Bilingual lexicon induction focuses on learning word translation pairs,also known as bitexts,from monolingual corpora by establishing a mapping between the source and target embedding spaces.Despite recent advancement...Bilingual lexicon induction focuses on learning word translation pairs,also known as bitexts,from monolingual corpora by establishing a mapping between the source and target embedding spaces.Despite recent advancements,bilingual lexicon induction is limited to inducing bitexts consisting of individual words,lacking the ability to handle semantics-rich phrases.To bridge this gap and support downstream cross-lingual tasks,it is practical to develop a method for bilingual phrase induction that extracts bilingual phrase pairs from monolingual corpora without relying on cross-lingual knowledge.In this paper,the authors propose a novel phrase embedding training method based on the skip-gram structure.Specifically,a local hard negative sampling strategy that utilises negative samples of central tokens in sliding windows to enhance phrase embedding learning is introduced.The proposed method achieves competitive or superior performance compared to baseline approaches,with exceptional results recorded for distant languages.Additionally,we develop a phrase representation learning method that leverages multilingual pre-trained language models.These mPLMs-based representations can be combined with the above-mentioned static phrase embeddings to further improve the accuracy of the bilingual phrase induction task.We manually construct a dataset of bilingual phrase pairs and integrate it with MUSE to facilitate the bilingual phrase induction task.展开更多
Ultra-wide-band (UWB) signals are suitable for localization, since their high time resolution can provide precise time of arrival (TOA) estimation. However, one major challenge in UWB signal processing is the requirem...Ultra-wide-band (UWB) signals are suitable for localization, since their high time resolution can provide precise time of arrival (TOA) estimation. However, one major challenge in UWB signal processing is the requirement of high sampling rate which leads to complicated signal processing and expensive hardware. In this paper, we present a novel UWB signal sampling method called UWB signal sampling via temporal sparsity (USSTS). Its sampling rate is much lower than Nyquist rate. Moreover, it is implemented in one step and no extra processing unit is needed. Simulation results show that USSTS can not recover the signal precisely, but for the use in localization, the accuracy of TOA estimation is the same as that in traditional methods. Therefore, USSTS gives a novel and effective solution for the use of UWB signals in localization.展开更多
Purpose: The present study aimed to assess the accuracies of arterial stimulation with simultaneous venous sampling(ASVS) in preoperative localization of insulinomas. Materials and Methods: A cohort consisting of 6 ma...Purpose: The present study aimed to assess the accuracies of arterial stimulation with simultaneous venous sampling(ASVS) in preoperative localization of insulinomas. Materials and Methods: A cohort consisting of 6 males and 14 females(median age, 48.5y; range, 28–62y) with pathologically proven insulinomas were included in this study. Selective angiographies were performed with the superior mesenteric artery(SMA), gastroduodenal artery(GDA), proximal splenic artery, and midsplenic artery in all individuals. Then ASVS procedures were followed after angiographies for these arteries. Clinical characteristics of the patient and the tumor number, location, and size were recorded. The accuracy of preoperative localization of insulinomas was tested. Results: A total of 22 tumors were identified by histopathological diagnosis. The mean size of the tumor was 1.40±0.60 cm. Five tumors were in the head/neck region and 17 in the body/tail region. ASVS accurately localized 17/20(85%) cases with only biochemical data and 19/20(95%) cases with biochemical data and angiography images. Variant pancreatic arterial anatomy was revealed in 2 false cases with inferior pancreatic artery replaced by the superior mesenteric artery. Conclusion: ASVS was highly accurate in localizing insulinomas and should be performed in most of the patients with suspected insulinomas before the operation.展开更多
Background:The local pivotal method(LPM)utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories(NFIs).Its performance compared to simple random samp...Background:The local pivotal method(LPM)utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories(NFIs).Its performance compared to simple random sampling(SRS)and LPM with geographical coordinates has produced promising results in simulation studies.In this simulation study we compared all these sampling methods to systematic sampling.The LPM samples were selected solely using the coordinates(LPMxy)or,in addition to that,auxiliary remote sensing-based forest variables(RS variables).We utilized field measurement data(NFI-field)and Multi-Source NFI(MS-NFI)maps as target data,and independent MS-NFI maps as auxiliary data.The designs were compared using relative efficiency(RE);a ratio of mean squared errors of the reference sampling design against the studied design.Applying a method in NFI also requires a proven estimator for the variance.Therefore,three different variance estimators were evaluated against the empirical variance of replications:1)an estimator corresponding to SRS;2)a Grafström-Schelin estimator repurposed for LPM;and 3)a Matérn estimator applied in the Finnish NFI for systematic sampling design.Results:The LPMxy was nearly comparable with the systematic design for the most target variables.The REs of the LPM designs utilizing auxiliary data compared to the systematic design varied between 0.74–1.18,according to the studied target variable.The SRS estimator for variance was expectedly the most biased and conservative estimator.Similarly,the Grafström-Schelin estimator gave overestimates in the case of LPMxy.When the RS variables were utilized as auxiliary data,the Grafström-Schelin estimates tended to underestimate the empirical variance.In systematic sampling the Matérn and Grafström-Schelin estimators performed for practical purposes equally.Conclusions:LPM optimized for a specific variable tended to be more efficient than systematic sampling,but all of the considered LPM designs were less efficient than the systematic sampling design for some target variables.The Grafström-Schelin estimator could be used as such with LPMxy or instead of the Matérn estimator in systematic sampling.Further studies of the variance estimators are needed if other auxiliary variables are to be used in LPM.展开更多
