• Asian-Australasian Journal of Animal Sciences
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• v.18 no.8
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• pp.1088-1097
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• 2005

Main objectives of this study were to investigate accuracy, bias and power of linear and threshold model segregation analysis methods for detection of major genes in categorical traits in farm animals. Maximum Likelihood Linear Model (MLLM), Bayesian Linear Model (BALM) and Bayesian Threshold Model (BATM) were applied to simulated data on normal, categorical and binary scales as well as to disease data in pigs. Simulated data on the underlying normally distributed liability (NDL) were used to create categorical and binary data. MLLM method was applied to data on all scales (Normal, categorical and binary) and BATM method was developed and applied only to binary data. The MLLM analyses underestimated parameters for binary as well as categorical traits compared to normal traits; with the bias being very severe for binary traits. The accuracy of major gene and polygene parameter estimates was also very low for binary data compared with those for categorical data; the later gave results similar to normal data. When disease incidence (on binary scale) is close to 50%, segregation analysis has more accuracy and lesser bias, compared to diseases with rare incidences. NDL data were always better than categorical data. Under the MLLM method, the test statistics for categorical and binary data were consistently unusually very high (while the opposite is expected due to loss of information in categorical data), indicating high false discovery rates of major genes if linear models are applied to categorical traits. With Bayesian segregation analysis, 95% highest probability density regions of major gene variances were checked if they included the value of zero (boundary parameter); by nature of this difference between likelihood and Bayesian approaches, the Bayesian methods are likely to be more reliable for categorical data. The BATM segregation analysis of binary data also showed a significant advantage over MLLM in terms of higher accuracy. Based on the results, threshold models are recommended when the trait distributions are discontinuous. Further, segregation analysis could be used in an initial scan of the data for evidence of major genes before embarking on molecular genome mapping.

• Proceedings of the Korean Vacuum Society Conference
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• 2000.02a
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• pp.195-195
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• 2000

TFT-LCD의 제조공정은 박막층의 식각 공정에 대해 기존의 습식 공정을 대치하는 건식식각이 선호되고 있다. 건식 식각 공정은 반도체 공저에 응용되면서 소자의 최소 선폰(CD)이 감소함에 따라 유도결합셩 프라즈마를 비롯한 고밀도 플라즈마 이용한 플라즈마 장비 사용이 증가하는 추세이다. 여기에 평판디스플레이의 공정을 위해서는 대면적과 사각형 기판에 대한 균일도를 보장할 수 있는 고밀도의 균일한 플라즈마 유지가 중요하다. 본 실험에서는 자장강화된 유도결합형 플라즈마의 플라즈마 밀도 및 균일도를 살펴보고 TFT-LCD에 gate 전극으로 사용되는 Al-Nd 박막의 식각을 통하여 식각균일도와 식각속도 및 식각 선택도 등의 건식 식각 특성을 보고자 한다. 영구자석 및 전자석의 설치는 사각형의 유도결합형 플라즈마는 소형 영구자석을 배열하여 부착하였으며, 외부에는 chamber와 같이 사각형태의 전자석을 500mm$\times$500mm의 크기를 갖는 z축 방향의 Helmholtz형으로 제작하였다. 더. 영구자석 배열에 대해서는 자석간의 거리와 세기 변화를 조합하여 magnetic cusping의 변화를 주었으며 전자석의 세기는 전류값을 기준으로 변화시켜 보았다. 실험을 통하여 플라즈마 균일도를 5% 이하로 개선하고 이러한 균일도를 유지하며 플라즈마 밀도를 높일 수 있는 조건을 찾을 수 있었다. 이러한 적합화된 조건에서 저장강화된 유도결합형 프라즈마를 Al-Nd 박막 식각에 응용한 결과, Al-Nd의 식각속도 및 식각 선택도는 유도결합형 프라즈마에 비해 크게 증가하였으며, 식각균일도가 개선되는 것을 관찰하였다. 또한 electrostatic probe(Hiden, Analytical)를 이용하여 Al-Nd 식각에 사용된 반응성 식각가스에 대한 저장강화된 유도결합형 플라즈마의 특성 분석을 수행하였다.c recoil detection, Rutherford backscattering spectroscopy, X-ray diffraction, secondary electron microscopy, atomic force microscoy, $\alpha$-step, Raman scattering spectroscopu, Fourier transform infrared spectroscopy 및 micro hardness tester를 이용하여 기판 bias 전압이 DLC 박막의 특성에 미치는 영향을 조사하였다. 분석결과 본 연구에서 제작된 DLC 박막은 탄소와 수소만으로 구성되어 있으며, 비정질 상태임을 알 수 있었다. 기판 bias 전압의 증가에 따라 박막의 두께가 감소됨을 알 수 있었고, -150V에서는 박막이 거의 만들어지지 않았으며, -200V에서는 기판 표면이 식각되었다. 이것은 기판 bias 전압과 ECR 플라즈마에 의한 이온충돌 효과 때문으로 판단되며, 150V 이하에서는 증착되는 양보다 re-sputtering 되는 양이 더 많을 것으로 생각된다. 기판 bias 전압을 증가시킬수록 플라즈마에 의한 이온충돌 현상이 두드러져 탄소와 결합하고 있던 수소원자들이 떨어져 나가는 탈수소화 (dehydrogenation) 현상을 확인할 수 있었으며, 이것은 C-H 결합에너지가 C-C 결합이나 C=C 결합보다 약하여 수소 원자가 비교적 해리가 잘되므로 이러한 현상이 일어난다고 판단된다. 결합이 끊어진 탄소 원자들은 다른 탄소원자들과 결합하여 3차원적 cross-link를 형성시켜 나가면서 내부 압축응력을 증가시키는 것으로 알려져 있으며, hardness 시험 결과로 이것을 확인할 수 있었다. 그리고 표면거칠기는 기판 bias 전압을 증가시킬수록 더 smooth 해짐을 확인하였다.인하였다.을 알 수 있

