• Journal of Internet Computing and Services
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• v.15 no.5
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• pp.17-24
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• 2014

Blind algorithms designed to maximize the probability that constant modulus errors become zero carry out some summation operations for a set of constant modulus errors at an iteration time inducing heavy complexity. For the purpose of reducing this computational burden induced from the summation, a new approach to the estimation of the zero-error probability (ZEP) of constant modulus errors (CME) and its gradient is proposed in this paper. The ZEP of CME at the next iteration time is shown to be calculated recursively based on the currently calculated ZEP of CME. It also is shown that the gradient for the weight update of the algorithm can be obtained by differentiating the ZEP of CME estimated recursively. From the simulation results that the proposed estimation method of ZEP-CME and its gradient produces exactly the same estimation results with a significantly reduced computational complexity as the block-processing method does.

• Journal of Aquaculture
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• v.10 no.3
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• pp.301-310
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• 1997

To investigate a possibility of the species genetic relationship for the soluble proteins analysis on the class Cephalopoda in the Yellow Sea, the isolate eye, muscle and liver proteins from five species (Sepia esculenta, Sepiella japonica, Loligo chinensis, Loligo beka and Octopus minor) were analysed using different electrophoretic techniques (Davis-polyacrylamide gel electrophoresis, SDS-PAGE, exponential gradient SDS-PAGE, thin-layer isoelectro-focusing and two-dimensional PAGE). The average molecular weight of the soluble eye and muscle proteins was estimated at 35-50 KDa, separated b the exponential gradient SDS-PAGE. It was corresponds to that of electrophoretic patterns by t재 dimensional PAGE. By which the thin layer IEF, the target proteins showed a reasonable specificity based on their isoelectric points (pI) 7.5-8.5.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.239-248
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• 2005

The reliability and efficiency of network must be considered in the large scale wireless sensor networks. Broadcast method must be used rather than unicast method to enhance the reliability of networks. In recently proposed GRAB (GRAdient Broadcast) can certainly enhance reliability of networks fy using broadcast but its efficiency regarding using energy of network is low due to using only one sink. Hence, the lifetime of networks is reduced. In the paper we propose the scheme of SMSGB (Selective Multi Sink Gradient Broadcast) which uses single sink of multi-sink networks. The broadcast based SMSGB can secure reliability of large scale wireless sensor networks. The SMSGB can also use the network's energy evenly via multi sink distribution. Our experiments show that using SMSGB was reliable as GRAB and it increased the network's lifetime by 18% than using GRAB.

• The Journal of Korean Institute of Information Technology
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• v.18 no.5
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• pp.55-64
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• 2020

Estimating the state of charge (SOC) of a battery is essential for increasing the stability and reliability of a photovoltaic system. In this study, battery SOC estimation methods were proposed using artificial neural networks (ANNs) with gradient descent (GD), Levenberg-Marquardt (LM), and scaled conjugate gradient (SCG), and an adaptive neuro-fuzzy inference system (ANFIS). The charge start voltage and the integrated charge current were used as input data and the SOC was used as output data. Four models (ANN-GD, ANN-LM, ANN-SCG, and ANFIS) were implemented for battery SOC estimation and compared using MATLAB. The experimental results revealed that battery SOC estimation using the ANFIS model had both the highest accuracy and highest convergence speed.

• Journal of Digital Convergence
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• v.17 no.10
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• pp.251-258
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• 2019

In the future, various products are created in various fields using artificial intelligence. In this age, it is a very important problem to know the operation principle of artificial intelligence learning method and to use it correctly. This paper introduces artificial intelligence learning methods that have been known so far. Learning of artificial intelligence is based on the fixed point iteration method of mathematics. The GD(Gradient Descent) method, which adjusts the convergence speed based on the fixed point iteration method, the Momentum method to summate the amount of gradient, and finally, the Adam method that mixed these methods. This paper describes the advantages and disadvantages of each method. In particularly, the Adam method having adaptivity controls learning ability of machine learning. And we analyze how these methods affect digital signals. The changes in the learning process of digital signals are the basis of accurate application and accurate judgment in the future work and research using artificial intelligence.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.11a
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• pp.331-332
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• 2011

