• Journal of Microbiology and Biotechnology
• /
• v.17 no.11
• /
• pp.1885-1889
• /
• 2007

The tissue blot immunobinding assay (TBIA) is widely used for the detection and localization of plant viruses in various plant tissues. The basic experimental procedures of TBIA sampling and blotting were simplified using commercially available micropipette tips. This method was termed the ring-blot immunobinding assay (R-BIA), as the blot on the membrane forms a ring shape. The detection efficacy of R-BIA was tested for two chili pepper viruses, pepper mild mottle tobamovirus (PMMoV) and pepper mottle potyvirus (PepMoV), following the optimized serological procedures of TBIA (length of the incubation period and BSA concentration, and primary and secondary antibodies). Sensitivity of the R-BIA was about 1 ng/ml of purified PMMoV in pepper leaf sap from a healthy pepper plant. R-BIA also showed high specificity in the detection of PMMoV and PepMoV. Moreover, the modified sampling and blotting procedures were simpler and more reliable than other TBIA methods (such as whole-leaf blotting and crushed-leaf blotting), suggesting that the R-BIA may be used for medium- to large-scale detection of plant viruses in laboratories with minimal facilities.

• Journal of the Semiconductor & Display Technology
• /
• v.22 no.4
• /
• pp.108-112
• /
• 2023

Precision manufacturing technology is rapidly developing due to the extreme miniaturization of semiconductor processes to comply with Moore's Law. Accurate and precise alignment, which is one of the key elements of the semiconductor pre-process and post-process, is very important in the semiconductor process. The center detection of wafer align marks plays a key role in improving yield by reducing defects and research on accurate detection methods for this is necessary. Methods for accurate alignment using traditional image sensors can cause problems due to changes in image brightness and noise. To solve this problem, engineers must go directly into the line and perform maintenance work. This paper emphasizes that the development of AI technology can provide innovative solutions in the semiconductor process as high-resolution image and image processing technology also develops. This study proposes a new wafer center detection method through variable thresholding. And this study introduces a method for detecting the center that is less sensitive to the brightness of LEDs by utilizing a high-performance object detection model such as YOLOv8 without relying on existing algorithms. Through this, we aim to enable precise wafer focus detection using artificial intelligence.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.6
• /
• pp.2781-2800
• /
• 2016

Emerging attacks aim to access proprietary assets and steal data for business or political motives, such as Operation Aurora and Operation Shady RAT. Skilled Intruders would likely remove their traces on targeted hosts, but their network movements, which are continuously recorded by network devices, cannot be easily eliminated by themselves. However, without complete knowledge about both inbound/outbound and internal traffic, it is difficult for security team to unveil hidden traces of intruders. In this paper, we propose an autonomous anomaly detection system based on behavior profiling and relation mining. The single-hop access profiling model employ a novel linear grouping algorithm PSOLGA to create behavior profiles for each individual server application discovered automatically in historical flow analysis. Besides that, the double-hop access relation model utilizes in-memory graph to mine time-sequenced access relations between different server applications. Using the behavior profiles and relation rules, this approach is able to detect possible anomalies and violations in real-time detection. Finally, the experimental results demonstrate that the designed models are promising in terms of accuracy and computational efficiency.

• Journal of Sensor Science and Technology
• /
• v.31 no.6
• /
• pp.441-447
• /
• 2022

This study describes a novel method for detecting and measuring partial discharge (PD) on an electrical facility such as an insulated power cable or switchgear using fiber optic sensing technology, and a distributed acoustic sensing (DAS) system. This method has distinct advantages over traditional PD sensing techniques based on an electrical method, including immunity to electromagnetic interference (EMI), long range detection, simultaneous detection for multiple points, and exact location. In this study, we present a DAS system for PD detection with performance evaluation and experimental results in a simulated environment. The results show that the system can be applied to PD detection.

• Journal of Internet Computing and Services
• /
• v.22 no.3
• /
• pp.53-58
• /
• 2021

There is little research on actual business activities in the field of security control. Therefore, in this paper, we intend to present a practical research methodology that can contribute to the calculation of the size of the appropriate input personnel through the modeling of the threat information detection response time of the security control and to analyze the effectiveness of the latest security solutions. The total threat information detection response time performed by the security control center is defined as TIDRT (Total Intelligence Detection & Response Time). The total threat information detection response time (TIDRT) is composed of the sum of the internal intelligence detection & response time (IIDRT) and the external intelligence detection & response time (EIDRT). The internal threat information detection response time (IIDRT) can be calculated as the sum of the five steps required. The ultimate goal of this study is to model the major business activities of the security control center with an equation to calculate the cyber threat information detection response time calculation formula of the security control center. In Chapter 2, previous studies are examined, and in Chapter 3, the calculation formula of the total threat information detection response time is modeled. Chapter 4 concludes with a conclusion.

