• Journal of the Korea Society of Computer and Information
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• v.16 no.5
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• pp.93-100
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• 2011

In this paper, we propose a FDR(False Data Reduction) strategy for P2P environment that reduces false data. The key idea of our strategy is that we use FDR algorithm to stop transmitting of false data and to delete that. If a user recognizes false data in downloaded-data and the user's peer requests the others to stop the transmission of the false data immediately. Also, the FDR algorithm notifies the other peers to prohibit spreading of the false data in the environment. All this procedure is possible to be executed in each peer without any lookup server. The FDR algorithm needs only a little data exchange among peers. Through simulation, we show that it is more effective to reduce the network traffic than the previous P2P strategy. We also show that the proposed strategy improves the performance of network compared to previous P2P strategy. As a result, The FDR strategy is decreased 9.78 ~ 16.84% of mean true data transmission time.

• Hip & pelvis
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• v.30 no.4
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• pp.219-225
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• 2018

Purpose: Ischiofemoral impingement (IFI)-primarily diagnosed by magnetic resonance imaging (MRI)-is an easily overlooked disease due to its low incidence. The purpose of this study was to evaluate the usefulness of false profile view as a screening test for IFI. Materials and Methods: Fifty-eight patients diagnosed with IFI between June 2013 and July 2017 were enrolled in this retrospective study. A control group (n=58) with matching propensity scores (age, gender, and body mass index) were also included. Ischiofemoral space (IFS) was measured as the shortest distance between the lateral cortex of the ischium and the medial cortex of lesser trochanter in weight bearing hip anteroposterior (AP) view and false profile view. MRI was used to measure IFS and quadratus femoris space (QFS). The receiver operating characteristics (ROC), area under the ROC curve (AUC) and cutoff point of the IFS were measured by false profile images, and the correlation between the IFS and QFS was analyzed using the MRI scans. Results: In the false profile view and hip AP view, patients with IFI had significantly decreased IFS (P<0.01). In the false profile view, ROC AUC (0.967) was higher than in the hip AP view (0.841). Cutoff value for differential diagnosis of IFI in the false profile view was 10.3 mm (sensitivity, 88.2%; specificity, 88.4%). IFS correlated with IFS (r=0.744) QFS (0.740) in MRI and IFS (0.621) in hip AP view (P<0.01). Conclusion: IFS on false profile view can be used as a screening tool for potential IFI.

• Analytical Science and Technology
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• v.35 no.3
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• pp.124-128
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• 2022

Blood-sensitive reagents may exhibit false positives or negatives under the influence of substances other than blood. Since these reactions lead to the misinterpretation of blood evidence, it is essential to investigate the possibility of false-positive and -negative reactions of blood-sensitive reagents. Acidic hydrogen peroxide (AHP) is a recently discovered blood-sensitive reagent, and it is not yet known whether it causes false-positive or -negative reactions. To confirm this, 20 µL of blood was placed on metal surfaces, plastic surfaces, paper surfaces, paint surfaces, foods, vegetable oils, detergents, and petroleum hydrocarbons, and then AHP was applied. The blood was observed through an orange filter under a 505-nm light source, and no false-positive or false-negative reactions were observed with any of the substances/materials. However, it was confirmed that polyethylene terephthalate surfaces, polyvinylchloride surfaces, some paint surfaces, and foods exhibit their own photoluminescence under the conditions of blood observation, which interferes with blood observation.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.1
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• pp.100-105
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• 2011

Generally, because received signals from radar are very bulky, the data are divided into manageable size called section, and sections are distributed into several digital signal processors. And then, target detection algorithms are applied simultaneously in each processor. CFAR(Constant False Alarm Rate) algorithm, which is the most popular target detection algorithm, can estimate accurate threshold values to determine which signals are targets or noises within center-cut of section allocated to each processor. However, its estimation precision is diminished in section edge data because of insufficient surrounding data to be referred. Especially this edge problem of CFAR is too serious if we have many sections to be processed, because it causes many false alarms in most every section edges. This paper describes false alarm issues on MCA(Minimum Cell Average)-CFAR, and proposes a false alarm elimination method by changing section size alternatively. Real received data from multi-function radar were used to evaluate a proposed method, and we show that our method drastically decreases false alarms without missing real targets, and improves detection performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.2115-2130
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• 2022

With the development of over-the-top (OTT) services, the demand for content is increasing, and you can easily and conveniently acquire various content in the online environment. As a result, copyrighted content can be easily copied and distributed, resulting in serious copyright infringement. Some special forms of online service providers (OSP) use filtering-based technologies to protect copyrights, but illegal uploaders use methods that bypass traditional filters. Uploading with a title that bypasses the filter cannot use a similar search method to detect illegal content. In this paper, we propose a technique for profiling the Heavy Uploader by normalizing the bypassed content title and efficiently detecting illegal content. First, the word is extracted from the normalized title and converted into a bit-array to detect illegal works. This Bloom Filter method has a characteristic that there are false positives but no false negatives. The false positive rate has a trade-off relationship with processing performance. As the false positive rate increases, the processing performance increases, and when the false positive rate decreases, the processing performance increases. We increased the detection rate by directly comparing the word to the result of increasing the false positive rate of the Bloom Filter. The processing time was also as fast as when the false positive rate was increased. Afterwards, we create a function that includes information about overall piracy and identify clustering-based heavy uploaders. Analyze the behavior of heavy uploaders to find the first uploader and detect the source site.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.2
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• pp.100-105
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• 2022

