• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1997.10a
• /
• pp.52-55
• /
• 1997

In this paper, both the generalization of one artificial variable technique to the general bound problem and the efficient implementation of the technique are suggested. When the steepest-edge method is used as a pricing rule in the simplex method, it is easy to update the reduced cost and the simplex multiplier every iteration. Therefore, one artificial variable technique is more efficient than Wolfe's method in which the reduced cost and simplex multiplier must be recalculated in every iteration. When implementing the one artificial variable technique on the LP problems with the general bound restraints on the variables, an arbitrary basic solution which satisfies the bound restraints is sought first, and the artificial column which adjusts the infeasibility is introduced. The phase one of the simplex method minimizes the one artificial variable. The efficient implementation technique includes the splitting, scaling, storage of the artificial column, and the cure of infeasibility problem.

• KIEAE Journal
• /
• v.14 no.3
• /
• pp.71-77
• /
• 2014

This study aimed at developing and evaluating performance of the two logics for respectively operating two-position- and variable-heating systems. Both logics control the heating system and openings of the double skin facade buildings in an integrated manner. Artificial neural network models were applied for the predictive and adaptive controls in order to optimally condition the indoor thermal environment. Numerical computer simulation methods using the MATLAB (Matrix Laboratory) and TRNSYS (Transient Systems Simulation) were employed for the performance tests of the logics in the test module. Analysis on the test results revealed that the variable control logic provided more comfortable and stable temperature conditions with the increased comfortable period and the decreased standard deviation from the center of the comfortable range. In addition, the amount of heat supply to the indoor space was significantly reduced by the variable control logic. Thus, it can be concluded that the optimal control method using the artificial neural network model can work more effectively when it is applied to the variable heating systems.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.504-507
• /
• 1997

It is well known that Variable Structure Controller(VSC) is robust to parameters variation and disturbance but its performance depends on the design parameters such as switching gain and slope of sliding surface. This paper proposes a more robust VSC that is composed of local VSC's. Each local VSC considers the local system dynamics with narrow parameter variation and disturbance. First we optimize the local VSC's by use of Evolution Strategy, and next we use Artificial Neural Network to generalize the local VSC's and construct the overall VSC in order to cover the whole range of parameter variation and disturbance. Simulation on BLDC motor current control shows that the proposed VSC is superior to the conventional VSC.

• The Journal of Asian Finance, Economics and Business
• /
• v.9 no.3
• /
• pp.257-263
• /
• 2022

The objective of this study was to see how artificial intelligence applications affected organizational innovation in Jordanian commercial banks. Both independent and dependent variables were measured in three dimensions: expert systems, neural network systems, and fuzzy logic systems for artificial intelligence applications variable. Product innovation, process innovation, and management innovation for the organizational innovation variable. To achieve study objectives, a questionnaire was developed and distributed to a sample of one hundred fifty-three managers in Jordanian commercial banks, who were selected according to the simple random sampling method. Except for the neural network systems dimension, which comes in at an average level, the study indicated that there is a high level of organizational innovation and artificial intelligence applications. Furthermore, the findings revealed that artificial intelligence applications have a significant impact on organizational innovation in Jordanian commercial banks, with the most important artificial intelligence application being a fuzzy logic system. The study suggested keeping track of technological advancements in the field of artificial intelligence applications and incorporating them into banking operations by benchmarking with the best commercial bank practices and allocating a portion of the budget to technological applications and infrastructure development, as well as balancing between technology use and information security risks to ensure client privacy is protected.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.36 no.3
• /
• pp.261-265
• /
• 2023

