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
This paper presents,a new approach of Medical Image Pixels Clustering(MIPC),aims to trace the dissimilar patterns over the Magnetic Resonance(MR)image through the process of automatically identify the appropriate numb...This paper presents,a new approach of Medical Image Pixels Clustering(MIPC),aims to trace the dissimilar patterns over the Magnetic Resonance(MR)image through the process of automatically identify the appropriate number of distinct clusters based on different improved unsupervised clustering schemes for enrichment,pattern predication and deeper investigation.The proposed MIPC consists of two stages:clustering and validation.In the clustering stage,the MIPC automatically identifies the distinct number of dissimilar clusters over the gray scale MR image based on three different improved unsupervised clustering schemes likely improved Limited Agglomerative Clustering(iLIAC),Dynamic Automatic Agglomerative Clustering(DAAC)and Optimum N-Means(ONM).In the second stage,the performance of MIPC approach is estimated by measuring Intra intimacy and Intra contrast of each individual cluster in the result of MR image based on proposed validation method namely Shreekum Intra Cluster Measure(SICM).Experimental results showthat the MIPC approach is better suited for automatic identification of highly relative dissimilar clusters over the MR cancer images with higher Intra closeness and lower Intra contrast based on improved unsupervised clustering schemes.展开更多
An unsupervised clustering\|based intrusion detection algorithm is discussed in this paper. The basic idea of the algorithm is to produce the cluster by comparing the distances of unlabeled training data sets. With th...An unsupervised clustering\|based intrusion detection algorithm is discussed in this paper. The basic idea of the algorithm is to produce the cluster by comparing the distances of unlabeled training data sets. With the classified data instances, anomaly data clusters can be easily identified by normal cluster ratio and the identified cluster can be used in real data detection. The benefit of the algorithm is that it doesn't need labeled training data sets. The experiment concludes that this approach can detect unknown intrusions efficiently in the real network connections via using the data sets of KDD99.展开更多
Multi-cluster perforation and multi-staged fracturing of horizontal well is one of the main technologies in volumetric fracturing stimulation of unconventional oil and gas reservoirs,but unconventional reservoirs in C...Multi-cluster perforation and multi-staged fracturing of horizontal well is one of the main technologies in volumetric fracturing stimulation of unconventional oil and gas reservoirs,but unconventional reservoirs in China are generally of strong heterogeneity,which causes different fracture initiation pressures in different positions of lateral,making it difficult to ensure the balanced fracture initiation and propagation between clusters in multi-cluster perforating.It is in urgent need to precisely evaluate the difference in rock strength in lateral and determine the well section with similar rock strength to deploy fractures,so as to reach the goal of balanced stimulation.Based on the drilling and logging data,this paper establishes an unsupervised clustering model of mechanical specific energy of bit at the bottomhole the lateral.Then,the influence of drill string friction,composite drilling and jet-assisted rock breaking on the mechanical specific energy is analyzed,and the distribution and clustering categories of bottomhole mechanical specific energy with decimeter spatial resolution are obtained.Finally,a fracture deployment optimization method for horizontal well volumetric fracturing aiming balanced stimulation is developed by comprehensively considering inter-fracture interference,casing collar position,plug position,and clustering result of bottomhole mechanical specific energy.The following results are obtained.First,compared with brittleness index,Poisson's ratioandstressdifference,perforation erosion area isina strongercorrelationwith themechanical specific energy,andthemechanical specific energy can effectively characterize the difference in the amount of proppant injected into the perforation clusters in the lateral,so it can be served as one of the important indicators for the selection of fracture deployment position.Second,the drilling and logging data cleaning and smoothing and the clustering number selection by the elbow method are the key steps to obtain the clustering results of bottomhole mechanical specific energy,which can tell the difference in the mechanical specific energy with decimeter-level resolution.Third,the interval with mechanical specific energy within 10%of the averagevalue in the section is selected for deploying perforation clusters,and the compiled computer algorithm can automatically determine the optimal position of fracturing section and cluster,so as to realize the differential design of stage spacing and cluster spacing.In conclusion,the research results can further improve the fractures deployment efficiency and balanced stimulation of volumetric fracturing in unconventional oil andgasreservoirs,and this technology is expected to provide ideas andnew methods forthe fracturedeployment optimization of horizontal well volumetric fracturing in unconventional oil and gas reservoirs.展开更多
Many existing immune detection algorithms rely on a large volume of labeled self-training samples,which are often difficult to obtain in practical scenarios,thus limiting the training of detection models.Furthermore,n...Many existing immune detection algorithms rely on a large volume of labeled self-training samples,which are often difficult to obtain in practical scenarios,thus limiting the training of detection models.Furthermore,noise inherent in the samples can substantially degrade the detection accuracy of these algorithms.To overcome these challenges,we propose an immune generation algorithm that leverages clustering and a rebound mechanism for label propagation(LP-CRI).The dataset is randomly partitioned into multiple subsets,each of which undergoes clustering followed by label propagation and evaluation.The rebound mechanism assesses the model’s performance after propagation and determines whether to revert to its previous state,initiating a subsequent round of propagation to ensure stable and effective training.Experimental results demonstrate that the proposed method is both computationally efficient and easy to train,significantly enhancing detector performance and outperforming traditional immune detection algorithms.展开更多
Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subse...Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.展开更多
