856 research outputs found
Experimental investigations on the aerodynamic and aeroacoustic characteristics of small UAS propellers
Unmanned aerial system (UAS) is a hot topic in both industry and academia fields. As a popular planform, the rotary-wing system gains more attentions. The small UAS propeller is the most important component in this system, which transfers electric energy into kinetic energy to accomplish fly missions.
In the present work, several experimental studies have been performed to investigate the aerodynamic and aeroacoustic characteristics of small UAS propellers. First of all, by conducting force and flow filed measurements, the unsteady dynamic thrust and the wake structure of the propeller has been studied to explore the fundamental physics to help researchers and engineers to obtain a better understanding. Secondly, two kinds of bio-inspired the propellers have been designed and manufactured. Through a set of force, sound, and flow filed measurements, the aerodynamic and aeroacoustic performance of these propellers has been compared to the baseline propeller to evaluate the effects of aerodynamic efficiency and noise attenuation. It was found that the serrated trailing edge propeller could reduce the turbulent trailing edge noise up to 2 dB, and the maple seed inspired propeller could reduce the noise up to 4 dB with no effect on the aerodynamic performance. In addition, since the rotary-wing system consists more than one propeller, the rotor to rotor interaction on the aerodynamic and aeroacoustic performance also has been studied. By enlarging the separation distance between two propellers, the thrust fluctuation and noise generation could be restricted. Not only the design of the device itself has effect on the flying performance, the extreme weather also would affect it. Therefore, an icing research study on the small UAS propeller has been conducted to illustrate how does the ice formed on the propeller and how does the icing influence the aerodynamics performance and power consumption.
During these experimental studies, the force measurements were achieved by a high sensitive force and moment transducer (JR3 load cell), which had a precision of ñ0.1N (ñ 0.25% of the full range). The sound measurements were conducted inside of the anechoic chamber located in the aerospace engineering department at Iowa State University. This chamber has a physical dimensions of 12ÃÂ12ÃÂ9 feet with a cut-off frequency of 100 Hz. The detailed flow structure downstream of the propeller was measured by a high-resolution digital PIV system. The PIV system was used to elucidate the streamwise flow structure downstream of the propeller. Both “free-run” and “phase-locked” PIV measurements were conducted to achieve the ensemble-average flow structure and detailed flow structure at certain phase angles
Dynamics of entanglement in the transverse Ising model
We study the evolution of nearest-neighbor entanglement in the one
dimensional Ising model with an external transverse field. The system is
initialized as the so called "thermal ground state" of the pure Ising model. We
analyze properties of generation of entanglement for different regions of
external transverse fields. We find that the derivation of the time at which
the entanglement reaches its first maximum with respect to the reciprocal
transverse field has a minimum at the critical point. This is a new indicator
of quantum phase transition.Comment: To be published in PR
Medical Image Segmentation Based on Multi-Modal Convolutional Neural Network: Study on Image Fusion Schemes
Image analysis using more than one modality (i.e. multi-modal) has been
increasingly applied in the field of biomedical imaging. One of the challenges
in performing the multimodal analysis is that there exist multiple schemes for
fusing the information from different modalities, where such schemes are
application-dependent and lack a unified framework to guide their designs. In
this work we firstly propose a conceptual architecture for the image fusion
schemes in supervised biomedical image analysis: fusing at the feature level,
fusing at the classifier level, and fusing at the decision-making level.
Further, motivated by the recent success in applying deep learning for natural
image analysis, we implement the three image fusion schemes above based on the
Convolutional Neural Network (CNN) with varied structures, and combined into a
single framework. The proposed image segmentation framework is capable of
analyzing the multi-modality images using different fusing schemes
simultaneously. The framework is applied to detect the presence of soft tissue
sarcoma from the combination of Magnetic Resonance Imaging (MRI), Computed
Tomography (CT) and Positron Emission Tomography (PET) images. It is found from
the results that while all the fusion schemes outperform the single-modality
schemes, fusing at the feature level can generally achieve the best performance
in terms of both accuracy and computational cost, but also suffers from the
decreased robustness in the presence of large errors in any image modalities.Comment: Zhe Guo and Xiang Li contribute equally to this wor
EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching from Scratch
Designing the structure of neural networks is considered one of the most
challenging tasks in deep learning, especially when there is few prior
knowledge about the task domain. In this paper, we propose an
Ecologically-Inspired GENetic (EIGEN) approach that uses the concept of
succession, extinction, mimicry, and gene duplication to search neural network
structure from scratch with poorly initialized simple network and few
constraints forced during the evolution, as we assume no prior knowledge about
the task domain. Specifically, we first use primary succession to rapidly
evolve a population of poorly initialized neural network structures into a more
diverse population, followed by a secondary succession stage for fine-grained
searching based on the networks from the primary succession. Extinction is
applied in both stages to reduce computational cost. Mimicry is employed during
the entire evolution process to help the inferior networks imitate the behavior
of a superior network and gene duplication is utilized to duplicate the learned
blocks of novel structures, both of which help to find better network
structures. Experimental results show that our proposed approach can achieve
similar or better performance compared to the existing genetic approaches with
dramatically reduced computation cost. For example, the network discovered by
our approach on CIFAR-100 dataset achieves 78.1% test accuracy under 120 GPU
hours, compared to 77.0% test accuracy in more than 65, 536 GPU hours in [35].Comment: CVPR 201
A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning
Automatic decision-making approaches, such as reinforcement learning (RL),
have been applied to (partially) solve the resource allocation problem
adaptively in the cloud computing system. However, a complete cloud resource
allocation framework exhibits high dimensions in state and action spaces, which
prohibit the usefulness of traditional RL techniques. In addition, high power
consumption has become one of the critical concerns in design and control of
cloud computing systems, which degrades system reliability and increases
cooling cost. An effective dynamic power management (DPM) policy should
minimize power consumption while maintaining performance degradation within an
acceptable level. Thus, a joint virtual machine (VM) resource allocation and
power management framework is critical to the overall cloud computing system.
Moreover, novel solution framework is necessary to address the even higher
dimensions in state and action spaces. In this paper, we propose a novel
hierarchical framework for solving the overall resource allocation and power
management problem in cloud computing systems. The proposed hierarchical
framework comprises a global tier for VM resource allocation to the servers and
a local tier for distributed power management of local servers. The emerging
deep reinforcement learning (DRL) technique, which can deal with complicated
control problems with large state space, is adopted to solve the global tier
problem. Furthermore, an autoencoder and a novel weight sharing structure are
adopted to handle the high-dimensional state space and accelerate the
convergence speed. On the other hand, the local tier of distributed server
power managements comprises an LSTM based workload predictor and a model-free
RL based power manager, operating in a distributed manner.Comment: accepted by 37th IEEE International Conference on Distributed
Computing (ICDCS 2017
Vector-tracking-based GNSS/INS Deep Coupling and Experiment Platform for Urban Scenarios
Peer reviewe
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