Tarq geochemical 1:100,000 Sheet is located in Isfahan province which is investigated by Iran’s Geological and Explorations Organization using stream sediment analyzes. This area has stratigraphy of Precambrian to Qu...Tarq geochemical 1:100,000 Sheet is located in Isfahan province which is investigated by Iran’s Geological and Explorations Organization using stream sediment analyzes. This area has stratigraphy of Precambrian to Quaternary rocks and is located in the Central Iran zone. According to the presence of signs of gold mineralization in this area, it is necessary to identify important mineral areas in this area. Therefore, finding information is necessary about the relationship and monitoring the elements of gold, arsenic, and antimony relative to each other in this area to determine the extent of geochemical halos and to estimate the grade. Therefore, a well-known and useful K-means method is used for monitoring the elements in the present study, this is a clustering method based on minimizing the total Euclidean distances of each sample from the center of the classes which are assigned to them. In this research, the clustering quality function and the utility rate of the sample have been used in the desired cluster (S(i)) to determine the optimum number of clusters. Finally, with regard to the cluster centers and the results, the equations were used to predict the amount of the gold element based on four parameters of arsenic and antimony grade, length and width of sampling points.展开更多
The classification of the Northeast China Cold Vortex(NCCV)activity paths is an important way to analyze its characteristics in detail.Based on the daily precipitation data of the northeastern China(NEC)region,and the...The classification of the Northeast China Cold Vortex(NCCV)activity paths is an important way to analyze its characteristics in detail.Based on the daily precipitation data of the northeastern China(NEC)region,and the atmospheric circulation field and temperature field data of ERA-Interim for every six hours,the NCCV processes during the early summer(June)seasons from 1979 to 2018 were objectively identified.Then,the NCCV processes were classified using a machine learning method(k-means)according to the characteristic parameters of the activity path information.The rationality of the classification results was verified from two aspects,as follows:(1)the atmospheric circulation configuration of the NCCV on various paths;and(2)its influences on the climate conditions in the NEC.The obtained results showed that the activity paths of the NCCV could be divided into four types according to such characteristics as the generation origin,movement direction,and movement velocity of the NCCV.These included the generation-eastward movement type in the east of the Mongolia Plateau(eastward movement type or type A);generation-southeast longdistance movement type in the upstream of the Lena River(southeast long-distance movement type or type B);generationeastward less-movement type near Lake Baikal(eastward less-movement type or type C);and the generation-southward less-movement type in eastern Siberia(southward less-movement type or type D).There were obvious differences observed in the atmospheric circulation configuration and the climate impact of the NCCV on the four above-mentioned types of paths,which indicated that the classification results were reasonable.展开更多
Grade estimation is an important phase of mining projects, and one that is considered a challenge due in part to the structural complexities in mineral ore deposits.To overcome this challenge, various techniques have ...Grade estimation is an important phase of mining projects, and one that is considered a challenge due in part to the structural complexities in mineral ore deposits.To overcome this challenge, various techniques have been used in the past. This paper introduces an approach for estimating Au ore grades within a mining deposit using k-means and principal component analysis(PCA). The Khooni district was selected as the case study. This region is interesting geologically, in part because it is considered an important gold source. The study area is situated approximately 60km northeast of the Anarak city and 270km from Esfahan. Through PCA, we sought to understand the relationship between the elements of gold,arsenic, and antimony. Then, by clustering, the behavior of these elements was investigated. One of the most famous and efficient clustering methods is k-means, based on minimizing the total Euclidean distance from each class center. Using the combined results and characteristics of the cluster centers, the gold grade was determined with a correlation coefficient of 91%. An estimation equation for gold grade was derived based on four parameters: arsenic and antimony content, and length and width of the sampling points. The results demonstrate that this approach is faster and more accurate than existing methodologies for ore grade estimation.展开更多
A rarely studied open cluster,King 1 is observed using the 1.3-m telescope equipped with a 2 k×4 k CCD at Vainu Bappu Observatory,India.We analyze the photometric data obtained from CCD observations in both B and...A rarely studied open cluster,King 1 is observed using the 1.3-m telescope equipped with a 2 k×4 k CCD at Vainu Bappu Observatory,India.We analyze the photometric data obtained from CCD observations in both B and V bands.Out of 132 detected stars in the open cluster King 1 field,we have identified four stellar variables,and two among them are reported as newly detected binary systems.The parallax values from Gaia DR2 suggest that the open cluster King 1 is in the background of these two detected binary systems,falling along the same line of sight,giving rise to different parallax values.Periodogram analysis was carried out using Phase Dispersion Minimization(PDM)and the Lomb-Scargle(LS)method for all the detected variables.PHysics Of Eclipsing Binari Es(PHOEBE)is extensively employed to model various stellar parameters of both the detected binary systems.Based on the modeling results obtained from this work,one of the binary systems is reported for the first time as an Eclipsing Detached(ED)and the other as an Eclipsing Contact(EC)binary of W-type W UMa.展开更多