• Journal of Astronomy and Space Sciences
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• v.26 no.3
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• pp.305-316
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• 2009

In this study, we suggest an empirical forecast of CIR (Corotating Interaction Regions) and geomagnetic storm based on the information of coronal holes (CH). For this we used CH data obtained from He I $10830{\AA}$ maps at National Solar Observatory-Kitt Peak from January 1996 to November 2003 and the CIR and storm data that Choi et al. (2009) identified. Considering the relationship among coronal holes, CIRs, and geomagnetic storms (Choi et al. 2009), we propose the criteria for geoeffective coronal holes; the center of CH is located between $N40^{\circ}$ and $S40^{\circ}$ and between $E40^{\circ}$ and $W20^{\circ}$, and its area in percentage of solar hemispheric area is larger than the following areas: (1) case 1: 0.36%, (2) case 2: 0.66%, (3) case 3: 0.36% for 1996-2000, and 0.66% for 2001-2003. Then we present contingency tables between prediction and observation for three cases and their dependence on solar cycle phase. From the contingency tables, we determined several statistical parameters for forecast evaluation such as PODy (the probability of detection yes), FAR (the false alarm ratio), Bias (the ratio of "yes" predictions to "yes" observations) and CSI (critical success index). Considering the importance of PODy and CSI, we found that the best criterion is case 3; CH-CIR: PODy=0.77, FAR=0.66, Bias=2.28, CSI=0.30. CH-storm: PODy=0.81, FAR=0.84, Bias=5.00, CSI=0.16. It is also found that the parameters after the solar maximum are much better than those before the solar maximum. Our results show that the forecasting of CIR based on coronal hole information is meaningful but the forecast of goemagnetic storm is challenging.

• Korean Journal of Cognitive Science
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• v.20 no.1
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• pp.51-77
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• 2009

Using a modified Posner's cue-target paradigm, we investigated whether negative cues attract more attention than neutral cues in anxious people. Previous studies used commonly an unbalanced proportion of valid and invalid trials(75% vs. 25% respectively). But in the present study, an equivalent proportion of valid and invalids trials was used for measuring detection speed of cues without participant's expectancy caused by the unbalanced proportion. Emotional words(Experiment 1) and facial expressions(Experiment 2) were used as cues for target locations. The result of Experiment 1 and 2 showed that threatening cues facilitated target detection in valid trials and interfered with it in invalid trials in anxious participants and a, reverse response patterns were found in non-anxious participants. This indicates that threatening cues attract more attention to the cued location in anxious people and in contrast, non-anxious people avoid threatening stimuli. In Experiment 3, we investigated the difference of validity effect across anxiety levels. The results showed that anxious participants gave less attention to cued location when the cues were non-informative whereas non-anxious participants gave more attention to cued locations in the same condition. We discussed two kinds of cognitive bias caused by anxiety levels: attentional bias and proportion related bias.

• Journal of Astronomy and Space Sciences
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• v.25 no.4
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• pp.481-490
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• 2008

Reaction wheel is one of the actuators for spacecraft attitude control, which generates torque by changing an inertial rotor speed inside of the wheel. In order to generate required torque accurately and estimate an accurate angular momentum, wheel speed should be measured as close to the actual speed as possible. In this study, two conventional speed detection methods for high speed motor with digital tacho pulse (Elapsed-time method and Pulse-count method) and their resolutions are analyzed. For satellite attitude maneuvering and control, reaction wheel shall be operated in bi directional and low speed operation is sometimes needed for emergency case. Thus the bias error at low speed with constant acceleration (or deceleration) is also analysed. As a result, the speed detection error of elapsed-time method is largely influenced upon the high-speed clock frequency at high speed and largely effected on the number of tacho pulses used in elapsed time calculation at low speed, respectively.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.40 no.4
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• pp.201-209
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• 2007

