• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.591-598
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• 2018

Short term load forecasts complexly affected by socioeconomic factors and weather variables have non-linear characteristics. Thus far, researchers have improved load forecast technologies through diverse techniques such as artificial neural networks, fuzzy theories, and statistical methods in order to enhance the accuracy of load forecasts. Short term load forecast errors for special days are relatively much higher than that of weekdays. The errors are mainly caused by the irregularity of social activities and insufficient similar past data required for constructing load forecast models. In this study, the load characteristics of Lunar New Year's Day holidays well known for the highest error occurrence holiday period are analyzed to propose a load forecast technique for Lunar New Year's Day holidays. To solve the insufficient input data problem, the similarity of the load patterns of past Lunar New Year's Day holidays having similar patterns was judged by Euclid distance. Lunar New Year's Day holidays periods for 2011-2012 were forecasted by the proposed method which shows that the proposed algorithm yields better results than the comprehensive analysis method or the knowledge-based method.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.8
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• pp.471-480
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• 2003

The design of a measurement system to perform Harmonic State Estimation (HSE) is a very complex problem. Among the reasons for its complexity are the system size, conflicting requirements of estimator accuracy, reliability in the presence of transducer noise and data communication failures, adaptability to change in the network topology and cost minimization. In particular, the number of harmonic instruments available is always limited. Therefore, a systematic procedure is needed to design the optimal placement of measurement points. This paper presents a new HSE algorithm which is based on an optimal placement of measurement points using Genetic Algorithms (GAs) which is widely used in areas such as: optimization of the objective function, learning of neural networks, tuning of fuzzy membership functions, machine learning, system identification and control. This HSE has been applied to the Simulation Test Power System for the validation of the new HSE algorithm. The study results have indicated an economical and effective method for optimal placement of measurement points using Genetic Algorithms (GAs) in the Harmonic State Estimation (HSE).

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.621-652
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• 2012

Software Estimations provide an inclusive set of directives for software project developers, project managers, and the management in order to produce more realistic estimates based on deficient, uncertain, and noisy data. A range of estimation models are being explored in the industry, as well as in academia, for research purposes but choosing the best model is quite intricate. Estimation by Analogy (EbA) is a form of case based reasoning, which uses fuzzy logic, grey system theory or machine-learning techniques, etc. for optimization. This research compares the estimation accuracy of some conventional data mining models with a hybrid model. Different data mining models are under consideration, including linear regression models like the ordinary least square and ridge regression, and nonlinear models like neural networks, support vector machines, and multivariate adaptive regression splines, etc. A precise and comprehensible predictive model based on the integration of GRA and regression has been introduced and compared. Empirical results have shown that regression when used with GRA gives outstanding results; indicating that the methodology has great potential and can be used as a candidate approach for software effort estimation.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2206-2208
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• 2003

LMTT(Linear Motor-based Transfer Technology) is horizontal transfer system in the maritime container terminal for the port automation. The system is modeled PMLSM(Permanent Magnetic Linear Synchronous Motor) that is consists of stator modules on the rail and shuttle car(mover). Because of large variant of movers weight by loading and unloading containers, the difference of each characteristic of stator modules, and a stator module's default etc., LMCS(Linear Motor Conveyance System) is considered as that the system is changed its model suddenly and variously. In this paper, we will introduce the soft-computing method of a multi-step prediction control for LMCS using DR-FNN(Dynamically Constructed Recurrent Fuzzy Neural Network). The proposed control system is used two networks for multi-step prediction. Consequently, the system has an ability to adapt for external disturbance, cogging force, force ripple, and sudden changes of itself.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.352-357
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• 2006

매년 도로와 주차공간의 확장보다 차량의 수가 빠르게 증가하여 그에 따라 불법 주차 관리의 어려움이 증가하고 있다. 이러한 문제점을 해결하기 위해 지능형 주차 관리 시스템이 필요하게 되었다. 본 논문에서는 획득된 차량 영상에서 수직 에지의 특징을 이용하여 번호판 영역과 개별 코드를 추출하고, 추출된 개별 코드를 퍼지 신경망 알고리즘을 제안하여 학습 및 인식한다. 본 논문에서는 차량 번호판 영역을 검출하기 위해 프리윗 마스크를 적용하여 수직 에지를 찾고, 차량 번호판의 정보를 이용하여 잡음을 제거한 후에 차량 번호판 영역을 추출한다. 추출된 차량 번호판 영역은 반복 이진화방법을 적용하여 이진화하고, 이진화된 차량 번호판 영역에 대해서 수직 분포도와 수평 분포도를 이용하여 번호판의 개별 코드를 추출한다 추출된 개별 코드는 제안된 퍼지 신경망 알고리즘을 적용하여 인식한다. 제안된 퍼지 신경망은 입력층과 중간층간의 학습 구조로는 FCM 알고리즘을 적용하고 중간층과 출력층간의 학습 구조는 Max_Min 신경망을 적용한다. 제안된 방법의 추출 및 인식 성능을 평가하기 위하여 실제 차량 영상 150장을 대상으로 실험한 결과, 기존의 차량 번호판 인식 방법보다 효율적이고 인식 성능이 개선된 것을 확인하였다.