云平台多容器集群数据量大、涉及种类多,导致异常状态监控难度大,为此提出基于Prometheus的监控算法。在云平台中,利用小波分解法获取多容器集群数据的实时状态序列,结合二叉树分解描述法划分不同类型的集群数据特征。根据Prometheus技...云平台多容器集群数据量大、涉及种类多,导致异常状态监控难度大,为此提出基于Prometheus的监控算法。在云平台中,利用小波分解法获取多容器集群数据的实时状态序列,结合二叉树分解描述法划分不同类型的集群数据特征。根据Prometheus技术具备的分布式储存管理特点划分监控空间,并设定监控类中心,对比多容器集群数据与该节点中心相似性,相似性最强的数据即异常。仿真实验证明,方法监控异常状态数据入侵信号在800~1200测试点位间出现大幅度变动,与实际number format exception (NFE)异常状态数据入侵监控结果十分接近,CPU耗用率较低,最小值为15%,对异常监控的响应耗时平均值为1.7 s,可为云平台稳定运行提供帮助。展开更多
A two-stage hybrid method is proposed to predict the phosphorus content of molten steel at the endpoint of steelmaking in BOF(Basic Oxygen Furnace). At the first clustering stage, the weighted K-means is performed to ...A two-stage hybrid method is proposed to predict the phosphorus content of molten steel at the endpoint of steelmaking in BOF(Basic Oxygen Furnace). At the first clustering stage, the weighted K-means is performed to produce clusters with homogeneous data. At the second predicting stage, each fuzzy neural network is carried out on each cluster and the results from all fuzzy neural networks are combined to be the final result of the hybrid method. The hybrid method and single fuzzy neural network are compared and the results show that the hybrid method outperforms single fuzzy neural network.展开更多
In Zhu,Wang and Gao(SIAM J.Sci.Comput.,43(2021),pp.A3009–A3031),we proposed a new framework of troubled-cell indicator(TCI)using K-means clustering and the numerical results demonstrate that it can detect the trouble...In Zhu,Wang and Gao(SIAM J.Sci.Comput.,43(2021),pp.A3009–A3031),we proposed a new framework of troubled-cell indicator(TCI)using K-means clustering and the numerical results demonstrate that it can detect the troubled cells accurately using the KXRCF indication variable.The main advantage of this TCI framework is its great potential of extensibility.In this follow-up work,we introduce three more indication variables,i.e.,the TVB,Fu-Shu and cell-boundary jump indication variables,and show their good performance by numerical tests to demonstrate that the TCI framework offers great flexibility in the choice of indication variables.We also compare the three indication variables with the KXRCF one,and the numerical results favor the KXRCF and the cell-boundary jump indication variables.展开更多
Photometric observations of AH Cnc, a W UMa-type system in the open cluster M67, were car- fled out by using the 50BIN telescope. About 100h of time-series/3- and V-band data were taken, based on which eight new times...Photometric observations of AH Cnc, a W UMa-type system in the open cluster M67, were car- fled out by using the 50BIN telescope. About 100h of time-series/3- and V-band data were taken, based on which eight new times of light minima were determined. By applying the Wilson-Devinney method, the light curves were modeled and a revised photometric solution of the binary system was derived. We con- firmed that AH Cnc is a deep contact （f = 51%）, low mass-ratio （q - 0.156） system. Adopting the distance modulus derived from study of the host cluster, we have re-calculated the physical parameters of the binary system, namely the masses and radii. The masses and radii of the two components were estimated to be respectively 1.188（4-0.061） Me, 1.332（4-0.063） RQ for the primary component and 0.185（4-0.032） Me, 0.592（4-0.051） Re for the secondary. By adding the newly derived minimum timings to all the available data, the period variations of AH Cnc were studied. This shows that the orbital period of the binary is con- tinuously increasing at a rate of dp/dt = 4.29 x 10-10 d yr-1. In addition to the long-term period increase, a cyclic variation with a period of 35.26 yr was determined, which could be attributed to an unresolved tertiary component of the system.展开更多
Cluster ensemble has testified to be a good choice for addressing cluster analysis issues, which is composed of two processes: creating a group of clustering results from a same data set and then combining these resul...Cluster ensemble has testified to be a good choice for addressing cluster analysis issues, which is composed of two processes: creating a group of clustering results from a same data set and then combining these results into a final clustering results. How to integrate these results to produce a final one is a significant issue for cluster ensemble. This combination process aims to improve the quality of individual data clustering results. A novel image segmentation algorithm using the Binary k-means and the Adaptive Affinity Propagation clustering (CEBAAP) is designed in this paper. It uses a Binary k-means method to generate a set of clustering results and develops an Adaptive Affinity Propagation clustering to combine these results. The experiments results show that CEBAAP has good image partition effect.展开更多
