• Journal of the Korean Society of Food Science and Nutrition
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• v.32 no.1
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• pp.1-7
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• 2003

Radiation induced markers were investigated for the detection of irradiated oranges imported from America. In the DNA comet assay, the non-irradiated and irradiated samples showed the comets with long tails in both seed and flesh. Though this tendency was maintained for 6 weeks, identification of non-irradiated or irradiated samples was impossible. In the thermoluminescence (TL) measurement, the non-irradiated samples revealed a glow curve with low intensity at about 28$0^{\circ}C$, while the irradiated samples showed with higher intensity at around 18$0^{\circ}C$. There were no remarkable changes in detection properties for 6 weeks after irradiation. The TL ratio of area for TL$_1$ glow curve to TL$_2$ was below 0.1 for the non-irradiated samples and 0.5 or more for the irradiated ones during storage. In the electron spin resonance (RSR) measurement, irradiated oranges showed an unspecific central signal in all parts (seed, flesh and peel), so the detection for radiation treatment of oranges was impossible. Based on the results, DNA comet assay and ESR were not useful for the detection, but TL was appropriate to search radiation induced markers of oranges during storage period. The detectable period during storage is confirmed by sensory evaluation.

• Parasites, Hosts and Diseases
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• v.57 no.1
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• pp.9-15
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• 2019

Melting temperature shift ($T_m-shift$) is a new detection method that analyze the melting curve on real-time PCR thermocycler using SYBR Green I fluorescent dye. To establish a $T_m-shift$ method for the detection of Ancylostoma ceylanicum and A. tubaeforme in cats, specific primers, with GC tail of unequal length attached to their 5' end, were designed based on 2 SNP loci (ITS101 and ITS296) of the internal transcribed spacer 1 (ITS1) sequences. The standard curve of $T_m-shift$ was established using the standard plasmids of A. ceylanicum (AceP) and A. tubaeforme (AtuP). The $T_m-shift$ method stability, sensitivity, and accuracy were tested with reference to the standard curve, and clinical fecal samples were also examined. The results demonstrated that the 2 sets of primers based on the 2 SNPs could accurately distinguish between A. ceylanicum and A. tubaeforme. The coefficient of variation (CV) of $T_m$- values of AceP and AtuP was 0.07% and 0.06% in ITS101 and was 0.06% and 0.08% in ITS296, respectively. The minimum detectable DNA concentration was $5.22{\times}10^{-6}$ and $5.28{\times}10^{-6}ng/{\mu}l$ samples of AceP and AtuP, respectively. The accuracy of $T_m-shift$ method reached 100% based on examination of 10 hookworm DNA samples with known species. In the clinical detection of hookworm in 69 stray cat fecal sample, the $T_m-shift$ detection results were consistent with the microscopic examination and successfully differentiated between the 2-hookworm species. In conclusion, the developed method is a rapid, sensitive and accurate technique and can provide a promising tool for clinical detection and epidemiological investigation of cat-derived hookworms.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1408-1410
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• 2015

Information about the painted road markings and other painted road objects play an important part in keeping safety of drivers. Some researchers have presented research approaches and dealt with road markings detection. In this paper, we present comprehensive survey of these techniques, and review some of them like a machine learning method, template matching method for road markings detection and classification, method of detection and classification of road markings using curve-based prototype fitting, signed edge signature method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3815-3835
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• 2022

Density-based outlier detection is one of the hot issues in data mining. A point is determined as outlier on basis of the density of points near them. The existing density-based detection algorithms have high time complexity, in order to reduce the time complexity, a new outlier detection algorithm DODMD (Density-based Outlier Detection in Multidimensional Datasets) is proposed. Firstly, on the basis of ZH-tree, the concept of micro-cluster is introduced. Each leaf node is regarded as a micro-cluster, and the micro-cluster is calculated to achieve the purpose of batch filtering. In order to obtain n sets of approximate outliers quickly, a greedy method is used to calculate the boundary of LOF and mark the minimum value as LOF_min. Secondly, the outliers can filtered out by LOF_min, the real outliers are calculated, and then the result set is updated to make the boundary closer. Finally, the accuracy and efficiency of DODMD algorithm are verified on real dataset and synthetic dataset respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.1
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• pp.22-29
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• 2016

Many studies in detection and classification of the targets in the underwater environments have been conducted for military purposes, as well as for non-military purpose. Due to the complicated characteristics of underwater acoustic signal reflecting multipath environments and spatio-temporal varying characteristics, active sonar target detection technique has been considered as a difficult technique. In this paper, we describe the basic concept of Fractional Fourier transform and optimal transform order. Then we analyze the relationship between time-frequency characteristics of an LFM signal and its spectrum using Fractional Fourier transform. Based on the analysis results, we present active sonar target detection method. To verify the performance of proposed methods, we compared the results with conventional FFT-based matched filter. The experimental results demonstrate the superiority of the proposed method compared to the conventional method in the aspect of AUC(Area Under the ROC Curve).

