20,673 research outputs found
Neutral network-PID control algorithm for semi-active suspensions with magneto-rheological damper
In this paper, a semi-active suspension control system based on Magneto-Rheological (MR) damper is designed for a commercial vehicle to improve the ride comfort and driving stability. A mathematical model of MR damper based on the Bouc-Wen hysteresis model is built. The mathematical model could precisely describe the characteristics of MR damper compared with the bench test results. The neural network-PID controller is designed for the semi-active suspension systems. According to the numerical results, the proposed controller can constrain vehicle vibrations and roll angle significantly. A detailed multi-body dynamic model of the light vehicle with four semi-active suspensions are established, and an actual vehicle handling and stability tests are carried out to verify the control performances of the proposed controller. It can be concluded that MR semi-active suspension systems can play a key role in coordination between the ride comfort and handling stability for the commercial vehicle
State estimation based on unscented Kalman filter for semi-active suspension systems
In this paper, a novel approach to estimate vehicle vibration state information in real time is proposed; it is based on unscented Kalman filter (UKF) theory. The UKF is based on the unscented transfer technique which considers high order terms during the measurement and update stage during the estimation. The proposed observer uses easily accessible measurements such as accelerations and suspension deflections to estimate the sprung and unspring mass vertical velocity for the suspension systems of full vehicle under unknown road disturbance. And it is with low sensitivity and robust to the unknown road surfaces. Matlab/Carsim co-simulation experiments are carried out to validate the performance of the estimator under two typical road excitations. The simulation results clearly indicate that the proposed UKF sate observer is precise
Energy-Efficient Flow Scheduling and Routing with Hard Deadlines in Data Center Networks
The power consumption of enormous network devices in data centers has emerged
as a big concern to data center operators. Despite many
traffic-engineering-based solutions, very little attention has been paid on
performance-guaranteed energy saving schemes. In this paper, we propose a novel
energy-saving model for data center networks by scheduling and routing
"deadline-constrained flows" where the transmission of every flow has to be
accomplished before a rigorous deadline, being the most critical requirement in
production data center networks. Based on speed scaling and power-down energy
saving strategies for network devices, we aim to explore the most energy
efficient way of scheduling and routing flows on the network, as well as
determining the transmission speed for every flow. We consider two general
versions of the problem. For the version of only flow scheduling where routes
of flows are pre-given, we show that it can be solved polynomially and we
develop an optimal combinatorial algorithm for it. For the version of joint
flow scheduling and routing, we prove that it is strongly NP-hard and cannot
have a Fully Polynomial-Time Approximation Scheme (FPTAS) unless P=NP. Based on
a relaxation and randomized rounding technique, we provide an efficient
approximation algorithm which can guarantee a provable performance ratio with
respect to a polynomial of the total number of flows.Comment: 11 pages, accepted by ICDCS'1
LSTM Pose Machines
We observed that recent state-of-the-art results on single image human pose
estimation were achieved by multi-stage Convolution Neural Networks (CNN).
Notwithstanding the superior performance on static images, the application of
these models on videos is not only computationally intensive, it also suffers
from performance degeneration and flicking. Such suboptimal results are mainly
attributed to the inability of imposing sequential geometric consistency,
handling severe image quality degradation (e.g. motion blur and occlusion) as
well as the inability of capturing the temporal correlation among video frames.
In this paper, we proposed a novel recurrent network to tackle these problems.
We showed that if we were to impose the weight sharing scheme to the
multi-stage CNN, it could be re-written as a Recurrent Neural Network (RNN).
This property decouples the relationship among multiple network stages and
results in significantly faster speed in invoking the network for videos. It
also enables the adoption of Long Short-Term Memory (LSTM) units between video
frames. We found such memory augmented RNN is very effective in imposing
geometric consistency among frames. It also well handles input quality
degradation in videos while successfully stabilizes the sequential outputs. The
experiments showed that our approach significantly outperformed current
state-of-the-art methods on two large-scale video pose estimation benchmarks.
We also explored the memory cells inside the LSTM and provided insights on why
such mechanism would benefit the prediction for video-based pose estimations.Comment: Poster in IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), 201
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