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A resource-adaptive tensor decomposition method for convolutional neural networks 认领 引用 被引量：1
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作者 XIE Xiaoyan REN Xun +3 位作者 ZHU Yun YU Jinhao JIN Luochen YANG Tianjiao 《High Technology Letters》 EI CAS 2025年第4期355-364,共10页
To enhance the inference efficiency of convolutional neural network(CNN),tensor parallelism is employed to improve the parallelism within operators.However,existing methods are customized to specific networks and hard... To enhance the inference efficiency of convolutional neural network(CNN),tensor parallelism is employed to improve the parallelism within operators.However,existing methods are customized to specific networks and hardware,limiting their generalizability.This paper proposes an approach called resource-adaptive tensor decomposition(RATD)for CNN operators,which aims to achieve an optimal match between computational resources and parallel computing tasks.Firstly,CNN is represented with fine-grained tensors at the lower graph level,thereby decoupling tensors that can be computed in parallel within operators.Secondly,the convolution and pooling operators are fused,and the decoupled tensor blocks are scheduled in parallel.Finally,a cost model is constructed,based on runtime and resource utilization,to iteratively refine the process of tensor block decomposition and automatically determine the optimal tensor decomposition.Experimental results demonstrate that the proposed RATD improves the accuracy of the model by 11%.Compared with CUDA(compute unified device architecture)deep neural network library(cuDNN),RATD achieves an average speedup ratio of 1.21 times in inference time across various convolution kernels,along with a 12%increase in computational resource utilization. 展开更多
关键词 tensor decomposition operator parallelism convolutional neural network(CNN)
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Tensor decomposition reveals trans-regulated gene modules in maize drought response 认领 引用
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作者 Jiawen Lu Yuxin Xie +2 位作者 Chunhui Li Jinliang Yang Junjie Fu 《Journal of Genetics and Genomics》 SCIE CAS CSCD 2025年第6期786-798,共13页
When plants respond to drought stress,dynamic cellular changes occur,accompanied by alterations in gene expression,which often act through trans-regulation.However,the detection of trans-acting genetic variants and ne... When plants respond to drought stress,dynamic cellular changes occur,accompanied by alterations in gene expression,which often act through trans-regulation.However,the detection of trans-acting genetic variants and networks of genes is challenged by the large number of genes and markers.Using a tensor decomposition method,we identify trans-acting expression quantitative trait loci(trans-eQTLs)linked to gene modules,rather than individual genes,which were associated with maize drought response.Module-to-trait association analysis demonstrates that half of the modules are relevant to drought-related traits.Genome-wide association studies of the expression patterns of each module identify 286 trans-eQTLs linked to drought-responsive modules,the majority of which cannot be detected based on individual gene expression.Notably,the trans-eQTLs located in the regions selected during maize improvement tend towards relatively strong selection.We further prioritize the genes that affect the transcriptional regulation of multiple genes in trans,as exemplified by two transcription factor genes.Our analyses highlight that multidimensional reduction could facilitate the identification of trans-acting variations in gene expression in response to dynamic environments and serve as a promising technique for high-order data processing in future crop breeding. 展开更多
关键词 Maize Drought stress Tensor decomposition Gene expression trans-eQTL
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Topology-aware tensor decomposition for meta-graph learning 认领 引用
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作者 Hansi Yang Quanming Yao 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第3期891-901,共11页
