• Journal of the Institute of Electronics and Information Engineers
• /
• v.54 no.1
• /
• pp.3-10
• /
• 2017

Counting Bloom filters (CBFs) have been popularly used in many network algorithms and applications for the membership queries of dynamic sets, since CBFs can provide delete operations, which are not provided in a standard 1-bit vector Bloom filter. However, because of the counting functions, a CBF can have overflows and accordingly false negatives. CBFs composed of 4-bit counters are generally used, but the 4-bit CBF wastes memory spaces by allocating 4 bits for every counter. In this paper, we propose a simple alternative of a 4-bit CBF named ternary Bloom filter (TBF). In the proposed TBF structure, if two or more elements are mapped to a counter in programming, the counters are not used for insertion or deletion operations any more. When the TBF consumes the same amount of memory space as a 4-bit CBF, it is shown through simulation that the TBF provides a better false positive rate than the CBF as well as the TBF does not generate false negatives.

• Journal of Korean Society of Transportation
• /
• v.31 no.4
• /
• pp.85-94
• /
• 2013

The Continuous Risk Profile (CRP) has been well known to be the most accurate and efficient among existing network screening methods. However, the classical CRP uses safety performance functions (SPFs) which require a huge investment to construct a database system. This study aims to suggest a new CRP method using average crash frequencies of homogeneous groups, instead of SPFs, as rescaling factors. Hierarchical clustering analysis is performed to classify freeway segments into homogeneous groups based on the data of AADT and number of lanes. Using the data from I-880 in California, the proposed method is compared to other several network screening methods. The results show that the proposed method decrease false positive rates while it does not produce any false negatives. The method developed in this study can be easily applied to screen freeway networks without any additional complex database systems, and contribute to the improvement of freeway safety management systems.

• The KIPS Transactions:PartC
• /
• v.13C no.1 s.104
• /
• pp.45-54
• /
• 2006

Nowadays, a lot of techniques have been applied for the detection of malicious behavior. However, the current techniques taken into practice are facing with the challenge of much variations of the original malicious behavior, and it is impossible to respond the new forms of behavior appropriately and timely. There are also some limitations can not be solved, such as the error affirmation (positive false) and mistaken obliquity (negative false). With the questions above, we suggest a new method here to improve the current situation. To detect the malicious code, we put forward dealing with the basic source code units through the conceptual graph. Basically, we use conceptual graph to define malicious behavior, and then we are able to compare the similarity relations of the malicious behavior by testing the formalized values which generated by the predefined graphs in the code. In this paper, we show how to make a conceptual graph and propose an efficient method for similarity measure to discern the malicious behavior. As a result of our experiment, we can get more efficient detection rate.

• Journal of Digital Contents Society
• /
• v.18 no.2
• /
• pp.373-382
• /
• 2017

As the number of scientific and technical contents increases, services that support efficient search of scientific and technical contents are required. When an author's affiliation is used as a keyword, not only the contents produced by the affiliation can be searched, but also the identification rate of the search result using the author and the term as keyword can be improved. Because of the ambiguity and vagueness of the data used as a search keyword, the search result may include false negative or false positive. However, the previous research on the control through identification of the search keyword is mainly focused on the author data and terminology data. In this paper, we propose the algorithm to identify affiliations and experiment with show the experiment with scientific and technological contents held by the Korea Institute of Science and Technology Information.

• Korean Journal of Radiology
• /
• v.21 no.7
• /
• pp.891-899
• /
• 2020

Objective: To assess the diagnostic performance of a deep learning-based algorithm for automated detection of acute and chronic rib fractures on whole-body trauma CT. Materials and Methods: We retrospectively identified all whole-body trauma CT scans referred from the emergency department of our hospital from January to December 2018 (n = 511). Scans were categorized as positive (n = 159) or negative (n = 352) for rib fractures according to the clinically approved written CT reports, which served as the index test. The bone kernel series (1.5-mm slice thickness) served as an input for a detection prototype algorithm trained to detect both acute and chronic rib fractures based on a deep convolutional neural network. It had previously been trained on an independent sample from eight other institutions (n = 11455). Results: All CTs except one were successfully processed (510/511). The algorithm achieved a sensitivity of 87.4% and specificity of 91.5% on a per-examination level [per CT scan: rib fracture(s): yes/no]. There were 0.16 false-positives per examination (= 81/510). On a per-finding level, there were 587 true-positive findings (sensitivity: 65.7%) and 307 false-negatives. Furthermore, 97 true rib fractures were detected that were not mentioned in the written CT reports. A major factor associated with correct detection was displacement. Conclusion: We found good performance of a deep learning-based prototype algorithm detecting rib fractures on trauma CT on a per-examination level at a low rate of false-positives per case. A potential area for clinical application is its use as a screening tool to avoid false-negative radiology reports.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.6
• /
• pp.1113-1120
• /
• 2017

