• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.50 no.3
• /
• pp.191-196
• /
• 2005

Fusarium head blight (FHB) is a severe disease problem that affects the quality and yield of barley grain. The evaluation of FHB resistance is difficult because environmental conditions greatly influence FHB infection and development. The objectives of this study were to: 1) establish an efficient screening method for selecting resistant barley to FHB, 2) compare FHB severity between the cut-spike method and pot-plant method for development of mass screening, and 3) estimate FHB resistance for barley germplasms. Barley cultivars and lines were evaluated for reaction to FHB in controlled-greenhouse condition. Spikes were spray-inoculated with a suspension $(5.0\times10^5\;macroconidia\;mL^{-1})$ of Fusarium graminearum SCK-O4 strain, and then kept in a greenhouse at $18-25^{\circ}C$ with $80-100\%$ relative humidity. Inoculation were employed at 3 different heading growth stages (heading date, three days after heading, and five days after heading). The inoculation was performed in 2 consecutive days in order to avoid escapes. The inoculated plants were maintained in the greenhouse at 4 different free moisture periods (1, 3, 5, and 7 days). The percentage of FHB severity was scored from 0 to 9 according to the rate of infected kernels per spike, and three spikes were evaluated per replication with 3 replicates. There were significant differences of FHB severity depending on the different free moisture periods, but not by the inoculation at different heading stages. The optimum evaluation point of FHB severity in the greenhouse condition was on the 7th day under free moisture condition after inoculation at the heading date. Infection level in cut-spike method highly correlated with that in pot-plant method. This suggested that cut-spike method is useful in evaluating of FHB resistance in barley. Six cultivars, such as Jinkwang, Buheung, Atahualpha 92, Chevron-b, Gobernadora-d, and MNBrite-c, were selected as resistant varieties to FHB. Correlation coefficient for the FHB severity evaluated by the pot-plant method between two seasons was 0.794, indicating the stability and accuracy of the screening method.

• Journal of Microbiology and Biotechnology
• /
• v.15 no.6
• /
• pp.1323-1329
• /
• 2005

A mathematical model was developed for predicting the growth kinetics of Listeria monocytogenes in tryptic soy broth (TSB) as a function of combined effects of temperature, pH, and NaCl. The TSB containing four different concentrations of NaCl (2, 4, 5, and $10\%$) was initially adjusted to six different pH levels (pH 5, 6, 7, 8, 9, and 10) and incubated at 4, 10, 25, or 37$^{circ}C$. In all experimental variables, the primary growth curves were well fitted ($r^{2}$=0.982 to 0.998) to a Gompertz equation to obtain the lag time (LT) and specific growth rate (SGR). Surface response models were identified as appropriate secondary models for LT and SGR on the basis of coefficient determination ($r^{2}$=0.907 for LT, 0.964 for SGR), mean square error (MSE=3.389 for LT, 0.018 for SGR), bias factor ($B_{1}$B,=0.706 for LT, 0.836 for SGR), and accuracy factor ($A_{f}$=1.567 for LT, 1.213 for SGR). Therefore, the developed secondary model proved reliable predictions of the combined effect of temperature, NaCl, and pH on both LT and SGR for L. monocytogenes in TSB.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.32 no.1
• /
• pp.63-73
• /
• 2014

Recently, researches on DBRN(DataBase Referenced Navigation) system are being carried out to replace GNSS(Global Navigation Satellite System), as weaknesses of GNSS were found that are caused by the intentional interference and the jamming of the satellite signal. This paper describes the gravity gradient modeling and the construction of EKF(Extended Kalman Filter) based GGRN(Gravity Gradient Referenced Navigation). To analyze the performance of GGRN, fourteen flight trajectories were made for simulations over whole South Korea. During the simulations, we considered the errors in both DB(DataBase) and sensor as well as the flight altitudes. Accurate performances were found, when errors in the DB and the sensor are small and they located at lower altitude. For comparative evaluation, the traditional TRN(Terrain Referenced Navigation) was also developed and performances were analyzed relative to those from the GGRN. In fact, most of GGRN performed better in low altitude, but both of precise gravity gradient DB and gradiometer were required to obtain similar level of precisions at the high altitude. In the future, additional tests and evaluations on the GGRN need to be performed to investigate on more factors such as DB resolution, flight speed, and the update rate.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.1
• /
• pp.105-109
• /
• 2010

