As for urban water dispatch, multiple steps of water pressure prediction are necessary. Due to the strong irregularity of water pressure, both machine learning and original deep learning methods can't accurately p...
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As for urban water dispatch, multiple steps of water pressure prediction are necessary. Due to the strong irregularity of water pressure, both machine learning and original deep learning methods can't accurately predict water pressure. In order to predict the water pressure with significant irregularity, this paper has developed a prediction model. The CEEMDAN approach seeks to reduce the noise of the raw water pressure data, which is directly recorded by sensors. The extraction of pump state signals has the ability to introduce extra useful information to the training model. Gradient Correction Methodology is a new strategy for increasing the precision of water pressure prediction in multi-step models. It could alleviate the gradient disappearing issue for long horizon prediction. In general, the designed prediction model on water pressure performs better than other models with long step sizes.
Social media is a platform for people to share their lives and interact with others. Image sharing is an integral component. Privacy information will inevitably be compromised during the process of sharing whole photo...
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Fault diagnosis and isolation is important for industrial system. In this paper, a kernel canonical variate analysis(KCVA) is proposed for fault isolation. KCVA is originally used as a data dimension reduction techniq...
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Fault diagnosis and isolation is important for industrial system. In this paper, a kernel canonical variate analysis(KCVA) is proposed for fault isolation. KCVA is originally used as a data dimension reduction technique which can account for nonlinearity and correlations in the industrial dynamical process data. But there are some difficulties using KCVA in the construction of the contribution for the fault isolation. On the one hand, it is difficult to compute the contributions of individual variables because it is scarcely possible to find an inverse mapping from the feature space to the original space. On the other hand, a smearing effect is hardly avoided. To solve the problem, a KCVA-based contributions is proposed using the state subspace and the residual subspace which can isolate the faulty variables effectively. Simulations are conducted on the Tennessee Eastman process to verify the performance of the proposed method.
The interoperability of heterogeneous networks, including hybrid industrial wired/wireless protocols, is a vital aspect of the Industrial Internet of Things (IIoT) since various industrial protocols have coexisted. Th...
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Aiming at the landing problem of Unmanned Aerial Vehicle (UAV) without landmarks and global coordinates, this paper proposes an efficient vision-based UAV autonomous landing solution. Firstly, image information is obt...
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Fully-polarised (FP) array interferometric Synthetic Aperture Radar (FP-Array-InSAR) is an important technology in three-dimensional (3D) reconstruction and image interpretation of various scattering mechanisms (SMs) ...
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This paper studies the power switch open-circuit(OC) fault in two-level three-phase (2L3P) voltage source inverter (VSI). According to the topological symmetry of the inverter, when it works in healthy conditions, its...
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In recent years, study of spatially structured light has become an attractive and promising area, providing more ways to explore unusual and anomalous effects in optics. Here, we propose a kind of polar-segmented vect...
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Predicting the future trajectory of a target becomes particularly challenging while dealing with occlusion conditions,where current information is unavailable,and historical data becomes the only basis for *** this pa...
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ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
Predicting the future trajectory of a target becomes particularly challenging while dealing with occlusion conditions,where current information is unavailable,and historical data becomes the only basis for *** this paper,we propose a trajectory prediction model that utilizes scaled dot product attention mechanism,a convolution neural network,and a bidirectional long short term memory *** scaled dot product attention mechanism gauges the impact of historical sequences,while the convolution neural network extracts intricate feature information from these ***,these data is fed into the bidirectional long short term memory network to forecast the target's navigation *** results highlight that the Attentive CNN-Bi LSTM(ACBL) network which we proposed has lower root mean square error(RMSE) by 23% on average than current methods.
This paper investigates the distributed model predictive control for an asynchronous nonlinear multi-agent system with external interference via a self-triggered generator and a prediction horizon regulator. First, a ...
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