In the smart logistics industry,unmanned forklifts that intelligently identify logistics pallets can improve work efficiency in warehousing and transportation and are better than traditional manual forklifts driven by...In the smart logistics industry,unmanned forklifts that intelligently identify logistics pallets can improve work efficiency in warehousing and transportation and are better than traditional manual forklifts driven by humans.Therefore,they play a critical role in smart warehousing,and semantics segmentation is an effective method to realize the intelligent identification of logistics pallets.However,most current recognition algorithms are ineffective due to the diverse types of pallets,their complex shapes,frequent blockades in production environments,and changing lighting conditions.This paper proposes a novel multi-feature fusion-guided multiscale bidirectional attention(MFMBA)neural network for logistics pallet segmentation.To better predict the foreground category(the pallet)and the background category(the cargo)of a pallet image,our approach extracts three types of features(grayscale,texture,and Hue,Saturation,Value features)and fuses them.The multiscale architecture deals with the problem that the size and shape of the pallet may appear different in the image in the actual,complex environment,which usually makes feature extraction difficult.Our study proposes a multiscale architecture that can extract additional semantic features.Also,since a traditional attention mechanism only assigns attention rights from a single direction,we designed a bidirectional attention mechanism that assigns cross-attention weights to each feature from two directions,horizontally and vertically,significantly improving segmentation.Finally,comparative experimental results show that the precision of the proposed algorithm is 0.53%–8.77%better than that of other methods we compared.展开更多
Pedestrian trajectory prediction is pivotal and challenging in applications such as autonomous driving,social robotics,and intelligent surveillance systems.Pedestrian trajectory is governed not only by individual inte...Pedestrian trajectory prediction is pivotal and challenging in applications such as autonomous driving,social robotics,and intelligent surveillance systems.Pedestrian trajectory is governed not only by individual intent but also by interactions with surrounding agents.These interactions are critical to trajectory prediction accuracy.While prior studies have employed Convolutional Neural Networks(CNNs)and Graph Convolutional Networks(GCNs)to model such interactions,these methods fail to distinguish varying influence levels among neighboring pedestrians.To address this,we propose a novel model based on a bidirectional graph attention network and spatio-temporal graphs to capture dynamic interactions.Specifically,we construct temporal and spatial graphs encoding the sequential evolution and spatial proximity among pedestrians.These features are then fused and processed by the Bidirectional Graph Attention Network(Bi-GAT),which models the bidirectional interactions between the target pedestrian and its neighbors.The model computes node attention weights(i.e.,similarity scores)to differentially aggregate neighbor information,enabling fine-grained interaction representations.Extensive experiments conducted on two widely used pedestrian trajectory prediction benchmark datasets demonstrate that our approach outperforms existing state-of-theartmethods regarding Average Displacement Error(ADE)and Final Displacement Error(FDE),highlighting its strong prediction accuracy and generalization capability.展开更多
Mechanical vibration defect is the key factor leading to sudden failure of gas-insulated switchgear(GIS)equipment.It is important to realise effective prediction of the me-chanical vibration state development trend of...Mechanical vibration defect is the key factor leading to sudden failure of gas-insulated switchgear(GIS)equipment.It is important to realise effective prediction of the me-chanical vibration state development trend of GIS equipment in order to improve its active safety protection level.This paper carried out research on the accurate prediction method and experimental validation of the mechanical vibration state and its defect severity development trend for the GIS equipment.Firstly,the deep and shallow vibration feature parameters for different mechanical defect signals were jointly extracted by time-domain features and deep belief network methods.Secondly,a new prediction model,incorporating the attention mechanism and the bidirectional gated recurrent unit(BiGRU),was constructed with the deep and shallow vibration feature parameters as inputs.Finally,the prediction trend effectiveness was verified based on the real-type GIS mechanical simulation platform and the field operation GIS equipment.Results show that the deep and shallow vibration feature extraction method proposed in this paper can characterise the mechanical defect information more comprehensively.The new prediction method of the vibration state trend based on the attention-BiGRU model shows ideal accuracy,and the predicted vibration state development trend is highly consistent with the actual,with an average absolute error of 0.063.The root mean square error(ERMSE)value of the prediction method is<5%,which reduces the relative error value at least 37% compared with the traditional prediction models.This paper provides a valuable reference for the proactive defence of GIS mechanical failure.展开更多
