Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with ...Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with the nearest neighbor classifier (NNC) is proposed. The principal component analysis (PCA) is used to reduce the dimension and extract features. Then one-against-all stratedy is used to train the SVM classifiers. At the testing stage, we propose an al-展开更多
In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (...In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the main statistical tools were reviewed. The aim was to examine and compare these nonparametric classification methods on the following attributes: robustness to training data, sensitivity to changes, data fitting, stability, ability to handle large data sizes, sensitivity to noise, time invested in parameter tuning, and accuracy. The performances, strengths and shortcomings of each of the algorithms were examined, and finally, a conclusion was arrived at on which one has higher performance. It was evident from the literature reviewed that RF is too sensitive to small changes in the training dataset and is occasionally unstable and tends to overfit in the model. KNN is easy to implement and understand but has a major drawback of becoming significantly slow as the size of the data in use grows, while the ideal value of K for the KNN classifier is difficult to set. SVM and RF are insensitive to noise or overtraining, which shows their ability in dealing with unbalanced data. Larger input datasets will lengthen classification times for NN and KNN more than for SVM and RF. Among these nonparametric classification methods, NN has the potential to become a more widely used classification algorithm, but because of their time-consuming parameter tuning procedure, high level of complexity in computational processing, the numerous types of NN architectures to choose from and the high number of algorithms used for training, most researchers recommend SVM and RF as easier and wieldy used methods which repeatedly achieve results with high accuracies and are often faster to implement.展开更多
Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats...Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats present in the MIT-BIH Arrhythmia database. We have tested our classifier on ~ 103100 beats for six beat types present in the database. Fuzzy KNN (FKNN) can be implemented very easily but large number of training examples used for classification can be very time consuming and requires large storage space. Hence, we have proposed a time efficient Arif-Fayyaz pruning algorithm especially suitable for FKNN which can maintain good classification accuracy with appropriate retained ratio of training data. By using Arif-Fayyaz pruning algorithm with Fuzzy KNN, we have achieved a beat classification accuracy of 97% and geometric mean of sensitivity of 94.5% with only 19% of the total training examples. The accuracy and sensitivity is comparable to FKNN when all the training data is used. Principal Component Analysis is used to further reduce the dimension of feature space from eleven to six without compromising the accuracy and sensitivity. PFKNN was found to robust against noise present in the ECG data.展开更多
Forest structure analysis is important for understanding the properties and development of a forest community,and its outcomes can be influenced by how trees are measured in sampled plots.Although there is a general c...Forest structure analysis is important for understanding the properties and development of a forest community,and its outcomes can be influenced by how trees are measured in sampled plots.Although there is a general consensus on the height at which tree diameter should be measured[1.3 m:diameter at breast height(DBH)],the minimum measureddiameter(MMD)often varies in different studies.In this study,we assumed that the outcomes of forest structure analysis can be influenced by MMD and,to this end,we applied g(r)function and stand spatial structural parameters(SSSPs)to investigate how different MMDs affect forest spatial structure analysis in two pine-oak mixed forests(30 and 57 years old)in southwest China and one old-growth oak forest(>120years old)from northwest China.Our results showed that 1)MMD was closely related to the distribution patterns of forest trees.Tree distribution patterns at each observational scale(r=0-20 m)tended tobecome random as the MMD increased.The older the community,the earlier this random distribution pattern appeared.2)As the MMD increased,neighboring trees became more regularly distributed around a reference tree.In most cases,however,nearest neighbors of a reference tree were randomly distributed.3)Tree species mingling decreased with increasing diameter,but it decreased slowly in older forests.4)No correlations can be found between individual tree size differentiation and MMD.We recommend that comparisons of spatial structures between communities would be more effective if using a unified MMD criterion.展开更多
