• 한국멀티미디어학회논문지
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• 제7권6호
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• pp.841-850
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• 2004

This paper proposes a GA and GDM-based method for removing unnecessary rules and generating relevant rules from the fuzzy rules corresponding to several fuzzy partitions. The aim of proposed method is to find a minimum set of fuzzy rules that can correctly classify all the training patterns. When the fine fuzzy partition is used with conventional methods, the number of fuzzy rules has been enormous and the performance of fuzzy inference system became low. This paper presents the application of GA as a means of finding optimal solutions over fuzzy partitions. In each rule, the antecedent part is made up the membership functions of a fuzzy set, and the consequent part is made up of a real number. The membership functions and the number of fuzzy inference rules are tuned by means of the GA, while the real numbers in the consequent parts of the rules are tuned by means of the gradient descent method. It is shown that the proposed method has improved than the performance of conventional method in formulating and solving a combinatorial optimization problem that has two objectives: to maximize the number of correctly classified patterns and to minimize the number of fuzzy rules.

• International Journal of Internet, Broadcasting and Communication
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• 제13권3호
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• pp.63-72
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• 2021

Mobile crowd-sensing (MCS) is a promising sensing paradigm that leverages mobile users with smart devices to perform large-scale sensing tasks in order to provide services to specific applications in various domains. However, MCS sensing tasks may not always be successfully completed or timely completed for various reasons, such as accidentally leaving the tasks incomplete by the users, asynchronous transmission, or connection errors. This results in missing sensing data at specific locations and times, which can degrade the performance of the applications and lead to serious casualties. Therefore, in this paper, we propose a missing data inference approach, called missing data approximation with probabilistic tensor factorization (MDI-PTF), to approximate the missing values as closely as possible to the actual values while taking asynchronous data transmission time and different sensing locations of the mobile users into account. The proposed method first normalizes the data to limit the range of the possible values. Next, a probabilistic model of tensor factorization is formulated, and finally, the data are approximated using the gradient descent method. The performance of the proposed algorithm is verified by conducting simulations under various situations using different datasets.

• International journal of advanced smart convergence
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• 제12권2호
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• pp.56-66
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• 2023

The goal of deep learning is to extract complex features from multidimensional data use the features to create models that connect input and output. Deep learning is a process of learning nonlinear features and functions from complex data, and the user data that is employed to train deep learning models has become the focus of privacy concerns. Companies that collect user's sensitive personal information, such as users' images and voices, own this data for indefinite period of times. Users cannot delete their personal information, and they cannot limit the purposes for which the data is used. The study has designed a deep learning method that employs privacy protection technology that uses distributed collaborative learning so that multiple participants can use neural network models collaboratively without sharing the input datasets. To prevent direct leaks of personal information, participants are not shown the training datasets during the model training process, unlike traditional deep learning so that the personal information in the data can be protected. The study used a method that can selectively share subsets via an optimization algorithm that is based on modified distributed stochastic gradient descent, and the result showed that it was possible to learn with improved learning accuracy while protecting personal information.

• 스마트미디어저널
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• 제3권2호
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• pp.9-13
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• 2014

본 논문은 형광 X선 분석 시스템에서 관찰되는 스펙트럼에서 원하는 원소의 피크값을 검출하는데 쓰이는 마쿼트 알고리즘을 모바일 환경에서 더욱 효과적으로 사용하는 데에 있다. 이러한 마쿼트 알고리즘은 본래 잡음이 섞이기 전의 순수한 데이터가 무엇인지 알아가기 위한 유추해 가는 과정의 방법이다. 이러한 마쿼트 알고리즘에서 매우 중요한 역할을 하는 매개변수가 있는데 이 매개변수에 따라서 구하고자 하는 변수 값을 더욱 빠르게 구할 수도 있고 아닐 수도 있다. 기존의 방법에서 불필요한 계산량을 줄이기 위하여 매우 중요한 역할을 하는 매개변수인 ${\mu}$ 자리에 이 매개변수 대신 다른 매개변수를 도입한다. 또한 하드웨어적 측면을 고려시, 여러개의 정규분포의 모양으로 되어있는 함수를 여러개의 정규분포로 나누어서 생각하면 원하는 값을 구하기 더욱 간단해지지만 신뢰도 문제가 발생할 수 있다. 이러한 문제를 해결할 새로운 시스템을 제시한다.