Signals are often of random character since they cannot bear any information if they are predictable for any time t, they are usually modelled as stationary random processes .On the other hand, because of the inertia ...Signals are often of random character since they cannot bear any information if they are predictable for any time t, they are usually modelled as stationary random processes .On the other hand, because of the inertia of the measurement apparatus, measured sampled values obtained in practice may not be the precise value of the signal X(t) at time tk (k∈Z), but only local averages of X(t) near tk. In this paper, it is presented that a wide (or weak ) sense stationary stochastic process can be approximated by generalized sampling series with local average samples.展开更多
In this paper, auxiliary information is used to determine an estimator of finite population total using nonparametric regression under stratified random sampling. To achieve this, a model-based approach is adopted by ...In this paper, auxiliary information is used to determine an estimator of finite population total using nonparametric regression under stratified random sampling. To achieve this, a model-based approach is adopted by making use of the local polynomial regression estimation to predict the nonsampled values of the survey variable y. The performance of the proposed estimator is investigated against some design-based and model-based regression estimators. The simulation experiments show that the resulting estimator exhibits good properties. Generally, good confidence intervals are seen for the nonparametric regression estimators, and use of the proposed estimator leads to relatively smaller values of RE compared to other estimators.展开更多
Although real-world experiences show that preparing one image per person is more convenient, most of the appearance-based face recognition methods degrade or fail to work if there is only a single sample per person(SS...Although real-world experiences show that preparing one image per person is more convenient, most of the appearance-based face recognition methods degrade or fail to work if there is only a single sample per person(SSPP). In this work, we introduce a novel supervised learning method called supervised locality preserving multimanifold(SLPMM) for face recognition with SSPP. In SLPMM, two graphs: within-manifold graph and between-manifold graph are made to represent the information inside every manifold and the information among different manifolds, respectively. SLPMM simultaneously maximizes the between-manifold scatter and minimizes the within-manifold scatter which leads to discriminant space by adopting locality preserving projection(LPP) concept. Experimental results on two widely used face databases FERET and AR face database are presented to prove the efficacy of the proposed approach.展开更多
Let B^pΩ, 1 ≤ p 〈 ∞, be the space of all bounded functions from Lp（R） which can be extended to entire functions of exponential type Ω. The uniform error bounds for truncated Whittaker-Kotelnikov-Shannon series ...Let B^pΩ, 1 ≤ p 〈 ∞, be the space of all bounded functions from Lp（R） which can be extended to entire functions of exponential type Ω. The uniform error bounds for truncated Whittaker-Kotelnikov-Shannon series based on local sampling are derived for functions f ∈ B^pΩ without decay assumption at infinity. Then the optimal bounds of the aliasing error and truncation error of Whittaker-Kotelnikov-Shannon expansion for non-bandlimited functions from Sobolev classes L/（Wp（R）） are determined up to a logarithmic factor.展开更多
Sampling principle and characteristics and edge effect of orthogonal wavelet transform of signals are researched. Two samples of signals and wavelet bases must be taken in wavelet transform. In the second sample sampl...Sampling principle and characteristics and edge effect of orthogonal wavelet transform of signals are researched. Two samples of signals and wavelet bases must be taken in wavelet transform. In the second sample sampling interval or sampling length in different frequency range will be automatically adjusted. Wavelet transform can detect singular points. Both ends of signals are singular points. Edge effect is not avoidable.展开更多
Synchronous sampling is very essential in underwater multilinear array seismic exploration system in which every acquisition node(AN)samples analog signals by its own analog-digital converter(ADC).Aiming at the proble...Synchronous sampling is very essential in underwater multilinear array seismic exploration system in which every acquisition node(AN)samples analog signals by its own analog-digital converter(ADC).Aiming at the problems of complex synchronous sampling method and long locking time after varying sampling rate in traditional underwater seismic exploration system,an improved synchronous sampling model based on the master-slave synchronous model and local clock asynchronous drive with non phase locked loop(PLL)is built,and a high-precision synchronous sampling method is proposed,which combines the short-term stability of local asynchronous driving clock with the master-slave synchronous calibration of local sampling clock.Based on the improved synchronous sampling model,the influence of clock stability,transmission delay and phase jitter on synchronous sampling error is analyzed,and a high-precision calibration method of synchronous sampling error based on step-by-step compensation of transmission delay is proposed.The model and method effectively realize the immunity of phase jitter on synchronous sampling error in principle,and compensate the influence of signal transmission delay on synchronous sampling error.At the same time,it greatly reduces the complexity of software and hardware implementation of synchronous sampling,and solves the problem of long locking time after changing the sampling rate in traditional methods.The experimental system of synchronous sampling for dual linear array is built,and the synchronous sampling accuracy is better than 5 ns.展开更多
