• Title/Summary/Keyword: regressor

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Dynamic Parameters Identification of Robotic Manipulator using Momentum (모멘텀을 이용한 로봇 동역학 파라미터 식별)

  • Choi, Young-Jin
    • The Journal of Korea Robotics Society
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    • v.7 no.3
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    • pp.222-230
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    • 2012
  • The paper presents a momentum-based regressor by using Hamiltonian dynamics representation for robotic manipulator. It has an advantage in that the proposed regressor does not require the acceleration measurement for the identification of dynamic parameters. Also, the identification algorithm is newly suggested by solving a minimization problem with constraint. The developed algorithm is easy to implement in real-time. Finally, the effectiveness of the proposed momentum-based regressor and identification method is shown through numerical simulations.

Lagged Unstable Regressor Models and Asymptotic Efficiency of the Ordinary Least Squares Estimator

  • Shin, Dong-Wan;Oh, Man-Suk
    • Journal of the Korean Statistical Society
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    • v.31 no.2
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    • pp.251-259
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    • 2002
  • Lagged regressor models with general stationary errors independent of the regressors are considered. The regressor process is unstable having characteristic roots on the unit circle. If the order of the lag matches the number of roots on the unit circle, the ordinary least squares estimator (OLSE) is asymptotically efficient in that it has the same limiting distribution as the generalized least squares estimator (GLSE) under the same normalization. This result extends the well-known result of Grenander and Rosenblatt (1957) for asymptotic efficiency of the OLSE in deterministic polynomial and/or trigonometric regressor models to a class of models with stochastic regressors.

Asymptotic Properties of a Robust Estimator for Regression Models with Random Regressor

  • Chang, Sook-Hee;Kim, Hae-Kyung
    • Communications for Statistical Applications and Methods
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    • v.6 no.2
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    • pp.345-356
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    • 1999
  • This paper deals with the problem of estimating regression coefficients in nonlinear regression model having random regressor. The sufficient conditions for consistency of the $L_1$-estimator with random regressor are given and discussed in this paper. An example is given to illustrate the application of the main results.

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Non-regressor Based Adaptive Tracking Control of an Underwater Vehicle-mounted Manipulator (수중 선체에 장착된 로봇팔 궤적의 비귀환형 적응제어)

  • 여준구
    • Journal of Ocean Engineering and Technology
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    • v.14 no.2
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    • pp.7-12
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    • 2000
  • This paper presents a non-regressor based adaptive control scheme for the trajectory tracking of underwater vehicle-mounted manipulator systems(UVMS). The adaptive control system includes a class of unmodeled effects is applied to the trajectory control of an UVMS. The only information required to implement this scheme ios the upper bound and lowe bound of the system parameter matrices the upper bound of unmodeled effects the number of joints the position and attitude of the vehicle and trajectory commands. The adaptive control law estimates control gains defined by the combinations of the bounded constants of system parameter matrices and of a filtered error equation. To evaluate the performance of the non-regressor based adaptive controller computer simulation was performed with a two-link planar robot model mounted on an underwater vehicle. The hydrodynamic effects acting on the manipulator are included. It is assumed that the vehicle's motion is slow and can be predicted with a proper compensator.

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On the Behavior of the Signed Regressor Least Mean Squares Adaptation with Gaussian Inputs (가우시안 입력신호에 대한 Signed Regressor 최소 평균자승 적응 방식의 동작 특성)

  • 조성호
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.18 no.7
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    • pp.1028-1035
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    • 1993
  • The signed regressor (SR) algorithm employs one bit quantization on the input regressor (or tap input) in such a way that the quantized input sequences become +1 or -1. The algorithm is computationally more efficient by nature than the popular least mean square (LMS) algorithm. The behavior of the SR algorithm unfortunately is heavily dependent on the characteristics of the input signal, and there are some Inputs for which the SR algorithm becomes unstable. It is known, however, that such a stability problem does not take place with the SR algorithm when the input signal is Gaussian, such as in the case of speech processing. In this paper, we explore a statistical analysis of the SR algorithm. Under the assumption that signals involved are zero-mean and Gaussian, and further employing the commonly used independence assumption, we derive a set of nonlinear evolution equations that characterizes the mean and mean-squared behavior of the SR algorithm. Experimental results that show very good agreement with our theoretical derivations are also presented.

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An Adaptive Iterative Learning Control and Identification for Uncertain Robotic Systems (불확실한 로봇 시스템을 위한 적응 반복 학습 제어 및 식별)

  • 최준영
    • Journal of Institute of Control, Robotics and Systems
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    • v.10 no.5
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    • pp.395-401
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    • 2004
  • We present an AILC(Adaptive Iterative Learning Control) scheme and a sufficient condition for system parameter identification for uncertain robotic systems that perform the same tasks repetitively. It is guaranteed that the joint velocity and position asymptotically converge to the reference joint velocity and position, respectively. In addition, it is proved that a sufficient condition for parameter identification is the PE(Persistent Excitation) condition on the regressor matrix evaluated at the reference trajectory during the operation period. Since the regressor matrix on the reference trajectory can be easily computed prior to the real robot operation, the proposed algorithm provides a useful method to verify whether the parameter error converges to zero or not.