Missing values are prevalent in real-world datasets and they may reduce predictive performance of a learning algorithm. Dissolved Gas Analysis (DGA), one of the most deployable methods for detecting and predicting inc...Missing values are prevalent in real-world datasets and they may reduce predictive performance of a learning algorithm. Dissolved Gas Analysis (DGA), one of the most deployable methods for detecting and predicting incipient faults in power transformers is one of the casualties. Thus, this paper proposes filling-in the missing values found in a DGA dataset using the k-nearest neighbor imputation method with two different distance metrics: Euclidean and Cityblock. Thereafter, using these imputed datasets as inputs, this study applies Support Vector Machine (SVM) to built models which are used to classify transformer faults. Experimental results are provided to show the effectiveness of the proposed approach.展开更多
The core of smoothed particle hydrodynamics （SPH） is the nearest neighbor search subroutine. In this paper, a nearest neighbor search algorithm which is based on multiple background grids and support variable smooth...The core of smoothed particle hydrodynamics （SPH） is the nearest neighbor search subroutine. In this paper, a nearest neighbor search algorithm which is based on multiple background grids and support variable smooth length is introduced. Through tested on lid driven cavity flow, it is clear that this method can provide high accuracy. Analysis and experiments have been made on its parallelism, and the results show that this method has better parallelism and with adding processors its accuracy become higher, thus it achieves that efficiency grows in pace with accuracy.展开更多
By using the method of density-matrix renormalization-group to solve the different spin spin correlation functions, the nearest-neighbouring entanglement （NNE） and the next-nearest-neighbouring entanglement （NNNE）...By using the method of density-matrix renormalization-group to solve the different spin spin correlation functions, the nearest-neighbouring entanglement （NNE） and the next-nearest-neighbouring entanglement （NNNE） of one-dimensional alternating Heisenberg XY spin chain are investigated in the presence of alternating the-nearestneighbouring interaction of exchange couplings, external magnetic fields and the next-nearest neighbouring interaction. For a dimerised ferromagnetic spin chain, the NNNE appears only above a critical dimerized interaction, meanwhile, the dimerized interaction a effects a quantum phase transition point and improves the NNNE to a large extent. We also study the effect of ferromagnetic or antiferromagnetic next-nearest neighbouring （NNN） interaction on the dynamics of NNE and NNNE. The ferromagnetic NNN interaction increases and shrinks the NNE below and above a critical frustrated interaction respectively, while the antiferromagnetic NNN interaction always reduces the NNE. The antiferromagnetic NNN interaction results in a large value of NNNE compared with the case where the NNN interaction is ferromagnetic.展开更多
As climate change negotiations progress,monitoring biomass and carbon stocks is becoming an important part of the current forest research.Therefore,national governments are interested in developing forest-monitoring s...As climate change negotiations progress,monitoring biomass and carbon stocks is becoming an important part of the current forest research.Therefore,national governments are interested in developing forest-monitoring strategies using geospatial technology.Among statistical methods for mapping biomass,there is a nonparametric approach called k-nearest neighbor(kNN).We compared four variations of distance metrics of the kNN for the spatially-explicit estimation of aboveground biomass in a portion of the Mexican north border of the intertropical zone.Satellite derived,climatic,and topographic predictor variables were combined with the Mexican National Forest Inventory(NFI)data to accomplish the purpose.Performance of distance metrics applied into the kNN algorithm was evaluated using a cross validation leave-one-out technique.The results indicate that the Most Similar Neighbor(MSN)approach maximizes the correlation between predictor and response variables(r=0.9).Our results are in agreement with those reported in the literature.These findings confirm the predictive potential of the MSN approach for mapping forest variables at pixel level under the policy of Reducing Emission from Deforestation and Forest Degradation(REDD+).展开更多
It is well known that in pyroxene structure, there are two metal sites, M1 and M2.Generally speaking, ferrous iron in each of these sites would normally be expected to give rise to a doublet. However, anomies have bee...It is well known that in pyroxene structure, there are two metal sites, M1 and M2.Generally speaking, ferrous iron in each of these sites would normally be expected to give rise to a doublet. However, anomies have been found in the relative areas of the peaks in the room temperature spectra of some clinopyroxene (CPX) when the above assigninent is folowed. Ac-cording to the calculation of Next Nearest Neighbor configurations of divalent cations in M1,we found that the four configurations of M1 can be divided into two groups. One group is 3Ca configuration that increases with the content of Ca (p. f. u); the other group is made up of three No-3Ca configurations that decrease with the content of Ca. The two groups contribute to the spectrum structure of M1, so in this study we fit two doublets for ferrous iron in M1.Though there were severa reports on Fe3+ in tetrahedral site previously, it wa not sure that Fe3+ occupies the T site is a universal fact in CPX, despite of the content of A1. We found that the Fe3+ in the T site fitted by Medauer spectroscopy is negatively correlated to the Si content in the T site and positively correlated to the Fe3 + in the T site estimated on the suppo-sition that Fe3+ and Al occupy the T site randomly. If it is true, it is important in the model-ing of ion exchange geobarometries and gepthermornetries.展开更多
