• Title/Summary/Keyword: Artificial Neural Network

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Optimization of Culture Conditions and Bench-Scale Production of $_L$-Asparaginase by Submerged Fermentation of Aspergillus terreus MTCC 1782

  • Gurunathan, Baskar;Sahadevan, Renganathan
    • Journal of Microbiology and Biotechnology
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    • v.22 no.7
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    • pp.923-929
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    • 2012
  • Optimization of culture conditions for L-asparaginase production by submerged fermentation of Aspergillus terreus MTCC 1782 was studied using a 3-level central composite design of response surface methodology and artificial neural network linked genetic algorithm. The artificial neural network linked genetic algorithm was found to be more efficient than response surface methodology. The experimental $_L$-asparaginase activity of 43.29 IU/ml was obtained at the optimum culture conditions of temperature $35^{\circ}C$, initial pH 6.3, inoculum size 1% (v/v), agitation rate 140 rpm, and incubation time 58.5 h of the artificial neural network linked genetic algorithm, which was close to the predicted activity of 44.38 IU/ml. Characteristics of $_L$-asparaginase production by A. terreus MTCC 1782 were studied in a 3 L bench-scale bioreactor.

Process Design of a Hot Forged Product Using the Artificial Neural Network and the Statistical Design of Experiments (신경망과 실험계획법을 이용한 열간 단조품의 공정설계)

  • 김동환;김동진;김호관;김병민;최재찬
    • Journal of the Korean Society for Precision Engineering
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    • v.15 no.9
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    • pp.15-24
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    • 1998
  • In this research. we have proposed a new technique to determine .the combination of design parameters for the process design of a hot forged product using artificial neural network(ANN) and statistical design of experiments(DOE). The investigated problem involves the adequate selection of the aspect ratio of billet, the ram velocity and the friction factor as design parameters. An optimal billet satisfying the forming limitation, die filling, load and energy as well as more uniform distribution of effective strain, is determined by applying the ability of artificial neural network and considering the analysis of mean and variation on the functional requirement. This methodology will be helpful in designing and controlling parameters on the shop floor which would yield the best design solution.

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Pacman Game Reinforcement Learning Using Artificial Neural-network and Genetic Algorithm (인공신경망과 유전 알고리즘을 이용한 팩맨 게임 강화학습)

  • Park, Jin-Soo;Lee, Ho-Jeong;Hwang, Doo-Yeon;Cho, Soosun
    • IEMEK Journal of Embedded Systems and Applications
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    • v.15 no.5
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    • pp.261-268
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    • 2020
  • Genetic algorithms find the optimal solution by mimicking the evolution of natural organisms. In this study, the genetic algorithm was used to enable Pac-Man's reinforcement learning, and a simulator to observe the evolutionary process was implemented. The purpose of this paper is to reinforce the learning of the Pacman AI of the simulator, and utilize genetic algorithm and artificial neural network as the method. In particular, by building a low-power artificial neural network and applying it to a genetic algorithm, it was intended to increase the possibility of implementation in a low-power embedded system.

Patterns recognition via artificial neural network systems

  • Sugisaka, M.;Sagara, S.;Ueno, S.
    • 제어로봇시스템학회:학술대회논문집
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    • 1990.10b
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    • pp.929-932
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    • 1990
  • This paper considers the problem of patterns recognition using the artificial neural network systems. The artificial neural network systems provide an effective tool for classifying patterns and/or characters by learning them in a certain repeated hashion. The mechanism of the learning process and the structure of neural network systems used are main concerns in the accurate and fast classification of the patterns which are slightly different each other. The neural network system employed in this study has three layers structure which is composed of input, intermidiate, and output layers. Our main concern is to develope an effective learning mechanism how to learn the patterns fastly and accurately. The experimental study performed shows that there exists an effective learning method to get higher recognition ratio in classifying the several different patterns by artificial neural network system constructed.

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Development of In process Condition Monitoring System on Turning Process using Artificial Neural Network. (신경회로망 모델을 이용한 선삭 공정의 실시간 이상진단 시스템의 개발)

    • Journal of the Korean Society of Manufacturing Technology Engineers
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    • v.7 no.3
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    • pp.14-21
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    • 1998
  • The in-process detection of the state of cutting tool is one of the most important technical problem in Intelligent Machining System. This paper presents a method of detecting the state of cutting tool in turning process, by using Artificial Neural Network. In order to sense the state of cutting tool. the sensor fusion of an acoustic emission sensor and a force sensor is applied in this paper. It is shown that AErms and three directional dynamic mean cutting forces are sensitive to the tool wear. Therefore the six pattern features that is, the four sensory signal features and two cutting conditions are selected for the monitoring system with Artificial Neural Network. The proposed monitoring system shows a good recogniton rate for the different cutting conditions.

