• The Plant Pathology Journal
• /
• 제39권5호
• /
• pp.486-493
• /
• 2023

Cowpea mild mottle virus (CPMMV) is a global plant virus that poses a threat to the production and quality of legume crops. Early and accurate diagnosis is essential for effective managing CPMMV outbreaks. With the advancement in isothermal recombinase polymerase amplification and lateral flow strips technologies, more rapid and sensitive methods have become available for detecting this pathogen. In this study, we have developed a reverse transcription recombinase polymerase amplification combined with lateral flow strips (RT-RPA-LFS) method for the detection of CPMMV, specifically targeting the CPMMV coat protein (CP) gene. The RT-RPA-LFS assay only requires 20 min at 40℃ and demonstrates high specificity. Its detection limit was 10 copies/µl, which is approximately up to 100 times more sensitive than RT-PCR on agarose gel electrophoresis. The developed RT-RPA-LFS method offers a rapid, convenient, and sensitive approach for field detection of CPMMV, which contribute to controlling the spread of the virus.

• Journal of Veterinary Science
• /
• 제21권1호
• /
• pp.4.1-4.9
• /
• 2020

Fast and accurate detection of viral RNA pathogens is important in apiculture. A polymerase chain reaction (PCR)-based detection method has been developed, which is simple, specific, and sensitive. In this study, we rapidly (in 1 min) synthesized cDNA from the RNA of deformed wing virus (DWV)-infected bees (Apis mellifera), and then, within 10 min, amplified the target cDNA by ultra-rapid qPCR. The PCR products were hybridized to a DNA-chip for confirmation of target gene specificity. The results of this study suggest that our method might be a useful tool for detecting DWV, as well as for the diagnosis of RNA virus-mediated diseases on-site.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권1호
• /
• pp.323-343
• /
• 2020

With the rapid development of computer and communication techniques, copyright protection of vector geographic data has attracted considerable research attention because of the high cost of such data. A novel adaptive watermark detection algorithm is proposed for vector geographic data that can be used to qualitatively analyze the robustness of watermarks against data addition attacks. First, a watermark was embedded into the vertex coordinates based on coordinate mapping and quantization. Second, the adaptive watermark detection model, which is capable of calculating the detection threshold, false positive error (FPE) and false negative error (FNE), was established, and the characteristics of the adaptive watermark detection algorithm were analyzed. Finally, experiments were conducted on several real-world vector maps to show the usability and robustness of the proposed algorithm.

• The Plant Pathology Journal
• /
• 제23권4호
• /
• pp.260-265
• /
• 2007

Testing a large number of samples from field monitoring and routine indexing is cumbersome and the available virus detection tools were labor intensive and expensive. To circumvent these problems we established tissue blot immunoassay (TBIA) method an alternative detection tool to detect Barley mild mosaic virus (BaMMV) and Barley yellow mosaic virus (BaYMV) infection in the field and greenhouse inoculated plants for monitoring and routine indexing applications, respectively. Initially, leaf and stem were tested to determine suitable plant tissue for direct blotting on nitrocellulose membrane. The dilutions of antibodies were optimized for more efficient and economical purposes. Results showed that stem tissue was more suitable for direct blotting for it had no background that interferes in the reaction. Therefore, this technique was referred as direct stem blot immunoassay or DSBIA, in this study. Re-used diluted (1:1000) antiserum and conjugate up to 3 times with the addition of half strength amount of concentrated antibodies was more effective in detecting the virus. The virus blotted on the nitrocellulose membrane from stem tissues kept at room temperature for 3 days were still detectable. The efficiency of DSBIA and RT-PCR in detecting BaMMV and BaYMV were relatively comparable. Results further proved that DSBIA is a rapid, reliable and economical detection method suitable for monitoring BaMMV and BaYMV infection in the field and practical method in indexing large scale of barley materials for virus resistance screening.

• 한국재난정보학회 논문집
• /
• 제10권3호
• /
• pp.452-457
• /
• 2014

화학재난 발생시 현장대응 요원들은 사건 원인물질의 성상과 잔류오염 농도를 신속 정확하게 파악해야 한다. 또한 화학재난 현장에서의 적절한 대응절차 진행을 위해서는 화학물질의 성상과 오염농도 확인은 필수적이다. 이를 위해 현장에서 사용하는 각 장비의 특징을 알아보고자 한다. 현장대응장비는 모든 화학물질을 확인할 수 없으며, 각 장비별로 물질탐지에 제한적이다. 장비별 물질탐지 범위와 상호보완성을 고려해야 한다. 본 연구에서는 현장 활용장비인 간이탐지 킷과 검지관식 탐지장비, 전자식 탐지장비의 신속한 현장 활용을 위한 대응절차를 마련하여 현장대응에 도움을 주고자 한다.

