South America’s climatic diversity is a product of its vast geographical expanse, encompassing tropical to subtropical latitudes. The variations in precipitation and temperature across the region stem from the influe...South America’s climatic diversity is a product of its vast geographical expanse, encompassing tropical to subtropical latitudes. The variations in precipitation and temperature across the region stem from the influence of distinct atmospheric systems. While some studies have characterized the prevailing systems over South America, they often lacked the utilization of statistical techniques for homogenization. On the other hand, other research has employed multivariate statistical methods to identify homogeneous regions regarding temperature and precipitation, but their focus has been limited to specific areas, such as the south, southeast, and northeast. Surprisingly, there is a lack of work that compares various multivariate statistical techniques to determine homogeneous regions across the entirety of South America concerning temperature and precipitation. This paper aims to address this gap by comparing three such techniques: Cluster Analysis (K-means and Ward) and Self Organizing Maps, using data from different sources for temperature (ERA5, ERA5-Land, and CRU) and precipitation (ERA5, ERA5-Land, and CPC). Spatial patterns and time series were generated for each region over the period 1981-2010. The results from this analysis of spatially homogeneous regions concerning temperature and precipitation have the potential to significantly benefit climate analysis and forecasts. Moreover, they can offer valuable insights for various climatological studies, guiding decision-making processes in diverse fields that rely on climate information, such as agriculture, disaster management, and water resources planning.展开更多
Multiplicative noise removal problems have attracted much attention in recent years.Unlike additive noise,multiplicative noise destroys almost all information of the original image,especially for texture images.Motiva...Multiplicative noise removal problems have attracted much attention in recent years.Unlike additive noise,multiplicative noise destroys almost all information of the original image,especially for texture images.Motivated by the TV-Stokes model,we propose a new two-step variational model to denoise the texture images corrupted by multiplicative noise with a good geometry explanation in this paper.In the first step,we convert the multiplicative denoising problem into an additive one by the logarithm transform and propagate the isophote directions in the tangential field smoothing.Once the isophote directions are constructed,an image is restored to fit the constructed directions in the second step.The existence and uniqueness of the solution to the variational problems are proved.In these two steps,we use the gradient descent method and construct finite difference schemes to solve the problems.Especially,the augmented Lagrangian method and the fast Fourier transform are adopted to accelerate the calculation.Experimental results show that the proposed model can remove the multiplicative noise efficiently and protect the texture well.展开更多
Designing a sparse array with reduced transmit/receive modules(TRMs)is vital for some applications where the antenna system’s size,weight,allowed operating space,and cost are limited.Sparse arrays exhibit distinct ar...Designing a sparse array with reduced transmit/receive modules(TRMs)is vital for some applications where the antenna system’s size,weight,allowed operating space,and cost are limited.Sparse arrays exhibit distinct architectures,roughly classified into three categories:Thinned arrays,nonuniformly spaced arrays,and clustered arrays.While numerous advanced synthesis methods have been presented for the three types of sparse arrays in recent years,a comprehensive review of the latest development in sparse array synthesis is lacking.This work aims to fill this gap by thoroughly summarizing these techniques.The study includes synthesis examples to facilitate a comparative analysis of different techniques in terms of both accuracy and efficiency.Thus,this review is intended to assist researchers and engineers in related fields,offering a clear understanding of the development and distinctions among sparse array synthesis techniques.展开更多
Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards.Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized charact...Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards.Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized characteristics of mining slopes,this study introduces a new method that fuses model data from Unmanned aerial vehicles(UAV)tilt photogrammetry and 3D laser scanning through a data alignment algorithm based on control points.First,the mini batch K-Medoids algorithm is utilized to cluster the point cloud data from ground 3D laser scanning.Then,the elbow rule is applied to determine the optimal cluster number(K0),and the feature points are extracted.Next,the nearest neighbor point algorithm is employed to match the feature points obtained from UAV tilt photogrammetry,and the internal point coordinates are adjusted through the distanceweighted average to construct a 3D model.Finally,by integrating an engineering case study,the K0 value is determined to be 8,with a matching accuracy between the two model datasets ranging from 0.0669 to 1.0373 mm.Therefore,compared with the modeling method utilizing K-medoids clustering algorithm,the new modeling method significantly enhances the computational efficiency,the accuracy of selecting the optimal number of feature points in 3D laser scanning,and the precision of the 3D model derived from UAV tilt photogrammetry.This method provides a research foundation for constructing mine slope model.展开更多
With the development of green data centers,a large number of Uninterruptible Power Supply(UPS)resources in Internet Data Center(IDC)are becoming idle assets owing to their low utilization rate.The revitalization of th...With the development of green data centers,a large number of Uninterruptible Power Supply(UPS)resources in Internet Data Center(IDC)are becoming idle assets owing to their low utilization rate.The revitalization of these idle UPS resources is an urgent problem that must be addressed.Based on the energy storage type of the UPS(EUPS)and using renewable sources,a solution for IDCs is proposed in this study.Subsequently,an EUPS cluster classification method based on the concept of shared mechanism niche(CSMN)was proposed to effectively solve the EUPS control problem.Accordingly,the classified EUPS aggregation unit was used to determine the optimal operation of the IDC.An IDC cost minimization optimization model was established,and the Quantum Particle Swarm Optimization(QPSO)algorithm was adopted.Finally,the economy and effectiveness of the three-tier optimization framework and model were verified through three case studies.展开更多
