• Bulletin of the Korean Chemical Society
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• v.31 no.6
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• pp.1561-1567
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• 2010

In this study, we have developed a new detection method using Si field effect transistor (FET)-type biosensors, which enables the direct monitoring of antigen-antibody binding within very high-ionic-strength solutions such as 1$\times$PBS and human serum. In the new method, as no additional dilution or desalting processes are required, the FET-type biosensors can be more suitable for ultrasensitive and real-time analysis of raw sample solutions. The new detection scheme is based on the observation that the strength of antigen-antibody-specific binding is significantly influenced by the ionic strength of the reaction solutions. For a prostate specific antigen (PSA), in some conditions, the binding reaction between PSA and anti-PSA in a low-ionic strength reaction solution such as 10 ${\mu}M$ phosphate buffer is weak (reversible), while that in high-ionic strength reaction solutions such as 1$\times$PBS or human serum is strong.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.9-14
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• 2016

This study evaluated a digital locking device design using detection distance of 13.4mm of a human body sensing type magnetic field coil. In contrast to digital locking devices that are used nowadays, the existing serial number entering buttons, lighting, number cover, corresponding pcb, exterior case, and data delivery cables have been deleted and are only composed of control ON/OFF power switches and emergency terminals. When the magnetic field coil substrates installed inside the inner case detects the electric resistance delivered from the opposite side of the 12mm interval exterior contacting the glass body part, the corresponding induced current flows. At this time, the magnetic field coil takes the role as a sensor when coil frequency of the circular coil is transformed. The magnetic coil as a sensor detects a change in the oscillation frequency output before and after the body is detected. This is then amplified to larger than 2,000%, transformed into digital signals, and delivered to exclusive software to compare and search for embedded data. The detection time followed by the touch area of the body standard to a $12.8{\emptyset}$ magnetic field coil was 30% contrast at 0.08sec and 80% contrast at 0.03sec, in which the detection distance was 13.4mm, showing the best level.

• Journal of the Korean Society of Radiology
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• v.15 no.1
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• pp.15-20
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• 2021

CT is important role in the medical field, such as disease diagnosis, but the number of examination and CT images are increasing. Recently, deep learning has been actively used in the medical field, and it has been used to diagnose auxiliary disease through object detection during deep learning using medical images. The purpose of study to evaluate accuracy by detecting kidney and vertebrae during abdominal CT using object detection deep learning in YOLOv3. As a results of the study, the detection accuracy of the kidney and vertebrae was 83.00%, 82.45%, and can be used as basic data for the object detection of medical images using deep learning.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

• Bulletin of the Korean Chemical Society
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• v.29 no.1
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• pp.153-158
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• 2008

A microchip-based capillary gel electrophoresis (MCGE) technique was developed for the ultra-fast detection and differentiation of Candidatus Mycoplasma haemominutum (Candidatus M. haemominutum, California strain) and Mycoplasma haemofelis (M. haemofelis, Ohio strain) in Korean feral cats through the application of programmed field strength gradients (PFSG) in a conventional glass double-T microchip. The effects of the poly (ethyleneoxide) (PEO) concentration and electric field strength on the separation of DNA fragments were investigated. The PCR-amplified products of Candidatus M. haemominutum (202-bp) and M. haemofelis (273-bp) were analyzed by MCGE within 75 s under a constant applied electric field of 117.6 V/cm and a sieving matrix of 0.3% PEO (Mr 8 000 000). When the PFSG was applied, MCGE analysis generated results 6.8-times faster without any loss of resolution or reproducibility. The MCGE-PFSG technique was also applied to eleven samples selected randomly from 33 positive samples. The samples were detected and differentiated within 11 s. The analysis time of the MCGE-PFSG technique was approximately 980-times faster than that using conventional slab gel electrophoresis.

• Journal of the Optical Society of Korea
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• v.14 no.4
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• pp.316-320
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• 2010

We report an application of full-field optical coherence tomography (FF-OCT) for identifying counterfeit bank notes. The depth-resolved imaging capability of FF-OCT was used for tomographic identification of superficially-identical objects. By retrieving the internal structures of the security feature (cash hologram) of an original banknote, we could demonstrate the feasibility of FF-OCT to identify counterfeit money. The FF-OCT images showed that the hologram consisted of micron scale multi-coated layers including an air gap. Therefore, it is expected that FF-OCT has potential as a new non-invasive tool to discern imitation of currency, and it would find applications in a wide field of counterfeit sciences.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.378-383
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• 1998

Needless to say, the importance of the quality of observed values shall be emphasized in the field of 'TQM', because, the first step of 'TQM' should be some data - observed values. Usually, meaning of the quality of observed values should be, a) accuracy (trueness and precision), b) detection limit, c) cost and so on. However, the authors will describe mainly on b), in this paper. The definitions of technical terms related to 'Detection Limit' are defined in ISO l1843-1 Capability of detection - Part1:Terms and definitions (1998). The most important terms extracted from the above standard are shown in the following table. The application of the 'Detection Limit' to the actual measurement is discussed in this paper.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.83.3-83.3
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• 2015

During a six-month period (autumn 2014 within the framework of a research & education project) we have constructed a professional double-station video-meteor detection network at the SOAO and BOAO mountain summits. Meteor detection is achieved by pixel-to-pixel motion-detection trigger. Each station is nearly autonomous and has three cameras with fixed viewing angles monitoring part of the night-sky over Korea. Various field of views are in use for testing purpose and captured video-meteor data is automatically transferred to a central FTP server on a nightly basis. Data is publicly available. The network has been operational since September 2014 and could serve as a prototype system for a more extended national network for meteor/fireball monitoring and detection in Korean airspace. We will report on the network construction, technical setup and present first results of detected meteors and fireballs. Further information: Meteors@KASI: http://meteor.kasi.re.kr.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.62.2-63
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• 2015

During a six-month period (autumn 2014 within the framework of a research & education project) we have constructed a professional double-station video-meteor detection network at the SOAO and BOAO mountain summits. Meteor detection is achieved by pixel-to-pixel motion-detection trigger. Each station is nearly autonomous and has three cameras with fixed viewing angles monitoring part of the night-sky over Korea. Various field of views are in use for testing purpose and captured video-meteor data is automatically transferred to a central FTP server on a nightly basis. Data is publicly available. The network has been operational since September 2014 and could serve as a prototype system for a more extended national network for meteor/fireball monitoring and detection in Korean airspace. We will report on the network construction, technical setup and present first results of detected meteors and fireballs. Further information: Meteors@KASI: http://meteor.kasi.re.kr.

• Journal of Information Processing Systems
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• v.12 no.1
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• pp.100-108
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• 2016

In this paper, we propose a method to achieve improved number plate detection for mobile devices by applying a multiple convolutional neural network (CNN) approach. First, we processed supervised CNN-verified car detection and then we applied the detected car regions to the next supervised CNN-verifier for number plate detection. In the final step, the detected number plate regions were verified through optical character recognition by another CNN-verifier. Since mobile devices are limited in computation power, we are proposing a fast method to recognize number plates. We expect for it to be used in the field of intelligent transportation systems.