In this paper, the control problem is addressed for a hybrid PDE-ODE system that describes a nonuniform gantry crane system with constrained tension. A bottom payload hangs from the top gantry by connecting a flexible...
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Acquiring high resolution images in deep regions is challenging in ultrasound imaging due to limited probe aperture size and low transmit frequency usage. The concept of synthetic tracked aperture ultrasound (STRATUS)...
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ISBN:
(纸本)9781467398985
Acquiring high resolution images in deep regions is challenging in ultrasound imaging due to limited probe aperture size and low transmit frequency usage. The concept of synthetic tracked aperture ultrasound (STRATUS) imaging is introduced to extend the effective aperture size by moving the probe while accurately tracking its orientation and translation. Based on the synthetic aperture technique, sub-apertures from each pose can be synthesized to construct a high-resolution image. In particular, we propose a mechanical tracking configuration using a 6 degree-of-freedom (DOF) robotic arm with force sensors that not only provides a robust tracking accuracy, but also enables co-operative control. The ultrasound probe is moved by an operator, while a virtual fixture uses force feedback of the robotic arm to constrain the motion to be on a desired plane or trajectory. Furthermore, we developed an algorithm to mitigate the potential errors between consecutive poses, such as tracking inaccuracy, tissue deformation, and phase aberration. Those errors were extracted by computing subtle image shift through cross-correlation for all neighboring poses, and the procedure is dynamically applied to the entire image. Comparing the STRATUS image to a conventional single pose image, the full width at the half maximum (FWHM) of a point target located at a depth of around 85 mm improved from 3.13 mm to 2.78 mm, and SNR improved from 28.96 dB to 30.27 dB. In addition, the dynamic error compensation further improved the FWHM and SNR to be 1.15 mm and 33.17 dB, respectively. The results proved the feasibility of the co-robotic STRATUS imaging, and dynamic error compensation improved the system's tolerance to errors.
In this paper, an approach to detect stator winding short-circuit faults in squirrel-cage induction motors based on Random Forest and Park's Vector is proposed. This is accomplished by scoring the unbalance in the...
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ISBN:
(纸本)9781509042821
In this paper, an approach to detect stator winding short-circuit faults in squirrel-cage induction motors based on Random Forest and Park's Vector is proposed. This is accomplished by scoring the unbalance in the current and voltage waveforms as well as in Park's Vector, both for current and voltage. To score the unbalance in the d-q space, a Principal Component Analysis is applied to Park's Vector and with the first two principal components the eccentricity is calculated, while the first principal component is used to determine the phase in short-circuit. The proposed strategy has been experimentally tested on a special 400-V, 50-Hz, 4-pole, 2.2-kW induction motor with reconfigurable stator windings in which it was possible to emulate different types of inter-turn short-circuits. The results are quite promising, even only using 1-kHz sampling frequency to acquire the current and voltage waveforms in the three phases, and the use of the Fast Fourier Transform is avoided. The developed solution may be used for tele-monitoring of the motor condition and to implement advanced predictive maintenance strategies.
This paper presents a system which constantly monitors the level of attention of a driver in traffic. The vehicle is instrumented and can identify the state of traffic-lights, as well as obstacles on the road. If the ...
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ISBN:
(纸本)9781467380270
This paper presents a system which constantly monitors the level of attention of a driver in traffic. The vehicle is instrumented and can identify the state of traffic-lights, as well as obstacles on the road. If the driver is inattentive and fails to recognize a threat, the assistance system produces a warning. Therefore, the system helps the driver to focus on crucial traffic situations. Our system consists of three components: computer vision detection of traffic-lights and other traffic participants, an eye tracking device used also for head localization, and finally, a human machine interface consisting of a head-up display and an acoustic module used to provide warnings to the driver. The orientation of the driver's head is detected using fiducial markers visible in video frames. We describe how the system was integrated using an autonomous car as experimental ADAS platform.
Mutual adaptation is critical for effective team collaboration. This paper presents a formalism for human-robot mutual adaptation in collaborative tasks. We propose the bounded-memory adaptation model (BAM), which cap...