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Prediction of Jominy Curve using Artificial Neural Network (인공 신경망 모델을 활용한 조미니 곡선 예측)

  • Lee, Woonjae;Lee, Seok-Jae
    • Journal of the Korean Society for Heat Treatment
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    • v.31 no.1
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    • pp.1-5
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    • 2018
  • This work demonstrated the application of an artificial neural network model for predicting the Jominy hardness curve by considering 13 alloying elements in low alloy steels. End-quench Jominy tests were carried out according to ASTM A255 standard method for 1197 samples. The hardness values of Jominy sample were measured at different points from the quenched end. The developed artificial neural network model predicted the Jominy curve with high accuracy ($R^2=0.9969$ for training and $R^2=0.9956$ for verification). In addition, the model was used to investigate the average sensitivity of input variables to hardness change.

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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    • 1995.10a
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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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Comparison of the BOD Forecasting Ability of the ARIMA model and the Artificial Neural Network Model (ARIMA 모형과 인공신경망모형의 BOD예측력 비교)

  • 정효준;이홍근
    • Journal of Environmental Health Sciences
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    • v.28 no.3
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    • pp.19-25
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    • 2002
  • In this paper, the water quality forecast was performed on the BOD of the Chungju Dam using the ARIMA model, which is a nonlinear statistics model, and the artificial neural network model. The monthly data of water quality were collected from 1991 to 2000. The most appropriate ARIMA model for Chungju dam was found to be the multiplicative seasonal ARIMA(1,0,1)(1,0,1)$_{12}$, model. While the artificial neural network model, which is used relatively often in recent days, forecasts new data by the strength of a learned matrix like human neurons. The BOD values were forecasted using the back-propagation algorithm of multi-layer perceptrons in this paper. Artificial neural network model was com- posed of two hidden layers and the node number of each hidden layer was designed fifteen. It was demonstrated that the ARIMA model was more appropriate in terms of changes around the overall average, but the artificial neural net-work model was more appropriate in terms of reflecting the minimum and the maximum values.s.

Artificial Neural Network Models in Prediction of the Moisture Content of a Spray Drying Process

  • Taylan, Osman;Haydar, Ali
    • Journal of the Korean Ceramic Society
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    • v.41 no.5
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    • pp.353-358
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    • 2004
  • Spray drying is a unique drying process for powder production. Spray dried product must be free-flowing in order to fill the pressing dies rapidly, especially in the ceramic production. The important powder characteristics are; the particle size distribu-tion and moisture content of the finished product that can be estimated and adjusted by the spray dryer operation, within limits, through regulation of atomizer and drying conditions. In order to estimate the moisture content of the resultant dried product, we modeled the control system of the drying process using two different Artificial Neural Network (ANN) approaches, namely the Back-Propagation Multiplayer Perceptron (BPMLP) algorithm and the Radial Basis Function (RBF) network. It was found out that the performance of both of the artificial neural network models were quite significant and the total testing error for the 100 data was 0.8 and 0.7 for the BPMLP algorithm and the RBF network respectively.

Comparison between Logistic Regression and Artificial Neural Networks as MMPI Discriminator (MMPI 분석도구로서 인공신경망 분석과 로지스틱 회귀분석의 비교)

  • Lee, Jaewon;Jeong, Bum Seok;Kim, Mi Sug;Choi, Jee Wook;Ahn, Byung Un
    • Korean Journal of Biological Psychiatry
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    • v.12 no.2
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    • pp.165-172
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    • 2005
  • Objectives:The purpose of this study is to 1) conduct a discrimination analysis of schizophrenia and bipolar affective disorder using MMPI profile through artificial neural network analysis and logistic regression analysis, 2) to make a comparison between advantages and disadvantages of the two methods, and 3) to demonstrate the usefulness of artificial neural network analysis of psychiatric data. Procedure:The MMPI profiles for 181 schizophrenia and bipolar affective disorder patients were selected. Of these profiles, 50 were randomly placed in the learning group and the remaining 131 were placed in the validation group. The artificial neural network was trained using the profiles of the learning group and the 131 profiles of the validation group were analyzed. A logistic regression analysis was then conducted in a similar manner. The results of the two analyses were compared and contrasted using sensitivity, specificity, ROC curves, and kappa index. Results:Logistic regression analysis and artificial neural network analysis both exhibited satisfactory discriminating ability at Kappa index of greater than 0.4. The comparison of the two methods revealed artificial neural network analysis is superior to logistic regression analysis in its discriminating capacity, displaying higher values of Kappa index, specificity, and AUC(Area Under the Curve) of ROC curve than those of logistic regression analysis. Conclusion:Artificial neural network analysis is a new tool whose frequency of use has been increasing for its superiority in nonlinear applications. However, it does possess insufficiencies such as difficulties in understanding the relationship between dependent and independent variables. Nevertheless, when used in conjunction with other analysis tools which supplement it, such as the logistic regression analysis, it may serve as a powerful tool for psychiatric data analysis.

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