The distinctive distribution of acoustic emission(AE)characteristic parameters generated during tensile testing of low-temperature tempered AISI 4140 steel was investigated.Two clusters of acoustic emission signals we...The distinctive distribution of acoustic emission(AE)characteristic parameters generated during tensile testing of low-temperature tempered AISI 4140 steel was investigated.Two clusters of acoustic emission signals were distinguished using power-law distribution fitting and k-means clustering methods.These clusters were identified as resulting from dislocation motion during yielding and dislocation entanglement during uniform plastic deformation.The conclusion is further confirmed by transmission electron microscopy images at different strains.In particular,the unique"arch-shaped"distribution of the acoustic emission energy during yielding implies a change in unusual dislocation motion modes.The effect of carbide precipitation was qualitatively discussed as not considering the primary cause of the formation of this arch-shaped distribution.The evolution of dislocation motion modes during yielding of low-temperature tempered martensite was elucidated by comparing the significant difference in cumulative energy values during yielding of annealed and low-temperature tempered specimens.Dislocations emit from Frank–Read or grain boundary sources and slip along short free paths,contributing to the initial increase in AE signals energy.Subsequently,the primary source of acoustic emission energy“arch-shaped”peak during yielding was generated by the avalanche behavior of accumulated dislocations,leading to the accelerated dislocation motion.展开更多
Internet services and web-based applications play pivotal roles in various sensitive domains, encompassing e-commerce, e-learning, e-healthcare, and e-payment. However, safeguarding these services poses a significant ...Internet services and web-based applications play pivotal roles in various sensitive domains, encompassing e-commerce, e-learning, e-healthcare, and e-payment. However, safeguarding these services poses a significant challenge, as the need for robust security measures becomes increasingly imperative. This paper presented an innovative method based on differential analyses to detect abrupt changes in network traffic characteristics. The core concept revolves around identifying abrupt alterations in certain characteristics such as input/output volume, the number of TCP connections, or DNS queries—within the analyzed traffic. Initially, the traffic is segmented into distinct sequences of slices, followed by quantifying specific characteristics for each slice. Subsequently, the distance between successive values of these measured characteristics is computed and clustered to detect sudden changes. To accomplish its objectives, the approach combined several techniques, including propositional logic, distance metrics (e.g., Kullback-Leibler Divergence), and clustering algorithms (e.g., K-means). When applied to two distinct datasets, the proposed approach demonstrates exceptional performance, achieving detection rates of up to 100%.展开更多
The use of machine learning algorithms to identify characteristics in Distributed Denial of Service (DDoS) attacks has emerged as a powerful approach in cybersecurity. DDoS attacks, which aim to overwhelm a network or...The use of machine learning algorithms to identify characteristics in Distributed Denial of Service (DDoS) attacks has emerged as a powerful approach in cybersecurity. DDoS attacks, which aim to overwhelm a network or service with a flood of malicious traffic, pose significant threats to online systems. Traditional methods of detection and mitigation often struggle to keep pace with the evolving nature of these attacks. Machine learning, with its ability to analyze vast amounts of data and recognize patterns, offers a robust solution to this challenge. The aim of the paper is to demonstrate the application of ensemble ML algorithms, namely the K-Means and the KNN, for a dual clustering mechanism when used with PySpark to collect 99% accurate data. The algorithms, when used together, identify distinctive features of DDoS attacks that prove a very accurate reflection of reality, so they are a good combination for this aim. Impressively, having preprocessed the data, both algorithms with the PySpark foundation enabled the achievement of 99% accuracy when tuned on the features of a DDoS big dataset. The semi-supervised dataset tabulates traffic anomalies in terms of packet size distribution in correlation to Flow Duration. By training the K-Means Clustering and then applying the KNN to the dataset, the algorithms learn to evaluate the character of activity to a greater degree by displaying density with ease. The study evaluates the effectiveness of the K-Means Clustering with the KNN as ensemble algorithms that adapt very well in detecting complex patterns. Ultimately, cross-reaching environmental results indicate that ML-based approaches significantly improve detection rates compared to traditional methods. Furthermore, ensemble learning methods, which combine two plus multiple models to improve prediction accuracy, show greatness in handling the complexity and variability of big data sets especially when implemented by PySpark. The findings suggest that the enhancement of accuracy derives from newer software that’s designed to reflect reality. However, challenges remain in the deployment of these systems, including the need for large, high-quality datasets and the potential for adversarial attacks that attempt to deceive the ML models. Future research should continue to improve the robustness and efficiency of combining algorithms, as well as integrate them with existing security frameworks to provide comprehensive protection against DDoS attacks and other areas. The dataset was originally created by the University of New Brunswick to analyze DDoS data. The