While numerous information services are provided on the Web as a core infrastructure of information society, semantic services using the Semantic Web technologies still stay deployed number and application range. This situation would be mainly originated from the failure of securing reliability to the user. Thus, this paper introduces an evaluation method for measuring reliabilities of the semantic services comparatively. To measure the reliability of the compared systems, the observer assesses 'precision in task performance' as a quantitative analysis and 'reliability of expectation-result' as a qualitative analysis described by the test persons. On the other hand, the test person should rate the functional reliability and reliability of the served information on the vector graph by himself with a scale from 0 to 5. Experimental results show that assessment by the observer is very similar to rating value by test persons, and that the accuracy of the served information has a close effect on the functional reliability. Through this paper, we can verify the essential factors for evaluating the reliability of semantic service systems. These are functional reliability and reliability of served information resulting from function execution. In particular, it has been proven that the reliability of the semantic information services largely influences the "Quality in Use" and therefore determines the major factors of the semantic service reliability.

• Korean Journal of Cognitive Science
• /
• v.22 no.3
• /
• pp.255-270
• /
• 2011

The purpose of this study was to examine the effects of nonspecific and specific cue on decision making during situation awareness. Participants were 36 male college students who were randomly assigned to one of three groups: (1) nonspecific situation awareness, (2) specific situation awareness, and (3) a control group. Every participant was in the level 3-4.5 according to American National Tennis Level Program. Participants were asked to watch tennis single defence, single offence, double defence rally and when the screen stops, they were required to push the button(left, middle, or right) appropriate for the ball's direction to return as soon as possible. The experiment was designed to be analyzed for group(3)${\times}$condition(3)${\times}$area(7) using three-way ANOVA. The dependent variables were reaction time, accuracy rate, and amplitude and latency of P300. The result showed that the latency of the nonspecific situation awareness group and the specific situation awareness group was shorter and their amplitudes were higher than the control group. Fz, Cz, Pz were prominent among areas, and the single defence condition was more prominent than the single offence and the double defence condition. As a result of the study, it can be suggested that the information about situation awareness provided beforehand directly affects the brain's information processing. In addition, it shows that ERP can be a useful index for studying situation awareness.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.26 no.6
• /
• pp.681-688
• /
• 2020

Approximately 6,000 chemicals are transported through the sea, including hazardous and noxious substances (HNS), which cause marine pollution and are harmful to marine life. The HNS discharged into the sea during the maritime transportation process undergoes physical and chemical changes on the sea surface and in seawater, and some types of HNS sink and are deposited on the seabed. The HNS deposited on the seabed adversely affects the benthic ecosystem, and hence, it is desirable to detect, treat, and recover the HNS on the seabed. Therefore, this study was conducted to analyze the performance requirements that should be considered as the top priority when developing a mechanical system for recovering the HNS deposited on the seabed. Various types of existing dredging devices used for collecting and recovering pollutants from river beds and seabeds were investigated, and 10 performance indices for the mechanical devices were selected. The new performance requirements for the development of the seabed-deposited HNS recovery system were proposed using performance indices. By considering the depth of water in domestic seaports, some of the performance requirements of the mechanical system for recovering deposited HNS from the seabed were obtained as follows: production rate (50-300 ㎥/hr), maximum operation depth (50 m), sediment type (most forms), percentage of solids (10 % or higher), horizontal operating accuracy (±10 cm), limiting currents (3-5 knots). These performance requirements are expected to be useful in the conceptual and basic design of mechanical systems for recovering seabed-deposited HNS.

