• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.513-520
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• 2021

Recently, research on edutech, which combines education and technology in the e-learning field called learning, education and training, has been actively conducted, but it is still insufficient to collect and utilize data tailored to individual learners based on learning activity data that can be automatically collected from digital devices. Therefore, this study attempts to detect questions in textbooks or problem papers using artificial intelligence computer vision technology that plays the same role as human eyes. The textbook or questionnaire item detection model proposed in this study can help collect, store, and analyze offline learning activity data in connection with intelligent education services without digital conversion of textbooks or questionnaires to help learners provide personalized learning services even in offline learning.

• Journal of Korea Multimedia Society
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• v.17 no.4
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• pp.506-513
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• 2014

In recent years, research on user's activity recognition using a smart phone has attracted a lot of attentions. A smart phone has various sensors, such as camera, GPS, accelerometer, audio, etc. In addition, smart phones are carried by many people throughout the day. Therefore, we can collect log data from smart phone sensors. The log data can be used to analyze user activities. This paper proposes an approach to inferring a user's physical activities based on the tri-axis accelerometer of smart phone. We propose recognition method for four activity which is physical activity; sitting, standing, walking, running. We have to convert accelerometer raw data so that we can extract features to categorize activities. This paper introduces a recognition method that is able to high detection accuracy for physical activity modes. Using the method, we developed an application system to recognize the user's physical activity mode in real-time. As a result, we obtained accuracy of over 80%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4661-4680
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• 2016

Cognitive radio (CR) technology is an effective solution to the spectrum scarcity issue. Collaborative spectrum sensing is known as a promising technique to improve the performance of spectrum sensing in cognitive radio networks (CRNs). However, collaborative spectrum sensing is vulnerable to spectrum data falsification (SSDF) attack, where malicious users (MUs) may send false sensing data to mislead other secondary users (SUs) to make an incorrect decision about primary user (PUs) activity, which is one of the key adversaries to the performance of CRNs. In this paper, we propose a coalition based malicious users detection (CMD) algorithm to detect the malicious user in CRNs. The proposed CMD algorithm can efficiently detect MUs base on the Geary'C theory and be modeled as a coalition formation game. Specifically, SSDF attack is one of the key issues to affect the resource allocation process. Focusing on the security issues, in this paper, we analyze the power allocation problem with MUs, and propose MUs detection based power allocation (MPA) algorithm. The MPA algorithm is divided into two steps: the MUs detection step and the optimal power allocation step. Firstly, in the MUs detection step, by the CMD algorithm we can obtain the MUs detection probability and the energy consumption of MUs detection. Secondly, in the optimal power allocation step, we use the Lagrange dual decomposition method to obtain the optimal transmission power of each SU and achieve the maximum utility of the whole CRN. Numerical simulation results show that the proposed CMD and MPA scheme can achieve a considerable performance improvement in MUs detection and power allocation.

• Food Science of Animal Resources
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• v.29 no.1
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• pp.1-14
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• 2009

This study was to review the classification, detection and control of bacteriophage in fermented dairy products. Bacteriophage has lytic and/or lysogenic life cycles. Epidemiologically speaking, detected major phages are c2, 936 and p335. Among them p335 has been the largest concern in dairy industry. Traditionally, various analytical technologies, such as spot, starter activity, indicator test, ATP measurement and conductimetric analysis, have been used for the phage detection. In recent years, advanced methods such as flow cytometric method, petrifilm, enzyme linked immunosorbent assay (ELISA) and multiflex PCR diagnostic kit have been deveoloped. The phage contamination has been controlled by using heat, high-pressure treatment, and the combinations of heat and pressure, and/or chemical. Also some starter cultures with phage-resistant character have been developed to minimize the concentration of phages in dairy product. Bacteriophage inhibition media such as calcium medium was also mentioned. To prevent the contamination of bacteriophage in dairy industry, further researches on the detection and control of phage, and phage resistant starters are necessary in the future.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.

• Journal of IKEEE
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• v.18 no.4
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• pp.456-462
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• 2014

Scintillators were polished in four steps using polishing paper, to reduce the optical loss occurring at their cross section when radiation detectors are fabricated with plastic scintillators. We studied the correlation between the polishing steps and detection efficiency and assessed the detection characteristics that are dependent in the polishing steps. Our results showed that the detection efficiency increased by approximately 7.75 times for a detector that used a scintillator polished in four steps, compared to a detector that used an depolished scintillator. For detectors fabricated using scintillators polished in different steps, better detection characteristics were obtained in terms of the activity, distance, and location of radiation, compared to detectors fabricated with an depolished scintillator.