dataset itself was based on logs of the university’s servers, which found various DoS attacks throughout the publicly available period to totally generate 80 attributes with a 6.40GB size. In this dataset, the label and binary column become a very important portion of the final classification. In the last column, this means the normal traffic would be differentiated by the attack traffic. Further analysis is then ripe for investigation. Finally, malicious traffic alert software, as an example, should be trained on packet influx to Flow Duration dependence, which creates a mathematical scope for averages to enact. In achieving such high accuracy, the project acts as an illustration (referenced in the form of excerpts from my Google Colab account) of many attempts to tune. Cybersecurity advocates for more work on the character of brute-force attack traffic and normal traffic features overall since most of our investments as humans are digitally based in work, recreational, and social environments.展开更多
Clustering approaches are one of the probabilistic load flow(PLF)methods in distribution networks that can be used to obtain output random variables,with much less computation burden and time than the Monte Carlo simu...Clustering approaches are one of the probabilistic load flow(PLF)methods in distribution networks that can be used to obtain output random variables,with much less computation burden and time than the Monte Carlo simulation(MCS)method.However,a challenge of the clustering methods is that the statistical characteristics of the output random variables are obtained with low accuracy.This paper presents a hybrid approach based on clustering and Point estimate methods.In the proposed approach,first,the sample points are clustered based on the𝑙-means method and the optimal agent of each cluster is determined.Then,for each member of the population of agents,the deterministic load flow calculations are performed,and the output variables are calculated.Afterward,a Point estimate-based PLF is performed and the mean and the standard deviation of the output variables are obtained.Finally,the statistical data of each output random variable are modified using the Point estimate method.The use of the proposed method makes it possible to obtain the statistical properties of output random variables such as mean,standard deviation and probabilistic functions,with high accuracy and without significantly increasing the burden of calculations.In order to confirm the consistency and efficiency of the proposed method,the 10-,33-,69-,85-,and 118-bus standard distribution networks have been simulated using coding in Python®programming language.In simulation studies,the results of the proposed method have been compared with the results obtained from the clustering method as well as the MCS method,as a criterion.展开更多
Exploration strategy design is a challenging problem in reinforcement learning(RL),especially when the environment contains a large state space or sparse rewards.During exploration,the agent tries to discover unexplor...Exploration strategy design is a challenging problem in reinforcement learning(RL),especially when the environment contains a large state space or sparse rewards.During exploration,the agent tries to discover unexplored(novel)areas or high reward(quality)areas.Most existing methods perform exploration by only utilizing the novelty of states.The novelty and quality in the neighboring area of the current state have not been well utilized to simultaneously guide the agent’s exploration.To address this problem,this paper proposes a novel RL framework,called clustered reinforcement learning(CRL),for efficient exploration in RL.CRL adopts clustering to divide the collected states into several clusters,based on which a bonus reward reflecting both novelty and quality in the neighboring area(cluster)of the current state is given to the agent.CRL leverages these bonus rewards to guide the agent to perform efficient exploration.Moreover,CRL can be combined with existing exploration strategies to improve their performance,as the bonus rewards employed by these existing exploration strategies solely capture the novelty of states.Experiments on four continuous control tasks and six hard-exploration Atari-2600 games show that our method can outperform other state-of-the-art methods to achieve the best performance.展开更多
文摘Tarq geochemical 1:100,000 Sheet is located in Isfahan province which is investigated by Iran’s Geological and Explorations Organization using stream sediment analyzes. This area has stratigraphy of Precambrian to Quaternary rocks and is located in the Central Iran zone. According to the presence of signs of gold mineralization in this area, it is necessary to identify important mineral areas in this area. Therefore, finding information is necessary about the relationship and monitoring the elements of gold, arsenic, and antimony relative to each other in this area to determine the extent of geochemical halos and to estimate the grade. Therefore, a well-known and useful K-means method is used for monitoring the elements in the present study, this is a clustering method based on minimizing the total Euclidean distances of each sample from the center of the classes which are assigned to them. In this research, the clustering quality function and the utility rate of the sample have been used in the desired cluster (S(i)) to determine the optimum number of clusters. Finally, with regard to the cluster centers and the results, the equations were used to predict the amount of the gold element based on four parameters of arsenic and antimony grade, length and width of sampling points.
