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Interpreting the vulnerability of power systems in cascading failures using multi-graph convolutional networks Research Article

Supaporn LONAPALAWONG, Changsheng CHEN, Can WANG, Wei CHEN

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12, Pages 1848-1861 doi: 10.1631/FITEE.2200035

Abstract: Analyzing the of in is generally regarded as a challenging problem. Although existing studies can extract some critical rules, they fail to capture the complex subtleties under different operational conditions. In recent years, several deep learning methods have been applied to address this issue. However, most of the existing deep learning methods consider only the grid topology of a power system in terms of topological connections, but do not encompass a power system's spatial information such as the electrical distance to increase the accuracy in the process of graph convolution. In this paper, we construct a novel power- that uses power system topology and spatial information to optimize the edge weight assignment of the line graph. Then we propose a multi-graph convolutional network (MGCN) based on a graph classification task, which preserves a power system's spatial correlations and captures the relationships among physical components. Our model can better handle the problem with that have parallel lines, where our method can maintain desirable accuracy in modeling systems with these extra topology features. To increase the interpretability of the model, we present the MGCN using layer-wise relevance propagation and quantify the contributing factors of model classification.

Keywords： Power systems Vulnerability Cascading failures Multi-graph convolutional networks Weighted line graph

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Forecasting traffic flows in irregular regions with multi-graph convolutional network and gated recurrent unit Research Articles

Dewen Seng, Fanshun Lv, Ziyi Liang, Xiaoying Shi, Qiming Fang,sengdw@hdu.edu.cn,172050041@hdu.edu.cn,liangziyi2020@163.com,shixiaoying@hdu.edu.cn,fangqiming@hdu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 9, Pages 1179-1193 doi: 10.1631/FITEE.2000243

Abstract: The prediction of regional traffic flows is important for traffic control and management in an intelligent trafﬁc system. With the help of deep neural networks, the convolutional neural network or residual neural network, which can be applied only to regular grids, is adopted to capture the spatial dependence for flow prediction. However, the obtained regions are always irregular considering the road network and administrative boundaries; thus, dividing the city into grids is inaccurate for prediction. In this paper, we propose a new model based on and (MGCN-GRU) to predict traffic flows for . Specifically, we first construct heterogeneous inter-region graphs for a city to reflect the relationships among regions. In each graph, nodes represent the and edges represent the relationship types between regions. Then, we propose a to fuse different inter-region graphs and additional attributes. The GRU is further used to capture the temporal dependence and to predict future traffic flows. Experimental results based on three real-world large-scale datasets (public bicycle system dataset, taxi dataset, and dockless bike-sharing dataset) show that our MGCN-GRU model outperforms a variety of existing methods.

Keywords：交通流量预测；多图卷积网络；门控循环单元；不规则区域

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Video summarization with a graph convolutional attention network Research Articles

Ping Li, Chao Tang, Xianghua Xu,patriclouis.lee@gmail.com

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 6, Pages 902-913 doi: 10.1631/FITEE.2000429

Abstract: has established itself as a fundamental technique for generating compact and concise video, which alleviates managing and browsing large-scale video data. Existing methods fail to fully consider the local and global relations among frames of video, leading to a deteriorated summarization performance. To address the above problem, we propose a graph convolutional attention network (GCAN) for . GCAN consists of two parts, embedding learning and , where embedding learning includes the temporal branch and graph branch. In particular, GCAN uses dilated temporal convolution to model local cues and temporal self-attention to exploit global cues for video frames. It learns graph embedding via a multi-layer to reveal the intrinsic structure of frame samples. The part combines the output streams from the temporal branch and graph branch to create the context-aware representation of frames, on which the importance scores are evaluated for selecting representative frames to generate video summary. Experiments are carried out on two benchmark databases, SumMe and TVSum, showing that the proposed GCAN approach enjoys superior performance compared to several state-of-the-art alternatives in three evaluation settings.