In this paper,an image processing algorithm which is able to synthesize material textures of arbitrary shapes is proposed.The presented approach uses an arbitrary image to construct a structure layer of the material.T...In this paper,an image processing algorithm which is able to synthesize material textures of arbitrary shapes is proposed.The presented approach uses an arbitrary image to construct a structure layer of the material.The resulting structure layer is then used to constrain the material texture synthesis.The field of second-moment matrices is used to represent the structure layer.Many tests with various constraint images are conducted to ensure that the proposed approach accurately reproduces the visual aspects of the input material sample.The results demonstrate that the proposed algorithm is able to accurately synthesize arbitrary-shaped material textures while respecting the local characteristics of the exemplar.This paves the way toward the synthesis of 3D material textures of arbitrary shapes from 2D material samples,which has a wide application range in virtual material design and materials characterization.展开更多
Bagging is not quite suitable for stable classifiers such as nearest neighbor classifiers due to the lack of diversity and it is difficult to be directly applied to face recognition as well due to the small sample si...Bagging is not quite suitable for stable classifiers such as nearest neighbor classifiers due to the lack of diversity and it is difficult to be directly applied to face recognition as well due to the small sample size （SSS） property of face recognition. To solve the two problems,local Bagging （L-Bagging） is proposed to simultaneously make Bagging apply to both nearest neighbor classifiers and face recognition. The major difference between L-Bagging and Bagging is that L-Bagging performs the bootstrap sampling on each local region partitioned from the original face image rather than the whole face image. Since the dimensionality of local region is usually far less than the number of samples and the component classifiers are constructed just in different local regions,L-Bagging deals with SSS problem and generates more diverse component classifiers. Experimental results on four standard face image databases （AR,Yale,ORL and Yale B） indicate that the proposed L-Bagging method is effective and robust to illumination,occlusion and slight pose variation.展开更多
为保证虚拟手术系统中的网格质量,提出一种基于Loose r sample理论的快速表面网格重建算法。记录满足Loose r sample采样定理的点集,用以描述物体的轮廓。通过约束Delaunay方法对该点集进行三角化,标记顶点和Delaunay单元,重构新的网格...为保证虚拟手术系统中的网格质量,提出一种基于Loose r sample理论的快速表面网格重建算法。记录满足Loose r sample采样定理的点集,用以描述物体的轮廓。通过约束Delaunay方法对该点集进行三角化,标记顶点和Delaunay单元,重构新的网格。实验结果表明,该算法能够保证生成网格的质量,简化仿真复杂度。展开更多
Due to the complexity of the real engineering environment, the arrival measurement inevitably contains outliers and leads to serious location errors. In order to eliminate the influence of the outliers effectively,thi...Due to the complexity of the real engineering environment, the arrival measurement inevitably contains outliers and leads to serious location errors. In order to eliminate the influence of the outliers effectively,this paper proposes a novel robust AE/MS source localization method using optimized M-estimate consensus sample. First, a sample subset is selected from the entire arrival set to obtain fitting model and its parameters. Second, consensus set is determined by checking the arrivals with the fitting model instantiated by the estimated model parameters. Third, optimization process is performed to further optimize the consensus set. The above steps are iterated, and the final source coordinates are obtained by using all the elements in the optimal consensus set. The novel method is validated by a pencil-lead breaks experiment. The results indicate that the novel method has better location accuracy of less than 5 mm compared to existing methods, regardless of the presence or absence of outliers. With the increase of outlier scale and outlier ratio, the location result of the proposed method is always more stable and accurate than that of the existing methods. Mine blasting experiments further demonstrate that the new method holds good prospects for engineering applications.展开更多
When sampling from a finite population there is often auxiliary information available on unit level. Such information can be used to improve the estimation of the target parameter. We show that probability samples tha...When sampling from a finite population there is often auxiliary information available on unit level. Such information can be used to improve the estimation of the target parameter. We show that probability samples that are well spread in the auxiliary space are balanced, or approximately balanced, on the auxiliary variables. A consequence of this balancing effect is that the Horvitz-Thompson estimator will be a very good estimator for any target variable that can be well approximated by a Lipschitz continuous function of the auxiliary variables. Hence we give a theoretical motivation for use of well spread probability samples. Our conclusions imply that well spread samples, combined with the Horvitz- Thompson estimator, is a good strategy in a varsity of situations.展开更多
A localization method based on distance function of projected features is presented to solve the accuracy reduction or failure problem due to occlusion and blurring caused by smog, when dealing with vision based local...A localization method based on distance function of projected features is presented to solve the accuracy reduction or failure problem due to occlusion and blurring caused by smog, when dealing with vision based localization for target oil and gas wellhead （OGWH）. Firstly, the target OGWH is modeled as a cylinder with marker, and a vector with redundant parameter is used to describe its pose. Secondly, the explicit mapping relationship between the pose vector with redundant parameter and projected features is derived. Then, a 2D-point-to-feature distance function is proposed, as well as its derivative. Finally, based on this distance function and its derivative, an algorithm is proposed to estimate the pose of target OGWH directly according to the 2D image information, and the validity of the method is verified by both synthetic data and real image experiments. The results show that this method is able to accomplish the localization in the case of occlusion and blurring, and its anti-noise ability is good especially with noise ratio of less than 70%.展开更多