• Analytical Science and Technology
• /
• v.24 no.2
• /
• pp.142-149
• /
• 2011

This study was conducted to define metabolite of the resveratrol by gas chromatography- time-offlight mass spectrometric detection. From these results, we suppose that the structure of metabolite is the result of reduction of double-bond attached by two-phenyl groups. Also, validity of method for determining metabolite of resveratrol and endogenous steroids was tested. The recoveries ranged from 96.47 to 114.74%, and intraand inter-day precision ranged 11.40 - 10.87% and 1.10 - 10.93%, accuracy ranged 80.03 - 119.92% and 80.02 - 119.56%, respectively. Resveratrol and endogenous steroids had correlation coefficients above or equal to 0.996. The method was successfully validated for the determination of resveratrol and endogenous steroids. Urinary samples from volunteers dosed resveratrol were analyzed to confirm a correlation resveratrol and its metabolite. From these results, the highest level of resveratrol and its metabolite was excreted in 10 - 15 hr more slowly than common drug, and conversion rate of metabolite was higher in woman than that in man. In addition, endogenous steroids were shown same the highest level of 10 - 15 hr. For estrone and estradiol, sensitivity was relatively higher in female than in man. And there were no significant changes of excretion patterns in the other endogenous steroids. Thus, we assumed that activation of resveratrol has impact on woman than man.

• Analytical Science and Technology
• /
• v.26 no.4
• /
• pp.221-227
• /
• 2013

A sensitive and selective liquid chromatography-tandem mass spectrometry (LC-MS/MS) was developed for the investigation of the ginsenoside Rg1 in human plasma. After addition of internal standard (digoxin), plasma was diluted with acetone and methanol (80:20), the supernatant was concentrated and analyzed by LC-MS/MS. The optimal chromatographic separation was achieved on an Agilent Eclipse XDB-C18 column ($4.6{\times}150mm$, $5{\mu}m$) with a mobile phase of 0.1% formic acid in water and 0.1% formic acid in methanol at a flow rate of 0.9 mL/min gradient mode. The standard calibration curve for ginsenoside Rg1 was linear ($r^2=0.9995$) over the concentration range 1~500 ng/mL in human plasma. The intra- and inter-day precision over the concentration range of ginsenoside Rg1 was lower than 7.53% (correlation of variance, CV), and accuracy exceeded 98.28%. This LC-MS/MS assay of ginsenoside Rg1 in human plasma is applicable for quantifying in the pharmacokinetic study.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.1
• /
• pp.75-84
• /
• 2018

Increasing the operation rate of components and stabilizing the operation through timely management of the core parts are crucial for improving the efficiency of the railroad maintenance industry. The demand for diagnosis technology to assess the condition of rolling stock components, which employs history management and automated big data analysis, has increased to satisfy both aspects of increasing reliability and reducing the maintenance cost of the core components to cope with the trend of rapid maintenance. This study developed a big data platform-based system to manage the rolling stock component condition to acquire, process, and analyze the big data generated at onboard and wayside devices of railroad cars in real time. The system can monitor the conditions of the railroad car component and system resources in real time. The study also proposed a machine learning technique that enabled the distributed and parallel processing of the acquired big data and automatic component fault diagnosis. The test, which used the virtual instance generation system of the Amazon Web Service, proved that the algorithm applying the distributed and parallel technology decreased the runtime and confirmed the fault diagnosis model utilizing the random forest machine learning for predicting the condition of the bearing and wheel parts with 83% accuracy.

• Journal of KIISE:Computer Systems and Theory
• /
• v.36 no.4
• /
• pp.274-282
• /
• 2009

Various location tracking sensors; such as GPS, INS, radar, and optical equipment; are used for tracking moving targets. In order to effectively track moving targets, it is necessary to develop an effective fusion method for these heterogeneous devices. There have been studies in which the estimated values of each sensors were regarded as different models and fused together, considering the different error characteristics of the sensors for the improvement of tracking performance using heterogeneous multi-sensor. However, the rate of errors for the estimated values of other sensors has increased, in that there has been a sharp increase in sensor errors and the attempts to change the estimated sensor values for the Sensor Probability could not be applied in real time. In this study, the Sensor Probability is obtained by comparing the RMSE (Root Mean Square Error) for the difference between the updated and measured values of the Kalman filter for each sensor. The process of substituting the new combined values for the Kalman filter input values for each sensor is excluded. There are improvements in both the real-time application of estimated sensor values, and the tracking performance for the areas in which the sensor performance has rapidly decreased. The proposed algorithm adds the error characteristic of each sensor as a conditional probability value, and ensures greater accuracy by performing the track fusion with the sensors with the most reliable performance. The trajectory of a UAV is generated in an experiment and a performance analysis is conducted with other fusion algorithms.