988 research outputs found
A Multilayer Feed Forward Small-World Neural Network Controller and Its Application on Electrohydraulic Actuation System
Being difficult to attain the precise mathematical models, traditional control methods such as proportional integral (PI) and proportional integral differentiation (PID) cannot meet the demands for real time and robustness when applied in some nonlinear systems. The neural network controller is a good replacement to overcome these shortcomings. However, the performance of neural network controller is directly determined by neural network model. In this paper, a new neural network model is constructed with a structure topology between the regular and random connection modes based on complex network, which simulates the brain neural network as far as possible, to design a better neural network controller. Then, a new controller is designed under small-world neural network model and is investigated in both linear and nonlinear systems control. The simulation results show that the new controller basing on small-world network model can improve the control precision by 30% in the case of system with random disturbance. Besides the good performance of the new controller in tracking square wave signals, which is demonstrated by the experiment results of direct drive electro-hydraulic actuation position control system, it works well on anti-interference performance
Centralizers of derived-from-Anosov systems on : rigidity versus triviality
In this paper, we study the centralizer of a partially hyperbolic
diffeomorphism on which is homotopic to an Anosov automorphism,
and we show that either its centralizer is virtually trivial or such
diffeomorphism is smoothly conjugate to its linear part.Comment: 24 page
Age-related differences in takeover performance:A comparative analysis of older and younger drivers in prolonged partially automated driving
Objective: Vehicle automation technologies have the potential to address the mobility needs of older adults. However, age-related cognitive declines may pose new challenges for older drivers when they are required to take back or “takeover” control of their automated vehicle. This study aims to explore the impact of age on takeover performance under partially automated driving conditions and the interaction effect between age and voluntary non-driving-related tasks (NDRTs) on takeover performance. Method: A total of 42 older drivers (M = 65.5 years, SD = 4.4) and 40 younger drivers (M = 37.2 years, SD = 4.5) participated in this mixed-design driving simulation experiment (between subjects: age [older drivers vs. younger drivers] and NDRT engagement [road monitoring vs. voluntary NDRTs]; within subjects: hazardous event occurrence time [7.5th min vs. 38.5th min]). Results: Older drivers exhibited poorer visual exploration performance (i.e., longer fixation point duration and smaller saccade amplitude), lower use of advanced driving assistance systems (ADAS; e.g., lower percentage of time adaptive cruise control activated [ACCA]) and poorer takeover performance (e.g., longer takeover time, larger maximum resulting acceleration, and larger standard deviation of lane position) compared to younger drivers. Furthermore, older drivers were less likely to experience driving drowsiness (e.g., lower percentage of time the eyes are fully closed and Karolinska Sleepiness Scale levels); however, this advantage did not compensate for the differences in takeover performance with younger drivers. Older drivers had lower NDRT engagement (i.e., lower percentage of fixation time on NDRTs), and NDRTs did not significantly affect their drowsiness but impaired takeover performance (e.g., higher collision rate, longer takeover time, and larger maximum resulting acceleration). Conclusions: These findings indicate the necessity of addressing the impaired takeover performance due to cognitive decline in older drivers and discourage them from engaging in inappropriate NDRTs, thereby reducing their crash risk during automated driving.</p
A multi-channel photometric detector for multi-component analysis in flow injection analysis
The detector, a multi-channel photometric detector, described in this
paper was developed using multi-wavelength LEDs (light emitting
diode) and phototransistors for absorbance measurement controlled
by an Intel 8031 8-bit single chip microcomputer. Up to four flow
cells can be attached to the detector. The LEDs and phototransistors
are both inexpensive, and reliable. The results given by the detector
for simultaneous determination of trace amounts of cobalt and
cadmium in zinc sulphate electrolyte are reported. Because of the
newly developed detector, this approach employs much less hardware
apparatus than by employing conventional photometric detectors
Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System
The rapid growth of the ride-hailing industry has revolutionized urban
transportation worldwide. Despite its benefits, equity concerns arise as
underserved communities face limited accessibility to affordable ride-hailing
services. A key issue in this context is the vehicle rebalancing problem, where
idle vehicles are moved to areas with anticipated demand. Without equitable
approaches in demand forecasting and rebalancing strategies, these practices
can further deepen existing inequities. In the realm of ride-hailing, three
main facets of fairness are recognized: algorithmic fairness, fairness to
drivers, and fairness to riders. This paper focuses on enhancing both
algorithmic and rider fairness through a novel vehicle rebalancing method. We
introduce an approach that combines a Socio-Aware Spatial-Temporal Graph
Convolutional Network (SA-STGCN) for refined demand prediction and a
fairness-integrated Matching-Integrated Vehicle Rebalancing (MIVR) model for
subsequent vehicle rebalancing. Our methodology is designed to reduce
prediction discrepancies and ensure equitable service provision across diverse
regions. The effectiveness of our system is evaluated using simulations based
on real-world ride-hailing data. The results suggest that our proposed method
enhances both accuracy and fairness in forecasting ride-hailing demand,
ultimately resulting in more equitable vehicle rebalancing in subsequent
operations. Specifically, the algorithm developed in this study effectively
reduces the standard deviation and average customer wait times by 6.48% and
0.49%, respectively. This achievement signifies a beneficial outcome for
ride-hailing platforms, striking a balance between operational efficiency and
fairness.Comment: 31 pages, 6 figure
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