In this paper, a hardware-in-loop experiment that integrated controller area network (CAN) monitor and evaluation function for control system is the research content, and a rule-based energy management system of plug-in hybrid electric bus (PHEB) is developed. The real-time kernel of PHEB model was downloaded into VTSystem platform for the real-time simulation system development. An driver and energy management system-in-loop experiment was carried out to verify the energy management strategy under the China Transit Bus Driving Cycle (CTBDC), and the CAN bus performance features were evaluated by CANoe software. The energy consumption per 100km includes 14.1L diesel and 11.9 kW·h electricity with an initial SoC of 85%. et al. / Energy Procedia 88 ( 2016 ) 950 -956 951
Wireless sensor network has many sensor nodes with characteristics of limited cost, collecting data, good fault tolerance and storage. It has been used in environmental monitoring, health care, military and commercial. Coverage control is a significant issue that needs to be solved in wireless sensor networks. In order to solve the problem of overlapping coverage for environmental monitoring and improve coverage rate, an improved immune fuzzy genetic algorithm (IIFGA) based on cluster head selection is proposed. the mathematical model is systematically described. In the experiments, ant colony optimization (ACO) and simulated annealing (SA) are given to compare the performance of IIFGA. The experiments show the proposed coverage control algorithm has a higher convergence speed and improve the coverage rate.
Rice sheath blight is one of the main diseases in rice production. The traditional detection method, which needs manual recognition, is usually inefficient and slow. In this study, a recognition method for identifying rice sheath blight based on a backpropagation (BP) neural network is posed. Firstly, the sample image is smoothed by median filtering and histogram equalization, and the edge of the lesion is segmented using a Sobel operator, which largely reduces the background information and significantly improves the image quality. Then, the corresponding feature parameters of the image are extracted based on color and texture features. Finally, a BP neural network is built for training and testing with excellent tunability and easy optimization. The results demonstrate that when the number of hidden layer nodes is set to 90, the recognition accuracy of the BP neural network can reach up to 85.8%. Based on the color and texture features of the rice sheath blight image, the recognition algorithm constructed with a BP neural network has high accuracy and can effectively make up for the deficiency of manual recognition.
As a layer of soft fibrous tissue,
the periodontal ligament (PDL)
protects against mechanical shock when transmitting mastication force
from tooth to its surrounding alveolar bone. Currently, no quantitative
method is available to estimate the shock resistance ability of the
PDL. To solve this problem, in the present study we developed a finite
element (FE) model of the tooth-PDL-bone complex and analyzed the
energy storage and dissipation during the mastication movements. Displacement
and Mises stress of tooth-PDL-bone complex show that the PDL is able
to protect the alveolar bone from mechanical shock by shielding the
transfer of deformation and stress. During mastication, the energy
of the PDL is stored up to ∼161.5 J/mm3 at the period
of loading and dissipated about one-tenth of the stored energy when
unloading. The energy storage is displacement-dependent but time-independent
because of the hyperelasticity of PDL. However, the energy dissipation
is time- and displacement-dependent because of the viscoelasticity
of PDL. The present study helps to understand the periodontal potential
and the origin of dental diseases such as tooth concussion and occlusal
trauma from the view of energy conversion.
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