• Title/Summary/Keyword: Detection Techniques

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LSTM-based Early Fire Detection System using Small Amount Data

  • Seonhwa Kim;Kwangjae Lee
    • Journal of the Semiconductor & Display Technology
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    • v.23 no.1
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    • pp.110-116
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    • 2024
  • Despite the continuous advancement of science and technology, fire accidents continue to occur without decreasing over time, so there is a constant need for a system that can accurately detect fires at an early stage. However, because most existing fire detection systems detect fire in the early stage of combustion when smoke is generated, rapid fire prevention actions may be delayed. Therefore we propose an early fire detection system that can perform early fire detection at a reasonable cost using LSTM, a deep learning model based on multi-gas sensors with high selectivity in the early stage of decomposition rather than the smoke generation stage. This system combines multiple gas sensors to achieve faster detection speeds than traditional sensors. In addition, through window sliding techniques and model light-weighting, the false alarm rate is low while maintaining the same high accuracy as existing deep learning. This shows that the proposed fire early detection system is a meaningful research in the disaster and engineering fields.

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Efficient DRG Fraud Candidate Detection Method Using Data Mining Techniques (데이터마이닝 기법을 이용한 효율적인 DRG 확인심사대상건 검색방법)

  • Lee, Jung-Kyu;Jo, Min-Woo;Park, Ki-Dong;Lee, Moo-Song;Lee, Sang-Il;Kim, Chang-Yup;Kim, Yong-Ik;Hong, Du-Ho
    • Journal of Preventive Medicine and Public Health
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    • v.36 no.2
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    • pp.147-152
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    • 2003
  • Objectives : To develop a Diagnosis-Related Group (DRG) fraud candidate detection method, using data mining techniques, and to examine the efficiency of the developed method. Methods ; The Study included 79,790 DRGs and their related claims of 8 disease groups (Lens procedures, with or without, vitrectomy, tonsillectomy and/or adenoidectomy only, appendectomy, Cesarean section, vaginal delivery, anal and/or perianal procedures, inguinal and/or femoral hernia procedures, uterine and/or adnexa procedures for nonmalignancy), which were examined manually during a 32 months period. To construct an optimal prediction model, 38 variables were applied, and the correction rate and lift value of 3 models (decision tree, logistic regression, neural network) compared. The analyses were peformed separately by disease group. Results : The correction rates of the developed method, using data mining techniques, were 15.4 to 81.9%, according to disease groups, with an overall correction rate of 60.7%. The lift values were 1.9 to 7.3 according to disease groups, with an overall lift value of 4.1. Conclusions : The above findings suggested that the applying of data mining techniques is necessary to improve the efficiency of DRG fraud candidate detection.

Change Detection of a Small Town Area from Multi-Temporal Aerial Photos using Image Differencing and Image Ratio Techniques (다시기 항공사진으로부터 영상대차법과 영상대비법을 이용한 소도읍 지역의 변화 검출)

  • Lee, Jin-Duk;Yeon, Sang-Ho;Lee, Dong-Ho
    • Journal of the Korean Association of Geographic Information Studies
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    • v.11 no.1
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    • pp.116-124
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    • 2008
  • This study presents the application of multi-temporal and multi-scale panchromatic aerial photos for change detection in a small urban area. For aerial photos of the scale of 1:20,000 taken in 1987 and 1996 and the scale of 1:37,500 taken in 2000. Pre-processing that make the same conditions to all of the aerial photos was carried out through geometric correction, registration, contrasting, resamplimg, and mosaicking and then change detection were carried out respectively by image differencing and image ratio techniques. As a result, the change of urban features and landcover were able to be detected from panchromatic aerial photos that is single-band images and then the detected change results were compared between both techniques.

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Modified File Title Normalization Techniques for Copyright Protection (저작권 보호를 위한 변형된 파일 제목 정규화 기법)

  • Hwang, Chan Woong;Ha, Ji Hee;Lee, Tea Jin
    • Convergence Security Journal
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    • v.19 no.4
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    • pp.133-142
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    • 2019
  • Although torrents and P2P sites or web hard are frequently used by users simply because they can be easily downloaded freely or at low prices, domestic torrent and P2P sites or web hard are very sensitive to copyright. Techniques have been researched and applied. Among these, title and string comparison method filtering techniques that block the number of cases such as file titles or combinations of key words are blocked by changing the title and spacing. Bypass is easy through. In order to detect and block illegal works for copyright protection, a technique for normalizing modified file titles is essential. In this paper, we compared the detection rate by searching before and after normalizing the modified file title of illegal works and normalizing the file title. Before the normalization, the detection rate was 77.72%, which was unfortunate while the detection rate was 90.23% after the normalization. In the future, it is expected that better handling of nonsense terms, such as common date and quality display, will yield better results.

Technique for Malicious Code Detection using Stacked Convolution AutoEncoder (적층 콘볼루션 오토엔코더를 활용한 악성코드 탐지 기법)

  • Choi, Hyun-Woong;Heo, Junyoung
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.20 no.2
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    • pp.39-44
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    • 2020
  • Malicious codes cause damage to equipments while avoiding detection programs(vaccines). The reason why it is difficult to detect such these new malwares using the existing vaccines is that they use "signature-based" detection techniques. these techniques effectively detect already known malicious codes, however, they have problems about detecting new malicious codes. Therefore, most of vaccines have recognized these drawbacks and additionally make use of "heuristic" techniques. This paper proposes a technology to detecting unknown malicious code using deep learning. In addition, detecting malware skill using Supervisor Learning approach has a clear limitation. This is because, there are countless files that can be run on the devices. Thus, this paper utilizes Stacked Convolution AutoEncoder(SCAE) known as Semi-Supervisor Learning. To be specific, byte information of file was extracted, imaging was carried out, and these images were learned to model. Finally, Accuracy of 98.84% was achieved as a result of inferring unlearned malicious and non-malicious codes to the model.