基金This research was jointly supported by the National Natural Science Foundation of China(Grant No.42005037)the Liaoning Provincial Natural Science Foundation Project(PhD Start-up Research Fund 2019-BS-214),the Special Scientific Research Project for the Forecaster(Grant No.CMAYBY2018-018)+2 种基金a Key Technical Project of Liaoning Meteorological Bureau(Grant No.LNGJ201903)the National Key Research and Development Project(Grant No.2018YFC1505601)the Open Foundation Project of the Institute of Atmospheric Environment,China Meteorological Administration(Grant Nos.2020SYIAE08 and 2020SYIAEZD5).
文摘The classification of the Northeast China Cold Vortex(NCCV)activity paths is an important way to analyze its characteristics in detail.Based on the daily precipitation data of the northeastern China(NEC)region,and the atmospheric circulation field and temperature field data of ERA-Interim for every six hours,the NCCV processes during the early summer(June)seasons from 1979 to 2018 were objectively identified.Then,the NCCV processes were classified using a machine learning method(k-means)according to the characteristic parameters of the activity path information.The rationality of the classification results was verified from two aspects,as follows:(1)the atmospheric circulation configuration of the NCCV on various paths;and(2)its influences on the climate conditions in the NEC.The obtained results showed that the activity paths of the NCCV could be divided into four types according to such characteristics as the generation origin,movement direction,and movement velocity of the NCCV.These included the generation-eastward movement type in the east of the Mongolia Plateau(eastward movement type or type A);generation-southeast longdistance movement type in the upstream of the Lena River(southeast long-distance movement type or type B);generationeastward less-movement type near Lake Baikal(eastward less-movement type or type C);and the generation-southward less-movement type in eastern Siberia(southward less-movement type or type D).There were obvious differences observed in the atmospheric circulation configuration and the climate impact of the NCCV on the four above-mentioned types of paths,which indicated that the classification results were reasonable.
文摘Grade estimation is an important phase of mining projects, and one that is considered a challenge due in part to the structural complexities in mineral ore deposits.To overcome this challenge, various techniques have been used in the past. This paper introduces an approach for estimating Au ore grades within a mining deposit using k-means and principal component analysis(PCA). The Khooni district was selected as the case study. This region is interesting geologically, in part because it is considered an important gold source. The study area is situated approximately 60km northeast of the Anarak city and 270km from Esfahan. Through PCA, we sought to understand the relationship between the elements of gold,arsenic, and antimony. Then, by clustering, the behavior of these elements was investigated. One of the most famous and efficient clustering methods is k-means, based on minimizing the total Euclidean distance from each class center. Using the combined results and characteristics of the cluster centers, the gold grade was determined with a correlation coefficient of 91%. An estimation equation for gold grade was derived based on four parameters: arsenic and antimony content, and length and width of the sampling points. The results demonstrate that this approach is faster and more accurate than existing methodologies for ore grade estimation.
基金MHRD TEQIP-Ⅲ for awarding fellowships for pursuing Ph.D.s at Gauhati UniversityFunding for the DPAC has been provided by national institutions,in particular the institutions participating in the Gaia Multilateral Agreement.
文摘A rarely studied open cluster,King 1 is observed using the 1.3-m telescope equipped with a 2 k×4 k CCD at Vainu Bappu Observatory,India.We analyze the photometric data obtained from CCD observations in both B and V bands.Out of 132 detected stars in the open cluster King 1 field,we have identified four stellar variables,and two among them are reported as newly detected binary systems.The parallax values from Gaia DR2 suggest that the open cluster King 1 is in the background of these two detected binary systems,falling along the same line of sight,giving rise to different parallax values.Periodogram analysis was carried out using Phase Dispersion Minimization(PDM)and the Lomb-Scargle(LS)method for all the detected variables.PHysics Of Eclipsing Binari Es(PHOEBE)is extensively employed to model various stellar parameters of both the detected binary systems.Based on the modeling results obtained from this work,one of the binary systems is reported for the first time as an Eclipsing Detached(ED)and the other as an Eclipsing Contact(EC)binary of W-type W UMa.
文摘云平台多容器集群数据量大、涉及种类多,导致异常状态监控难度大,为此提出基于Prometheus的监控算法。在云平台中,利用小波分解法获取多容器集群数据的实时状态序列,结合二叉树分解描述法划分不同类型的集群数据特征。根据Prometheus技术具备的分布式储存管理特点划分监控空间,并设定监控类中心,对比多容器集群数据与该节点中心相似性,相似性最强的数据即异常。仿真实验证明,方法监控异常状态数据入侵信号在800~1200测试点位间出现大幅度变动,与实际number format exception (NFE)异常状态数据入侵监控结果十分接近,CPU耗用率较低,最小值为15%,对异常监控的响应耗时平均值为1.7 s,可为云平台稳定运行提供帮助。
基金Item Sponsored by Beijing Higher Education Young Elite Teacher Project(YETP0382)2012 Ladder Plan Project of Beijing Key Laboratory of Knowledge Engineering for Materials Science of China(Z121101002812005)
文摘A two-stage hybrid method is proposed to predict the phosphorus content of molten steel at the endpoint of steelmaking in BOF(Basic Oxygen Furnace). At the first clustering stage, the weighted K-means is performed to produce clusters with homogeneous data. At the second predicting stage, each fuzzy neural network is carried out on each cluster and the results from all fuzzy neural networks are combined to be the final result of the hybrid method. The hybrid method and single fuzzy neural network are compared and the results show that the hybrid method outperforms single fuzzy neural network.
基金We thank the anonymous reviewers and the editor for their valuable comments and suggestions.The research of Z.Gao is partially supported by the National Key R&D Program of China(No.2021YFF0704002)The four authors,Z.Wang,Z.Gao,H.Wang and H.Zhu,want to acknowledge the funding support by NSFC grant No.11871443+3 种基金The research of Z.Wang and H.Zhu is also partially sponsored by NUPTSF(Grant No.NY220040)Natural Science Foundation of Jiangsu Province of China(No.BK20191375)Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant No.KYCX200787The research of Q.Zhang is partially supported by NSFC grant No.12071214.
文摘In Zhu,Wang and Gao(SIAM J.Sci.Comput.,43(2021),pp.A3009–A3031),we proposed a new framework of troubled-cell indicator(TCI)using K-means clustering and the numerical results demonstrate that it can detect the troubled cells accurately using the KXRCF indication variable.The main advantage of this TCI framework is its great potential of extensibility.In this follow-up work,we introduce three more indication variables,i.e.,the TVB,Fu-Shu and cell-boundary jump indication variables,and show their good performance by numerical tests to demonstrate that the TCI framework offers great flexibility in the choice of indication variables.We also compare the three indication variables with the KXRCF one,and the numerical results favor the KXRCF and the cell-boundary jump indication variables.
