• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1033-1043
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• 2007

The multi layer perceptron for segmenting objects in images only uses the input windows that are made from a image in a fixed size. These windows are recognized so each independent learning data that they make the performance of the multi layer perceptron poor. The poor performance is caused by not considering the position information and effect of input windows in input images. So we propose a new approach to add the position information and effect of input windows to the multi layer perceptron#s input layer. Our new approach improves the performance as well as the learning time in the multi layer perceptron. In our experiment, we can find our new algorithm good.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.2
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• pp.31-38
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• 2011

Skin color is a very important information for an automatic face recognition. In this paper, we proposed a skin region extraction method using the MLP(Multi-Layer Perceptron) and skin color model. We use the adaptive lighting compensation technique for improved performance of skin region extraction. Also, using an preprocessing filter, normally large areas of easily distinct non-skin pixels, are eliminated from further processing. Experimental results show that the proposed method has better performance than the conventional methods, and reduces processing time by 31~49% on average.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.3
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• pp.79-89
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• 1998

The Error Back-Propagation(EBP) algorithm is widely applied to train a multi-layer perceptron, which is a neural network model frequently used to solve complex problems such as pattern recognition, adaptive control, and global optimization. However, the EBP is basically a gradient descent method, which may get stuck in a local minimum, leading to failure in finding the globally optimal solution. Moreover, a multi-layer perceptron suffers from locking a systematic determination of the network structure appropriate for a given problem. It is usually the case to determine the number of hidden nodes by trial and error. In this paper, we propose a new algorithm to efficiently train a multi-layer perceptron. OUr algorithm uses stochastic perturbation in the weight space to effectively escape from local minima in multi-layer perceptron learning. Stochastic perturbation probabilistically re-initializes weights associated with hidden nodes to escape a local minimum if the probabilistically re-initializes weights associated with hidden nodes to escape a local minimum if the EGP learning gets stuck to it. Addition of new hidden nodes also can be viewed asa special case of stochastic perturbation. Using stochastic perturbation we can solve the local minima problem and the network structure design in a unified way. The results of our experiments with several benchmark test problems including theparity problem, the two-spirals problem, andthe credit-screening data show that our algorithm is very efficient.

• Smart Media Journal
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• v.7 no.1
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• pp.31-36
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• 2018

In this paper, we propose automatic screen on / off method of OST-HMD screen using gaze depth estimation technique. The proposed method uses MLP (Multi-layer Perceptron) to learn the user's gaze information and the corresponding distance of the object, and inputs the gaze information to estimate the distance. In the learning phase, eye-related features obtained using a wearable eye-tracker. These features are then entered into the Multi-layer Perceptron (MLP) for learning and model generation. In the inference step, eye - related features obtained from the eye tracker in real time input to the MLP to obtain the estimated depth value. Finally, we use the results of this calculation to determine whether to turn the display of the HMD on or off. A prototype was implemented and experiments were conducted to evaluate the feasibility of the proposed method.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.51-59
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• 2019

Due to the exponential growth of access information on the web, the need for predicting web users' next access has increased. Various models such as markov models, deep neural networks, support vector machines, and fuzzy inference models were proposed to handle web access prediction. For deep learning based on neural network models, training time on large-scale web usage data is very huge. To address this problem, deep neural network models are trained on cluster of computers in parallel. In this paper, we investigated impact of several important spark parameters related to data partitions, shuffling, compression, and locality (basic spark parameters) for training Multi-Layer Perceptron model on Spark standalone cluster. Then based on the investigation, we tuned basic spark parameters for training Multi-Layer Perceptron model and used it for tuning Spark when training Multi-Layer Perceptron model for web access prediction. Through experiments, we showed the accuracy of web access prediction based on our proposed web access prediction model. In addition, we also showed performance improvement in training time based on our spark basic parameters tuning for training Multi-Layer Perceptron model over default spark parameters configuration.