As the size of the data is getting larger and larger due to improvement of the telecommunication techniques, it would be main issues to develop and process the database. The bloom filter used to lookup a particular element under the given set is very useful structure because of the space efficiency. In this paper, we analyse the main factor of the false positive and propose the new parallel type bloom filter in order to minimize the false positive which is caused by other hash functions. The proposed method uses the memory as large as the conventional bloom filter use, but it can improve the processing speed using parallel processing. In addition, if we use the perfect hash function, the insertion and deletion function in the proposed bloom filter would be possible.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2004.05a
• /
• pp.1083-1086
• /
• 2004

Switched Network는 Shared Network 에 비해서 스니핑에 안전하다. 하지만 비교우위일뿐 절대적으로 스니핑에 안전한 것은 아니다. 이미 Switched Network 상에서 스니핑을 할 수 있는 공격툴들이 많이 소개되어 있다. 본 논문에서는 Switched Network 상에서 ARP(Address Resolution Protocol) 스푸핑을 통한 ARP 캐시 오염을 통하여 스니핑이 가능한 시나리오를 기술한다. 이러한 시나리오를 탐지하기 위한 기존의 방법은 DHCP와 같은 동적인 환경이 포함된 경우 False Positive 를 자주 발생시키기 때문에 문제가 된다. 여기에서는 이러한 False Positive를 줄인 탐지 방법을 제시하고자 한다.

• Journal of Genetic Medicine
• /
• v.15 no.2
• /
• pp.79-86
• /
• 2018

Purpose: This study aimed to evaluate the clinical usefulness of non-invasive prenatal testing (NIPT) as an alternative testing of invasive diagnostic testing in pregnancies with ultrasound abnormalities. Materials and Methods: This was a retrospective study of pregnant women with abnormal ultrasound findings before 24 weeks of gestation between April 2016 and March 2017. Abnormal ultrasound findings included isolated increased nuchal translucency, structural anomalies, and soft markers. The NIPT or diagnostic test was conducted and NIPT detected trisomy 21 (T21), T18, T13 and sex chromosomal abnormalities. We analyzed the false positive and residual risks of NIPT based on the ultrasound findings. Results: During the study period, 824 pregnant women had abnormal ultrasound findings. Among the study population, 139 patients (16.9%) underwent NIPT. When NIPT was solely performed in the patients with abnormal ultrasound findings, overall false positive risk was 2.2% and this study found residual risks of NIPT. However, the discordant results of NIPT differed according to the type of abnormal ultrasound findings. Discordant results were significant in the group with structural anomalies with 4.4% false positive rate. However, no discordant results were found in the group with single soft markers. Conclusion: This study found different efficacy of NIPT according to the ultrasound findings. The results emphasize the importance of individualized counseling for prenatal screening or diagnostic test based on the type of abnormal ultrasound.

• Journal of the Society of Disaster Information
• /
• v.19 no.4
• /
• pp.968-975
• /
• 2023

Purpose: We would like to confirm that the false positive rate of flames/smoke is high when detecting fires. Propose a method and dataset to recognize and classify fire situations to reduce the false detection rate. Method: Using the video as learning data, the characteristics of the fire situation were extracted and applied to the classification model. For evaluation, the model performance of Yolov8 and Slowfast were compared and analyzed using the fire dataset conducted by the National Information Society Agency (NIA). Result: YOLO's detection performance varies sensitively depending on the influence of the background, and it was unable to properly detect fires even when the fire scale was too large or too small. Since SlowFast learns the time axis of the video, we confirmed that detects fire excellently even in situations where the shape of an atypical object cannot be clearly inferred because the surrounding area is blurry or bright. Conclusion: It was confirmed that the fire detection rate was more appropriate when using a video-based artificial intelligence detection model rather than using image data.

• Journal of the Korea Society of Computer and Information
• /
• v.16 no.4
• /
• pp.61-71
• /
• 2011

In this paper, we proposed the method for detecting text region on image using DCT-coefficient and transition-map analysis. The detecting rate of traditional method for detecting text region using DCT-coefficient analysis is high, but false positive detecting rate also is high and the method using transition-map often reject true text region in step of verification because of sticky threshold. To overcome these problems, we generated PTRmap(Promising Text Region map) through DCT-coefficient analysis and applied PTRmap to method for detecting text region using transition map. As the result, the false positive detecting rate decreased as compared with the method using DCT-coefficient analysis, and the detecting rate increased as compared with the method using transition map.