• Journal of Animal Science and Technology
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• v.64 no.5
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• pp.813-829
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• 2022

Internal and external defects of eggs should be detected to prevent cross-contamination of intact eggs by abnormal eggs during storage. Emerging detection technologies for abnormal eggs were introduced as an alternative to human inspection. The advanced technologies could rapidly detect abnormal eggs. Abnormal egg detection technologies using acoustic response, machine vision, and spectroscopy have been commercialized in the poultry industry. Non-destructive egg quality assessment methods meanwhile could preserve the value of eggs and improve detection efficiency. In order to improve detection efficiency, it is essential to select a proper algorithm for classifying the types of abnormal eggs. This review deals with the performance of the detection technologies for various types of abnormal eggs in recently published resources. In addition, the discriminant methods and detection algorithms of abnormal eggs reported in the published literature were investigated. Although the majority of the studies were conducted on a laboratory scale, the developed detection technologies for internal and external defects in eggs were technically feasible to obtain the excellent detection accuracy. To apply the developed detection technologies to the poultry industry, it is necessary to achieve the detection rates required from the industry.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.481-484
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• 2011

Recent Automotive technology is driven from mechanical device to the electronic components which improve the vehicle's safety and convenience. The future competitiveness of the car will come from safety issues and energy efficiency, convenience and the application of the technologies. In this study, various techniques for driver drowsiness detection are introduced and compared with each others. The advantages and disadvantages of commercially available technologies and developed technologies are compared. To enhance the detection resolution, multiple sensing technologies are introduced in this paper. The feasibility of two drowsiness detection methods, that is, existing camera image recognition method and bio signal analysis method, are tested. The direct drowsiness detection by the camera image of eyes and driver's vital signs detected indirectly are combined and analyzed by the developed noble algorithm for stress, fatigue, drowsiness detection with a more accurate high-drowsiness detection.

• Structural Monitoring and Maintenance
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• v.7 no.1
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• pp.13-25
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• 2020

One of the most important aspects in structural health monitoring is the detection of fatigue damage. Structural components such as heavy-duty bolts work under high dynamic loads, and thus are prone to accumulate fatigue damage and cracks may originate. Those heavy-duty bolts are used, for example, in wind power generation and mining equipment. Therefore, the investigation of new and more effective monitoring technologies attracts a great interest. In this study the acoustic emission (AE) technology was employed to detect incipient damage during fatigue testing of a M36 bolt. Initial results showed that the AE signals have a high level of background noise due to how the load is applied by the fatigue testing machine. Thus, an advanced signal processing method in the time-frequency domain, the Hilbert-Huang Spectrum (HHS), was applied to reveal AE components buried in background noise in form of high-frequency peaks that can be associated with damage progression. Accordingly, the main contribution of the present study is providing insights regarding the detection of incipient damage during fatigue testing using AE signals and providing recommendations for further research.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.225-226
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• 2022

The construction industry, which has a labour-intensive and conservative nature, is exclusive to adopt new technologies. However, the construction industry is viably introducing the 4th Industrial Revolution technologies represented by artificial intelligence, Internet of Things, robotics and unmanned transportation to promote change into a smart industry. An image-based artificial intelligence technology is a field of computer vision technology that refers to machines mimicking human visual recognition of objects from pictures or videos. The purpose of this article is to explore image-based artificial intelligence technologies which would be able to apply to the construction sites. In this study, we show two examples which is one for a construction waste classification model and another for cast in-situ anchor bolts defection detection model. Image-based intelligence technologies would be used for various measurement, classification, and detection works that occur in the construction projects.

• Biomedical Science Letters
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• v.21 no.4
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• pp.165-171
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• 2015

Microbial pathogens are responsible for most of the rapidly-spreading deadly infectious diseases against humans. Thus, there is an urgent need for efficient and rapid detection methods for infectious microorganisms. The detection methods should not only be targeted and specific, but they have to be encompassing of potential changes of the pathogen as it evolves and mutates quickly during an epidemic or pandemic. The existing diagnostics such as the antibody-based ELISA immunoassay and PCR methods are too selective and narrowly focused; they are insufficient to capture newly evolved mutant strains of the pathogen. Here, we introduce a fresh perspective on some new technologies, including aptamers and next generation sequencing for pathogen detection. These technologies are not in their infancy; they are reasonably mature and ready, and they hold great promise for unparalleled applications in pathogen detection.