[Objectives]The paper was to screen new varieties of long cowpea that are suitable for autumn cultivation in Hunan,as well as to develop a comprehensive evaluation method to assess their adaptability and performance.[...[Objectives]The paper was to screen new varieties of long cowpea that are suitable for autumn cultivation in Hunan,as well as to develop a comprehensive evaluation method to assess their adaptability and performance.[Methods]A total of 48 long cowpea varieties were introduced,and a range of comprehensive evaluation methods was employed to assess these varieties through the collection and analysis of field data.[Results]The square Euclidean distance of 14 allowed for the classification of all varieties into eight distinct groups.Groups II,III,and V belong to the autumn dominant group within this region,while groups I and VIII belong to the intermediate group.Additionally,groups IV,VI,and VII belong to the autumn inferior group in this area.Through a comparative analysis of various comprehensive evaluation methods,it was determined that the common factor comprehensive evaluation,grey correlation method,and fuzzy evaluation method were appropriate for application in the selection of long cowpea varieties.Furthermore,the evaluation outcomes were largely consistent with the cluster pedigree diagram.[Conclusions]Through comprehensive index method,ten varieties demonstrating superior performance in autumn cultivation have been identified,including C20,C42,C29,C40,C3,C14,C18,C25,C15,and C47.The selected varieties exhibit several advantageous traits,such as a reduced growth duration,a lower position of initial flower nodes,a decreased number of branches,predominantly green young pods,elongated pod strips,thicker pod structures,an increased number of pods per plant,and higher overall yields.These characteristics render them particularly valuable for extensive cultivation.展开更多
High fidelity analysis models,which are beneficial to improving the design quality,have been more and more widely utilized in the modern engineering design optimization problems.However,the high fidelity analysis mode...High fidelity analysis models,which are beneficial to improving the design quality,have been more and more widely utilized in the modern engineering design optimization problems.However,the high fidelity analysis models are so computationally expensive that the time required in design optimization is usually unacceptable.In order to improve the efficiency of optimization involving high fidelity analysis models,the optimization efficiency can be upgraded through applying surrogates to approximate the computationally expensive models,which can greately reduce the computation time.An efficient heuristic global optimization method using adaptive radial basis function（RBF） based on fuzzy clustering（ARFC） is proposed.In this method,a novel algorithm of maximin Latin hypercube design using successive local enumeration（SLE） is employed to obtain sample points with good performance in both space-filling and projective uniformity properties,which does a great deal of good to metamodels accuracy.RBF method is adopted for constructing the metamodels,and with the increasing the number of sample points the approximation accuracy of RBF is gradually enhanced.The fuzzy c-means clustering method is applied to identify the reduced attractive regions in the original design space.The numerical benchmark examples are used for validating the performance of ARFC.The results demonstrates that for most application examples the global optima are effectively obtained and comparison with adaptive response surface method（ARSM） proves that the proposed method can intuitively capture promising design regions and can efficiently identify the global or near-global design optimum.This method improves the efficiency and global convergence of the optimization problems,and gives a new optimization strategy for engineering design optimization problems involving computationally expensive models.展开更多
Temperature-dependent photoluminescence characteristics of organic-inorganic halide perovskite CH3NH3Pb I3-xClx films prepared using a two-step method on ZnO/FTO substrates were investigated. Surface morphology and ab...Temperature-dependent photoluminescence characteristics of organic-inorganic halide perovskite CH3NH3Pb I3-xClx films prepared using a two-step method on ZnO/FTO substrates were investigated. Surface morphology and absorption characteristics of the films were also studied. Scanning electron microscopy revealed large crystals and substrate coverage. The orthorhombic-to-tetragonal phase transition temperature was-140 K. The films＇ exciton binding energy was 77.6 ± 10.9 meV and the energy of optical phonons was 38.8 ± 2.5 meV. These results suggest that perovskite CH3NH3Pb I（3-x）Clx films have excellent optoelectronic characteristics which further suggests their potential usage in perovskitebased optoelectronic devices.展开更多
Tarq geochemical 1:100,000 Sheet is located in Isfahan province which is investigated by Iran’s Geological and Explorations Organization using stream sediment analyzes. This area has stratigraphy of Precambrian to Qu...Tarq geochemical 1:100,000 Sheet is located in Isfahan province which is investigated by Iran’s Geological and Explorations Organization using stream sediment analyzes. This area has stratigraphy of Precambrian to Quaternary rocks and is located in the Central Iran zone. According to the presence of signs of gold mineralization in this area, it is necessary to identify important mineral areas in this area. Therefore, finding information is necessary about the relationship and monitoring the elements of gold, arsenic, and antimony relative to each other in this area to determine the extent of geochemical halos and to estimate the grade. Therefore, a well-known and useful K-means method is used for monitoring the elements in the present study, this is a clustering method based on minimizing the total Euclidean distances of each sample from the center of the classes which are assigned to them. In this research, the clustering quality function and the utility rate of the sample have been used in the desired cluster (S(i)) to determine the optimum number of clusters. Finally, with regard to the cluster centers and the results, the equations were used to predict the amount of the gold element based on four parameters of arsenic and antimony grade, length and width of sampling points.展开更多