Heterogeneous graphs generally refer to graphs with different types of nodes and edges.A common approach for extracting useful information from heterogeneous graphs is to use meta-graphs,which can be seen as a special... Heterogeneous graphs generally refer to graphs with different types of nodes and edges.A common approach for extracting useful information from heterogeneous graphs is to use meta-graphs,which can be seen as a special kind of directed acyclic graph with same node and edge types as the heterogeneous graph.However,how to design proper metagraphs is challenging.Recently,there have been many works on learning suitable metagraphs from a heterogeneous graph.Existing methods generally introduce continuous weights for edges that are independent of each other,which ignores the topological structures of meta-graphs and can be ineffective.To address this issue,the authors propose a new viewpoint from tensor on learning meta-graphs.Such a viewpoint not only helps interpret the limitation of existing works by CANDECOMP/PARAFAC(CP)decomposition,but also inspires us to propose a topology-aware tensor decomposition,called TENSUS,that reflects the structure of DAGs.The proposed topology-aware tensor decomposition is easy to use and simple to implement,and it can be taken as a plug-in part to upgrade many existing works,including node classification and recommendation on heterogeneous graphs.Experimental results on different tasks demonstrate that the proposed method can significantly improve the state-of-the-arts for all these tasks. 展开更多
关键词 graph neural network heterogeneous graph polymorphic network tensor decomposition
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Multi-Aspect Incremental Tensor Decomposition Based on Distributed In-Memory Big Data Systems 认领 引用 被引量：3
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作者 Hye-Kyung Yang Hwan-Seung Yong 《Journal of Data and Information Science》 CSCD 2020年第2期13-32,共20页
Purpose:We propose In Par Ten2,a multi-aspect parallel factor analysis three-dimensional tensor decomposition algorithm based on the Apache Spark framework.The proposed method reduces re-decomposition cost and can han... Purpose:We propose In Par Ten2,a multi-aspect parallel factor analysis three-dimensional tensor decomposition algorithm based on the Apache Spark framework.The proposed method reduces re-decomposition cost and can handle large tensors.Design/methodology/approach:Considering that tensor addition increases the size of a given tensor along all axes,the proposed method decomposes incoming tensors using existing decomposition results without generating sub-tensors.Additionally,In Par Ten2 avoids the calculation of Khari–Rao products and minimizes shuffling by using the Apache Spark platform.Findings:The performance of In Par Ten2 is evaluated by comparing its execution time and accuracy with those of existing distributed tensor decomposition methods on various datasets.The results confirm that In Par Ten2 can process large tensors and reduce the re-calculation cost of tensor decomposition.Consequently,the proposed method is faster than existing tensor decomposition algorithms and can significantly reduce re-decomposition cost.Research limitations:There are several Hadoop-based distributed tensor decomposition algorithms as well as MATLAB-based decomposition methods.However,the former require longer iteration time,and therefore their execution time cannot be compared with that of Spark-based algorithms,whereas the latter run on a single machine,thus limiting their ability to handle large data.Practical implications:The proposed algorithm can reduce re-decomposition cost when tensors are added to a given tensor by decomposing them based on existing decomposition results without re-decomposing the entire tensor.Originality/value:The proposed method can handle large tensors and is fast within the limited-memory framework of Apache Spark.Moreover,In Par Ten2 can handle static as well as incremental tensor decomposition. 展开更多
关键词 PARAFAC Tensor decomposition Incremental tensor decomposition Apache Spark Big data
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A Novel Tensor Decomposition-Based Efficient Detector for Low-Altitude Aerial Objects With Knowledge Distillation Scheme 认领 引用 被引量：9
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作者 Nianyin Zeng Xinyu Li +2 位作者 Peishu Wu Han Li Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第2期487-501,共15页
Unmanned aerial vehicles(UAVs) have gained significant attention in practical applications, especially the low-altitude aerial(LAA) object detection imposes stringent requirements on recognition accuracy and computati... Unmanned aerial vehicles(UAVs) have gained significant attention in practical applications, especially the low-altitude aerial(LAA) object detection imposes stringent requirements on recognition accuracy and computational resources. In this paper, the LAA images-oriented tensor decomposition and knowledge distillation-based network(TDKD-Net) is proposed,where the TT-format TD(tensor decomposition) and equalweighted response-based KD(knowledge distillation) methods are designed to minimize redundant parameters while ensuring comparable performance. Moreover, some robust network structures are developed, including the small object detection head and the dual-domain attention mechanism, which enable the model to leverage the learned knowledge from small-scale targets and selectively focus on salient features. Considering