In this study, GF/EP composite laminates with an epoxy matrix modified by carbon black (CB) nanoparticles of 2.0 wt% and copper chloride (CC) were manufactured using the vacuum assisted resin infusion (VARI) method. T...
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ISBN:
(纸本)9788888785332
In this study, GF/EP composite laminates with an epoxy matrix modified by carbon black (CB) nanoparticles of 2.0 wt% and copper chloride (CC) were manufactured using the vacuum assisted resin infusion (VARI) method. The addition of CB nanoparticles and CC substantially improves electrical conductivity of GF/EP composites in all three principal directions with percolation network of CB nanoparticles. The addition of CB nanoparticles increases both initiation and propagation values of modes I and II interlaminar fracture toughness of GF/EP composite laminates. The change in electrical resistance was used as a damage index to monitor delamination growth. With the damage index for individual pathways of a network in GF/EP quasi-isotropic laminates with CB and CC. An electrical resistivity tomography method was successfully developed to locate and quantitatively assess growth of transverse impact damage in the GF/EP composite laminates.
＜正＞A multi-layer model of a kinetic energy length is developed by employing recent results of the *** theory predicts the complete,mean streamwise turbulent kinetic-energy profile （MKP）,in good agreement with emp...
＜正＞A multi-layer model of a kinetic energy length is developed by employing recent results of the *** theory predicts the complete,mean streamwise turbulent kinetic-energy profile （MKP）,in good agreement with empirical data for a wide range
Turbulent rough pipe flow has been a theoretical challenge for nearly eighty *** recent findings include several ways of obtaining a single collapsed curve,called scaling function,for a series of roughness heights and...
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Turbulent rough pipe flow has been a theoretical challenge for nearly eighty *** recent findings include several ways of obtaining a single collapsed curve,called scaling function,for a series of roughness heights and Reynolds numbers(Re).Here,we
Analysis of direct numerical simulation（DNS） of spatially developing compressible turbulent boundary layers（CTBL） is performed using ratios of the components of Reynolds stress tensor to its trace,Γ.It is found t...
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Analysis of direct numerical simulation（DNS） of spatially developing compressible turbulent boundary layers（CTBL） is performed using ratios of the components of Reynolds stress tensor to its trace,Γ.It is found that the bulk flow is characterized by four universal constants:Γ≈0.5,Γ≈0.2,Γ≈0.3,
In this paper, the distributed output regulation problem of multi-agent linear systems with communication time delays is studied, by means of both dynamic state and output feedback control laws. Given bounded communic...
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In this paper, the distributed output regulation problem of multi-agent linear systems with communication time delays is studied, by means of both dynamic state and output feedback control laws. Given bounded communication delays, distributed control laws are designed via linear matrix inequalities (LMI). Both delay-independent and delaydependent conditions are discussed.
The k-nearest neighbor (k-NN) nonparametric regression is a classic model for single point short-term traffic flow forecasting. The traffic flows of the same clock time of the days are viewed as neighbors to each othe...
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The k-nearest neighbor (k-NN) nonparametric regression is a classic model for single point short-term traffic flow forecasting. The traffic flows of the same clock time of the days are viewed as neighbors to each other, and the neighbors with the most similar values are regarded as nearest neighbors and are used for the prediction. In this method, only the information of the neighbors is considered. However, it is observed that the “trends” in the traffic flows are useful for the prediction. Taking a sequence of consecutive time periods and viewing the a sequence of “increasing”, “equal” or “decreasing” of the traffic flows of two consecutive periods as a pattern, it is observed that the patterns can be used for prediction, despite the patterns are not from the same clock time period of the days. Based on this observation, a pattern recognition algorithm is proposed. Moreover, empirically, we find that the patterns from different clock time of the days can have different contributions to the prediction. For example, if both to predict the traffic flow in the morning, the pattern from the morning can lead to better prediction than same patterns from afternoon or evening. In one sentence, we argue that both the pattern and the clock time of the pattern contain useful information for the prediction and we propose the weighted pattern recognition algorithm (WPRA). We give different weights to the same patterns of different clock time for the prediction. In this way, we take both virtues of the k-NN method and the PRA method. We use the root mean square error (RMSE) between the actual traffic flows and the predicted traffic flows as the measurement. By applying the results to actual data and the simulated data, about 20% improvement compare with the PRA is obtained.
