• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.139-147
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• 2012

In this paper, we discuss the structure of biological knowledge discovery system based on text mining and automatic inference. Given a set of biology documents, the system produces a new hypothesis in an integrated manner. The text mining module of the system first extracts the 'event' information of predefined types from the documents. The inference module then produces a new hypothesis based on the extracted results. Such an integrated system can use information more up-to-date and diverse than other automatic knowledge discovery systems use. However, for the success of such an integrated system, the precision of the text mining module becomes crucial, as any hypothesis based on a single piece of false positive information would highly likely be erroneous. In this paper, we propose a probabilistic filtering method that filters out false positives from the extraction results. Our proposed method shows higher performance over an occurrence-based baseline method.

• ETRI Journal
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• v.37 no.2
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• pp.251-261
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• 2015

We present a novel method for real-time automatic license plate detection in high-resolution videos. Although there have been extensive studies of license plate detection since the 1970s, the suggested approaches resulting from such studies have difficulties in processing high-resolution imagery in real-time. Herein, we propose a novel cascade structure, the fastest classifier available, by rejecting false positives most efficiently. Furthermore, we train the classifier using the core patterns of various types of license plates, improving both the computation load and the accuracy of license plate detection. To show its superiority, our approach is compared with other state-of-the-art approaches. In addition, we collected 20,000 images including license plates from real traffic scenes for comprehensive experiments. The results show that our proposed approach significantly reduces the computational load in comparison to the other state-of-the-art approaches, with comparable performance accuracy.

• Proceedings of the KSAR Conference
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• 2004.06a
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• pp.229-229
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• 2004

The identification of embryo-specific genes would provide insights into early embryonic development. However, the current methods employed to identify the genes that are expressed at a specific developmental stage are labor intensive and suffer from high rates of false positives. Here we employed a new and accurate reverse transcription-polymerase chain reaction (RT-PCR) technology that involves annealing control primers (ACPs) to identify the genes that are specifically or prominently expressed in bovine early blastocysts and hatched blastocysts produced in vitro. (omitted)

• Proceedings of the Korean Information Science Society Conference
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• 2011.06b
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• pp.79-82
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• 2011

무인기용 비행제어 소프트웨어는 인터럽트 핸들러에서 비결정적인 수행결과를 조래하는 경합이 발생될 수 있다. 이러한 유형의 경합을 탐지하기 위한 기존 방법은 원시 프로그램의 인터럽트 핸들러를 스레드로 변환하여 정적 경합탐지 도구를 사용하므로 프로그램 수행 시 실제 발생하지 않는 부정확한 경합(false positives)를 보고한다. 본 연구는 부정확한 경합 보고를 줄이기 위해서 원시 프로그램을 POSIX 실시간 스레브 프로그램으로 변환하고 Lockset기반 탐지기법 의해서 탐지된 공유변수를 대상으로 Happens-before 관계 분석기법을 이용하여 경합을 탐지하는 동적 경합탐지 도구를 사용한다. 제시된 방법의 실험을 위해서 Knob Assembly에 탑재되는 비행제어 소프트웨어를 대상으로 정적 경합탐지 도구와 동적 경합탐지 도구의 경합탐지 결과를 비교 분석한다.

• Journal of Communications and Networks
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• v.12 no.4
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• pp.375-381
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• 2010

A flooding attack, such as DoS or Worm, can be easily created or even downloaded from the Internet, thus, it is one of the main threats to servers on the Internet. This paper presents an online real-time network response system, which can determine whether a LAN is suffering from a flooding attack within a very short time unit. The detection engine of the system is based on the incremental mining of fuzzy association rules from network packets, in which membership functions of fuzzy variables are optimized by a genetic algorithm. The incremental mining approach makes the system suitable for detecting, and thus, responding to an attack in real-time. This system is evaluated by 47 flooding attacks, only one of which is missed, with no false positives occurring. The proposed online system belongs to anomaly detection, not misuse detection. Moreover, a mechanism for dynamic firewall updating is embedded in the proposed system for the function of eliminating suspicious connections when necessary.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.1
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• pp.58.1-58.1
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• 2012

