This paper focuses on the development of a proximate time optimal control method for two-dimensional rigid body systems, eg. XY positioning tables. Our approach is based on the traditional proximate time-optimal servo...
详细信息
We study a networked state estimation problem for a linear system with multiple sensors, each of which transmits its measurements to a central estimator via a lossy communication network for computing the minimum mean...
详细信息
It has been recognized by many researchers that accurate bus travel time prediction is critical for successful deployment of traffic signal priority (TSP) systems. Although there exist a lot of studies on travel time ...
详细信息
It has been recognized by many researchers that accurate bus travel time prediction is critical for successful deployment of traffic signal priority (TSP) systems. Although there exist a lot of studies on travel time prediction for Advanced Traveler Information Systems (ATIS), this problem for TSP purpose is a little different and the amount of literature is limited. This paper proposes a deep learning based approach for continuous travel time prediction problem. Parameters of the deep network are fine-tuned following a layer-by-layer pre-training procedure on a dataset generated by traffic simulations. Variables that may affect continuous travel time are selected carefully. Experiments are conducted to validate the performance of the proposed model. The results indicate that the proposed model produces prediction with mean absolute error less than 4 seconds, which is accurate enough for TSP operations. This paper also reveals that, except for obvious factors like speed, travel distance and traffic density, the signal time when the prediction is made is also an important factor affecting travel time.
The visualization of layer distribution of furnace burden in the time-radial-height dimension was presented based on array radar measured data. Abnormal data mining and data sampling method were adopted to obtain vali...
详细信息
To avoid structural damage, a wind turbine is equipped with a safety supervisor that triggers an emergency shutdown procedure in case of internal faults or large wind gusts. This paper leverages the (compositional) ba...
详细信息
Inspired by the biological RNA, a circular genetic operators based RNA genetic algorithm (cRNA-GA) is proposed to estimate the model parameters of the proton exchange membrane fuel cell (PEMFC). To maintain the popula...
详细信息
In order to get better performance at different speed regions in Direct Torque control (DTC) for induction motor system, a speed adaptive strategy combining two control models are established - hexagon flux linkage co...
详细信息
The value of stator and rotor resistance, inductance is important to controller design and condition monitoring of an asynchronous motor system. This paper proposes an improved least squarest to identify the parameter...
详细信息
In this article, a method to obtain spatial coordinate of spherical robot's moving platform using a single camera is proposed, and experimentally verified. The proposed method is an accurate, flexible and low-cost...
详细信息
ISBN:
(纸本)9781467372350
In this article, a method to obtain spatial coordinate of spherical robot's moving platform using a single camera is proposed, and experimentally verified. The proposed method is an accurate, flexible and low-cost tool for the kinematic calibration of spherical-workspace mechanisms to achieve the desired accuracy in position. The sensitivity and efficiency of the provided method is thus evaluated. Furthermore, optimization of camera location is outlined subject to the prescribed cost functions. Finally, experimental analysis of the proposed calibration method on ARAS Eye surgery Robot (DIAMOND) is presented;In which the accuracy is obtained from three to six times better than the previous calibration.
This paper proposes a stator flux and rotor speed estimation method for induction motor (IM) based on extended complex Kalman filter (ECKF). A complex-valued model is adopted that allows a simpler and more effective s...
详细信息
暂无评论