查看更多>>摘要：In this paper,we mainly study the dis-crete approximation about average differential entropy of continuous bounded space-time random field.The estima-tion of differential entropy on random variable is a classic problem,and there are many related studies.Space-time random field is a theoretical extension of adding random variables to space-time parameters,but studies on dis-crete estimation of entropy on space-time random field are relatively few.The differential entropy forms of con-tinuous bounded space-time random field and discrete es-timations are discussed,and three estimation forms of dif-ferential entropy in the case of random variables are gen-erated in this paper.Furthermore,it is concluded that under the condition that the entropy estimation formula after space-time segmentation converges with probability 1,the average entropy in the bounded space-time region can also converge with probability 1,and three general-ized entropies are verified respectively.In addition,we also carried out numerical experiments on the conver-gence of average entropy estimation based on parameters,and the numerical results are consistent with the theoret-ical results,which indicting further study of the average entropy estimation problem of space-time random fields is significant in the future.
查看更多>>摘要：By solving the existing expectation-sig-nal-to-noise ratio(expectation-SNR)based inequality model of the closed-form instantaneous cross-correlation function type of Choi-Williams distribution(CICFCWD),the linear canonical transform(LCT)free parameters se-lection strategies obtained are usually unsatisfactory.Since the second-order moment variance outperforms the first-order moment expectation in accurately characteriz-ing output SNRs,this paper uses the variance analysis technique to improve parameters selection strategies.The CICFCWD's average variance of deterministic signals em-bedded in additive zero-mean stationary circular Gaussi-an noise processes is first obtained.Then the so-called variance-SNRs are defined and applied to model a vari-ance-SNR based inequality.A stronger inequalities sys-tem is also formulated by integrating expectation-SNR and variance-SNR based inequality models.Finally,a dir-ect application of the system in noisy one-component and bi-component linear frequency-modulated(LFM)signals detection is studied.Analytical algebraic constraints on LCT free parameters newly derived seem more accurate than the existing ones,achieving better noise suppression effects.Our methods have potential applications in optic-al,radar,communication and medical signal processing.
查看更多>>摘要：Satellites based positioning has been widely applied to many areas in our daily lives and thus become indispensable,which also leads to increasing de-mand for high-positioning accuracy.In some complex en-vironments(such as dense urban,valley),multipath inter-ference is one of the main error sources deteriorating posi-tioning accuracy,and it is difficult to eliminate via differ-ential techniques due to its uncertainty of occurrence and irrelevance in different instants.To address this problem,we propose a positioning method for global navigation satellite systems(GNSS)by adopting a modified teaching-learning based optimization(TLBO)algorithm after the positioning problem is formulated as an optimization problem.Experiments are conducted by using actual satellite data.The results show that the proposed posi-tioning algorithm outperforms other algorithms,such as particle swarm optimization based positioning algorithm,differential evolution based positioning algorithm,vari-able projection method,and TLBO algorithm,in terms of accuracy and stability.
查看更多>>摘要：Recently,many deep learning models have shown excellent performance in hyperspectral image(HSI)classification.Among them,networks with mul-tiple convolution kernels of different sizes have been proved to achieve richer receptive fields and extract more representative features than those with a single convolu-tion kernel.However,in most networks,different-sized convolution kernels are usually used directly on multi-branch structures,and the image features extracted from them are fused directly and simply.In this paper,to fully and adaptively explore the multiscale information in both spectral and spatial domains of HSI,a novel multi-scale weighted kernel network(MSWKNet)based on an adapt-ive receptive field is proposed.First,the original HSI cu-bic patches are transformed to the input features by com-bining the principal component analysis and one-dimen-sional spectral convolution.Then,a three-branch net-work with different convolution kernels is designed to convolve the input features,and adaptively adjust the size of the receptive field through the attention mechanism of each branch.Finally,the features extracted from each branch are fused together for the task of classification.Experiments on three well-known hyperspectral data sets show that MSWKNet outperforms many deep learning networks in HSI classification.
查看更多>>摘要：To reduce the overhead and complexity of channel state information acquisition in interference alignment,the topological interference management(TIM)was proposed to manage interference,which only relied on the network topology information.The previous research on topological interference management via the low-rank matrix completion approach is known to be NP-hard.This paper considers the clustering method for the topological interference management problem,namely,the low-rank matrix completion for TIM is applied with-in each cluster.Based on the clustering result,we solve the low-rank matrix completion problem via nuclear norm minimization and Frobenius norm minimization function.Simulation results demonstrate that the proposed cluster-ing method combined with TIM leads to significant gain on the achievable degrees of freedom.
