Resource Type

Journal Article 1007

Conference Videos 57

Conference Information 13

Year

2024 2

2023 105

2022 126

2021 136

2020 100

2019 73

2018 68

2017 73

2016 59

2015 18

2014 7

2013 14

2012 18

2011 17

2010 22

2009 23

2008 20

2007 25

2006 35

2005 38

open ︾

Keywords

Deep learning 16

neural network 16

sustainable development 13

Qinba Mountain Area 10

Artificial intelligence 9

Machine learning 9

Multi-agent system 8

Neural network 8

Reinforcement learning 7

Software-defined networking (SDN) 7

the Qinba Mountain Area 7

Multi-agent systems 6

forecast 6

cyberspace 5

cyberspace security 5

6G 4

food security 4

pattern recognition 4

rough sets 4

open ︾

Search scope:

排序： Display mode:

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    

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

Cell-Zone Method: An Engineering Approach to Predict Smoke Movement in Large Scale Building Fire

Hu Longhua,Huo Ran,Liy Uanzhou,Wang Haobo

Strategic Study of CAE 2003, Volume 5, Issue 8,   Pages 59-63

Abstract:

In large scale building fire, it is improper to predict smoke descending using traditional simple two-layer zone model, which divides the total space of the building into upper hot smoke layer and lower cool air layer. In this paper, an improved method, named Cell-Zone Method, is used to solve this problem, which first divides the total space into some small subspaces and then uses traditional two-layer zone model in each subspace. Comparison is carried out between these two methods in fire smoke development simulation in typical large space buildings by CFAST4.02 software package. Results show that Cell-Zone Method demonstrates more applicability than traditional simple two-layer zone model in large scale building, especially in buildings having large scale in one direction.

Keywords： large scale building     smoke movement     cell-zone method     zone-model    

HTML PDF Collect

A distributed EEMDN-SABiGRU model on Spark for passenger hotspot prediction

夏大文,耿建,黄瑞曦,申冰琪,胡杨,李艳涛,李华青

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 9,   Pages 1316-1331 doi: 10.1631/FITEE.2200621

Abstract: To address the imbalance problem between supply and demand for taxis and passengers, this paper proposes a distributed ensemble empirical mode decomposition with normalization of spatial attention mechanism based bi-directional gated recurrent unit (EEMDN-SABiGRU) model on Spark for accurate passenger hotspot prediction. It focuses on reducing blind cruising costs, improving carrying efficiency, and maximizing incomes. Specifically, the EEMDN method is put forward to process the passenger hotspot data in the grid to solve the problems of non-smooth sequences and the degradation of prediction accuracy caused by excessive numerical differences, while dealing with the eigenmodal EMD. Next, a spatial attention mechanism is constructed to capture the characteristics of passenger hotspots in each grid, taking passenger boarding and alighting hotspots as weights and emphasizing the spatial regularity of passengers in the grid. Furthermore, the bi-directional GRU algorithm is merged to deal with the problem that GRU can obtain only the forward information but ignores the backward information, to improve the accuracy of feature extraction. Finally, the accurate prediction of passenger hotspots is achieved based on the EEMDN-SABiGRU model using real-world taxi GPS trajectory data in the Spark parallel computing framework. The experimental results demonstrate that based on the four datasets in the 00-grid, compared with LSTM, EMD-LSTM, EEMD-LSTM, GRU, EMD-GRU, EEMD-GRU, EMDN-GRU, CNN, and BP, the mean absolute percentage error, mean absolute error, root mean square error, and maximum error values of EEMDN-SABiGRU decrease by at least 43.18%, 44.91%, 55.04%, and 39.33%, respectively.

Keywords： Passenger hotspot prediction     Ensemble empirical mode decomposition (EEMD)     Spatial attention mechanism     Bi-directional gated recurrent unit (BiGRU)     GPS trajectory     Spark    

HTML Collect

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    

HTML PDF Collect

Study on realtime method of traffic flow guidance system

Cui Jianming,Ye Huaizhen

Strategic Study of CAE 2008, Volume 10, Issue 10,   Pages 64-66

Abstract:

Real-time guidance system of traffic flow is information reconstruct in unit time. If events occut in short periodic time, these events will inflict the normal traffic guidance. In this paper, a new and simple solution scheme is proposed by analyzing status traffic situation. In this plan, systematic revision for read-time guidance in burst event is added to deduce reaction time. This scheme is imporved technology built on the current guidance system, therefore, this conclusion can be widly applied to traffic flow guidance system.

