• Title, Summary, Keyword: ANN

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Design of the Fixed Size Systolic Array for the Back-propagation ANN (역전파 ANN을 위한 고정 크기 시스톨릭 어레이 설계)

  • 김지연;장명숙;박기현
    • Proceedings of the Korean Information Science Society Conference
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    • pp.691-693
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    • 1998
  • A parallel processing systolic array reduces execution time of the Back-propagation ANN. But, systolic array must be designed whenever the number of neurons in the ANN differ. To use the systolic array which is aready designed ad a fixed size VLSI chip, partition of the problem size systolic array must be performed. This paper presents a design method of the fixed size systolic array for the Back-propagation algorthm using LSGP and LPGS partion method

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A Method of Transient Stability Analysis Using ANN (신경회로망 부하모델을 이용한 과도안정도 해석기법)

  • Lee, J.P.;Lim, J.Y.;Kim, S.S.;Ji, P.S.
    • Proceedings of the KIEE Conference
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    • pp.329-331
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    • 2006
  • Load models are important for improving the accuracy of stability analysis. Load characteristics are changed for voltage and frequency condition. In this research, ANN with LMBP learning rule is used to construct the load model. Characteristics of some residential loads are tested under various voltage and frequency conditions. Acquired data are used to construct load models by ANN. Constructed ANN load model are applied to transient stability analysis.

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RELATION BETWEEN ANN-CATEGORIES AND RING CATEGORIES

  • Phung, Che Thi Kim;Quang, Nguyen Tien;Thuy, Nguyen Thu
    • Communications of the Korean Mathematical Society
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    • v.25 no.4
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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.

Application of Artificial Neural Network Model for Environmental Load Estimation of Pre-Stressed Concrete Beam Bridge (PSC Beam교 환경부하량 추정을 위한 인공신경망 모델 적용 연구)

  • Kim, Eu Wang;Yun, Won Gun;Kim, Kyong Ju
    • Korean Journal of Construction Engineering and Management
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    • v.19 no.4
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    • pp.82-92
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    • 2018
  • Considering that earlier stage of construction project has a great influence on the possibility of lowering of environmental load, it is important to build and utilize system that can support effective decision making at the initial stage of the project. In this study, we constructed an environmental load estimation model that can be used at the early stage of the project using basic design factors. The model was constructed by using the artificial neural network to estimate environmental load by applying to planning stage (ANN-1), basic design stage (ANN-2). The result of test, shows that average of absolute measuring efficiency and standard deviation of ANN-1 and ANN-2 were 11.19% / 5.30% and 9.59% / 3.09% each. This result indicates that the model using the input variables extended with the project progress has high reliability and it is considered to be effective in decision support at the initial design stage of the project.

A study on Development of Artificial Neural Network (ANN) for Preliminary Design of Urban Deep Ex cavation and Tunnelling (도심지 지하굴착 및 터널시공 예비설계를 위한 인공신경망 개발에 관한 연구)

  • Yoo, Chungsik;Yang, Jaewon
    • Journal of the Korean Geosynthetics Society
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    • v.19 no.1
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    • pp.11-23
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    • 2020
  • In this paper development artificial neural networks (ANN) for preliminary design and prediction of urban tunnelling and deep excavation-induced ground settlement was presented. In order to form training and validation data sets for the ANN development, field design and measured data were collected for various tunnelling and deep-excavation sites. The field data were then used as a database for the ANN training. The developed ANN was validated against a testing set and the unused field data in terms of statistical parameters such as R2, RMSE, and MAE. The practical use of ANN was demonstrated by applying the developed ANN to hypothetical conditions. It was shown that the developed ANN can be effectively used as a tool for preliminary excavation design and ground settlement prediction for urban excavation problems.