In this paper, a combined method of unsupervised clustering and learning vector quantity (LVQ) is presented to forecast the occurrence of solar flare. Three magnetic parameters including the maximum horizontal gradien...In this paper, a combined method of unsupervised clustering and learning vector quantity (LVQ) is presented to forecast the occurrence of solar flare. Three magnetic parameters including the maximum horizontal gradient, the length of the neutral line, and the number of singular points are extracted from SOHO/MDI longitudinal magnetograms as measures. Based on these pa- rameters, the sliding-window method is used to form the sequential data by adding three days evolutionary information. Con- sidering the imbalanced problem in dataset, the K-means clustering, as an unsupervised clustering algorithm, is used to convert imbalanced data to balanced ones. Finally, the learning vector quantity is employed to predict the flares level within 48 hours. Experimental results indicate that the performance of the proposed flare forecasting model with sequential data is improved.展开更多
Traditional unsupervised seismic facies analysis techniques need to assume that seismic data obey mixed Gaussian distribution.However,fi eld seismic data may not meet this condition,thereby leading to wrong classifi c...Traditional unsupervised seismic facies analysis techniques need to assume that seismic data obey mixed Gaussian distribution.However,fi eld seismic data may not meet this condition,thereby leading to wrong classifi cation in the application of this technology.This paper introduces a spectral clustering technique for unsupervised seismic facies analysis.This algorithm is based on on the idea of a graph to cluster the data.Its kem is that seismic data are regarded as points in space,points can be connected with the edge and construct to graphs.When the graphs are divided,the weights of the edges between the different subgraphs are as low as possible,whereas the weights of the inner edges of the subgraph should be as high as possible.That has high computational complexity and entails large memory consumption for spectral clustering algorithm.To solve the problem this paper introduces the idea of sparse representation into spectral clustering.Through the selection of a small number of local sparse representation points,the spectral clustering matrix of all sample points is approximately represented to reduce the cost of spectral clustering operation.Verifi cation of physical model and fi eld data shows that the proposed approach can obtain more accurate seismic facies classification results without considering the data meet any hypothesis.The computing efficiency of this new method is better than that of the conventional spectral clustering method,thereby meeting the application needs of fi eld seismic data.展开更多
Interference signals recognition plays an important role in anti-jamming communication.With the development of deep learning,many supervised interference signals recognition algorithms based on deep learning have emer...Interference signals recognition plays an important role in anti-jamming communication.With the development of deep learning,many supervised interference signals recognition algorithms based on deep learning have emerged recently and show better performance than traditional recognition algorithms.However,there is no unsupervised interference signals recognition algorithm at present.In this paper,an unsupervised interference signals recognition method called double phases and double dimensions contrastive clustering(DDCC)is proposed.Specifically,in the first phase,four data augmentation strategies for interference signals are used in data-augmentation-based(DA-based)contrastive learning.In the second phase,the original dataset’s k-nearest neighbor set(KNNset)is designed in double dimensions contrastive learning.In addition,a dynamic entropy parameter strategy is proposed.The simulation experiments of 9 types of interference signals show that random cropping is the best one of the four data augmentation strategies;the feature dimensional contrastive learning in the second phase can improve the clustering purity;the dynamic entropy parameter strategy can improve the stability of DDCC effectively.The unsupervised interference signals recognition results of DDCC and five other deep clustering algorithms show that the clustering performance of DDCC is superior to other algorithms.In particular,the clustering purity of our method is above 92%,SCAN’s is 81%,and the other three methods’are below 71%when jammingnoise-ratio(JNR)is−5 dB.In addition,our method is close to the supervised learning algorithm.展开更多
In recent years, functional data has been widely used in finance, medicine, biology and other fields. The current clustering analysis can solve the problems in finite-dimensional space, but it is difficult to be direc...In recent years, functional data has been widely used in finance, medicine, biology and other fields. The current clustering analysis can solve the problems in finite-dimensional space, but it is difficult to be directly used for the clustering of functional data. In this paper, we propose a new unsupervised clustering algorithm based on adaptive weights. In the absence of initialization parameter, we use entropy-type penalty terms and fuzzy partition matrix to find the optimal number of clusters. At the same time, we introduce a measure based on adaptive weights to reflect the difference in information content between different clustering metrics. Simulation experiments show that the proposed algorithm has higher purity than some algorithms.展开更多
With the development of information technology,radio communication technology has made rapid progress.Many radio signals that have appeared in space are difficult to classify without manually labeling.Unsupervised rad...With the development of information technology,radio communication technology has made rapid progress.Many radio signals that have appeared in space are difficult to classify without manually labeling.Unsupervised radio signal clustering methods have recently become an urgent need for this situation.Meanwhile,the high complexity of deep learning makes it difficult to understand the decision results of the clustering models,making it essential to conduct interpretable analysis.This paper proposed a combined loss function for unsupervised clustering based on autoencoder.The combined loss function includes reconstruction loss and deep clustering loss.Deep clustering loss is added based on reconstruction loss,which makes similar deep features converge more in feature space.In addition,a features visualization method for signal clustering was proposed to analyze the interpretability of autoencoder utilizing Saliency Map.Extensive experiments have been conducted on a modulated signal dataset,and the results indicate the superior performance of our proposed method over other clustering algorithms.In particular,for the simulated dataset containing six modulation modes,when the SNR is 20dB,the clustering accuracy of the proposed method is greater than 78%.The interpretability analysis of the clustering model was performed to visualize the significant features of different modulated signals and verified the high separability of the features extracted by clustering model.展开更多
