Objectives:In order to improve the prediction accuracy of forced-air pre-cooling for blueberries,a mathematical model of forced-air pre-cooling for blueberries based on the micro-cluster method was established.Materia...Objectives:In order to improve the prediction accuracy of forced-air pre-cooling for blueberries,a mathematical model of forced-air pre-cooling for blueberries based on the micro-cluster method was established.Materials and Methods:In order to determine the optimal micro-cluster model parameters suitable for forced air pre-cooling of blueberries,three factors controlling the micro-cluster geometry parameters were evaluated by 7/8 pre-cooling time,uniformity,and convective heat transfer coeffcient.Results:It was found that the optimal values of the number of micro-clusters(n3),the distance between individual units within a micro-cluster(a)and the distance between micro-clusters(c)were 3,0.75,and 0.2,respectively.Under these optimal values,the temperature error of the micro-cluster method remained below 1°C,achieving highly accurate temperature predictions during the blueberry pre-cooling process.The results showed that the micro-cluster method effectively solved the challenges of complex confguration,long simulation time,and low accuracy compared to the porous medium and equivalent sphere methods.Conclusion:Based on the above analysis,it can be concluded that the micro-cluster method provids a theoretical basis for optimizing forced-air pre-cooling processes and making informed control decisions.展开更多
Clustering data streams has drawn lots of attention in the last few years due to their ever-growing presence. Data streams put additional challenges on clustering such as limited time and memory and one pass clusterin...Clustering data streams has drawn lots of attention in the last few years due to their ever-growing presence. Data streams put additional challenges on clustering such as limited time and memory and one pass clustering. Furthermore, discovering clusters with arbitrary shapes is very important in data stream applications. Data streams are infinite and evolving over time, and we do not have any knowledge about the number of clusters. In a data stream environment due to various factors, some noise appears occasionally. Density-based method is a remarkable class in clustering data streams, which has the ability to discover arbitrary shape clusters and to detect noise. Furthermore, it does not need the nmnber of clusters in advance. Due to data stream characteristics, the traditional density-based clustering is not applicable. Recently, a lot of density-based clustering algorithms are extended for data streams. The main idea in these algorithms is using density- based methods in the clustering process and at the same time overcoming the constraints, which are put out by data streanFs nature. The purpose of this paper is to shed light on some algorithms in the literature on density-based clustering over data streams. We not only summarize the main density-based clustering algorithms on data streams, discuss their uniqueness and limitations, but also explain how they address the challenges in clustering data streams. Moreover, we investigate the evaluation metrics used in validating cluster quality and measuring algorithms＇ performance. It is hoped that this survey will serve as a steppingstone for researchers studying data streams clustering, particularly density-based algorithms.展开更多
基金supported by the Natural Science Foundation of Shandong Province,China(No.ZR2021QC186)the China Postdoctoral Science Foundation(No.2023M743923).
文摘Objectives:In order to improve the prediction accuracy of forced-air pre-cooling for blueberries,a mathematical model of forced-air pre-cooling for blueberries based on the micro-cluster method was established.Materials and Methods:In order to determine the optimal micro-cluster model parameters suitable for forced air pre-cooling of blueberries,three factors controlling the micro-cluster geometry parameters were evaluated by 7/8 pre-cooling time,uniformity,and convective heat transfer coeffcient.Results:It was found that the optimal values of the number of micro-clusters(n3),the distance between individual units within a micro-cluster(a)and the distance between micro-clusters(c)were 3,0.75,and 0.2,respectively.Under these optimal values,the temperature error of the micro-cluster method remained below 1°C,achieving highly accurate temperature predictions during the blueberry pre-cooling process.The results showed that the micro-cluster method effectively solved the challenges of complex confguration,long simulation time,and low accuracy compared to the porous medium and equivalent sphere methods.Conclusion:Based on the above analysis,it can be concluded that the micro-cluster method provids a theoretical basis for optimizing forced-air pre-cooling processes and making informed control decisions.
基金supported by the University of Malaya Research under Grant No.RG097-12ICT
文摘Clustering data streams has drawn lots of attention in the last few years due to their ever-growing presence. Data streams put additional challenges on clustering such as limited time and memory and one pass clustering. Furthermore, discovering clusters with arbitrary shapes is very important in data stream applications. Data streams are infinite and evolving over time, and we do not have any knowledge about the number of clusters. In a data stream environment due to various factors, some noise appears occasionally. Density-based method is a remarkable class in clustering data streams, which has the ability to discover arbitrary shape clusters and to detect noise. Furthermore, it does not need the nmnber of clusters in advance. Due to data stream characteristics, the traditional density-based clustering is not applicable. Recently, a lot of density-based clustering algorithms are extended for data streams. The main idea in these algorithms is using density- based methods in the clustering process and at the same time overcoming the constraints, which are put out by data streanFs nature. The purpose of this paper is to shed light on some algorithms in the literature on density-based clustering over data streams. We not only summarize the main density-based clustering algorithms on data streams, discuss their uniqueness and limitations, but also explain how they address the challenges in clustering data streams. Moreover, we investigate the evaluation metrics used in validating cluster quality and measuring algorithms＇ performance. It is hoped that this survey will serve as a steppingstone for researchers studying data streams clustering, particularly density-based algorithms.
