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Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images Research Article

Xiaolong CHEN, Xiaoqian MU, Jian GUAN, Ningbo LIU, Wei ZHOU,cxlcxl1209@163.com,guanjian_68@163.com

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 4,   Pages 630-643 doi: 10.1631/FITEE.2000611

Abstract: As a classic deep learning target detection algorithm, Faster R-CNN (region convolutional neural network) has been widely used in high-resolution synthetic aperture radar (SAR) and inverse SAR (ISAR) image detection. However, for most common low-resolution radar , it is difficult to achieve good performance. In this paper, taking PPI images as an example, a method based on the Marine-Faster R-CNN algorithm is proposed in the case of complex background (e.‍g., sea clutter) and target characteristics. The method performs feature extraction and target recognition on PPI images generated by radar echoes with the . First, to improve the accuracy of detecting marine targets and reduce the false alarm rate, Faster R-CNN was optimized as the Marine-Faster R-CNN in five respects: new backbone network, anchor size, dense target detection, data sample balance, and scale normalization. Then, JRC (Japan Radio Co., Ltd.) was used to collect echo data under different conditions to build a marine target dataset. Finally, comparisons with the classic Faster R-CNN method and the constant false alarm rate (CFAR) algorithm proved that the proposed method is more accurate and robust, has stronger generalization ability, and can be applied to the detection of marine targets for . Its performance was tested with datasets from different observation conditions (sea states, radar parameters, and different targets).

Keywords： Marine target detection     Navigation radar     Plane position indicator (PPI) images     Convolutional neural network (CNN)     Faster R-CNN (region convolutional neural network) method    

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Deep 3D reconstruction: methods, data, and challenges Review Article

Caixia Liu, Dehui Kong, Shaofan Wang, Zhiyong Wang, Jinghua Li, Baocai Yin,lcxxib@emails.bjut.edu.cn,wangshaofan@bjut.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5,   Pages 615-766 doi: 10.1631/FITEE.2000068

Abstract: Three-dimensional (3D) reconstruction of shapes is an important research topic in the fields of computer vision, computer graphics, pattern recognition, and virtual reality. Existing 3D reconstruction methods usually suffer from two bottlenecks: (1) they involve multiple manually designed states which can lead to cumulative errors, but can hardly learn semantic features of 3D shapes automatically; (2) they depend heavily on the content and quality of images, as well as precisely calibrated cameras. As a result, it is difficult to improve the reconstruction accuracy of those methods. 3D reconstruction methods based on deep learning overcome both of these bottlenecks by automatically learning semantic features of 3D shapes from low-quality images using deep networks. However, while these methods have various architectures, in-depth analysis and comparisons of them are unavailable so far. We present a comprehensive survey of 3D reconstruction methods based on deep learning. First, based on different deep learning model architectures, we divide 3D reconstruction methods based on deep learning into four types, , , , and based methods, and analyze the corresponding methodologies carefully. Second, we investigate four representative databases that are commonly used by the above methods in detail. Third, we give a comprehensive comparison of 3D reconstruction methods based on deep learning, which consists of the results of different methods with respect to the same database, the results of each method with respect to different databases, and the robustness of each method with respect to the number of views. Finally, we discuss future development of 3D reconstruction methods based on deep learning.

Keywords： 深度学习模型；三维重建；循环神经网络；深度自编码器；生成对抗网络；卷积神经网络    

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A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG Regular Papers

Lu-di WANG, Wei ZHOU, Ying XING, Na LIU, Mahmood MOVAHEDIPOUR, Xiao-guang ZHOU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 3,   Pages 405-413 doi: 10.1631/FITEE.1700413

Abstract:

Reconstruction of a 12-lead electrocardiogram (ECG) from a serial 3-lead ECG has been researched in the past to satisfy the need for more wearing comfort and ambulatory situations. The accuracy and real-time performance of traditional methods need to be improved. In this study, we present a novel method based on convolutional neural networks (CNNs) for the synthesis of missing precordial leads. The results show that the proposed method receives better similarity and consumes less time using the PTB database. Particularly, the presented method shows outstanding performance in reconstructing the pathological ECG signal, which is crucial for cardiac diagnosis. Our CNN-based method is shown to be more accurate and time-saving for deployment in non-hospital situations to synthesize a standard 12-lead ECG from a reduced lead-set ECG recording. This is promising for real cardiac care.