the imbalance of bounding box regression samples and the inaccuracy of regression geometric factors, the focal and efficient IoU(intersection of union) loss with optimal transport assignment(F-EIoU-OTA)mechanism is proposed to improve the detection accuracy. The proposed TDKD-Net is comprehensively evaluated through extensive experiments, and the results have demonstrated the effectiveness and superiority of the developed methods in comparison to other advanced detection algorithms, which also present high generalization and strong robustness. As a resource-efficient precise network, the complex detection of small and occluded LAA objects is also well addressed by TDKD-Net, which provides useful insights on handling imbalanced issues and realizing domain adaptation. 展开更多
关键词 Attention mechanism knowledge distillation(KD) object detection tensor decomposition(TD) unmanned aerial vehicles(UAVs)
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Key Exchange Protocol Based on Tensor Decomposition Problem 认领 引用 被引量：2
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作者 MAO Shaowu ZHANG Huanguo +3 位作者 WU Wanqing ZHANG Pei SONG Jun LIU Jinhui 《China Communications》 SCIE CSCD 2016年第3期174-183,共10页
The hardness of tensor decomposition problem has many achievements, but limited applications in cryptography, and the tensor decomposition problem has been considered to have the potential to resist quantum computing.... The hardness of tensor decomposition problem has many achievements, but limited applications in cryptography, and the tensor decomposition problem has been considered to have the potential to resist quantum computing. In this paper, we firstly proposed a new variant of tensor decomposition problem, then two one-way functions are proposed based on the hard problem. Secondly we propose a key exchange protocol based on the one-way functions, then the security analysis, efficiency, recommended parameters and etc. are also given. The analyses show that our scheme has the following characteristics: easy to implement in software and hardware, security can be reduced to hard problems, and it has the potential to resist quantum computing.Besides the new key exchange can be as an alternative comparing with other classical key protocols. 展开更多
关键词 key exchange resistant quantum hard problem tensor decomposition
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A multispectral image compression and encryption algorithm based on tensor decomposition and chaos 认领 引用 被引量：1
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作者 XU Dongdong DU Limin 《High Technology Letters》 EI CAS 2022年第2期134-141,共8页
A multi spectral image compression and encryption algorithm that combines Karhunen-Loeve(KL) transform,tensor decomposition and chaos is proposed for solving the security problem of multi-spectral image compression an... A multi spectral image compression and encryption algorithm that combines Karhunen-Loeve(KL) transform,tensor decomposition and chaos is proposed for solving the security problem of multi-spectral image compression and transmission.Firstly,in order to eliminate residual spatial redundancy and most of the spectral redundancy,the image is performed by KL transform.Secondly,to further eliminate spatial redundancy and reduce block effects in the compression process,two-dimensional discrete 9/7 wavelet transform is performed,and then Arnold transform and encryption processing on the transformed coefficients are performed.Subsequently,the tensor is decomposed to keep its intrinsic structure intact and eliminate residual space redundancy.Finally,differential pulse filters are used to encode the coefficients,and Tent mapping is used to implement confusion diffusion encryption on the code stream.The experimental results show that the method has high signal-to-noise ratio,fast calculation speed,and large key space,and it is sensitive to keys and plaintexts with a positive effect in spectrum assurance at the same time. 展开更多
关键词 Karhunen-Loeve(KL)transform tensor decomposition differential pulse filter Tent map
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Recommender Systems Based on Tensor Decomposition 认领 引用
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作者 Zhoubao Sun Xiaodong Zhang +2 位作者 Haoyuan Li Yan Xiao Haifeng Guo 《Computers, Materials & Continua》 SCIE EI 2021年第1期621-630,共10页