• Microbiology and Biotechnology Letters
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• v.37 no.4
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• pp.340-349
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• 2009

A CJ9 bacterial strain, which showed antifungal and antibacterial activities, was isolated from meju and identified as Bacillus polyfermenticus based on Gram staining, biochemical properties, as well as its 16S rRNA sequence. B. polyfermenticus CJ9 showed the antimicrobial activity against the various pathogenic molds, yeasts, and bacteria. The antibacterial activity was stable in the pH 5.0~9.0, but the activity was lost at $37^{\circ}C$ for 24 hr. The antifungal activity was stable in the pH range of 3.0~9.0 and reduced at $121^{\circ}C$ for 15 min, but antifungal activity was not completely destroyed. The antibacterial activity was completely inactivated by proteinase K, protease, trypsin, and $\alpha$-chymotrypsin. The antifungal activity was also completely inactivated by protease and $\alpha$-chymotrypsin, and reduced its activity by proteinase which indicated that the antifungal and antibacterial compounds have proteineous nature. The apparent molecular mass of the partially purified antifungal compound, as indicated by using the direct detection method in Tricine-SDS-PAGE, was approximately 1.4 kDa. The molecular mass of the antibacterial compound could not be determined because of its heat-liable characteristic.

• 한국생물공학회:학술대회논문집
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• 2000.04a
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• pp.554-557
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• 2000

Strain JV3 was isolated from commercial Swiss cheese products and identified as a bacteriocin producer, which has bactericidal activity against Leuconostoc mesenteroides KCCM 11324. Strain JW3 was identified tentatively as Lactococcus lactis by the API test. The activity of lacticin JW3, named tentatively as the bacteriocin produced by Lactococcus lactis JW3, was detected during the mid-log growth phase, and reached a maximum during the early stationary phase, and decreased after the late stationary phase. Its antimicrobial activity on sensitive indicator cells was completely disappeared by protease IV. The inhibitory activities of lacticin JW3 were detected during treatments of up to $121\'^{circ}C$ for 15 min. Lacticin JW3 was very stable over a pH range of 2.0 to 9.0 The apparent molecular mass of lacticin JW3 was estimated to be in the region of 3-3.5kDa, which was determined by the direct detection of bactericidal activity after SDS-PAGE.

• Journal of Microbiology and Biotechnology
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• v.5 no.2
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• pp.105-109
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• 1995

Anticancer activity of a fraction of the ethanol extract from the root of Korean angelica (Angelica gigas Nakai) was recognized in human cancer cell lines HeLa $S_3$, K-562, and Hep $G_2$. The extract blocked the phorbol ester-inducing megakaryocytic differentiation of K-562 cells, which indicated the modification of protein kinase C (PKC) activity. In vitro assay showed the activation of PKC by the extract. An effective fraction of the Angelica gigas extract, of which $R_f$ value was 0.64 in a thin layer chromatography, was a different component from those of European angelicas. The $ED_50$ value of the fraction was 8, 9, and $16\;\mu\textrm{m}/ml$ against HeLa $S_3\;Hep\;G_2$, and K-562 cells, respectively, while the fraction showed higher $ED_50$ values against normal cell lines.

• The Journal of the Korean Society for Microbiology
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• v.22 no.1
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• pp.23-33
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• 1987

To detect lepotospiral strains which produce hemolysin and determine the optimal condition for assaying hemolysin, we screened reference strains and observed some property of hemolysin. Hemolysin activity was assayed with cell free culture liquids and erythrocyte suspension. The production of hemolysin by local strains isolated in Korea was assayed and compared with that of reference strains. The hemolysin was produced by 18 strains among 38 reference strains and 3 local strains isolated in Korea. The production of hemolysin began with growth of Leptospira cultured in EMJH medium and reached maximum at stationary phase. The optimum temperature for hemolytic activity was $37^{\circ}C$. At lower temperature the activity of hemolysin was decreased progressively. The hemolytic activity was completely inactivated after :30 minutes' exposure at $56^{\circ}C$. Hemolysis pattern was "hot-cold type" which showed increased hemolysis after cold incubation. The hemolysin was most active on sheep erythrocyte and less active on ox, goat, human and guinea pig erythrocyte with the decreasing order.