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