Short-term traffic flow is one of the core technologies to realize traffic flow guidance. In this article, in view of the characteristics that the traffic flow changes repeatedly, a short-term traffic flow forecasting...Short-term traffic flow is one of the core technologies to realize traffic flow guidance. In this article, in view of the characteristics that the traffic flow changes repeatedly, a short-term traffic flow forecasting method based on a three-layer K-nearest neighbor non-parametric regression algorithm is proposed. Specifically, two screening layers based on shape similarity were introduced in K-nearest neighbor non-parametric regression method, and the forecasting results were output using the weighted averaging on the reciprocal values of the shape similarity distances and the most-similar-point distance adjustment method. According to the experimental results, the proposed algorithm has improved the predictive ability of the traditional K-nearest neighbor non-parametric regression method, and greatly enhanced the accuracy and real-time performance of short-term traffic flow forecasting.展开更多
Individual tree detection (ITD) and the area-based approach (ABA) are combined to generate tree-lists using airborne LiDAR data. ITD based on the Canopy Height Model (CHM) was applied for overstory trees, while ABA ba...Individual tree detection (ITD) and the area-based approach (ABA) are combined to generate tree-lists using airborne LiDAR data. ITD based on the Canopy Height Model (CHM) was applied for overstory trees, while ABA based on nearest neighbor (NN) imputation was applied for understory trees. Our approach is intended to compensate for the weakness of LiDAR data and ITD in estimating understory trees, keeping the strength of ITD in estimating overstory trees in tree-level. We investigated the effects of three parameters on the performance of our proposed approach: smoothing of CHM, resolution of CHM, and height cutoff (a specific height that classifies trees into overstory and understory). There was no single combination of those parameters that produced the best performance for estimating stems per ha, mean tree height, basal area, diameter distribution and height distribution. The trees in the lowest LiDAR height class yielded the largest relative bias and relative root mean squared error. Although ITD and ABA showed limited explanatory powers to estimate stems per hectare and basal area, there could be improvements from methods such as using LiDAR data with higher density, applying better algorithms for ITD and decreasing distortion of the structure of LiDAR data. Automating the procedure of finding optimal combinations of those parameters is essential to expedite forest management decisions across forest landscapes using remote sensing data.展开更多
Aggregate nearest neighbor(ANN) search retrieves for two spatial datasets T and Q, segment(s) of one or more trajectories from the set T having minimum aggregate distance to points in Q. When interacting with large am...Aggregate nearest neighbor(ANN) search retrieves for two spatial datasets T and Q, segment(s) of one or more trajectories from the set T having minimum aggregate distance to points in Q. When interacting with large amounts of trajectories, this process would be very time-consuming due to consecutive page loads. An approximate method for finding segments with minimum aggregate distance is proposed which can improve the response time. In order to index large volumes of trajectories, scalable and efficient trajectory index(SETI) structure is used. But some refinements are provided to temporal index of SETI to improve the performance of proposed method. The experiments were performed with different number of query points and percentages of dataset. It is shown that proposed method besides having an acceptable precision, can reduce the computation time significantly. It is also shown that the main fraction of search time among load time, ANN and computing convex and centroid, is related to ANN.展开更多