• 한국멀티미디어학회논문지
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• 제16권11호
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• pp.1338-1347
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• 2013

Face recognition is a science of automatically identifying individuals based their unique facial features. In order to avoid overfitting and reduce the computational reduce the computational burden, a new face recognition algorithm using PCA-fisher linear discriminant (PCA-FLD) and fuzzy radial basis function neural network (RBFNN) is proposed in this paper. First, face features are extracted by the principal component analysis (PCA) method. Then, the extracted features are further processed by the Fisher's linear discriminant technique to acquire lower-dimensional discriminant patterns, the processed features will be considered as the input of the fuzzy RBFNN. As a widely applied algorithm in fuzzy RBF neural network, BP learning algorithm has the low rate of convergence, therefore, an improved learning algorithm based on Levenberg-Marquart (L-M) for fuzzy RBF neural network is introduced in this paper, which combined the Gradient Descent algorithm with the Gauss-Newton algorithm. Experimental results on the ORL face database demonstrate that the proposed algorithm has satisfactory performance and high recognition rate.

• 대한전기학회논문지
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• 제40권5호
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• pp.453-459
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• 1991

This paper presents a new algorithm to select optimal parameters and location of power system stabilizer (PSS). A new performance measure, which evaluates the share of a particular mode among state responses, is introduced. The gradient of the performance measure with respect to PSS parametes is derived in an explicit form, so optimal parameters of PSS can be obtained by the steepest descent method. The machine, with which it is most probable to reduce the performance measure, is identified as the optimum site for PSS application.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2017년도 추계학술발표대회
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• pp.770-773
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• 2017

Linear Regression 문제를 토대로 변형하여 선형회귀분석, 2차함수모형 회귀분석, '단조 증가(감소)' 3차 함수 모형 회귀분석과 그에 따라 변형되는 gradient descent 알고리즘을 기술한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2696-2698
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• 2000

본 연구에서는 실수형 염색체들로 구성된 개체에 대해 감수분열을 적용하여 개체를 만들고, 이 생식체들의 랜덤한 선택과 교배에 의해 세대가 진화함에 따라 탐색을 수행하는 감수분열 유전알고리즘을 이용하여 퍼지모델의 최적 구조와 파라미터를 탐색하고 Gradient Descent 알고리즘으로 파라미터를 정밀 조정하는 방안을 제안한다. 제안된 방안을 적용하여 Box-Jenkins의 가스로 데이터에 대한 퍼지모델을 구성하고 그 적용 가능성을 보인다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.179-184
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• 1998

In this paper, a new hierarchical fuzzy inference system (HFIS) using structured Takagi-Sugeno type fuzzy inference units(FIUs) is proposed. The proposed HFIS not only solves the rule explosion problem in conventional HFIS, but also overcomes the readability problem caused by the structure where outputs of previous level FIUs are used as input variables directly. Gradient descent algorithm is used for adaptation of fuzzy rules. The ball and beam control is performed in computer simulation to illustrate the performance of the proposed controller.

• Journal of the Optical Society of Korea
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• 제19권1호
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• pp.45-54
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• 2015

In this study, a novel parallel wavefront correction system architecture is proposed, and a model-based tabu search (MBTS) algorithm is introduced for this new system to compensate wavefront aberration caused by atmospheric turbulence in a free-space optical (FSO) communication system. The algorithm flowchart is presented, and a simple hypothetical design for the parallel correction system with multiple adaptive optical (AO) subsystems is given. The simulated performance of MBTS for an AO-FSO system is analyzed. The results indicate that the proposed algorithm offers better performance in wavefront aberration compensation, coupling efficiency, and convergence speed than a stochastic parallel gradient descent (SPGD) algorithm.