Classification of clustered microseismic events in a coal mine using machine learning

Discrimination of seismicity distributed in different areas is essential for reliable seismic risk assessment in mines.Although machine learning has been widely applied in seismic data processing,feasibility and reliability of applying this technique to classify spatially clustered seismic events in underground mines are yet to be investigated.In this research,two groups of seismic events with a minimum local magnitude(ML) of-3 were observed in an underground coal mine.They were respectively located around a dyke and the longwall face.Additionally,two types of undesired signals were also recorded.Four machine learning methods,i.e.random forest(RF),support vector machine(SVM),deep convolutional neural network(DCNN),and residual neural network(ResNN),were used for classifying these signals.The results obtained based on a primary dataset showed that these seismic events could be classified with at least 91% accuracy.The DCNN using seismogram images as the inputs reached the best performance with more than 94% accuracy.As mining is a dynamic progress which could change the characteristics of seismic signals,the temporal variance in the prediction performance of DCNN was also investigated to assess the reliability of this classifier during mining.A cascaded workflow consisting of database update,model training,signal prediction,and results review was established.By progressively calibrating the DCNN model,it achieved up to 99% prediction accuracy.The results demonstrated that machine learning is a reliable tool for the automatic discrimination of spatially clustered seismicity in underground mining.

A NEW EVIDENCE UPDATING RULE BASED ON CONDITIONAL EVENT

Updating or conditioning a body of evidence modeled within the DS framework plays an important role in most of Artificial Intelligence (AI) applications. Rule is one of the most important methods to represent knowledge in AI. The appearance of uncertain reasoning urges us to measure the belief of rule. Now,most of uncertain reasoning models represent the belief of rule by conditional probability. However,it has many limitations when standard conditional probability is used to measure the belief of expert system rule. In this paper,AI rule is modelled by conditional event and the belief of rule is measured by conditional event probability,then we use random conditional event to construct a new evidence updating method. It can overcome the drawback of the existed methods that the forms of focal sets influence updating result. Some examples are given to illustrate the effectiveness of the proposed method.

基于事件响应event-response的动态工作流系统

提出了一种在企业工作流管理系统中运用由协作的各企业提供的对事件的响应 (event response)的方案来实现动态的工作流系统 .通过工作流引擎灵活地调度、控制 ,利用基于约束的动态绑定机制动态地将事件的响应请求绑定到合适的event response和响应提供者 (provider) ,从而柔性地 ,协同地完成共同的目标 .

基于数据挖潜的精神科护理不良事件分析

目的基于数据挖潜思路对数据进行回顾性分析,旨在发现精神科护理不良事件的相关因素和变量之间的关联规则,为拟定精神科护理安全管理策略和风险预警机制提供科学依据。方法运用Apriori算法,以支持度、置信度和提升度为指标,对2016年—2021年某精神病专科医院上报的684例护理不良事件的影响因素变量进行关联挖掘。结果设置最低条件支持度为5,最小规则置信度为85%,最大前项数为3,得到关联规则共90条,经专业判断,最终选择16条关联。其中,科室、护士班次和当班人力、患者总数、患者年龄、性别和住院天数均与跌倒/坠床不良事件有关联;患者总数、精神症状、住院天数、性别和当班护士年龄与暴力攻击不良事件关联;患者的精神症状与自杀地点有关联。结论采用数据挖潜技术,可以深入分析精神科护理不良事件相关因素之间的关联规则,发现精神科护理不良事件发生的规律和特点,为针对性地开展精神科护理风险管理提供科学依据;针对不良事件的关联规则拟定风险预警机制,对于保障患者安全、减少精神科护士职业伤害有一定指导意义。

典型事件化规则库编制要点分析及创新点提取

针对现有监控告警技术存在的监控信息量大、事故处置效率低、容易漏判误判等问题,新一代集控系统将采用监控信息事件化告警技术来解决这些问题。该技术以典型事件化规则库作为信息基础,通过对信号进行关联分析和归纳,形成对设备事件的判断。与原来仅以时序排列展示的散乱信号相比,监控系统将以“设备事件”的形式展示,为监控人员的进一步处置提供参考。通过详细介绍典型事件化规则库中挖掘设备故障事件规则及挖掘设备异常事件规则的编制流程,在国网基本规则库的基础上,研发新一代集控典型规则库的创新点。

