Before a train departs a yard, many aspects of the freight cars and locomotives undergo inspection, including their safety appliances. Safety appliances are handholds, ladders and other objects that serve as the inter...
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Before a train departs a yard, many aspects of the freight cars and locomotives undergo inspection, including their safety appliances. Safety appliances are handholds, ladders and other objects that serve as the interface between humans and railcars during transportation. Federal safety rules govern the design and condition of safety appliances. The current car inspection process is primarily visual making it laborious, redundant, and generally lacking of memory. There exists a potential to increase both the effectiveness and efficiency of safety appliance inspections by utilizing machine vision technology to enhance the railcar inspection process. Machine vision consists of capturing digital video and using algorithms capable of detecting and analyzing the particular objects or patterns of interest. computer algorithms can objectively inspect railcars without tiring or becoming distracted and can also focus on certain parts of the railcar not easily seen by an inspector on the ground. Thus far, algorithms have been developed that can detect deformed ladders, handholds, and brake wheels on open-top gondolas and hoppers. Next, visual learning will be employed to teach the algorithm the differences between Federal Railroad Administration (FRA) safety appliance defects and other types of deformation not requiring a car to be bad ordered. The final product will be a wayside inspection system capable of detecting safety appliance defects on passing railcars.
In a recent result, we proposed a dynamic dual high-gain observer/controller architecture for nonlinear systems of the generalized output-feedback canonical form. The designed output-feedback controller was shown to b...
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In a recent result, we proposed a dynamic dual high-gain observer/controller architecture for nonlinear systems of the generalized output-feedback canonical form. The designed output-feedback controller was shown to be robust to functional and parametric uncertainties coupled with all states. However, a magnitude bound on the unknown parameters was required. In this paper, we propose two output-feedback controllers, one time-varying and the other time-invariant that can handle time-varying nonlinear parametric uncertainty coupled with all states without requiring any a priori magnitude bounds on the unknown parameters. The proposed dynamic controller designs provide globally asymptotically stabilizing output-feedback solutions for the benchmark open problem proposed in our earlier work with no magnitude bounds or sign information on the unknown parameter being necessary.
We propose an adaptive output-feedback controller for a general class of nonlinear triangular (strict-feedback-like) systems. The design is based on our recent results on a dual high-gain observer and controller archi...
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ISBN:
(纸本)1424401704;9781424401703
We propose an adaptive output-feedback controller for a general class of nonlinear triangular (strict-feedback-like) systems. The design is based on our recent results on a dual high-gain observer and controller architecture with a dynamic scaling. The technique provides strong robustness properties and allows the system class to contain unknown functions dependent on all states and involving unknown parameters (with no magnitude bounds required). Unlike our earlier result on this problem where a time-varying design of the high-gain scaling parameter was utilized, the technique proposed here achieves an autonomous dynamic controller by introducing a novel design of the observer, the scaling parameter, and the adaptation parameter. This provides a time-invariant dynamic output-feedback controller for the benchmark open problem proposed in our earlier work with no magnitude bounds or sign information on the unknown parameter being necessary
This paper describes a robotic application that tracks a moving object by utilizing a mobile robot with multiple sensors. The robotic platform uses a visual camera to sense the movement of the desired object and a ran...
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This paper describes a robotic application that tracks a moving object by utilizing a mobile robot with multiple sensors. The robotic platform uses a visual camera to sense the movement of the desired object and a range sensor to help the robot detect and then avoid obstacles in real time while continuing to track and follow the desired object. In terms of real-time obstacle avoidance capacity, this paper also presents a modified potential field algorithm called dynamic goal potential field algorithm (DGPF) for this robotic application specifically. Experimental results show that the robotic and intelligent system can fulfill the requirements of tracking an object and avoiding obstacles simultaneously when the object is moving
In this paper, the SPT (Singular Perturbation Theory) is applied to decompose the VCM's (Voice Coil Motor) and induced PZT active suspension's dynamics into fast and slow subsystems, respectively. The control ...
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The goal of our research is to develop a high-level controller to provide reference trajectories automatically to the low-level controller of a rehabilitation robotic device. The high-level controller, which is the su...
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The goal of our research is to develop a high-level controller to provide reference trajectories automatically to the low-level controller of a rehabilitation robotic device. The high-level controller, which is the supervisory controller, is an event driven, asynchronous discrete event system (DES), described by a finite state automaton. Extensive simulations are performed using the supervisory controller for a rehabilitation task. The results demonstrate the feasibility of the proposed supervisory controller
We have developed a system which integrates the information output from several pose estimation algorithms and from several views of the scene. It is tested in a real setup with a robotic manipulator. It is shown that...
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We have developed a system which integrates the information output from several pose estimation algorithms and from several views of the scene. It is tested in a real setup with a robotic manipulator. It is shown that integrating pose estimates from several algorithms increases the overall performance of the pose estimation accuracy as well as the robustness as compared to using only a single algorithm. It is shown that increased robustness can be achieved by using pose estimation algorithms based on complementary features, so called algorithmic multi-cue integration (AMC). Furthermore it is also shown that increased accuracy can be achieved by integrating pose estimation results from different views of the scene, so-called temporal multi-cue integration (TMC). Temporal multi-cue integration is the most interesting aspect of this paper
In this work, we explore various ideas and approaches to deal with the inherent uncertainty and image noise in motion analysis, and develop a low-complexity, accurate and reliable scheme to estimate the motion fields ...
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We propose a new inventory control technique for large-scale bidirectional (or closed-loop) supply chains including repairs. It is well-known that available optimization techniques are computationally intractable for ...
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We propose a new inventory control technique for large-scale bidirectional (or closed-loop) supply chains including repairs. It is well-known that available optimization techniques are computationally intractable for bidirectional stochastic supply chains and also necessitate several simplifying assumptions. In contrast, the proposed approach is an adaptive scheme which scales well to practically interesting large-scale multi-item supply chains. Furthermore, practical issues such as stochastic transport delays, manufacturing times, and repair times and probabilistic characterization of part repair success are handled in a unified framework. The control scheme is based on a hierarchical two-level architecture comprised of an adaptive set-point generator and a lower-level order-up-to policy. An application to aircraft supply chains involving multiple OEMs, depots, bases, squadrons, and planes is also investigated
Electronic fetal monitoring is an essential tool for fetal surveillance during labor. It is mainly based on the monitoring and evaluation of the fetal heart rate (FHR) signal, which has to be interpreted online. Evalu...
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Electronic fetal monitoring is an essential tool for fetal surveillance during labor. It is mainly based on the monitoring and evaluation of the fetal heart rate (FHR) signal, which has to be interpreted online. Evaluation and interpretation of FHR gives an indication of the fetal condition. A lot of research efforts have been done towards the development of automatic and reliable methods for processing and evaluating FHR. This research work introduces an integrated methodology for processing and classifying FHR based on the novel approach of grammatical evolution for feature construction and selection. The proposed methodology is presented, and it is applied to a data set. Experimental results are promising paving the way for further research in that direction
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