In frequency division duplex(FDD)massive multiple-input multiple-output(MIMO)systems,a bidirectional positional attention network(BPANet)was proposed to address the high computational complexity and low accuracy of ex...In frequency division duplex(FDD)massive multiple-input multiple-output(MIMO)systems,a bidirectional positional attention network(BPANet)was proposed to address the high computational complexity and low accuracy of existing deep learning-based channel state information(CSI)feedback methods.Specifically,a bidirectional position attention module(BPAM)was designed in the BPANet to improve the network performance.The BPAM captures the distribution characteristics of the CSI matrix by integrating channel and spatial dimension information,thereby enhancing the feature representation of the CSI matrix.Furthermore,channel attention is decomposed into two one-dimensional(1D)feature encoding processes effectively reducing computational costs.Simulation results demonstrate that,compared with the existing representative method complex input lightweight neural network(CLNet),BPANet reduces computational complexity by an average of 19.4%and improves accuracy by an average of 7.1%.Additionally,it performs better in terms of running time delay and cosine similarity.展开更多
Automatically generating a brief summary for legal-related public opinion news(LPO-news,which contains legal words or phrases)plays an important role in rapid and effective public opinion disposal.For LPO-news,the cri...Automatically generating a brief summary for legal-related public opinion news(LPO-news,which contains legal words or phrases)plays an important role in rapid and effective public opinion disposal.For LPO-news,the critical case elements which are significant parts of the summary may be mentioned several times in the reader comments.Consequently,we investigate the task of comment-aware abstractive text summarization for LPO-news,which can generate salient summary by learning pivotal case elements from the reader comments.In this paper,we present a hierarchical comment-aware encoder(HCAE),which contains four components:1)a traditional sequenceto-sequence framework as our baseline;2)a selective denoising module to filter the noisy of comments and distinguish the case elements;3)a merge module by coupling the source article and comments to yield comment-aware context representation;4)a recoding module to capture the interaction among the source article words conditioned on the comments.Extensive experiments are conducted on a large dataset of legal public opinion news collected from micro-blog,and results show that the proposed model outperforms several existing state-of-the-art baseline models under the ROUGE metrics.展开更多
基金supported by the Postgraduate Scientific Research Innovation Project of Hunan Province under Grant QL20210212the Scientific Innovation Fund for Postgraduates of Central South University of Forestry and Technology under Grant CX202102043.
文摘In the smart logistics industry,unmanned forklifts that intelligently identify logistics pallets can improve work efficiency in warehousing and transportation and are better than traditional manual forklifts driven by humans.Therefore,they play a critical role in smart warehousing,and semantics segmentation is an effective method to realize the intelligent identification of logistics pallets.However,most current recognition algorithms are ineffective due to the diverse types of pallets,their complex shapes,frequent blockades in production environments,and changing lighting conditions.This paper proposes a novel multi-feature fusion-guided multiscale bidirectional attention(MFMBA)neural network for logistics pallet segmentation.To better predict the foreground category(the pallet)and the background category(the cargo)of a pallet image,our approach extracts three types of features(grayscale,texture,and Hue,Saturation,Value features)and fuses them.The multiscale architecture deals with the problem that the size and shape of the pallet may appear different in the image in the actual,complex environment,which usually makes feature extraction difficult.Our study proposes a multiscale architecture that can extract additional semantic features.Also,since a traditional attention mechanism only assigns attention rights from a single direction,we designed a bidirectional attention mechanism that assigns cross-attention weights to each feature from two directions,horizontally and vertically,significantly improving segmentation.Finally,comparative experimental results show that the precision of the proposed algorithm is 0.53%–8.77%better than that of other methods we compared.
基金funded by the National Natural Science Foundation of China,grant number 624010funded by the Natural Science Foundation of Anhui Province,grant number 2408085QF202+1 种基金funded by the Anhui Future Technology Research Institute Industry Guidance Fund Project,grant number 2023cyyd04funded by the Project of Research of Anhui Polytechnic University,grant number Xjky2022150.
文摘Pedestrian trajectory prediction is pivotal and challenging in applications such as autonomous driving,social robotics,and intelligent surveillance systems.Pedestrian trajectory is governed not only by individual intent but also by interactions with surrounding agents.These interactions are critical to trajectory prediction accuracy.While prior studies have employed Convolutional Neural Networks(CNNs)and Graph Convolutional Networks(GCNs)to model such interactions,these methods fail to distinguish varying influence levels among neighboring pedestrians.To address this,we propose a novel model based on a bidirectional graph attention network and spatio-temporal graphs to capture dynamic interactions.Specifically,we construct temporal and spatial graphs encoding the sequential evolution and spatial proximity among pedestrians.These features are then fused and processed by the Bidirectional Graph Attention Network(Bi-GAT),which models the bidirectional interactions between the target pedestrian and its neighbors.The model computes node attention weights(i.e.,similarity scores)to differentially aggregate neighbor information,enabling fine-grained interaction representations.Extensive experiments conducted on two widely used pedestrian trajectory prediction benchmark datasets demonstrate that our approach outperforms existing state-of-theartmethods regarding Average Displacement Error(ADE)and Final Displacement Error(FDE),highlighting its strong prediction accuracy and generalization capability.