• Journal of the Korean Society for Nondestructive Testing
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• v.26 no.6
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• pp.380-389
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• 2006

A new laser-based ultrasonic receiver that is based on multi-channel interferometry is shown to be well suited for robust and sensitive detection of ultrasound in industrial environment. The proposed architecture combines random-quadrature detection with detector arrays and parallel multi-speckle processing. The high sensitivity is reached, thanks to the random phase distribution of laser speckle caused by surface roughness. High-density parallel signal processing is achieved by using a simple demodulation technique based on signal rectification. This simple detection scheme is also demonstrated for rejection of the laser intensity noise, making possible the use of lower cost laser without reduction in performances. Results demonstrating this new principle of operation and its performances are presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.494-510
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• 2024

Internet users are exposed to sophisticated cyberattacks that intrusion detection systems have difficulty detecting. Therefore, research is increasing on intrusion detection methods that use artificial intelligence technology for detecting novel cyberattacks. Unsupervised learning-based methods are being researched that learn only from normal data and detect abnormal behaviors by finding patterns. This study developed an anomaly-detection method based on unsupervised machines and deep learning for a network intrusion detection system (NIDS). We present a hybrid anomaly detection approach based on unsupervised learning techniques using the autoencoder (AE), Isolation Forest (IF), and Local Outlier Factor (LOF) algorithms. An oversampling approach that increased the detection rate was also examined. A hybrid approach that combined deep learning algorithms and traditional machine learning algorithms was highly effective in setting the thresholds for anomalies without subjective human judgment. It achieved precision and recall rates respectively of 88.2% and 92.8% when combining two AEs, IF, and LOF while using an oversampling approach to learn more unknown normal data improved the detection accuracy. This approach achieved precision and recall rates respectively of 88.2% and 94.6%, further improving the detection accuracy compared with the hybrid method. Therefore, in NIDS the proposed approach provides high reliability for detecting cyberattacks.

• 한국정보디스플레이학회:학술대회논문집
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• 2006.08a
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• pp.1447-1450
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• 2006

The high speed and sensitivity array test system has been developed and utilized for massproduction of advanced LTPS displays including SOG and OLED. It realizes fast enough TACT enabling 100% inspection with better than 1fF sensitivity. The result of actual measurement shows its superior TACT and sensitivity, and also shows MURA detection of OLED panel.

• Journal of Microbiology and Biotechnology
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• v.33 no.5
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• pp.559-573
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• 2023

Shiga toxin (Stxs)-producing enterohaemorrhagic Escherichia coli (EHEC) and Shigella dysenteriae serotype 1 are major causative agents of severe bloody diarrhea (known as hemorrhagic colitis) and hemolytic uremic syndrome (HUS) associated with extraintestinal complications such as acute renal failure and neurologic impairment in infected patients under 9 years of age. Extreme nephrotoxicity of Stxs in HUS patients is associated with severe outcomes, highlighting the need to develop technologies to detect low levels of the toxin in environmental or food samples. Currently, the conventional polymerase chain reaction (PCR) or immunoassay is the most broadly used assay to detect the toxin. However, these assays are laborious, time-consuming, and costly. More recently, numerous studies have described novel, highly sensitive, and portable methods for detecting Stxs from EHEC. To contextualize newly emerging Stxs detection methods, we briefly explain the basic principles of these methods, including lateral flow assays, optical detection, and electrical detection. We subsequently describe existing and newly emerging rapid detection technologies to identify and measure Stxs.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2006.02a
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• pp.78-78
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• 2006

Gene expression analysis can be performed by one-color (intensity-based) or two-color (ratio-based) microarray platforms depending on the specific applications and needs of the researcher. The traditional two-color approach is well founded from a historical and scientific standpoint, and the one-color approach, when paired with high quality microarrays and a robust workflow, offers additional flexibility in experimental design. Two of the major requirements of any microarray platform are system reproducibility, which provides the means for high confidence experiments and accurate comparison across multiple samples; and high sensitivity, for the detection of significant gene expression changes, including small fold changes across multiple gene sets. Each of these requirements is fulfilled by the Agilent One-color Gene Expression Platform as illustrated by the data included in this study. As a result, researchers have the ability to take advantage of the enhanced performance and sensitivity of Agilent's 60-mer oligonucleotide microarrays, and experience the first commercial microarray platform compatible with both one- and two-color detection.