To improve the recognition ability of communication jamming signals,Siamese Neural Network-based Open World Recognition(SNNOWR)is proposed.The algorithm can recognize known jamming classes,detect new(unknown)jamming c...To improve the recognition ability of communication jamming signals,Siamese Neural Network-based Open World Recognition(SNNOWR)is proposed.The algorithm can recognize known jamming classes,detect new(unknown)jamming classes,and unsupervised cluseter new classes.The network of SNN-OWR is trained supervised with paired input data consisting of two samples from a known dataset.On the one hand,the network is required to have the ability to distinguish whether two samples are from the same class.On the other hand,the latent distribution of known class is forced to approach their own unique Gaussian distribution,which is prepared for the subsequent open set testing.During the test,the unknown class detection process based on Gaussian probability density function threshold is designed,and an unsupervised clustering algorithm of the unknown jamming is realized by using the prior knowledge of known classes.The simulation results show that when the jamming-to-noise ratio is more than 0d B,the accuracy of SNN-OWR algorithm for known jamming classes recognition,unknown jamming detection and unsupervised clustering of unknown jamming is about 95%.This indicates that the SNN-OWR algorithm can make the effect of the recognition of unknown jamming be almost the same as that of known jamming.展开更多
Air traffic controllers face challenging initiatives due to uncertainty in air traffic.One way to support their initiatives is to identify similar operation scenes.Based on the operation characteristics of typical bus...Air traffic controllers face challenging initiatives due to uncertainty in air traffic.One way to support their initiatives is to identify similar operation scenes.Based on the operation characteristics of typical busy area control airspace,an complexity measurement indicator system is established.We find that operation in area sector is characterized by aggregation and continuity,and that dimensionality and information redundancy reduction are feasible for dynamic operation data base on principle components.Using principle components,discrete features and time series features are constructed.Based on Gaussian kernel function,Euclidean distance and dynamic time warping(DTW)are used to measure the similarity of the features.Then the matrices of similarity are input in Spectral Clustering.The clustering results show that similar scenes of trend are not ideal and similar scenes of modes are good base on the indicator system.Finally,actual vertical operation decisions for area sector and results of identification are compared,which are visualized by metric multidimensional scaling(MDS)plots.We find that identification results can well reflect the operation at peak hours,but controllers make different decisions under the similar conditions before dawn.The compliance rate of busy operation mode and division decisions at peak hours is 96.7%.The results also show subjectivity of actual operation and objectivity of identification.In most scenes,we observe that similar air traffic activities provide regularity for initiatives,validating the potential of this approach for initiatives and other artificial intelligence support.展开更多
Information on survival and growth during the early life stage is essential to understand the mechanism of interannual variations in fish recruitment.Chub mackerel Scomber japonicus is a commercially important pelagic...Information on survival and growth during the early life stage is essential to understand the mechanism of interannual variations in fish recruitment.Chub mackerel Scomber japonicus is a commercially important pelagic fish widely distributed in the northwestern Pacific.Its catch showed large fluctuations with changes in distribution and migration under climate change and strong fishing.We determined the hatch dates and growth rates of young-of-the-year of chub mackerel through otolith microstructure using samples collected in the Oyashio water in autumn 2018.Results show that the ages of young chub mackerel ranged between 120 and 180 d,and the estimated hatch date lasted from midJanuary to late May with a peak from mid-March to mid-April.Average otolith daily increment width during the early life stages(from hatching to 25 d)showed an increasing trend.Chub mackerel grows slowly in the first 10 d,and then grows faster during the 10thto 25thd.Three groups with dissimilar growth histories and migration routes were identified using unsupervised random forest clustering analysis,but all eventually converge on the same nursery ground.The faster growth of young-of-the-year chub mackerel leads to better recruitment due to the hypothesis of growth-dependent mortality.Most chub mackerels hatched in March and April,the spawning period is longer and earlier,which could lead to strong year classes.These findings on population composition and life history traits of young-of-the-year of chub mackerel provide valuable information on its recruitment processes during the period of stock recovery.展开更多
After decades of low year classes,the stock of Japanese sardine(Sardinops melanostictus)has begun to recover since the mid-2000s.The hatch dates and otolith growth rates of age-0 juvenile sardine,which were collected ...After decades of low year classes,the stock of Japanese sardine(Sardinops melanostictus)has begun to recover since the mid-2000s.The hatch dates and otolith growth rates of age-0 juvenile sardine,which were collected in the subarctic Oyashio waters in autumn 2018,were determined from an otolith microstructure analysis.The sardines were hatched from late January to late April,while mostly in February and March.The otolith growth rate increased continuously up to 60 d after hatching and thereafter de-creased.The revealed growth rate in a crucial growth period is faster than that reported for juvenile sardines collected in the 1990s,which is coincided with the recent recovery trend of the sardine stock.Two groups with different hatch dates,growth histories,and migration routes were identified using unsupervised random forest clustering analysis.They were considered inshore and offshore migration individuals in accordance with recent researches.In the offshore group,a high proportion of sardine juveniles hatched late and grew faster in the Kuroshio-Oyashio transitional waters,a finding consistent with the hypothesis of growth-rate-dependent re-cruitment.This finding on the population composition and growth rate of juvenile sardine in the Oyashio waters can be a basis for an improved prediction of their survival and provides us with valuable information on the recruitment processes of this stock during the period of stock recovery.展开更多