Keywords： traffic flow guidance system     simulation     emergent events     real-time guidance    

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

Study on area forecast of coal and gas outburst based on coupling of neural network and genetic algorithm

Shi Shiliang,Wu Aiyou

Strategic Study of CAE 2009, Volume 11, Issue 9,   Pages 91-96

Abstract:

The coal and gas outburst is a dynamic pheaomenon in the underground exploitation of coal mine,and the strong dynamic effect can result in damage of belongs and death of workers of coal mine. Therefore,it is very important to advance coal industry healthy and continual in forecast the area of coal and gas outburst reasonablely.This paper aimed at the defect that neural network is easy to fall into some extremely local smallness and cause the unreasonable distribution of the weight value of the forecast indexes,ade the area forecast model of the coal and gas outburst was established based on coupling of the neural network and the genetic algorithm according to the natural conditions and the characteristics of the geologic structure. The coupling forecast model was validated with the practical example.The study results has proved the validity of the model, and laid the foundation of the area forecast of the coal and gas outburst based on coupling of the neural network and genetic algorithm.

Keywords： coal and gas outburst     area forecast     neural network     genetic algorithm     isoneph of outburst    

HTML PDF Collect

A highly efficient reconfigurable rotation unit based on an inverse butterfly network Article

Chao MA, Zi-bin DAI, Wei LI, Hai-juan ZANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1784-1794 doi: 10.1631/FITEE.1601265

Abstract: We propose a reconfigurable control-bit generation algorithm for rotation and sub-word rotation operations. The algorithm uses a self-routing characteristic to configure an inverse butterfly network. In addition to being highly parallelized and inexpensive, the algorithm integrates the rotation-shift, bi-directional rotation-shift, and sub-word rotation-shift operations. To our best knowledge, this is the first scheme to accommodate a variety of rotation operations into the same architecture. We have developed the highly efficient reconfigurable rotation unit (HERRU) and synthesized it into the Semiconductor Manufacturing International Corporation (SMIC)’s 65-nm process. The results show that the overall efficiency (relative area×relative latency) of our HERRU is higher by at least 23% than that of other designs with similar functions. When executing the bi-directional rotation operations alone, HERRU occupies a significantly smaller area with a lower latency than previously proposed designs.

Keywords： Rotation operations     Self-routing     Control-bit generation algorithm     Inverse butterfly network    

HTML PDF Collect

Integrated Network Design and Demand Forecast for On-Demand Urban Air Mobility Article

Zhiqiang Wu, Yu Zhang

Engineering 2021, Volume 7, Issue 4,   Pages 473-487 doi: 10.1016/j.eng.2020.11.007

Abstract:

Urban air mobility (UAM) is an emerging concept proposed in recent years that uses electric vertical take-off and landing vehicles (eVTOLs). UAM is expected to offer an alternative way of transporting passengers and goods in urban areas with significantly improved mobility by making use of low-altitude airspace. In addition to other essential elements, ground infrastructure of vertiports is needed to transition UAM from concept to operation. This study examines the network design of UAM on-demand service, with a particular focus on the use of integer programming and a solution algorithm to determine the optimal locations of vertiports, user allocation to vertiports, and vertiport access- and egress-mode choices while considering the interactions between vertiport locations and potential UAM travel demand. A case study based on simulated disaggregate travel demand data of the Tampa Bay area in Florida, USA was conducted to demonstrate the effectiveness of the proposed model. Candidate vertiport locations were obtained by analyzing a  three-dimensional (3D) geographic information system (GIS) map developed from lidar data of Florida and physical and regulation constraints of eVTOL operations at vertiports. Optimal locations of vertiports were determined to achieve the minimal total generalized cost; however, the modeling structure allows each user to select a better mode between ground transportation and UAM in terms of generalized cost. The outcomes of the case study reveal that although the percentage of trips that switched from ground mode to multimodal UAM was small, users choosing the UAM service benefited from significant time saving. In addition, the impact of different parameter settings on the demand for UAM service was explored from the supply side, and different pricing strategies were tested that might influence potential demand and revenue generation for UAM operators. The combined effects of the number of vertiports and pricing strategies were also analyzed. The findings from this study offer in-depth planning and managerial insights for municipal decision-makers and UAM operators. The conclusion of this paper discusses caveats to the study, ongoing efforts by the authors, and future directions in UAM research.