The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, d...The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis.展开更多
Cluster science as a bridge linking atomic molecular physics and condensed matter inspired the nanomaterials development in the past decades, ranging from the single-atom catalysis to ligand-protected noble metal clus...Cluster science as a bridge linking atomic molecular physics and condensed matter inspired the nanomaterials development in the past decades, ranging from the single-atom catalysis to ligand-protected noble metal clusters. The corresponding studies not only have been restricted to the search for the geometrical structures of clusters, but also have promoted the development of cluster-assembled materials as the building blocks. The CALYPSO cluster prediction method combined with other computational techniques have significantly stimulated the development of the cluster-based nanomaterials. In this review, we will summarize some good cases of cluster structure by CALYPSO method, which have also been successfully identified by the photoelectron spectra experiments. Beginning with the alkali-metal clusters, which serve as benchmarks, a series of studies are performed on the size-dependent elemental clusters which possess relatively high stability and interesting chemical physical properties. Special attentions are paid to the boron-based clusters because of their promising applications. The NbSi12 and BeB16 clusters, for example, are two classic representatives of the silicon-and boron-based clusters, which can be viewed as building blocks of nanotubes and borophene. This review offers a detailed description of the structural evolutions and electronic properties of medium-sized pure and doped clusters, which will advance fundamental knowledge of cluster-based nanomaterials and provide valuable information for further theoretical and experimental studies.展开更多
The selection of refracturing candidate is one of the most important jobs faced by oilfield engineers. However, due to the complicated multi-parameter relationships and their comprehensive influence, the selection of ...The selection of refracturing candidate is one of the most important jobs faced by oilfield engineers. However, due to the complicated multi-parameter relationships and their comprehensive influence, the selection of refracturing candidate is often very difficult. In this paper, a novel approach combining data analysis techniques and fuzzy clustering was proposed to select refracturing candidate. First, the analysis techniques were used to quantitatively calculate the weight coefficient and determine the key factors. Then, the idealized refracturing well was established by considering the main factors. Fuzzy clustering was applied to evaluate refracturing potential. Finally, reservoirs numerical simulation was used to further evaluate reservoirs energy and material basis of the optimum refracturing candidates. The hybrid method has been successfully applied to a tight oil reservoir in China. The average steady production was 15.8 t/d after refracturing treatment, increasing significantly compared with previous status. The research results can guide the development of tight oil and gas reservoirs effectively.展开更多
In order to improve the accuracy and efficiency of 3D model retrieval,the method based on affinity propagation clustering algorithm is proposed. Firstly,projection ray-based method is proposed to improve the feature e...In order to improve the accuracy and efficiency of 3D model retrieval,the method based on affinity propagation clustering algorithm is proposed. Firstly,projection ray-based method is proposed to improve the feature extraction efficiency of 3D models. Based on the relationship between model and its projection,the intersection in 3D space is transformed into intersection in 2D space,which reduces the number of intersection and improves the efficiency of the extraction algorithm. In feature extraction,multi-layer spheres method is analyzed. The two-layer spheres method makes the feature vector more accurate and improves retrieval precision. Secondly,Semi-supervised Affinity Propagation ( S-AP) clustering is utilized because it can be applied to different cluster structures. The S-AP algorithm is adopted to find the center models and then the center model collection is built. During retrieval process,the collection is utilized to classify the query model into corresponding model base and then the most similar model is retrieved in the model base. Finally,75 sample models from Princeton library are selected to do the experiment and then 36 models are used for retrieval test. The results validate that the proposed method outperforms the original method and the retrieval precision and recall ratios are improved effectively.展开更多
The distribution of remaining oil is often described qualitatively. The remaining oil distributed in the whole reservoir is calculated according to the characteristics of the space distribution of the saturation of re...The distribution of remaining oil is often described qualitatively. The remaining oil distributed in the whole reservoir is calculated according to the characteristics of the space distribution of the saturation of remaining oil. Logging data are required to accomplish this. However, many such projects cannot be completed. Since the old study of remaining oil distribution could not be quantified efficiently, the ＂dynamic two-step method＂ is presented. Firstly, the water cut of every flow unit in one well at one time is calculated according to the comprehensive water cut of a single well at one time. Secondly, the remaining oil saturation of the flow unit of the well at one time is calculated based on the water cut of the flow unit at a given time. The results show that ＂dynamic two-step method＂ has characteristics of simplicity and convenience, and is especially suitable for the study of remaining oil distribution at high water-cut stage. The distribution of remaining oil presented banding and potato form, remaining oil was relatively concentrated in faultage neighborhood and imperfect well netting position, and the net thickness of the place was great. This proposal can provide an effective way to forecast remaining oil distribution and enhance oil recovery, especially applied at the high water-cut stage.展开更多