Recommender system is an effective tool to solve the problems of information overload.The traditional recommender systems,especially the collaborative filtering ones,only consider the two factors of users and items.Wh... Recommender system is an effective tool to solve the problems of information overload.The traditional recommender systems,especially the collaborative filtering ones,only consider the two factors of users and items.While social networks contain abundant social information,such as tags,places and times.Researches show that the social information has a great impact on recommendation results.Tags not only describe the characteristics of items,but also reflect the interests and characteristics of users.Since the traditional recommender systems cannot parse multi-dimensional information,in this paper,a tensor decomposition model based on tag regularization is proposed which incorporates social information to benefit recommender systems.The original Singular Value Decomposition(SVD)model is optimized by mining the co-occurrence and mutual exclusion of tags,and their features are constrained by the relationship between tags.Experiments on real dataset show that the proposed algorithm achieves superior performance to existing algorithms. 展开更多
关键词 Recommender system social information tensor decomposition tag
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TdBrnn:An Approach to Learning Users’Intention to Legal Consultation with Normalized Tensor Decomposition and Bi-LSTM 认领 引用
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作者 Xiaoding Guo Hongli Zhang +1 位作者 Lin Ye Shang Li 《Computers, Materials & Continua》 SCIE EI 2020年第4期315-336,共22页
With the development of Internet technology and the enhancement of people’s concept of the rule of law,online legal consultation has become an important means for the general public to conduct legal consultation.Howe... With the development of Internet technology and the enhancement of people’s concept of the rule of law,online legal consultation has become an important means for the general public to conduct legal consultation.However,different people have different language expressions and legal professional backgrounds.This phenomenon may lead to the phenomenon of different descriptions of the same legal consultation.How to accurately understand the true intentions behind different users’legal consulting statements is an important issue that needs to be solved urgently in the field of legal consulting services.Traditional intent understanding algorithms rely heavily on the lexical and semantic information between the original data,and are not scalable,and often require taxing manual annotation work.This article proposes a new approach TdBrnn which is based on the normalized tensor decomposition method and Bi-LSTM to learn users’intention to legal consulting.First,we present the users’legal consulting statements as a tensor.And then we use the normalized tensor decomposition layer proposed by this article to extract the tensor elements and structural information of the original tensor which can best represent users’intention of legal consultation,namely the core tensor.The core tensor relies less on the lexical and semantic information of the original users’legal consulting statements data,it reduces the dimension of the original tensor,and greatly reduces the computational complexity of the subsequent Bi-LSTM algorithm.Furthermore,we use a large number of core tensors obtained by the tensor decomposition layer with users’legal consulting statements tensors as inputs to continuously train Bi-LSTM,and finally derive the users’legal consultation intention classification model which can comprehensively understand the user’s legal consultation intention.Experiments show that our method has faster convergence speed and higher accuracy than traditional recurrent neural networks. 展开更多
关键词 Normalized tensor decomposition Bi-LSTM legal consultation users’intention
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Efficient tensor decomposition method for noncircular source in colocated coprime MIMO radar 认领 引用
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作者 Qian-Peng Xie Xiao-Yi Pan Shun-Ping Xiao 《Chinese Physics B》 SCIE EI CAS CSCD 2020年第5期333-345,共13页
An effective method via tensor decomposition is proposed to deal with the joint direction-of-departure(DOD)and direction-of-arrival(DOA)estimation of noncircular sources in colocated coprime MIMO radar.By decomposing ... An effective method via tensor decomposition is proposed to deal with the joint direction-of-departure(DOD)and direction-of-arrival(DOA)estimation of noncircular sources in colocated coprime MIMO radar.By decomposing the transmitter and receiver into two sparse subarrays,noncircular property of source can be used to construct new extended received signal model for two sparse subarrays.The new received model can double the virtual array aperture due to the elliptic covariance of imping sources is nonzero.To further exploit the multidimensional structure of the noncircular received model,we stack the subarray output and its conjugation according to mode-1 unfolding and mode-2 unfolding of a third-order tensor,respectively.Thus,the corresponding extended tensor model consisted of noncircular information for DOA and DOD can be obtained.Then,the higher-order singular value decomposition technique is utilized to estimate the accurate signal subspace and angular parameter can be automatically paired via the rotational invariance relationship.Specifically,the ambiguous angle can be eliminated and the true targets can be achieved with the aid of the coprime property.Furthermore,a closed-form expression for the deterministic CRB under the NC sources scenario is also derived.Simulation results verify the superiority of the proposed estimator. 展开更多
