As inland waterway usage intensifies, it increasingly intersects and conflicts with railway and road transportation modes, highlighting the need for efficient management of these critical junctures. In particular, mov...
As inland waterway usage intensifies, it increasingly intersects and conflicts with railway and road transportation modes, highlighting the need for efficient management of these critical junctures. In particular, movable bridges represent a key intersection of highway and waterway Traffic but also a potential source of conflict. This paper proposes and analyzes the use of automatic schedulers that consider both highway and waterway Traffic, reduce conflicts, and make key decisions about bridge use. The METANET macroscopic Traffic model is elaborated to allow the simulation of drawbridge openings on the highway mainline, based on the modelling of mainstream metering. Several MPC-based schedulers are proposed using the designed highway Traffic model and key vessel information, aiming to study the impact of the bridge opening and scheduling on highway Traffic. The simulation results indicate a significant reduction in Traffic conflicts at drawbridge intersections due to the implementation of these schedulers. The functioning of the schedulers is shown to be robust and demonstrate verifiable behavior, indicating their high potential in real-world applications.
Mobile robots represented by smart wheelchairs can assist elderly people with mobility *** paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,which can assist...
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Mobile robots represented by smart wheelchairs can assist elderly people with mobility *** paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,which can assist users to implement accurate navigation(e.g.,docking)in the environment without prior *** order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms,this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic *** last,comparative experiments were carried out in the real *** show that our method is superior in terms of safety,comfort and docking accuracy.
The Automotive Headlamp Leveling system (ALS), aims to ensure the stability of the illumination range of the headlamp by vertically rotate the headlamp using motors when the body attitude changes. This paper proposes ...
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Digital-to-analogue converters (DACs) exhibit several non-ideal effects that deteriorate performance. Methods in feedback control can reduce such effects. Due to implementation limitations, the feedback signal in exis...
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FEA (Finite Element Analysis) of conventional hysteresis motors has been difficult due to the specificity of the rotor. Equivalent circuit-based analysis is common, and it is difficult to expect the torque ripple due ...
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ISBN:
(数字)9784886864406
ISBN:
(纸本)9798350379105
FEA (Finite Element Analysis) of conventional hysteresis motors has been difficult due to the specificity of the rotor. Equivalent circuit-based analysis is common, and it is difficult to expect the torque ripple due to the slot structure. However, recent advances in FEM (Finite Element Method) technology have made it possible to consider the nonlinear characteristics of the rotor. By utilizing this, the torque ripple and torque depending on the stator bridge angle, which affects the torque ripple of the motor, were compared. This is expected to enable geometry optimization of hysteresis motors, which is difficult with conventional methods.
The paper introduces a Koopman bilinear model predictive control (KBMPC) as a local planner for smart wheelchair systems. This approach leverages the Koopman Operator to streamline the nonlinear terms in MPC, aiming t...
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The exploration of PINN (Physics Informed Neural Network) in research is still in its nascent stages globally, with a notable dearth of studies focusing on electromagnetic field analysis. In response to this gap, this...
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ISBN:
(数字)9798350348958
ISBN:
(纸本)9798350348965
The exploration of PINN (Physics Informed Neural Network) in research is still in its nascent stages globally, with a notable dearth of studies focusing on electromagnetic field analysis. In response to this gap, this paper introduces a novel approach for eddy current analysis employing a transfer learning-based discrete differentiation method within the framework of a PINN. While discrete differentiation methods offer the advantage of low model complexity and rapid analysis, they encounter challenges in eddy current analysis due to the need for learning at each time step. This paper addresses these challenges through the application of transfer learning techniques. Our findings demonstrate that the proposed method significantly reduces the total analysis time in time-dependent scenarios compared to traditional Finite Element Method (FEM) approaches.
This paper investigates the prediction of magnetization surfaces in Switched Reluctance Motors using three distinct methods: classical Neural Networks, Radial Basis Function networks, and Physics-Informed Neural Netwo...
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In motor fault diagnosis, auto-encoder based methods are effective in detecting abnormal patterns by utilizing only normal data. However, this approach has limitations in classifying various fault types, as it is prim...
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ISBN:
(数字)9784886864406
ISBN:
(纸本)9798350379105
In motor fault diagnosis, auto-encoder based methods are effective in detecting abnormal patterns by utilizing only normal data. However, this approach has limitations in classifying various fault types, as it is primarily trained with normal data. To solve this problem, a voting technique using an ensemble model based on an auto-encoder is proposed in this study. The proposed method combines several auto-encoder models to synthesize the prediction results of each model, showing higher performance in fault type classification than traditional single model approaches. This method has the advantage of more clearly distinguishing the boundaries between failure types and maintaining stable performance even in various failure scenarios. The experimental results confirm that the proposed method can classify various types of faults with high accuracy, even though it was trained only on normal data.
Solving the explicit model predictive control (MPC) problem entails enumerating a list of critical regions and their ancillary feedback laws. Unfortunately, their number and the time required to compute them increase ...
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