Keywords： Convolutional neural networks (CNNs)     Electrocardiogram (ECG) synthesis     E-health    

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Recent advances in efficient computation of deep convolutional neural networks Review

Jian CHENG, Pei-song WANG, Gang LI, Qing-hao HU, Han-qing LU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1,   Pages 64-77 doi: 10.1631/FITEE.1700789

Abstract: Deep neural networks have evolved remarkably over the past few years and they are currently the fundamental tools of many intelligent systems. At the same time, the computational complexity and resource consumption of these networks continue to increase. This poses a significant challenge to the deployment of such networks, especially in real-time applications or on resource-limited devices. Thus, network acceleration has become a hot topic within the deep learning community. As for hardware implementation of deep neural networks, a batch of accelerators based on a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC) have been proposed in recent years. In this paper, we provide a comprehensive survey of recent advances in network acceleration, compression, and accelerator design from both algorithm and hardware points of view. Specifically, we provide a thorough analysis of each of the following topics: network pruning, low-rank approximation, network quantization, teacher–student networks, compact network design, and hardware accelerators. Finally, we introduce and discuss a few possible future directions.

Keywords： Deep neural networks     Acceleration     Compression     Hardware accelerator    

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Research on Tracing Evaluation System in Virtual Enterprise Based on Neural Network

Wang Shuo,Tang Xiaowo

Strategic Study of CAE 2003, Volume 5, Issue 4,   Pages 65-69

Abstract:

The paper designed tracing evaluation index system in virtual enterprise and established neural network trace evaluation model. As a result, it was simple and nicety than traditional method, so it had wider application foreground.

Keywords： virtual enterprise     neural network     trace evaluation     system    

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Pressure in Gas-assisted Injection Molding

Ou Changjin

Strategic Study of CAE 2007, Volume 9, Issue 5,   Pages 27-32

Abstract:

In this study,  an effective control method and strategy based on fuzzy neural network has been developed for gas injection pressure accurate control in gas-assisted injection. A fuzzy neural network controller with five layers and its control algorithm are established.  The learning ability of neural network is used to optimize the rules of the fuzzy logic so as to improve the adaptability of system.  The simulation of the system capability and three segmental injected pressure control are carried out under the environment of MATLAB and the results show that this theoretic model is feasible, and the control system has good characteristics and control action.

Keywords： gas-assisted injection molding     fuzzy neural network     gas-injection pressure control    

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Aggregated context network for crowd counting

Si-yue Yu, Jian Pu,51174500148@stu.ecnu.edu.cn,jianpu@fudan.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 11,   Pages 1535-1670 doi: 10.1631/FITEE.1900481

Abstract: has been applied to a variety of applications such as video surveillance, traffic monitoring, assembly control, and other public safety applications. Context information, such as perspective distortion and background interference, is a crucial factor in achieving high performance for . While traditional methods focus merely on solving one specific factor, we aggregate sufficient context information into the network to tackle these problems simultaneously in this study. We build a fully convolutional network with two tasks, i.e., main density map estimation and auxiliary . The main task is to extract the multi-scale and spatial context information to learn the density map. The auxiliary task gives a comprehensive view of the background and foreground information, and the extracted information is finally incorporated into the main task by late fusion. We demonstrate that our network has better accuracy of estimation and higher robustness on three challenging datasets compared with state-of-the-art methods.

Keywords： 人群计数；卷积神经网络；密度估计；语义分割；多任务学习    

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Research on fuzzy neural network control method for high-frequency vacuum drying of wood

Jiang Bin,Sun Liping,Cao Jun and Zhou Zheng

Strategic Study of CAE 2014, Volume 16, Issue 4,   Pages 17-20

Abstract:

High- frequency vacuum combined wood drying is a kind of fast drying speed， low energy consumption，little environmental pollution of new drying technology. On the basis of theoretical analysis with high frequency in wood vacuum drying process，the fuzzy controller and fuzzy neural network controller of wood drying are designed in view of the neural network method to establish model of wood drying. The simulation experiment results show that fuzzy neural network control is better，such as the temperature rising fast，high control precision，good stability. The method to realize the automatic control of timber drying process has important research significance.

