• Biotechnology and Bioprocess Engineering:BBE
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• 제11권4호
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• pp.351-356
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• 2006

This study developed an artificial neural network (ANN) to estimate the growth of microorganisms during a fermentation process. The ANN relies solely on the cumulative consumption of alkali and the buffer capacity, which were measured on-line from the on/off control signal and pH values through automatic pH control. The two input variables were monitored on-line from a series of different batch cultivations and used to train the ANN to estimate biomass. The ANN was refined by optimizing the network structure and by adopting various algorithms for its training. The software estimator successfully generated growth profiles that showed good agreement with the measured biomass of separate batch cultures carried out between at 25 and $35^{\circ}C$.

• 한국수자원학회논문집
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• 제53권11호
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• pp.929-938
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• 2020

본 연구에서는 청미천 유역에서의 플럭스타워에서 산출되는 증발산량의 결측값을 보완하기 위해 인공신경망(Artificial Neural Network, ANN)을 사용하였다. 비교 평가를 위해, Mean Diurnal Variation(MDV), Food and Agriculture Organization Penman-Monteith(FAO-PM) 방법들을 이용하여 증발산량을 산정하였고, ANN 방법을 이용한 결과와 비교하였다. 비교 평가 방법으로 시계열 방법 및 통계 분석(결정계수, IOA, RMSE, MAE)이 사용되었다. 각 gap-filling 모델의 검증을 위해 2015년의 30분 단위 데이터를 이용하였으며, 121개의 결측값 중 MDV, FAO-PM, ANN 방법 순으로 각각 70, 53, 54개의 결측값을 보완하여 모든 데이터가 관측되지 않은 36개의 데이터를 제외하면 각각 82.4%, 62.4%, 63.5%의 성능을 보였다. 결정계수(MDV, FAO-PM, ANN 방법 순으로 각각 0.673, 0.784, 0.841)와 IOA(MDV, FAO-PM, ANN 방법 순으로 각각 0.899, 0.890, 0.951)를 분석한 결과, 3가지 방법 모두 양질의 상관성을 보여 활용성이 충분하다고 판단되며, 이 중 ANN 모델이 가장 높은 적합도와 양질의 성능을 나타내었다. 본 연구를 기반으로 기계학습방법을 이용한 플럭스 타워 자료의 gap-filing 연구에 보다 적절하게 활용될 수 있을 것이다.

• Asian Pacific Journal of Cancer Prevention
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• 제15권14호
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• pp.5883-5888
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• 2014

Background: Breast cancer is the most common cancers in female populations. The exact cause is not known, but is most likely to be a combination of genetic and environmental factors. Log-logistic model (LLM) is applied as a statistical method for predicting survival and it influencing factors. In recent decades, artificial neural network (ANN) models have been increasingly applied to predict survival data. The present research was conducted to compare log-logistic regression and artificial neural network models in prediction of breast cancer (BC) survival. Materials and Methods: A historical cohort study was established with 104 patients suffering from BC from 1997 to 2005. To compare the ANN and LLM in our setting, we used the estimated areas under the receiver-operating characteristic (ROC) curve (AUC) and integrated AUC (iAUC). The data were analyzed using R statistical software. Results: The AUC for the first, second and third years after diagnosis are 0.918, 0.780 and 0.800 in ANN, and 0.834, 0.733 and 0.616 in LLM, respectively. The mean AUC for ANN was statistically higher than that of the LLM (0.845 vs. 0.744). Hence, this study showed a significant difference between the performance in terms of prediction by ANN and LLM. Conclusions: This study demonstrated that the ability of prediction with ANN was higher than with the LLM model. Thus, the use of ANN method for prediction of survival in field of breast cancer is suggested.

• 한국지반공학회논문집
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• 제19권6호
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• pp.343-350
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• 2003

인공신경망(Artificial Neural Network, ANN) 해석기술을 지반공학 분야에서 활용하는 경우가 점점 다양해지고 있다. 이 연구에서는 초음파에 의해 증가된 토양수세법의 효율성을 해석하는 모델개발에 인공신경망기법을 적용하였다. 실내시험을 통하여 인공신경망을 위한 입력자료를 확보한 뒤 이를 이용하여 모델을 학습시킨 후 모델검증을 실시하였다. 해석 변수, 즉 모멘텀항, 학습률, 전이함수 종류, 은닉층 수 및 노드 수 등을 달리하여 연구를 수행하였으며 최적의 조건을 도출한 후 개발된 모델의 검증을 실시하였다. 개발된 모델의 검증결과 측정값과 예측값의 상관관계가 매우 높게 나타났으며 이를 통하여 수학적 모델 수립이 곤란한 토양수세 초음파 기법의 전반적인 고찰의 기초를 확립하였다.

