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A data-driven method for estimating the target position of low-frequency sound sources in shallow seas Research Articles

Xianbin Sun, Xinming Jia, Yi Zheng, Zhen Wang,robin_sun@qut.edu.cn,jiaxinming_123@163.com

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 7,   Pages 1020-1030 doi: 10.1631/FITEE.2000181

Abstract: Estimating the target position of low-frequency sound sources in a environment is difficult due to the high cost of hydrophone placement and the complexity of the propagation model. We propose a compressed (C-RNN) model that compresses the signal received by a into a dynamic sound intensity signal and compresses the target position of the sound source into a GeoHash code. Two types of data are used to carry out prior training on the , and the trained network is subsequently used to estimate the target position of the sound source. Compared with traditional mathematical models, the C-RNN model functions independently under the complex sound field environment and terrain conditions, and allows for real-time positioning of the sound source under low-parameter operating conditions. Experimental results show that the average error of the model is 56 m for estimating the target position of a low-frequency sound source in a environment.

Keywords： 矢量水听器；浅海；低频；位置估计；循环神经网络    

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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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Current Source Active Power Filter Control UsingNeural Network Technologies

Wang Ping,Zhang Ke, Xu Huijun

Strategic Study of CAE 2007, Volume 9, Issue 1,   Pages 40-43

Abstract:

In this paper,the application of neural network to power converter control is discussed.A new hysteresis comparator constructed by using neural network is introduced.Hysteresis band control is an effective and simple control method.It can easily run without many system parameters.But the switch frequency of system is not fixed.So it not only makes the system unstable but also may lessen the life span of the switches. The control method that combines the neural network technology with the hysteresis band technology has a high performance in response of current.Through training the neural network can learn the control rules by itself and can replace the real hysteresis comparator in power converter control.The computer simulation results are given in this paper and they can demonstrate the effectiveness of the proposed method.The neural network is realized by using DSP.

Keywords： source filter     neural network     hysteresis comparator    

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A Survey of Accelerator Architectures for Deep Neural Networks Review

Yiran Chen, uan Xie, Linghao Song, Fan Chen, Tianqi Tang

Engineering 2020, Volume 6, Issue 3,   Pages 264-274 doi: 10.1016/j.eng.2020.01.007

Abstract:

Recently, due to the availability of big data and the rapid growth of computing power, artificial intelligence (AI) has regained tremendous attention and investment. Machine learning (ML) approaches have been successfully applied to solve many problems in academia and in industry. Although the explosion of big data applications is driving the development of ML, it also imposes severe challenges of data processing speed and scalability on conventional computer systems. Computing platforms that are dedicatedly designed for AI applications have been considered, ranging from a complement to von Neumann platforms to a “must-have” and standalone technical solution. These platforms, which belong to a larger category named “domain-specific computing,” focus on specific customization for AI. In this article, we focus on summarizing the recent advances in accelerator designs for deep neural networks (DNNs)—that is, DNN accelerators. We discuss various architectures that support DNN executions in terms of computing units, dataflow optimization, targeted network topologies, architectures on emerging technologies, and accelerators for emerging applications. We also provide our visions on the future trend of AI chip designs.

Keywords： Deep neural network     Domain-specific architecture     Accelerator    

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A Rough Fuzzy Neural Classifier

Zeng Huanglin,Wang Xiao

Strategic Study of CAE 2003, Volume 5, Issue 12,   Pages 60-65

Abstract:

In this paper, the concepts of rough sets are used to define equivalence classes encoding input data sets, and eliminate redundant or insignificant attributes in data sets so that to reduce the complexity of system construction. In order to deal with ill-defined or real experimental data, an input object is represented as a fuzzy variable by fuzzy membership function, and the significant factor of the input feature corresponding to output pattern classification is incorporated to constitute a fuzzy inference so that to enhance nonlinear mapping classification. A new kind of rough fuzzy neural classifier and a learning algorithm with LSE are proposed in this paper. A integration of the merits of fuzzy and neural network technologies can not only accommodate overlapping classification and therefore increase the performance of nonlinear mapping classification, but ensure more efficiently to handle real life ambiguous and changing situations and to achieve tractability, robustness, and low-cost solutions.

Keywords： fuzzy sets     rough sets     neural networks     pattern classification    

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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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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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Analysis of Characteristics and CMAC Neural Networks Controller of Electrohydraulic Servo System of the 6-DOF Paralle Platform

Zhai Chuanrun,ZhanXingqun,Zhang Yanhua,Ran Xianglai,Zhao Keding

Strategic Study of CAE 2001, Volume 3, Issue 10,   Pages 36-40

Abstract:

This paper analyzed the kinematics and dynamics of the 6-DOF platform, and adopted CMAC Neural Networks as controller to realize tailing track. Simulation results showed that the analysis of kinematics and dynamics was correct and the controller had the capabilities of resisting disturbance and good robustness.

Keywords： 6-DOF in-parallel platform     CMAC neural network     kinematics     kinetics    

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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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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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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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Title Author Date Type Operation

A data-driven method for estimating the target position of low-frequency sound sources in shallow seas

Xianbin Sun, Xinming Jia, Yi Zheng, Zhen Wang,robin_sun@qut.edu.cn,jiaxinming_123@163.com

Journal Article

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

Current Source Active Power Filter Control UsingNeural Network Technologies

Wang Ping,Zhang Ke, Xu Huijun

Journal Article

A Survey of Accelerator Architectures for Deep Neural Networks

Yiran Chen, uan Xie, Linghao Song, Fan Chen, Tianqi Tang

Journal Article

A Rough Fuzzy Neural Classifier

Zeng Huanglin,Wang Xiao

Journal Article

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

Wang Shuo,Tang Xiaowo

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

Analysis of Characteristics and CMAC Neural Networks Controller of Electrohydraulic Servo System of the 6-DOF Paralle Platform

Zhai Chuanrun,ZhanXingqun,Zhang Yanhua,Ran Xianglai,Zhao Keding

Journal Article

Pressure in Gas-assisted Injection Molding

Ou Changjin

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

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