Improving Lane Marking Detection by Combining Horizontal 1-D LoG Filtered Scale Space and Variable Thresholding (수평 1-D LoG 필터링 스케일 공간과 가변적 문턱처리의 결합에 의한 차선 마킹 검출 개선)

  • Yoo, Hyeon-Joong
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.49 no.4
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    • pp.85-94
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    • 2012
  • Lane marking detection is essential to both ITS and DAS systems. The objective of this paper is to provide more robust technique for lane marking detection than traditional techniques by using scale-space technique. Variable thresholding that is based on the local statistics may be very effective for detecting such objects as lane markings that have prominent intensities. However, such techniques that only rely on local statistics have limitations containing irrelevant areas as well. We reduce the candidate areas by combining the variable thresholding result with cost-efficient horizontal 1D LoG filtered scale space. Through experiments using practical images, we could achieve significant improvement over the techniques based not only on the variable thresholding but also on the Hough transform that is another very popular technique for this purpose.

Rapid Detection Methods for Food-Borne Pathogens in Dairy Products by Polymerase Chain Reaction (PCR 방법을 이용한 우유 및 유제품에서 발생하는 식중독 균의 신속 검출법)

  • Kwak, Hyelim;Han, Seonkyeong;Kim, Eiseul;Hong, Yeun;Kim, Haeyeong
    • Journal of Dairy Science and Biotechnology
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    • v.31 no.2
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    • pp.171-177
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    • 2013
  • The dairy industry has consistently grown via the expansion of dairy-based food categories. Dairy product consumption is stable since the nutrient composition in dairy products is ideal for human health. However, dairy products are highly susceptible to food-borne pathogens. Controlling the safety of dairy products is thus important when considering the nutrient-rich matrix of this food category. Currently, immunoassays or molecular biology techniques have been used to evaluate the safety of dairy products in Korea. These methods are based on the detection of proteins and thus have low reproducibility and sensitivity. Recent techniques to detect food-borne pathogens have focused on genetic analyses. Rapid detection methods for food-borne pathogens in milk and dairy products using polymerase chain reaction (PCR) techniques, such as conventional PCR, real-time PCR, repetitive sequence-based (rep)-PCR, PCR-denaturing gradient gel electrophoresis (DGGE), and digital PCR, are reviewed in this article. The aim of this review was to contribute knowledge of the relationship between microflora and the quality of dairy products. This study will also assist in the immediate monitoring of food-borne pathogens in milk and dairy products when an outbreak related to this food category occurs.

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An automatic detection scheme of anti-debugging routines to the environment for analysis (분석 환경에 따른 안티 디버깅 루틴 자동 탐지 기법)

  • Park, Jin-Woo;Park, Yong-Su
    • Journal of Internet Computing and Services
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    • v.15 no.6
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    • pp.47-54
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    • 2014
  • Anti-debugging is one of the techniques implemented within the computer code to hinder attempts at reverse engineering so that attackers or analyzers will not be able to use debuggers to analyze the program. The technique has been applied to various programs and is still commonly used in order to prevent malware or malicious code attacks or to protect the programs from being analyzed. In this paper, we will suggest an automatic detection scheme for anti-debugging routines. With respect to the automatic detection, debuggers and a simulator were used by which trace information on the Application Program Interface(API) as well as executive instructions were extracted. Subsequently, the extracted instructions were examined and compared so as to detect points automatically where suspicious activity was captured as anti-debugging routines. Based on experiments to detect anti-debugging routines using such methods, 21 out of 25 anti-debugging techniques introduced in this paper appear to be able to detect anti-debugging routines properly. The technique in the paper is therefore not dependent upon a certain anti-debugging method. As such, the detection technique is expected to also be available for anti-debugging techniques that will be developed or discovered in the future.

Development of Biosensors for Rapid Detection of Foodborne Pathogenic Bacteria using CRISPR/Cas (CRISPR/Cas 시스템 기술을 활용한 고위험성 식중독 세균 신속 검출을 위한 바이오센서 개발)

  • Seon Yeong Jo;Jong Pil Park
    • Journal of Food Hygiene and Safety
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    • v.38 no.5
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    • pp.279-286
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    • 2023
  • Rapid and accurate detection of pathogenic bacteria is crucial for various applications, including public health and food safety. However, existing bacteria detection techniques have several drawbacks as they are inconvenient and require time-consuming procedures and complex machinery. Recently, the precision and versatility of CRISPR/Cas system has been leveraged to design biosensors that offer a more efficient and accurate approach to bacterial detection compared to the existing techniques. Significant research has been focused on developing biosensors based on the CRISPR/Cas system which has shown promise in efficiently detecting pathogenic bacteria or virus. In this review, we present a biosensor based on the CRISPR/Cas system that has been specifically developed to overcome these limitations and detect different pathogenic bacteria effectively including Vibrio parahaemolyticus, Salmonella, E. coli O157:H7, and Listeria monocytogenes. This biosensor takes advantage of the CRISPR/Cas system's precision and versatility for more efficiently accurately detecting bacteria compared to the previous techniques. The biosensor has potential to enhance public health and ensure food safety as the biosensor's design can revolutionize method of detecting pathogenic bacteria. It provides a rapid and reliable method for identifying harmful bacteria and it can aid in early intervention and preventive measures, mitigating the risk of bacterial outbreaks and their associated consequences. Further research and development in this area will lead to development of even more advanced biosensors capable of detecting an even broader range of bacterial pathogens, thereby significantly benefiting various industries and helping in safeguard human health