Keywords：时序学习；自注意力机制；图卷积网络；上下文融合；视频摘要

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Amultimodal dense convolution network for blind image quality assessment Research Article

Nandhini CHOCKALINGAM, Brindha MURUGAN

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11, Pages 1601-1615 doi: 10.1631/FITEE.2200534

Abstract: Technological advancements continue to expand the communications industry’s potential. Images, which are an important component in strengthening communication, are widely available. Therefore, image quality assessment (IQA) is critical in improving content delivered to end users. Convolutional neural networks (CNNs) used in IQA face two common challenges. One issue is that these methods fail to provide the best representation of the image. The other issue is that the models have a large number of parameters, which easily leads to overfitting. To address these issues, the dense convolution network (DSC-Net), a model with fewer parameters, is proposed for . Moreover, it is obvious that the use of multimodal data for has improved the performance of applications. As a result, fuses the texture features extracted using the gray-level co-occurrence matrix (GLCM) method and spatial features extracted using DSC-Net and predicts the image quality. The performance of the proposed framework on the benchmark synthetic datasets LIVE, TID2013, and KADID-10k demonstrates that the MDSC-Net approach achieves good performance over state-of-the-art methods for the NR-IQA task.

Keywords： No-reference image quality assessment (NR-IQA) Blind image quality assessment Multimodal dense convolution network (MDSC-Net) Deep learning Visual quality Perceptual quality

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Multi-focus image fusion based on fully convolutional networks Research Articles

Rui Guo, Xuan-jing Shen, Xiao-yu Dong, Xiao-li Zhang,zhangxiaoli@jlu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7, Pages 963-1118 doi: 10.1631/FITEE.1900336

Abstract: We propose a method, in which a fully convolutional network for focus detection (FD-FCN) is constructed. To obtain more precise focus detection maps, we propose to add s in the network to make both detailed and abstract visual information available when using FD-FCN to generate maps. A new training dataset for the proposed network is constructed based on dataset CIFAR-10. The image fusion algorithm using FD-FCN contains three steps: focus maps are obtained using FD-FCN, decision map generation occurs by applying a morphological process on the focus maps, and image fusion occurs using a decision map. We carry out several sets of experiments, and both subjective and objective assessments demonstrate the superiority of the proposed fusion method to state-of-the-art algorithms.

Keywords：多焦距图像融合；全卷积网络；跳层；性能评估

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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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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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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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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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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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Development Roadmap of Polymorphic Intelligence Network Technology Toward 2035

Li Junfei, Hu Yuxiang, Yi Peng, Wu Jiangxing

Strategic Study of CAE 2020, Volume 22, Issue 3, Pages 141-147 doi: 10.15302/J-SSCAE-2019.11.010

Abstract:

The current infrastructure and technology system of the Internet are facing major challenges in terms of intelligence, diversification, personalization, robustness, and efficiency. It is urgent to change the network infrastructure and build a polymorphic definable and intelligent network. This paper studies and judges the development trend of network technologies in China and abroad, proposes the development goal of polymorphic definable and intelligent network, and extracts a list of key cutting-edge technologies for foresight. Based on these, the development roadmap of polymorphic definable and intelligent network in China for 2035 is constructed. China should prospect the research and development of key technologies including network architecture, addressing and routing, full-dimensional definability, network intelligence, and network robust control, and guide the development of relevant industries through the key contents including core chips, products, and systems. Demonstration projects can be deployed to promote the implementation of intelligent network business, including projects concerning information infrastructure, vertical industry network, space–earth integrated network, ubiquitous interconnection of human and things. In addition, this paper proposes some suggestions on the guarantee measures of polymorphic definable and intelligent network in China from the aspects of policy guarantee, scientific research platform support and joint research, international communication expansion, and human resource cultivation and employment.

