• 한국군사과학기술학회지
• /
• 제10권3호
• /
• pp.208-216
• /
• 2007

As the propellant mass is being accelerated out of the gun chamber along with the projectile, a continuous pressure gradient exists between the end of chamber and the base of the projectile. For this reason, the base pressure-travel curve is very important to design a conventional gun barrel in the interior ballistics, but it is not obtained briefly by empirical or theoretical method. In this paper, a simple relation between chamber pressure and base pressure was determined by the factor of base pressure(Cb) obtained from the experimental method. The simple relation gives a reasonable prediction for the reduction of pressure between the breech and the base of projectile owing to the axial gradient in the gun tube. The predictions have been validated by the infrared screen sensor and the PRODAS(PROjectile Design and Analysis System) for interior ballistic systems. Therefore, the base pressure-travel curve could be calculated from the chamber pressure measured by piezoelectric sensor. The base pressure-travel curve obtained from the simple relation offers initial information to gun barrel designer and is used for calculation of muzzle velocity.

• Journal of Astronomy and Space Sciences
• /
• 제32권3호
• /
• pp.189-200
• /
• 2015

In this study, we present the results of orbit determination (OD) using satellite laser ranging (SLR) data for the Science and Technology Satellite (STSAT)-2C by a short-arc analysis. For SLR data processing, the NASA/GSFC GEODYN II software with one year (2013/04 - 2014/04) of normal point observations is used. As there is only an extremely small quantity of SLR observations of STSAT-2C and they are sparsely distribution, the selection of the arc length and the estimation intervals for the atmospheric drag coefficients and the empirical acceleration parameters was made on an arc-to-arc basis. For orbit quality assessment, the post-fit residuals of each short-arc and orbit overlaps of arcs are investigated. The OD results show that the weighted root mean square post-fit residuals of short-arcs are less than 1 cm, and the average 1-day orbit overlaps are superior to 50/600/900 m for the radial/cross-track/along-track components. These results demonstrate that OD for STSAT-2C was successfully achieved with cm-level range precision. However its orbit quality did not reach the same level due to the availability of few and sparse measurement conditions. From a mission analysis viewpoint, obtaining the results of OD for STSAT-2C is significant for generating enhanced orbit predictions for more frequent tracking.

• Structural Engineering and Mechanics
• /
• 제46권1호
• /
• pp.39-51
• /
• 2013

An extremum method is presented to predict the wrinkling characteristics of the inflated cone in bending. The wrinkling factor is firstly defined so as to obtain the wrinkling condition. The initial wrinkling location is then determined by searching the maximum of the wrinkling factor. The critical wrinkling load is finally obtained by determining the ratio of the wrinkling moment versus the initial wrinkling location. The extremum method is proposed based on the assumption of membrane material of beam wall, and it is extended to consider beam wall with thin-shell material in the end. The nondimensional analyses show that the initial wrinkling location is closely related to the taper ratio. When the taper ratio is higher than the critical value, the initial wrinkles will be initiated at a different location. The nondimensional critical wrinkling load nonlinearly increases as the taper ratio increases firstly, and then linearly increases after the critical taper ratio. The critical taper ratio reflects the highest load-carrying efficiency of the inflated cone in bending, and it can be regarded as a measure to optimize the geometry of the inflated cone. The comparative analysis shows fairly good agreement between analytical and numerical results. Over the whole range of the comparison, the mean differences are lower than 3%. This gives confidence to use extremum method for bending-wrinkling analysis of inflated conical cantilever beam.

• 천문학회지
• /
• 제52권6호
• /
• pp.217-225
• /
• 2019

We apply a modified Convolutional Neural Network (CNN) model in conjunction with transfer learning to predict whether an active region (AR) would produce a ≥C-class or ≥M-class flare within the next 24 hours. We collect line-of-sight magnetogram samples of ARs provided by the SHARP from May 2010 to September 2018, which is a new data product from the HMI onboard the SDO. Based on these AR samples, we adopt the approach of shuffle-and-split cross-validation (CV) to build a database that includes 10 separate data sets. Each of the 10 data sets is segregated by NOAA AR number into a training and a testing data set. After training, validating, and testing our model, we compare the results with previous studies using predictive performance metrics, with a focus on the true skill statistic (TSS). The main results from this study are summarized as follows. First, to the best of our knowledge, this is the first time that the CNN model with transfer learning is used in solar physics to make binary class predictions for both ≥C-class and ≥M-class flares, without manually engineered features extracted from the observational data. Second, our model achieves relatively high scores of TSS = 0.640±0.075 and TSS = 0.526±0.052 for ≥M-class prediction and ≥C-class prediction, respectively, which is comparable to that of previous models. Third, our model also obtains quite good scores in five other metrics for both ≥C-class and ≥M-class flare prediction. Our results demonstrate that our modified CNN model with transfer learning is an effective method for flare forecasting with reasonable prediction performance.

• Interdisciplinary Bio Central
• /
• 제5권2호
• /
• pp.3.1-3.7
• /
• 2013

Recently, the productivity of drug discovery has gradually decreased as the limitations of single-target-based drugs for various and complex diseases become exposed. To overcome these limitations, drug combinations have been proposed, and great efforts have been made to predict efficacious drug combinations by statistical methods using drug databases. However, previous methods which did not take into account biological networks are insufficient for elaborate predictions. Also, increased evidences to support the fact that drug effects are closely related to metabolic enzymes suggested the possibility for a new approach to the study drug combinations. Therefore, in this paper we suggest a novel approach for analyzing drug combinations using a metabolic network in a systematic manner. The influence of a drug on the metabolic network is described using the distance between the drug target and an enzyme. Target-enzyme distances are converted into influence scores, and from these scores we calculated the correlations between drugs. The result shows that the influence score derived from the targetenzyme distance reflects the mechanism of drug action onto the metabolic network properly. In an analysis of the correlation score distribution, efficacious drug combinations tended to have low correlation scores, and this tendency corresponded to the known properties of the drug combinations. These facts suggest that our approach is useful for prediction drug combinations with an advanced understanding of drug mechanisms.