Motif-based graph local clustering(MGLC)algorithms are gen-erally designed with the two-phase framework,which gets the motif weight for each edge beforehand and then conducts the local clustering algorithm on the weig...Motif-based graph local clustering(MGLC)algorithms are gen-erally designed with the two-phase framework,which gets the motif weight for each edge beforehand and then conducts the local clustering algorithm on the weighted graph to output the result.Despite correctness,this frame-work brings limitations on both practical and theoretical aspects and is less applicable in real interactive situations.This research develops a purely local and index-adaptive method,Index-adaptive Triangle-based Graph Local Clustering(TGLC+),to solve the MGLC problem w.r.t.triangle.TGLC+combines the approximated Monte-Carlo method Triangle-based Random Walk(TRW)and deterministic Brute-Force method Triangle-based Forward Push(TFP)adaptively to estimate the Personalized PageRank(PPR)vector without calculating the exact triangle-weighted transition probability and then outputs the clustering result by conducting the standard sweep procedure.This paper presents the efficiency of TGLC+through theoretical analysis and demonstrates its effectiveness through extensive experiments.To our knowl-edge,TGLC+is the first to solve the MGLC problem without computing the motif weight beforehand,thus achieving better efficiency with comparable effectiveness.TGLC+is suitable for large-scale and interactive graph analysis tasks,including visualization,system optimization,and decision-making.展开更多
Target detection of small samples with a complex background is always difficult in the classification of remote sensing images.We propose a new small sample target detection method combining local features and a convo...Target detection of small samples with a complex background is always difficult in the classification of remote sensing images.We propose a new small sample target detection method combining local features and a convolutional neural network(LF-CNN)with the aim of detecting small numbers of unevenly distributed ground object targets in remote sensing images.The k-nearest neighbor method is used to construct the local neighborhood of each point and the local neighborhoods of the features are extracted one by one from the convolution layer.All the local features are aggregated by maximum pooling to obtain global feature representation.The classification probability of each category is then calculated and classified using the scaled expected linear units function and the full connection layer.The experimental results show that the proposed LF-CNN method has a high accuracy of target detection and classification for hyperspectral imager remote sensing data under the condition of small samples.Despite drawbacks in both time and complexity,the proposed LF-CNN method can more effectively integrate the local features of ground object samples and improve the accuracy of target identification and detection in small samples of remote sensing images than traditional target detection methods.展开更多
The crowdsourcing-based WLAN indoor localization system has been widely promoted for the effective reduction of the workload from the offline phase data collection while constructing radio maps.Aiming at the problem o...The crowdsourcing-based WLAN indoor localization system has been widely promoted for the effective reduction of the workload from the offline phase data collection while constructing radio maps.Aiming at the problem of the inaccurate location annotation of the crowdsourced samples,the existing invalid access points(APs)in collected samples,and the uneven sample distribution,as well as the diverse terminal devices,which will result in the construction of the wrong radio map,an effective WLAN indoor radio map construction scheme(WRMCS)is proposed based on crowdsourced samples.The WRMCS consists of 4 main modules:outlier detection,key AP selection,fingerprint interpolation,and terminal device calibration.Moreover,an online localization algorithm is put forward to estimate the position of the online test fingerprint.The simulation results show that the proposed scheme can achieve higher localization accuracy than the peer schemes,and possesses good effectiveness and robustness at the same time.展开更多
基金National Key Research and Development Program of China,Grant/Award Number:2023YFC3305003National Natural Science Foundation of China,Grant/Award Number:62376076。
文摘Bilingual lexicon induction focuses on learning word translation pairs,also known as bitexts,from monolingual corpora by establishing a mapping between the source and target embedding spaces.Despite recent advancements,bilingual lexicon induction is limited to inducing bitexts consisting of individual words,lacking the ability to handle semantics-rich phrases.To bridge this gap and support downstream cross-lingual tasks,it is practical to develop a method for bilingual phrase induction that extracts bilingual phrase pairs from monolingual corpora without relying on cross-lingual knowledge.In this paper,the authors propose a novel phrase embedding training method based on the skip-gram structure.Specifically,a local hard negative sampling strategy that utilises negative samples of central tokens in sliding windows to enhance phrase embedding learning is introduced.The proposed method achieves competitive or superior performance compared to baseline approaches,with exceptional results recorded for distant languages.Additionally,we develop a phrase representation learning method that leverages multilingual pre-trained language models.These mPLMs-based representations can be combined with the above-mentioned static phrase embeddings to further improve the accuracy of the bilingual phrase induction task.We manually construct a dataset of bilingual phrase pairs and integrate it with MUSE to facilitate the bilingual phrase induction task.