Keywords： intelligence network polymorphic network architecture full-dimension definable robust control development roadmap

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A Combined Optimization Model for Transportation Modes of Multimodal Transport

Wang Tao,Wang Gang

Strategic Study of CAE 2005, Volume 7, Issue 10, Pages 46-50

Abstract:

To solve the prevalent problem of the low informatization level and lack of relevant decision support systems for the multimodal transport in China, transportation characteristics of various transportation modes are analyzed firstly, and choice basis is put forward after comparison. Then a virtual transportation network of multimodal transport is set up. Finally, a combined optimization model for various transportation modes is deduced based on the above and algorithm is also proposed.

Keywords： multimodal transport transportation mode combined optimization

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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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Shot classification and replay detection for sports video summarization Research Article

Ali JAVED, Amen ALI KHAN,ali.javed@uettaxila.edu.pk

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 5, Pages 790-800 doi: 10.1631/FITEE.2000414

Abstract: Automated analysis of sports is challenging due to variations in cameras, replay speed, illumination conditions, editing effects, game structure, genre, etc. To address these challenges, we propose an effective framework based on and for field sports videos. Accurate is mandatory to better structure the input video for further processing, i.e., key events or . Therefore, we present a based method for . Then we analyze each shot for and specifically detect the successive batch of logo transition frames that identify the replay segments from the sports videos. For this purpose, we propose local octa-pattern features to represent video frames and train the for classification as replay or non-replay frames. The proposed framework is robust to variations in cameras, replay speed, shot speed, illumination conditions, game structure, sports genre, broadcasters, logo designs and placement, frame transitions, and editing effects. The performance of our framework is evaluated on a dataset containing diverse YouTube sports videos of soccer, baseball, and cricket. Experimental results demonstrate that the proposed framework can reliably be used for and to summarize field sports videos.

Keywords： Extreme learning machine Lightweight convolutional neural network Local octa-patterns Shot classification Replay detection Video summarization

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A multistandard and resource-efficient Viterbi decoder for a multimode communication system None

Yi-qi XIE, Zhi-guo YU, Yang FENG, Lin-na ZHAO, Xiao-feng GU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 4, Pages 536-543 doi: 10.1631/FITEE.1601596

Abstract: We present a novel standard convolutional symbols generator (SCSG) block for a multi-parameter reconfigurable Viterbi decoder to optimize resource consumption and adaption of multiple parameters. The SCSG block generates all the states and calculates all the possible standard convolutional symbols corresponding to the states using an iterative approach. The architecture of the Viterbi decoder based on the SCSG reduces resource consumption for recalculating the branch metrics and rearranging the correspondence between branch metrics and transition paths. The proposed architecture supports constraint lengths from 3 to 9, code rates of 1/2, 1/3, and 1/4, and fully optional polynomials. The proposed Viterbi decoder has been implemented on the Xilinx XC7VX485T device with a high throughput of about 200 Mbps and a low resource consumption of 162k logic gates.

Keywords： Reconfigurable Viterbi decoder Multi-parameter Low resource consumption Standard convolutional symbols generator (SCSG) Fully optional polynomials

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Title Author Date Type Operation

Interpreting the vulnerability of power systems in cascading failures using multi-graph convolutional networks

Supaporn LONAPALAWONG, Changsheng CHEN, Can WANG, Wei CHEN

Journal Article

Forecasting traffic flows in irregular regions with multi-graph convolutional network and gated recurrent unit

Dewen Seng, Fanshun Lv, Ziyi Liang, Xiaoying Shi, Qiming Fang,sengdw@hdu.edu.cn,172050041@hdu.edu.cn,liangziyi2020@163.com,shixiaoying@hdu.edu.cn,fangqiming@hdu.edu.cn

Journal Article

Video summarization with a graph convolutional attention network

Ping Li, Chao Tang, Xianghua Xu,patriclouis.lee@gmail.com

Journal Article

Amultimodal dense convolution network for blind image quality assessment

Nandhini CHOCKALINGAM, Brindha MURUGAN

Journal Article

Multi-focus image fusion based on fully convolutional networks

Rui Guo, Xuan-jing Shen, Xiao-yu Dong, Xiao-li Zhang,zhangxiaoli@jlu.edu.cn

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