This paper applies the arc detection algorithm to prevent the false detection in photo voltaic series arc detection circuit, which is required not only to detect the series arc quickly, but also not falsely detect the arc for the non-arc noise. For this purpose, this study proposes a rapid and preventive false detection method of single peak noise and short noise signals. First, to prevent false detection by single peak noise, Discrete wavelet transform (DWT)-based characteristic parameters are applied to determine the shape and the amplitude of the noise. In addition, arc fault detection within a few milliseconds is performed with the DWT iterative algorithm to quickly prevent false detection for short noise signals, considering the continuity of serial arc noise. Thus, the method operates not only to detect series arc, but also to avoid false arc detection for peak and short noises. The proposed algorithm is applied to real-time serial arc detection circuit based on the TMS320F28335 DSP. The serial arc detection and peak noise filtering performances are verified in the built simulated arc test facility. Furthermore, the filtering performance of short noise generated through DC switch operation is confirmed.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.366-371
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• 2023

신경망 기반의 검색 모델이 활발히 연구됨에 따라 효과적인 대조학습을 위한 다양한 네거티브 샘플링 방법이 제안되고 있다. 대표적으로, ANN전략은 하드 네거티브 샘플링 방법으로 질문에 대해 검색된 후보 문서들 중에서 정답 문서를 제외한 상위 후보 문서를 네거티브로 사용하여 검색 모델의 성능을 효과적으로 개선시킨다. 하지만 질문에 부착된 정답 문서를 통해 후보 문서를 네거티브로 구분하기 때문에 실제로 정답을 유추할 수 있는 후보 문서임에도 불구하고 네거티브로 분류되어 대조학습을 진행할 수 있다는 문제점이 있다. 이러한 가짜 네거티브 문제(False Negative Problem)는 학습과정에서 검색 모델을 혼란스럽게 하며 성능을 감소시킨다. 본 논문에서는 False Negative Problem를 분석하고 이를 완화시키기 위해 가짜 네거티브 분류기(False Negative Classifier)를 소개한다. 실험은 오픈 도메인 질의 응답 데이터셋인 Natural Question에서 진행되었으며 실제 False Negative를 확인하고 이를 판별하여 기존 성능보다 더 높은 성능을 얻을 수 있음을 보여준다.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.419-432
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• 2019

Most of deep learning model training was proceeded by supervised learning, which is to train labeling data composed by inputs and corresponding outputs. Labeling data was directly generated manually, so labeling accuracy of data is relatively high. However, it requires heavy efforts in securing data because of cost and time. Additionally, the main goal of supervised learning is to improve detection performance for 'True Positive' data but not to reduce occurrence of 'False Positive' data. In this paper, the occurrence of unpredictable 'False Positive' appears by trained modes with labeling data and 'True Positive' data in monitoring of deep learning-based CCTV accident detection system, which is under operation at a tunnel monitoring center. Those types of 'False Positive' to 'fire' or 'person' objects were frequently taking place for lights of working vehicle, reflecting sunlight at tunnel entrance, long black feature which occurs to the part of lane or car, etc. To solve this problem, a deep learning model was developed by simultaneously training the 'False Positive' data generated in the field and the labeling data. As a result, in comparison with the model that was trained only by the existing labeling data, the re-inference performance with respect to the labeling data was improved. In addition, re-inference of the 'False Positive' data shows that the number of 'False Positive' for the persons were more reduced in case of training model including many 'False Positive' data. By training of the 'False Positive' data, the capability of field application of the deep learning model was improved automatically.

• Textile Coloration and Finishing
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• v.12 no.2
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• pp.79-88
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• 2000

Draw textured yarns have many differences with yarn quality as well as wearing, due to the bulkiness, thermal and physical properties according to the false twist texturing system. In order to improve such property, many studies have been accomplished for developing good textured yarns and their fabrics, but these have been essentially obtained by experimental data or mathematical analysis. This study surveyed various properties of DTY produced by false twist texturing system, namely pin and crossed-belt false twist insertion systems. And 6 yarn specimens of 2 group(pin twisting type, belt twisting type) were measured and analysed.

• 한국정보디스플레이학회:학술대회논문집
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• 2002.08a
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• pp.617-620
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• 2002

Quantitative analysis of dynamic false contours on PDP is essential to evaluate the performance of algorithms for false contour reduction. It also serves as an optimization criterion for selecting the subfield pattern. In this paper, a color difference in uniform color space is defined as a new measure for dynamic false contours. Unlike the measures in previous works, it accounts for the channel dependencies among the RGB color channels.