By closely examining hue, saturation and value （HSV） images of the solids holdup distribution in a riser, it can be seen that a ＂cluster＂ is the combination of a relatively stable core cluster of the highest solid...By closely examining hue, saturation and value （HSV） images of the solids holdup distribution in a riser, it can be seen that a ＂cluster＂ is the combination of a relatively stable core cluster of the highest solids holdups and constantly changing cluster clouds of solids holdups that are higher than the dilute phase. Based on this analysis, a threshold selection method maximizing the inter-class variance between the background and foreground classes is introduced. A systematic cluster identification process is therefore proposed that： （｜） applies the threshold selection method to obtain the critical solids holdup threshold ~c to discriminate dense and dilute phases and （2） applies the method again in the dense phase regions to obtain the cluster solids holdup threshold Ssct that identifies the core clusters. Using this systematic process, clusters of different shapes and sizes and a relatively clear boundary can be visualized clearly and identified accurately. Using ~sct, the core cluster fraction is calculated by dividing the total number of pixels in the core cluster by the total number of image pixels. The variation of the core cluster fraction according to operating conditions is also discussed.展开更多
The thermal-induced error is a very important sour ce of machining errors of machine tools. To compensate the thermal-induced machin ing errors, a relationship model between the thermal field and deformations was need...The thermal-induced error is a very important sour ce of machining errors of machine tools. To compensate the thermal-induced machin ing errors, a relationship model between the thermal field and deformations was needed. The relationship can be deduced by virtual of FEM (Finite Element Method ), ANN (Artificial Neural Network) or MRA (Multiple Regression Analysis). MR A is on the basis of a total understanding of the temperature distribution of th e machine tool. Although the more the temperatures measured are, the more accura te the MRA is, too more temperatures will hinder the analysis calculation. So it is necessary to identify the key temperatures of the machine tool. The selectio n of key temperatures decides the efficiency and precision of MRA. Because of th e complexities and multi-input and multi-output structure of the relationships , the exact quantitative portions as well as the unclear portions must be taken into consideration together to improve the identification of key temperatures. I n this paper, a fuzzy cluster analysis was used to select the key temperatures. The substance of identifying the key temperatures is to group all temperatures b y their relativity, and then to select a temperature from each group as the repr esentation. A fuzzy cluster analysis can uncover the relationships between t he thermal field and deformations more truly and thoroughly. A fuzzy cluster ana lysis is the cluster analysis based on fuzzy sets. Given U={u i|i=0,...,N}, in which u i is the temperature measured, a fuzzy matrix R can be obta ined. The transfer close package t(R) can be deduced from R. A fuzzy clu ster of U then conducts on the basis of t(R). Based on the fuzzy cluster analysis discussed above, this paper identified the k ey temperatures of a horizontal machining center. The number of the temperatures measured was reduced to 4 from 32, and then the multiple regression relationshi p models between the 4 temperatures and the thermal deformations of the spindle were drawn. The remnant errors between the regression models and measured deform ations reached a satisfying low level. At the same time, the decreasing of tempe rature variable number improved the efficiency of measure and analysis greatly.展开更多
In this study,the advanced machine learning algorithm NESTORE(Next Strong Related Earthquake)was applied to the Japan Meteorological Agency catalog(1973-2024).It calculates the probability that the aftershocks will re...In this study,the advanced machine learning algorithm NESTORE(Next Strong Related Earthquake)was applied to the Japan Meteorological Agency catalog(1973-2024).It calculates the probability that the aftershocks will reach or exceed a magnitude equal to the magnitude of the mainshock minus one and classifies the clusters as type A or type B,depending on whether this condition is met or not.It has been shown useful in the tests in Italy,western Slovenia,Greece,and California.Due to Japan’s high and complex seismic activity,new algorithms were developed to complement NESTORE:a hybrid cluster identification method,which uses both ETAS-based stochastic declustering and deterministic graph-based selection,and REPENESE(RElevant features,class imbalance PErcentage,NEighbour detection,SElection),an algorithm for detecting outliers in skewed class distributions,which takes in account if one class has a larger number of samples with respect to the other(class imbalance).Trained with data from 1973 to 2004(7 type A and 43 type B clusters)and tested from 2005 to 2023(4 type A and 27 type B clusters),the method correctly forecasted 75%of A clusters and 96%of B clusters,achieving a precision of 0.75 and an accuracy of 0.94 six hours after the mainshock.It accurately classified the 2011 Tōhoku event cluster.Near-real-time forecasting was applied to the sequence after the April 17,2024 M6.6 earthquake in Shikoku,correctly classifying it as a“Type B cluster”.These results highlight the potential for the forecasting of strong aftershocks in regions with high seismicity and class imbalance,as evidenced by the high recall,precision and accuracy values achieved in the test phase.展开更多
