• 대한수학회논문집
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• 제25권4호
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• pp.523-535
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• 2010

There are different categorifications of the notion of a ring such as Ann-category due to N. T. Quang, ring category due to M. M. Kapranov and V. A. Voevodsky. The main result of this paper is to prove that every axiom in the definition of a ring category, but the axiom $x_0 = y_0$, can be deduced from the axiomatics of an Ann-category.

• 대한수학회논문집
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• 제27권1호
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• pp.23-36
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• 2012

Dual monoidal category $\mathcal{C}^*$ of a monoidal functor F : $\mathcal{C}\;{\rightarrow}\;\mathcal{V}$ has been constructed by S. Majid. In this paper, we extend the construction of dual structures for an Ann-functor F : $\mathcal{B}\;{\rightarrow}\;\mathcal{A}$. In particular, when F = $id_{\mathcal{A}}$, then the dual category $\mathcal{A}^*$ is indeed the center of $\mathcal{A}$ an this is a braided Ann-category.

• Computers and Concrete
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• 제26권6호
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• pp.533-546
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• 2020

The prediction of spall response of reinforced concrete members like columns and slabs have been attempted by earlier researchers with analytical solutions, as well as with empirical models developed from data generated from physical or numerical experiments, with different degrees of success. In this article, compared to the empirical models, more versatile and accurate models are developed based on model-free approach of artificial neural network (ANN). Synthetic data extracted from the results of numerical experiments from literature have been utilized for the purpose of training and testing of the ANN models. For two concrete members, namely, slabs and columns, different sets of ANN models were developed, each of which proved to have definite advantages over the corresponding empirical model reported in literature. In case of slabs, for all three categories of spall, the ANN model results were superior to the empirical models as evaluated by the various performance metrics, such as correlation, root mean square error, mean absolute error, maximum overestimation and maximum underestimation. The ANN models for each category of column spall could handle three variables together: namely, depth, spacing of longitudinal and transverse reinforcement, as contrasted to the empirical models that handled one variable at a time, and at the same time yielded comparable performance. The application of the ANN models for spall prediction of concrete slabs and columns developed in this study has been discussed along with their limitations.

• Computers and Concrete
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• 제13권6호
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• pp.765-780
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• 2014

Many building codes use the empirical equation to determine fundamental period of vibration where in effect of length, width and the stiffness of the building is not explicitly accounted for. In the present study, ANN models are developed in three categories, varying the number of input parameters in each category. Input parameters are chosen to represent mass, stiffness and geometry of the buildings indirectly. Total numbers of 206 buildings are analyzed out of which, data set of 142 buildings is used to develop these models. It is demonstrated through developed ANN models that geometry of the building and the sizes of the columns are significant parameters in the dynamic analysis of building frames. The testing dataset of these three models is used to obtain the empirical relationship between the height of the building and fundamental period of vibration and compared with the similar equations proposed by other researchers. Experiments are conducted on Mild Steel frames using uniaxial shake table. It is seen that the values obtained through the ANN models are close to the experimental values. The validity of ANN technique is verified by experimental values.

• 대한공간정보학회지
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• 제13권3호
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• pp.33-40
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• 2005

위성영상에서 실제 지표면의 형태와 지상물체를 구분하여 분류하는 것은 원격탐사의 중요한 목적중의 하나이다. 다중분광영상을 이용한 분류는 일반적인 토지피복도의 제작에 이용되어지고 있으며 영상분류의 방법에는 많은 이론들이 사용되어지고 있다. 본 연구는 대구 달성군 지역의 IKONOS 영상을 MLC(Maximum Likelihood Classification), ANN(Artificial neural network), SVM(Support Vector Machine), Naive Bayes 분류기법들을 이용하여 각각의 분류정확도를 비교 분석하였다. 또한 PCA/ICA 전처리 과정을 거친 분류기법들 결과와, Boosting 알고리즘 과정을 거친 후의 결과를 비교하였다. 본 연구의 목적은 적절한 전처리과정과 분류기법을 수행함으로써 가장 효율적인 지형분류 방법을 획득하는데 그 목적이 있다.

• Nuclear Engineering and Technology
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• 제54권8호
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• pp.3043-3050
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• 2022

In Korea, all nuclear power plants (NPPs) participate in annual performance tests including in vivo measurements using the FastScan, a stand type whole body counter (WBC), manufactured by Canberra. In 2018, all Korean NPPs satisfied the testing criterion, the root mean square error (RMSE) ≤ 0.25, for the whole body configuration, but three NPPs which participated in an additional lung configuration test in the fission and activation product category did not meet the criterion. Due to the low resolution of the FastScan NaI(Tl) detectors, the conventional peak analysis (PA) method of the FastScan did not show sufficient performance to meet the criterion in the presence of interfering radioisotopes (RIs), ¹³⁴Cs and ¹³⁷Cs. In this study, we developed an artificial neural network (ANN) to improve the performance of the FastScan in the lung configuration. All of the RMSE values derived by the ANN satisfied the criterion, even though the photopeaks of ¹³⁴Cs and ¹³⁷Cs interfered with those of the analytes or the analyte photopeaks were located in a low-energy region below 300 keV. Since the ANN performed better than the PA method, it would be expected to be a promising approach to improve the accuracy and precision of in vivo FastScan measurement for the lung configuration.

