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MFF-Net: Multimodal Feature Fusion Network for 3D Object Detection

Computers, Materials & Continua 2023年第6期75卷 5615-5637页

作者： Peicheng Shi Zhiqiang Liu Heng Qi Aixi Yang School of Mechanical Engineering Anhui Polytechnic UniversityWuhu241000Anhui ProvinceChina School of Mechanical Polytechnic Institute of Zhejiang UniversityHangzhou310000Zhejiang ProvinceChina

In complex traffic environment scenarios,it is very important for autonomous vehicles to accurately perceive the dynamic information of other vehicles around the vehicle in advance.The accuracy of 3D object detection will be affected by problems such as illumination changes,object occlusion,and object detection distance.To this purpose,we face these challenges by proposing a multimodal feature fusion network for 3D object detection(MFF-Net).In this research,this paper first uses the spatial transformation projection algorithm to map the image features into the feature space,so that the image features are in the same spatial dimension when fused with the point cloud features.Then,feature channel weighting is performed using an adaptive expression augmentation fusion network to enhance important network features,suppress useless features,and increase the directionality of the network to features.Finally,this paper increases the probability of false detection and missed detection in the non-maximum suppression algo-rithm by increasing the one-dimensional threshold.So far,this paper has constructed a complete 3D target detection network based on multimodal feature fusion.The experimental results show that the proposed achieves an average accuracy of 82.60%on the Karlsruhe Institute of Technology and Toyota Technological Institute(KITTI)dataset,outperforming previous state-of-the-art multimodal fusion networks.In Easy,Moderate,and hard evaluation indicators,the accuracy rate of this paper reaches 90.96%,81.46%,and 75.39%.This shows that the MFF-Net network has good performance in 3D object detection.

关键词： 3D object detection multimodal fusion neural network autonomous driving attention mechanism

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The Short-Term Prediction ofWind Power Based on the Convolutional Graph attention Deep Neural Network

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Energy Engineering 2024年第2期121卷 359-376页

作者： Fan Xiao Xiong Ping Yeyang Li Yusen Xu Yiqun Kang Dan Liu Nianming Zhang State Grid Hubei Electric Power Research Institute Wuhan430077China College of Energy and Electrical Engineering Hohai UniversityNanjing210098China

The fluctuation of wind power affects the operating safety and power consumption of the electric power grid and restricts the grid connection of wind power on a large scale.Therefore,wind power forecasting plays a key role in improving the safety and economic benefits of the power grid.This paper proposes a wind power predicting method based on a convolutional graph attention deep neural network with multi-wind farm data.Based on the graph attention network and attention mechanism,the method extracts spatial-temporal characteristics from the data of multiple wind farms.Then,combined with a deep neural network,a convolutional graph attention deep neural network model is constructed.Finally,the model is trained with the quantile regression loss function to achieve the wind power deterministic and probabilistic prediction based on multi-wind farm spatial-temporal data.A wind power dataset in the U.S.is taken as an example to demonstrate the efficacy of the proposed model.Compared with the selected baseline methods,the proposed model achieves the best prediction performance.The point prediction errors(i.e.,root mean square error(RMSE)and normalized mean absolute percentage error(NMAPE))are 0.304 MW and 1.177%,respectively.And the comprehensive performance of probabilistic prediction(i.e.,con-tinuously ranked probability score(CRPS))is 0.580.Thus,the significance of multi-wind farm data and spatial-temporal feature extraction module is self-evident.

关键词： Format wind power prediction deep neural network graph attention network attention mechanism quantile regression

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Semantic segmentation for remote sensing images based on an AD-HRNet model

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International Journal of Digital Earth 2022年第1期15卷 2376-2399页

作者： Xue Yang Xiang Fan Mingjun Peng Qingfeng Guan Luliang Tang School of Geography and Information Engineering China University of GeosciencesWuhanPeople’s Republic of China Wuhan Geomatics Institute WuhanPeople’s Republic of China State Key Laboratory of Information Engineering in Surveying Mappingand Remote SensingWuhan UniversityWuhanPeople’s Republic of China