We present a comprehensive re-analysis of stellar photometric variability in the field of open cluster M37, using our new high-precision light curves. This dataset provides a rare opportunity to explore different types of variability between short (-minutes) and long (-one month) time-scales. To investigate the variability properties of -30,000 objects, we developed new algorithms for detecting periodic, aperiodic, and sporadic variability in their light curves. About 7.5% (2,284) of the total sample exhibits convincing variations that are induced by flares, pulsations, eclipses, starspots and, in some cases, unknown causes. The benefits of our new photometry and analysis package are evident. The discovery rate of new variables is increased by 63% in comparison with the existing catalog of variables, and 51 previously identified variables were found to be false positives resulting from time-dependent systematic effects. Based on extended and improved catalog of variables, we will review the basic properties (e.g., periodicity, amplitude, type) of the variability and how different they are for different spectral types and for cluster memberships.

• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.81-88
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• 2017

In this paper, we classified the weaknesses of C/C++ programs listed in CWE based on the diagnostic information produced at each stage of program compilation. Our classification identifies which stages should be responsible for analyzing the weaknesses. We also present algorithmic frameworks for detecting typical weaknesses belonging to the classes to demonstrate validness of our scheme. For the weaknesses that cannot be analyzed by using the diagnostic information, we separated them as a group that are often detectable by the analyses that simulate program execution, for instance, symbolic execution and abstract interpretation. We expect that classification of weaknesses, and diagnostic information accordingly, would contribute to systematic development of static analyzers that minimizes false positives and negatives.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.2
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• pp.569-575
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• 2014

Although mammography is the primary imaging modality for the breast, it has its limitations especially with dense breast parenchyma. Breast magnetic resonance imaging (MRI) has evolved into an important adjunctive tool as it is currently the most sensitive technique for breast cancer detection. Despite this high sensitivity, overlap in the appearances of some benign and malignant breast lesions results in additional unnecessary intervention with negative results. These false positives, in addition to high cost and limited availability, necessitate establishing proper indications for breast MRI. The literature was here reviewed for recent clinical trials, meta-analyses and review papers which have studied this important subject. PubMed; the US national library of medicine, was utilized to review the literature in the last twenty years. Using the obtained information, current uses of breast MRI are discussed in this paper to determine the indications which are relevant to clinical practice.

• Journal of Communications and Networks
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• v.13 no.2
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• pp.125-131
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• 2011

Rapid strides are being made to bring to reality the technology of wearable sensors for monitoring patients' physiological data.We study the problem of automatically detecting anomalies in themeasured blood glucose levels. The normal daily measurements of the patient are used to train a hidden Markov model (HMM). The structure of the HMM-its states and output symbols-are selected to accurately model the typical transitions in blood glucose levels throughout a 24-hour period. The learning of the HMM is done using historic data of normal measurements. The HMM can then be used to detect anomalies in blood glucose levels being measured, if the inferred likelihood of the observed data is low in the world described by the HMM. Our simulation results show that our technique is accurate in detecting anomalies in glucose levels and is robust (i.e., no false positives) in the presence of reasonable changes in the patient's daily routine.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.116-125
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• 2019

The fire should extinguish as soon as possible because it causes economic loss and loses precious life. In this study, we propose a new atypical fire and smoke detection algorithm using deep learning and color histogram of fire and smoke. First, input frame images obtain from the ONVIF surveillance camera mounted in factory search motion candidate frame by motion detection algorithm and mean square error (MSE). Second deep learning (Faster R-CNN) is used to extract the fire and smoke candidate area of motion frame. Third, we apply a novel algorithm to detect the fire and smoke using color histogram algorithm with local area motion, similarity, and MSE. In this study, we developed a novel fire and smoke detection algorithm applied the local motion and color histogram method. Experimental results show that the surveillance camera with the proposed algorithm showed good fire and smoke detection results with very few false positives.