The knowledge of bubble profiles in gas-liquid two-phase flows is crucial for analyzing the kinetic processes such as heat and mass transfer, and this knowledge is contained in field data obtained by surface-resolved ...The knowledge of bubble profiles in gas-liquid two-phase flows is crucial for analyzing the kinetic processes such as heat and mass transfer, and this knowledge is contained in field data obtained by surface-resolved computational fluid dynamics (CFD) simulations. To obtain this information, an efficient bubble profile reconstruction method based on an improved agglomerative hierarchical clustering (AHC) algorithm is proposed in this paper. The reconstruction method is featured by the implementations of a binary space division preprocessing, which aims to reduce the computational complexity, an adaptive linkage criterion, which guarantees the applicability of the AHC algorithm when dealing with datasets involving either non-uniform or distorted grids, and a stepwise execution strategy, which enables the separation of attached bubbles. To illustrate and verify this method, it was applied to dealing with 3 datasets, 2 of them with pre-specified spherical bubbles and the other obtained by a surface-resolved CFD simulation. Application results indicate that the proposed method is effective even when the data include some non-uniform and distortion.展开更多
To make the quantitative results of nuclear magnetic resonance(NMR) transverse relaxation(T;) spectrums reflect the type and pore structure of reservoir more directly, an unsupervised clustering method was developed t...To make the quantitative results of nuclear magnetic resonance(NMR) transverse relaxation(T;) spectrums reflect the type and pore structure of reservoir more directly, an unsupervised clustering method was developed to obtain the quantitative pore structure information from the NMR T;spectrums based on the Gaussian mixture model(GMM). Firstly, We conducted the principal component analysis on T;spectrums in order to reduce the dimension data and the dependence of the original variables. Secondly, the dimension-reduced data was fitted using the GMM probability density function, and the model parameters and optimal clustering numbers were obtained according to the expectation-maximization algorithm and the change of the Akaike information criterion. Finally, the T;spectrum features and pore structure types of different clustering groups were analyzed and compared with T;geometric mean and T;arithmetic mean. The effectiveness of the algorithm has been verified by numerical simulation and field NMR logging data. The research shows that the clustering results based on GMM method have good correlations with the shape and distribution of the T;spectrum, pore structure, and petroleum productivity, providing a new means for quantitative identification of pore structure, reservoir grading, and oil and gas productivity evaluation.展开更多
The idea of modified water masses is introduced and a cluster analysis is used for determining the boundary of modified water masses and its variety in the shallow water area of the Huanghai Sea (Yellow Sea) and the E...The idea of modified water masses is introduced and a cluster analysis is used for determining the boundary of modified water masses and its variety in the shallow water area of the Huanghai Sea (Yellow Sea) and the East China Sea. According to the specified standards to make the cluster, we have determined the number and boundary of the water masses and the mixed zones.The results obtained by the cluster method show that there are eight modified water masses in this area. According to the relative index of temperature and salinity,the modified water masses are divided into nine different characteristic parts. The water, masses may also be divided into three salinity types. On the TS-Diagram, the points concerning temperature and safinity of different modified mater masses are distributed around a curve, from which the characteristics of gradual modification may be embodied. The variation ranges of different modified water masses are all large, explaining the intensive modification of water masses in展开更多
The sixth Data Release （DR6） of the Sloan Digital Sky Survey （SDSS） provides more photometric regions, new features and more accurate data around globular cluster Palomar 5. A new method, Back Propagation Neural N...The sixth Data Release （DR6） of the Sloan Digital Sky Survey （SDSS） provides more photometric regions, new features and more accurate data around globular cluster Palomar 5. A new method, Back Propagation Neural Network （BPNN）, is used to estimate the cluster membership probability in order to detect its tidal tails. Cluster and field stars, used for training the networks, are extracted over a 40 × 20 deg^2 field by color-magnitude diagrams （CMDs）. The best BPNNs with two hidden layers and a Levenberg-Marquardt （LM） training algorithm are determined by the chosen cluster and field samples. The membership probabilities of stars in the whole field are obtained with the BPNNs, and contour maps of the probability distribution show that a tail extends .5.42° to the north of the cluster and another tail extends 3.77° to the south. The tails are similar to those detected by Odenkirchen et al., but no more debris from the cluster is found to the northeast in the sky. The radial density profiles are investigated both along the tails and near the cluster center. Quite a few substructures are discovered in the tails. The number density profile of the cluster is fitted with the King model and the tidal radius is determined as 14.28＇. However, the King model cannot fit the observed profile at the outer regions （R 〉 8＇） because of the tidal tails generated by the tidal force. Luminosity functions of the cluster and the tidal tails are calculated, which confirm that the tails originate from Palomar 5.展开更多
Applying calculation method in alloy design should be an important tendency due to its characters of inexpensive cost, high efficiency and prediction. DOS calculations of AuSn, AsSn and SbSn Sn- based alloys have ...Applying calculation method in alloy design should be an important tendency due to its characters of inexpensive cost, high efficiency and prediction. DOS calculations of AuSn, AsSn and SbSn Sn- based alloys have been investigated by employing DV - Xa method, in which different cluster models were adopted to calculate electron structure.It is proved that some regulations must be taken into ac- count in order to carry out alloy design calculation successfully,which are described in this paper in detail.展开更多