This paper presents an unsupervised clustering random-forest-based metric for affinity estimation in large and high-dimensional data.The criterion used for node splitting during forest construction can handle rank-def...This paper presents an unsupervised clustering random-forest-based metric for affinity estimation in large and high-dimensional data.The criterion used for node splitting during forest construction can handle rank-deficiency when measuring cluster compactness.The binary forest-based metric is extended to continuous metrics by exploiting both the common traversal path and the smallest shared parent node.The proposed forest-based metric efficiently estimates affinity by passing down data pairs in the forest using a limited number of decision trees.A pseudo-leaf-splitting(PLS)algorithm is introduced to account for spatial relationships,which regularizes affinity measures and overcomes inconsistent leaf assign-ments.The random-forest-based metric with PLS facilitates the establishment of consistent and point-wise correspondences.The proposed method has been applied to automatic phrase recognition using color and depth videos and point-wise correspondence.Extensive experiments demonstrate the effectiveness of the proposed method in affinity estimation in a comparison with the state-of-the-art.展开更多
This paper exposes some intrlnsic chsracterlstlca of the spectral clustering method by using the tools from the mstrlx perturbation theory. We construct s welght mstrix of s graph and study Its elgenvalues and elgenve...This paper exposes some intrlnsic chsracterlstlca of the spectral clustering method by using the tools from the mstrlx perturbation theory. We construct s welght mstrix of s graph and study Its elgenvalues and elgenvectors. It shows that the number of clusters Is equal to the number of elgenvslues that are larger than 1, and the number of polnts In each of the clusters can be spproxlmsted by the associated elgenvslue. It also shows that the elgenvector of the weight rnatrlx can be used dlrectly to perform clusterlng; that Is, the dlrectlonsl angle between the two-row vectors of the mstrlx derlved from the elgenvectors Is s sultable distance measure for clustsrlng. As s result, an unsupervised spectral clusterlng slgorlthm based on welght mstrlx （USCAWM） Is developed. The experlmental results on s number of srtlficisl and real-world data sets show the correctness of the theoretical analysis.展开更多
Nanoscale surface topography is an effective approach in modulating cell-material interactions,significantly impacting cellular and nuclear morphologies,as well as their functionality.However,the adaptive changes in c...Nanoscale surface topography is an effective approach in modulating cell-material interactions,significantly impacting cellular and nuclear morphologies,as well as their functionality.However,the adaptive changes in cellular metabolism induced by the mechanical and geometrical microenvironment of the nanotopography remain poorly understood.In this study,we investigated the metabolic activities in cells cultured on engineered nanopillar substrates by using a label-free multimodal optical imaging platform.This multimodal imaging platform,integrating two photon fluorescence(TPF)and stimulated Raman scattering(SRS)microscopy,allowed us to directly visualize and quantify metabolic activities of cells in 3D at the subcellular scale.We discovered that the nanopillar structure significantly reduced the cell spreading area and circularity compared to flat surfaces.Nanopillar-induced mechanical cues significantly modulate cellular metabolic activities with variations in nanopillar geometry further influencing these metabolic processes.Cells cultured on nanopillars exhibited reduced oxidative stress,decreased protein and lipid synthesis,and lower lipid unsaturation in comparison to those on flat substrates.Hierarchical clustering also revealed that pitch differences in the nanopillar had a more significant impact on cell metabolic activity than diameter variations.These insights improve our understanding of how engineered nanotopographies can be used to control cellular metabolism,offering possibilities for designing advanced cell culture platforms which can modulate cell behaviors and mimic natural cellular environment and optimize cell-based applications.By leveraging the unique metabolic effects of nanopillar arrays,one can develop more effective strategies for directing the fate of cells,enhancing the performance of cell-based therapies,and creating regenerative medicine applications.展开更多
Spiropyran derivatives are prototype mechanophores with a promising application as molecular sensors because of their changeable structure under external force stimuli.However,the chemical structure evolution under ex...Spiropyran derivatives are prototype mechanophores with a promising application as molecular sensors because of their changeable structure under external force stimuli.However,the chemical structure evolution under external stimuli remains unclear due to the uncertainty and difficulty in distinguishing the structures of different ring-opened merocyanine isomers generated in the force-induced reaction.Here we identify the structure of isomers produced by the force-induced reaction of spiropyran derivatives using a single-molecule conductance measurement and an unsupervised clustering algorithm.We found that the original data from the single-molecule conductance measurement can be divided into four clusters through unsupervised clustering.By introducing a photoinduced reaction and theoretical calculation,we identified and attributed the four clusters of data to the multiple states of the molecular junctions.Our work demonstrates that a single-molecule break junction measurement can distinguish the isomers in the force-induced reaction,suggesting the great potential of single-molecule conductance measurement and unsupervised clustering approaches for structural analysis.展开更多
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.
基金This work is supported by Faculty of Science and Technology,University of the Faroe Islands,Faroe Islands,Denmark and REVA University,Bengaluru.
