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Register a new userGlobal optimization framework for real-time route guidance via variable message sign
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Added by
Bai Liu
Publication date
2016
fields
Informatics Engineering
and research's language is
English
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Variable message sign (VMS) is an effective traffic management tool for congestion mitigation. The VMS is primarily used as a means of providing factual travel information or genuine route guidance to travelers. However, this may be rendered sub-optimal on a network level by potential network paradoxes and lack of consideration for its cascading effect on the rest of the network. This paper focuses on the design of optimal display strategy of VMS in response to real-time traffic information and its coordination with other intelligent transportation systems such as signal control, in order to explore the full potential of real-time route guidance in combating congestion. We invoke the linear decision rule framework to design the optimal on-line VMS strategy, and test its effectiveness in conjunction with on-line signal control. A simulation case study is conducted on a real-world test network in China, which shows the advantage of the proposed adaptive VMS display strategy over genuine route guidance, as well as its synergies with on-line signal control for congestion mitigation.
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The paper proves convergence to global optima for a class of distributed algorithms for nonconvex optimization in network-based multi-agent settings. Agents are permitted to communicate over a time-varying undirected graph. Each agent is assumed to possess a local objective function (assumed to be smooth, but possibly nonconvex). The paper considers algorithms for optimizing the sum function. A distributed algorithm of the consensus+innovations type is proposed which relies on first-order information at the agent level. Under appropriate conditions on network connectivity and the cost objective, convergence to the set of global optima is achieved by an annealing-type approach, with decaying Gaussian noise independently added into each agents update step. It is shown that the proposed algorithm converges in probability to the set of global minima of the sum function.
With the development of robotics, there are growing needs for real time motion planning. However, due to obstacles in the environment, the planning problem is highly non-convex, which makes it difficult to achieve real time computation using existing non-convex optimization algorithms. This paper introduces the convex feasible set algorithm (CFS) which is a fast algorithm for non-convex optimization problems that have convex costs and non-convex constraints. The idea is to find a convex feasible set for the original problem and iteratively solve a sequence of subproblems using the convex constraints. The feasibility and the convergence of the proposed algorithm are proved in the paper. The application of this method on motion planning for mobile robots is discussed. The simulations demonstrate the effectiveness of the proposed algorithm.
Any industrial system goes along with objectives to be met (e.g. economic performance), disturbances to handle (e.g. market fluctuations, catalyst decay, unexpected variations in uncontrolled flow rates and compositions,...), and uncertainties about its behavior. In response to these, decisions must be taken and instructions be sent to the operators to drive and maintain the plant at satisfactory, yet potentially changing operating conditions. Over the past thirty years many methods have been created and developed to answer these questions. In particular, the field of Real-Time Optimization (RTO) has emerged that, among others, encompasses methods that allow the systematic improvement of the performances of the industrial system, using plant measurements and a potentially inaccurate tool to predict its behaviour, generally in the form of a model. Even though the definition of RTO can differ between authors, inside and outside the process systems engineering community, there is currently no RTO method, which is deemed capable of fully automating the aforementioned decision-making process. This thesis consists of a series of contributions in this direction, which brings RTO closer to being capable of a full plant automation. Keywords: Real-time optimization, Decision-making, Optimization, Reduced-order-model optimization, Autopilot for steady-state processes, Operational research.
Simultaneous localization and mapping (SLAM) has been a hot research field in the past years. Against the backdrop of more affordable 3D LiDAR sensors, research on 3D LiDAR SLAM is becoming increasingly popular. Furthermore, the re-localization problem with a point cloud map is the foundation for other SLAM applications. In this paper, a template matching framework is proposed to re-localize a robot globally in a 3D LiDAR map. This presents two main challenges. First, most global descriptors for point cloud can only be used for place detection under a small local area. Therefore, in order to re-localize globally in the map, point clouds and descriptors(templates) are densely collected using a reconstructed mesh model at an offline stage by a physical simulation engine to expand the functional distance of point cloud descriptors. Second, the increased number of collected templates makes the matching stage too slow to meet the real-time requirement, for which a cascade matching method is presented for better efficiency. In the experiments, the proposed framework achieves 0.2-meter accuracy at about 10Hz matching speed using pure python implementation with 100k templates, which is effective and efficient for SLAM applications.
Distillation-based learning boosts the performance of the miniaturized neural network based on the hypothesis that the representation of a teacher model can be used as structured and relatively weak supervision, and thus would be easily learned by a miniaturized model. However, we find that the representation of a converged heavy model is still a strong constraint for training a small student model, which leads to a high lower bound of congruence loss. In this work, inspired by curriculum learning we consider the knowledge distillation from the perspective of curriculum learning by routing. Instead of supervising the student model with a converged teacher model, we supervised it with some anchor points selected from the route in parameter space that the teacher model passed by, as we called route constrained optimization (RCO). We experimentally demonstrate this simple operation greatly reduces the lower bound of congruence loss for knowledge distillation, hint and mimicking learning. On close-set classification tasks like CIFAR100 and ImageNet, RCO improves knowledge distillation by 2.14% and 1.5% respectively. For the sake of evaluating the generalization, we also test RCO on the open-set face recognition task MegaFace.
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