Semantic segmentation for remote sensing images faces challenges of unbalanced category weight,rich context causing difficulties of recognition,blurred boundaries of multi-scale objects,and so on.To address these problems,we propose a new model by combining HRNet with attention mechanisms and dilated convolution,denoted as:AD-HRNet for the semantic segmentation of remote sensing images.In the framework of AD-HRNet,we obtained the weight value of each category based on an improved weighted cross-entropy function by introducing the median frequency balance method to solve the issue of class weight imbalance.The Shuffle-CBAM module with channel attention and spatial attention in AD-HRNet framework was applied to extract more global context information of images through slightly increasing the amount of computation.To address the problem of blurred boundaries caused by multi-scale object segmentation and edge segmentation,we developed an MDC-DUC module in AD-HRNet framework to capture the context information of multi-scale objects and the edge information of many irregular objects.Taking Postdam,Vaihingen,and SAMA-VTOL datasets as materials,we verified the performance of AD-HRNet by comparing with eight typical semantic segmentation models.Experimental results shown that AD-HRNet increases the mIoUs to 75.59%and 71.58%based on the Postdam and Vaihingen datasets,respectively.

关键词： Semantic segmentation convolutional neural networks dilated convolution attention mechanism remote sensing

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Distribution network state estimation based on attention-enhanced recurrent neural network pseudo-measurement modeling

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Protection and Control of Modern Power Systems 2023年第2期8卷 244-259页

作者： Yaojian Wang Jie Gu Lyuzerui Yuan Department of Electrical Engineering School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong University209 of Smart Grid Building800 Dong Chuan RdShanghai 200240China

Because there is insufficient measurement data when implementing state estimation in distribution networks,this paper proposes an attention-enhanced recurrent neural network(A-RNN)-based pseudo-measurement modeling metho.First,based on analyzing the power series at the source and load end in the time and frequency domains,a period-dependent extrapolation model is established to characterize the power series in those domains.The complex mapping functions in the model are automatically represented by A-RNNs to obtain an A-RNNs-based period-dependent pseudo-measurement generation model.The distributed dynamic state estimation model of the distribution network is established,and the pseudo-measurement data generated by the model in real time is used as the input of the state estimation model together with the measurement data.The experimental results show that the method proposed can explore in depth the complex sequence characteristics of the measurement data such that the accuracy of the pseudo-measurement data is further improved.The results also show that the state estimation accuracy of a distribution network is very poor when there is a lack of measurement data,but is greatly improved by adding the pseudo-measurement data generated by the model proposed.

关键词： State estimation Pseudo measurement Recurrent neural network attention mechanism Time-frequency domain analysis Distribution network

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Research on PM_(2.5) Concentration Prediction Algorithm Based on Temporal and Spatial Features

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Computers, Materials & Continua 2023年第6期75卷 5555-5571页

作者： Song Yu Chen Wang School of Computer Science and Engineering Central South UniversityChangsha410000China

PM2.5 has a non-negligible impact on visibility and air quality as an important component of haze and can affect cloud formation and rainfall and thus change the climate,and it is an evaluation indicator of air pollution level.Achieving PM2.5 concentration prediction based on relevant historical data mining can effectively improve air pollution forecasting ability and guide air pollution prevention and control.The past methods neglected the impact caused by PM2.5 flow between cities when analyzing the impact of inter-city PM2.5 concentrations,making it difficult to further improve the prediction accuracy.However,factors including geographical information such as altitude and distance and meteorological information such as wind speed and wind direction affect the flow of PM2.5 between cities,leading to the change of PM2.5 concentration in cities.So a PM2.5 directed flow graph is constructed in this paper.Geographic and meteorological data is introduced into the graph structure to simulate the spatial PM2.5 flow transmission relationship between cities.The introduction of meteorological factors like wind direction depicts the unequal flow relationship of PM2.5 between cities.Based on this,a PM2.5 concentration prediction method integrating spatial-temporal factors is proposed in this paper.A spatial feature extraction method based on weight aggregation graph attention network(WGAT)is proposed to extract the spatial correlation features of PM2.5 in the flow graph,and a multi-step PM2.5 prediction method based on attention gate control loop unit(AGRU)is proposed.The PM2.5 concentration prediction model WGAT-AGRU with fused spatiotemporal features is constructed by combining the two methods to achieve multi-step PM2.5 concentration prediction.Finally,accuracy and validity experiments are conducted on the KnowAir dataset,and the results show that the WGAT-AGRU model proposed in the paper has good performance in terms of prediction accuracy and validates the effectiveness of the model.

