• 제목/요약/키워드: Linear Prediction Algorithm

검색결과 289건 처리시간 0.025초

오피니언 마이닝과 머신러닝을 이용한 페이스북 인기 게시물 예측 시스템 (Prediction System of Facebook's popular post using Opinion Mining and Machine Learning)

  • 안현우;문남미
    • 한국방송∙미디어공학회:학술대회논문집
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    • 한국방송∙미디어공학회 2017년도 추계학술대회
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    • pp.70-73
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    • 2017
  • 페이스북 SNS 플랫폼에서 제공하는 데이터 수집 프로토콜을 이용해 콘텐츠들의 인기 점수와 사용자 의견들을 수집하고 수집된 정보를 가공하여 기계학습을 진행한다. 오피니언 데이터를 학습함으로 인해 인간의 관점을 모방하게 되며 결과적으로 콘텐츠의 질을 판단하는 요소로써 작용하도록 한다. 데이터의 수집은 페이스북 측에서 제공하는 Graph API 와 Python 을 이용하여 진행한다. Graph API 는 HTTP GET 방식의 프로토콜을 이용하여 요청 하고 JSON 형식으로 결과를 반환한다. 학습은 Multiple Linear Regression 과 Gradient Descent Algorithm(GDA)을 사용하여 진행한다. 이후 학습이 진행된 프로그램에 사용자 의견 데이터를 건네주면 최종인기 점수를 예측하는 시스템을 설명한다.

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Fractal behavior identification for monitoring data of dam safety

  • Su, Huaizhi;Wen, Zhiping;Wang, Feng
    • Structural Engineering and Mechanics
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    • 제57권3호
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    • pp.529-541
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    • 2016
  • Under the interaction between dam body, dam foundation and external environment, the dam structural behavior presents the time-varying nonlinear characteristics. According to the prototypical observations, the correct identification on above nonlinear characteristics is very important for dam safety control. It is difficult to implement the description, analysis and diagnosis for dam structural behavior by use of any linear method. Based on the rescaled range analysis approach, the algorithm is proposed to identify and extract the fractal feature on observed dam structural behavior. The displacement behavior of one actual dam is taken as an example. The fractal long-range correlation for observed displacement behavior is analyzed and revealed. The feasibility and validity of the proposed method is verified. It is indicated that the mechanism evidence can be provided for the prediction and diagnosis of dam structural behavior by using the fractal identification method. The proposed approach has a high potential for other similar applications.

CDMA 네트워크에서의 ECG 압축 알고리즘의 성능 평가 (Performance Evaluation of Wavelet-based ECG Compression Algorithms over CDMA Networks)

  • 김병수;유선국
    • 대한전기학회논문지:시스템및제어부문D
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    • 제53권9호
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    • pp.663-669
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    • 2004
  • The mobile tole-cardiology system is the new research area that support an ubiquitous health care based on mobile telecommunication networks. Although there are many researches presenting the modeling concepts of a GSM-based mobile telemedical system, practical application needs to be considered both compression performance and error corruption in the mobile environment. This paper evaluates three wavelet ECG compression algorithms over CDMA networks. The three selected methods are Rajoub using EPE thresholding, Embedded Zerotree Wavelet(EZW) and Wavelet transform Higher Order Statistics Coding(WHOSC) with linear prediction. All methodologies protected more significant information using Forward Error Correction coding and measured not only compression performance in noise-free but also error robustness and delay profile in CDMA environment. In addition, from the field test we analyzed the PRD for movement speed and the features of CDMA 1X. The test results show that Rajoub has low robustness over high error attack and EZW contributes to more efficient exploitation in variable bandwidth and high error. WHOSC has high robustness in overall BER but loses performance about particular abnormal ECG.

Text Mining and Sentiment Analysis for Predicting Box Office Success

  • Kim, Yoosin;Kang, Mingon;Jeong, Seung Ryul
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제12권8호
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    • pp.4090-4102
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    • 2018
  • After emerging online communications, text mining and sentiment analysis has been frequently applied into analyzing electronic word-of-mouth. This study aims to develop a domain-specific lexicon of sentiment analysis to predict box office success in Korea film market and validate the feasibility of the lexicon. Natural language processing, a machine learning algorithm, and a lexicon-based sentiment classification method are employed. To create a movie domain sentiment lexicon, 233,631 reviews of 147 movies with popularity ratings is collected by a XML crawling package in R program. We accomplished 81.69% accuracy in sentiment classification by the Korean sentiment dictionary including 706 negative words and 617 positive words. The result showed a stronger positive relationship with box office success and consumers' sentiment as well as a significant positive effect in the linear regression for the predicting model. In addition, it reveals emotion in the user-generated content can be a more accurate clue to predict business success.

적응 DPCM에 의한 영상정보 감축 (Image Information Compression by Adaptive Differential Pulse Code Modulation)

  • 조병걸;한영오;오진성;이세현;장인호;이웅천;박상희
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1988년도 전기.전자공학 학술대회 논문집
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    • pp.229-231
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    • 1988
  • In this paper, we design adaptive DPCM by the two dimensional linear prediction using the covariance method and propose adaptive algorithm which determines analysis frame size by the compassion of the local standard deviation and the global standard deviation of images.

