Analysis and Experiment of an Ultra-light Flapping Wing Aircraft

II Inspired by flying animals in nature especially birds, human has designed and attempted to achieve man-powered flapping wing aircraft in very early aviation history. Limited by the understanding of the aerodynamic theory and materials in practise, the bird-like aircraft remains as a dream and ambition for over a contrary. As the relevant knowledge and technology are fast developing in the last decade, the research topic becomes attractive again with encouraging results from a few full scale aircraft flight tests. Although it is suspected that a manned scale flapping wing may not be as efficient as fixed wing, the unique advantages of high manoeuvrability and short take-off and landing capability will keep flapping wing as one of the most potential type of personal and aerobatic aircraft in the future market. The aim of this project is to investigate into the feasibility and development of a bio-inspired bird-like man-powered ultra-light flapping wing aircraft (ULFWA). The project is based on analytical and experimental study of a scaled model taking an existing hang glider as the baseline airframe. Based on the characteristics of flying animals in nature and manmade hang glider properties, this thesis focuses its study on evaluating the feasibility and analysis of primarily a human powered aircraft. For this purpose, there are four main features as guidance in the ULFWA design. Firstly the flapping frequency was limited to below 2Hz. Secondly the hang glider airframe was adapted with a simple flapping mechanism design. Thirdly the flapping wing stroke and kinematics has been kept with the simplest and resonant movement to achieve high mechanical efficiency. Finally the wing structure has flexible rib of chord wise unsymmetrical bending stiffness to offset the aerodynamic lift loss in upstroke. An engine powered mechanism design was also studied as additional option of the ULFWA. The initial design and aerodynamic calculation of the ULFWA was based on the hang glider data including dimensions, MTOW (226 kg) and cruising speed. The unsteady aerodynamic lift and thrust forces were calculated based on Theodorsen’s theory and unsteady panel method in 2D and extended to 3D using strip theory. A set of optimal flapping kinematic parameters such as amplitude and combination of the heaving and pitching motion of the 2D wing section were determined by calculation and comparison in the limited range. Considering the maximum power and lag motion that human could achieve, the flapping frequency in the ULFWA design is limited to 1Hz. This slow motion leads to a much lower propulsive efficiency in terms of the optimum Strouhal Number (St=0.2-0.4), which was used as the design reference. Mechanism and structure design with inertia force calculation was then completed based on the kinematics. This led to the evaluation of power requirement, which was divided into two components, drag and inertia forces. The results show that the ULFWA needs minimum 2452.25W (equals to 3.29Bhp) to maintain sustainable cruise flight. In order to demonstrate the ULFWA flapping mechanism and structure design, a 1:10 scaled model with two pairs of wings of different stiffness were built for testing and measurement. Two servomotors were used as to simulate human power actuation. With this model, simplified structure and one of mechanism designs was shown. Four experiments were carried out to measure the model’s lift and thrust force. Because of the limited response of the servo motors, the maximum flapping frequency achieved is only 0.75 Hz in the specified flapping amplitude which is close to reality and has improvement margin. By reducing the flapping amplitude, the frequency can be increased to gain higher thrust. Although it is fund that the result from scaled model test is a little lower than theoretical result, it has demonstrated the feasibility and potential of human powered flapping wings aircraft

3D Shape Estimation from 2D Landmarks: A Convex Relaxation Approach

We investigate the problem of estimating the 3D shape of an object, given a set of 2D landmarks in a single image. To alleviate the reconstruction ambiguity, a widely-used approach is to confine the unknown 3D shape within a shape space built upon existing shapes. While this approach has proven to be successful in various applications, a challenging issue remains, i.e., the joint estimation of shape parameters and camera-pose parameters requires to solve a nonconvex optimization problem. The existing methods often adopt an alternating minimization scheme to locally update the parameters, and consequently the solution is sensitive to initialization. In this paper, we propose a convex formulation to address this problem and develop an efficient algorithm to solve the proposed convex program. We demonstrate the exact recovery property of the proposed method, its merits compared to alternative methods, and the applicability in human pose and car shape estimation.Comment: In Proceedings of CVPR 201

Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data

This paper presents a new method for shadow removal using unpaired data, enabling us to avoid tedious annotations and obtain more diverse training samples. However, directly employing adversarial learning and cycle-consistency constraints is insufficient to learn the underlying relationship between the shadow and shadow-free domains, since the mapping between shadow and shadow-free images is not simply one-to-one. To address the problem, we formulate Mask-ShadowGAN, a new deep framework that automatically learns to produce a shadow mask from the input shadow image and then takes the mask to guide the shadow generation via re-formulated cycle-consistency constraints. Particularly, the framework simultaneously learns to produce shadow masks and learns to remove shadows, to maximize the overall performance. Also, we prepared an unpaired dataset for shadow removal and demonstrated the effectiveness of Mask-ShadowGAN on various experiments, even it was trained on unpaired data.Comment: Accepted to ICCV 201

Direction-aware Spatial Context Features for Shadow Detection

Shadow detection is a fundamental and challenging task, since it requires an understanding of global image semantics and there are various backgrounds around shadows. This paper presents a novel network for shadow detection by analyzing image context in a direction-aware manner. To achieve this, we first formulate the direction-aware attention mechanism in a spatial recurrent neural network (RNN) by introducing attention weights when aggregating spatial context features in the RNN. By learning these weights through training, we can recover direction-aware spatial context (DSC) for detecting shadows. This design is developed into the DSC module and embedded in a CNN to learn DSC features at different levels. Moreover, a weighted cross entropy loss is designed to make the training more effective. We employ two common shadow detection benchmark datasets and perform various experiments to evaluate our network. Experimental results show that our network outperforms state-of-the-art methods and achieves 97% accuracy and 38% reduction on balance error rate.Comment: Accepted for oral presentation in CVPR 2018. The journal version of this paper is arXiv:1805.0463