Estimation of Tire-Road Friction Coefficient using Observers (관측기를 이용한 노면과 타이어 간의 마찰계수 추정)

  • 정태영;이경수;송철기
    • Journal of Institute of Control, Robotics and Systems
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    • v.4 no.6
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    • pp.722-728
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    • 1998
  • In this paper real-time estimation methods for identifying the tire-road friction coefficient are presented. Taking advantage of the Magic Formula Tire Model, the similarity technique and the specific model for the vehicle dynamics, a reduced order observer/filtered-regressor-based method is proposed. The Proposed method is evaluated on simulations of a full-vehicle model with an eight state nonlinear vehicle/transmission model and nonlinear suspension model. It has been shown through simulations that it is possible to estimate the tire-road friction from measurements of engine rpm, transmission output speed and wheel speeds using the proposed identification method. The proposed method can be used as a useful option as a part of vehicle collision warning/avoidance systems and will be useful in the implementation of a warning algorithm since the tire-road friction can be estimated only using RPM sensors.

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Real-time 3D Pose Estimation of Both Human Hands via RGB-Depth Camera and Deep Convolutional Neural Networks (RGB-Depth 카메라와 Deep Convolution Neural Networks 기반의 실시간 사람 양손 3D 포즈 추정)

  • Park, Na Hyeon;Ji, Yong Bin;Gi, Geon;Kim, Tae Yeon;Park, Hye Min;Kim, Tae-Seong
    • Proceedings of the Korea Information Processing Society Conference
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    • 2018.10a
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    • pp.686-689
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    • 2018
  • 3D 손 포즈 추정(Hand Pose Estimation, HPE)은 스마트 인간 컴퓨터 인터페이스를 위해서 중요한 기술이다. 이 연구에서는 딥러닝 방법을 기반으로 하여 단일 RGB-Depth 카메라로 촬영한 양손의 3D 손 자세를 실시간으로 인식하는 손 포즈 추정 시스템을 제시한다. 손 포즈 추정 시스템은 4단계로 구성된다. 첫째, Skin Detection 및 Depth cutting 알고리즘을 사용하여 양손을 RGB와 깊이 영상에서 감지하고 추출한다. 둘째, Convolutional Neural Network(CNN) Classifier는 오른손과 왼손을 구별하는데 사용된다. CNN Classifier 는 3개의 convolution layer와 2개의 Fully-Connected Layer로 구성되어 있으며, 추출된 깊이 영상을 입력으로 사용한다. 셋째, 학습된 CNN regressor는 추출된 왼쪽 및 오른쪽 손의 깊이 영상에서 손 관절을 추정하기 위해 다수의 Convolutional Layers, Pooling Layers, Fully Connected Layers로 구성된다. CNN classifier와 regressor는 22,000개 깊이 영상 데이터셋으로 학습된다. 마지막으로, 각 손의 3D 손 자세는 추정된 손 관절 정보로부터 재구성된다. 테스트 결과, CNN classifier는 오른쪽 손과 왼쪽 손을 96.9%의 정확도로 구별할 수 있으며, CNN regressor는 형균 8.48mm의 오차 범위로 3D 손 관절 정보를 추정할 수 있다. 본 연구에서 제안하는 손 포즈 추정 시스템은 가상 현실(virtual reality, VR), 증강 현실(Augmented Reality, AR) 및 융합 현실 (Mixed Reality, MR) 응용 프로그램을 포함한 다양한 응용 분야에서 사용할 수 있다.

A Design and Implement of Efficient Agricultural Product Price Prediction Model

  • Im, Jung-Ju;Kim, Tae-Wan;Lim, Ji-Seoup;Kim, Jun-Ho;Yoo, Tae-Yong;Lee, Won Joo
    • Journal of the Korea Society of Computer and Information
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    • v.27 no.5
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    • pp.29-36
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    • 2022
  • In this paper, we propose an efficient agricultural products price prediction model based on dataset which provided in DACON. This model is XGBoost and CatBoost, and as an algorithm of the Gradient Boosting series, the average accuracy and execution time are superior to the existing Logistic Regression and Random Forest. Based on these advantages, we design a machine learning model that predicts prices 1 week, 2 weeks, and 4 weeks from the previous prices of agricultural products. The XGBoost model can derive the best performance by adjusting hyperparameters using the XGBoost Regressor library, which is a regression model. The implemented model is verified using the API provided by DACON, and performance evaluation is performed for each model. Because XGBoost conducts its own overfitting regulation, it derives excellent performance despite a small dataset, but it was found that the performance was lower than LGBM in terms of temporal performance such as learning time and prediction time.

Equivalence of GLS and Difference Estimator in the Linear Regression Model under Seasonally Autocorrelated Disturbances

  • Seuck Heun Song;Jong Hyup Lee
    • Communications for Statistical Applications and Methods
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    • v.1 no.1
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    • pp.112-118
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    • 1994
  • The generalized least squares estimator in the linear regression model is equivalent to difference estimator irrespective of the particular form of the regressor matrix when the disturbances are generated by a seasonally autoregressive provess and autocorrelation is closed to unity.

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