A robotic dolphin with a pair of 3-DOF flippers, two turning units and a multi-link oscillatory propulsion mechanism is designed. The mechanical, hardware and software designs of the robotic dolphin are given. The fli...
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Advancements in complexity, complexsystems, and the intelligence sciences, particularly smart city technologies, have shown great potential in aiding to ease traffic congestion. The overall approach and the main idea...
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Advancements in complexity, complexsystems, and the intelligence sciences, particularly smart city technologies, have shown great potential in aiding to ease traffic congestion. The overall approach and the main ideas in building smart transportation for smart cities, particularly ACP (artificial system, computational experiment, and parallel execution)-based parallel transportation management and controlsystems (PTMS), are presented. PTMS can be expanded to the new generation of intelligent transportation systems. The main components of the proposed architecture include social signal and social traffic, ITS clouds and services, agent-based traffic control, and transportation knowledge automation. Some technical details of these components are discussed. Finally, one case study is introduced, and the effectiveness is analyzed.
Feature correspondence lays the foundation for many tasks in computer vision and pattern recognition. In this paper the directed structural model is utilized to represent the feature set, and the correspondence proble...
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Feature correspondence lays the foundation for many tasks in computer vision and pattern recognition. In this paper the directed structural model is utilized to represent the feature set, and the correspondence problem is then formulated as the structural model matching. Compared with the undirected structural model, the proposed directed model provides more discriminating ability and invariance against rotation and scale transformations. Finally, the recently proposed convex-concave relaxation procedure (CCRP) is generalized to approximately solve the problem. Extensive experiments on synthetic and real data witness the effectiveness of the proposed method. (C) 2014 Elsevier B.V. All rights reserved.
In this paper we propose the graduated nonconvexity and concavity procedure (GNCCP) as a general optimization framework to approximately solve the combinatorial optimization problems defined on the set of partial perm...
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In this paper we propose the graduated nonconvexity and concavity procedure (GNCCP) as a general optimization framework to approximately solve the combinatorial optimization problems defined on the set of partial permutation matrices. GNCCP comprises two sub-procedures, graduated nonconvexity which realizes a convex relaxation and graduated concavity which realizes a concave relaxation. It is proved that GNCCP realizes exactly a type of convex-concave relaxation procedure (CCRP), but with a much simpler formulation without needing convex or concave relaxation in an explicit way. Actually, GNCCP involves only the gradient of the objective function and is therefore very easy to use in practical applications. Two typical related NP-hard problems, partial graph matching and quadratic assignment problem (QAP), are employed to demonstrate its simplicity and state-of-the-art performance.
It remains a great challenge for a biomimetic dolphin robot to leap out of the water by reason of requirements for very high speed and exquisite motion control. In this paper, we estimate the minimum exit speed that a...
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It remains a great challenge for a biomimetic dolphin robot to leap out of the water by reason of requirements for very high speed and exquisite motion control. In this paper, we estimate the minimum exit speed that allows the dolphin to completely leap out of the water, and for the first time, we build a self-contained leaping dolphin robot with commercially available actuators and power supply. To quantify the possible impact of the body length on the minimum exit speed during the leap, we employ a rigid bodymodel rather than a particle model to numerically evaluate the leaping process. Furthermore, a robotic prototype intended for leap motions is created, with particular emphasis on streamlining and high-thrust tail propulsive mechanism designs in conjunction with a passive control strategy for the dorsoventral propulsion. Underwater tests on the untethered dolphin robot demonstrate the effectiveness of the proposed methods and mechatronic designs. We found that the dolphin robot successfully performed leaps with a length-specific speed of over 2.3 body lengths per second, and an emergence angle ranging between 35 and 60 degrees.
Vehicle recognition plays an important role in traffic surveillance systems, advanced driver-assistance systems, and autonomous vehicles. This paper presents a novel approach for multivehicle recognition that consider...