基金National Key R&D Program of China,Grant/Award Numbers:2022YFB2403700,2022YFB2403705Natural Science Foundation of Chongqing,Grant/Award Number:CSTB2022NSCQ-MSX1247。
文摘Mechanical vibration defect is the key factor leading to sudden failure of gas-insulated switchgear(GIS)equipment.It is important to realise effective prediction of the me-chanical vibration state development trend of GIS equipment in order to improve its active safety protection level.This paper carried out research on the accurate prediction method and experimental validation of the mechanical vibration state and its defect severity development trend for the GIS equipment.Firstly,the deep and shallow vibration feature parameters for different mechanical defect signals were jointly extracted by time-domain features and deep belief network methods.Secondly,a new prediction model,incorporating the attention mechanism and the bidirectional gated recurrent unit(BiGRU),was constructed with the deep and shallow vibration feature parameters as inputs.Finally,the prediction trend effectiveness was verified based on the real-type GIS mechanical simulation platform and the field operation GIS equipment.Results show that the deep and shallow vibration feature extraction method proposed in this paper can characterise the mechanical defect information more comprehensively.The new prediction method of the vibration state trend based on the attention-BiGRU model shows ideal accuracy,and the predicted vibration state development trend is highly consistent with the actual,with an average absolute error of 0.063.The root mean square error(ERMSE)value of the prediction method is<5%,which reduces the relative error value at least 37% compared with the traditional prediction models.This paper provides a valuable reference for the proactive defence of GIS mechanical failure.
基金supported by the National Natural Science Foundation of China(12005108)the Shandong Provincial Natural Science Foundation Youth Project(ZR2020QF016)the National Natural Science Foundation of China(U2006222)。
文摘In frequency division duplex(FDD)massive multiple-input multiple-output(MIMO)systems,a bidirectional positional attention network(BPANet)was proposed to address the high computational complexity and low accuracy of existing deep learning-based channel state information(CSI)feedback methods.Specifically,a bidirectional position attention module(BPAM)was designed in the BPANet to improve the network performance.The BPAM captures the distribution characteristics of the CSI matrix by integrating channel and spatial dimension information,thereby enhancing the feature representation of the CSI matrix.Furthermore,channel attention is decomposed into two one-dimensional(1D)feature encoding processes effectively reducing computational costs.Simulation results demonstrate that,compared with the existing representative method complex input lightweight neural network(CLNet),BPANet reduces computational complexity by an average of 19.4%and improves accuracy by an average of 7.1%.Additionally,it performs better in terms of running time delay and cosine similarity.
基金supported by the National Key Research and Development Program of China (2018YFC0830105,2018YFC 0830101,2018YFC0830100)the National Natural Science Foundation of China (Grant Nos.61972186,61762056,61472168)+1 种基金the Yunnan Provincial Major Science and Technology Special Plan Projects (202002AD080001)the General Projects of Basic Research in Yunnan Province (202001AT070046,202001AT070047).
文摘Automatically generating a brief summary for legal-related public opinion news(LPO-news,which contains legal words or phrases)plays an important role in rapid and effective public opinion disposal.For LPO-news,the critical case elements which are significant parts of the summary may be mentioned several times in the reader comments.Consequently,we investigate the task of comment-aware abstractive text summarization for LPO-news,which can generate salient summary by learning pivotal case elements from the reader comments.In this paper,we present a hierarchical comment-aware encoder(HCAE),which contains four components:1)a traditional sequenceto-sequence framework as our baseline;2)a selective denoising module to filter the noisy of comments and distinguish the case elements;3)a merge module by coupling the source article and comments to yield comment-aware context representation;4)a recoding module to capture the interaction among the source article words conditioned on the comments.Extensive experiments are conducted on a large dataset of legal public opinion news collected from micro-blog,and results show that the proposed model outperforms several existing state-of-the-art baseline models under the ROUGE metrics.