Water distribution network(WDN)leakage management has received increased attention in recent years.One of the most successful leakage-control strategies is to divide the network into District Metered Areas(DMAs).As a ...Water distribution network(WDN)leakage management has received increased attention in recent years.One of the most successful leakage-control strategies is to divide the network into District Metered Areas(DMAs).As a multi-staged technique,the generation of DMAs is a difficult task in design and implementation(i.e.,clustering,sectorization,and performance evaluation).Previous studies on DMAs implementation did not consider the potential use of existing valves in achieving the objective.In this work,a methodology is proposed for detecting clusters and reducing the cost of additional valves and DMA sectorization by considering existing valves as much as possible.The procedure of DMAs identification has been divided into three stages,i.e.,a)clusters identification;b)sectorization or boundaries optimization and c)performance evaluation of the partitioned network.The proposed methodology is evaluated on a simple network and a real-world water network with the findings provided and compared to the DMAs,established for a raw water network with no existing valves.It is found that there is an adequate difference in cost of strategy implementation in both the cases for the network under consideration and the existing valve system achieved better network performance in terms of resilience index.展开更多
Sequencing-based spatial transcriptomics(ST)is an emerging technology to study in situ gene expression patterns at the whole-genome scale.Currently,ST data analysis is still complicated by high technical noises and lo...Sequencing-based spatial transcriptomics(ST)is an emerging technology to study in situ gene expression patterns at the whole-genome scale.Currently,ST data analysis is still complicated by high technical noises and low resolution.In addition to the transcriptomic data,matched histopathological images are usually generated for the same tissue sample along the ST experiment.The matched high-resolution histopathological images provide complementary cellular phenotypical information,providing an opportunity to mitigate the noises in ST data.We present a novel ST data analysis method called transcriptome and histopathological image integrative analysis for ST(TIST),which enables the identification of spatial clusters(SCs)and the enhancement of spatial gene expression patterns by integrative analysis of matched transcriptomic data and images.TIST devises a histopathological feature extraction method based on Markov random field(MRF)to learn the cellular features from histopathological images,and integrates them with the transcriptomic data and location information as a network,termed TIST-net.Based on TIST-net,SCs are identified by a random walk-based strategy,and gene expression patterns are enhanced by neighborhood smoothing.We benchmark TIST on both simulated datasets and 32 real samples against several state-of-the-art methods.Results show that TIST is robust to technical noises on multiple analysis tasks for sequencing-based ST data and can find interesting microstructures in different biological scenarios.TIST is available at http://lifeome.net/software/tist/and https://ngdc.cncb.ac.cn/biocode/tools/BT007317.展开更多
文摘By closely examining hue, saturation and value （HSV） images of the solids holdup distribution in a riser, it can be seen that a ＂cluster＂ is the combination of a relatively stable core cluster of the highest solids holdups and constantly changing cluster clouds of solids holdups that are higher than the dilute phase. Based on this analysis, a threshold selection method maximizing the inter-class variance between the background and foreground classes is introduced. A systematic cluster identification process is therefore proposed that： （｜） applies the threshold selection method to obtain the critical solids holdup threshold ~c to discriminate dense and dilute phases and （2） applies the method again in the dense phase regions to obtain the cluster solids holdup threshold Ssct that identifies the core clusters. Using this systematic process, clusters of different shapes and sizes and a relatively clear boundary can be visualized clearly and identified accurately. Using ~sct, the core cluster fraction is calculated by dividing the total number of pixels in the core cluster by the total number of image pixels. The variation of the core cluster fraction according to operating conditions is also discussed.
文摘The thermal-induced error is a very important sour ce of machining errors of machine tools. To compensate the thermal-induced machin ing errors, a relationship model between the thermal field and deformations was needed. The relationship can be deduced by virtual of FEM (Finite Element Method ), ANN (Artificial Neural Network) or MRA (Multiple Regression Analysis). MR A is on the basis of a total understanding of the temperature distribution of th e machine tool. Although the more the temperatures measured are, the more accura te the MRA is, too more temperatures will hinder the analysis calculation. So it is necessary to identify the key temperatures of the machine tool. The selectio n of key temperatures decides the efficiency and precision of MRA. Because of th e complexities and multi-input and multi-output structure of the relationships , the exact quantitative portions as well as the unclear portions must be taken into consideration together to improve the identification of key temperatures. I n this paper, a fuzzy cluster analysis was used to select the key temperatures. The substance of identifying the key temperatures is to group all temperatures b y their relativity, and then to select a temperature from each group as the repr esentation. A fuzzy cluster analysis can uncover the relationships between t he thermal field and deformations more truly and thoroughly. A fuzzy cluster ana lysis is the cluster analysis based on fuzzy sets. Given U={u i|i=0,...,N}, in which u i is the temperature measured, a fuzzy matrix R can be obta ined. The transfer close package t(R) can be deduced from R. A fuzzy clu ster of U then conducts on the basis of t(R). Based on the fuzzy cluster analysis discussed above, this paper identified the k ey temperatures of a horizontal machining center. The number of the temperatures measured was reduced to 4 from 32, and then the multiple regression relationshi p models between the 4 temperatures and the thermal deformations of the spindle were drawn. The remnant errors between the regression models and measured deform ations reached a satisfying low level. At the same time, the decreasing of tempe rature variable number improved the efficiency of measure and analysis greatly.