Keywords： high-frequency vacuum     wood drying     fuzzy neural network    

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De-scattering and edge-enhancement algorithms for underwater image restoration Research Papers

Pan-wang PAN, Fei YUAN, En CHENG

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 6,   Pages 862-871 doi: 10.1631/FITEE.1700744

Abstract:

Image restoration is a critical procedure for underwater images, which suffer from serious color deviation and edge blurring. Restoration can be divided into two stages: de-scattering and edge enhancement. First, we introduce a multi-scale iterative framework for underwater image de-scattering, where a convolutional neural network is used to estimate the transmission map and is followed by an adaptive bilateral filter to refine the estimated results. Since there is no available dataset to train the network, a dataset which includes 2000 underwater images is collected to obtain the synthetic data. Second, a strategy based on white balance is proposed to remove color casts of underwater images. Finally, images are converted to a special transform domain for denoising and enhancing the edge using the non-subsampled contourlet transform. Experimental results show that the proposed method significantly outperforms state-of-the-art methods both qualitatively and quantitatively.

Keywords： Image de-scattering     Edge enhancement     Convolutional neural network     Non-subsampled contourlet transform    

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Simulation Algorithm of Flightdeck Airflow Based on Neural Network

Xun Wensheng,Lin Ming

Strategic Study of CAE 2003, Volume 5, Issue 5,   Pages 76-79

Abstract:

The airflow on the flightdeck is an important factor which influences helicopter flight safety. The airflow velocity distribution characteristics directly influences simulation accuracy of helicopter flight dynamics. Based on the Navier-Stokes equations, the method to determine the airflow velocity in real-time is discussed using BP neural network. This method can be used for flightdeck airflow real-time simulation, and it can improve helicopter flight simulation accuracy.

Keywords： flow     finite element     neural network    

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Prediction model for residual strength of stiffened panels with multiple site damage based on artificial neural network

Yang Maosheng,Chen Yueliang,Yu Dazhao

Strategic Study of CAE 2008, Volume 10, Issue 5,   Pages 46-50

Abstract:

A prediction model for residual strength of stiffened panels with multiple site damage based on artificial neural network (ANN) is developed, and the results obtained from the trained BP model are compared to the analytical and experimental data available in the literature. The results obtained indicate that the neural network model predictions are in the best agreement with the experimental data than any other methods, and the modified linkup models predict better than the linkup model proposed by Swift. In the end several simulations are carried out to predict the trends with varying input parameters. The results show that the residual strength decreases linearly as the half-crack length of lead crack increases and increases linearly as the ligament length increases for both kinds of stiffened panels, but the one-bay stiffened panels are more sensitive to the change than the two-bay stiffened panels.

Keywords： neural network     multiple site damage     stiffened panel     residual strength    

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An Improving Method of BP Neural Network and Its Application

Li Honggang,Lü Hui,Li Gang

Strategic Study of CAE 2005, Volume 7, Issue 5,   Pages 63-65

Abstract:

Seeing on that in BPNN the small learning gene will make the long training time, but the large learning gene will make the BPNN surging, this paper brings forward a way to modify the learning gene, that is, adding a proportion gene before the learning gene, The proportion gene will change when the weight of the BPNN needs to be modified. This can shorten the training time and make convergence better as well. The simulating results show that the new algorithm is much better than the old one during BPNN scouting the missile command.

Keywords： BPNN     improved algorithm     simulation    

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Study of Forecast of Building Cost Based on Neural Network

Nie Guihua,Liu Pingfeng,He Liu

Strategic Study of CAE 2005, Volume 7, Issue 10,   Pages 56-59

Abstract:

In the constantly changing marketing economy, it has become an urgent task for construction industry to find a rapid, simple and practical way to organize construction project budget. To solve this problem, this paper adopts the model of the back-propagation neural network, takes the features of construction as input variables, trains the network using actual data as samples and optimizes the network structure by contribution analysis. It shows the validity of the model in the forecast of construction project budget.