A novel model of fuzzy clustering using kernel methods is proposed. This model is called kernel modified possibilistic c-means （KMPCM） model. The proposed model is an extension of the modified possibilistic c-means ...A novel model of fuzzy clustering using kernel methods is proposed. This model is called kernel modified possibilistic c-means （KMPCM） model. The proposed model is an extension of the modified possibilistic c-means （MPCM） algorithm by using kernel methods. Different from MPCM and fuzzy c-means （FCM） model which are based on Euclidean distance, the proposed model is based on kernel-induced distance. Furthermore, with kernel methods the input data can be mapped implicitly into a high-dimensional feature space where the nonlinear pattern now appears linear. It is unnecessary to do calculation in the high-dimensional feature space because the kernel function can do it. Numerical experiments show that KMPCM outperforms FCM and MPCM.展开更多
文摘South America’s climatic diversity is a product of its vast geographical expanse, encompassing tropical to subtropical latitudes. The variations in precipitation and temperature across the region stem from the influence of distinct atmospheric systems. While some studies have characterized the prevailing systems over South America, they often lacked the utilization of statistical techniques for homogenization. On the other hand, other research has employed multivariate statistical methods to identify homogeneous regions regarding temperature and precipitation, but their focus has been limited to specific areas, such as the south, southeast, and northeast. Surprisingly, there is a lack of work that compares various multivariate statistical techniques to determine homogeneous regions across the entirety of South America concerning temperature and precipitation. This paper aims to address this gap by comparing three such techniques: Cluster Analysis (K-means and Ward) and Self Organizing Maps, using data from different sources for temperature (ERA5, ERA5-Land, and CRU) and precipitation (ERA5, ERA5-Land, and CPC). Spatial patterns and time series were generated for each region over the period 1981-2010. The results from this analysis of spatially homogeneous regions concerning temperature and precipitation have the potential to significantly benefit climate analysis and forecasts. Moreover, they can offer valuable insights for various climatological studies, guiding decision-making processes in diverse fields that rely on climate information, such as agriculture, disaster management, and water resources planning.
文摘Multiplicative noise removal problems have attracted much attention in recent years.Unlike additive noise,multiplicative noise destroys almost all information of the original image,especially for texture images.Motivated by the TV-Stokes model,we propose a new two-step variational model to denoise the texture images corrupted by multiplicative noise with a good geometry explanation in this paper.In the first step,we convert the multiplicative denoising problem into an additive one by the logarithm transform and propagate the isophote directions in the tangential field smoothing.Once the isophote directions are constructed,an image is restored to fit the constructed directions in the second step.The existence and uniqueness of the solution to the variational problems are proved.In these two steps,we use the gradient descent method and construct finite difference schemes to solve the problems.Especially,the augmented Lagrangian method and the fast Fourier transform are adopted to accelerate the calculation.Experimental results show that the proposed model can remove the multiplicative noise efficiently and protect the texture well.
基金supported by the National Natural Science Foundation of China under Grant No.U2341208.
文摘Designing a sparse array with reduced transmit/receive modules(TRMs)is vital for some applications where the antenna system’s size,weight,allowed operating space,and cost are limited.Sparse arrays exhibit distinct architectures,roughly classified into three categories:Thinned arrays,nonuniformly spaced arrays,and clustered arrays.While numerous advanced synthesis methods have been presented for the three types of sparse arrays in recent years,a comprehensive review of the latest development in sparse array synthesis is lacking.This work aims to fill this gap by thoroughly summarizing these techniques.The study includes synthesis examples to facilitate a comparative analysis of different techniques in terms of both accuracy and efficiency.Thus,this review is intended to assist researchers and engineers in related fields,offering a clear understanding of the development and distinctions among sparse array synthesis techniques.
基金funded by National Natural Science Foundation of China(Grant Nos.42272333,42277147).
文摘Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards.Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized characteristics of mining slopes,this study introduces a new method that fuses model data from Unmanned aerial vehicles(UAV)tilt photogrammetry and 3D laser scanning through a data alignment algorithm based on control points.First,the mini batch K-Medoids algorithm is utilized to cluster the point cloud data from ground 3D laser scanning.Then,the elbow rule is applied to determine the optimal cluster number(K0),and the feature points are extracted.Next,the nearest neighbor point algorithm is employed to match the feature points obtained from UAV tilt photogrammetry,and the internal point coordinates are adjusted through the distanceweighted average to construct a 3D model.Finally,by integrating an engineering case study,the K0 value is determined to be 8,with a matching accuracy between the two model datasets ranging from 0.0669 to 1.0373 mm.Therefore,compared with the modeling method utilizing K-medoids clustering algorithm,the new modeling method significantly enhances the computational efficiency,the accuracy of selecting the optimal number of feature points in 3D laser scanning,and the precision of the 3D model derived from UAV tilt photogrammetry.This method provides a research foundation for constructing mine slope model.