본 논문에서는 Markov random field (MRF) 프레임워크와 영상의 기울기(gradient) 정보를 이용한 필드영상의 공간적 디인터레이싱(deinterlacing) 알고리즘을 제안한다. 기존의 디인터레이싱 결과를 보면 때때로 에지 부분의 연결이 정밀하지 못하여 눈에 거슬리는 재깅(jagging) 현상 등의 결함이 나타나기도 하는데, 제안하는 알고리즘은 이러한 현상을 줄이고자 영상의 기울기 도메인(gradient domain)에서 디인터레이싱을 수행한다. 즉, 제안하는 방식은 필드 영상으로부터 기울기 영상을 얻고 이를 보간한 후 필드영상과 복원된 기울기 영상을 토대로 원본 영상을 복원한다. 이 과정에서 각각의 픽셀마다 기울기 영상의 보간을 위한 에지 방향의 추정이 필요한데, 이 과정에서는 MRF 모델을 기반으로 에너지 함수를 설계하고 최적화시킴으로써 보다 강건한 추정결과를 얻도록 하였다. 프레임 영상 복원은 기울기 영상과 필드 영상 정보를 사전 정보로 하여 선형 방정식을 세우고 푸는 과정으로 이루어진다. 실험한 결과, 제안된 방법의 결과가 기존 방법에 비하여 눈에 띄는 결함을 줄이고 좋은 성능을 보임을 확인할 수 있다.

• Journal of Digital Contents Society
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• v.11 no.1
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• pp.39-45
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• 2010

In this paper we propose a new fast peak searching method using the gradient and present simulation results. The proposed method is a solution to the problem that finds the peak(maximum) of the unimodal function on a finite interval with minimum searching steps. Its main application is the auto-focus in the mobile phone. We propose the three steps to find the peak; periodic search, gradient-based search and detail search. In simulation we generated the Gaussian functions with white noise and have the result of about 8 searching steps and 1.04 errors on average.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.4
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• pp.812-820
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• 2012

In this paper, multiple pedestrians tracking system using Histogram of Oriented Gradient and occlusion detection is proposed. The proposed system is applicable to Intelligent Surveillance System. First, we detect pedestrian in a image sequence using pedestrian's feature. To get pedestrian's feature, we make block-histogram using gradient's direction histogram based on HOG(Histogram of Oriented Gradient), after that a pedestrian region is classified by using Linear-SVM(Support Vector Machine) training. Next, moving objects are tracked by using position information of the classified pedestrians. And we create motion trajectory descriptor which is used for content based event retrieval. The experimental results show that the proposed method is more fast, accurate and effective than conventional methods.

• Journal of Biomedical Engineering Research
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• v.20 no.1
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• pp.53-58
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• 1999

Eddy current in MRI systems degrades gradient field linearity and distorts gradient waveform. When the waveform distortion is spatially variant, it is very difficult to perform special imaging techniques such as the echo planar imaging technique or the fast spin echo imaging technique. In this study, we have developed a new technique to estimate the distorted gradient waveforms at any points inside the imaging region using the finite element method. After obtaining the eddy-current-effect transfer function, which represents magnitude and phase characteristics of the gradient field at a particular point, we have used the transfer function to estimate the actual gradient waveforms at the point. To verify the proposed technique, we have compared the estimated gradient waveforms with the measured ones.

• Journal of the Korea Society for Simulation
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• v.18 no.2
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• pp.49-55
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• 2009

In this paper, we investigate an optimal allocation of constant service resources in stochastic system to optimize the expected performance of interest. For this purpose, we use the control variates to estimate the gradients of expected performance with respect to given resource parameters, and apply these estimated gradients in stochastic optimization algorithm to find the optimal allocation of resources. The proposed gradient estimation method is advantageous in that it uses simulation results of a single design point without increasing the number of design points in simulation experiments and does not need to describe the logical relationship among realized performance of interest and perturbations in input parameters. We consider the applications of this research to various models and extension of input parameter space as the future research.