1,721,698 research outputs found
A novel active noise control system with online secondary-path filter based on a stepsize controller
No full textIn active noise control (ANC) systems, it is important to estimate the control filter and the secondary-path filter simultaneously since the secondary path can be changed by changing the position of the loudspeaker in real time. However, it is very difficult to estimate two unknown filters at the same time since the estimation errors of the two filters can affect the each estimation. In this paper, a stepsize controller was proposed to improve the convergence stability and noise reduction ability. The stepsize controller assumes that the errors in the estimation of the secondary path are large measurement noises in terms of estimation of the control filter and adjusts the stepsize of the control filter until the measurement noises are reduced. It can provide the stable ANC systems since the estimation of the control filter is automatically controlled according to the estimation of the secondary-path filter. The proposed ANC system was also applied to the scheduled-stepsize algorithm [1]. The simulation results show stable estimations of the filters and improved noises reduction levels. © 2019 JSME.1
An advanced time-delay controller for robust trajectory control of manipulator in the excavator
No full textAn advanced time-delay controller with a hold function is designed. The hold function, which consists of hyperbolic tangent, improves an error convergence by substituting the error to a modified error. Kinematics and dynamics of the 3-link arm are analyzed as the manipulator of the excavator consists of a 3-link arm, which are called boom, arm and bucket. To show the robustness of the advanced time-delay controller, proportional integral derivative controller and adaptive inertia-related controller are designed and compared. The simulations are performed according to the weight of the bucket tip and the trajectory time by using the MATLAB Simulink. © 2021 IEEE.1
Online secondary path estimation in Active Noise Control Systems using a scheduled step size algorithm
No full textIn an active noise control (ANC) system, a fast and robust estimation of secondary path filter is important for reducing a primary noise effectively. A destructive interference signal of the noise generated by the actuator can effectively remove the primary noise at the target point when the secondary path filter is estimated quickly and precisely. In this paper, a scheduled-step size NLMS (SS-NLMS) algorithm is applied to estimate the secondary path filter in the ANC system. Analyzing the curve of mean square deviation (MSD) geometrically, it is divided into a transient stage and a steady state stage. In a specific value of a step size, the value of MSD is decreased in the transient stage and is kept in the steady state stage. Using the analysis, the step size in the interval (0,1) was scheduled in each iteration for fast convergence speed and low steady state error. An advantage of the proposed system is not required to additional computations for fast and robust estimation of the secondary path filter. In the ANC system, the SS-NLMS algorithm is an innovative algorithm which has not existed. © 2017 IEEE.1
Two-stage active noise control with online secondary-path filter based on an adapted scheduled-stepsize NLMS algorithm
No full textA two-stage active noise control (ANC) system is proposed for non-stationary environments: a secondary-path filtering (SPF) stage and a control filtering (CF) stage. The secondary-path filter is roughly trained as quickly as possible in the SPF stage. Based on the trained secondary-path filter, the control filter is trained to minimize the residual errors sensed by an error microphone in the CF stage. A stage-switching algorithm is designed to exchange between the SPF stage and the CF stage based only on signals from the error microphone, which moves the CF stage to the SPF stage whenever the residual errors reach up to a certain level in which the control filter cannot suppress the residual errors mainly caused by the change of the secondary path. To train the secondary-path filter and the control filter quickly and robustly, a scheduled-stepsize normalized least mean square (NLMS) algorithm is adapted to handle not only measurement noises but also disturbances mutually generated between the training of the secondary-path filter and that of the control filter. Since the adapted scheduled-stepsize NLMS algorithm presets the optimal stepsizes for each iteration, the proposed ANC system trains quickly the filters without the additional computations and reduces the residual errors over other ANC systems. (C) 2019 Elsevier Ltd. All rights reserved.11Nsciescopu
Implementation of acid concentration model based on MSPRNN for a steel pickling process
No full textThis paper presents a implementation of acid concentration model based on multi-step prediction recurrent neural network (MSPRNN) for a steel pickling process. The MSPRNN for predicting the values not in only one-step future but in multi-step future is applied to predict the acid concentration in the steel pickling process. The basic MSPRNN is a recursive structure predicting the multi-step future targets using the distant past inputs and the previous predicted targets. On the other hand, the proposed MSPRNN is a structure that predicts the multi-step future targets using distant past inputs and distant past targets. Even though the nonlinearity is strong because of the large time difference between the available inputs and the targets to be predicted, the proposed MSPRNN maintains robust prediction of the acid concentration in the multi-step future. © 2020 IEEE.1
Transmission of 40 Gb/s x 8 WDM channels with 0.4 (bit/s)/Hz spectral efficiency using short-period dispersion managed fiber
No full textThe Monitoring of Complex Active Rules with Vector Representation
No full textSIGIR: ACM Special Interest Group on Information Retrieval
SIGLINK: Hypertext, Hypermedia, and Web
SIGART: ACM Special Interest Group on Artificial Intelligenc
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