查看更多>>摘要：The triangular geometry is the basis of near field array accurate distance estimation algorithms.The Fisher expression of traditional distance estimation is derived by utilizing the Taylor series.To improve conver-gence rate and estimation accuracy,a novel iterative dis-tance estimation algorithm is proposed with differential equations based on the triangular geometry.Firstly,its convergence performance is analysed in detail.Secondly,the selection of the initial value and the number of itera-tions are respectively studied.Thirdly,compared with the traditional estimation algorithms by utilizing the Fisher approximation,the proposed algorithm has a higher con-vergence rate and estimation accuracy.Moreover,its pseudocode is presented.Finally,the experiment results and performance analysis are provided to verify the ef-fectiveness of the proposed algorithm.
查看更多>>摘要：A common but critical task in biological ontologies data analysis is to compare the difference between ontologies.There have been numerous ontology-based semantic-similarity measures proposed in specific ontology domain,but it still remains a challenge for cross-domain ontologies comparison.An ontology contains the scientific natural language description for the correspond-ing biological aspect.Therefore,we develop a new meth-od based on natural language processing(NLP)represent-ation model bidirectional encoder representations from transformers(BERT)for cross-domain semantic repres-entation of biological ontologies.This article uses the BERT model to represent the word-level of the ontolo-gies as a set of vectors,facilitating the semantic analysis or comparing the biomedical entities named in an onto-logy or associated with ontology terms.We evaluated the ability of our method in two experiments:calculating sim-ilarities of pair-wise disease ontology and human pheno-type ontology terms and predicting the pair-wise of pro-teins interaction.The experimental results demonstrated the comparative performance.This gives promise to the development of NLP methods in biological data analysis.
查看更多>>摘要：Pre-mRNA splicing is an essential pro-cedure for gene transcription.Through the cutting of in-trons and exons,the DNA sequence can be decoded into different proteins related to different biological functions.The cutting boundaries are defined by the donor and ac-ceptor splice sites.Characterizing the nucleotides pat-terns in detecting splice sites is sophisticated and chal-lenges the conventional methods.Recently,the deep learning frame has been introduced in predicting splice sites and exhibits high performance.It extracts high di-mension features from the DNA sequence automatically rather than infers the splice sites with prior knowledge of the relationships,dependencies,and characteristics of nucleotides in the DNA sequence.This paper proposes the AttentionSplice model,a hybrid construction com-bined with multi-head self-attention,convolutional neural network,bidirectional long short-term memory network.The performance of AttentionSplice is evaluated on the Homo sapiens(Human)and Caenorhabditis Elegans(Worm)datasets.Our model outperforms state-of-the-art models in the classification of splice sites.To provide in-terpretability of AttentionSplice models,we extract im-portant positions and key motifs which could be essential for splice site detection through the attention learned by the model.Our result could offer novel insights into the underlying biological roles and molecular mechanisms of gene expression.
查看更多>>摘要：Protein localization information is essen-tial for understanding protein functions and their roles in various biological processes.The image-based prediction methods of protein subcellular localization have emerged in recent years because of the advantages of microscopic images in revealing spatial expression and distribution of proteins in cells.However,the image-based prediction is a very challenging task,due to the multi-instance nature of the task and low quality of images.In this paper,we pro-pose a multi-task learning strategy and mask generation to enhance the prediction performance.Furthermore,we also investigate effective multi-instance learning schemes.We collect a large-scale dataset from the Human Protein Atlas database,and the experimental results show that the proposed multi-task multi-instance learning model outperforms both single-instance learning and common multi-instance learning methods by large margins.
查看更多>>摘要：Improving transportation efficiency is an eternal research hotspot in rail transit system.In recent years,the train operation control method based on virtu-al coupling has attracted the attention of many scholars.The method of train coordination and anti-collision con-trol is not only the key to realize the virtual coupling of train,but also the key to ensure the safety of train opera-tion.Therefore,based on the existing research,a virtual coupled train dynamics model with nonlinear dynamics is established.Then,the parameters of the operation pro-cess model of the nonlinear virtual coupled train are iden-tified by the recursive least squares method based on real-time data,which is applied to the variable parameter arti-ficial potential field(VAPF)for parameter identification.A fusion controller based on feature-based generalized model prediction(GPC)and VAPF is used to control the virtual coupled train and prevent collision.Finally,the validity of the proposed method is verified by using real high-speed railway data.
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