52,426 research outputs found
Informative windowed forecasting of continuous-time linear systems for mutual information-based sensor planning
No full textThis paper presents an expression of mutual information that defines the information gain in planning of sensing resources, when the goal is to reduce the forecast uncertainty of some quantities of interest and the system dynamics is described as a continuous-time linear system. The method extends the smoother approach in Choi and How (2010b) to handle a more general notion of the verification entity continuous sequence of variables over some finite time window in the future. The expression of mutual information for this windowed forecasting case is derived and quantified, taking advantage of an underlying conditional independence structure and utilizing a two-filter formula for fixed-interval smoothing with correlated noises. Two numerical examples on (a) a two-state linear system with time-varying one-way coupling dynamics, and (b) idealized weather forecasting with moving verification paths demonstrate the validity of the proposed quantification methodology.
Super Resolution based on Deep Learning Technique for constructing Digital Elevation Model
No full textIn this paper, the additional learning method on the pre-trained convolutional neural network (CNN) for image super-resolution (SR) and its usage for lunar image postprocessing is proposed. Transfer learning is a popular method in convolutional network (ConvNet) research because training a ConvNet to learn basic features for classification and detection is prohibitively time consuming. Transfer learning enables the re-training of the latter layer of a ConvNet to perform a different task. In SR, the overall ConvNet structure is much different from a ConvNet structure used for classification and detection, as the size of the input and output data must be identical. Inspired by the transfer learning method, an additional CNN structure is added to the base CNN for SR, and the additional ConvNet structure is newly trained. Results show a small improvement in performance over the base ConvNet structure in some example images. The CNN for SR algorithm outperforms the Bicubic interpolation method in restoring a sample lunar image to its original resolution. Possible applications include enhancing the resolution of lunar images to perform shape from shading, de-noising, and template matching the lunar surface image to a given DEM
A potential game approach for distributed cooperative sensing for maximum mutual information
No full textImproving Computational Efficiency in Crowded Task Allocation Games with Coupled Constraints
No full textMulti-agent task allocation is a well-studied field with many proven algorithms. In real-world applications, many tasks have complicated coupled relationships that affect the feasibility of some algorithms. In this paper, we leverage on the properties of potential games and introduce a scheduling algorithm to provide feasible solutions in allocation scenarios with complicated spatial and temporal dependence. Additionally, we propose the use of random sampling in a Distributed Stochastic Algorithm to enhance speed of convergence. We demonstrate the feasibility of such an approach in a simulated disaster relief operation and show that feasibly good results can be obtained when the confirmation and sample size requirements are properly selected
Alignment and ortho-rectification of lunar surface image using the NASA Ames Stereo Pipeline
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Hybrid Control Trajectory Optimization for Air-breathing Hypersonic Vehicle
No full textTrajectory optimization problem for air-breathing hypersonic vehicle is addressed in this paper. The engine of hypersonic vehicle is assumed as a dual-mode scramjet engine which can be operated as a ramjet and scramjet for wide range of flight Mach number. Boost-skipping trajectory was proposed for range maximization of hypersonic vehicle, and based on this trajectory, flight modes of dual-mode scramjet are divided into three modes, which are ram mode, scram mode, non-powered mode. Hypersonic vehicle was modelled with consideration of changes of physical quantities over mode transition. To deal with discrete mode changes as well as continuous control, hybrid optimal control method is applied to this problem. Simulation results demonstrate that the optimized trajectory with hybrid control has better performance compared to cyclic mode transition trajectory. Also, a vehicle which imitates the characteristics of dual-mode scramjet vehicle is implemented to optimize the trajectory. The results suggest that the hybrid optimal control can be applied to the trajectory optimization of a dual-mode scramjet vehicle considering the mode transition in infinite time horizon. Copyright (C) 2020 The Authors
Aircraft Trajectory Segmentation-Based Contrastive Coding: A Framework for Self-Supervised Trajectory Representation
No full textAir traffic trajectory recognition has gained significant interest within the air traffic management community, particularly for fundamental tasks such as classification and clustering. This paper introduces Aircraft Trajectory Segmentation-based Contrastive Coding (ATSCC), a self-supervised time series representation learning framework designed to capture semantic information in air traffic trajectory data, resulting in a data representation that best explains the trajectory instances. The framework leverages the segmentable characteristic of trajectories and ensures consistency within the self-assigned segments. Intensive experiments were conducted on datasets from three different airports, totaling four datasets, to compare the performance of the learned representation in downstream classification and clustering with that of other state-of-the-art representation learning techniques. The results show that ATSCC outperforms the baseline methods by aligning the representation with maneuvering procedures. Moreover, ATSCC is adaptable to various airport configurations and applicable to incomplete trajectories. This research has expanded upon existing capabilities, achieving these improvements independently without predefined inputs such as airport configurations, maneuvering procedures, or labeled data. The trajectory datasets used in this paper are available at huggingface.co/datasets/petchthwr/ATFMTraj. The implementation code is publicly available at github.com/petchthwr/ATSCC
SVM-Based Fault Type Classification Method for Navigation of Formation Control Systems
No full textIn this paper, we propose a fault type classification algorithm for a networked multi-robot formation control. Both actuator and sensor faults of a robot are considered as node fault on the networked system. The Support Vector Machine (SVM) based classification scheme is proposed in order to classify the fault type accurately. Basically, the graph-theoretic approach is used for modeling the multi-agent communication and to generate the formation control law. A numerical simulation is presented to confirm the performance of proposed fault type classification method. © Springer International Publishing AG, part of Springer Nature 2019
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