Collaborative representation-based classification(CRC) is a distance based method, and it obtains the original contributions from all samples to solve the sparse representation coefficient. We find out that it helps t...Collaborative representation-based classification(CRC) is a distance based method, and it obtains the original contributions from all samples to solve the sparse representation coefficient. We find out that it helps to enhance the discrimination in classification by integrating other distance based features and/or adding signal preprocessing to the original samples. In this paper, we propose an improved version of the CRC method which uses the Gabor wavelet transformation to preprocess the samples and also adapts the nearest neighbor(NN)features, and hence we call it GNN-CRC. Firstly, Gabor wavelet transformation is applied to minimize the effects from the background in face images and build Gabor features into the input data. Secondly, the distances solved by NN and CRC are fused together to obtain a more discriminative classification. Extensive experiments are conducted to evaluate the proposed method for face recognition with different instantiations. The experimental results illustrate that our method outperforms the naive CRC as well as some other state-of-the-art algorithms.展开更多
We report a theoretical work on the properties of modulational instability and bright type nonlinear localized modes in one-dimensional easy-axis weak ferromagnetic spin lattices involving next-nearest-neighbor coupli...We report a theoretical work on the properties of modulational instability and bright type nonlinear localized modes in one-dimensional easy-axis weak ferromagnetic spin lattices involving next-nearest-neighbor couplings.With a linear stability analysis, we calculate the growth rates of the modulational instability, and plot the instability regions.When the strength of the next-nearest-neighbor coupling is large enough, two new asymmetric modulational instability regions appear near the boundary of the first Brillouin zone.Furthermore, analytical forms of the bright nonlinear localized modes are constructed by means of a quasi-discreteness approach.The influence of the next-nearest-neighbor coupling on the Brillouin zone center mode and boundary mode are discussed.In particular, we discover a reversal phenomenon of the propagation direction of the Brillouin zone boundary mode.展开更多
Nearest polynomial with given properties has many applications in control theory and applied mathematics. Given a complex univariate polynomial f（z） and a zero α, in this paper we explore the problem of computing a...Nearest polynomial with given properties has many applications in control theory and applied mathematics. Given a complex univariate polynomial f（z） and a zero α, in this paper we explore the problem of computing a complex polynomial f^^（z） such that f^^（a）=0 and the distance ｜｜f^^-f｜｜ is minimal. Considering most of the existing works focus on either certain polynonfial basis or certain vector norm, we propose a common computation framework based on both general polynomial basis and general vector norm, and summarize the computing process into a four-step algorithm. Further, to find the explicit expression of f（z）, we focus on two specific norms which generalize the familiar lp-norm and mixed norm studied in the existing works, and then compute f^^（z） explicitly based on the proposed algorithm. We finally give a numerical example to show the effectiveness of our method.展开更多
基金This project was supported by Shanghai Shu Guang Project.
文摘Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with the nearest neighbor classifier (NNC) is proposed. The principal component analysis (PCA) is used to reduce the dimension and extract features. Then one-against-all stratedy is used to train the SVM classifiers. At the testing stage, we propose an al-
文摘In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the main statistical tools were reviewed. The aim was to examine and compare these nonparametric classification methods on the following attributes: robustness to training data, sensitivity to changes, data fitting, stability, ability to handle large data sizes, sensitivity to noise, time invested in parameter tuning, and accuracy. The performances, strengths and shortcomings of each of the algorithms were examined, and finally, a conclusion was arrived at on which one has higher performance. It was evident from the literature reviewed that RF is too sensitive to small changes in the training dataset and is occasionally unstable and tends to overfit in the model. KNN is easy to implement and understand but has a major drawback of becoming significantly slow as the size of the data in use grows, while the ideal value of K for the KNN classifier is difficult to set. SVM and RF are insensitive to noise or overtraining, which shows their ability in dealing with unbalanced data. Larger input datasets will lengthen classification times for NN and KNN more than for SVM and RF. Among these nonparametric classification methods, NN has the potential to become a more widely used classification algorithm, but because of their time-consuming parameter tuning procedure, high level of complexity in computational processing, the numerous types of NN architectures to choose from and the high number of algorithms used for training, most researchers recommend SVM and RF as easier and wieldy used methods which repeatedly achieve results with high accuracies and are often faster to implement.