基金supported by the National Natural Science Foundation of China(Nos. U1131121,11303021,U1231202,11473037 and 11373073)
文摘Photometric observations of AH Cnc, a W UMa-type system in the open cluster M67, were car- fled out by using the 50BIN telescope. About 100h of time-series/3- and V-band data were taken, based on which eight new times of light minima were determined. By applying the Wilson-Devinney method, the light curves were modeled and a revised photometric solution of the binary system was derived. We con- firmed that AH Cnc is a deep contact （f = 51%）, low mass-ratio （q - 0.156） system. Adopting the distance modulus derived from study of the host cluster, we have re-calculated the physical parameters of the binary system, namely the masses and radii. The masses and radii of the two components were estimated to be respectively 1.188（4-0.061） Me, 1.332（4-0.063） RQ for the primary component and 0.185（4-0.032） Me, 0.592（4-0.051） Re for the secondary. By adding the newly derived minimum timings to all the available data, the period variations of AH Cnc were studied. This shows that the orbital period of the binary is con- tinuously increasing at a rate of dp/dt = 4.29 x 10-10 d yr-1. In addition to the long-term period increase, a cyclic variation with a period of 35.26 yr was determined, which could be attributed to an unresolved tertiary component of the system.
基金This work was supported by Natural Science Foundation of Heilongjiang province of China (F201406) and Liaoning Science and Technology Project (2014302006).
文摘Cluster ensemble has testified to be a good choice for addressing cluster analysis issues, which is composed of two processes: creating a group of clustering results from a same data set and then combining these results into a final clustering results. How to integrate these results to produce a final one is a significant issue for cluster ensemble. This combination process aims to improve the quality of individual data clustering results. A novel image segmentation algorithm using the Binary k-means and the Adaptive Affinity Propagation clustering (CEBAAP) is designed in this paper. It uses a Binary k-means method to generate a set of clustering results and develops an Adaptive Affinity Propagation clustering to combine these results. The experiments results show that CEBAAP has good image partition effect.
基金The authors acknowledge financial support from the National Natural Science Foundation of China(Grant Nos.51771114,51371117).
文摘The distinctive distribution of acoustic emission(AE)characteristic parameters generated during tensile testing of low-temperature tempered AISI 4140 steel was investigated.Two clusters of acoustic emission signals were distinguished using power-law distribution fitting and k-means clustering methods.These clusters were identified as resulting from dislocation motion during yielding and dislocation entanglement during uniform plastic deformation.The conclusion is further confirmed by transmission electron microscopy images at different strains.In particular,the unique"arch-shaped"distribution of the acoustic emission energy during yielding implies a change in unusual dislocation motion modes.The effect of carbide precipitation was qualitatively discussed as not considering the primary cause of the formation of this arch-shaped distribution.The evolution of dislocation motion modes during yielding of low-temperature tempered martensite was elucidated by comparing the significant difference in cumulative energy values during yielding of annealed and low-temperature tempered specimens.Dislocations emit from Frank–Read or grain boundary sources and slip along short free paths,contributing to the initial increase in AE signals energy.Subsequently,the primary source of acoustic emission energy“arch-shaped”peak during yielding was generated by the avalanche behavior of accumulated dislocations,leading to the accelerated dislocation motion.
文摘Internet services and web-based applications play pivotal roles in various sensitive domains, encompassing e-commerce, e-learning, e-healthcare, and e-payment. However, safeguarding these services poses a significant challenge, as the need for robust security measures becomes increasingly imperative. This paper presented an innovative method based on differential analyses to detect abrupt changes in network traffic characteristics. The core concept revolves around identifying abrupt alterations in certain characteristics such as input/output volume, the number of TCP connections, or DNS queries—within the analyzed traffic. Initially, the traffic is segmented into distinct sequences of slices, followed by quantifying specific characteristics for each slice. Subsequently, the distance between successive values of these measured characteristics is computed and clustered to detect sudden changes. To accomplish its objectives, the approach combined several techniques, including propositional logic, distance metrics (e.g., Kullback-Leibler Divergence), and clustering algorithms (e.g., K-means). When applied to two distinct datasets, the proposed approach demonstrates exceptional performance, achieving detection rates of up to 100%.