关键词： Spatiotemporal fusion PM2.5 concentration prediction graph neural network recurrent neural network attention mechanism

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An unsupervised heterogeneous change detection method based on image translation network and post-processing algorithm

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International Journal of Digital Earth 2022年第1期15卷 1056-1080页

作者： Decheng Wang Feng Zhao Hui Yi Yinan Li Xiangning Chen Space Information Academy Space Engineering UniversityBeijingPeople’s Republic of China China Satellite Maritime Tracking and Control Department JiangyinPeople’s Republic of China

The change detection(CD)of heterogeneous remote sensing images is an important but challenging task.The difficulty is to obtain the change information by directly comparing the different statistical characteristics of the images acquired by different sensors.This paper proposes an unsupervised method for heterogeneous image CD based on an image domain transfer network.First,an attention mechanism is added to the Cycle-generative adversarial networks(Cycle-GANs)to obtain a more consistent feature expression by transferring bi-temporal heterogeneous images to the common domain.The Euclidean distance of the corresponding pixels is calculated in the common domain to form a difference map,and a threshold algorithm is applied to get a rough change map.Finally,the proposed adaptive Discrete Cosine Transform(DCT)algorithm reduces the noise introduced by false detection,and the final change map is obtained.The proposed method is verified on three real heterogeneous CD datasets and compared with the current state-of-the-art methods.The results show that the proposed method is accurate and robust for performing heterogeneous CD tasks.

关键词： Unsupervised change detection heterogeneous images cycle-generative adversarial networks(Cycle-GANs) attention mechanism domain transfer

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A Novel Feature Aggregation Approach for Image Retrieval Using Local and Global Features

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Computer Modeling in Engineering & Sciences 2022年第4期131卷 239-262页

作者： Yuhua Li Zhiqiang He Junxia Ma Zhifeng Zhang Wangwei Zhang Prasenjit Chatterjee Dragan Pamucar Software Engineering College Zhengzhou University of Light IndustryZhengzhou450001China China Mobile Group Henan Company Limited Xinxiang453000China Department of Mechanical Engineering MCKV Institute of EngineeringHowrah711204India Department of Logistics University of Defence in BelgradeBelgrade11000Serbia

The current deep convolution features based on retrievalmethods cannot fully use the characteristics of the salient image regions.Also,they cannot effectively suppress the background noises,so it is a challenging task to retrieve objects in cluttered scenarios.To solve the problem,we propose a new image retrieval method that employs a novel feature aggregation approach with an attention mechanism and utilizes a combination of local and global features.The method first extracts global and local features of the input image and then selects keypoints from local features by using the attention mechanism.After that,the feature aggregation mechanism aggregates the keypoints to a compact vector representation according to the scores evaluated by the attention mechanism.The core of the aggregation mechanism is to allow features with high scores to participate in residual operations of all cluster centers.Finally,we get the improved image representation by fusing aggregated feature descriptor and global feature of the input image.To effectively evaluate the proposedmethod,we have carried out a series of experiments on large-scale image datasets and compared them with other state-of-the-art methods.Experiments show that this method greatly improves the precision of image retrieval and computational efficiency.

关键词： attention mechanism image retrieval descriptor aggregation convolutional neural network

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Acoustic Emission Recognition Based on a Three-Streams Neural Network with attention

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Computer Systems Science & Engineering 2023年第9期46卷 2963-2974页

作者： Kang Xiaofeng Hu Kun Ran Li College of Information and Engineering Xuzhou University of TechnologyXuzhouJiangsu221000China College of Electrical and Power Engineering China University of Mining and TechnologyXuzhouJiangsu221116China Department of Electrical Electronic and Computer EngineeringUniversity of Western AustraliaPerthAustralia

Acoustic emission(AE)is a nondestructive real-time monitoring technology,which has been proven to be a valid way of monitoring dynamic damage to materials.The classification and recognition methods of the AE signals of the rotor are mostly focused on machine learning.Considering that the huge success of deep learning technologies,where the Recurrent Neural Network(RNN)has been widely applied to sequential classification tasks and Convolutional Neural Network(CNN)has been widely applied to image recognition tasks.A novel three-streams neural network(TSANN)model is proposed in this paper to deal with fault detection tasks.Based on residual connection and attention mechanism,each stream of the model is able to learn the most informative representation from Mel Frequency Cepstrum Coefficient(MFCC),Tempogram,and short-time Fourier transform(STFT)spectral respectively.Experimental results show that,in comparison with traditional classification methods and single-stream CNN networks,TSANN achieves the best overall performance and the classification error rate is reduced by up to 50%,which demonstrates the availability of the model proposed.

