• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.444-450
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• 2013

For intelligent vehicle technology, it is very important to recognize the states of around vehicles and assess the collision risk for safety driving of the vehicle. Specifically, it is very fatal the collision with the vehicle coming from opposite direction. In this paper, a centerlane violation prediction method is proposed. Only radar signal based prediction makes lots of false alarm cause of measurement noise and the false alarm can make more danger situation than the non-prediction situation. We proposed the novel prediction method using IMM algorithm and fuzzy logic to increase accuracy and get rid of false positive. Fuzzy logic adjusts the radar signal and the IMM algorithm appropriately. It is verified by the computer simulation that shows stable prediction result and fewer number of false alarm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.4
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• pp.327-332
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• 2009

This paper proposes a method to improve the accuracy of a short-term electrical load forecasting (STLF) system based on neuro-fuzzy models. The proposed method compensates load forecasts based on the error obtained during the previous prediction. The basic idea behind this approach is that the error of the current prediction is highly correlated with that of the previous prediction. This simple compensation scheme using error information drastically improves the performance of the STLF based on neuro-fuzzy models. The viability of the proposed method is demonstrated through the simulation studies performed on the load data collected by Korea Electric Power Corporation (KEPCO) in 1996 and 1997.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.342-347
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• 2003

It is essential to develop an algorithm for the estimate of link travel velocity and for the supply and control of travel information in the context of intelligent transportation information system. The paper proposes the fuzzy logic based prediction of link travel velocity. Three factors such as time, date and velocity are considered as major components to represent the travel situation. In the fuzzy modeling, those factors were expressed by fuzzy membership functions. We acquire position／velocity data through GPS antenna with PDA embedded probe vehicles. The link travel velocity is calculated using refined GPS data and the prediction results are compared with actual data for its accuracy.

• Journal of Computational Design and Engineering
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• v.3 no.1
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• pp.1-13
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• 2016

The increase of consumer needs for quality metal cutting related products with more precise tolerances and better product surface roughness has driven the metal cutting industry to continuously improve quality control of metal cutting processes. In this paper, two different approaches are discussed. First, design of experiments (DOE) is used to determine the significant factors and then fuzzy logic approach is presented for the prediction of surface roughness. The data used for the training and checking the fuzzy logic performance is derived from the experiments conducted on a CNC milling machine. In order to obtain better surface roughness, the proper sets of cutting parameters are determined before the process takes place. The factors considered for DOE in the experiment were the depth of cut, feed rate per tooth, cutting speed, tool nose radius, the use of cutting fluid and the three components of the cutting force. Finally the significant factors were used as input factors for fuzzy logic mechanism and surface roughness is predicted with empirical formula developed. Test results show good agreement between the actual process output and the predicted surface roughness.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.4
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• pp.61-67
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• 1999

In turning the chip may be produced in the form of continuous chip or discontinuous one. Continuous chips produced at high speed machining may hit the newly cut workpiece surface and adversely affect the appearance of the surface finish and may interfere with tool and sometimes induce tool fracture. In this study relationship between AE signal and chip form was experimentally investigated, The experimental results show that types of chip form are possible to be classified from the AE signal using fuzzy logic.

• Journal of the Korean Society of Industry Convergence
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• v.4 no.2
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• pp.127-132
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• 2001

In turning, the chip may be produced in the form of continuous chip or discontinuous chip. The continuous chips are dangerous to the operator and difficult to be handled at high speed machining. The signal of AE(Acoustic Emission) is found out to be related to cutting conditions, tool materials, test conditions and tool geometry in turning. In this study, the relationship between AE signal and chip form was experimentally investigated. The experimental results show that the types of chip form are possible to be classified from the AE signal using fuzzy logic.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.778-780
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• 1996

The predictive maximum demand controllers often bring about large number of control actions during the every integrating period and/or undesirable load-disconnecting operations during the begining stage if the integrating period. To solve these problems, a fuzzy predictive maximum demand control algorithm is proposed, which determines the sensitivity if control action by urgency if the load interrupting action along with the predicted demand reading to the target or the time arriving at the end stage if the integrating period. A prototype controller employing the proposed algorithm also is developed and its performances are tested by PROCOM SYSTEMS Corperation of Korea.

• Journal of IKEEE
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• v.23 no.1
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• pp.334-337
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• 2019

In response to the increasing demand for energy, research and development of next-generation energy is actively carried out around the world to replace fossil fuels. Among them, the specific gravity of solar power generation systems using infinity and pollution-free solar energy is increasing. However, solar power generation is so different from solar energy that it is difficult to provide stable power and the power production itself depends on the solar energy by region. To solve these problems in this paper, we have collected meteorological data such as actual regional solar irradiance, precipitation, temperature and humidity, and proposed a solar power output prediction system using logic-based fuzzy Neural Network.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.351-360
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• 2016

Rapid population growth with high living standards and high electronics use for personal comfort has raised the electricity demand exponentially. To fulfill this elevated demand, conventional energy sources are shifting towards low production cost and long term usable alternative energy sources. Hybrid renewable energy systems (HRES) are becoming popular as stand-alone power systems for providing electricity in remote areas due to advancement in renewable energy technologies and subsequent rise in prices of petroleum products. Wind and solar power are considered feasible replacement to fossil fuels as the prediction of the fuel shortage in the near future, forced all operators involved in energy production to explore this new and clean source of power. Presented paper proposes fuzzy logic based Energy Management System (EMS) for Wind Turbine (WT), Photo Voltaic (PV) and Diesel Generator (DG) hybrid micro-grid configuration. Battery backup system is introduced for worst environmental conditions or high load demands. Dump load along with dump load controller is implemented for over voltage and over speed protection. Fuzzy logic based supervisory control system performs the power flow control between different scenarios such as battery charging, battery backup, dump load activation and DG backup in most intellectual way.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.43-58
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• 2014

As the use of trading systems has increased rapidly, many researchers have become interested in developing effective stock market prediction models using artificial intelligence techniques. Stock market prediction involves multifaceted interactions between market-controlling factors and unknown random processes. A successful stock prediction model achieves the most accurate result from minimum input data with the least complex model. In this research, we develop a combination model of ${\pi}$-fuzzy logic and support vector machine (SVM) models, using a genetic algorithm to optimize the parameters of the SVM and ${\pi}$-fuzzy functions, as well as feature subset selection to improve the performance of stock market prediction. To evaluate the performance of our proposed model, we compare the performance of our model to other comparative models, including the logistic regression, multiple discriminant analysis, classification and regression tree, artificial neural network, SVM, and fuzzy SVM models, with the same data. The results show that our model outperforms all other comparative models in prediction accuracy as well as return on investment.