• The Journal of the Acoustical Society of Korea
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• v.12 no.1
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• pp.55-60
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• 1993

본 논문은 불특정 화자의 단모음 인식에 관한 연구로써, VQ(Vectro Quantization)와 MLP(multi-layer perceptron)에 의한 음성 인식 방법을 제안한다. 이 방법은 VQ codebook을 구하고 이를 이용해서 관측열(observation sequence)을 구해각 codeword가 데이터로부터 가질 수 있는 확률값을 계산하여 이 값을 신경 회로망의 입력으로 사용하는 방법이다. 인식 대상으로는 한국어 단모음을 선정하였으며 10명의 남성 화자가 8개의 단모음을 10번씩 발음한 것으로 시스템의 효율성을 알아보기 위해 VQ/HMM(hidden markov model)에 의한 인식과 비교 실험한다. 실험 결과에 의하면, 시스템의 단순성에도 불구하고 학습능력애 뛰어난 관계로 VQ/HMM보다 VQ와 MLP에 의한 음성 인식률이 향상됨을 보여준다.

• International Journal of Contents
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• v.12 no.1
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• pp.49-53
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• 2016

"Hahoe Village" in Andong region is an UNESCO World Heritage Site. It should be protected against various disasters such as fire, flooding, earthquake, etc. Among these disasters, flooding has drastic impact on the lives and properties in a wide area. Since "Hahoe Village" is adjacent to Nakdong River, it is important to monitor the water level near the village. In this paper, we developed a hydrological modelling using multi-layer perceptron (MLP) to predict the water level of Nakdong River near "Hahoe Village". To develop the prediction model, error back-propagation (EBP) algorithm was used to train the MLP with water level data near the village and rainfall data at the upper reaches of the village. After training with data in 2012 and 2013, we verified the prediction performance of MLP with untrained data in 2014.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.85-96
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• 2020

It is important to extract salient object image and to solve the invariance problem for image recognition. In this paper we propose a new hybrid algorithm for invariance and improved classification performance in image recognition, whose algorithm is combined by FT(Frequency-tuned Salient Region Detection) algorithm, Guided filter, Zernike moments, and a simple artificial neural network (Multi-layer Perceptron). The conventional FT algorithm is used to extract initial salient object image, the guided filtering to preserve edge details, Zernike moments to solve invariance problem, and a classification to recognize the extracted image. For guided filtering, guided filter is used, and Multi-layer Perceptron which is a simple artificial neural networks is introduced for classification. Experimental results show that this algorithm can achieve a superior performance in the process of extracting salient object image and invariant moment feature. And the results show that the algorithm can also classifies the extracted object image with improved recognition rate.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.491-496
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• 2004

The oscillation of the fluid caused by external forces is called sloshing, which occurs in moving vehicles with contained liquid masses, such as aircraft. cars and liquid rocket and so on. This sloshing effect could be a severe problem in vehicle stability and control. So, various baffles are used in order to reduce the sloshing. The Lagrangian, Eulerian and ALE numerical method is widely used on the analysis of sloshing presently. But, these numerical methods are needed so many CPU time. In this study, for the reduction of the sloshing analysis time, me multi.layer perceptron artificial neural network is introduced and analysis results are presented.

• Journal of Korea Multimedia Society
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• v.25 no.6
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• pp.783-792
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• 2022

Versatile Video Coding (VVC) is the latest video coding standard, which had been developed by the Joint Video Experts Team (JVET) of ITU-T Video Coding Experts Group (VCEG) and ISO/IEC Moving Picture Experts Group (MPEG) in 2020. Although VVC can provide powerful coding performance, it requires tremendous computational complexity to determine the optimal block structures during the encoding process. In this paper, we propose a fast ternary tree decision method using two neural networks with 7 nodes as input vector based on the multi-layer perceptron structure, names STH-NN and STV-NN. As a training result of neural network, the STH-NN and STV-NN achieved accuracies of 85% and 91%, respectively. Experimental results show that the proposed method reduces the encoding complexity up to 25% with unnoticeable coding loss compared to the VVC test model (VTM).