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Stochastic Processes And Filtering Theory

1.Introduction to Composite Materials 2. Macrochemical Behavior of a Lamina 3.Micromechanical Behavior of a Lamina 4.Macromechanical Behavior of a Laminate 5.Bending, Buckling, and Vibration of Laminated Plates 6.Other Analysis and Behavior Topics 7.Introduction to Design of Composite Structures Appendix A.Matrices and Tensors Appendix B.Maxima and Minima of Functions of a Single Variable Appendix C.Typical Stress-Strain Curves Appendix D.Governing Equations for Beam Equilibrium and Plate Equilibrium, Buckling, and Vibration Index

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Mechanics of composite materials

1. Control Methodology 2. Dynamical Systems 3. Applications to Social and Environmental Problems

Dynamics and Control

where xt+1 is the position and orientation of the robot (with respect to a reference frame) at time t + 1, with (ξ, η) giving the x and y coordinates and θ the angle (with respect to the x-axis) that the robot is facing. The robot has two drive wheels, of radius r on an axle of length d. During time period t the right wheel is believed to rotate at a velocity of φRt and the left at a velocity of φLt. In this example, these velocities are fixed with φRt = 0.4 and φLt = 0.1. The state update function, F , calculates where the robot should be at each time point, given its previous position. However, in reality, there is some random fluctuation in the velocity of the wheels, for example, due to slippage. Therefore the actual position of the robot and the position given by equation F will differ. The magnitude of these random fluctuations is included in the model through Σx.

Unscented Kalman Filterの計測への応用に関する研究

In this paper, we investigate the day-to-day regularity of urban congestion patterns. We first partition link speed data every 10 min into 3D clusters that propose a parsimonious sketch of the congestion pulse. We then gather days with similar patterns and use consensus clustering methods to produce a unique global pattern that fits multiple days, uncovering the day-to-day regularity. We show that the network of Amsterdam over 35 days can be synthesized into only 4 consensual 3D speed maps with 9 clusters. This paves the way for a cutting-edge systematic method for travel time predictions in cities. By matching the current observation to historical consensual 3D speed maps, we design an efficient real-time method that successfully predicts 84% trips travel times with an error margin below 25%. The new concept of consensual 3D speed maps allows us to extract the essence out of large amounts of link speed observations and as a result reveals a global and previously mostly hidden picture of traffic dynamics at the whole city scale, which may be more regular and predictable than expected.

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Revealing the day-to-day regularity of urban congestion patterns with 3D speed maps.

Travel time information is a vital component of many intelligent transportation systems (ITS) applications. In recent years, the number of vehicles in India has increased tremendously, leading to severe traffic congestion and pollution in urban areas, particularly during peak periods. A desirable strategy to deal with such issues is to shift more people from personal vehicles to public transport by providing better service (comfort, convenience and so on). In this context, advanced public transportation systems (APTS) are one of the most important ITS applications, which can significantly improve the traffic situation in India. One such application will be to provide accurate information about bus arrivals to passengers, leading to reduced waiting times at bus stops. This needs a real-time data collection technique, a quick and reliable prediction technique to calculate the expected travel time based on real-time data and informing the passengers regarding the same. The scope of this study is to use global positioning system data collected from public transportation buses plying on urban roadways in the city of Chennai, India, to predict travel times under heterogeneous traffic conditions using an algorithm based on the Kalman filtering technique. The performance of the proposed algorithm is found to be promising and expected to be valuable in the development of APTS in India. The work presented here is one of the first attempts at real-time short-term prediction of travel time for ITS applications in Indian traffic conditions.

Travel time prediction under heterogeneous traffic conditions using global positioning system data from buses

The accuracy of travel time information given to passengers plays a key role in the success of any Advanced Public Transportation Systems (APTS) application. In order to improve the accuracy of such applications, one should carefully develop a prediction method. A majority of the available prediction methods considered the variation in travel time either spatially or temporally. The present study developed a prediction method that considers both temporal and spatial variations in travel time. The conservation of vehicles equation in terms of flow and density was first re-written in terms of speed in the form of a partial differential equation using traffic stream models. Then, the developed speed based equation was discretized using the Godunov scheme and used in the prediction scheme that was based on the Kalman filter. From the results, it was found that the proposed method was able to perform better than historical average, regression, and ANN methods and the methods that considered either temporal or spatial variations alone. Finally, a formulation was developed to check the effect of side roads on prediction accuracy and it was found that the additional requirement in terms of location based data did not result in an appreciable change in the prediction accuracy. This clearly demonstrated that the proposed approach based on using vehicle tracking data is good enough for the considered application of bus travel time prediction.

Bus travel time prediction using a time-space discretization approach

This paper deals with the development of a fault-free model of the pneumatic subsystem of an air brake system that is used in commercial vehicles. Our objective is to use this model in brake control and diagnostic applications. The development of a diagnostic system would be useful in automating enforcement inspections and also in monitoring the condition of the brake system in real-time. This paper presents a detailed description of the development of this model and of the experimental setup used to corroborate this model for various realistic test runs.

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Modeling the Pneumatic Subsystem of an S-cam Air Brake System

In this paper, a rear-wheel-driven series hybrid electric vehicle which has a mechanically operated friction brake system is studied. A new cooperative control of regenerative braking and friction braking called ‘combined braking’ is proposed for this vehicle configuration. A mechanism to adjust the proportions of regenerative braking and friction braking was proposed in this paper. Further, the braking force distribution between the front wheels and the rear wheels was analysed to ensure stable braking. The brake system characteristics were considered to ensure that the driver’s feel remains the same in the new proposed combined braking strategy. The simulation results under urban driving and across the Modified Indian Driving Cycle and vehicle road testing results show that the proposed combined braking can regenerate more than twice the braking energy of conventional parallel braking. Also, with combined braking, the braking force distribution between the front wheels and the rear wheels is closer to t...

Cooperative control of regenerative braking and friction braking for a hybrid electric vehicle

The accuracy of Bus Traveler Information Systems (BTIS) depends on several factors such as accuracy of the input data, speed of data transfer, data quality control and performance of the prediction scheme. A majority of the existing BTIS in India does not take into account the real-time data and the quality control of data. Also, there is a scope for improving the performance of the underlying prediction schemes. There are several studies on real-time bus arrival time prediction under homogeneous traffic conditions. However, the traffic condition in India is different and direct implementation of those studies may not yield the best results. One of the main components of bus travel time is the delay time at bus stops, in addition to the other common delays. These delays need to be incorporated in the prediction scheme for better accuracy, which is not the case currently in most studies. Also, there is a need to develop an accurate automated bus arrival time prediction system using real-time data under heterogeneous traffic conditions. This study presents a model-based algorithm that uses real-time data from field and takes delays automatically into account for an accurate prediction of bus arrival time. The results obtained are compared with the currently adopted field method and show a clear improvement in the prediction accuracy.

Shankar C. Subramanian

Papers

Travel time prediction under heterogeneous traffic conditions using global positioning system data from buses

Bus travel time prediction using a time-space discretization approach

Modeling the Pneumatic Subsystem of an S-cam Air Brake System

Cooperative control of regenerative braking and friction braking for a hybrid electric vehicle

Development of a real-time bus arrival prediction system for Indian traffic conditions