基金supported by the Key Technology Projects of the China Southern Power Grid Corporation(STKJXM20200059)the Key Support Project of the Joint Fund of the National Natural Science Foundation of China(U22B20123)。
文摘With the development of green data centers,a large number of Uninterruptible Power Supply(UPS)resources in Internet Data Center(IDC)are becoming idle assets owing to their low utilization rate.The revitalization of these idle UPS resources is an urgent problem that must be addressed.Based on the energy storage type of the UPS(EUPS)and using renewable sources,a solution for IDCs is proposed in this study.Subsequently,an EUPS cluster classification method based on the concept of shared mechanism niche(CSMN)was proposed to effectively solve the EUPS control problem.Accordingly,the classified EUPS aggregation unit was used to determine the optimal operation of the IDC.An IDC cost minimization optimization model was established,and the Quantum Particle Swarm Optimization(QPSO)algorithm was adopted.Finally,the economy and effectiveness of the three-tier optimization framework and model were verified through three case studies.
基金Supported by China Agricultural Industry Research System(CARS-23-G31)Technology Innovation Guidance Project of Changde City(CDKJJ20220265,CDKJJ2023YF33).
文摘[Objectives]The paper was to screen new varieties of long cowpea that are suitable for autumn cultivation in Hunan,as well as to develop a comprehensive evaluation method to assess their adaptability and performance.[Methods]A total of 48 long cowpea varieties were introduced,and a range of comprehensive evaluation methods was employed to assess these varieties through the collection and analysis of field data.[Results]The square Euclidean distance of 14 allowed for the classification of all varieties into eight distinct groups.Groups II,III,and V belong to the autumn dominant group within this region,while groups I and VIII belong to the intermediate group.Additionally,groups IV,VI,and VII belong to the autumn inferior group in this area.Through a comparative analysis of various comprehensive evaluation methods,it was determined that the common factor comprehensive evaluation,grey correlation method,and fuzzy evaluation method were appropriate for application in the selection of long cowpea varieties.Furthermore,the evaluation outcomes were largely consistent with the cluster pedigree diagram.[Conclusions]Through comprehensive index method,ten varieties demonstrating superior performance in autumn cultivation have been identified,including C20,C42,C29,C40,C3,C14,C18,C25,C15,and C47.The selected varieties exhibit several advantageous traits,such as a reduced growth duration,a lower position of initial flower nodes,a decreased number of branches,predominantly green young pods,elongated pod strips,thicker pod structures,an increased number of pods per plant,and higher overall yields.These characteristics render them particularly valuable for extensive cultivation.
基金supported by National Natural Science Foundation of China (Grant Nos. 50875024,51105040)Excellent Young Scholars Research Fund of Beijing Institute of Technology,China (Grant No.2010Y0102)Defense Creative Research Group Foundation of China(Grant No. GFTD0803)
文摘High fidelity analysis models,which are beneficial to improving the design quality,have been more and more widely utilized in the modern engineering design optimization problems.However,the high fidelity analysis models are so computationally expensive that the time required in design optimization is usually unacceptable.In order to improve the efficiency of optimization involving high fidelity analysis models,the optimization efficiency can be upgraded through applying surrogates to approximate the computationally expensive models,which can greately reduce the computation time.An efficient heuristic global optimization method using adaptive radial basis function（RBF） based on fuzzy clustering（ARFC） is proposed.In this method,a novel algorithm of maximin Latin hypercube design using successive local enumeration（SLE） is employed to obtain sample points with good performance in both space-filling and projective uniformity properties,which does a great deal of good to metamodels accuracy.RBF method is adopted for constructing the metamodels,and with the increasing the number of sample points the approximation accuracy of RBF is gradually enhanced.The fuzzy c-means clustering method is applied to identify the reduced attractive regions in the original design space.The numerical benchmark examples are used for validating the performance of ARFC.The results demonstrates that for most application examples the global optima are effectively obtained and comparison with adaptive response surface method（ARSM） proves that the proposed method can intuitively capture promising design regions and can efficiently identify the global or near-global design optimum.This method improves the efficiency and global convergence of the optimization problems,and gives a new optimization strategy for engineering design optimization problems involving computationally expensive models.
基金supported by the International Science and Technology Cooperation Program of Science and Technology Bureau of Changchun City,China(Grant No.12ZX68)
文摘Temperature-dependent photoluminescence characteristics of organic-inorganic halide perovskite CH3NH3Pb I3-xClx films prepared using a two-step method on ZnO/FTO substrates were investigated. Surface morphology and absorption characteristics of the films were also studied. Scanning electron microscopy revealed large crystals and substrate coverage. The orthorhombic-to-tetragonal phase transition temperature was-140 K. The films＇ exciton binding energy was 77.6 ± 10.9 meV and the energy of optical phonons was 38.8 ± 2.5 meV. These results suggest that perovskite CH3NH3Pb I（3-x）Clx films have excellent optoelectronic characteristics which further suggests their potential usage in perovskitebased optoelectronic devices.
文摘Tarq geochemical 1:100,000 Sheet is located in Isfahan province which is investigated by Iran’s Geological and Explorations Organization using stream sediment analyzes. This area has stratigraphy of Precambrian to Quaternary rocks and is located in the Central Iran zone. According to the presence of signs of gold mineralization in this area, it is necessary to identify important mineral areas in this area. Therefore, finding information is necessary about the relationship and monitoring the elements of gold, arsenic, and antimony relative to each other in this area to determine the extent of geochemical halos and to estimate the grade. Therefore, a well-known and useful K-means method is used for monitoring the elements in the present study, this is a clustering method based on minimizing the total Euclidean distances of each sample from the center of the classes which are assigned to them. In this research, the clustering quality function and the utility rate of the sample have been used in the desired cluster (S(i)) to determine the optimum number of clusters. Finally, with regard to the cluster centers and the results, the equations were used to predict the amount of the gold element based on four parameters of arsenic and antimony grade, length and width of sampling points.