文摘This paper presents,a new approach of Medical Image Pixels Clustering(MIPC),aims to trace the dissimilar patterns over the Magnetic Resonance(MR)image through the process of automatically identify the appropriate number of distinct clusters based on different improved unsupervised clustering schemes for enrichment,pattern predication and deeper investigation.The proposed MIPC consists of two stages:clustering and validation.In the clustering stage,the MIPC automatically identifies the distinct number of dissimilar clusters over the gray scale MR image based on three different improved unsupervised clustering schemes likely improved Limited Agglomerative Clustering(iLIAC),Dynamic Automatic Agglomerative Clustering(DAAC)and Optimum N-Means(ONM).In the second stage,the performance of MIPC approach is estimated by measuring Intra intimacy and Intra contrast of each individual cluster in the result of MR image based on proposed validation method namely Shreekum Intra Cluster Measure(SICM).Experimental results showthat the MIPC approach is better suited for automatic identification of highly relative dissimilar clusters over the MR cancer images with higher Intra closeness and lower Intra contrast based on improved unsupervised clustering schemes.
文摘An unsupervised clustering\|based intrusion detection algorithm is discussed in this paper. The basic idea of the algorithm is to produce the cluster by comparing the distances of unlabeled training data sets. With the classified data instances, anomaly data clusters can be easily identified by normal cluster ratio and the identified cluster can be used in real data detection. The benefit of the algorithm is that it doesn't need labeled training data sets. The experiment concludes that this approach can detect unknown intrusions efficiently in the real network connections via using the data sets of KDD99.
基金supported by the Science Fund for Excellent Youth of the National Natural Science Foundation of China"Well hydrodynamics and engineering"(No.52122401).
文摘Multi-cluster perforation and multi-staged fracturing of horizontal well is one of the main technologies in volumetric fracturing stimulation of unconventional oil and gas reservoirs,but unconventional reservoirs in China are generally of strong heterogeneity,which causes different fracture initiation pressures in different positions of lateral,making it difficult to ensure the balanced fracture initiation and propagation between clusters in multi-cluster perforating.It is in urgent need to precisely evaluate the difference in rock strength in lateral and determine the well section with similar rock strength to deploy fractures,so as to reach the goal of balanced stimulation.Based on the drilling and logging data,this paper establishes an unsupervised clustering model of mechanical specific energy of bit at the bottomhole the lateral.Then,the influence of drill string friction,composite drilling and jet-assisted rock breaking on the mechanical specific energy is analyzed,and the distribution and clustering categories of bottomhole mechanical specific energy with decimeter spatial resolution are obtained.Finally,a fracture deployment optimization method for horizontal well volumetric fracturing aiming balanced stimulation is developed by comprehensively considering inter-fracture interference,casing collar position,plug position,and clustering result of bottomhole mechanical specific energy.The following results are obtained.First,compared with brittleness index,Poisson's ratioandstressdifference,perforation erosion area isina strongercorrelationwith themechanical specific energy,andthemechanical specific energy can effectively characterize the difference in the amount of proppant injected into the perforation clusters in the lateral,so it can be served as one of the important indicators for the selection of fracture deployment position.Second,the drilling and logging data cleaning and smoothing and the clustering number selection by the elbow method are the key steps to obtain the clustering results of bottomhole mechanical specific energy,which can tell the difference in the mechanical specific energy with decimeter-level resolution.Third,the interval with mechanical specific energy within 10%of the averagevalue in the section is selected for deploying perforation clusters,and the compiled computer algorithm can automatically determine the optimal position of fracturing section and cluster,so as to realize the differential design of stage spacing and cluster spacing.In conclusion,the research results can further improve the fractures deployment efficiency and balanced stimulation of volumetric fracturing in unconventional oil andgasreservoirs,and this technology is expected to provide ideas andnew methods forthe fracturedeployment optimization of horizontal well volumetric fracturing in unconventional oil and gas reservoirs.
基金granted by Key Project of Beijing Municipal Social Science Foundation(No.15ZHA004)Key Project of Beijing Municipal Social Science Foundation and Beijing Municipal Education Commission Social Science Program(No.SZ20231123202).
文摘Many existing immune detection algorithms rely on a large volume of labeled self-training samples,which are often difficult to obtain in practical scenarios,thus limiting the training of detection models.Furthermore,noise inherent in the samples can substantially degrade the detection accuracy of these algorithms.To overcome these challenges,we propose an immune generation algorithm that leverages clustering and a rebound mechanism for label propagation(LP-CRI).The dataset is randomly partitioned into multiple subsets,each of which undergoes clustering followed by label propagation and evaluation.The rebound mechanism assesses the model’s performance after propagation and determines whether to revert to its previous state,initiating a subsequent round of propagation to ensure stable and effective training.Experimental results demonstrate that the proposed method is both computationally efficient and easy to train,significantly enhancing detector performance and outperforming traditional immune detection algorithms.
基金supported in part by NIH grants R01NS39600,U01MH114829RF1MH128693(to GAA)。
文摘Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.