基金supported by National science foundation(No. 60772035): Key technique study on heterogeneous network convergenceDoctoral grant(No.20070004010)s: Study on cross layer design for heterogeneous network convergence+1 种基金National 863 Hi-Tech Projects(No.2007AA01Z277): Pa-rameter design based electromagnetic compatibility study in cognitive radio communication systemNational science foundation(No. 60830001): Wireless communication fundamentals and key techniuqes for high speed rail way control and safety data transmission
文摘Ultra-wide-band (UWB) signals are suitable for localization, since their high time resolution can provide precise time of arrival (TOA) estimation. However, one major challenge in UWB signal processing is the requirement of high sampling rate which leads to complicated signal processing and expensive hardware. In this paper, we present a novel UWB signal sampling method called UWB signal sampling via temporal sparsity (USSTS). Its sampling rate is much lower than Nyquist rate. Moreover, it is implemented in one step and no extra processing unit is needed. Simulation results show that USSTS can not recover the signal precisely, but for the use in localization, the accuracy of TOA estimation is the same as that in traditional methods. Therefore, USSTS gives a novel and effective solution for the use of UWB signals in localization.
基金This work was supported by the Shanghai Pujiang Program(16PJ1406200)the Scientific Research Innovation Projects of Shanghai Municipal Education Commission(15ZZ060)
文摘Purpose: The present study aimed to assess the accuracies of arterial stimulation with simultaneous venous sampling(ASVS) in preoperative localization of insulinomas. Materials and Methods: A cohort consisting of 6 males and 14 females(median age, 48.5y; range, 28–62y) with pathologically proven insulinomas were included in this study. Selective angiographies were performed with the superior mesenteric artery(SMA), gastroduodenal artery(GDA), proximal splenic artery, and midsplenic artery in all individuals. Then ASVS procedures were followed after angiographies for these arteries. Clinical characteristics of the patient and the tumor number, location, and size were recorded. The accuracy of preoperative localization of insulinomas was tested. Results: A total of 22 tumors were identified by histopathological diagnosis. The mean size of the tumor was 1.40±0.60 cm. Five tumors were in the head/neck region and 17 in the body/tail region. ASVS accurately localized 17/20(85%) cases with only biochemical data and 19/20(95%) cases with biochemical data and angiography images. Variant pancreatic arterial anatomy was revealed in 2 false cases with inferior pancreatic artery replaced by the superior mesenteric artery. Conclusion: ASVS was highly accurate in localizing insulinomas and should be performed in most of the patients with suspected insulinomas before the operation.
基金the Ministry of Agriculture and Forestry key project“Puuta liikkeelle ja uusia tuotteita metsästä”(“Wood on the move and new products from forest”)Academy of Finland(project numbers 295100 , 306875).
