• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.11
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• pp.82-88
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• 2013

It is important to give lightning warning prior to a cloud-to-ground (CG) discharge within an Area of Concern (AOC) because most of lightning damage and victim are usually occurred by the first lightning in the AOC. The aim of this study is to find the optimal operation conditions of the automated lightning warning systems in order to make the best use of the available data. In this paper, the test-operated results of the automated lightning alert and risk management system (ALARM) based on detections of CG discharge and eletrostatic field and optimized at probability of lightning have been described. It was possible to obtain the following warning performance parameters: probability of detection (POD), false alarm ratio (FAR), probability of lightning (POL) and failure-to-warn rate (FTW). The data obtained from trial operation for 5months were not sufficient but the first analysis of domestic lightning warning was carried out. We have observed that the evaluated statistical results through trial operation depend on the various factors such as analysis methods and criteria, topographical conditions, etc. Also we suggest some methods for improvement of POL and POD including the finding of the optimal electric field threshold level to be used, based on the high values of FAR and FTW found in this work.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.1
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• pp.123-128
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• 2018

In this paper, we propose a multi-vehicle object detection algorithm using feature point matching that tracks efficient vehicle objects. The proposed algorithm extracts the feature points of the vehicle using the FAST algorithm for efficient vehicle object tracking. And True if the feature points are included in the image segmented into the 5X5 region. If the feature point is not included, it is processed as False and the corresponding area is blacked to remove unnecessary object information excluding the vehicle object. Then, the post processed area is set as the maximum search window size of the vehicle. And A minimum search window using the outermost feature points of the vehicle is set. By using the set search window, we compensate the disadvantages of the search window size of mean-shift algorithm and track vehicle object. In order to evaluate the performance of the proposed method, SIFT and SURF algorithms are compared and tested. The result is about four times faster than the SIFT algorithm. And it has the advantage of detecting more efficiently than the process of SUFR algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.6
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• pp.37-42
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• 2009

Cognitive radio (CR), which is proposed as a technology that utilizes the frequency resources effectively, has studied to relive scarcity of the frequency resources. Also, FCC revises the regulation to reuse the TV white spaces for applying CR system and allows to use the TV white spaces by CR devices and use of CR device may be regularized after conversion by DTV in 2009. If CR system's signal appeared in pilot's position, pilot sensing performs false alarm detection because pilot sensing scheme confuse DTV signal and CR system. In order to improve false alarm detection, we propose distinction scheme between DTV and CR system using PN511 sequence in this paper.

• Genomics & Informatics
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• v.3 no.1
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• pp.15-23
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• 2005

Background MicroRNAs (miRNAs) are a class of noncoding RNAs found in various organisms such as plants and mammals. However, most of the mRNAs regulated by miRNAs are unknown. Furthermore, miRNA targets in genomes cannot be identified by standard sequence comparison since their complementarity to the target sequence is imperfect in general. In this paper, we propose a kernel-based method for the efficient prediction of miRNA targets. To help in distinguishing the false positives from potentially valid targets, we elucidate the features common in experimentally confirmed targets. Results The performance of our prediction method was evaluated by five-fold cross-validation. Our method showed 0.64 and 0.98 in sensitivity and in specificity, respectively. Also, the proposed method reduced the number of false positives by half compared with TargetScan. We investigated the effect of feature sets on the classification of miRNA targets. Finally, we predicted miRNA targets for several miRNAs in the Caenorhabditis elegans (C. elegans) 3' untranslated region (3' UTR) database. Condusions The targets predicted by the suggested method will help in validating more miRNA targets and ultimately in revealing the role of small RNAs in the regulation of genomes. Our algorithm for miRNA target site detection will be able to be improved by additional experimental­knowledge. Also, the increase of the number of confirmed targets is expected to reveal general structural features that can be used to improve their detection.

• Korean Journal of Veterinary Service
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• v.27 no.1
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• pp.95-101
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• 2004

Babesia bovis rap-1 and B equi ema-1 intergenic(IG) nucleotides were analyzed and compared for identifying putative promoter sites using computer programs. The reason to initiate this research was to determine if IG nucleotides of Babesia genes that are predicted to be involved in erythrocyte invasion have functions regulating gene transcription and translation, which can be applied to functional gene knockout. Four IG sequences used included BbIG5(B bovis rap-1 5' IG), BblG3(B bovis rap-1 3' IG), BeIG5(B equi ema-1 5' IG) and BeIG3(B equi ema-1 3' IG). BbIG5 contained a putative promoter at nucleotide 197-246 with a predicted TATA-box and a transcription start site. BbIG3 had a putative promoter at nucleotide 270-320 with two predicted TATA-boxes and a transcription start site. BeIG3 had a putative promoter at nucleotide 155-205 with a predicted TATA-box and a transcription start site. Putative promoter sites in these three sequences mentioned above were identified with score cutoff 0.8, which means detection of about 40% recognized promoters with 0.1-0.4% false positives. In contrast, BeIG5 had a putative promoter at nucleotide 163-213 with score cutoff 0.8, but neither TATA-box nor transcription start site were recognized. However, BeIG5 had a putative promoter at nucleotide 388-438 with a predicted TATA-box and a transcription start site when score cutoff was decreased to 0.18, which means detection of about 70% recognized promoters with 2.2-5.3% false positives. These sequences with putative promoters can be tested if they have functions regulating gene transcription and translation.

