• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
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• pp.18-20
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• 2005

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a neural network used for identification of the process dynamics of s signal input and signal output system and it was shown that this method offered superior capability over the conventional back propagation algorithm. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal estimate input signal about controller using neural networks.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 심포지엄 논문집 정보 및 제어부문
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• pp.89-91
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• 2004

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And in practice since it is difficult to the PID gains suitably lots of researches have been reported with respect to turning schemes of PID gains. A Neural Network-based PID control scheme is proposed, which extracts skills of human experts as PID gains. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based PID control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal is convergence speed progress about applied gain of PID controller using the neural networks.

• 응용통계연구
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• 제31권2호
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• pp.265-285
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• 2018

본 연구에서는 미세먼지 $PM_{10}$의 4가지 분류 등급인 '좋음, 보통, 나쁨, 매우 나쁨' 그리고 2가지 분류 등급인 '좋음 혹은 보통, 나쁨 혹은 매우 나쁨'을 예측하기 위해서 심층 신경망모형을 사용하였다. 2010년부터 2015년까지 국내 6개 대도시 지역에서 관측한 일별 미세먼지 데이터에 대하여 기존 분류기법인 신경망모형, 다항 로지스틱 회귀모형, Support Vector Machine, Random Forest을 적용했을 때에 비해서 심층 신경망모형의 정확도는 더 높아졌다.

• 전자공학회논문지B
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• 제32B권12호
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• pp.1581-1590
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• 1995

Recently, advanced video coding techniques using segmentation technique have been actively researched as candidates for video coding of MPEG-4 standard. The conventional segmentation techniques are unsuitable for real-time process because they have sequential structure. In this paper, we propose a new image segmentation technique using competitive learning neural network for vector quantization. The proposed segmentation procedure consist of prefiltering, primary and secondary segmentation, and a small region ellimination process. Primary segmentation segments input image in detail. Secondary segmentation merges similar region using a repetitive FSCL(Frequency sensitive competive learning) neural network. In this process, it is possible to segment an image from high resolution to low resolution by adjusting the number of repetition. Finally, small regions are merged into adjacent regions. Experimental results show that the procedure described yields reconstructed images of reasonably acceptable quality at bit rates of 0. 25 - 0.3 bit/pel.

• IEIE Transactions on Smart Processing and Computing
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• 제4권2호
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• pp.110-114
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• 2015

This paper reports a novel object-detection technique in video sequences. The proposed algorithm consists of detection of objects in illuminated and occluded videos by using object features and a neural network technique. It consists of two functional modules: region-based object feature extraction and continuous detection of objects in video sequences with region features. This scheme is proposed as an enhancement of the Lowe's scale-invariant feature transform (SIFT) object detection method. This technique solved the high computation time problem of feature generation in the SIFT method. The improvement is achieved by region-based feature classification in the objects to be detected; optimal neural network-based feature reduction is presented in order to reduce the object region feature dataset with winner pixel estimation between the video frames of the video sequence. Simulation results show that the proposed scheme achieves better overall performance than other object detection techniques, and region-based feature detection is faster in comparison to other recent techniques.

• Computers and Concrete
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• 제30권3호
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• pp.209-224
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• 2022

In this paper, the artificial neural network (ANN) is employed to predict the flexural behavior of reinforced concrete (RC) beams retrofitted with carbon fiber/epoxy composites modified by carbon nanotubes (CNTs). Multiple techniques are used to improve the accuracy of the ANN prediction, as the data represents a multivalued function. These techniques include static ANN modeling, ANN modeling with load history, and ANN modeling with double load history. The developed ANN models are used to predict the load-displacement profiles of beams retrofitted with either CFRP or CNTs modified CFRP, flexural capacity, and maximum displacement of the beams. The results demonstrate that the ANN is able to predict the flexural behavior of the retrofitted RC beams as well as the effect of each parameter including the type of the used epoxy and the presence of the CNTs.

• International Journal of Computer Science & Network Security
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• 제24권5호
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• pp.212-216
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• 2024

Feature Selection have turned into the main point of investigations particularly in bioinformatics where there are numerous applications. Deep learning technique is a useful asset to choose features, anyway not all calculations are on an equivalent balance with regards to selection of relevant features. To be sure, numerous techniques have been proposed to select multiple features using deep learning techniques. Because of the deep learning, neural systems have profited a gigantic top recovery in the previous couple of years. Anyway neural systems are blackbox models and not many endeavors have been made so as to examine the fundamental procedure. In this proposed work a new calculations so as to do feature selection with deep learning systems is introduced. To evaluate our outcomes, we create relapse and grouping issues which enable us to think about every calculation on various fronts: exhibitions, calculation time and limitations. The outcomes acquired are truly encouraging since we figure out how to accomplish our objective by outperforming irregular backwoods exhibitions for each situation. The results prove that the proposed method exhibits better performance than the traditional methods.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1995년도 추계학술대회발표논문집; 서울대학교, 서울; 30 Sep. 1995
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• pp.334-343
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• 1995

In recent years, neural networks have been developed as an alternative to traditional statistical techniques. In this study, a neural network model was compared to traditional forecasting models in terms of their capabilities to forecast passenger traffic for flights between U.S. and Korea. The results show that the forecasting ability of the neural networks was superior to the traditional models. In terms of accuracy, the performance of the neural networks was quite encouraging. Using mean absolute deviation, the neural network performed best. The new technique is easy to learn and apply with commercial neural network software. Therefore, airline decision makers should benefit from using neural networks in forecasting passenger loads.

• 전산구조공학
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• 제10권2호
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• pp.265-272
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• 1997

본 논문은 신경망 근사 해석 모델 개발을 궁극적인 목적으로 하는 기초적 연구로서, 기존의 수치해석 알고리즘과의 성능 비교를 통하여 신경망 알고리즘의 특성과 역할을 수치해석의 관점에서 정확히 판단하는데 목적이 있다. 신경망 알고리즘을 변형하여 선형 연립 방정식의 해를 구하는 두가지 방법을 제안하였고, 회귀분석, 보간법과의 비교를 통하여 광범위한 근사자(universal approximator)로서의 역할을 보였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.717-719
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• 2003

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And in practice since it is difficult to the PID gains suitably lots of researches have been reported with respect to turning schemes of PID gains. A Neural Network-based PID control scheme is proposed, which extracts skills of human experts as PID gains. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based PID control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident.