Density based clustering algorithms(DBCLAs)rely on the notion of density to identify clusters of arbitrary shapes,sizes with varying densities.Existing surveys on DB-CLAs cover only a selected set of algorithms.These ...Density based clustering algorithms(DBCLAs)rely on the notion of density to identify clusters of arbitrary shapes,sizes with varying densities.Existing surveys on DB-CLAs cover only a selected set of algorithms.These surveys fail to provide an extensive information about a variety of DBCLAs proposed till date including a taxonomy of the algorithms.In this paper we present a comprehensive survey of various DB-CLAS over last two decades along with their classification.We group the DBCLAs in each of the four categories:density definition,parameter sensitivity,execution mode and nature of*data and further divide them into various classes under each of these categories.In addition,we compare the DBCLAs through their common features and variations in citation and conceptual dependencies.We identify various application areas of DBCLAS in domains such as astronomy,earth sciences,molecular biology,geography,multimedia.Our survey also identifies probable future directions of DBCLAs where involvement of density based methods may lead to favorable results.展开更多
The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of ...The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.展开更多
A modified DBSCAN algorithm is presented for deinterleaving of radar pulses in modern EW environments.A main characteristic of the proposed method is that using only time of arrival of pulses,the method can sort the p...A modified DBSCAN algorithm is presented for deinterleaving of radar pulses in modern EW environments.A main characteristic of the proposed method is that using only time of arrival of pulses,the method can sort the pulses efficiently.Other PDW information such as rise time,carrier frequency,pulse width,modulation on pulse,fall time and direction of arrival are not required.To identify the valid PRIs in a set of interleaved pulses,an innovative modification of the DBSCAN algorithm is introduced which is accurate and easy to implement.The proposed method determines valid PRIs more accurately and neglects the spurious ones more efficiently as compared to the classical histogram based algorithms such as SDIF.Furthermore,without specifying any input parameter,the proposed method can deinterleave radar pulses while up to 30%jitter is present in the associated PRI.The accuracy and efficiency of the proposed method are verified by computer simulations and real data results.Experimental simulations are based on different real and operational scenarios where the presence of missing and spurious pulses are also considered.So,the simulation results can be of practical significance.展开更多
Process safety in chemical industries is considered to be one of the important goals towards sustainable development.This is due to the fact that,major accidents still occur and continue to exert significant reputatio...Process safety in chemical industries is considered to be one of the important goals towards sustainable development.This is due to the fact that,major accidents still occur and continue to exert significant reputational and financial impacts on process industries.Alarm systems constitute an indispensable component of automation as they draw the attention of process operators to any abnormal conditi on in the plant.Therefore,if deployed properly,alarm systems can play a critical role in helping plant operators ensure process safety and profitability.How-ever,in practice,many process plants suffer from poor alarm system configuration which leads to nuisance alarms and alarm floods that compromise safety.A vast amount of research has primarily focused on developing sophisticated alarm management algorithms to address specific issues.In this article,we provide a simple,practical,systematic approach that can be applied by plant engineers(i.e.,non-experts)to improve industrial alarm system performance.The proposed approach is demonstrated using an industrial power plant case study.展开更多
The combination of increased amount of“space junk”,lack of precise tracking information and control of items orbiting Earth,and debris from past collisions posed important threats to human space missions.In this stu...The combination of increased amount of“space junk”,lack of precise tracking information and control of items orbiting Earth,and debris from past collisions posed important threats to human space missions.In this study,a strategy enabling“multispacecraft to multidebris”debris removal task is proposed.By analyzing the current distribution of space debris,282 large debris and rocket bodies are selected as target database.The traditional-density-based spatial clustering of applications with noise algorithm is innovatively modified by defining the speed increment as threshold parameter for dividing target debris into different clusters.Then,the Hungarian-algorithm-based target allocation strategy is used to assign multispacecraft to different debris clusters for the removal mission.Simulations verify the effectiveness of the proposed“multispacecraft to multidebris”strategy that is able to remove as much as 83.33%of the total 282 target debris.展开更多
Caused by the environment clutter,the radar false alarm plots are unavoidable.Suppressing false alarm points has always been a key issue in Radar plots procession.In this paper,a radar false alarm plots elimination me...Caused by the environment clutter,the radar false alarm plots are unavoidable.Suppressing false alarm points has always been a key issue in Radar plots procession.In this paper,a radar false alarm plots elimination method based on multi-feature extraction and classification is proposed to effectively eliminate false alarm plots.Firstly,the density based spatial clustering of applications with noise(DBSCAN)algorithm is used to cluster the radar echo data processed by constant false-alarm rate(CFAR).The multi-features including the scale features,time domain features and transform domain features are extracted.Secondly,a feature evaluation method combining pearson correlation coefficient(PCC)and entropy weight method(EWM)is proposed to evaluate interrelation among features,effective feature combination sets are selected as inputs of the classifier.Finally,False alarm plots classified as clutters are eliminated.The experimental results show that proposed method can eliminate about 90%false alarm plots with less target loss rate.展开更多
The historical information of loadings on substation helps in evaluation of size of photovoltaic(PV)generation and energy storages for peak shaving and distribution system upgrade deferral. A method, based on consumpt...The historical information of loadings on substation helps in evaluation of size of photovoltaic(PV)generation and energy storages for peak shaving and distribution system upgrade deferral. A method, based on consumption data, is proposed to separate the unusual consumption and to form the clusters of similar regular consumption. The method does optimal partition of the load pattern data into core points and border points, high and less dense regions, respectively. The local outlier factor, which does not require fixed probability distribution of data and statistical measures, ranks the unusual consumptions on only the border points, which are a few percent of the complete data. The suggested method finds the optimal or close to optimal number of clusters of similar shape of load patterns to detect regular peak and valley load demands on different days. Furthermore,identification and characterization of features pertaining to unusual consumptions in load pattern data have been done on border points only. The effectiveness of the proposed method and characterization is tested on two practical distribution systems.展开更多
Immunohistochemistry(IHC)is a vital technique for detecting specific proteins and antigens in tissue sections using antibodies,aiding in the analysis of tumor growth and metastasis.However,IHC is costly and time-consu...Immunohistochemistry(IHC)is a vital technique for detecting specific proteins and antigens in tissue sections using antibodies,aiding in the analysis of tumor growth and metastasis.However,IHC is costly and time-consuming,making it challenging to implement on a large scale.To address this issue,we introduce a method that enables virtual IHC staining directly on Hematoxylin and Eosin(H&E)images.Firstly,we have developed a novel registration technique,called Bi-stage Registration based on density Clustering(BiReC),to enhance the registration efficiency between H&E and IHC images.This method involves automatically generating numerous Regions Of Interest(ROI)labels on the H&E image for model training,with the labels being determined by the intensity of IHC staining.Secondly,we propose a novel two-branch network architecture,called SeaConvNeXt,which integrates a lightweight Squeeze-Enhanced Axial(SEA)attention mechanism to efficiently extract and fuse multi-level local and global features from H&E images for direct prediction of specific proteins and antigens.The SeaConvNeXt consists of a ConvNeXt branch and a global fusion branch.The ConvNeXt branch extracts multi-level local features at four stages,while the global fusion branch,including an SEA Transformer module and three global blocks,is designed for global feature extraction and multiple feature fusion.Our experiments demonstrate that SeaConvNeXt outperforms current state-of-the-art methods on two public datasets with corresponding IHC and H&E images,achieving an AUC of 90.7%on the HER2SC dataset and 82.5%on the CRC dataset.These results suggest that SeaConvNeXt has great potential for predicting virtual IHC staining on H&E images.展开更多
文摘Density based clustering algorithms(DBCLAs)rely on the notion of density to identify clusters of arbitrary shapes,sizes with varying densities.Existing surveys on DB-CLAs cover only a selected set of algorithms.These surveys fail to provide an extensive information about a variety of DBCLAs proposed till date including a taxonomy of the algorithms.In this paper we present a comprehensive survey of various DB-CLAS over last two decades along with their classification.We group the DBCLAs in each of the four categories:density definition,parameter sensitivity,execution mode and nature of*data and further divide them into various classes under each of these categories.In addition,we compare the DBCLAs through their common features and variations in citation and conceptual dependencies.We identify various application areas of DBCLAS in domains such as astronomy,earth sciences,molecular biology,geography,multimedia.Our survey also identifies probable future directions of DBCLAs where involvement of density based methods may lead to favorable results.
文摘The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.
文摘A modified DBSCAN algorithm is presented for deinterleaving of radar pulses in modern EW environments.A main characteristic of the proposed method is that using only time of arrival of pulses,the method can sort the pulses efficiently.Other PDW information such as rise time,carrier frequency,pulse width,modulation on pulse,fall time and direction of arrival are not required.To identify the valid PRIs in a set of interleaved pulses,an innovative modification of the DBSCAN algorithm is introduced which is accurate and easy to implement.The proposed method determines valid PRIs more accurately and neglects the spurious ones more efficiently as compared to the classical histogram based algorithms such as SDIF.Furthermore,without specifying any input parameter,the proposed method can deinterleave radar pulses while up to 30%jitter is present in the associated PRI.The accuracy and efficiency of the proposed method are verified by computer simulations and real data results.Experimental simulations are based on different real and operational scenarios where the presence of missing and spurious pulses are also considered.So,the simulation results can be of practical significance.
文摘Process safety in chemical industries is considered to be one of the important goals towards sustainable development.This is due to the fact that,major accidents still occur and continue to exert significant reputational and financial impacts on process industries.Alarm systems constitute an indispensable component of automation as they draw the attention of process operators to any abnormal conditi on in the plant.Therefore,if deployed properly,alarm systems can play a critical role in helping plant operators ensure process safety and profitability.How-ever,in practice,many process plants suffer from poor alarm system configuration which leads to nuisance alarms and alarm floods that compromise safety.A vast amount of research has primarily focused on developing sophisticated alarm management algorithms to address specific issues.In this article,we provide a simple,practical,systematic approach that can be applied by plant engineers(i.e.,non-experts)to improve industrial alarm system performance.The proposed approach is demonstrated using an industrial power plant case study.
基金supported in part by the National Natural Science Foundation of China(project nos.12172013 and 12322202).
文摘The combination of increased amount of“space junk”,lack of precise tracking information and control of items orbiting Earth,and debris from past collisions posed important threats to human space missions.In this study,a strategy enabling“multispacecraft to multidebris”debris removal task is proposed.By analyzing the current distribution of space debris,282 large debris and rocket bodies are selected as target database.The traditional-density-based spatial clustering of applications with noise algorithm is innovatively modified by defining the speed increment as threshold parameter for dividing target debris into different clusters.Then,the Hungarian-algorithm-based target allocation strategy is used to assign multispacecraft to different debris clusters for the removal mission.Simulations verify the effectiveness of the proposed“multispacecraft to multidebris”strategy that is able to remove as much as 83.33%of the total 282 target debris.
文摘Caused by the environment clutter,the radar false alarm plots are unavoidable.Suppressing false alarm points has always been a key issue in Radar plots procession.In this paper,a radar false alarm plots elimination method based on multi-feature extraction and classification is proposed to effectively eliminate false alarm plots.Firstly,the density based spatial clustering of applications with noise(DBSCAN)algorithm is used to cluster the radar echo data processed by constant false-alarm rate(CFAR).The multi-features including the scale features,time domain features and transform domain features are extracted.Secondly,a feature evaluation method combining pearson correlation coefficient(PCC)and entropy weight method(EWM)is proposed to evaluate interrelation among features,effective feature combination sets are selected as inputs of the classifier.Finally,False alarm plots classified as clutters are eliminated.The experimental results show that proposed method can eliminate about 90%false alarm plots with less target loss rate.
基金supported by the Department of Science and Technology(DST),New Delhi,India(No.DST/EE/2014127)
文摘The historical information of loadings on substation helps in evaluation of size of photovoltaic(PV)generation and energy storages for peak shaving and distribution system upgrade deferral. A method, based on consumption data, is proposed to separate the unusual consumption and to form the clusters of similar regular consumption. The method does optimal partition of the load pattern data into core points and border points, high and less dense regions, respectively. The local outlier factor, which does not require fixed probability distribution of data and statistical measures, ranks the unusual consumptions on only the border points, which are a few percent of the complete data. The suggested method finds the optimal or close to optimal number of clusters of similar shape of load patterns to detect regular peak and valley load demands on different days. Furthermore,identification and characterization of features pertaining to unusual consumptions in load pattern data have been done on border points only. The effectiveness of the proposed method and characterization is tested on two practical distribution systems.
基金supported by the National Key R&D Program of China(No.2023YFC3402800)the National Natural Science Foundation of China(Nos.62371276,62272288,and 82272084)the Fundamental Research Funds for the Central Universities,Shaanxi Normal University(No.GK202302006).
文摘Immunohistochemistry(IHC)is a vital technique for detecting specific proteins and antigens in tissue sections using antibodies,aiding in the analysis of tumor growth and metastasis.However,IHC is costly and time-consuming,making it challenging to implement on a large scale.To address this issue,we introduce a method that enables virtual IHC staining directly on Hematoxylin and Eosin(H&E)images.Firstly,we have developed a novel registration technique,called Bi-stage Registration based on density Clustering(BiReC),to enhance the registration efficiency between H&E and IHC images.This method involves automatically generating numerous Regions Of Interest(ROI)labels on the H&E image for model training,with the labels being determined by the intensity of IHC staining.Secondly,we propose a novel two-branch network architecture,called SeaConvNeXt,which integrates a lightweight Squeeze-Enhanced Axial(SEA)attention mechanism to efficiently extract and fuse multi-level local and global features from H&E images for direct prediction of specific proteins and antigens.The SeaConvNeXt consists of a ConvNeXt branch and a global fusion branch.The ConvNeXt branch extracts multi-level local features at four stages,while the global fusion branch,including an SEA Transformer module and three global blocks,is designed for global feature extraction and multiple feature fusion.Our experiments demonstrate that SeaConvNeXt outperforms current state-of-the-art methods on two public datasets with corresponding IHC and H&E images,achieving an AUC of 90.7%on the HER2SC dataset and 82.5%on the CRC dataset.These results suggest that SeaConvNeXt has great potential for predicting virtual IHC staining on H&E images.