关键词 colocated coprime MIMO radar noncircular signal tensor decomposition DOD and DOA estimation
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Detection of T-wave Alternans in ECG Signals Using FRFT and Tensor Decomposition 认领 引用
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作者 Chuanbin Ge Shuli Zhao Yi Xin 《Journal of Beijing Institute of Technology》 EI CAS 2021年第3期290-294,共5页
T-wave alternans(TWA)refers to the periodic beat-to-beat variation in the amplitude of T-wave in the electrocardiogram(ECG)signal in an ABAB-pattern.TWA has been proven to be a very important indicator of malignant ar... T-wave alternans(TWA)refers to the periodic beat-to-beat variation in the amplitude of T-wave in the electrocardiogram(ECG)signal in an ABAB-pattern.TWA has been proven to be a very important indicator of malignant arrhythmia risk stratification.A new method to detect TWA by combining fractional Fourier transform(FRFT)and tensor decomposition is proposed.First,the T-wave vector is extracted from the ECG of each heartbeat,and its FRFT amplitudes at multiple orders are arranged to form a T-wave matrix.Then,a third-order tensor is composed of T-wave matrices of several consecutive heart beats.After tensor decomposition,projection matrices are obtained in three dimensions.The complexity of the projection matrix is measured by Shannon entropy to obtain feature vector to detect the presence of TWA.Results show that the sensitivity,specificity,and accuracy of the algorithm on the MIT-BIH database are 91.16%,94.25%,and 92.68%,respectively.This method effectively utilizes the fractional domain information of ECG,and shows the promising potential of the FRFT in ECG signal processing. 展开更多
关键词 T-wave alternans(TWA) electrocardiogram(ECG) fractional Fourier transform tensor decomposition
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Relations between cubic equation, stress tensor decomposition, and von Mises yield criterion 认领 引用
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作者 Haoyuan GUO Liyuan ZHANG +1 位作者 Yajun YIN Yongxin GAO 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2015年第10期1359-1370,共12页
Inspired by Cardano＇s method for solving cubic scalar equations, the addi- tive decomposition of spherical/deviatoric tensor （DSDT） is revisited from a new view- point. This decomposition simplifies the cubic tenso... Inspired by Cardano＇s method for solving cubic scalar equations, the addi- tive decomposition of spherical/deviatoric tensor （DSDT） is revisited from a new view- point. This decomposition simplifies the cubic tensor equation, decouples the spher- ical/deviatoric strain energy density, and lays the foundation for the von Mises yield criterion. Besides, it is verified that under the precondition of energy decoupling and the simplest form, the DSDT is the only possible form of the additive decomposition with physical meanings. 展开更多
关键词 Cardano＇s method Caylay-Hamilton theorem cubic tensor equation decomposition of spherical/deviatoric tensor （DSDT） von Mises yield criterion
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A Three-Layer Weighted Ensemble Clustering Approach Based on Tensor Decomposition 认领 引用
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作者 Ting Gao Xiufang Xu +5 位作者 Xuesheng Bian Naixuan Guo Zhaofeng Chen Sen Xu Shanliang Yao Tian Zhou 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2026年第3期1737-1749,共13页
Existing ensemble clustering approaches usually construct a co-association matrix by combining clustering members and then use it to achieve the clustering result.However,the co-association matrix is easily influenced... Existing ensemble clustering approaches usually construct a co-association matrix by combining clustering members and then use it to achieve the clustering result.However,the co-association matrix is easily influenced by low-quality clustering members,leading to poor clustering results.To address the problem,we propose a three-layer weighted ensemble clustering approach based on tensor decomposition.Our approach firstly computes weights for the partition,cluster,and point layers using the normalized mutual information,the information entropy,and the quadratic function,respectively,to reduce the influence of lowquality clustering members.Next,we construct a coherent-link matrix and a three-layer weighted coassociation matrix and stack them into a 3-dimensional tensor.Finally,we leverage the tensor's low-rank structure to propagate reliable information from the coherent-link matrix,refining the three-layer weighted coassociation matrix and obtaining the final matrix with enhanced clustering performance.Experimental results on ten datasets demonstrate that the proposed approach significantly outperforms ten existing ensemble clustering approaches.Specifically,the proposed approach achieves an average improvement of 8.5%in the normalized mutual information,7.2%in the adjusted rand index,and 6.8%in the F-measure compared to the best-performing baseline approach. 展开更多