Deadlock is an undesired situation in a highly automated system due to the fact that no system can allow its occurrence which may produce some unnecessary economic losses or serious consequences. There are three mathe...
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Deadlock is an undesired situation in a highly automated system due to the fact that no system can allow its occurrence which may produce some unnecessary economic losses or serious consequences. There are three mathematical tools to handle deadlocks in resource allocation systems: graph theory, finite state machine, and Petri net. Due to its inherent characteristics, Petri nets are widely applied to manufacturing systems. Generally, these existing deadlock methods are classified into three strategies: deadlock detection and recovery, deadlock avoidance, and deadlock prevention. In this paper, a review of deadlock prevention policies and merits and drawbacks of these policies are presented. Then it gives the possible trend of the research in the future.
This paper proposes a novel algorithm for parking motion of a Car-like mobile robot. The algorithm presented here addresses calculating equations for planning a parking path in real time. Moreover, by incorporating th...
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This paper proposes a novel algorithm for parking motion of a Car-like mobile robot. The algorithm presented here addresses calculating equations for planning a parking path in real time. Moreover, by incorporating the constraints of the mechanical and kinematical characteristics of the car and the geometry of the parking lot in the path planning, we can turn a parking problem into solving algebraic equations. By tracking a planned path, the Car-like mobile robot can drive into the parking area without hitting any boundaries. The efficiency of the proposed algorithm is demonstrated by simulation.
Pedestrians are key participants in transportation systems, so pedestrian detection in video surveillance systems is of great significance to the research and application of Intelligent Transportation systems (ITS). W...
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Pedestrians are key participants in transportation systems, so pedestrian detection in video surveillance systems is of great significance to the research and application of Intelligent Transportation systems (ITS). We review some methods and models for vision-based pedestrian detection in recent years. In this paper, the pedestrian detection techniques are divided into macroscopic and microscopic according to different application in transportation systems. Macroscopic pedestrian detection aims to estimate crowd density without distinguishing each pedestrian, and microscopic pedestrian detection focuses on detection and recognition of individual pedestrians. The latter detection style is deeply studied, so it is presented in detail in this paper, especially for the feature-classifier-based detection method. Finally, the pedestrian detection algorithms are discussed and concluded from the viewpoint of video surveillance and ITS. Existing problems and future trends are presented in that section.
In video surveillance system, detection and tracking of vehicles are two foundational and significant tasks. In this paper, a vehicle detection and tracking method based on rear lamp pairs is proposed. The proposed me...
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In video surveillance system, detection and tracking of vehicles are two foundational and significant tasks. In this paper, a vehicle detection and tracking method based on rear lamp pairs is proposed. The proposed method combines color with motion information to perform vehicle detection. In order to adapt to different weather conditions like night, the rear lamps are divided into two categories: unlit lamps and lighted lamps. First, threshold segmentation are used to extract both the unlit and lighted lamp candidates in hue-saturation-value (HSV) color space and the thresholds are selected automatically by maximally stable extremal region (MSER) method. Then, all lamp candidates are tracked by using Kalman filter and lamp candidates with short-lived trajectories are removed to avoid disturbances. Next, two adjacent lamp candidates with similar speed are bound together as a region of interest (ROI), which represents a potential pair of lamps. Image cross-correlation symmetry analysis based on Gabor filter is utilized to find the ROIs with symmetrical texture and these symmetric ROIs can be regarded as pairs of lamps. The experimental results show that the proposed method can effectively deal with various illumination conditions and improve the accuracy and robustness of vehicle detection. In addition, this method can perform vehicle detection and tracking under complex traffic conditions and the Gabor filter based symmetry analysis can successfully suppress subtle difference between the left and right parts of a vehicle as well as environment noises.
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