• KIPS Transactions on Software and Data Engineering
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• v.3 no.7
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• pp.277-284
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• 2014

The detection method of curves for the lanes which is powerful for the variation by utilizing the lane variation vector and cardinal spline on the inverse perspective transformation screen images which do not required the camera parameters are suggested in this paper. This method detects the lane area by setting the expected lane area in the s frame and next s+1 frame where the inverse perspective transformation and entire process of the lane filter are adapted, and expects the points of lane location in the next frames with the lane variation vector calculation from the detected lane areas. The scan area is set from the nextly expected lane position and new lane positions are detected within these areas, and the lane variation vectors are renewed with the detected lane position and the lanes are detected with application of cardinal spline for the control points inside the lane areas. The suggested method is a powerful method for curved lane detection, but it was adopted to the linear lanes too. It showed an excellent lane detection speed of about 20ms in processing a frame.

• Journal of KIISE:Information Networking
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• v.30 no.4
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• pp.504-512
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• 2003

In this paper, we propose a new adaptive dissolve detection method based on the analysis of a dissolve modeling error which is the difference between an ideally modeled dissolve curve with no correlation and an actual dissolve curve including a correlation. The proposed dissolve detection method consists of two steps. First, candidate dissolve regions are extracted using the characteristics of a downward convex parabola, then each candidate region is verified based oil the dissolve modeling error. If the dissolve modeling error for a candidate region is less than a threshold defined by the target modeling error with a target correlation, the candidate region is determined as a resolve region with a lower correlation than the target correlation. The threshold is adaptively determined based on the variances between the candidate regions and the target correlation. By considering the correlation between neighbor scenes, the proposed method is able to be a semantic scene-change detector. The proposed method was tested on various types of data and its performance proved to be more accurate and reliable regardless of variation of variance of test sequences when compared with other commonly use methods.

• The Journal of the Acoustical Society of Korea
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• v.40 no.4
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• pp.337-346
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• 2021

Detection of abnormal signal generally can be done by using features of normal signals as main information because of data imbalance. This paper propose an efficient method for abnormal signal detection using parallel AutoEncoder (AE) which can use features of abnormal signals as well. The proposed Parallel AE (PAE) is composed of a normal and an abnormal reconstructors having identical AE structure and train features of normal and abnormal signals, respectively. The PAE can effectively solve the imbalanced data problem by sequentially training normal and abnormal data. For further detection performance improvement, additional binary classifier can be added to the PAE. Through experiments using public acoustic data, we obtain that the proposed PAE shows Area Under Curve (AUC) improvement of minimum 22 % at the expenses of training time increased by 1.31 ~ 1.61 times to the single AE. Furthermore, the PAE shows 93 % AUC improvement in detecting abnormal underwater acoustic signal when pre-trained PAE is transferred to train open underwater acoustic data.

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.199-209
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• 2022

Typical anomaly detection algorithms are trained by using prior data. Thus the batch learning based algorithms cause inevitable performance degradation when characteristics of newly incoming normal data change over time. We propose an online anomaly detection algorithm which can consider the gradual characteristic changes of incoming normal data. The proposed algorithm based on one-class classification model includes both offline and online learning procedures. In offline learning procedure, the algorithm learns the prior data to be close to centroid of the latent space and then updates the centroid of the latent space incrementally by new incoming data. In the online learning, the algorithm continues learning by using the updated centroid. Through experiments using public underwater acoustic data, the proposed online anomaly detection algorithm takes only approximately 2 % additional learning time for the incremental centroid update and learning. Nevertheless, the proposed algorithm shows 19.10 % improvement in Area Under the receiver operating characteristic Curve (AUC) performance compared to the offline learning model when new incoming normal data comes.

• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.369-385
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• 1999

For the growth curve model with arbitrary covariance structure, known as unstructured covariance matrix, the problems of detecting outliers are discussed in this paper. In order to detect outliers in the growth curve model, the likelihood ratio testing statistics in mean shift model is established and its distribution is derived. After we detected outliers in growth curve model, we test homo and/or hetero-geneous covariance matrices using PSR Quasi-Bayes Criterion. For illustration, one numerical example is discussed, which compares between before and after outlier deleting.