability. The proposed method utilizes failure rate predicted by the software on the basis of MIL-HDBK-217F and failure mode analyzed through FMEA (Failure Mode and Effect Analysis) using data generated from the actual field. The failure detection rate of using the proposed method is applied to set periodic test of the actual guided missile. The proposed method in this paper has advantages in accuracy and objectivity because it utilizes a large amount of data generated in the actual field.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.571-585
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• 2021

Applications for object detection are expanding as it is automated through artificial intelligence-based processing, such as deep learning, on a large volume of images and videos. High dependence on training data and a non-transparent way to find answers are the common characteristics of deep learning. Attacks on training data and training models have emerged, which are closely related to the nature of deep learning. Privacy, integrity, and robustness for the extracted information are important security issues because deep learning enables object recognition in images and videos. This paper summarizes the security issues that need to be addressed for future applications and analyzes the state-of-the-art security studies related to robustness, privacy, and integrity of object detection for images and videos.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.