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Vehicle recognition plays an important role in traffic surveillance systems, advanced driver-assistance systems, and autonomous vehicles. This paper presents a novel approach for multivehicle recognition that considers vehicle space location and classification as a coupled optimization problem. It can speed up the detection process with more accurate vehicle region proposals and can recognize multiple vehicles using a single model. The proposed detector is implemented in three stages: 1) obtaining candidate vehicle locations with prior objectness measure;2) classifying vehicle region proposals to distinguish the three common types of vehicles (i.e., car, taxi, and bus) by a single convolutional neural network (CNN);and 3) coupling classification results with the detection process, which leads to fewer false positives. In experiments on high-resolution traffic images, our method achieves unique characteristics: 1) It matches the state-of-the-art detection accuracy;2) it is more efficient in generating a smaller set of high-quality vehicle windows;3) its searching time is decreased by about 30 times compared with the other two popular detection schemes;and 4) it recognizes different vehicles in each image using a single CNN model with eight layers.
Traffic data provide the basis for both research and applications in transportation control, management, and evaluation, but real-world traffic data collected from loop detectors or other sensors often contain corrupt...
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Traffic data provide the basis for both research and applications in transportation control, management, and evaluation, but real-world traffic data collected from loop detectors or other sensors often contain corrupted or missing data points which need to be imputed for traffic analysis. For this end, here we propose a deep learning model named denoising stacked autoencoders for traffic data imputation. We tested and evaluated the model performance with consideration of both temporal and spatial factors. Through these experiments and evaluation results, we developed an algorithm for efficient realization of deep learning for traffic data imputation by training the model hierarchically using the full set of data from all vehicle detector stations. Using data provided by Caltrans PeMS, we have shown that the mean absolute error of the proposed realization is under 10 veh/5-min, a better performance compared with other popular models: the history model, ARIMA model and BP neural network model. We further investigated why the deep leaning model works well for traffic data imputation by visualizing the features extracted by the first hidden layer. Clearly, this work has demonstrated the effectiveness as well as efficiency of deep learning in the field of traffic data imputation and analysis. (C) 2016 Elsevier Ltd. All rights reserved.
In this paper, a novel iterative adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. When the iterative control law and ite...
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In this paper, a novel iterative adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. When the iterative control law and iterative performance index function in each iteration cannot be accurately obtained, it is shown that the iterative controls can make the performance index function converge to within a finite error bound of the optimal performance index function. Stability properties are presented to show that the system can be stabilized under the iterative control law which makes the present iterative ADP algorithm feasible for implementation both on-line and off-line. Neural networks are used to approximate the iterative performance index function and compute the iterative control policy, respectively, to implement the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.
In this paper, a reinforcement-learning-based direct adaptive control is developed to deliver a desired tracking performance for a class of discrete-time (DT) nonlinear systems with unknown bounded disturbances. We in...
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In this paper, a reinforcement-learning-based direct adaptive control is developed to deliver a desired tracking performance for a class of discrete-time (DT) nonlinear systems with unknown bounded disturbances. We investigate multi-input-multi-output unknown nonaffine nonlinear DT systems and employ two neural networks (NNs). By using Implicit Function Theorem, an action NN is used to generate the control signal and it is also designed to cancel the nonlinearity of unknown DT systems, for purpose of utilizing feedback linearization methods. On the other hand, a critic NN is applied to estimate the cost function, which satisfies the recursive equations derived from heuristic dynamic programming. The weights of both the action NN and the critic NN are directly updated online instead of offline training. By utilizing Lyapunov's direct method, the closed-loop tracking errors and the NN estimated weights are demonstrated to be uniformly ultimately bounded. Two numerical examples are provided to show the effectiveness of the present approach. (C) 2014 Elsevier Ltd. All rights reserved.
This brief presents the real-time dynamic Dubins-Helix (RDDH) method for trajectory smoothing, which consists of Dubins-Helix trajectory generation and pitch angle smoothing. The generated 3-D trajectory is called the...
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This brief presents the real-time dynamic Dubins-Helix (RDDH) method for trajectory smoothing, which consists of Dubins-Helix trajectory generation and pitch angle smoothing. The generated 3-D trajectory is called the RDDH trajectory. On one hand, the projection of 3-D trajectory on the horizontal plane is partially generated by Dubins path planner such that the curvature radius constraint is satisfied. On the other hand, the Helix curve is constructed to satisfy the pitch angle constraint, even in the case that the initial and final poses are close. Furthermore, by analyzing the relationship between the parameters and the effectiveness of the RDDH trajectory, the smoothing algorithm is designed to obtain appropriate parameters for a shorter and smoother trajectory. In the end, the numerical results show the proposed method can generate an effective trajectory under diverse initial conditions and achieve real-time computation.
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