Over the last four years efforts have been devoted towards the development and validation of mechanical test result models relating to a range of alloy steels. Several neural-network based models have been developed, ...
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Over the last four years efforts have been devoted towards the development and validation of mechanical test result models relating to a range of alloy steels. Several neural-network based models have been developed, two of which are related to the mechanical test results of Ultimate Tensile Strength (UTS) and Reduction of Area (ROA). The ultimate aim of developing these models is to pave the way to process optimisation through better predictions of mechanical properties. In this research we propose to exploit such neural network models in order to determine the optimal alloy composition and heat treatment temperatures required, given certain predefined mechanical properties such as the UTS and ROA. Generic Algorithms are used for this purpose. The results obtained are very encouraging.
This paper describes an experimental comparison of two alternative nonlinear automotive speed controlsystems. The first approach is based on interpolation of multiple linear controllers designed using multiple local ...
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This paper describes an experimental comparison of two alternative nonlinear automotive speed controlsystems. The first approach is based on interpolation of multiple linear controllers designed using multiple local linear models. The second approach is based upon geometric nonlinear control theory and utilises feedback linearisation. This paper focuses on the engineering aspects and experimental comparison in a test vehicle. The controllers are tested in a range of speed-profile tracking tasks, and in a disturbance rejection task (the vehicle is driven up a 10% slope). For comparison, linear PI/PID controllers are implemented.
Reduced manning is the process (and the result) of removing human functions from a system while retaining or improving system operability and effectiveness. Reliability and maintainability characterize a system's ...
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Reduced manning is the process (and the result) of removing human functions from a system while retaining or improving system operability and effectiveness. Reliability and maintainability characterize a system's operability and effectiveness. Reduced manning impacts system reliability by changing the characteristics of (1) human error associated with system operation and maintenance, (2) time to repair failed components, and (3) mean-time-between-failures (MBTF) in a reduced manning environment. Simply reducing manning without compensating for system dependence on human involvement generally has a negative impact on system maintainability. Methods to address this include (1) human-system integration design of maintenance interfaces and (2) design of operations activities that are closely related to device failures. After demonstrating reliable performance through testing and operation, ship commanders can be assured that fewer people can effectively operate and maintain Navy ships and systems.
This paper describes two computing paradigms known as neural computing and evolutionary computing, and their potential contribution to building intelligent software systems. The paper begins by giving a brief introduc...
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This paper describes two computing paradigms known as neural computing and evolutionary computing, and their potential contribution to building intelligent software systems. The paper begins by giving a brief introduction to the origins of each paradigm. Then two sections introduce the basic principles, and identify the role of each paradigm in intelligent system design. Each section ends with a number of applications that have been or are being investigated. These include connection admission control, modem communication, adaptive model-based control, face and handwriting recognition, frequency assignment, help-desk scheduling, financial time-series prediction, face recognition and evolving agent behavior. A section introduces the idea of using communications theory to design neural networks and the paper concludes with the authors' views on the future of neural and evolutionary computing for intelligent software systems.
Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. ...
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Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. To address this issue, this paper proposes a novel approach to extracting vehicle velocity and acceleration, enabling the learning of vehicle dynamics and encoding them as auxiliary information. The VDI-LSTM model is designed, incorporating graph convolution and attention mechanisms to capture vehicle interactions using trajectory data and dynamic information. Specifically, a dynamics encoder is designed to capture the dynamic information, a dynamic graph is employed to represent vehicle interactions, and an attention mechanism is introduced to enhance the performance of LSTM and graph convolution. To demonstrate the effectiveness of our model, extensive experiments are conducted, including comparisons with several baselines and ablation studies on real-world highway datasets. Experimental results show that VDI-LSTM outperforms other baselines compared, which obtains a 3% improvement on the average RMSE indicator over the five prediction steps.
The Preisach distribution function in electrical steel at different magnetizing frequencies has been studied by the combination of the experimental measurements in an Epstein frame and the numerical identification by ...
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research results using some of the most advanced soft computing techniques in intelligent robotic systems are presented. The main purpose of this book is to show how the power of soft computing techniques can be explo...
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ISBN:
(数字)9783790818826
ISBN:
(纸本)9783662130032
research results using some of the most advanced soft computing techniques in intelligent robotic systems are presented. The main purpose of this book is to show how the power of soft computing techniques can be exploited in intelligent robotic systems. The main emphasis is on control system for a mobile robot, behavior arbitration for a mobile robot, reinforcement learning of a robot, manipulation of a robot, collision avoidance and automatic design of robots.;This book will be useful for application engineers, scientists and researchers who wish to use some of the most advanced soft computing techniques in robotics.
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