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다층 신경망을 이용한 비선형 예측 알고리즘에 관한 연구 (A Study on the Non-linear Prediction Algorithm using Multi-layer Neural Network)

  • 박형근;김선엽
    • 한국산학기술학회:학술대회논문집
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    • 한국산학기술학회 2007년도 추계학술발표논문집
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    • pp.155-158
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    • 2007
  • 현대사회의 발전으로 인해 생성되는 수많은 정보는 시간과 공간의 제약이 없는 다차원적인 특성을 갖고 있으며, 사회 전반에 걸쳐 보다 나은 결과를 위한 의사결정에 활용되고 있다. 또한 우리생활에서 발생하는 많은 현상을 보다 합리적이고 과학적인 방법으로 분석하여 정확한 예측이 이루어진다면 미래의 불확실성에 대한 불안을 해소하고, 현재의 의사 결정을 하는데 큰 도움이 될 수 있다. 따라서 본 논문에서는 다층 신경망을 이용하여 비선형적 관계를 표현할 수 있는 적응 능력을 갖을 뿐만 아니라 비선형 통계예측에 적용이 가능한 알고리즘을 제안하고 분석하였다.

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Online Selective-Sample Learning of Hidden Markov Models for Sequence Classification

  • Kim, Minyoung
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제15권3호
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    • pp.145-152
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    • 2015
  • We consider an online selective-sample learning problem for sequence classification, where the goal is to learn a predictive model using a stream of data samples whose class labels can be selectively queried by the algorithm. Given that there is a limit to the total number of queries permitted, the key issue is choosing the most informative and salient samples for their class labels to be queried. Recently, several aggressive selective-sample algorithms have been proposed under a linear model for static (non-sequential) binary classification. We extend the idea to hidden Markov models for multi-class sequence classification by introducing reasonable measures for the novelty and prediction confidence of the incoming sample with respect to the current model, on which the query decision is based. For several sequence classification datasets/tasks in online learning setups, we demonstrate the effectiveness of the proposed approach.

The Application of FBNWT in Wave Overtopping Analysis

  • ;;현범수
    • 한국해양공학회지
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    • 제22권1호
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    • pp.1-5
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    • 2008
  • A 2-D Fluent-based numerical wave tank(FBNWT) capable of simulating wave propagating and overtopping is presented. The FBNWT model is based on the Reynolds averaged Naiver-Stokes equations and VOF free surface tracking method. The piston wave maker system is realized by dynamic mesh technology(DMT) and user defined function(UDF). The non-iteration time advancement(NITA) PISO algorithm is employed for the velocity and pressure coupling. The FBNWT numerical solutions of linear wave propagation have been validated by analytical solutions. Several overtopping problems are simulated and the prediction results show good agreements with the experimental data, which demonstrates that the present model can be utilized in the corresponding analysis.

Simulation of Reservoir Sediment Deposition in Low-head Dams using Artificial Neural Networks

  • Idrees, Muhammad Bilal;Sattar, Muhammad Nouman;Lee, Jin-Young;Kim, Tae-Woong
    • 한국수자원학회:학술대회논문집
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    • 한국수자원학회 2019년도 학술발표회
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    • pp.159-159
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    • 2019
  • In this study, the simulation of sediment deposition at Sangju weir reservoir, South Korea, was carried out using artificial neural networks. The ANNs have typically been used in water resources engineering problems for their robustness and high degree of accuracy. Three basic variables namely turbid water inflow, outflow, and water stage have been used as input variables. It was found that ANNs were able to establish valid relationship between input variables and target variable of sedimentation. The R value was 0.9806, 0.9091, and 0.8758 for training, validation, and testing phase respectively. Comparative analysis was also performed to find optimum structure of ANN for sediment deposition prediction. 3-14-1 network architecture using BR algorithm outperformed all other combinations. It was concluded that ANN possess mapping capabilities for complex, non-linear phenomenon of reservoir sedimentation.

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Active Trajectory Tracking Control of AMR using Robust PID Tunning

  • Tae-Seok Jin
    • 한국산업융합학회 논문집
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    • 제27권4_1호
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    • pp.753-758
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    • 2024
  • Trajectory tracking of the AMR robot is one research for the AMR robot navigation. For the control system of the Autonomous mobile robot(AMR) being in non-honolomic system and the complex relations among the control parameters, it is d ifficult to solve the problem based on traditional mathematics model. In this paper, we presents a simple and effective way of implementing an adaptive tracking controller based on the PID for AMR robot trajectory tracking. The method uses a non-linear model of AMR robot kinematics and thus allows an accurate prediction of the future trajectories. The proposed controller has a parallel structure that consists of PID controller with a fixed gain. The control law is constructed on the basis of Lyapunov stability theory. Computer simulation for a differentially driven non-holonomic AMR robot is carried out in the velocity and orientation tracking control of the non-holonomic AMR. The simulation results of wheel type AMR robot platform show that the proposed controller is more robust than the conventional back-stepping controller to show the effectiveness of the proposed algorithm.