文摘Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats present in the MIT-BIH Arrhythmia database. We have tested our classifier on ~ 103100 beats for six beat types present in the database. Fuzzy KNN (FKNN) can be implemented very easily but large number of training examples used for classification can be very time consuming and requires large storage space. Hence, we have proposed a time efficient Arif-Fayyaz pruning algorithm especially suitable for FKNN which can maintain good classification accuracy with appropriate retained ratio of training data. By using Arif-Fayyaz pruning algorithm with Fuzzy KNN, we have achieved a beat classification accuracy of 97% and geometric mean of sensitivity of 94.5% with only 19% of the total training examples. The accuracy and sensitivity is comparable to FKNN when all the training data is used. Principal Component Analysis is used to further reduce the dimension of feature space from eleven to six without compromising the accuracy and sensitivity. PFKNN was found to robust against noise present in the ECG data.
基金financially supported by the National Science Foundation of China (grant no. 31400542 31460196)+1 种基金Guangxi Natural Science Foundation (grant 2016GXNSFBA380233)Guangxi special fund project for innovation-driven development (AA 17204087-8)
文摘Forest structure analysis is important for understanding the properties and development of a forest community,and its outcomes can be influenced by how trees are measured in sampled plots.Although there is a general consensus on the height at which tree diameter should be measured[1.3 m:diameter at breast height(DBH)],the minimum measureddiameter(MMD)often varies in different studies.In this study,we assumed that the outcomes of forest structure analysis can be influenced by MMD and,to this end,we applied g(r)function and stand spatial structural parameters(SSSPs)to investigate how different MMDs affect forest spatial structure analysis in two pine-oak mixed forests(30 and 57 years old)in southwest China and one old-growth oak forest(>120years old)from northwest China.Our results showed that 1)MMD was closely related to the distribution patterns of forest trees.Tree distribution patterns at each observational scale(r=0-20 m)tended tobecome random as the MMD increased.The older the community,the earlier this random distribution pattern appeared.2)As the MMD increased,neighboring trees became more regularly distributed around a reference tree.In most cases,however,nearest neighbors of a reference tree were randomly distributed.3)Tree species mingling decreased with increasing diameter,but it decreased slowly in older forests.4)No correlations can be found between individual tree size differentiation and MMD.We recommend that comparisons of spatial structures between communities would be more effective if using a unified MMD criterion.
文摘Missing values are prevalent in real-world datasets and they may reduce predictive performance of a learning algorithm. Dissolved Gas Analysis (DGA), one of the most deployable methods for detecting and predicting incipient faults in power transformers is one of the casualties. Thus, this paper proposes filling-in the missing values found in a DGA dataset using the k-nearest neighbor imputation method with two different distance metrics: Euclidean and Cityblock. Thereafter, using these imputed datasets as inputs, this study applies Support Vector Machine (SVM) to built models which are used to classify transformer faults. Experimental results are provided to show the effectiveness of the proposed approach.
基金Project supported by the National Natural Science Foundation of China(Grant No.11002086)the Shanghai Leading Academic Discipline Project(Grant No.J50103)
文摘The core of smoothed particle hydrodynamics （SPH） is the nearest neighbor search subroutine. In this paper, a nearest neighbor search algorithm which is based on multiple background grids and support variable smooth length is introduced. Through tested on lid driven cavity flow, it is clear that this method can provide high accuracy. Analysis and experiments have been made on its parallelism, and the results show that this method has better parallelism and with adding processors its accuracy become higher, thus it achieves that efficiency grows in pace with accuracy.
基金Project supported by the Key Higher Education Program of Hubei Province, China (Grant No Z20052201)Natural Science Foundation of Hubei Province, China (Grant No 2006ABA055)Postgraduate Program of Hubei Normal University of China(Grant No 2007D20)
文摘By using the method of density-matrix renormalization-group to solve the different spin spin correlation functions, the nearest-neighbouring entanglement （NNE） and the next-nearest-neighbouring entanglement （NNNE） of one-dimensional alternating Heisenberg XY spin chain are investigated in the presence of alternating the-nearestneighbouring interaction of exchange couplings, external magnetic fields and the next-nearest neighbouring interaction. For a dimerised ferromagnetic spin chain, the NNNE appears only above a critical dimerized interaction, meanwhile, the dimerized interaction a effects a quantum phase transition point and improves the NNNE to a large extent. We also study the effect of ferromagnetic or antiferromagnetic next-nearest neighbouring （NNN） interaction on the dynamics of NNE and NNNE. The ferromagnetic NNN interaction increases and shrinks the NNE below and above a critical frustrated interaction respectively, while the antiferromagnetic NNN interaction always reduces the NNE. The antiferromagnetic NNN interaction results in a large value of NNNE compared with the case where the NNN interaction is ferromagnetic.