• Asian-Australasian Journal of Animal Sciences
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• 제31권11호
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• pp.1700-1713
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• 2018

Objective: The aim of this study was to verify the usefulness of artificial neural networks (ANN), multivariate adaptive regression splines (MARS), naïve Bayes classifier (NBC), general discriminant analysis (GDA), and logistic regression (LR) for dystocia detection in Polish Holstein-Friesian Black-and-White heifers and cows and to indicate the most influential predictors of calving difficulty. Methods: A total of 1,342 and 1,699 calving records including six categorical and four continuous predictors were used. Calving category (difficult vs easy or difficult, moderate and easy) was the dependent variable. Results: The maximum sensitivity, specificity and accuracy achieved for heifers on the independent test set were 0.855 (for ANN), 0.969 (for NBC), and 0.813 (for GDA), respectively, whereas the values for cows were 0.600 (for ANN), 1.000 and 0.965 (for NBC, GDA, and LR), respectively. With the three categories of calving difficulty, the maximum overall accuracy for heifers and cows was 0.589 (for MARS) and 0.649 (for ANN), respectively. The most influential predictors for heifers were an average calving difficulty score for the dam's sire, calving age and the mean yield of the farm, where the heifer was kept, whereas for cows, these additionally included: calf sex, the difficulty of the preceding calving, and the mean daily milk yield for the preceding lactation. Conclusion: The potential application of the investigated models in dairy cattle farming requires, however, their further improvement in order to reduce the rate of dystocia misdiagnosis and to increase detection reliability.

• Wind and Structures
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• 제32권2호
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• pp.127-142
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• 2021

Wind load acting on a standalone structure is different from that acting on a similar structure which is surrounded by other structures in close proximity. The presence of other structures in the surrounding can change the wind flow regime around the principal structure and thus causing variation in wind loads compared to a standalone case. This variation on wind loads termed as interference effect depends on several factors like terrain category, geometry of the structure, orientation, wind incident angle, interfering distances etc., In the present study, a three building configuration is considered and the mean pressure coefficients on each face of principle building are determined in presence of two interfering buildings. Generally, wind loads on interfering buildings are determined from wind tunnel experiments. Computational fluid dynamic studies are being increasingly used to determine the wind loads recently. Whereas, wind tunnel tests are very expensive, the CFD simulation requires high computational cost and time. In this scenario, Artificial Neural Network (ANN) technique and Support Vector Regression (SVR) can be explored as alternative tools to study wind loads on structures. The present study uses these data-driven approaches to predict mean pressure coefficients on each face of principle building. Three typical arrangements of three building configuration viz. L shape, V shape and mirror of L shape arrangement are considered with varying interfering distances and wind incidence angles. Mean pressure coefficients (Cp mean) are predicted for 45 degrees wind incidence angle through ANN and SVR. Further, the critical faces of principal building, critical interfering distances and building arrangement which are more prone to wind loads are identified through this study. Among three types of building arrangements considered, a maximum of 3.9 times reduction in Cp mean values are noticed under Case B (V shape) building arrangement with 2.5B interfering distance. Effect of interfering distance and building arrangement on suction pressure on building faces has also been studied. Accordingly, Case C (mirror of L shape) building arrangement at a wind angle of 45º shows less suction pressure. Through this study, it was also observed that the increase of interfering distance may increase the suction pressure for all the cases of building configurations considered.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.183-189
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• 2023

A stroke is a medical disease where a blood vessel in the brain ruptures, causes damage to the brain. If the flow of blood and different nutrients to the brain is intermittent, symptoms may occur. Stroke is other reason for loss of life and widespread disorder. The prevalence of stroke is high in growing countries, with ischemic stroke being the high usual category. Many of the forewarning signs of stroke can be recognized the seriousness of a stroke can be reduced. Most of the earlier stroke detections and prediction models uses image examination tools like CT (Computed Tomography) scan or MRI (Magnetic Resonance Imaging) which are costly and difficult to use for actual-time recognition. Machine learning (ML) is a part of artificial intelligence (AI) that makes software applications to gain the exact accuracy to predict the end results not having to be directly involved to get the work done. In recent times ML algorithms have gained lot of attention due to their accurate results in medical fields. Hence in this work, Stroke disease identification system by using Machine Learning algorithm is presented. The ML algorithm used in this work is Artificial Neural Network (ANN). The result analysis of presented ML algorithm is compared with different ML algorithms. The performance of the presented approach is compared to find the better algorithm for stroke identification.

• 자원환경지질
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• 제46권4호
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• pp.301-312
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• 2013

산사태는 지형, 지질, 임상, 토양 등과 같은 다양한 요인들이 복합적으로 작용하여 발생한다. 따라서 산사태 발생위치와 산사태 유발 요인 사이의 상관관계를 파악할 수 있는 다양한 분석 기법이 사용되고 있으며 본 연구에서는 산사태 위험지역을 정량적으로 예측할 수 있는 효과적인 기법을 제안하고자 퍼지관계 기법과 인공신경망 기법을 이용하여 포항지역의 산사태 취약성을 분석하였다. 취약성 분석을 위해 먼저 산사태 위치를 파악하여 현황도를 작성하였으며, 산사태 발생과 관련 있는 11개의 요인들에 대한 공간 데이터베이스를 구축하였다. 퍼지관계 기법에서는 cosine amplitude method를 이용해 각 요인 별 퍼지 소속 함수 값을 획득하고 퍼지관계 함수 연산을 이용하여 취약성도를 작성하였다. 인공신경망 기법에서는 오류 역전파 알고리즘을 이용하여 산사태와 관련 요인들 간의 상대적 가중치를 결정하고 취약성도를 작성하였다. 두 기법으로 도출된 산사태 취약성도의 ROC(Receiver Operating Characteristic)와 AUC(Area Under the Curve)를 통한 검증 결과는 82.18%와 87.4%로 나타났다. 퍼지 관계 및 인공신경망 기법 모두 높은 예측 정확도를 보여 취약성 분석 기법으로서의 적용 가능성이 있는 것으로 분석되었다. 한편 본 연구지역의 경우 인공신경망 기법이 퍼지관계 기법에 비해 좀 더 나은 예측 정확도를 보이는 것으로 분석되었다.