• Journal of Multimedia Information System
• /
• 제6권1호
• /
• pp.1-6
• /
• 2019

In the field of industrial applications, the real-time performance of the target detection problem is very important. The most serious time consumption in the pedestrian detection process is the extraction phase of the candidate window. To accelerate the speed, in this paper, a fast extraction of pedestrian candidate window based on the BING (Binarized Normed Gradients) algorithm replaces the traditional sliding window scanning. The BING features are extracted with the positive and negative samples and input into the two-stage SVM (Support Vector Machine) classifier for training. The obtained BING template may include a pedestrian candidate window. The trained template is loaded during detection, and the extracted candidate windows are input into the classifier. The experimental results show that the proposed method can extract fewer candidate window and has a higher recall rate with more rapid speed than the traditional sliding window detection method, so the method improves the detection speed while maintaining the detection accuracy. In addition, the real-time requirement is satisfied.

• 전자통신동향분석
• /
• 제38권5호
• /
• pp.71-80
• /
• 2023

With the rapid advancement of the Internet, the use of encrypted traffic has surged in order to protect data during transmission. Simultaneously, network attacks have also begun to leverage encrypted traffic, leading to active research in the field of encrypted traffic analysis to overcome the limitations of traditional detection methods. In this paper, we provide an overview of the encrypted traffic analysis field, covering the analysis process, domains, models, evaluation methods, and research trends. Specifically, it focuses on the research trends in the field of anomaly detection in encrypted network traffic analysis. Furthermore, considerations for model development in encrypted traffic analysis are discussed, including traffic dataset composition, selection of traffic representation methods, creation of analysis models, and mitigation of AI model attacks. In the future, the volume of encrypted network traffic will continue to increase, particularly with a higher proportion of attack traffic utilizing encryption. Research on attack detection in such an environment must be consistently conducted to address these challenges.

• 한국감성과학회:학술대회논문집
• /
• 한국감성과학회 2009년도 춘계학술대회
• /
• pp.219-222
• /
• 2009

Rapid object recognition is one of the main stream research themes focusing to reveal how human recognizes object and interacts with environment in natural world. This field of study is of consequence in that it is highly important in evolutionary perspective to quickly see the external objects and judge their characteristics to plan future reactions. In this study, we investigated how human detect natural scene objects and categorize them in a limited time frame. We applied Magnetoencepahlogram (MEG) while participants were performing detection (e.g. object vs. texture) or basic-level categorization (e.g. cars vs. dogs) tasks to track the dynamic interaction in human brain for rapid object recognition process. The results revealed that detection and categorization involves different temporal and functional connections that correlated for the successful recognition process as a whole. These results imply that dynamics in the brain are important for our interaction with environment. The implication from this study can be further extended to investigate the effect of subconscious emotional factors on the dynamics of brain interactions during the rapid recognition process.

• International journal of advanced smart convergence
• /
• 제13권2호
• /
• pp.195-204
• /
• 2024

In this paper, we investigate the comparative performance of two leading object detection architectures, YOLOv7 and Detection Transformer (DETR), across varying levels of data augmentation using CycleGAN. Our experiments focus on chest scan images within the context of biomedical informatics, specifically targeting the detection of abnormalities. The study reveals that YOLOv7 consistently outperforms DETR across all levels of augmented data, maintaining better performance even with 75% augmented data. Additionally, YOLOv7 demonstrates significantly faster convergence, requiring approximately 30 epochs compared to DETR's 300 epochs. These findings underscore the superiority of YOLOv7 for object detection tasks, especially in scenarios with limited data and when rapid convergence is essential. Our results provide valuable insights for researchers and practitioners in the field of computer vision, highlighting the effectiveness of YOLOv7 and the importance of data augmentation in improving model performance and efficiency.

• Proceedings of the National Institute of Ecology of the Republic of Korea
• /
• 제5권3호
• /
• pp.94-98
• /
• 2024

Field surveys for reports on suspected invasive termite species were received at the National Institute of Ecology's Invasive Species Reporting Center. We collected 10 termites and performed DNA sequence analysis for species identification. Specimens were confirmed as Coptotermes formosanus. This is the reconfirmation of C. formosanus in South Korea, highlighting the importance of early detection and rapid response and reaffirming the possibility of C. formosanus invading South Korea.