• Journal of Industrial Technology
• /
• v.21 no.B
• /
• pp.91-98
• /
• 2001

In this paper, center detection and motion analysis of a moving object are studied. Kohonen's self-organizing neural network models are used for the moving objects tracking and time delay neural networks are used for dynamic characteristic analysis. Instead of objects brightness, neuron projections by Kohonen Networks are used. The motion of target objects can be analyzed by using the differential neuron image between the two projections. The differential neuron image which is made by two consecutive neuron projections is used for center detection and moving objects tracking. The two differential neuron images which are made by three consecutive neuron projections are used for the moving trajectory estimation. It is possible to distinguish 8 directions of a moving trajectory with two frames and 16 directions with three frames.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1199-1201
• /
• 2005

In the synthetic aperture radar (SAR) system, the motion error is the main phase error sources and the motion compensation is very important. The phase gradient autofocus (PGA) is a state of art technique for phase error correction of SAR. It exploits the redundancy of the phase-error information among range bins by selecting the strongest scatter for each range bin and synthesizes them. The motivation of this paper is based on the observation that the redundancy of phase error is also among the cross-range direction. Moreover, the proposed method applies the weighting function to better utilize the phase error information. The validity of the proposed scheme for PGA is tested with some numerical simulation.

• Journal of Dairy Science and Biotechnology
• /
• v.33 no.4
• /
• pp.271-275
• /
• 2015

Colloidal semiconductor CdSe-ZnS core-shell nanocrystal quantum dot (Qdot) are luminescent inorganic fluorophores that show potential to overcome some of the functional limitations encountered with organic dyes in fluorescence labeling applications. Salmonella Enteritidis has emerged as a major cause of human salmonellosis worldwide since the 1980s. A rapid, specific, and sensitive method for the detection of Salmonella Enteritidis was developed using Qdot as a fluorescence marker coupled with immunomagnetic separation. Magnetic beads coated with anti-Salmonella Enteritidis antibodies were employed to selectively capture the target bacteria, and biotin-conjugated anti-Salmonella antibodies were added to form sandwich immune complexes. After magnetic separation, the immune complexes were labeled with Qdot via biotin-streptavidin conjugation, and fluorescence measurement was carried out using a fluorescence measurement system. The detection limit of the Qdot method was a Salmonella Enteritidis concentration of $10^3$ colony-forming units (CFU)/mL, whereas the conventional fluorescein isothiocyanate-based method required over $10^5CFU/mL$. The total detection time was within 2 h. In addition to the potential for general nanotechnology development, these results suggest a new rapid detection method of various pathogenic bacteria from a complex food matrix.

• International Journal of Highway Engineering
• /
• v.14 no.3
• /
• pp.77-85
• /
• 2012

Most of Automatic Accident Detection Algorithm has a problem of detecting an accident as traffic congestion. Actually, center's managers deal with accidents depend on watching CCTV or accident report by drivers even though they run the Automatic Accident Detection system. It is because of the system's detecting errors such as detecting non-accidents as accidents, and it makes decreasing in the system's overall reliability. It means that Automatic Accident Detection Algorithm should not only have high detection probability but also have low false alarm probability, and it has to detect accurate accident spot. The study tries to verify and evaluate the effectiveness of using Gaussian Mixture Model and individual vehicle tracking to adapt Accident Detection Algorithm to Center Management System by measuring accident detection probability and false alarm probability's frequency in the real accident.

• Journal of Biomedical Engineering Research
• /
• v.30 no.2
• /
• pp.122-128
• /
• 2009

Cancer serum biomarkers have advanced our ability to more accurately predict tumor classification, prognostic/metastatic potential, and response potential to novel chemotherapies. Serum amyloid A (SAA) and Vascular endothelial growth factor (VEGF) have potential utility as a serum biomarker for lung cancer. Quantum dots, nanometer-sized crystals, have a high quantum yield, sensitivity, and pronounced photostability. The properties of quantum dots can be efficiently applied to the detection of serum biomarkers in immunoassays as fluorescent probe. We used quantum dots as fluorescent probes in immunoassays and attempted to detect serum amyloid A and vascular endothelial growth factor as serum biomarkers of lung cancer. This fluorescence immunoassay based on the properties of quantum dots is applicable to the detection of serum biomarkers for lung cancer. The fluorescence immunoassay with quantum dots should allow the efficient and specific detection of serum amyloid A (SAA) for the possible diagnosis of lung cancer.