文摘Background:The local pivotal method(LPM)utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories(NFIs).Its performance compared to simple random sampling(SRS)and LPM with geographical coordinates has produced promising results in simulation studies.In this simulation study we compared all these sampling methods to systematic sampling.The LPM samples were selected solely using the coordinates(LPMxy)or,in addition to that,auxiliary remote sensing-based forest variables(RS variables).We utilized field measurement data(NFI-field)and Multi-Source NFI(MS-NFI)maps as target data,and independent MS-NFI maps as auxiliary data.The designs were compared using relative efficiency(RE);a ratio of mean squared errors of the reference sampling design against the studied design.Applying a method in NFI also requires a proven estimator for the variance.Therefore,three different variance estimators were evaluated against the empirical variance of replications:1)an estimator corresponding to SRS;2)a Grafström-Schelin estimator repurposed for LPM;and 3)a Matérn estimator applied in the Finnish NFI for systematic sampling design.Results:The LPMxy was nearly comparable with the systematic design for the most target variables.The REs of the LPM designs utilizing auxiliary data compared to the systematic design varied between 0.74–1.18,according to the studied target variable.The SRS estimator for variance was expectedly the most biased and conservative estimator.Similarly,the Grafström-Schelin estimator gave overestimates in the case of LPMxy.When the RS variables were utilized as auxiliary data,the Grafström-Schelin estimates tended to underestimate the empirical variance.In systematic sampling the Matérn and Grafström-Schelin estimators performed for practical purposes equally.Conclusions:LPM optimized for a specific variable tended to be more efficient than systematic sampling,but all of the considered LPM designs were less efficient than the systematic sampling design for some target variables.The Grafström-Schelin estimator could be used as such with LPMxy or instead of the Matérn estimator in systematic sampling.Further studies of the variance estimators are needed if other auxiliary variables are to be used in LPM.
基金National Natural Science Foundation of China (No60572113,No10501026) and Liuhui Center for Applied Mathematics
文摘Signals are often of random character since they cannot bear any information if they are predictable for any time t, they are usually modelled as stationary random processes .On the other hand, because of the inertia of the measurement apparatus, measured sampled values obtained in practice may not be the precise value of the signal X(t) at time tk (k∈Z), but only local averages of X(t) near tk. In this paper, it is presented that a wide (or weak ) sense stationary stochastic process can be approximated by generalized sampling series with local average samples.
文摘In this paper, auxiliary information is used to determine an estimator of finite population total using nonparametric regression under stratified random sampling. To achieve this, a model-based approach is adopted by making use of the local polynomial regression estimation to predict the nonsampled values of the survey variable y. The performance of the proposed estimator is investigated against some design-based and model-based regression estimators. The simulation experiments show that the resulting estimator exhibits good properties. Generally, good confidence intervals are seen for the nonparametric regression estimators, and use of the proposed estimator leads to relatively smaller values of RE compared to other estimators.
文摘Although real-world experiences show that preparing one image per person is more convenient, most of the appearance-based face recognition methods degrade or fail to work if there is only a single sample per person(SSPP). In this work, we introduce a novel supervised learning method called supervised locality preserving multimanifold(SLPMM) for face recognition with SSPP. In SLPMM, two graphs: within-manifold graph and between-manifold graph are made to represent the information inside every manifold and the information among different manifolds, respectively. SLPMM simultaneously maximizes the between-manifold scatter and minimizes the within-manifold scatter which leads to discriminant space by adopting locality preserving projection(LPP) concept. Experimental results on two widely used face databases FERET and AR face database are presented to prove the efficacy of the proposed approach.
基金Supported by the National Natural Science Foundation of China (10971251, 11101220 and 11271199)the Program for new century excellent talents in University of China (NCET-10-0513)
文摘Let B^pΩ, 1 ≤ p 〈 ∞, be the space of all bounded functions from Lp（R） which can be extended to entire functions of exponential type Ω. The uniform error bounds for truncated Whittaker-Kotelnikov-Shannon series based on local sampling are derived for functions f ∈ B^pΩ without decay assumption at infinity. Then the optimal bounds of the aliasing error and truncation error of Whittaker-Kotelnikov-Shannon expansion for non-bandlimited functions from Sobolev classes L/（Wp（R）） are determined up to a logarithmic factor.
文摘Sampling principle and characteristics and edge effect of orthogonal wavelet transform of signals are researched. Two samples of signals and wavelet bases must be taken in wavelet transform. In the second sample sampling interval or sampling length in different frequency range will be automatically adjusted. Wavelet transform can detect singular points. Both ends of signals are singular points. Edge effect is not avoidable.
