• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1478-1481
• /
• 2005

As the model of most practical system cannot be obtained, the practice of typical control method is limited. Accordingly, numerous artificial intelligence control methods have been used widely. Fuzzy control and neural network control have been an important point in the developing process of the field. This paper is proposed adaptive fuzzy-neural network based on the vector controlled interior permanent magnet synchronous motor drive system. The fuzzy-neural network is first utilized for the speed control. A model reference adaptive scheme is then proposed in which the adaptation mechanism is executed using fuzzy-neural network. Also, this paper is proposed estimation of speed of interior permanent magnet synchronous motor using artificial neural network controller. The back-propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back-propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the analysis results to verify the effectiveness of the new method.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.10
• /
• pp.9-21
• /
• 2020

The aim of this paper is to predict the Borsa Istanbul (BIST) 30 index movements to determine the most accurate buy and sell decisions using the methods of Artificial Neural Networks (ANN) and Genetic Algorithm (GA). We combined these two methods to obtain a hybrid intelligence method, which we apply. In the financial markets, over 100 technical indicators can be used. However, several of them are preferred by analysts. In this study, we employed nine of these technical indicators. They are moving average convergence divergence (MACD), relative strength index (RSI), commodity channel index (CCI), momentum, directional movement index (DMI), stochastic oscillator, on-balance volume (OBV), average directional movement index (ADX), and simple moving averages (3-day moving average, 5-day moving average, 10-day moving average, 14-day moving average, 20-day moving average, 22-day moving average, 50-day moving average, 100-day moving average, 200-day moving average). In this regard, we combined these two techniques and obtained a hybrid intelligence method. By applying this hybrid model to each of these indicators, we forecast the movements of the Borsa Istanbul (BIST) 30 index. The experimental result indicates that our best proposed hybrid model has a successful forecast rate of 75%, which is higher than the single ANN or GA forecasting models.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.12 no.1
• /
• pp.103-110
• /
• 1998

The modeling of load characteristics is a difficult problem because of uncertainty of load. This research uses artificial neural networks which can approximate nonlinear problem to represent load characteristics. After the selection of typical load, active and reactive power for the variation of voltage and frequency is obtained from experiments. We constructed and learned ANN based on these data for component load identification. The learned ANN identified load characteristics for other voltage and/or frequency variation. In addition, the results of component load identification are presented to demonstrate the potentiality of the proposed method.method.

• Advances in concrete construction
• /
• v.14 no.2
• /
• pp.115-130
• /
• 2022

This paper presents an application of the maturation effect on the strength of high-strength concrete which is produced with different origin aggregates. While investigating the maturation effect on HSC 384 specimens were prepared with 22 different origin aggregates. These prepared specimens were subjected to the standard compressive tests which were applied after curing for 2, 7, 28, and 56 days under appropriate conditions. The test results revealed that bright surface-low adherence behavior is valid in normal strength concretes, but is not as effective as expected in high-strength concretes. The application of artificial neural networks (ANNs) to predict 2, 7, 28, and 56 day compressive strength of HSC is also investigated in this paper. An ANN model is built, trained, and tested using the available test data gathered from experimental studies. The ANN model is found to predict 2, 7, 28, and 56 days of compressive strength of high-strength concrete well within the ranges of the input parameters considered. These comparisons show that ANNs have strong potential as a feasible tool for predicting the compressive strength of high-strength concrete within the range of the input parameters considered.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.48 no.1
• /
• pp.72-81
• /
• 2012

Using artificial neural network (ANN) technique, auction prices for common mackerel were forecasted with the daily total sale and auction price data at the Busan Cooperative Fish Market before introducing Total Allowable Catch (TAC) system, when catch data had no limit in Korea. Virtual input data produced from actual data were used to improve the accuracy of prediction and the suitable neural network was induced for the prediction. We tested 35 networks to be retained 10, and found good performance network with regression ratio of 0.904 and determination coefficient of 0.695. There were significant variations between training and verification errors in this network. Ideally, it should require more training cases to avoid over-learning, which leads to improve performance and makes the results more reliable. And the precision of prediction was improved when environmental factors including physical and biological variables were added. This network for prediction of price and catch was considered to be applicable for other fishes.