文摘As climate change negotiations progress,monitoring biomass and carbon stocks is becoming an important part of the current forest research.Therefore,national governments are interested in developing forest-monitoring strategies using geospatial technology.Among statistical methods for mapping biomass,there is a nonparametric approach called k-nearest neighbor(kNN).We compared four variations of distance metrics of the kNN for the spatially-explicit estimation of aboveground biomass in a portion of the Mexican north border of the intertropical zone.Satellite derived,climatic,and topographic predictor variables were combined with the Mexican National Forest Inventory(NFI)data to accomplish the purpose.Performance of distance metrics applied into the kNN algorithm was evaluated using a cross validation leave-one-out technique.The results indicate that the Most Similar Neighbor(MSN)approach maximizes the correlation between predictor and response variables(r=0.9).Our results are in agreement with those reported in the literature.These findings confirm the predictive potential of the MSN approach for mapping forest variables at pixel level under the policy of Reducing Emission from Deforestation and Forest Degradation(REDD+).
文摘It is well known that in pyroxene structure, there are two metal sites, M1 and M2.Generally speaking, ferrous iron in each of these sites would normally be expected to give rise to a doublet. However, anomies have been found in the relative areas of the peaks in the room temperature spectra of some clinopyroxene (CPX) when the above assigninent is folowed. Ac-cording to the calculation of Next Nearest Neighbor configurations of divalent cations in M1,we found that the four configurations of M1 can be divided into two groups. One group is 3Ca configuration that increases with the content of Ca (p. f. u); the other group is made up of three No-3Ca configurations that decrease with the content of Ca. The two groups contribute to the spectrum structure of M1, so in this study we fit two doublets for ferrous iron in M1.Though there were severa reports on Fe3+ in tetrahedral site previously, it wa not sure that Fe3+ occupies the T site is a universal fact in CPX, despite of the content of A1. We found that the Fe3+ in the T site fitted by Medauer spectroscopy is negatively correlated to the Si content in the T site and positively correlated to the Fe3 + in the T site estimated on the suppo-sition that Fe3+ and Al occupy the T site randomly. If it is true, it is important in the model-ing of ion exchange geobarometries and gepthermornetries.
文摘Short-term traffic flow is one of the core technologies to realize traffic flow guidance. In this article, in view of the characteristics that the traffic flow changes repeatedly, a short-term traffic flow forecasting method based on a three-layer K-nearest neighbor non-parametric regression algorithm is proposed. Specifically, two screening layers based on shape similarity were introduced in K-nearest neighbor non-parametric regression method, and the forecasting results were output using the weighted averaging on the reciprocal values of the shape similarity distances and the most-similar-point distance adjustment method. According to the experimental results, the proposed algorithm has improved the predictive ability of the traditional K-nearest neighbor non-parametric regression method, and greatly enhanced the accuracy and real-time performance of short-term traffic flow forecasting.
文摘Individual tree detection (ITD) and the area-based approach (ABA) are combined to generate tree-lists using airborne LiDAR data. ITD based on the Canopy Height Model (CHM) was applied for overstory trees, while ABA based on nearest neighbor (NN) imputation was applied for understory trees. Our approach is intended to compensate for the weakness of LiDAR data and ITD in estimating understory trees, keeping the strength of ITD in estimating overstory trees in tree-level. We investigated the effects of three parameters on the performance of our proposed approach: smoothing of CHM, resolution of CHM, and height cutoff (a specific height that classifies trees into overstory and understory). There was no single combination of those parameters that produced the best performance for estimating stems per ha, mean tree height, basal area, diameter distribution and height distribution. The trees in the lowest LiDAR height class yielded the largest relative bias and relative root mean squared error. Although ITD and ABA showed limited explanatory powers to estimate stems per hectare and basal area, there could be improvements from methods such as using LiDAR data with higher density, applying better algorithms for ITD and decreasing distortion of the structure of LiDAR data. Automating the procedure of finding optimal combinations of those parameters is essential to expedite forest management decisions across forest landscapes using remote sensing data.