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ISBN:
(纸本)9781467383707
Mutual adaptation is critical for effective team collaboration. This paper presents a formalism for human-robot mutual adaptation in collaborative tasks. We propose the bounded-memory adaptation model (BAM), which captures human adaptive behaviors based on a bounded memory assumption. We integrate BAM into a partially observable stochastic model, which enables robot adaptation to the human. When the human is adaptive, the robot will guide the human towards a new, optimal collaborative strategy unknown to the human in advance. When the human is not willing to change their strategy, the robot adapts to the human in order to retain human trust. Human subject experiments indicate that the proposed formalism can significantly improve the effectiveness of human-robot teams, while human subject ratings on the robot performance and trust are comparable to those achieved by cross training, a state-of-the-art human-robot team training practice.
We present a hierarchical graph-based approach for unknown object discovery in RGB-D point clouds captured with a Kinect-like sensor from unstructured scenes. A two-step approach is proposed which first extracts meani...
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ISBN:
(纸本)9781467380270
We present a hierarchical graph-based approach for unknown object discovery in RGB-D point clouds captured with a Kinect-like sensor from unstructured scenes. A two-step approach is proposed which first extracts meaningful regions from an input scene through over-segmentation. Secondly, a procedure is introduced to detect compositions of such regions that can represent primitive-shaped object candidates like boxes or cylinders. Complex-shaped objects are interpreted as a composition of primitive-shaped objects, for instance, a teddy bear can consist of two convex-shaped arms, legs, a convex-shaped head and torso. An ensemble of classifiers is trained to learn patterns from the appearances of such neighboring primitive shapes that constitute complex-shaped objects. Therein the appearance is described by a set of features from the texture and geometry domain. For the experiments, a dataset was prepared which is publicly available, containing a set of scenes which consists of 296 human-annotated object instances in total. Experiments show that the proposed hierarchical approach is capable to extract meaningful regions: an under-segmentation rate of 2.6% has been achieved. Furthermore, objects are segmented with a segmentation rate of 92.9% which reflects the capability of our approach to detect potential object candidates within unstructured scenes.
Deep learning has rapidly transformed the state of the art algorithms used to address a variety of problems in computer vision and robotics. These breakthroughs have relied upon massive amounts of human annotated trai...
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Deep learning has rapidly transformed the state of the art algorithms used to address a variety of problems in computer vision and robotics. These breakthroughs have relied upon massive amounts of human annotated training data. This time consuming process has begun impeding the progress of these deep learning efforts. This paper describes a method to incorporate photo-realistic computer images from a simulation engine to rapidly generate annotated data that can be used for the training of machine learning algorithms. We demonstrate that a state of the art architecture, which is trained only using these synthetic annotations, performs better than the identical architecture trained on human annotated real-world data, when tested on the KITTI data set for vehicle detection. By training machine learning algorithms on a rich virtual world, real objects in real scenes can be learned and classified using synthetic data. This approach offers the possibility of accelerating deep learning's application to sensor-based classification problems like those that appear in self-driving cars. The source code and data to train and validate the networks described in this paper are made available for researchers.
Optimal placement of drones is a very challenging problem and it belongs to the group of hard optimization problems for which swarm intelligence algorithms were successfully applied. This paper presents an implementat...
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Optimal placement of drones is a very challenging problem and it belongs to the group of hard optimization problems for which swarm intelligence algorithms were successfully applied. This paper presents an implementation of the recent elephant herding optimization algorithm for solving the static drone location problem. The objective of the model applied in this paper is to establish monitoring of all targets with the least possible number of drones. In empirical tests we used two problem instances: one with 30 uniformly distributed targets, and one with 30 clustered targets. The simulation results show that the elephant herding optimization algorithm performs well in covering targets for both instances of the problem, especially considering the number of drones that were deployed.
In this paper, we present a histopathology image categorization method based on Fisher vector descriptors. While Fisher vector has been broadly successful for general computer vision and recently applied to microscopy...
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The disturbance observer has been widely employed in applications due to its powerful ability for disturbance rejection and robustness under plant uncertainties. However, it rejects the disturbance approximately rathe...
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