关键词 ensemble clustering tensor decomposition three-layer weighted co-association(CA)matrix
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Tensor Decomposition-assisted Multiview Subgroup Analysis 认领 引用
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作者 Xun Zhao Ling Zhou +1 位作者 Weijia Zhang Huazhen Lin 《Acta Mathematica Sinica,English Series》 SCIE CSCD 2025年第2期588-618,共31页
To learn the subgroup structure generated by multidimensional interaction, we propose a novel multiview subgroup integration technique based on tensor decomposition. Compared to the traditional subgroup analysis that ... To learn the subgroup structure generated by multidimensional interaction, we propose a novel multiview subgroup integration technique based on tensor decomposition. Compared to the traditional subgroup analysis that can only handle single-view heterogeneity, our proposed method achieves a greater level of homogeneity within the subgroups, leading to enhanced interpretability and predictive power. For computational readiness of the proposed method, we build an algorithm that incorporates pairwise shrinkage-encouraging penalties and ADMM techniques. Theoretically, we establish the asymptotic consistency and normality of the proposed estimators. Extensive simulation studies and real data analysis demonstrate that our proposal outperforms other methods in terms of prediction accuracy and grouping consistency. In addition, the analysis based on the proposed method indicates that intergenerational care significantly increases the risk of chronic diseases associated with diet and fatigue in all provinces while only reducing the risk of emotion-related chronic diseases in the eastern coastal and central regions of China. 展开更多
关键词 Multiview subgroup analysis tensor decomposition data integration ADMM algorithm
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A Novel Multichannel Audio Signal Compression Method Based on Tensor Representation and Decomposition 认领 引用 被引量：2
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作者 WANG Jing XIE Xiang KUANG Jingming 《China Communications》 SCIE CSCD 2014年第3期80-90,共11页
Multichannel audio signal is more difficult to be compressed than mono and stereo ones.A novel multichannel audio signal compression method based on tensor representation and decomposition is proposed in this paper.Th... Multichannel audio signal is more difficult to be compressed than mono and stereo ones.A novel multichannel audio signal compression method based on tensor representation and decomposition is proposed in this paper.The multichannel audio is represented with 3-order tensor space and is decomposed into core tensor with three factor matrices in the way of channel,time and frequency.Only the truncated core tensor is transmitted which will be multiplied by the pre-trained factor matrices to reconstruct the original tensor space.Objective and subjective experiments have been done to show a very noticeable compression capability with an acceptable output quality.The novelty of the proposed compression method is that it enables both high compression capability and backward compatibility with limited signal distortion to the hearing. 展开更多
关键词 multichannel audio signal compression tensor decomposition Tuckermodel core tensor
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An inexact alternating proximal gradient algorithm for nonnegative CP tensor decomposition 认领 引用 被引量：2
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作者 WANG DeQing CONG FengYu 《Science China(Technological Sciences)》 SCIE EI CAS CSCD 2021年第9期1893-1906,共14页