关键词： Convolutional neural network attention mechanism acoustic emission fault detection

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A Novel Foreign Object Detection Method in Transmission Lines Based on Improved YOLOv8n

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Computers, Materials & Continua 2024年第4期79卷 1263-1279页

作者： Yakui Liu Xing Jiang Ruikang Xu Yihao Cui Chenhui Yu Jingqi Yang Jishuai Zhou School of Mechanical and Automotive Engineering Qingdao University of TechnologyQingdao266520China State Key Laboratory of Electrical Insulation and Power Equipment Xi’an Jiaotong UniversityXi’an710049China Key Lab of Industrial Fluid Energy Conservation and Pollution Control Qingdao University of TechnologyMinistry of EducationQingdao266520China

The rapid pace of urban development has resulted in the widespread presence of construction equipment andincreasingly complex conditions in transmission corridors. These conditions pose a serious threat to the safeoperation of the power grid.Machine vision technology, particularly object recognition technology, has beenwidelyemployed to identify foreign objects in transmission line images. Despite its wide application, the technique faceslimitations due to the complex environmental background and other auxiliary factors. To address these challenges,this study introduces an improved YOLOv8n. The traditional stepwise convolution and pooling layers are replacedwith a spatial-depth convolution (SPD-Conv) module, aiming to improve the algorithm’s efficacy in recognizinglow-resolution and small-size objects. The algorithm’s feature extraction network is improved by using a LargeSelective Kernel (LSK) attention mechanism, which enhances the ability to extract relevant features. Additionally,the SIoU Loss function is used instead of the Complete Intersection over Union (CIoU) Loss to facilitate fasterconvergence of the algorithm. Through experimental verification, the improved YOLOv8n model achieves adetection accuracy of 88.8% on the test set. The recognition accuracy of cranes is improved by 2.9%, which isa significant enhancement compared to the unimproved algorithm. This improvement effectively enhances theaccuracy of recognizing foreign objects on transmission lines and proves the effectiveness of the new algorithm.

关键词： YOLOv8n data enhancement attention mechanism SPD-Conv Smoothed Intersection over Union(SIoU)Loss

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Improving Wind Forecasts Using a Gale-Aware Deep attention Network

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Journal of Meteorological Research 2023年第6期37卷 775-789页

作者： Keran CHEN Yuan ZHOU Ping WANG Pingping WANG Xiaojun YANG Nan ZHANG Di WANG School of Electrical and Information Engineering Tianjin UniversityTianjin300072China Joint Laboratory of Intelligent Identification&Nowcasting Service for Convective System CMA Public Meteorological Service CentreChina Meteorological Administration(CMA)Beijing100081China Qingdao Academy of Chinese Medical Science Shandong University of Traditional Chinese MedicineQingdao266112China Tianjin Meteorological Observatory Tianjin300074China

Numerical weather prediction of wind speed requires statistical postprocessing of systematic errors to obtain reliable and accurate forecasts.However,use of postprocessing models is often undesirable for extreme weather events such as gales.Here,we propose a postprocessing algorithm based on a gale-aware deep attention network to simultaneously improve wind speed forecasts and gale area warnings.Specifically,the algorithm includes both a galeaware loss function that focuses the model on potential gale areas,and an observation station supervision strategy that alleviates the problem of missing extreme values caused by data gridding.The effectiveness of the proposed model was verified by using data from 235 wind speed observation stations.Experimental results show that our model can produce wind speed forecasts with a root-mean-square error of 1.1547 m s^(-1),and a Hanssen–Kuipers discriminant score of 0.517,performance that is superior to that of the other postprocessing algorithms considered.

关键词： wind speed prediction deep attention network numerical model three-dimensional(3D)fully convolutional network attention mechanism

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