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Author

Huanzhang Lu

Bio: Huanzhang Lu is an academic researcher from National University of Defense Technology. The author has contributed to research in topics: Infrared signature & Convolutional neural network. The author has an hindex of 5, co-authored 25 publications receiving 285 citations.

Papers
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Journal ArticleDOI
TL;DR: A novel convolutional neural network framework is proposed for time series classification that can discover and extract the suitable internal structure to generate deep features of the input time series automatically by using convolution and pooling operations.

398 citations

Journal ArticleDOI
TL;DR: A novel spatial-temporal local contrast method is proposed for moving point target detection in space-based IR imaging system and significantly outperforms other methods in terms of background suppression and target detection.

23 citations

Journal ArticleDOI
TL;DR: In this paper, the authors explored a way of jointly estimating micromotion dynamics and geometrical shape parameters from the IR signals of targets in remote detection distance, and they found that the dynamic properties of the target would induce a periodic fluctuating variation on the IR irradiance intensity signature.
Abstract: The micromotion dynamics and geometrical shape are considered to be essential characteristics for exoatmospheric targets discrimination. Many methods have been investigated to retrieve the micromotion features using radar signals returned from targets of a given shape. We explore a way of jointly estimating micromotion dynamics and geometrical shape parameters from the infrared (IR) signals of targets in remote detection distance. It is found that the micromotion dynamics of the target would induce a periodic fluctuating variation on the IR irradiance intensity signature. In addition to the micromotion characteristics, the fluctuation could also reflect target structure properties, which offer a possible clue in extracting the features of micromotion dynamics and geometrical shape. Thus, the data model of target IR irradiance intensity signatures induced by micromotion patterns including spinning, coning, and tumbling is developed, and a method of parameters estimation based on joint optimization analysis techniques is proposed. Experimental results demonstrated that the parameters of target micromotion dynamics and geometrical shape can be effectively estimated using the proposed method, if the input signature contains multiple dominant frequency components.

18 citations

Journal ArticleDOI
TL;DR: A multi-scale convolutional neural network (MCNN) is proposed for feature learning and classification that can automatically extract features of objects at multi-timescales and multi-frequencies.
Abstract: Object discrimination plays an important role in an infrared (IR) imaging system. However, at a long observing distance, the presence of detector noise and the absence of robust features make space objects' discrimination difficult to tackle with. In this paper, a multi-scale convolutional neural network (MCNN) is proposed for feature learning and classification. It consists of three parts: transformation, local convolution, and full convolution. Different from previous objects' classification methods, the MCNN can automatically extract features of objects at multi-timescales and multi-frequencies. Low-level features are combined with high-level features to simultaneously capture long-term tendency and short-term fluctuations of the time sequences of IR radiation intensity. Training data are generated from IR radiation models considering micro-motion dynamics and inherent properties of space point objects under different scenarios. The simulation results indicate that our method not only promotes the performance but is also robust to the detector noise. The classification accuracy can reach 96% at a strong noise level (signal-to-noise ratio is 10 dB) in a simulation scenario.

14 citations

Proceedings ArticleDOI
25 Mar 2017
TL;DR: Experimental results show that CNN can obtain state of the art performance for waveforms classification in terms of classification accuracy and noise tolerance.
Abstract: A novel waveforms classification method based on convolutional neural networks (CNN) is proposed in this paper. Firstly, convolution and pooling operations are cross used for generating deep features, and then fully connected to the output layer for classification. Different from other traditional approaches which need human-designed features, CNN can discover and extract the suitable internal structure of the input waveform to obtain deep features for classification automatically. So that the generalization ability of this method is significantly improved comparing to other methods. Experimental results show that CNN can obtain state of the art performance for waveforms classification in terms of classification accuracy and noise tolerance.

9 citations


Cited by
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Journal ArticleDOI
TL;DR: This article proposes the most exhaustive study of DNNs for TSC by training 8730 deep learning models on 97 time series datasets and provides an open source deep learning framework to the TSC community.
Abstract: Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-of-the-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.

1,833 citations

Journal ArticleDOI
TL;DR: A 9-layer deep convolutional neural network (CNN) is developed to automatically identify 5 different categories of heartbeats in ECG signals to serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmicheartbeats.

938 citations

Journal ArticleDOI
TL;DR: This letter investigates the application of CNNs for classifying time-space waveforms from seismic shot gathers and picking FBs of both direct wave and refracted wave and illustrates that CNN is an efficient automatic data-driven classifier and picker.
Abstract: Regardless of successful applications of the convolutional neural networks (CNNs) in different fields, its application to seismic waveform classification and first-break (FB) picking has not been explored yet. This letter investigates the application of CNNs for classifying time-space waveforms from seismic shot gathers and picking FBs of both direct wave and refracted wave. We use representative subimage samples with two types of labeled waveform classification to supervise CNNs training. The goal is to obtain the optimal weights and biases in CNNs, which are solved by minimizing the error between predicted and target label classification. The trained CNNs can be utilized to automatically extract a set of time-space attributes or features from any subimage in shot gathers. These attributes are subsequently inputted to the trained fully connected layer of CNNs to output two values between 0 and 1. Based on the two-element outputs, a discriminant score function is defined to provide a single indication for classifying input waveforms. The FB is then located from the calculated score maps by sequentially using a threshold, the first local minimum rule of every trace and a median filter. Finally, we adopt synthetic and real shot data examples to demonstrate the effectiveness of CNNs-based waveform classification and FB picking. The results illustrate that CNN is an efficient automatic data-driven classifier and picker.

198 citations

Journal ArticleDOI
TL;DR: This work proposes a deep learning based approach for supervised multi-time series anomaly detection that combines a Convolutional Neural Network and a Recurrent Neural Network in different ways and refers to this architecture as Multi-head CNN–RNN.

196 citations

Journal ArticleDOI
TL;DR: This article employs a systematic literature survey approach to systematically review statistical and machine learning models in credit scoring, to identify limitations in literature, to propose a guiding machine learning framework, and to point to emerging directions.

141 citations