文摘The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.U1804121 and 11304167)
文摘Cluster science as a bridge linking atomic molecular physics and condensed matter inspired the nanomaterials development in the past decades, ranging from the single-atom catalysis to ligand-protected noble metal clusters. The corresponding studies not only have been restricted to the search for the geometrical structures of clusters, but also have promoted the development of cluster-assembled materials as the building blocks. The CALYPSO cluster prediction method combined with other computational techniques have significantly stimulated the development of the cluster-based nanomaterials. In this review, we will summarize some good cases of cluster structure by CALYPSO method, which have also been successfully identified by the photoelectron spectra experiments. Beginning with the alkali-metal clusters, which serve as benchmarks, a series of studies are performed on the size-dependent elemental clusters which possess relatively high stability and interesting chemical physical properties. Special attentions are paid to the boron-based clusters because of their promising applications. The NbSi12 and BeB16 clusters, for example, are two classic representatives of the silicon-and boron-based clusters, which can be viewed as building blocks of nanotubes and borophene. This review offers a detailed description of the structural evolutions and electronic properties of medium-sized pure and doped clusters, which will advance fundamental knowledge of cluster-based nanomaterials and provide valuable information for further theoretical and experimental studies.
基金Projects(51204054,51504203)supported by the National Natural Science Foundation of ChinaProject(2016ZX05023-001)supported by the National Science and Technology Major Project of China
文摘The selection of refracturing candidate is one of the most important jobs faced by oilfield engineers. However, due to the complicated multi-parameter relationships and their comprehensive influence, the selection of refracturing candidate is often very difficult. In this paper, a novel approach combining data analysis techniques and fuzzy clustering was proposed to select refracturing candidate. First, the analysis techniques were used to quantitatively calculate the weight coefficient and determine the key factors. Then, the idealized refracturing well was established by considering the main factors. Fuzzy clustering was applied to evaluate refracturing potential. Finally, reservoirs numerical simulation was used to further evaluate reservoirs energy and material basis of the optimum refracturing candidates. The hybrid method has been successfully applied to a tight oil reservoir in China. The average steady production was 15.8 t/d after refracturing treatment, increasing significantly compared with previous status. The research results can guide the development of tight oil and gas reservoirs effectively.
基金Sponsored by the National Natural Science Foundation of China (Grant No. 51075083)
文摘In order to improve the accuracy and efficiency of 3D model retrieval,the method based on affinity propagation clustering algorithm is proposed. Firstly,projection ray-based method is proposed to improve the feature extraction efficiency of 3D models. Based on the relationship between model and its projection,the intersection in 3D space is transformed into intersection in 2D space,which reduces the number of intersection and improves the efficiency of the extraction algorithm. In feature extraction,multi-layer spheres method is analyzed. The two-layer spheres method makes the feature vector more accurate and improves retrieval precision. Secondly,Semi-supervised Affinity Propagation ( S-AP) clustering is utilized because it can be applied to different cluster structures. The S-AP algorithm is adopted to find the center models and then the center model collection is built. During retrieval process,the collection is utilized to classify the query model into corresponding model base and then the most similar model is retrieved in the model base. Finally,75 sample models from Princeton library are selected to do the experiment and then 36 models are used for retrieval test. The results validate that the proposed method outperforms the original method and the retrieval precision and recall ratios are improved effectively.
文摘The distribution of remaining oil is often described qualitatively. The remaining oil distributed in the whole reservoir is calculated according to the characteristics of the space distribution of the saturation of remaining oil. Logging data are required to accomplish this. However, many such projects cannot be completed. Since the old study of remaining oil distribution could not be quantified efficiently, the ＂dynamic two-step method＂ is presented. Firstly, the water cut of every flow unit in one well at one time is calculated according to the comprehensive water cut of a single well at one time. Secondly, the remaining oil saturation of the flow unit of the well at one time is calculated based on the water cut of the flow unit at a given time. The results show that ＂dynamic two-step method＂ has characteristics of simplicity and convenience, and is especially suitable for the study of remaining oil distribution at high water-cut stage. The distribution of remaining oil presented banding and potato form, remaining oil was relatively concentrated in faultage neighborhood and imperfect well netting position, and the net thickness of the place was great. This proposal can provide an effective way to forecast remaining oil distribution and enhance oil recovery, especially applied at the high water-cut stage.