• Journal of the Korean Geotechnical Society
• /
• v.24 no.11
• /
• pp.17-24
• /
• 2008

It is known that scour at bridge piers is one of the leading causes of bridge failure. However, the mechanism of flow around a pier structure is so complicated that it is difficult to establish a general empirical model to provide accurate estimation for scour. Especially, each of the proposed empirical formula yields good results for a particular data set but can't show reliable predictability for various scouring data set. In this study, an alternative approach, that is, artificial neural networks (ANN), is proposed to estimate the local scour depth with numerous field data base. The local scour depth was modeled as a function of seven variables; pier shape, pier width, pier length, skew angle, stream velocity, water depth, $D_{50}$. 426 field data were used for the training and testing of ANN model. The predicted results showed that the neural network could provide a better alternative to the empirical equations.

• ETRI Journal
• /
• v.34 no.5
• /
• pp.696-705
• /
• 2012

Accurate synthesis models based on artificial neural networks (ANNs) are proposed to directly obtain the physical dimensions of an asymmetric coplanar waveguide with conductor backing and substrate overlaying (ACPWCBSO). First, the ACPWCBSO is analyzed with the conformal mapping technique (CMT) to obtain the training data. Then, a modified genetic-algorithm-Levenberg-Marquardt (GA-LM) algorithm is adopted to train ANNs. In the algorithm, the maximal relative error (MRE) is used as the fitness function of the chromosomes to guarantee that the MRE is small, while the mean square error is used as the error function in LM training to ensure that the average relative error is small. The MRE of ANNs trained with the modified GA-LM algorithm is less than 8.1%, which is smaller than those trained with the existing GA-LM algorithm and the LM algorithm (greater than 15%). Lastly, the ANN synthesis models are validated by the CMT analysis, electromagnetic simulation, and measurements.

• Wind and Structures
• /
• v.39 no.2
• /
• pp.101-110
• /
• 2024

Tall buildings are distinguished by their slenderness, making them sensitive to wind loads. A huge amount of resources is typically dedicated to controlling loads and vibrations caused by wind. Enhancing tall buildings' aerodynamic performance can save a large portion of these expenses. This enhancement can be achieved through aerodynamic optimization that can be tackled either by altering the outer shape of the building locally through modifying the corners (e.g., corner chamfering) or globally through changing the whole form of the building (e.g., twisting). In this paper, a newly developed aerodynamic optimization procedure (AOP) is adopted to enhance tall buildings' aerodynamic performance. This procedure is a combination of computational fluid dynamics (CFD), Artificial Neural Networks (ANN) and Genetic algorithm (GA). An ANN-based surrogate model is used to evaluate the aerodynamic parameters through the optimization procedure to reach a reliable aerodynamic shape. Helical twisting and corner modifications of the buildings are used to reduce the along-wind base moment.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.18 no.5
• /
• pp.126-141
• /
• 2019

One of the Cooperative Intelligent Transportation System(C-ITS) priority services is collision prevention support service. Several studies have considered V2I2V communication-based collision prevention support services using Artificial Neural Networks(ANN). However, such services still show some issues due to a low penetration of C-ITS devices and large delay, particularly when loading massive traffic data into the server in the C-ITS center. This study proposes the Artificial Neural Network-based Collision Warning Service(ACWS), which allows upstream vehicle to update pre-determined weights involved in the ANN by using real-time sectional traffic information. This research evaluates the proposed service with respect to various penetration rates and delays. The evaluation result shows the performance of the ACWS increases as the penetration rate of the C-ITS devices in the vehicles increases or the delay decreases. Furthermore, it reveals a better performance is observed in more advanced ANN model-based ACWS for any given set of conditions.

• Transactions of Materials Processing
• /
• v.29 no.2
• /
• pp.89-96
• /
• 2020

The finite element method has been widely applied in the sheet metal forming process. However, the finite element method is computationally expensive and time consuming. In order to tackle this problem, surrogate modeling methods have been proposed. An artificial neural network (ANN) is one such surrogate model and has been well studied over the past decades. However, when it comes to ANN with two or more layers, so called deep neural networks (DNN), there is distinct a lack of research. We chose to use DNNs our surrogate model to predict the behavior of sheet metal in the deep drawing process. Thickness variation is selected as an output of the DNN in order to evaluate workpiece feasibility. Input variables of the DNN are radius of die, die corner and blank holder force. Finite element analysis was conducted to obtain data for surrogate model construction and testing. Sampling points were determined by full factorial, latin hyper cube and monte carlo methods. We investigated the performance of the DNN according to its structure, number of nodes and number of layers, then it was compared with a radial basis function surrogate model using various sampling methods and numbers. The results show that our DNN could be used as an efficient surrogate model for the deep drawing process.