Many systems on our planet shift abruptly and irreversibly from the desired state to an undesired state when forced across a "tipping point". Some examples are mass extinctions within ecosystems, cascading f...
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Autonomous vehicles were experiencing rapid development in the past few years. However, achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic driving environment. Therefore, auton...
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Retrieving similar trajectories from a large trajectory dataset is important for a variety of applications, like transportation planning and mobility analysis. Unlike previous works based on fine-grained GPS trajector...
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The effective fault monitoring of the motor bearings not only can ensure the smooth and efficient operation of equipment, but also can detect and eliminate the running fault in time to prevent major accidents. Based o...
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ISBN:
(纸本)9781728116525
The effective fault monitoring of the motor bearings not only can ensure the smooth and efficient operation of equipment, but also can detect and eliminate the running fault in time to prevent major accidents. Based on deep learning algorithm, this paper constructs a stacked auto-encoder (SAE) network. The input data are compressed by introduced sparsity constraint, so that the network can accurately extract the fault characteristics of the input data. And the fault recognition ability of the network can be improved by introducing random noise to reconstruct input data. The simulation result shows that the SAE network can not only overcome the shortcomings of traditional fault diagnosis methods that requires personnel to distinguish fault samples and needs a large number of prior knowledge; but also realize the self-learning of fault feature information. The accuracy rates of fault identification reach 98%, 94%, 96% and 95.5% in four different working conditions. What's more, the network can adapt to the actual multi-load cases, demonstrate strong robustness under different working conditions.
A novel power forecasting approach for PV plant based on irradiance index and LSTM is presented in this paper. Firstly, we come up with a clustering algorithm according to the irradiance index after analyzing the peri...
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A novel power forecasting approach for PV plant based on irradiance index and LSTM is presented in this paper. Firstly, we come up with a clustering algorithm according to the irradiance index after analyzing the periodic characteristics of PV plant daily power curves. Then, the Long Short-Term Memory (LSTM) is employed to build forecasting models for each type of weather. An empirical study on a real dataset shows that the proposed method can effectively use multivariate time series information to predict the power for PV plants and obtain better performance than Extreme Learning Machine (ELM) and Artificial Neural Networks (ANN).
Position and attitude control of a quadrotor is discussed in this paper.A two-loop structure is adopted for the position and attitude control of the quadrotor,with the inner loop for the attitude and altitude,and the ...
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Position and attitude control of a quadrotor is discussed in this paper.A two-loop structure is adopted for the position and attitude control of the quadrotor,with the inner loop for the attitude and altitude,and the outer loop for the horizontal *** order to reject the unmeasured disturbance due to wind gust and other uncertainties,linear active disturbance rejection control（LADRC） is adopted in the inner loop,and the outer loop adopts two simple PD *** and tuning of the inner and outer controllers are *** results show that the proposed control strategy can track the desired position and attitude and meanwhile can reject the unmeasured disturbances and uncertainties of the quadrotor quickly.
In this paper, one kind of multivariate system identification algorithm based discrete state-space neural network (DSSNN) is presented for modelling gas turbine systems with non-Gaussian disturbances. An improved perf...
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In this paper, one kind of multivariate system identification algorithm based discrete state-space neural network (DSSNN) is presented for modelling gas turbine systems with non-Gaussian disturbances. An improved performance index is proposed to train DSSNNs by using Survival Information Potential (SIP) of identification errors. Compared with the traditional Mean Square Error (MSE) based identification algorithm, simulation results demonstrate that the proposed system identification algorithm can obtain better model for 8OkW gas turbine.
Video person re-identification (re-ID) plays an important role in surveillance video analysis. However, the performance of video re-ID degenerates severely under partial occlusion. In this paper, we propose a novel ne...
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Target selection has always been a popular research topic in the human-computer interaction(HCI)*** with continuous interactive spaces,such as video games,augmented reality(AR),and virtual reality(VR),are becom-
Target selection has always been a popular research topic in the human-computer interaction(HCI)*** with continuous interactive spaces,such as video games,augmented reality(AR),and virtual reality(VR),are becom-
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