基金National Key Research and Development Program of China(No.2018YFE0208200)National Natural Science Foundation of China(Nos.61971307,61905175,51775377)+5 种基金National Key Research and Development Plan Project(No.2020YFB2010800)The Fok Ying Tung Education Foundation(No.171055)China Postdoctoral Science Foundation(No.2020M680878)Guangdong Province Key Research and Development Plan Project(No.2020B0404030001)Tianjin Science and Technology Plan Project(No.20YDTPJC01660)Project of Foreign Affairs Committee of China Aviation Development Sichuan Gas Turbine Research Institute(Nos.GJCZ-2020-0040,GJCZ-2020-0041)。
文摘Synchronous sampling is very essential in underwater multilinear array seismic exploration system in which every acquisition node(AN)samples analog signals by its own analog-digital converter(ADC).Aiming at the problems of complex synchronous sampling method and long locking time after varying sampling rate in traditional underwater seismic exploration system,an improved synchronous sampling model based on the master-slave synchronous model and local clock asynchronous drive with non phase locked loop(PLL)is built,and a high-precision synchronous sampling method is proposed,which combines the short-term stability of local asynchronous driving clock with the master-slave synchronous calibration of local sampling clock.Based on the improved synchronous sampling model,the influence of clock stability,transmission delay and phase jitter on synchronous sampling error is analyzed,and a high-precision calibration method of synchronous sampling error based on step-by-step compensation of transmission delay is proposed.The model and method effectively realize the immunity of phase jitter on synchronous sampling error in principle,and compensate the influence of signal transmission delay on synchronous sampling error.At the same time,it greatly reduces the complexity of software and hardware implementation of synchronous sampling,and solves the problem of long locking time after changing the sampling rate in traditional methods.The experimental system of synchronous sampling for dual linear array is built,and the synchronous sampling accuracy is better than 5 ns.
文摘In this paper,an image processing algorithm which is able to synthesize material textures of arbitrary shapes is proposed.The presented approach uses an arbitrary image to construct a structure layer of the material.The resulting structure layer is then used to constrain the material texture synthesis.The field of second-moment matrices is used to represent the structure layer.Many tests with various constraint images are conducted to ensure that the proposed approach accurately reproduces the visual aspects of the input material sample.The results demonstrate that the proposed algorithm is able to accurately synthesize arbitrary-shaped material textures while respecting the local characteristics of the exemplar.This paves the way toward the synthesis of 3D material textures of arbitrary shapes from 2D material samples,which has a wide application range in virtual material design and materials characterization.
文摘Bagging is not quite suitable for stable classifiers such as nearest neighbor classifiers due to the lack of diversity and it is difficult to be directly applied to face recognition as well due to the small sample size （SSS） property of face recognition. To solve the two problems,local Bagging （L-Bagging） is proposed to simultaneously make Bagging apply to both nearest neighbor classifiers and face recognition. The major difference between L-Bagging and Bagging is that L-Bagging performs the bootstrap sampling on each local region partitioned from the original face image rather than the whole face image. Since the dimensionality of local region is usually far less than the number of samples and the component classifiers are constructed just in different local regions,L-Bagging deals with SSS problem and generates more diverse component classifiers. Experimental results on four standard face image databases （AR,Yale,ORL and Yale B） indicate that the proposed L-Bagging method is effective and robust to illumination,occlusion and slight pose variation.
文摘为保证虚拟手术系统中的网格质量,提出一种基于Loose r sample理论的快速表面网格重建算法。记录满足Loose r sample采样定理的点集,用以描述物体的轮廓。通过约束Delaunay方法对该点集进行三角化,标记顶点和Delaunay单元,重构新的网格。实验结果表明,该算法能够保证生成网格的质量,简化仿真复杂度。
基金the financial support provided by the National Natural Science Foundation of China (No. 41772313)Hunan Science and Technology Planning Project (No. 2019RS3001)+3 种基金the Science and Technology Innovation Program of Hunan Province (No. 2021RC1001)the National Natural Science Foundation for Young Scientists of China (No. 52104111)the Natural Science Foundation of Hunan (No. 2021JJ30819)Key Science and Technology Project of Guangxi Transportation Industry (Research on fine blasting and disaster control technology of mountain expressway tunnel)。
文摘Due to the complexity of the real engineering environment, the arrival measurement inevitably contains outliers and leads to serious location errors. In order to eliminate the influence of the outliers effectively,this paper proposes a novel robust AE/MS source localization method using optimized M-estimate consensus sample. First, a sample subset is selected from the entire arrival set to obtain fitting model and its parameters. Second, consensus set is determined by checking the arrivals with the fitting model instantiated by the estimated model parameters. Third, optimization process is performed to further optimize the consensus set. The above steps are iterated, and the final source coordinates are obtained by using all the elements in the optimal consensus set. The novel method is validated by a pencil-lead breaks experiment. The results indicate that the novel method has better location accuracy of less than 5 mm compared to existing methods, regardless of the presence or absence of outliers. With the increase of outlier scale and outlier ratio, the location result of the proposed method is always more stable and accurate than that of the existing methods. Mine blasting experiments further demonstrate that the new method holds good prospects for engineering applications.
文摘When sampling from a finite population there is often auxiliary information available on unit level. Such information can be used to improve the estimation of the target parameter. We show that probability samples that are well spread in the auxiliary space are balanced, or approximately balanced, on the auxiliary variables. A consequence of this balancing effect is that the Horvitz-Thompson estimator will be a very good estimator for any target variable that can be well approximated by a Lipschitz continuous function of the auxiliary variables. Hence we give a theoretical motivation for use of well spread probability samples. Our conclusions imply that well spread samples, combined with the Horvitz- Thompson estimator, is a good strategy in a varsity of situations.