Line transect sampling in a sighting survey is one of most widely used methods for assessing animal abundance. This study applied distance data, collected from three sighting surveys using line transects for finless porpoise that were conducted in 2004 and 2005 off the west coast of Korea, to four models (hazard-rate, uniform, half-normal and exponential) that can use a variety of detection functions, g (x). The hazard-rate model, a derived model for the detection function, should have a shoulder condition chosen using the AIC (Akaike Information Criterion), as the most suitable model. However, it did not describe a shoulder shape for the value of g(x) near the track tine and underestimated g (x), just as the exponential model did. The hazard-rate model showed a bias toward overestimating the densities of finless porpoises with a higher coefficient of variation (CV) than the other models did. The uniform model underestimated the densities of finless porpoise but had the lowest CV. The half-normal model described a detection function with a shape similar to that of the uniform model. The half-normal model was robust for finless porpoise data and should be able to avoid density underestimation. The estimated abundance of finless porpoise was 3,602 individuals (95% CI=1,251-10,371) inshore in 2005 and 33,045 individuals (95% CI=24,274-44,985) offshore in 2004.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.9-17
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• 2023

In this paper, we propose a one-class vibration anomaly detection system for bearing defect diagnosis. In order to reduce the economic and time loss caused by bearing failure, an accurate defect diagnosis system is essential, and deep learning-based defect diagnosis systems are widely studied to solve the problem. However, it is difficult to obtain abnormal data in the actual data collection environment for deep learning learning, which causes data bias. Therefore, a one-class classification method using only normal data is used. As a general method, the characteristics of vibration data are extracted by learning the compression and restoration process through AutoEncoder. Anomaly detection is performed by learning a one-class classifier with the extracted features. However, this method cannot efficiently extract the characteristics of the vibration data because it does not consider the frequency characteristics of the vibration data. To solve this problem, we propose an AutoEncoder model that considers the frequency characteristics of vibration data. As for classification performance, accuracy 0.910, precision 1.0, recall 0.820, and f1-score 0.901 were obtained. The network design considering the vibration characteristics confirmed better performance than existing methods.

• Journal of the Korean Society of Safety
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• v.23 no.2
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• pp.45-50
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• 2008

The purpose of the present study was to investigate the age difference in driving behavior(more specifically, left-turn). The participants were instructed to report whether they can turn left their car in the T-shape road(road and other vehicles' behavior relating to driver's tasks were recorded in advance and projected the simulation screen) after the leading vehicle passed them(i.e., before the target vehicle arrived). The participants' judgment accuracy and response bias were analyzed by using signal detection theory. The results showed that the old group tended to be less sensitive but more confident in their judgement of turning left their car. In particular, both age groups appeared to more depend on the distance from observation location to approaching vehicle rather than arrival times or driving speeds of the approaching vehicle.

• Transactions of the Korean Society of Automotive Engineers
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• v.5 no.1
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• pp.27-37
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• 1997

Internal combustion engine is the main source of environmental pollutants and therefore better technology is required to reduce harmful elements from the exhaust gases all over the world. Especially, harmful elements from the exhaust gases are caused by incomplete combustion of mixture inside the engine cylinder and this abnormal combustion like misfire or partial burning is the direct cause of the air pollution and engine performance degradation. the object of this research is to detect abnormal combustion like misfire and to keep the engine performance in the optimal operating state. Development of a new system therefore could be applied to a real car. To realize this, the spark-plug in a conventional ignition system is used as a misfire detection sensor and breakdown voltage is analyzed. In this research, bias voltage(about 3kV) was applied to the electrodes of spark-plug and breakdown voltage signal is obtained. This breakdown voltage signal is analyzed and found that a combustion phenomena in engine cylinder has close relationship with harmonic coefficient K which was introduced in this research. Newly developed combustion diagnostic method( breakdown voltage signal analysis) from this research can be used for the combustion diagnostic and combustion control system in an real car.

• Atmosphere
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• v.28 no.2
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• pp.163-174
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• 2018

In order to assimilate MHS satellite data into the convective scale model at KMA, ATOVS data are reprocessed to utilize the original high-resolution data. And then to improve the preprocessing experiments for cloud detection were performed and optimized to convective-scale model. The experiment which is land scattering index technique added to Observational Processing System to remove contaminated data showed the best result. The analysis fields with assimilation of MHS are verified against with ECMWF analysis fields and fit to other observations including Sonde, which shows improved results on relative humidity fields at sensitive level (850-300 hPa). As the relative humidity of upper troposphere increases, the bias and RMSE of geopotential height are decreased. This improved initial field has a very positive effect on the forecast performance of the model. According to improvement of model field, the Equitable Threat Score (ETS) of precipitation prediction of $1{\sim}20mm\;hr^{-1}$ was increased and this impact was maintained for 27 hours during experiment periods.