Nonnegative tensor decomposition has become increasingly important for multiway data analysis in recent years. The alternating proximal gradient(APG) is a popular optimization method for nonnegative tensor decompositi... Nonnegative tensor decomposition has become increasingly important for multiway data analysis in recent years. The alternating proximal gradient(APG) is a popular optimization method for nonnegative tensor decomposition in the block coordinate descent framework. In this study, we propose an inexact version of the APG algorithm for nonnegative CANDECOMP/PARAFAC decomposition, wherein each factor matrix is updated by only finite inner iterations. We also propose a parameter warm-start method that can avoid the frequent parameter resetting of conventional APG methods and improve convergence performance.By experimental tests, we find that when the number of inner iterations is limited to around 10 to 20, the convergence speed is accelerated significantly without losing its low relative error. We evaluate our method on both synthetic and real-world tensors.The results demonstrate that the proposed inexact APG algorithm exhibits outstanding performance on both convergence speed and computational precision compared with existing popular algorithms. 展开更多
关键词 tensor decomposition nonnegative CANDECOMP/PARAFAC block coordinate descent alternating proximal gradient inexact scheme
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Near-field joint estimation of multi-targets’position and velocity in a terahertz MIMO-OFDM system based on tensor decomposition 认领 引用
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作者 Ke LIU Shengfu ZHAO +4 位作者 Weixin CHEN Zhen WANG Lingxiang LI Zhi CHEN Qiang XU 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2024年第12期1708-1722,共15页
This paper investigates the joint estimation of multi-targets’position and velocity for a terahertz multi-input multi-output(MIMO)orthogonal frequency division multiplexing(OFDM)system operating in the near field bas... This paper investigates the joint estimation of multi-targets’position and velocity for a terahertz multi-input multi-output(MIMO)orthogonal frequency division multiplexing(OFDM)system operating in the near field based on tensor decomposition.The waveforms transmitted from shared antennas carry communication messages and are orthogonal to each other in the frequency domain.The estimation of the position and velocity of multiple targets in the considered near-field scenario is challenging because it involves spherical wavefronts.A signal model based on spherical wavefronts enables higher resolution on spatial position,which,if properly designed,can be used to improve the estimation accuracy.In this paper,we propose a CANDECOMP/PARAFAC(CP)decomposition-based near-field localization(CP-NFL)algorithm for the joint estimation of the position and velocity of multiple targets.In our proposed method,the received signal is expressed as a third-order tensor;based on its factor matrices we convert the original non-convex optimization problem into a convex one and solve it with CVX tools.Our analysis reveals that the uniqueness in CP decomposition can be guaranteed and the computational complexity of our proposed method is linear to the sum of the third powers of the number of sub-carriers,OFDM symbols,antennas,and targets.Numerical results show that our proposed method has a clear advantage over the existing method in terms of estimation accuracy and computational complexity. 展开更多
关键词 Terahertz Multi-input multi-output orthogonal frequency division multiplexing(MIMO-OFDM) Near-field localization(NFL) Velocity estimation Tensor decomposition
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TIDS: Tensor Based Intrusion Detection System (IDS) and Its Application in Large Scale DDoS Attack Detection 认领 引用 被引量：1
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作者 Hanqing Sun Xue Li +1 位作者 Qiyuan Fan Puming Wang 《Computers, Materials & Continua》 SCIE EI 2025年第7期1659-1679,共21页
The era of big data brings new challenges for information network systems(INS),simultaneously offering unprecedented opportunities for advancing intelligent intrusion detection systems.In this work,we propose a data-d... The era of big data brings new challenges for information network systems(INS),simultaneously offering unprecedented opportunities for advancing intelligent intrusion detection systems.In this work,we propose a data-driven intrusion detection system for Distributed Denial of Service(DDoS)attack detection.The system focuses on intrusion detection from a big data perceptive.As intelligent information processing methods,big data and artificial intelligence have been widely used in information systems.The INS system is an important information system in cyberspace.In advanced INS systems,the network architectures have become more complex.And the smart devices in INS systems collect a large scale of network data.How to improve the performance of a complex intrusion detection system with big data and artificial intelligence is a big challenge.To address the problem,we design a novel intrusion detection system(IDS)from a big data perspective.The IDS system uses tensors to represent large-scale and complex multi-source network data in a unified tensor.Then,a novel tensor decomposition(TD)method is developed to complete big data mining.The TD method seamlessly collaborates with the XGBoost(eXtreme Gradient Boosting)method to complete the intrusion detection.To verify the proposed IDS system,a series of experiments is conducted on two real network datasets.The results revealed that the proposed IDS system attained an impressive accuracy rate over 98%.Additionally,by altering the scale of the datasets,the proposed IDS system still maintains excellent detection performance,which demonstrates the proposed IDS system’s robustness. 展开更多