基金supported by National Natural Science Foundation of China(No.61403226)the State Key Laboratory of Tribology of China(No.SKLT09A03)
文摘A localization method based on distance function of projected features is presented to solve the accuracy reduction or failure problem due to occlusion and blurring caused by smog, when dealing with vision based localization for target oil and gas wellhead （OGWH）. Firstly, the target OGWH is modeled as a cylinder with marker, and a vector with redundant parameter is used to describe its pose. Secondly, the explicit mapping relationship between the pose vector with redundant parameter and projected features is derived. Then, a 2D-point-to-feature distance function is proposed, as well as its derivative. Finally, based on this distance function and its derivative, an algorithm is proposed to estimate the pose of target OGWH directly according to the 2D image information, and the validity of the method is verified by both synthetic data and real image experiments. The results show that this method is able to accomplish the localization in the case of occlusion and blurring, and its anti-noise ability is good especially with noise ratio of less than 70%.
基金supported by the Fundamental Research Funds for the Central Universities(No.2020JS005).
文摘Motif-based graph local clustering(MGLC)algorithms are gen-erally designed with the two-phase framework,which gets the motif weight for each edge beforehand and then conducts the local clustering algorithm on the weighted graph to output the result.Despite correctness,this frame-work brings limitations on both practical and theoretical aspects and is less applicable in real interactive situations.This research develops a purely local and index-adaptive method,Index-adaptive Triangle-based Graph Local Clustering(TGLC+),to solve the MGLC problem w.r.t.triangle.TGLC+combines the approximated Monte-Carlo method Triangle-based Random Walk(TRW)and deterministic Brute-Force method Triangle-based Forward Push(TFP)adaptively to estimate the Personalized PageRank(PPR)vector without calculating the exact triangle-weighted transition probability and then outputs the clustering result by conducting the standard sweep procedure.This paper presents the efficiency of TGLC+through theoretical analysis and demonstrates its effectiveness through extensive experiments.To our knowl-edge,TGLC+is the first to solve the MGLC problem without computing the motif weight beforehand,thus achieving better efficiency with comparable effectiveness.TGLC+is suitable for large-scale and interactive graph analysis tasks,including visualization,system optimization,and decision-making.
基金This work was partially supported by the Key Laboratory for Digital Land and Resources of Jiangxi Province,East China University of Technology(DLLJ202103)Science and Technology Commission Shanghai Municipality(No.19142201600)Graduate Innovation and Entrepreneurship Program in Shanghai University in China(No.2019GY04).
文摘Target detection of small samples with a complex background is always difficult in the classification of remote sensing images.We propose a new small sample target detection method combining local features and a convolutional neural network(LF-CNN)with the aim of detecting small numbers of unevenly distributed ground object targets in remote sensing images.The k-nearest neighbor method is used to construct the local neighborhood of each point and the local neighborhoods of the features are extracted one by one from the convolution layer.All the local features are aggregated by maximum pooling to obtain global feature representation.The classification probability of each category is then calculated and classified using the scaled expected linear units function and the full connection layer.The experimental results show that the proposed LF-CNN method has a high accuracy of target detection and classification for hyperspectral imager remote sensing data under the condition of small samples.Despite drawbacks in both time and complexity,the proposed LF-CNN method can more effectively integrate the local features of ground object samples and improve the accuracy of target identification and detection in small samples of remote sensing images than traditional target detection methods.
基金the National High Technology Research and Development Program of China(No.2012AA120802)National Natural Science Foundation of China(No.61771186)+1 种基金Postdoctoral Research Project of Heilongjiang Province(No.LBH-Q15121)Undergraduate University Project of Young Scientist Creative Talent of Heilongjiang Province(No.UNPYSCT-2017125).
文摘The crowdsourcing-based WLAN indoor localization system has been widely promoted for the effective reduction of the workload from the offline phase data collection while constructing radio maps.Aiming at the problem of the inaccurate location annotation of the crowdsourced samples,the existing invalid access points(APs)in collected samples,and the uneven sample distribution,as well as the diverse terminal devices,which will result in the construction of the wrong radio map,an effective WLAN indoor radio map construction scheme(WRMCS)is proposed based on crowdsourced samples.The WRMCS consists of 4 main modules:outlier detection,key AP selection,fingerprint interpolation,and terminal device calibration.Moreover,an online localization algorithm is put forward to estimate the position of the online test fingerprint.The simulation results show that the proposed scheme can achieve higher localization accuracy than the peer schemes,and possesses good effectiveness and robustness at the same time.