Keywords： BP neural network     building budget     forecast    

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Diffractive Deep Neural Networks at Visible Wavelengths Article

Hang Chen, Jianan Feng, Minwei Jiang, Yiqun Wang, Jie Lin, Jiubin Tan, Peng Jin

Engineering 2021, Volume 7, Issue 10,   Pages 1485-1493 doi: 10.1016/j.eng.2020.07.032

Abstract:

Optical deep learning based on diffractive optical elements offers unique advantages for parallel processing, computational speed, and power efficiency. One landmark method is the diffractive deep neural network (D2NN) based on three-dimensional printing technology operated in the terahertz spectral range. Since the terahertz bandwidth involves limited interparticle coupling and material losses, this paper
extends D2NN to visible wavelengths. A general theory including a revised formula is proposed to solve any contradictions between wavelength, neuron size, and fabrication limitations. A novel visible light D2NN classifier is used to recognize unchanged targets (handwritten digits ranging from 0 to 9) and targets that have been changed (i.e., targets that have been covered or altered) at a visible wavelength of 632.8 nm. The obtained experimental classification accuracy (84%) and numerical classification accuracy (91.57%) quantify the match between the theoretical design and fabricated system performance. The presented framework can be used to apply a D2NN to various practical applications and design other new applications.

Keywords： Optical computation     Optical neural networks     Deep learning     Optical machine learning     Diffractive deep neural networks    

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Application of Artificial Neural Network to Engineering Project Management

Wang Yingluo,Yang Yaohong

Strategic Study of CAE 2004, Volume 6, Issue 7,   Pages 26-33

Abstract:

Applications of ANN to engineering project management were summarized, including prediction and evaluation of risk, cost estimation, performance prediction, organization effectivity, engineering accident diagnoses, claim and litigation analysis, enter bidding decision, schedule/cost optimation and resource leveling. Problems existing in application were summarized and analyzed, some suggestions on how to develop application of ANN to engineering project management in China were submitted.

Keywords： engineering project management     ANN     prediction     optimization     DS    

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Title Author Date Type Operation

Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images

Xiaolong CHEN, Xiaoqian MU, Jian GUAN, Ningbo LIU, Wei ZHOU,cxlcxl1209@163.com,guanjian_68@163.com

Journal Article

Deep 3D reconstruction: methods, data, and challenges

Caixia Liu, Dehui Kong, Shaofan Wang, Zhiyong Wang, Jinghua Li, Baocai Yin,lcxxib@emails.bjut.edu.cn,wangshaofan@bjut.edu.cn

Journal Article

A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG

Lu-di WANG, Wei ZHOU, Ying XING, Na LIU, Mahmood MOVAHEDIPOUR, Xiao-guang ZHOU

Journal Article

Recent advances in efficient computation of deep convolutional neural networks

Jian CHENG, Pei-song WANG, Gang LI, Qing-hao HU, Han-qing LU

Journal Article

Research on Tracing Evaluation System in Virtual Enterprise Based on Neural Network

Wang Shuo,Tang Xiaowo

Journal Article

Pressure in Gas-assisted Injection Molding

Ou Changjin

Journal Article

Aggregated context network for crowd counting

Si-yue Yu, Jian Pu,51174500148@stu.ecnu.edu.cn,jianpu@fudan.edu.cn

Journal Article

Research on fuzzy neural network control method for high-frequency vacuum drying of wood

Jiang Bin,Sun Liping,Cao Jun and Zhou Zheng

Journal Article

De-scattering and edge-enhancement algorithms for underwater image restoration

Pan-wang PAN, Fei YUAN, En CHENG

Journal Article

Simulation Algorithm of Flightdeck Airflow Based on Neural Network

Xun Wensheng,Lin Ming

Journal Article

Prediction model for residual strength of stiffened panels with multiple site damage based on artificial neural network

Yang Maosheng,Chen Yueliang,Yu Dazhao

Journal Article

An Improving Method of BP Neural Network and Its Application

Li Honggang,Lü Hui,Li Gang

Journal Article

Study of Forecast of Building Cost Based on Neural Network

Nie Guihua,Liu Pingfeng,He Liu

Journal Article

Diffractive Deep Neural Networks at Visible Wavelengths

Hang Chen, Jianan Feng, Minwei Jiang, Yiqun Wang, Jie Lin, Jiubin Tan, Peng Jin

Journal Article

Application of Artificial Neural Network to Engineering Project Management

Wang Yingluo,Yang Yaohong

Journal Article