• Journal of Biomedical Engineering Research
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• v.16 no.2
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• pp.201-208
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• 1995

The information obtained from the analysis of respiratory muscle elecromyographic (EMG) activities provides a mean for studying muscular activity in relation to the ventilatory process. Thus, in order to comprehend the airflow pattern and its brain control, signal processing is required to characterize respiratory muscle activity. This paper presents a computerized method for the analysis of the electrical activity of the respiratory muscles of premature lambs, and focuses upon the automatic determination of respiratory timing points such as onset and cessation points of the burst activity. Based on experimental results, reliable timing points can be obtained using the proposed methodology.

• Korean Journal of Veterinary Research
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• v.29 no.4
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• pp.425-431
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• 1989

This study was undertaken to investigate the changes of enzyme activities resulted from low concentrated carbon monoxide poisoning on the caudate nucleus in rat. The activities of cytochrome oxidase, succinate dehydrogenase and lactate dehydragenase were observed histochemically, after the experimental animals were poisoned to 100ppm carbon monoxide for 8 hours every day from one day to 16 days. The materials were sliced from coronal section at the level of the optic chiasm and immediately frozen sections of $10{\mu}m$ thickness were cut on the cryostat at $-15^{\circ}C$ and incubated in the medium containing substrate for histochemical detection of cytochrome oxidase, succinate dehydrogenase and lactate dehydrogenase. The sections were mounted in glycerol gelatin and observed under light microscope. It was obtained that cytochrome oxidase activity decreased moderately and succinate dehydrogenase activity showed marked or moderate activity during entire poisoning period and lactate dehydrogenase activity showed marked or moderate activity from one to 8 days but recovered to normal condition at 16th day.

• Journal of Korean Society for Atmospheric Environment
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• v.10 no.E
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• pp.299-302
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• 1994

At a nuclear power plant, environmental radioactivity monitoring is routine work for the radiation safety management For the environmental monitoring of tritium($^3$H) activity in water sampled liquid scintillation counting( LSC) method is applied to measure low- energy beta activity from tritium in the samples. The $^3$H activity is measured using the BECKMAN 5801 system at the KRISS( Korea Research Institute of Standards and Science) for evaluating the lower limits of detection( LLD) of $^3$H measurement and the measuring capability of low-level $^3$H activity at four nuclear Power Plant sites. The LSC systems used for low-level $^3$H activity measurements at the nuclear Power Plants are confirmed to satisfy throughout an intercomparison study under the experimental arrangements by the KRISS.

• Mycobiology
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• v.39 no.2
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• pp.118-120
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• 2011

The ability of Ganoderma to produce extracellular enzymes, including ${\beta}$-glucosidase, cellulase, avicelase, pectinase, xylanase, protease, amylase, and ligninase was tested in chromogenic media. ${\beta}$-glucosidase showed the highest activity, among the eight tested enzymes. In particular, Ganoderma neo-japonicum showed significantly stronger activity for ${\beta}$-glucosidase than that of the other enzymes. Two Ganoderma lucidum isolates showed moderate activity for avicelase; however, Ganoderma neojaponicum showed the strongest activity. Moderate ligninase activity was only observed in Ganoderma neo-japonicum. In contrast, pectinase, amylase, protease, and cellulase were not present in Ganoderma. The results show that the degree of activity of the tested enzymes varied depending on the Ganoderma species tested.

• International Journal of Control, Automation, and Systems
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• v.5 no.1
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• pp.61-69
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• 2007

In this paper, we developed not only a reliable sound localization system including a VAD(Voice Activity Detection) component using three microphones but also a face tracking system using a vision camera. Moreover, we proposed a way to integrate three systems in the human-robot interaction to compensate errors in the localization of a speaker and to reject unnecessary speech or noise signals entering from undesired directions effectively. For the purpose of verifying our system's performances, we installed the proposed audio-visual system in a prototype robot, called IROBAA(Intelligent ROBot for Active Audition), and demonstrated how to integrate the audio-visual system.

• Food Science and Biotechnology
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• v.14 no.6
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• pp.743-746
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• 2005

A dipstick-type immunochemical biosensor for the detection of the organophosphorus insecticide fenthion was developed using a screen-printed electrode system as an amperometric transducer with polyclonal antibodies against fenthion as a bioreceptor. The assay of the biosensor involved competition between the pesticide in the sample and pesticide-glucose oxidase conjugate for binding to the antibody immobilized on the membrane. This was followed by measurement of the activity of the bound enzyme by the supply of the enzyme substrate (glucose) and amperometric determination of the enzyme reaction product ($H_2O_2$). The activity of the bound enzyme was inversely proportional to the concentration of pesticide. The optimized sensor system showed a linear response against the logarithm of the pesticide concentration ranging from $10^{-2}$ to $10^3\;{\mu}g/L$.

• Proceedings of the KSPS conference
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• 2005.11a
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• pp.59-62
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• 2005

Voice activity detection (VAD) is important in many areas of speech processing technology. Speech/nonspeech discrimination in noisy environments is a difficult task because the feature parameters used for the VAD are sensitive to the surrounding environments. Thus the VAD performance is severely degraded at low signal-to-noise ratios (SNRs). In this paper, a new VAD algorithm is proposed based on the degree of voicing and Quantile SNR (QSNR). These two feature parameters are more robust than other features such as energy and spectral entropy in noisy environments. The effectiveness of proposed algorithm is evaluated under the diverse noisy environments in the Aurora2 DB. According to out experiment, the proposed VAD outperforms the ETSI Advanced Frontend VAD.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1459-1466
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• 2018

Robots which detect human and recognize action are important factors for human interaction, and many researches have been conducted. Recently, deep learning technology has developed and learning based robot's technology is a major research area. These studies require a database to learn and evaluate for intelligent human perception. In this paper, we propose a multi-modal sensor-based image database condition considering the security task by analyzing the image database to detect the person in the outdoor environment and to recognize the behavior during the running of the robot.

• Proceedings of the KSPS conference
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• 2002.11a
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• pp.183-186
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• 2002

The speech detection is one of the important problems in real-time speech recognition. The accurate detection of speech boundaries is crucial to the performance of speech recognizer. In this paper, we propose a speech detector based on Mel-band selection through training. In order to show the excellence of the proposed algorithm, we compare it with a conventional one, so called, EPD-VAA (EndPoint Detector based on Voice Activity Detection). The proposed speech detector is trained in order to better extract keyword speech than other speech. EPD-VAA usually works well in high SNR but it doesn't work well any more in low SNR. But the proposed algorithm pre-selects useful bands through keyword training and decides the speech boundary according to the energy level of the sub-bands that is previously selected. The experimental result shows that the proposed algorithm outperforms the EPD-VAA.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.262-264
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• 2023

Internet of Things (IoT) networks currently employ an increased number of users and applications, raising their susceptibility to cyberattacks and data breaches, and endangering our security and privacy. Intrusion detection, which includes monitoring and analyzing incoming and outgoing traffic to detect and prohibit the hostile activity, is critical to ensure cybersecurity. Conventional intrusion detection systems (IDS) are centralized, making them susceptible to cyberattacks and other relevant privacy issues because all the data is gathered and processed inside a single entity. This research aims to create a blockchain-based architecture to support federated learning and improve cybersecurity and intrusion detection in IoT networks. In order to assess the effectiveness of the suggested approach, we have utilized well-known cybersecurity datasets along with centralized and federated machine learning models.