基金funded by a grant from the Italian Ministry of Foreign Affairs and International Cooperation and Co-funded within the RETURN Extended Partnership and received funding from the European Union Next-GenerationEU(National Recovery and Resilience Plan-NRRP,Mission 4,Component 2,Investment 1.3-D.D.12432/8/2022,PE0000005)the grant“Progetto INGV Pianeta Dinamico:Near real-time results of Physical and Statistical Seismology for earthquakes observations,modelling and forecasting(NEMESIS)”-code CUP D53J19000170001-funded by Italian Ministry MIUR(“Fondo Finalizzato al rilancio degli investimenti delle amministrazioni centrali dello Stato e allo sviluppo del Paese”,legge 145/2018)supported by the Japan Ministry of Education,Culture,Sports,Science and Technology(MEXT)project for seismology Toward Research innovation with data of earthquakes(STAR-E),Grant Number JPJ010217.
文摘In this study,the advanced machine learning algorithm NESTORE(Next Strong Related Earthquake)was applied to the Japan Meteorological Agency catalog(1973-2024).It calculates the probability that the aftershocks will reach or exceed a magnitude equal to the magnitude of the mainshock minus one and classifies the clusters as type A or type B,depending on whether this condition is met or not.It has been shown useful in the tests in Italy,western Slovenia,Greece,and California.Due to Japan’s high and complex seismic activity,new algorithms were developed to complement NESTORE:a hybrid cluster identification method,which uses both ETAS-based stochastic declustering and deterministic graph-based selection,and REPENESE(RElevant features,class imbalance PErcentage,NEighbour detection,SElection),an algorithm for detecting outliers in skewed class distributions,which takes in account if one class has a larger number of samples with respect to the other(class imbalance).Trained with data from 1973 to 2004(7 type A and 43 type B clusters)and tested from 2005 to 2023(4 type A and 27 type B clusters),the method correctly forecasted 75%of A clusters and 96%of B clusters,achieving a precision of 0.75 and an accuracy of 0.94 six hours after the mainshock.It accurately classified the 2011 Tōhoku event cluster.Near-real-time forecasting was applied to the sequence after the April 17,2024 M6.6 earthquake in Shikoku,correctly classifying it as a“Type B cluster”.These results highlight the potential for the forecasting of strong aftershocks in regions with high seismicity and class imbalance,as evidenced by the high recall,precision and accuracy values achieved in the test phase.
文摘Water distribution network(WDN)leakage management has received increased attention in recent years.One of the most successful leakage-control strategies is to divide the network into District Metered Areas(DMAs).As a multi-staged technique,the generation of DMAs is a difficult task in design and implementation(i.e.,clustering,sectorization,and performance evaluation).Previous studies on DMAs implementation did not consider the potential use of existing valves in achieving the objective.In this work,a methodology is proposed for detecting clusters and reducing the cost of additional valves and DMA sectorization by considering existing valves as much as possible.The procedure of DMAs identification has been divided into three stages,i.e.,a)clusters identification;b)sectorization or boundaries optimization and c)performance evaluation of the partitioned network.The proposed methodology is evaluated on a simple network and a real-world water network with the findings provided and compared to the DMAs,established for a raw water network with no existing valves.It is found that there is an adequate difference in cost of strategy implementation in both the cases for the network under consideration and the existing valve system achieved better network performance in terms of resilience index.
基金supported by the National Key R&D Program of China(Grant Nos.2020YFA0712403 and 2021YFF1200901)the National Natural Science Foundation of China(Grant Nos.61922047,81890993,61721003,and 62133006)+1 种基金the Beijing National Research Centre for Information Science and Technology Young Innovation Fund,China(Grant No.BNR2020RC01009)the Science and Technology Commission of Shanghai Municipality,China(Grant No.20PJ1408300)。
文摘Sequencing-based spatial transcriptomics(ST)is an emerging technology to study in situ gene expression patterns at the whole-genome scale.Currently,ST data analysis is still complicated by high technical noises and low resolution.In addition to the transcriptomic data,matched histopathological images are usually generated for the same tissue sample along the ST experiment.The matched high-resolution histopathological images provide complementary cellular phenotypical information,providing an opportunity to mitigate the noises in ST data.We present a novel ST data analysis method called transcriptome and histopathological image integrative analysis for ST(TIST),which enables the identification of spatial clusters(SCs)and the enhancement of spatial gene expression patterns by integrative analysis of matched transcriptomic data and images.TIST devises a histopathological feature extraction method based on Markov random field(MRF)to learn the cellular features from histopathological images,and integrates them with the transcriptomic data and location information as a network,termed TIST-net.Based on TIST-net,SCs are identified by a random walk-based strategy,and gene expression patterns are enhanced by neighborhood smoothing.We benchmark TIST on both simulated datasets and 32 real samples against several state-of-the-art methods.Results show that TIST is robust to technical noises on multiple analysis tasks for sequencing-based ST data and can find interesting microstructures in different biological scenarios.TIST is available at http://lifeome.net/software/tist/and https://ngdc.cncb.ac.cn/biocode/tools/BT007317.