Keywords： Advanced air mobility     Skyport     Travel mode choice     Low-altitude airspace     Unmanned systems    

HTML PDF Collect

A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Based on Multi-Factor Analysis and a Multi-Model Ensemble Article

Hui Liu, Rui Yang, Zhu Duan, Haiping Wu

Engineering 2021, Volume 7, Issue 12,   Pages 1751-1765 doi: 10.1016/j.eng.2020.10.023

Abstract:

Dissolved oxygen (DO) is an important indicator of aquaculture, and its accurate forecasting can effectively improve the quality of aquatic products. In this paper, a new DO hybrid forecasting model is proposed that includes three stages: multi-factor analysis, adaptive decomposition, and an optimization-based ensemble. First, considering the complex factors affecting DO, the grey relational (GR) degree method is used to screen out the environmental factors most closely related to DO. The consideration of multiple factors makes model fusion more effective. Second, the series of DO, water temperature, salinity, and oxygen saturation are decomposed adaptively into sub-series by means of the empirical wavelet transform (EWT) method. Then, five benchmark models are utilized to forecast the sub-series of EWT decomposition. The ensemble weights of these five sub-forecasting models are calculated by particle swarm optimization and gravitational search algorithm (PSOGSA). Finally, a multi-factor ensemble model for DO is obtained by weighted allocation. The performance of the proposed model is verified by time-series data collected by the pacific islands ocean observing system (PacIOOS) from the WQB04 station at Hilo. The evaluation indicators involved in the experiment include the nash-sutcliffe efficiency (NSE), kling-gupta efficiency (KGE), mean absolute percent error (MAPE), standard deviation of error (SDE), and coefficient of determination (R2). Example analysis demonstrates that: ① the proposed model can obtain excellent DO forecasting results; ② the proposed model is superior to other comparison models; and ③ the forecasting model can be used to analyze the trend of DO and enable managers to make better management decisions.

Keywords： Dissolved oxygen concentrations forecasting     Time-series multi-step forecasting     Multi-factor analysis     Empirical wavelet transform decomposition     Multi-model optimization ensemble    

HTML PDF Collect

A many-objective evolutionary algorithm based on decomposition with dynamic resource allocation for irregular optimization Research Articles

Ming-gang Dong, Bao Liu, Chao Jing,jingchao@glut.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 8,   Pages 1119-1266 doi: 10.1631/FITEE.1900321

Abstract: The multi-objective optimization problem has been encountered in numerous fields such as high-speed train head shape design, overlapping community detection, power dispatch, and unmanned aerial vehicle formation. To address such issues, current approaches focus mainly on problems with regular Pareto front rather than solving the . Considering this situation, we propose a many-objective evolutionary algorithm based on decomposition with (MaOEA/D-DRA) for irregular optimization. The proposed algorithm can dynamically allocate computing resources to different search areas according to different shapes of the problem’s Pareto front. An evolutionary population and an are used in the search process, and information extracted from the is used to guide the evolutionary population to different search regions. The evolutionary population evolves with the to decompose a problem into several subproblems, and all the subproblems are optimized in a collaborative manner. The is updated with the method of . The proposed algorithm is compared with five state-of-the-art many-objective evolutionary algorithms using a variety of test problems with . Experimental results show that the proposed algorithm outperforms these five algorithms with respect to convergence speed and diversity of population members. By comparison with the weighted-sum approach and penalty-based boundary intersection approach, there is an improvement in performance after integration of the into the proposed algorithm.

Keywords： Many-objective optimization problems     Irregular Pareto front     External archive     Dynamic resource allocation     Shift-based density estimation     Tchebycheff approach    

HTML PDF Collect

Title Author Date Type Operation

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

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

Supaporn LONAPALAWONG, Changsheng CHEN, Can WANG, Wei CHEN

Journal Article

Video summarization with a graph convolutional attention network

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

Journal Article

Cell-Zone Method: An Engineering Approach to Predict Smoke Movement in Large Scale Building Fire

Hu Longhua,Huo Ran,Liy Uanzhou,Wang Haobo

Journal Article

A distributed EEMDN-SABiGRU model on Spark for passenger hotspot prediction

夏大文,耿建,黄瑞曦,申冰琪,胡杨,李艳涛,李华青

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

Study on realtime method of traffic flow guidance system

Cui Jianming,Ye Huaizhen

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

Study on area forecast of coal and gas outburst based on coupling of neural network and genetic algorithm

Shi Shiliang,Wu Aiyou

Journal Article

A highly efficient reconfigurable rotation unit based on an inverse butterfly network

Chao MA, Zi-bin DAI, Wei LI, Hai-juan ZANG

Journal Article

Integrated Network Design and Demand Forecast for On-Demand Urban Air Mobility

Zhiqiang Wu, Yu Zhang

Journal Article

Petros Ioannou: Autopilot Connectivity and Traffic Flow Control (2019-9-18)

12 Aug 2021

Conference Videos

A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Based on Multi-Factor Analysis and a Multi-Model Ensemble

Hui Liu, Rui Yang, Zhu Duan, Haiping Wu

Journal Article

A many-objective evolutionary algorithm based on decomposition with dynamic resource allocation for irregular optimization

Ming-gang Dong, Bao Liu, Chao Jing,jingchao@glut.edu.cn

Journal Article