文摘Aggregate nearest neighbor(ANN) search retrieves for two spatial datasets T and Q, segment(s) of one or more trajectories from the set T having minimum aggregate distance to points in Q. When interacting with large amounts of trajectories, this process would be very time-consuming due to consecutive page loads. An approximate method for finding segments with minimum aggregate distance is proposed which can improve the response time. In order to index large volumes of trajectories, scalable and efficient trajectory index(SETI) structure is used. But some refinements are provided to temporal index of SETI to improve the performance of proposed method. The experiments were performed with different number of query points and percentages of dataset. It is shown that proposed method besides having an acceptable precision, can reduce the computation time significantly. It is also shown that the main fraction of search time among load time, ANN and computing convex and centroid, is related to ANN.
基金the National Natural Science Foundation of China(No.61502208)the Natural Science Foundation of Jiangsu Province of China(No.BK20150522)+1 种基金the Scientific and Technical Program of City of Huizhou(Nos.2016X0422037 and 2017C0405021)the Natural Science Foundation of Huizhou University(Nos.hzux1201606 and hzu201701)
文摘Collaborative representation-based classification(CRC) is a distance based method, and it obtains the original contributions from all samples to solve the sparse representation coefficient. We find out that it helps to enhance the discrimination in classification by integrating other distance based features and/or adding signal preprocessing to the original samples. In this paper, we propose an improved version of the CRC method which uses the Gabor wavelet transformation to preprocess the samples and also adapts the nearest neighbor(NN)features, and hence we call it GNN-CRC. Firstly, Gabor wavelet transformation is applied to minimize the effects from the background in face images and build Gabor features into the input data. Secondly, the distances solved by NN and CRC are fused together to obtain a more discriminative classification. Extensive experiments are conducted to evaluate the proposed method for face recognition with different instantiations. The experimental results illustrate that our method outperforms the naive CRC as well as some other state-of-the-art algorithms.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11604121 and 11875126)the Natural Science Fund Project of Hunan Province,China(Grant No.2017JJ3255)+1 种基金the National College Students’ Innovation Entrepreneurship Training Program,China(Grant No.201810531014)the Scientific Research Fund of Hunan Provincial Education Department,China(Grant No.17B212)
文摘We report a theoretical work on the properties of modulational instability and bright type nonlinear localized modes in one-dimensional easy-axis weak ferromagnetic spin lattices involving next-nearest-neighbor couplings.With a linear stability analysis, we calculate the growth rates of the modulational instability, and plot the instability regions.When the strength of the next-nearest-neighbor coupling is large enough, two new asymmetric modulational instability regions appear near the boundary of the first Brillouin zone.Furthermore, analytical forms of the bright nonlinear localized modes are constructed by means of a quasi-discreteness approach.The influence of the next-nearest-neighbor coupling on the Brillouin zone center mode and boundary mode are discussed.In particular, we discover a reversal phenomenon of the propagation direction of the Brillouin zone boundary mode.
基金Supported by the National Natural Science Foundation of China(Grant Nos.6143200311171052+3 种基金612723716132820611361005)the Research Programs of Gannan Normal University(Grant No.14zb21) College of Mathematics and Computer Science
文摘Nearest polynomial with given properties has many applications in control theory and applied mathematics. Given a complex univariate polynomial f（z） and a zero α, in this paper we explore the problem of computing a complex polynomial f^^（z） such that f^^（a）=0 and the distance ｜｜f^^-f｜｜ is minimal. Considering most of the existing works focus on either certain polynonfial basis or certain vector norm, we propose a common computation framework based on both general polynomial basis and general vector norm, and summarize the computing process into a four-step algorithm. Further, to find the explicit expression of f（z）, we focus on two specific norms which generalize the familiar lp-norm and mixed norm studied in the existing works, and then compute f^^（z） explicitly based on the proposed algorithm. We finally give a numerical example to show the effectiveness of our method.
