用多路望远镜探测器测量中子带电粒子核反应实验中的事例判选

介绍一个测量由中子引起的核反应的双微分截面实验的事例判选系统,由于反应截面非常小,而且本底相当大,为了最大程度地减少本底的影响,系统采用了二级硬件判选和一级软件判选。经过三级事例判选之后,可使本底计数降低４－５个数量级。     

Using CANARY event detection software for water quality analysis in the Milwaukee River

Urban water sources are susceptible to various contamination events as a result of natural, accidental, and human-induced occurrences. An early warning monitoring system provides timely information on changes in urban water quality. In this study, an analysis was made with CANARY event detection software (EDS) to monitor water quality parameters in river water and to identify the onset of anomalous water quality periods. Water quality signals including pH, conductivity, and turbidity from the Milwaukee River over specified periods during the summer season of 2018–2020 were employed as inputs to event detection algorithms in CANARY. The data analysis results show that CANARY can be useful as an early warning system for monitoring contamination in urban water sources and help to identify abnormal conditions quickly. The sensibility of the model relies on optimizing the configuration parameters, which involves selecting the ideal set of parameters for the event detection algorithm and adjusting the BED parameters to increase or decrease the probability of generating an alarm. The number of events reported between the Linear Prediction Correction Filter (LPCF) and Multivariate Nearest Neighbor (MVNN) algorithms varied as a result of different residual calculation mechanisms. Climate factors that contributed to the abnormal water quality events in the river were examined. The analysis of rainfall on water quality was carried out using a statistical method by determining whether there is a significant difference (p-value) between the seasonal mean water quality data and the mean value of water parameters during the sampling duration. Regression analysis was also performed to estimate the best model that describes the relationship between each of the water quality parameters and temperature.     

Identification methods for anomalous stress region in coal roadways based on microseismic information and numerical simulation

It is believed that the microseismicity induced by mining effect and gas gradient disturbance stress is a precursor to the essential characteristics of roadway unstability. In order to effectively identify and evaluate the stability of coal roadways in the process of mine development and extraction, a microseismic monitoring system was deployed for the study of the stress evolution process, damage degree and distribution characteristics in the tailgate and headgate. The mine under study is the 62113 outburst working face of Xin Zhuangzi coalmine in Huainan mining area. The whole process of microfractures initiation,extension, interaction and coalescence mechanisms during the progressive failure processes of the coal rock within the delineated and typical event clusters were investigated by means of a two dimensional realistic failure process analysis code(RFPA2D-Flow). The results show that the microseismic events gradually create different-sized event clusters. The microseismicity of the tailgate is significantly higher than that of the headgate. The study indicates that the greater anomalous stress region matches the area where microfractures continuously develop and finally connect to each other and form a fissure zone.Due to the mine layout and stress concentration, the ruptured area is mainly located on the left shoulder of the tailgate roof. The potential anomalous stress region of the coal roadway obtained by numerical simulation is relatively in good agreement with the trend of spatial macro evolution of coal rock microfractures captured by the microseismic monitoring system. The research results can provide important basis for understanding instability failure mechanism of deep roadway and microseismic activity law in complex geologic conditions, and it ultimately can be used to guide the selection and optimization of reinforcement and protection scheme.     

事件序列中的知识发现研究

事件的序列是数据的一种常见形式,其中的知识发现问题是近年来ＫＤＤ的一个活跃的研究领域。本文首先给出了事件序列中知识发现问题的形式描述和框架算法,然后讨论了数据库中发现大序列的问题,具体描述了发现数据库中大序列的算法。     

主动实时数据库事务特征

文中分析了主动实时事件的数据特征,时间特征,主动特征,定义了实时应用环境下的数据库正确性,介绍了主动实时事务处理的技术,提出了主动实时ＤＢＭＳ中各类事务的一种集成调度策略。     

PowerBuilder环境下通用打印设置窗口的实现

根据ＰｏｗｅｒＢｕｉｌｄｅｒ环境中Ｄａｔａ ｉｎｄｏｗ的Ｐｒｉｎｔ特性和打印函数,介绍一种能通过用户设置打印参数来实现Ｄａｔａｗｉｎｄｏｗ特殊打印的方法。     

Monitoring Distributed Real-Time Systems

指出了目前工业实时系统监测方面存在的问题,提出了一种在理论上和实际应用中监测分布式实时系统均具潜力的手段。结合目前作者对监测分布式实时系统的实际研究工作,讨论了实施这一手段监测系统的重要特性。给出了为实现这一目的而建立的ＶＴＡ原型软件构成和硬件结构。     

Workflow simulation for operational decision support using event graph through process mining

It is increasingly common to see computer-based simulation being used as a vehicle to model and analyze business processes in relation to process management and improvement. While there are a number of business process management (BPM) and business process simulation (BPS) methodologies, approaches and tools available, it is more desirable to have a systemic BPS approach for operational decision support, from constructing process models based on historical data to simulating processes for typical and common problems. In this paper, we have proposed a generic approach of BPS for operational decision support which includes business processes modeling and workflow simulation with the models generated. Processes are modeled with event graphs through process mining from workflow logs that have integrated comprehensive information about the control-flow, data and resource aspects of a business process. A case study of a credit card application is presented to illustrate the steps involved in constructing an event graph. The evaluation detail is also given in terms of precision, generalization and robustness. Based on the event graph model constructed, we simulate the process under different scenarios and analyze the simulation logs for three generic problems in the case study: 1) suitable resource allocation plan for different case arrival rates; 2) teamwork performance under different case arrival rates; and 3) evaluation and prediction for personal performances. Our experimental results show that the proposed approach is able to model business processes using event graphs and simulate the processes for common operational decision support which collectively play an important role in process management and improvement.     

A composite likelihood approach for spatially correlated survival data

The aim of this paper is to provide a composite likelihood approach to handle spatially correlated survival data using pairwise joint distributions. With e-commerce data, a recent question of interest in marketing research has been to describe spatially clustered purchasing behavior and to assess whether geographic distance is the appropriate metric to describe purchasing dependence. We present a model for the dependence structure of time-to-event data subject to spatial dependence to characterize purchasing behavior from the motivating example from e-commerce data. We assume the Farlie-Gumbel-Morgenstern (FGM) distribution and then model the dependence parameter as a function of geographic and demographic pairwise distances. For estimation of the dependence parameters, we present pairwise composite likelihood equations. We prove that the resulting estimators exhibit key properties of consistency and asymptotic normality under certain regularity conditions in the increasing-domain framework of spatial asymptotic theory.