基金supported by the National Natural Science Foundation of China (Grant No. 10973020)the Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality (Grant No. PHR200906210)+1 种基金the Funding Project for Base Construction of Scientific Research of Beijing Municipal Commission of Education (Grant No. WYJD200902)Beijing Philosophy and Social Science Planning Project (Grant No. 09BaJG258)
文摘In this paper, a combined method of unsupervised clustering and learning vector quantity (LVQ) is presented to forecast the occurrence of solar flare. Three magnetic parameters including the maximum horizontal gradient, the length of the neutral line, and the number of singular points are extracted from SOHO/MDI longitudinal magnetograms as measures. Based on these pa- rameters, the sliding-window method is used to form the sequential data by adding three days evolutionary information. Con- sidering the imbalanced problem in dataset, the K-means clustering, as an unsupervised clustering algorithm, is used to convert imbalanced data to balanced ones. Finally, the learning vector quantity is employed to predict the flares level within 48 hours. Experimental results indicate that the performance of the proposed flare forecasting model with sequential data is improved.
基金This work was supported by National Natural Science Foundation of China(Nos.U1562218,41604107,and 41804126).
文摘Traditional unsupervised seismic facies analysis techniques need to assume that seismic data obey mixed Gaussian distribution.However,fi eld seismic data may not meet this condition,thereby leading to wrong classifi cation in the application of this technology.This paper introduces a spectral clustering technique for unsupervised seismic facies analysis.This algorithm is based on on the idea of a graph to cluster the data.Its kem is that seismic data are regarded as points in space,points can be connected with the edge and construct to graphs.When the graphs are divided,the weights of the edges between the different subgraphs are as low as possible,whereas the weights of the inner edges of the subgraph should be as high as possible.That has high computational complexity and entails large memory consumption for spectral clustering algorithm.To solve the problem this paper introduces the idea of sparse representation into spectral clustering.Through the selection of a small number of local sparse representation points,the spectral clustering matrix of all sample points is approximately represented to reduce the cost of spectral clustering operation.Verifi cation of physical model and fi eld data shows that the proposed approach can obtain more accurate seismic facies classification results without considering the data meet any hypothesis.The computing efficiency of this new method is better than that of the conventional spectral clustering method,thereby meeting the application needs of fi eld seismic data.
基金This research was supported by the National Natural Science Foundation of China under Grant No.U19B2016.,and Zhejiang Provincial Key Lab of Data Storage and Transmission Technology,Hangzhou Dianzi University.
文摘Interference signals recognition plays an important role in anti-jamming communication.With the development of deep learning,many supervised interference signals recognition algorithms based on deep learning have emerged recently and show better performance than traditional recognition algorithms.However,there is no unsupervised interference signals recognition algorithm at present.In this paper,an unsupervised interference signals recognition method called double phases and double dimensions contrastive clustering(DDCC)is proposed.Specifically,in the first phase,four data augmentation strategies for interference signals are used in data-augmentation-based(DA-based)contrastive learning.In the second phase,the original dataset’s k-nearest neighbor set(KNNset)is designed in double dimensions contrastive learning.In addition,a dynamic entropy parameter strategy is proposed.The simulation experiments of 9 types of interference signals show that random cropping is the best one of the four data augmentation strategies;the feature dimensional contrastive learning in the second phase can improve the clustering purity;the dynamic entropy parameter strategy can improve the stability of DDCC effectively.The unsupervised interference signals recognition results of DDCC and five other deep clustering algorithms show that the clustering performance of DDCC is superior to other algorithms.In particular,the clustering purity of our method is above 92%,SCAN’s is 81%,and the other three methods’are below 71%when jammingnoise-ratio(JNR)is−5 dB.In addition,our method is close to the supervised learning algorithm.
文摘In recent years, functional data has been widely used in finance, medicine, biology and other fields. The current clustering analysis can solve the problems in finite-dimensional space, but it is difficult to be directly used for the clustering of functional data. In this paper, we propose a new unsupervised clustering algorithm based on adaptive weights. In the absence of initialization parameter, we use entropy-type penalty terms and fuzzy partition matrix to find the optimal number of clusters. At the same time, we introduce a measure based on adaptive weights to reflect the difference in information content between different clustering metrics. Simulation experiments show that the proposed algorithm has higher purity than some algorithms.
基金supported in part by the National Natural Science Foundation of China(No.62276206)the Key Research and Development Program of Shaanxi under Grant S2022-YF-YBGY-0921+2 种基金the State Key Program of National Natural Science of China(No.62231027)supported by the Science and Technology on Communication Information Security Control Laboratory。
文摘With the development of information technology,radio communication technology has made rapid progress.Many radio signals that have appeared in space are difficult to classify without manually labeling.Unsupervised radio signal clustering methods have recently become an urgent need for this situation.Meanwhile,the high complexity of deep learning makes it difficult to understand the decision results of the clustering models,making it essential to conduct interpretable analysis.This paper proposed a combined loss function for unsupervised clustering based on autoencoder.The combined loss function includes reconstruction loss and deep clustering loss.Deep clustering loss is added based on reconstruction loss,which makes similar deep features converge more in feature space.In addition,a features visualization method for signal clustering was proposed to analyze the interpretability of autoencoder utilizing Saliency Map.Extensive experiments have been conducted on a modulated signal dataset,and the results indicate the superior performance of our proposed method over other clustering algorithms.In particular,for the simulated dataset containing six modulation modes,when the SNR is 20dB,the clustering accuracy of the proposed method is greater than 78%.The interpretability analysis of the clustering model was performed to visualize the significant features of different modulated signals and verified the high separability of the features extracted by clustering model.
