Han Zhou

Bio: Han Zhou is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Communications system & Frequency allocation. The author has an hindex of 1, co-authored 3 publications receiving 17 citations.

Foreign object debris material recognition based on convolutional neural networks

Abstract: The material attributes of foreign object debris (FOD) are the most crucial factors to understand the level of damage sustained by an aircraft. However, the prevalent FOD detection systems lack an effective method for automatic material recognition. This paper proposes a novel FOD material recognition approach based on both transfer learning and a mainstream deep convolutional neural network (D-CNN) model. To this end, we create an FOD image dataset consisting of images from the runways of Shanghai Hongqiao International Airport and the campus of our research institute. We optimize the architecture of the D-CNN by considering the characteristics of the material distribution of the FOD. The results show that the proposed approach can improve the accuracy of material recognition by 39.6% over the state-of-the-art method. The work here will help enhance the intelligence capability of future FOD detection systems and encourage other practical applications of material recognition technology.

A Study on the Feasibility of Mobile Ad-Hoc Networks Applied to Foreign Objective Debris Detection System

Abstract: Foreign Object Debris (FOD) on the runways or taxiways may damage airplanes and injure personnel in the airport. There are two main types of commercial FOD detection systems, i.e. Fixed and mobile. Nowadays, only one of these two main types of FOD detection systems is deployed in some airports. In this paper, we propose a total solution to support both of FOD detection system types based on Shanghai Hongqiao International Airport FOD prevention's requirements, i.e. Mobile Ad-Hoc Network (MANET) based FOD detection system. In this paper, a novel FOD detection system architecture is proposed. Then we study the feasibility of MANET applied to the proposed FOD detection system. In this proposed MANET, Wi-Fi and IEEE 802.11 are introduced to physical and MAC layer of wireless node, OLSR is used to provide network routing function, in addition, a novel multi-path data fusion mechanism is proposed to decrease the packet loss ratio.

Modeling for spectrum congestion analysis of air-to-ground voice communications in China

Abstract: The traffic load of civil aviation, conjunction with the aircraft density, determines the number of frequency channels and ground radio sites in order to support various levels of future air-to-ground communications services. In the U.S. and Europe, the Very High Frequency (VHF) spectrum is highly congested due to the out of date Double Sideband Amplitude Modulation (DSB-AM) voice communication system. The U.S. and Europe have proceeded to update the DSB-AM voice communication system to confront with the increasing traffic load on the civil aviation system. Currently, DSB-AM voice communication system is still mainly used in Civil Aviation of China for air traffic services and it is still unknown that what the capability of this system is regarding the distribution feature of the traffic in China. Our main contribution of this paper is to establish a mathematical model which is able to allocate the spectrum according to the capability of DSB-AM voice communication system and analyze the spectrum congestion status in China. Based on this model, the spectrum allocation and the timeliness of the need for voice aviation communication system with respect to the impacts of the creation of voice communication can be inferred; the model provides a way to describe the ability of the communication infrastructure to meet the requirements of air traffic services. In this paper, the spectrum congestion status of aerodrome control service for voice communication around Yang Zi Jiang River is analyzed based on the geographic location of the airport sites as well as their traffic movements.

Convolutional neural network: a review of models, methodologies and applications to object detection

Abstract: Deep learning has developed as an effective machine learning method that takes in numerous layers of features or representation of the data and provides state-of-the-art results. The application of deep learning has shown impressive performance in various application areas, particularly in image classification, segmentation and object detection. Recent advances of deep learning techniques bring encouraging performance to fine-grained image classification which aims to distinguish subordinate-level categories. This task is extremely challenging due to high intra-class and low inter-class variance. In this paper, we provide a detailed review of various deep architectures and model highlighting characteristics of particular model. Firstly, we described the functioning of CNN architectures and its components followed by detailed description of various CNN models starting with classical LeNet model to AlexNet, ZFNet, GoogleNet, VGGNet, ResNet, ResNeXt, SENet, DenseNet, Xception, PNAS/ENAS. We mainly focus on the application of deep learning architectures to three major applications, namely (i) wild animal detection, (ii) small arm detection and (iii) human being detection. A detailed review summary including the systems, database, application and accuracy claimed is also provided for each model to serve as guidelines for future work in the above application areas.

Leading edge topography of blades - a critical review

Abstract: In turbomachinery, their blade leading edges are critical to performance and therefore fuel efficiency, emission, noise, running and maintenance costs. Leading edge damage and therefore roughness is either caused by subtractive processes such as foreign object damage (bird strikes and debris ingestion) and erosion (hail, rain droplets, sand particles, dust, volcanic ash and cavitation) and additive processes such as filming (from dirt, icing, fouling, insect build-up). Therefore, this review focuses on the changes in topography induced by during service to blade leading edges and the effect of roughness and form on performance and efforts to predict and model these changes. The applications considered are focused on wind, gas and tidal turbines and turbofan engines. Repair and protection strategies for leading edges of blades are also reviewed. The review shows additive processes are typically worse than subtractive processes, as the roughness or even form change is significant with icing and biofouling. Antagonism is reported between additive and subtractive roughness processes. There are gaps in the current understanding of the additive and subtractive processes that influence roughness and their interaction. Recent work paves the way forward where modelling and machine learning is used to predict coated wind turbine blade leading edge delamination and the effects this has on aerodynamic performance and what changes in blade angle would best capture the available wind energy with such damaged blades. To do this generically there is a need for better understanding of the environment that the blades see and the variation along their length, the material or coated material response to additive and/or subtractive mechanisms and thus the roughness/form evolution over time. This is turn would allow better understanding of the effects these changes have on aerodynamic/ hydrodynamic efficiency and the population of stress raisers and distribution of residual stresses that result. These in turn influence fatigue strength and remaining useful life of the blade leading edge as well as inform maintenance/repair needs

A Sequential CNN Approach for Foreign Object Detection in Hyperspectral Images

Abstract: This paper reports about potentials of hyperspectral imaging for object detection, especially on an application of foreign object detection (FOD) in meat products. A sequential deep-learning framework is proposed by using region-proposal networks (RPNs) and 3D convolutional networks (CNNs). Two independent datasets of images, contaminated with many types of foreign materials, were used for training and testing the proposed model. Results show that the proposed RPN model outperforms a selected search method in terms of accuracy, efficiency, and run-time. An FOD model based on RPN and 3D-CNN, or selected search with a 3D-CNN solve FOD with an average precision of 81.0% or 50.6%, respectively. This study demonstrates opportunities when using hyperspectral imaging systems for real-time object detection by using both spectral and spatial features combined.

Small Foreign Object Debris Detection for Millimeter-Wave Radar Based on Power Spectrum Features

Abstract: Foreign object debris (FOD) detection can be considered a kind of classification that distinguishes the measured signal as either containing FOD targets or only corresponding to ground clutter. In this paper, we propose a support vector domain description (SVDD) classifier with the particle swarm optimization (PSO) algorithm for FOD detection. The echo features of FOD and ground clutter received by the millimeter-wave radar are first extracted in the power spectrum domain as input eigenvectors of the classifier, followed with the parameters optimized by the PSO algorithm, and lastly, a PSO-SVDD classifier is established. However, since only ground clutter samples are utilized to train the SVDD classifier, overfitting inevitably occurs. Thus, a small number of samples with FOD are added in the training stage to further construct a PSO-NSVDD (NSVDD: SVDD with negative examples) classifier to achieve better classification performance. Experimental results based on measured data showed that the proposed methods could not only achieve a good detection performance but also significantly reduce the false alarm rate.