• Journal of IKEEE
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• v.23 no.2
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• pp.716-721
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• 2019

A novel switching differentiator-based backstepping controller for uncertain pure-feedback nonlinear systems is proposed. Using asymptotically convergent switching differentiator, time-derivatives of the virtual controls are directly estimated in every backstepping design steps. As a result, the control law has an extremely simple form and asymptotical stability of the tracking error is guaranteed regardless of parametric or unstructured uncertainties and unmatched disturbances in the considered system. It is required no universal approximators such as neural networks or fuzzy logic systems that are adaptively tuned online to cope with system uncertainties. Simulation results show the simplicity and performance of the proposed controller.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.7
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• pp.1588-1593
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• 2005

In coagulant control of water treatment plants, rule extraction, one of datamining categories, was performed for coagulant control of a water treatment plant. Clustering methods were applied to extract control rules from data. These control rules can be used for fully automation of water treatment plants instead of operator's knowledge for plant control. To perform fuzzy clustering, there are some coefficients to be determined and these kinds of studies have been performed over decades such as clustering indices. In this study, statistical indices were taken to calculate the number of clusters. Simultaneously, seed points were found out based on hierarchical clustering. These statistical approaches give information about features of clusters, so it can reduce computing cost and increase accuracy of clustering. The proposed algorithm can play an important role in datamining and knowledge discovery.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.4
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• pp.13-20
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• 2009

RFIDs have been used a lot of control systems such as library and security efficiently. Unlawful parking control is one of them and it will bring a lot of merit. Especially, it can be used vehicles. If a vehicle comes to unlawful parking place, reader system read the tag of a vehicle. RFID reader confirm the vehicle and record current time at the same time send information related the vehicle to the server system. After, it can be activated. If the vehicle move from unlawful parking place, RFID reader record departed time. In this paper, we proposed a monitoring system for unlawful parking cars. Especially, it is certain that this proposed modelling is very efficient and correct.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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• pp.137-142
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• 2012

The temporal subtraction technique as one of computer aided diagnosis has been introduced in medical fields to enhance the interval changes such as formation of new lesions and changes in existing abnormalities on deference image. With the temporal subtraction technique radiologists can easily detect lung nodules on visual screening. Until now, two-dimensional temporal subtraction imaging technique has been introduced for the clinical test. We have developed new temporal subtraction method to remove the subtraction artifacts which is caused by mis-registration on temporal subtraction images of lungs on MDCT images. In this paper, we propose a new computer aided diagnosis scheme for automatic enhancing the lung nodules from the temporal subtraction of thoracic MDCT images. At first, the candidates regions included nodules are detected by the multiple threshold technique in terms of the pixel value on the temporal subtraction images. Then, a rule-base method and artificial neural networks is utilized to remove the false positives of nodule candidates which is obtained temporal subtraction images. We have applied our detection of lung nodules to 30 thoracic MDCT image sets including lung nodules. With the detection method, satisfactory experimental results are obtained. Some experimental results are shown with discussion.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.5
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• pp.696-704
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• 2013

In this paper, we introduce the LED emotional lighting system realized with the aid of both intelligent algorithm and RGB LED combined with White LED. Generally, the illumination is known as a design factor to form the living place that affects human's emotion and action in the light- space as well as the purpose to light up the specific space. The LED emotional lighting system that can express emotional atmosphere as well as control the quantity of light is designed by using both RGB LED to form the emotional mood and W LED to get sufficient amount of light. RBFNNs is used as the intelligent algorithm and the network model designed with the aid of LED control parameters (viz. color coordinates (x and y) related to color temperature, and lux as inputs, RGBW current as output) plays an important role to build up the LED emotional lighting system for obtaining appropriate color space. Unlike conventional RBFNNs, Fuzzy C-Means(FCM) clustering method is used to obtain the fitness values of the receptive function, and the connection weights of the consequence part of networks are expressed by polynomial functions. Also, the parameters of RBFNN model are optimized by using PSO(Particle Swarm Optimization). The proposed LED emotional lighting can save the energy by using the LED light source and improve the ability to work as well as to learn by making an adequate mood under diverse surrounding conditions.