关键词 Intrusion detection system big data tensor decomposition multi-modal feature DDoS
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Approximation of Spatio-Temporal Random Processes Using Tensor Decomposition 认领 引用
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作者 Debraj Ghosh Anup Suryawanshi 《Communications in Computational Physics》 SCIE 2014年第6期75-95,共21页
A new representation of spatio-temporal random processes is proposed in this work.In practical applications,such processes are used to model velocity fields,temperature distributions,response of vibrating systems,to n... A new representation of spatio-temporal random processes is proposed in this work.In practical applications,such processes are used to model velocity fields,temperature distributions,response of vibrating systems,to name a few.Finding an efficient representation for any random process leads to encapsulation of information which makes it more convenient for a practical implementations,for instance,in a computational mechanics problem.For a single-parameter process such as spatial or temporal process,the eigenvalue decomposition of the covariance matrix leads to the well-known Karhunen-Lo`eve(KL)decomposition.However,for multiparameter processes such as a spatio-temporal process,the covariance function itself can be defined in multiple ways.Here the process is assumed to be measured at a finite set of spatial locations and a finite number of time instants.Then the spatial covariance matrix at different time instants are considered to define the covariance of the process.This set of square,symmetric,positive semi-definite matrices is then represented as a thirdorder tensor.A suitable decomposition of this tensor can identify the dominant components of the process,and these components are then used to define a closed-form representation of the process.The procedure is analogous to the KL decomposition for a single-parameter process,however,the decompositions and interpretations vary significantly.The tensor decompositions are successfully applied on(i)a heat conduction problem,(ii)a vibration problem,and(iii)a covariance function taken from the literature that was fitted to model a measured wind velocity data.It is observed that the proposed representation provides an efficient approximation to some processes.Furthermore,a comparison with KL decomposition showed that the proposed method is computationally cheaper than the KL,both in terms of computer memory and execution time. 展开更多
关键词 Random process spatio-temporal process tensor decomposition uncertainty quantification probabilistic mechanics.
Link Prediction based on Tensor Decomposition for the Knowledge Graph of COVID-19 Antiviral Drug 认领 引用 被引量：4
20
作者 Ting Jia Yuxia Yang +3 位作者 Xi Lu Qiang Zhu Kuo Yang Xuezhong Zhou 《Data Intelligence》 EI 2022年第1期134-148,共15页
Due to the large-scale spread of COVID-19,which has a significant impact on human health and social economy,developing effective antiviral drugs for COVID-19 is vital to saving human lives.Various biomedical associati... Due to the large-scale spread of COVID-19,which has a significant impact on human health and social economy,developing effective antiviral drugs for COVID-19 is vital to saving human lives.Various biomedical associations,e.g.,drug-virus and viral protein-host protein interactions,can be used for building biomedical knowledge graphs.Based on these sources,large-scale knowledge reasoning algorithms can be used to predict new links between antiviral drugs and viruses.To utilize the various heterogeneous biomedical associations,we proposed a fusion strategy to integrate the results of two tensor decomposition-based models(i.e.,CP-N3 and Compl Ex-N3).Sufficient experiments indicated that our method obtained high performance(MRR=0.2328).Compared with CP-N3,the mean reciprocal rank(MRR)is increased by 3.3%and compared with Compl Ex-N3,the MRR is increased by 3.5%.Meanwhile,we explored the relationship between the performance and relationship types,which indicated that there is a negative correlation(PCC=0.446,P-value=2.26 e-194)between the performance of triples predicted by our method and edge betweenness. 展开更多
关键词 Link prediction Knowledge graph COVID-19 Antiviral drug prediction Tensor decomposition
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