180 research outputs found
Controlling a Quadrotor Carrying a Cable-Suspended Load to Pass Through a Window
In this paper, we design an optimal control system for a quadrotor to carry a cable-suspended load flying through a window. As the window is narrower than the length of the cable, it is very challenging to design a practical control system to pass through it. Our solution includes a system identification component, a trajectory generation component, and a trajectory tracking control component. The exact dynamic model that usually derived from the first principles is assumed to be unavailable. Instead, a model identification approach is adopted, which relies on a simple but effective low order equivalent system (LOES) to describe the core dynamical characteristics of the system. After being excited by some specifically designed manoeuvres, the unknown parameters in the LOES are obtained by using a frequency based least square estimation algorithm. Based on the estimated LOES, a numerical optimization algorithm is then utilized for aggressive trajectory generation when relevant constraints are given. The generated trajectory can lead to the quadrotor and load system passing through a narrow window with a cascade PD trajectory tracking controller. Finally, a practical flight test based on an Astec Hummingbird quadrotor is demonstrated and the result validates the proposed approach
Case-Aware Adversarial Training
The neural network (NN) becomes one of the most heated type of models in
various signal processing applications. However, NNs are extremely vulnerable
to adversarial examples (AEs). To defend AEs, adversarial training (AT) is
believed to be the most effective method while due to the intensive
computation, AT is limited to be applied in most applications. In this paper,
to resolve the problem, we design a generic and efficient AT improvement
scheme, namely case-aware adversarial training (CAT). Specifically, the
intuition stems from the fact that a very limited part of informative samples
can contribute to most of model performance. Alternatively, if only the most
informative AEs are used in AT, we can lower the computation complexity of AT
significantly as maintaining the defense effect. To achieve this, CAT achieves
two breakthroughs. First, a method to estimate the information degree of
adversarial examples is proposed for AE filtering. Second, to further enrich
the information that the NN can obtain from AEs, CAT involves a weight
estimation and class-level balancing based sampling strategy to increase the
diversity of AT at each iteration. Extensive experiments show that CAT is
faster than vanilla AT by up to 3x while achieving competitive defense effect
Privacy-preserving smart IoT-based healthcare big data storage and self-adaptive access control system
Multi-view Graph Convolutional Networks with Differentiable Node Selection
Multi-view data containing complementary and consensus information can
facilitate representation learning by exploiting the intact integration of
multi-view features. Because most objects in real world often have underlying
connections, organizing multi-view data as heterogeneous graphs is beneficial
to extracting latent information among different objects. Due to the powerful
capability to gather information of neighborhood nodes, in this paper, we apply
Graph Convolutional Network (GCN) to cope with heterogeneous-graph data
originating from multi-view data, which is still under-explored in the field of
GCN. In order to improve the quality of network topology and alleviate the
interference of noises yielded by graph fusion, some methods undertake sorting
operations before the graph convolution procedure. These GCN-based methods
generally sort and select the most confident neighborhood nodes for each
vertex, such as picking the top-k nodes according to pre-defined confidence
values. Nonetheless, this is problematic due to the non-differentiable sorting
operators and inflexible graph embedding learning, which may result in blocked
gradient computations and undesired performance. To cope with these issues, we
propose a joint framework dubbed Multi-view Graph Convolutional Network with
Differentiable Node Selection (MGCN-DNS), which is constituted of an adaptive
graph fusion layer, a graph learning module and a differentiable node selection
schema. MGCN-DNS accepts multi-channel graph-structural data as inputs and aims
to learn more robust graph fusion through a differentiable neural network. The
effectiveness of the proposed method is verified by rigorous comparisons with
considerable state-of-the-art approaches in terms of multi-view semi-supervised
classification tasks
Architecture-based integrated management of diverse cloud resources
Cloud management faces with great challenges, due to the diversity of Cloud resources and ever-changing management requirements. For constructing a management system to satisfy a specific management requirement, a redevelopment solution based on existing management systems is usually more practicable than developing the system from scratch. However, the difficulty and workload of redevelopment are also very high. As the architecture-based runtime model is causally connected with the corresponding running system automatically, constructing an integrated Cloud management system based on the architecture-based runtime models of Cloud resources can benefit from the model-specific natures, and thus reduce the development workload. In this paper, we present an architecture-based approach to managing diverse Cloud resources. First, manageability of Cloud resources is abstracted as runtime models, which could automatically and immediately propagate any observable runtime changes of target resources to corresponding architecture models, and vice versa. Second, a customized model is constructed according to the personalized management requirement and the synchronization between the customized model and Cloud resource runtime models is ensured through model transformation. Thus, all the management tasks could be carried out through executing programs on the customized model. The experiment on a real-world cloud demonstrates the feasibility, effectiveness and benefits of the new approach to integrated management of Cloud resources ? 2014, Chen et al.; licensee Springer.EI11-15
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