基金supported by the National Natural Science Foundation of China(U19B2016)Zhejiang Provincial Key Lab of Data Storage and Transmission Technology,Hangzhou Dianzi University。
文摘To improve the recognition ability of communication jamming signals,Siamese Neural Network-based Open World Recognition(SNNOWR)is proposed.The algorithm can recognize known jamming classes,detect new(unknown)jamming classes,and unsupervised cluseter new classes.The network of SNN-OWR is trained supervised with paired input data consisting of two samples from a known dataset.On the one hand,the network is required to have the ability to distinguish whether two samples are from the same class.On the other hand,the latent distribution of known class is forced to approach their own unique Gaussian distribution,which is prepared for the subsequent open set testing.During the test,the unknown class detection process based on Gaussian probability density function threshold is designed,and an unsupervised clustering algorithm of the unknown jamming is realized by using the prior knowledge of known classes.The simulation results show that when the jamming-to-noise ratio is more than 0d B,the accuracy of SNN-OWR algorithm for known jamming classes recognition,unknown jamming detection and unsupervised clustering of unknown jamming is about 95%.This indicates that the SNN-OWR algorithm can make the effect of the recognition of unknown jamming be almost the same as that of known jamming.
基金the National Natural Science Foundation of China(Nos.71731001,61573181,71971114)the Fundamental Research Funds for the Central Universities(No.NS2020045)。
文摘Air traffic controllers face challenging initiatives due to uncertainty in air traffic.One way to support their initiatives is to identify similar operation scenes.Based on the operation characteristics of typical busy area control airspace,an complexity measurement indicator system is established.We find that operation in area sector is characterized by aggregation and continuity,and that dimensionality and information redundancy reduction are feasible for dynamic operation data base on principle components.Using principle components,discrete features and time series features are constructed.Based on Gaussian kernel function,Euclidean distance and dynamic time warping(DTW)are used to measure the similarity of the features.Then the matrices of similarity are input in Spectral Clustering.The clustering results show that similar scenes of trend are not ideal and similar scenes of modes are good base on the indicator system.Finally,actual vertical operation decisions for area sector and results of identification are compared,which are visualized by metric multidimensional scaling(MDS)plots.We find that identification results can well reflect the operation at peak hours,but controllers make different decisions under the similar conditions before dawn.The compliance rate of busy operation mode and division decisions at peak hours is 96.7%.The results also show subjectivity of actual operation and objectivity of identification.In most scenes,we observe that similar air traffic activities provide regularity for initiatives,validating the potential of this approach for initiatives and other artificial intelligence support.
基金Supported by the National Natural Science Foundation of China(No.41930534)the Third Institute of Oceanography through the National Program on Global Change and Air-Sea Interaction(No.GASI-02-PACYDaut)。
文摘Information on survival and growth during the early life stage is essential to understand the mechanism of interannual variations in fish recruitment.Chub mackerel Scomber japonicus is a commercially important pelagic fish widely distributed in the northwestern Pacific.Its catch showed large fluctuations with changes in distribution and migration under climate change and strong fishing.We determined the hatch dates and growth rates of young-of-the-year of chub mackerel through otolith microstructure using samples collected in the Oyashio water in autumn 2018.Results show that the ages of young chub mackerel ranged between 120 and 180 d,and the estimated hatch date lasted from midJanuary to late May with a peak from mid-March to mid-April.Average otolith daily increment width during the early life stages(from hatching to 25 d)showed an increasing trend.Chub mackerel grows slowly in the first 10 d,and then grows faster during the 10thto 25thd.Three groups with dissimilar growth histories and migration routes were identified using unsupervised random forest clustering analysis,but all eventually converge on the same nursery ground.The faster growth of young-of-the-year chub mackerel leads to better recruitment due to the hypothesis of growth-dependent mortality.Most chub mackerels hatched in March and April,the spawning period is longer and earlier,which could lead to strong year classes.These findings on population composition and life history traits of young-of-the-year of chub mackerel provide valuable information on its recruitment processes during the period of stock recovery.
基金supported by the National Natural Science Foundation of China(Nos.41930534,41861134-037,and 41876177)funding to the Third Institute of Oceanography through the National Program on Global Change and Air-Sea Interaction(No.GASI-02-PAC-YDaut).
文摘After decades of low year classes,the stock of Japanese sardine(Sardinops melanostictus)has begun to recover since the mid-2000s.The hatch dates and otolith growth rates of age-0 juvenile sardine,which were collected in the subarctic Oyashio waters in autumn 2018,were determined from an otolith microstructure analysis.The sardines were hatched from late January to late April,while mostly in February and March.The otolith growth rate increased continuously up to 60 d after hatching and thereafter de-creased.The revealed growth rate in a crucial growth period is faster than that reported for juvenile sardines collected in the 1990s,which is coincided with the recent recovery trend of the sardine stock.Two groups with different hatch dates,growth histories,and migration routes were identified using unsupervised random forest clustering analysis.They were considered inshore and offshore migration individuals in accordance with recent researches.In the offshore group,a high proportion of sardine juveniles hatched late and grew faster in the Kuroshio-Oyashio transitional waters,a finding consistent with the hypothesis of growth-rate-dependent re-cruitment.This finding on the population composition and growth rate of juvenile sardine in the Oyashio waters can be a basis for an improved prediction of their survival and provides us with valuable information on the recruitment processes of this stock during the period of stock recovery.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61876008 and 82071172Beijing Natural Science Foundation under Grant No.7192227the Research Center of Engineering and Technology for Digital Dentistry,the Ministry of Health.