In this paper, we analyzed the transformation of the power following by the angle of incidence of the solar, the angle of photovoltaic module and artificial solar changed from 30° to 90° and synchronously changed the distance from 0.1 m to 0.5 m. Setting the distance between the artificial solar and the luminometer from 0.1 m to 0.5 m and set the angles to 90°, 60°, 45°, and 30°, the angle was 90° and when the distance was 0.1 m, the maximum Illuminance was 19,580 lux, the light could be obtained more. If the angle of incidence between the Artificial solar and the photovoltaic module was 90° and the variable resistance was 1,000 Ω at a distance of 0.4 m, the maximum power reached 0.82 W. Provided that the angle of incidence between the artificial solar and the photovoltaic module was 90° and the distance was 0.2 m since the variable resistance had the maximum power of 500 Ω, the maximum power was 0.78 W. At 1,000 Ω, the maximum power is 0.80 W so the maximum power at the variable resistance 1,000 Ω could obtain higher power than the variable resistance 500 Ω. The variable resistance was 1,000 Ω and the angle of incidence between the Artificial solar and the photovoltaic module was 90° at a distance of 0.4 m, and the maximum power reached 0.82 W. The angle was 60° at 0.3 m and 0.4 m the maximum power reached 0.10 W. The angle was 45° at 0.2 m maximum power reached 0.020 W, the angle was 30° at 0.4 m, and the maximum power reached 0.004 W. In four results about maximum power depending on the angle of incidence between the artificial solar and the photovoltaic module, the luminous efficiency and maximum power can be got the best at an angle of 90°.

• Journal of the Korean Data and Information Science Society
• /
• v.15 no.2
• /
• pp.297-306
• /
• 2004

It is usual that multivariate data analysis produces related (small number of) artificial variates for data reduction. Among them, refer to MDS(multidimensional scaling), MDPREF(multidimensional preference analysis), CDA(canonical discriminant analysis), CCA(canonical correlation analysis) and FA(factor analysis). Varimax rotation of artificial variables which is originally invented in FA for easy interpretations is applied to diverse multivariate techniques mentioned above. Real data analysisis is performed in order to manifest that rotation improves interpretations of artificial variables.

• Journal of Welding and Joining
• /
• v.32 no.2
• /
• pp.43-47
• /
• 2014

Famous artificial neural network (ANN) is applied to predict proper process window of arc welding. Target weldment is variously combined lap joint fillet welding of automotive steel plates. ANN's system variable such as number of hidden layers, perceptrons and transfer function are carefully selected through case by case test. Input variables are welding condition and steel plate combination, for example, welding machine type, shield gas composition, current, speed and strength, thickness of base material. The number of each input variable referred in welding experiment is counted and provided to make it possible to presume the qualitative precision and limit of prediction. One of experimental process windows is excluded for predictability estimation and the rest are applied for neural network training. As expected from basic ANN theory, experimental condition composed of frequently referred input variables showed relatively more precise prediction while rarely referred set showed poorer result. As conclusion, application of ANN to arc welding process window derivation showed comparatively practical feasibility while it still needs more training for higher precision.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.20 no.2
• /
• pp.243-250
• /
• 2010

As the wind has become one of the fastest growing renewable energy sources, the key issue of wind energy conversion systems is how to efficiently operate the wind turbines in a wide range of wind speeds. Compared to fixed speed turbines, variable speed wind turbines feature higher energy yields, lower component stress and fewer grid connection power peaks. Generally, measurement of wind speed is required for the control of variable speed wind turbine system. However, wind speed measured by anemometers is not accurate owing to various reasons. In this work, a new control algorithm for variable speed wind turbine system based on Kalman filter which can be used for the estimation of wind speed and artificial neural network which can generate optimum rotor speed is proposed. Also, to verify the feasibility of the proposed scheme, various simulation studies are carried out by using Simulink in Matlab.

• 전기의세계
• /
• v.22 no.5
• /
• pp.12-18
• /
• 1973

This paper deals with Linear Programming by Signal Flow Graph technique which is different from that of Mason and Coates. The objective function is regarded as variable, and slack variable node, artificial variable node and objective function variable (constant) node are newly defined, which shows the process for optimization of solution very intuitively. Also methods for solving L.P. and examples with subject to Ax.leq.b, Ax=b and Ax.geq.b are presented.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.06a
• /
• pp.634-639
• /
• 1998

A methodology based on the concept of variable string length GA(VGA) is developed for determining automatically the number of hyperplanes and their appropriate arrangement for modeling the class boundaries of a given training data set in RN. The genetic operators and fitness functionare newly defined to take care of the variability in chromosome length. Experimental results on different artificial and real life data sets are provided.