• Molecular & Cellular Toxicology
• /
• 제1권3호
• /
• pp.157-163
• /
• 2005

Because of their widespread occurrence and substantial biological activity, halogenated aromatic hydrocarbons are one of the important classes of contaminants in the environment. We have performed comparative molecular field analysis (CoMFA) on structurally diverse ligands of Ah (dioxin) receptor to explore the physico-chemical requirements for binding. All CoMFA models have given $q^{2}$ value of more than 0.5 and $r^{2}$ value of more than 0.83. The predictive ability of the models was validated by an external test set, which gave satisfactory predictive $r^{2}$ values. Best predictions were obtained with CoMFA model of combined modified training set ($q^{2}=0.631,\;r^{2}=0.900$), giving predictive residual value = 0.002 log unit for the test compound. We have suggested a model comprises of four structurally different compounds, which offers a good predictability for various ligands. Our QSAR model is consistent with all previously established QSAR models with less structurally diverse ligands. The implications of the CoMFA/QSAR model presented herein are explored with respect to quantitative hazard identification of potential toxicants.

• 한국군사과학기술학회지
• /
• 제26권5호
• /
• pp.400-408
• /
• 2023

The tracking accuracy of buoy-based LBL(Long Base Line) systems can be significantly influenced by sea environmental conditions. Particularly, the position of buoys that may have drifted due to sea currents. Therefore it is necessary to predict and optimize the drifted-buoy positions in the deploying step. This research introduces a free-drift simulation model using ocean data from the European CMEMS. The simulation model's predictions are validated by comparing them to actual sea buoy drift tracks, showing a substantial match in averaged drift speed and direction. Using this drift model, we optimize the initial buoy layout and compare the tracking performance between the center hexagonal layout and close track layout. Our results verify that the optimized layout achieves lower tracking errors compared to the other two layout.

• 한국응용과학기술학회지
• /
• 제30권4호
• /
• pp.586-591
• /
• 2013

본 연구에서는 가속된 이온이 전기장이 걸려있는 freestanding 단결정 실리콘 나노 박막에 충돌했을 때 발생하는 열-전계 전자 방출 특성을 여러 전계 및 열적 조건 아래 체계적으로 분석하였다. 이온 충돌에 의한 열-전계 전자 방출은 쇼트키 효과 (schottky effect)의 선형영역의 특성에 의해 예측된 바와 같이 전계의 세기가 증가할수록 선형적으로 증가했으며, 이온 충돌에 의해 발생하는 열에너지의 제곱에 비례하는 특성을 보여주었다. 이러한 특성들은 실리콘 나노 박막의 질량 분석기용 이온 검출기로의 사용 가능성을 보여준다.

• 한국농공학회논문집
• /
• 제59권5호
• /
• pp.93-99
• /
• 2017

A model was developed using Artificial Neural Networks (ANNs) based on Principal Component Analysis (PCA), to accurately predict the air humidity inside an experimental greenhouse located in Daegu (latitude $35.53^{\circ}N$, longitude $128.36^{\circ}E$, and altitude 48 m), South Korea. The weather parameters, air temperature, relative humidity, solar radiation, and carbon dioxide inside and outside the greenhouse were monitored and measured by mounted sensors. Through the PCA of the data samples, three main components were used as the input data, and the measured inside humidity was used as the output data for the ALYUDA forecaster software of the ANN model. The Nash-Sutcliff Model Efficiency Coefficient (NSE) was used to analyze the difference between the experimental and the simulated results, in order to determine the predictive power of the ANN software. The results obtained revealed the variables that affect the inside air humidity through a sensitivity analysis graph. The measured humidity agreed well with the predicted humidity, which signifies that the model has a very high accuracy and can be used for predictions based on the computed $R^2$ and NSE values for the training and validation samples.

• 한국산업정보학회논문지
• /
• 제22권4호
• /
• pp.19-28
• /
• 2017

기업이나 정부에서는 연구나 기술 동향을 파악하고 예측하기 위해 주로 델파이 기법이 활용하여 왔다. 이 기법은 많은 시간과 비용이 소요되는 단점이 있기에 본 논문에서는 LDA 토픽모델링 기법을 활용하여 과학기술의 동향 및 예측에 관한 연구를 실시하였다. 이를 위해 미국 특허 문서중 AI(Artificial Intelligence) 초록을 대상으로 LDA 토픽모델링 기법을 활용하여 20개의 AI 세부기술을 추출하였다. 도출된 세부기술에 대해 핵심기술을 파악하고, 연도별 비중 추이 분석을 통하여 Hot기술과 Cold기술을 분류하였다. 텍스트 탐색, 컴퓨터 관리, 프로그래밍 구문, 네트워크 관리, 멀티미디어, 무선 네트워크 기술 등이 Hot 기술로 도출되었다. 이런 기술들은 최근 AI 분야에서 활발하게 연구되는 핵심 기술들이다. 본 논문에서 제시한 방법론은 사회문제나 지역혁신, 경영 등 다양한 분야에서의 동향분석이나 정책 도출 또는 기술 수요 예측에 활용되어 질 수 있을 것이다.