基金Projects(51634010,51676211) supported by the National Natural Science Foundation of ChinaProject(2017SK2253) supported by the Key Research and Development Program of Hunan Province,China
文摘The knowledge of bubble profiles in gas-liquid two-phase flows is crucial for analyzing the kinetic processes such as heat and mass transfer, and this knowledge is contained in field data obtained by surface-resolved computational fluid dynamics (CFD) simulations. To obtain this information, an efficient bubble profile reconstruction method based on an improved agglomerative hierarchical clustering (AHC) algorithm is proposed in this paper. The reconstruction method is featured by the implementations of a binary space division preprocessing, which aims to reduce the computational complexity, an adaptive linkage criterion, which guarantees the applicability of the AHC algorithm when dealing with datasets involving either non-uniform or distorted grids, and a stepwise execution strategy, which enables the separation of attached bubbles. To illustrate and verify this method, it was applied to dealing with 3 datasets, 2 of them with pre-specified spherical bubbles and the other obtained by a surface-resolved CFD simulation. Application results indicate that the proposed method is effective even when the data include some non-uniform and distortion.
基金Supported by the National Natural Science Foundation of China (42174142)National Science and Technology Major Project (2017ZX05039-002)+2 种基金Operation Fund of China National Petroleum Corporation Logging Key Laboratory (2021DQ20210107-11)Fundamental Research Funds for Central Universities (19CX02006A)Major Science and Technology Project of China National Petroleum Corporation (ZD2019-183-006)。
文摘To make the quantitative results of nuclear magnetic resonance(NMR) transverse relaxation(T;) spectrums reflect the type and pore structure of reservoir more directly, an unsupervised clustering method was developed to obtain the quantitative pore structure information from the NMR T;spectrums based on the Gaussian mixture model(GMM). Firstly, We conducted the principal component analysis on T;spectrums in order to reduce the dimension data and the dependence of the original variables. Secondly, the dimension-reduced data was fitted using the GMM probability density function, and the model parameters and optimal clustering numbers were obtained according to the expectation-maximization algorithm and the change of the Akaike information criterion. Finally, the T;spectrum features and pore structure types of different clustering groups were analyzed and compared with T;geometric mean and T;arithmetic mean. The effectiveness of the algorithm has been verified by numerical simulation and field NMR logging data. The research shows that the clustering results based on GMM method have good correlations with the shape and distribution of the T;spectrum, pore structure, and petroleum productivity, providing a new means for quantitative identification of pore structure, reservoir grading, and oil and gas productivity evaluation.
文摘The idea of modified water masses is introduced and a cluster analysis is used for determining the boundary of modified water masses and its variety in the shallow water area of the Huanghai Sea (Yellow Sea) and the East China Sea. According to the specified standards to make the cluster, we have determined the number and boundary of the water masses and the mixed zones.The results obtained by the cluster method show that there are eight modified water masses in this area. According to the relative index of temperature and salinity,the modified water masses are divided into nine different characteristic parts. The water, masses may also be divided into three salinity types. On the TS-Diagram, the points concerning temperature and safinity of different modified mater masses are distributed around a curve, from which the characteristics of gradual modification may be embodied. The variation ranges of different modified water masses are all large, explaining the intensive modification of water masses in
基金Supported by the National Natural Science Foundation of China
文摘The sixth Data Release （DR6） of the Sloan Digital Sky Survey （SDSS） provides more photometric regions, new features and more accurate data around globular cluster Palomar 5. A new method, Back Propagation Neural Network （BPNN）, is used to estimate the cluster membership probability in order to detect its tidal tails. Cluster and field stars, used for training the networks, are extracted over a 40 × 20 deg^2 field by color-magnitude diagrams （CMDs）. The best BPNNs with two hidden layers and a Levenberg-Marquardt （LM） training algorithm are determined by the chosen cluster and field samples. The membership probabilities of stars in the whole field are obtained with the BPNNs, and contour maps of the probability distribution show that a tail extends .5.42° to the north of the cluster and another tail extends 3.77° to the south. The tails are similar to those detected by Odenkirchen et al., but no more debris from the cluster is found to the northeast in the sky. The radial density profiles are investigated both along the tails and near the cluster center. Quite a few substructures are discovered in the tails. The number density profile of the cluster is fitted with the King model and the tidal radius is determined as 14.28＇. However, the King model cannot fit the observed profile at the outer regions （R 〉 8＇） because of the tidal tails generated by the tidal force. Luminosity functions of the cluster and the tidal tails are calculated, which confirm that the tails originate from Palomar 5.
文摘Applying calculation method in alloy design should be an important tendency due to its characters of inexpensive cost, high efficiency and prediction. DOS calculations of AuSn, AsSn and SbSn Sn- based alloys have been investigated by employing DV - Xa method, in which different cluster models were adopted to calculate electron structure.It is proved that some regulations must be taken into ac- count in order to carry out alloy design calculation successfully,which are described in this paper in detail.
基金Project supported by the 15th Plan for National Defence Preventive Research Project (Grant No.413030201)
文摘A novel model of fuzzy clustering using kernel methods is proposed. This model is called kernel modified possibilistic c-means （KMPCM） model. The proposed model is an extension of the modified possibilistic c-means （MPCM） algorithm by using kernel methods. Different from MPCM and fuzzy c-means （FCM） model which are based on Euclidean distance, the proposed model is based on kernel-induced distance. Furthermore, with kernel methods the input data can be mapped implicitly into a high-dimensional feature space where the nonlinear pattern now appears linear. It is unnecessary to do calculation in the high-dimensional feature space because the kernel function can do it. Numerical experiments show that KMPCM outperforms FCM and MPCM.