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2004년도 품질경영모델을 통한 가치 창출
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• pp.597-602
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• 2004

This study examined the forecasting of instant messinger's users choice using neural network. We used the statistical methods which were Logistic Regression, MDA(Multiple Discriminant Analysis), and ANN(Artificial Neural Network). In the result, the forecasting performance of the ANN was better than conventional model(Logistic Regression, MDA).

• 한국농공학회논문집
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• 제54권4호
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• pp.73-81
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• 2012

In this study, the settlement data of 32 center cored rockfill dams (total 39 monitored data) were collected and analyzed to develop the method to predict the crest settlement of a CCRD after impounding by using the internal settlement data occurred during construction. An artificial neural network (ANN) modeling was used in developing the method, which was considered to be a more reliable approach since in the ANN model dam height, core width, and core type were all considered as input variables in deriving the crest settlement, whereas in conventional methods, such as Clements's method, only dam height is used as a variable. The ANN analysis results showed a good agreement with the measured data, compared to those by the conventional methods using regression analysis. In addition, a simple procedure to use the ANN model for engineers in practice was provided by proposing the equations used for given input values.

• Macromolecular Research
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• 제10권1호
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• pp.13-17
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• 2002

An artificial neural network (ANN) was used to study the relationship between the glass transition temperature (T$_{g}$) and the structure of homopolymers. The input is very important for the ANN. In this paper, six kinds of input vectors were designed for the ANN. Of the six approaches, the best one gave the is T$_{g}$ of 251 polymers with a standard deviation of 8 K and a maximum error of 29 K. The trained ANN also predicted the T$_{g}$ of 20 polymers which are not included in the 251 polymers with a standard deviation of 7 K and a maximum error of 21 K. 21 K.

• Computers and Concrete
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• 제12권6호
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• pp.785-802
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• 2013

Ready-mixed soil material, known as a kind of controlled low-strength material, is a new way of soil cement combination. It can be used as backfill materials. In this paper, artificial neural network and nonlinear regression approach were applied to predict the compressive strength of ready-mixed soil material containing Portland cement, slag, sand, and soil in mixture. The data used for analyzing were obtained from our testing program. In the experiment, we carried out a mix design with three proportions of sand to soil (e.g., 6:4, 5:5, and 4:6). In addition, blast furnace slag partially replaced cement to improve workability, whereas the water-to-binder ratio was fixed. Testing was conducted on samples to estimate its engineering properties as per ASTM such as flowability, strength, and pulse velocity. Based on testing data, the empirical pulse velocity-strength correlation was established by regression method. Next, three topologies of neural network were developed to predict the strength, namely ANN-I, ANN-II, and ANN-III. The first two models are back-propagation feed-forward networks, and the other one is radial basis neural network. The results show that the compressive strength of ready-mixed soil material can be well-predicted from neural networks. Among all currently proposed neural network models, the ANN-I gives the best prediction because it is closest to the actual strength. Moreover, considering combination of pulse velocity and other factors, viz. curing time, and material contents in mixture, the proposed neural networks offer better evaluation than interpolated from pulse velocity only.

• ETRI Journal
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• 제31권1호
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• pp.92-94
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• 2009

We propose a system for mobile-phone fraud detection based on a bidirectional artificial neural network (bi-ANN). The key advantage of such a system is the ability to detect fraud not only by offline processing of call detail records (CDR), but also in real time. The core of the system is a bi-ANN that predicts the behavior of individual mobile-phone users. We determined that the bi-ANN is capable of predicting complex time series (Call_Duration parameter) that are stored in the CDR.

• Bulletin of the Korean Chemical Society
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• 제28권9호
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• pp.1472-1476
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• 2007

Artificial neural networks (ANNs) are successfully developed for the modeling and prediction of normalized polarity parameter (ETN) of 216 various solvents with diverse chemical structures using a quantitative-structure property relationship. ANN with architecture 5-9-1 is generated using five molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The most positive charge of a hydrogen atom (q+), total charge in molecule (qt), molecular volume of solvent (Vm), dipole moment (μ) and polarizability term (πI) are input descriptors and its output is ETN. It is found that properly selected and trained neural network with 192 solvents could fairly represent the dependence of normalized polarity parameter on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network is applied for prediction of the ETN values of 24 solvents in the prediction set, which are not used in the optimization procedure. Correlation coefficient (R) and root mean square error (RMSE) of 0.903 and 0.0887 for prediction set by MLR model should be compared with the values of 0.985 and 0.0375 by ANN model. These improvements are due to the fact that the ETN of solvents shows non-linear correlations with the molecular descriptors.