• KIEAE Journal
• /
• v.17 no.5
• /
• pp.69-76
• /
• 2017

Purpose: This study aimed at developing an Artificial Neural Network (ANN) model for predicting the amount of cooling energy consumption of the variable refrigerant flow (VRF) cooling system by the different set-points of the control variables, such as supply air temperature of air handling unit (AHU), condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. Applying the predicted results for the different set-points, the control algorithm, which embedded the ANN model, will determine the most energy efficient control strategy. Method: The ANN model was developed and tested its prediction accuracy by using matrix laboratory (MATLAB) and its neural network toolbox. The field data sets were collected for the model training and performance evaluation. For completing the prediction model, three major steps were conducted - i) initial model development including input variable selection, ii) model optimization, and iii) performance evaluation. Result: Eight meaningful input variables were selected in the initial model development such as outdoor temperature, outdoor humidity, indoor temperature, cooling load of the previous cycle, supply air temperature of AHU, condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. The initial model was optimized to have 2 hidden layers with 15 hidden neurons each, 0.3 learning rate, and 0.3 momentum. The optimized model proved its prediction accuracy with stable prediction results.

• Proceedings of the IEEK Conference
• /
• 2000.09a
• /
• pp.915-918
• /
• 2000

In this paper, we propose an efficient feature vector extraction and decision fusion methods for the automatic classification of power system disturbances. Here, FFT and WPT(wavelet packet transform) are und to extract an appropriate feature for classifying power quality disturbances with variable properties. In particular, the WPT can be utilized to develop an adaptable feature extraction algorithm using best basis selection. Furthermore. the extracted feature vectors are applied as input to the decision fusion system which combines the decisions of several classifiers having complementary performances, leading to improvement of the classification performance. Finally, the applicability of the proposed approach is demonstrated using some simulations results obtained by analyzing power quality disturbances data generated by using Matlab.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.19 no.1
• /
• pp.99-110
• /
• 1995

An industrial robot needs a simple and robust control algorithm obtaining high precision control performance in spite of disturbance and parameter's change. In this paper, for solving this problem, a new sliding mode control algorithm is proposed and applied to the trajectory control of a SCARA type robot. The proposed algorithm has diminished the chattering occurring in sliding mode by setting a dead band along the switching line on the phase plane. It shows that we can easily obtain a simple switching control input satisfying sliding mode in spite of regarding nonlinear terms of a manipulator and servo system as disturbance. A guideline for selection of dead-band width is determined by optimal value of cost function presenting magnitudes of chattering and error. By this algorithm, we can expect the high performance of the trajectory tracking of an industrial robot which needs a robust and simple algorithm.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2006.11a
• /
• pp.605-608
• /
• 2006

본 논문에서는 고정점 알고리즘의 독립성분분석과 상호정보 추정을 조합한 입력변수선택 기법을 제안하였다. 여기서 뉴우턴법에 기반을 둔 빠른 분석성능을 가지는 고정점 알고리즘의 독립성분분석은 입력변수 간의 독립성을 빠르게 찾기 위함이고, 입력변수의 확률밀도함수의 계산을 위해 적응적 분할을 이용한 상호정보의 추정은 변수상호간 종속성을 좀 더 정확하게 정량화하기 위함이다. 제안된 기법을 인위적으로 제시된 각 500개의 샘플을 가지는 6개의 독립신호와 1개의 종속신호를 대상으로 실험한 결과 빠르고 정확한 변수의 선택이 이루어짐을 확인하였다.

• International Journal of Computer Science & Network Security
• /
• v.22 no.2
• /
• pp.131-138
• /
• 2022

Soft sensors are used to anticipate complicated model parameters using data from classifiers that are comparatively easy to gather. The goal of this study is to use artificial intelligence techniques to design and build soft sensors. The combination of a Long Short-Term Memory (LSTM) network and Grey Wolf Optimization (GWO) is used to create a unique soft sensor. LSTM is developed to tackle linear model with strong nonlinearity and unpredictability of manufacturing applications in the learning approach. GWO is used to accomplish input optimization technique for LSTM in order to reduce the model's inappropriate complication. The newly designed soft sensor originally brought LSTM's superior dynamic modeling with GWO's exact variable selection. The performance of our proposal is demonstrated using simulations on real-world datasets.