• Biomedical Science Letters
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• v.21 no.1
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• pp.9-14
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• 2015

Mouse hepatitis virus (MHV) is a major pathogen in laboratory mice that usually leads to fatal diseases, such as hepatitis, multiple sclerosis, encephalitis, and respiratory disease. MHV has a high infection rate, and it needs to be detected as soon as possible to prevent its spread to other facilities. However, MHV detection by enzyme-linked immunosorbent assay (ELISA) often gives false positives; thus, it is very important that the results are confirmed as true positives in the early infection stage or distinguished as false positives with more accurate, reliable methods. Under microbiological screening, MHV ELISA-positive mice were found in four GFP-tagging transgenic mice. To verify the detection of the MHV antigen directly, reverse transcription polymerase chain reaction (RT-PCR) was performed, and the mice were determined to be MHV negative. Additional serum antibody-based screening was conducted with three different ELISA kits, and multiplexed fluorometric immunoassay (MFIA) was performed to confirm their accuracy/sensitivity. In brief, the ELISA kit for A59 nucleocapsid protein (MHV-A59N) revealed MHV ELISA positivity, while other ELISA kits (MHV-S lysate and MHV-JHM lysate) demonstrated MHV negativity. In MFIA, only the test for the recombinant A59 nucleocapsid antigen was MHV positive, which was consistent with the ELISA results. These results suggest that the ELISA kit with the recombinant A59 nucleocapsid antigen might induce non-specific MHV ELISA positivity and that confirmation is therefore essential.

• The KIPS Transactions:PartC
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• v.10C no.7
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• pp.843-850
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• 2003

Script viruses are easy to make a variation because they can be built easily and be spread in text format. Thus signature-based method has a limitation in detecting script viruses. In a consequence, many researches suggest simple heuristic methods, but high false-positive error is always being an obstacle. In order to overcome this problem, our previous study concentrated on analyzing data flow of codes and has low-false positive error, but still could not detect a polymorphic virus because polymorphic virus loads self body and changes it before make a descendent. We suggest a heuristic detection method which expands the detection range of previous method to include polymorphic script viruses. Expanded data flow analysis heuristic has an expanded grammar to detect Polymorphic copy Propagation. Finally, we will show the experimental result for the effectiveness of suggested method.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.5_6
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• pp.280-289
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• 2003

Deadlock recovery-based routing algorithms in wormhole networks have gained attraction due to low hardware complexity and high routing adaptability Progressive deadlock recovery techniques require a few dedicated resources to transmit deadlocked packets rather than killing them. Selection of deadlocked packets is primarily based on time-out value which should be carefully determined considering various traffic patterns or packet length. By its nature, current techniques using time-out accompany unignorable number of false deadlock detections especially in a heavy-loaded network or with long packet size. Moreover, when a deadlock occurs, more than one packet may be marked as deadlocked, which saturate the resources allocated for recovery. This paper proposes more accurate deadlock detection scheme which does not make use of time-out to declare deadlock. The proposed scheme reduces the probability to detect false deadlocks considerably. Furthermore, a single message is selected as deadlocked for each cycle of blocked messages, thereby eliminating recovery overheads.

• Analytical Science and Technology
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• v.34 no.2
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• pp.68-77
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• 2021

Cannabis is one of the most abused drugs in Korea. The main psychoactive component in cannabis, Δ9-tetrahydrocannabinol, is metabolized to 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (THCCOOH) and THCCOOH-glucuronide (THCCOOH-glu) in the human liver, whereby the amount of THCCOOH-glu found in urine is twice as high as that of THCCOOH. The analytical process adapted by the majority of urine drug-testing programs involves a two-step method consisting of an initial immunoassay-based screening test followed by a confirmatory test if the screening test result is positive. In this study, a qualitative gas chromatography-mass spectrometry (GC-MS) method was developed and validated for the detection of THCCOOH in human urine, where THCCOOH-glu was converted into THCCOOH by alkaline hydrolysis. For purification of the urine extract prior to instrumental analysis, high-speed centrifugation was used to minimize interference. In addition, an injection-port derivatization method using ethyl acetate and N,O-bis(trimethylsilyl)-trifluoroacetamide containing 1 % trimethylchlorosilane was employed to reduce the time required for derivatization, and an aliquot of the final solution was injected into the GC-MS. The method was validated by measuring the selectivity, limit of detection (LOD), and repeatability. The sensitivity, specificity, precision, accuracy, Kappa, F-measure, false positive, and false negative rate were determined by comparing the GC-MS results with those obtained using the immunoassay. The LOD was determined to be 0.32 ng/mL, while the repeatability was within 9.1 % for THCCOOH. Furthermore, a comparison study was carried out, whereby the screening immunoassay exhibited a sensitivity of 86.4 % and a specificity of 100 % compared to GC-MS. The applicability of the developed method was examined by analyzing spiked urine and forensic urine samples obtained from suspected cannabis abusers (n = 221).

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.863-874
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• 2021

In this paper, we propose a post-selection inference procedure for the fused lasso signal approximator (FLSA). The FLSA finds underlying sparse piecewise constant mean structure by applying total variation (TV) semi-norm as a penalty term. However, it is widely known that this convex relaxation can cause asymptotic inconsistency in change points detection. As a result, there can remain false change points even though we try to find the best subset of change points via a tuning procedure. To remove these false change points, we propose a post-selection inference for the FLSA. The proposed procedure applies a permutation test based on CUSUM statistic. Our post-selection inference procedure is an extension of the permutation test of Antoch and Hušková (2001) which deals with single change point problems, to multiple change points detection problems in combination with the FLSA. Numerical study results show that the proposed procedure is better than naïve z-tests and tests based on the limiting distribution of CUSUM statistics.