文摘The use of machine learning algorithms to identify characteristics in Distributed Denial of Service (DDoS) attacks has emerged as a powerful approach in cybersecurity. DDoS attacks, which aim to overwhelm a network or service with a flood of malicious traffic, pose significant threats to online systems. Traditional methods of detection and mitigation often struggle to keep pace with the evolving nature of these attacks. Machine learning, with its ability to analyze vast amounts of data and recognize patterns, offers a robust solution to this challenge. The aim of the paper is to demonstrate the application of ensemble ML algorithms, namely the K-Means and the KNN, for a dual clustering mechanism when used with PySpark to collect 99% accurate data. The algorithms, when used together, identify distinctive features of DDoS attacks that prove a very accurate reflection of reality, so they are a good combination for this aim. Impressively, having preprocessed the data, both algorithms with the PySpark foundation enabled the achievement of 99% accuracy when tuned on the features of a DDoS big dataset. The semi-supervised dataset tabulates traffic anomalies in terms of packet size distribution in correlation to Flow Duration. By training the K-Means Clustering and then applying the KNN to the dataset, the algorithms learn to evaluate the character of activity to a greater degree by displaying density with ease. The study evaluates the effectiveness of the K-Means Clustering with the KNN as ensemble algorithms that adapt very well in detecting complex patterns. Ultimately, cross-reaching environmental results indicate that ML-based approaches significantly improve detection rates compared to traditional methods. Furthermore, ensemble learning methods, which combine two plus multiple models to improve prediction accuracy, show greatness in handling the complexity and variability of big data sets especially when implemented by PySpark. The findings suggest that the enhancement of accuracy derives from newer software that’s designed to reflect reality. However, challenges remain in the deployment of these systems, including the need for large, high-quality datasets and the potential for adversarial attacks that attempt to deceive the ML models. Future research should continue to improve the robustness and efficiency of combining algorithms, as well as integrate them with existing security frameworks to provide comprehensive protection against DDoS attacks and other areas. The dataset was originally created by the University of New Brunswick to analyze DDoS data. The dataset itself was based on logs of the university’s servers, which found various DoS attacks throughout the publicly available period to totally generate 80 attributes with a 6.40GB size. In this dataset, the label and binary column become a very important portion of the final classification. In the last column, this means the normal traffic would be differentiated by the attack traffic. Further analysis is then ripe for investigation. Finally, malicious traffic alert software, as an example, should be trained on packet influx to Flow Duration dependence, which creates a mathematical scope for averages to enact. In achieving such high accuracy, the project acts as an illustration (referenced in the form of excerpts from my Google Colab account) of many attempts to tune. Cybersecurity advocates for more work on the character of brute-force attack traffic and normal traffic features overall since most of our investments as humans are digitally based in work, recreational, and social environments.
文摘Clustering approaches are one of the probabilistic load flow(PLF)methods in distribution networks that can be used to obtain output random variables,with much less computation burden and time than the Monte Carlo simulation(MCS)method.However,a challenge of the clustering methods is that the statistical characteristics of the output random variables are obtained with low accuracy.This paper presents a hybrid approach based on clustering and Point estimate methods.In the proposed approach,first,the sample points are clustered based on the𝑙-means method and the optimal agent of each cluster is determined.Then,for each member of the population of agents,the deterministic load flow calculations are performed,and the output variables are calculated.Afterward,a Point estimate-based PLF is performed and the mean and the standard deviation of the output variables are obtained.Finally,the statistical data of each output random variable are modified using the Point estimate method.The use of the proposed method makes it possible to obtain the statistical properties of output random variables such as mean,standard deviation and probabilistic functions,with high accuracy and without significantly increasing the burden of calculations.In order to confirm the consistency and efficiency of the proposed method,the 10-,33-,69-,85-,and 118-bus standard distribution networks have been simulated using coding in Python®programming language.In simulation studies,the results of the proposed method have been compared with the results obtained from the clustering method as well as the MCS method,as a criterion.
基金supported by the National Natural Science Foundation of China(Gtant No.62192783)Fundamental Research Funds for the Central Universities(No.020214380108).
文摘Exploration strategy design is a challenging problem in reinforcement learning(RL),especially when the environment contains a large state space or sparse rewards.During exploration,the agent tries to discover unexplored(novel)areas or high reward(quality)areas.Most existing methods perform exploration by only utilizing the novelty of states.The novelty and quality in the neighboring area of the current state have not been well utilized to simultaneously guide the agent’s exploration.To address this problem,this paper proposes a novel RL framework,called clustered reinforcement learning(CRL),for efficient exploration in RL.CRL adopts clustering to divide the collected states into several clusters,based on which a bonus reward reflecting both novelty and quality in the neighboring area(cluster)of the current state is given to the agent.CRL leverages these bonus rewards to guide the agent to perform efficient exploration.Moreover,CRL can be combined with existing exploration strategies to improve their performance,as the bonus rewards employed by these existing exploration strategies solely capture the novelty of states.Experiments on four continuous control tasks and six hard-exploration Atari-2600 games show that our method can outperform other state-of-the-art methods to achieve the best performance.