• Journal of the Semiconductor & Display Technology
• /
• v.20 no.4
• /
• pp.146-150
• /
• 2021

Plasma information based virtual metrology (PI-VM) that predicts wafer-to-wafer etch rate variation after wet cleaning of plasma facing parts was developed. As input parameters, plasma information (PI) variables such as electron temperature, fluorine density and hydrogen density were extracted from optical emission spectroscopy (OES) data for etch plasma. The PI-VM model was trained by stepwise variable selection method and multi-linear regression method. The expected etch rate by PI-VM showed high correlation coefficient with measured etch rate from SEM image analysis. The PI-VM model revealed that the root cause of etch rate variation after the wet cleaning was desorption of hydrogen from the cleaned parts as hydrogen combined with fluorine and decreased etchant density and etch rate.

• Computers and Concrete
• /
• v.19 no.5
• /
• pp.457-465
• /
• 2017

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.10 no.4
• /
• pp.343-350
• /
• 2000

In this paper, we propose the Multi-FPNN(Fuzzy Polynomial Neural Networks) model based on FNN and PNN(Polyomial Neural Networks) for optimal system identifacation. Here FNN structure is designed using fuzzy input space divided by each separated input variable, and urilized both in order to get better output performace. Each node of PNN structure based on GMDH(Group Method of Data handing) method uses two types of high-order polynomials such as linearane and quadratic, and the input of that node uses three kinds of multi-variable inputs such as linear and quadratic, and the input of that node and Genetic Algorithms(GAs) to identify both the structure and the prepocessing of parameters of a Multi-FPNN model. Here, HCM clustering method, which is carried out for data preproessing of process system, is utilized to determine the structure method, which is carried out for data preprocessing of process system, is utilized to determance index with a weighting factor is used to according to the divisions of input-output space. A aggregate performance inddex with a wegihting factor is used to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of this aggregate abjective function which it is acailable and effective to design to design and optimal Multi-FPNN model. The study is illustrated with the aid of two representative numerical examples and the aggregate performance index related to the approximation and generalization abilities of the model is evaluated and discussed.

• Journal of Korea Port Economic Association
• /
• v.38 no.2
• /
• pp.31-47
• /
• 2022

The purpose of this study is to develop a reliable tool that can classify and measure detailed indicators related to the performance of ESG implementation in the country and verify their applicability. Based on World Bank's data as input data, 67 types of ESG-related detailed indicators measured in a total of 239 countries were tested to derive an optimal model that could group detailed indicators into three categories: environment, society, and governance. As a result of the analysis, it was confirmed that a total of 10 detailed indicators had a statistically significant relationship with the country's ESG performance. In addition, the detailed indicators showed a positive correlation with the primary latent variables E, S, and G, and showed a high overall index in the suitability of the model to secure the validity and reliability of variable input. As a result, this study confirmed that several detailed performance indicators constituting ESG can be classified as latent variables, and it can be said that clear criteria for the selection method and input validity of variables were presented.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.11
• /
• pp.5164-5171
• /
• 2011

In fuzzy modeling for nonlinear process, typically using the given data, the fuzzy rules are formed by the input variables and the space division by selecting the input variable and dividing the input space for each input variables. The premise part of the fuzzy rule is identified by selection of the input variables, the number of space division and membership functions and the consequent part of the fuzzy rule is identified by polynomial functions in the form of simplified and linear inference. In general, formation of fuzzy rules for nonlinear processes using the given data have the problem that the number of fuzzy rules exponentially increases. To solve this problem complex nonlinear process can be modeled by separately forming the fuzzy rules by means of fuzzy division of each input space. Therefore, this paper utilizes individual input space to generate fuzzy rules. The premise parameters of the fuzzy rules are identified by Min-Max method using the minimum and maximum values of input data set and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. And lastly, using the data which is widely used in nonlinear process we evaluate the performance and the system characteristics.