Shear strength estimation of RC deep beams using the ANN and strut-and-tie approaches

  • Yavuz, Gunnur
    • Structural Engineering and Mechanics
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    • v.57 no.4
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    • pp.657-680
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    • 2016
  • Reinforced concrete (RC) deep beams are structural members that predominantly fail in shear. Therefore, determining the shear strength of these types of beams is very important. The strut-and-tie method is commonly used to design deep beams, and this method has been adopted in many building codes (ACI318-14, Eurocode 2-2004, CSA A23.3-2004). In this study, the efficiency of artificial neural networks (ANNs) in predicting the shear strength of RC deep beams is investigated as a different approach to the strut-and-tie method. An ANN model was developed using experimental data for 214 normal and high-strength concrete deep beams from an existing literature database. Seven different input parameters affecting the shear strength of the RC deep beams were selected to create the ANN structure. Each parameter was arranged as an input vector and a corresponding output vector that includes the shear strength of the RC deep beam. The ANN model was trained and tested using a multi-layered back-propagation method. The most convenient ANN algorithm was determined as trainGDX. Additionally, the results in the existing literature and the accuracy of the strut-and-tie model in ACI318-14 in predicting the shear strength of the RC deep beams were investigated using the same test data. The study shows that the ANN model provides acceptable predictions of the ultimate shear strength of RC deep beams (maximum $R^2{\approx}0.97$). Additionally, the ANN model is shown to provide more accurate predictions of the shear capacity than all the other computed methods in this study. The ACI318-14-STM method was very conservative, as expected. Moreover, the study shows that the proposed ANN model predicts the shear strengths of RC deep beams better than does the strut-and-tie model approaches.

Using Genetic Algorithms to Support Artificial Neural Networks for the Prediction of the Korea stock Price Index

  • Kim, Kyoung-jae;Ingoo han
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • pp.347-356
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    • 2000
  • This paper compares four models of artificial neural networks (ANN) supported by genetic algorithms the prediction of stock price index. Previous research proposed many hybrid models of ANN and genetic algorithms(GA) in order to train the network, to select the feature subsets, and to optimize the network topologies. Most these studies, however, only used GA to improve a part of architectural factors of ANN. In this paper, GA simultaneously optimized multiple factors of ANN. Experimental results show that GA approach to simultaneous optimization for ANN (SOGANN3) outperforms the other approaches.

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Indirect Cutting Force Estimation Using Artificial Neural Network (인공 신경망을 이용한 절삭력 간접 측정)

  • 최지현;김종원
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • pp.1054-1058
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    • 1995
  • There have been many research works for the indirect cutting force measurement in machining process, which deal with the case of one-axis cutting process. In multi-axis cutting process, the main difficulties to estimate the cutting forces occur when the feed direction is reversed. This paper presents the indirect cutting force measurement method in contour NC milling processes by using current signals of servo motors. An artificial neural network (ANN) system are suggested. An artificial neural network(ANN) system is also implemented with a training set of experimental cutting data to measure cutting force indirectly. The input variables of the ANN system are the motor currents and the feedrates of x and y-axis servo motors, and output variable is the cutting force of each axis. A series of experimental works on the circular interpolated contour milling process with the path of a complete circle has been performed. It is concluded that by comparing the ANN system with a dynamometer measuring cutting force directil, the ANN system has a good performance.

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Influences of the Input on ANN and QSPR of Homopolymers

  • Sun, Hong;Tang, Yingwu;Wu, Guoshi
    • Macromolecular research
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    • v.10 no.1
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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.

Estimating spatial distribution of water quality in landfill site

  • Yoon Hee-Sung;Lee Kang-Kun;Lee Seong-Soon;Lee Jin-Yong;Kim Jong-Ho
    • Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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    • pp.391-393
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    • 2006
  • In this study, the performance of artificial neural network (ANN) models for estimating spatial distribution of water quality was evaluated using electric conductivity (EC) values in landfill site. For the ANN model development, feedforward neural networks and backpropagation algorithm with gradient descent method were used. In Test 1, the interpolation ability of the ANN model was evaluated. Results of the ANN model were more precise than those of the Kriging model. In Test 2, spatial distributions of EC values were predicted using precipitation data. Results seemed to be reasonable, however, they showed a limitation of ANN models in extrapolations.

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