文摘This paper presents an unsupervised clustering random-forest-based metric for affinity estimation in large and high-dimensional data.The criterion used for node splitting during forest construction can handle rank-deficiency when measuring cluster compactness.The binary forest-based metric is extended to continuous metrics by exploiting both the common traversal path and the smallest shared parent node.The proposed forest-based metric efficiently estimates affinity by passing down data pairs in the forest using a limited number of decision trees.A pseudo-leaf-splitting(PLS)algorithm is introduced to account for spatial relationships,which regularizes affinity measures and overcomes inconsistent leaf assign-ments.The random-forest-based metric with PLS facilitates the establishment of consistent and point-wise correspondences.The proposed method has been applied to automatic phrase recognition using color and depth videos and point-wise correspondence.Extensive experiments demonstrate the effectiveness of the proposed method in affinity estimation in a comparison with the state-of-the-art.
基金Supported by the National Natural Science Foundation of China (Grant No. 60375003)the Aeronatical Science Foundation of China (Grant No. 03I53059)
文摘This paper exposes some intrlnsic chsracterlstlca of the spectral clustering method by using the tools from the mstrlx perturbation theory. We construct s welght mstrix of s graph and study Its elgenvalues and elgenvectors. It shows that the number of clusters Is equal to the number of elgenvslues that are larger than 1, and the number of polnts In each of the clusters can be spproxlmsted by the associated elgenvslue. It also shows that the elgenvector of the weight rnatrlx can be used dlrectly to perform clusterlng; that Is, the dlrectlonsl angle between the two-row vectors of the mstrlx derlved from the elgenvectors Is s sultable distance measure for clustsrlng. As s result, an unsupervised spectral clusterlng slgorlthm based on welght mstrlx （USCAWM） Is developed. The experlmental results on s number of srtlficisl and real-world data sets show the correctness of the theoretical analysis.
基金supported by the National Science Foundation(Grant ECCS-2025752)supported by Air Force Office of Scientific Research YIP award(AFOSR FA9550-23-1-0090)and Cancer research coordinating committee faculty seed grant to Z.J.We acknowledge support from NIH R01GM149976,NIH U01AI167892,NIH 5R01NS111039,NIH R21NS125395,U54CA132378,Sloan Research Fellow Award,and Hellman Fellow Award.
文摘Nanoscale surface topography is an effective approach in modulating cell-material interactions,significantly impacting cellular and nuclear morphologies,as well as their functionality.However,the adaptive changes in cellular metabolism induced by the mechanical and geometrical microenvironment of the nanotopography remain poorly understood.In this study,we investigated the metabolic activities in cells cultured on engineered nanopillar substrates by using a label-free multimodal optical imaging platform.This multimodal imaging platform,integrating two photon fluorescence(TPF)and stimulated Raman scattering(SRS)microscopy,allowed us to directly visualize and quantify metabolic activities of cells in 3D at the subcellular scale.We discovered that the nanopillar structure significantly reduced the cell spreading area and circularity compared to flat surfaces.Nanopillar-induced mechanical cues significantly modulate cellular metabolic activities with variations in nanopillar geometry further influencing these metabolic processes.Cells cultured on nanopillars exhibited reduced oxidative stress,decreased protein and lipid synthesis,and lower lipid unsaturation in comparison to those on flat substrates.Hierarchical clustering also revealed that pitch differences in the nanopillar had a more significant impact on cell metabolic activity than diameter variations.These insights improve our understanding of how engineered nanotopographies can be used to control cellular metabolism,offering possibilities for designing advanced cell culture platforms which can modulate cell behaviors and mimic natural cellular environment and optimize cell-based applications.By leveraging the unique metabolic effects of nanopillar arrays,one can develop more effective strategies for directing the fate of cells,enhancing the performance of cell-based therapies,and creating regenerative medicine applications.
基金supported by the National Natural Science Foundation of China(grant nos.22173075,21933012,61901402,31871877,and 21774106)the National Key R&D Program of China(grant no.2017YFA0204902)+1 种基金the Fundamental Research Funds for the Central Universities(grant nos.20720200068 and 20720190002)the Natural Science Foundation of Fujian Province(grant no.2018J06004).
文摘Spiropyran derivatives are prototype mechanophores with a promising application as molecular sensors because of their changeable structure under external force stimuli.However,the chemical structure evolution under external stimuli remains unclear due to the uncertainty and difficulty in distinguishing the structures of different ring-opened merocyanine isomers generated in the force-induced reaction.Here we identify the structure of isomers produced by the force-induced reaction of spiropyran derivatives using a single-molecule conductance measurement and an unsupervised clustering algorithm.We found that the original data from the single-molecule conductance measurement can be divided into four clusters through unsupervised clustering.By introducing a photoinduced reaction and theoretical calculation,we identified and attributed the four clusters of data to the multiple states of the molecular junctions.Our work demonstrates that a single-molecule break junction measurement can distinguish the isomers in the force-induced reaction,suggesting the great potential of single-molecule conductance measurement and unsupervised clustering approaches for structural analysis.