网络攻击和网络攻击事件判定研究

由于网络攻击技术的复杂性和攻击事件的紧耦合性,目前对网络攻击测评难以定量。对网络攻击、网络攻击事件的判定和计数规则进行研究,提出网络攻击、网络攻击事件分类判定和计数的原则,给出了其应用场景的具体建议。为网络攻击和网络攻击事件组织进行网络攻击和网络攻击事件判定提供技术支撑和标准建议,支撑网络攻击态势的共享和准确感知。

Applying Petri-Net-Based Reduction Approach for Verifying the Correctness of Workflow Models

The correctness of workflow models is one of the major challenges in context of workflow analysis. The aim of this paper is to provide an improved Petri net-based reduction approach for verifying the correctness of workflow models. To the end, how to represent well-behaved building blocks and control structures of business processes by Petri nets is given at first, and then how to build well-structured process nets is presented. According to the structural characteristics of well-structured process nets, a set of legacy reduction rules are improved and extended, and then a complete Petri-net-based verification approach is proposed. The sound ness and the complexity with polynomial time for the improved re duction method are also proven.

Reduction techniques of workflow verification and its implementation

Many workflow management systems have emerged in recent years, but few of them provide any form of support for verification. This frequently results in runtime errors that need to be corrected at prohibitive costs. In Ref.[1], a few reduction rules of verifying workflow graph are given. After analyzing the reduction rules, the overlapped reduction rule is found to be inaccurate. In this paper, the improved reduction rules are presented and the matrix-based implementing algorithm is given, so that the scope of the verification of workflow is expanded and the efficiency of the algorithm is enhanced. The method is simple and natural, and its implementation is easy too.

Smart home architecture based on event-driven DPWS～+

With the advances of electronic information technology and computer network, especially the embedded technology, smart home is no more just a vision but being practical. The interoperability of heterogeneous devices and flexibility of devices＇ usage are two key problems that challenge the implementation of smart home. To deal with these two issues, this paper proposes an event-driven service oriented architecture using device profile for web services （DPWS）. DPWS inherits the advantages of the traditional web services in achieving interoperability without dependence on platform, while improving service discovery and security as well as being optimized for deploying on resource constrained devices. By providing a visual interface for describing a service workflow （SW）, the user can easily customize the actions of devices by services composition. Devices automatically cooperate without user＇s intervention to complete required business logic. This is achieved by fully exploiting the eventing capabilities on DPWS enabled home devices. Finally, a home theater scenario is given to illustrate the event driven mechanism for the SW in the proposed smart home framework.

Supervisory Control of Fuzzy Discrete Event Systems Based on Agent

FDES(fuzzy discrete event systems) can effectively represent a kind of complicated systems involving deterministic uncertainties and vagueness as well as human subjective observation and judgement from the view of discrete events, here the information system is divided into some independent intelligent entitative Agents. The concept of information processing state based on Agents was proposed. The processing state of Agent can be judged by some assistant observation parameters about the Agent and its environment around, and the transition among these states can be represented by FDES based on rules. In order to ensure the harmony of the Agents for information processing, its upstream and downstream buffers are considered in the modeling of the Agent system, and the supervisory controller based on FDES is constructed. The processing state of Agent can be adjusted by the supervisory controller to improve the stability of the system and the efficiency of resource utilization during the process according to the control policies. The result of its application was provided to illustrate the validity of the supervisory adjustment.

A NOVEL FRAMEWORK FOR SOCCER GOAL DETECTION BASED ON SEMANTIC RULE

Focusing on the problem of goal event detection in soccer videos,a novel method based on Hidden Markov Model(HMM) and the semantic rule is proposed.Firstly,a HMM for a goal event is constructed.Then a Normalized Semantic Weighted Sum(NSWS) rule is established by defining a new feature of shots,semantic observation weight.The test video is detected based on the HMM and the NSWS rule,respectively.Finally,a fusion scheme based on logic distance is proposed and the detection results of the HMM and the NSWS rule are fused by optimal weights in the decision level,obtaining the final result.Experimental results indicate that the proposed method achieves 96.43% precision and 100% recall,which shows the effectiveness of this letter.

Flexible Workflow Management in Agile Supply Chain System

With the vehement development of global competition , Agile Supply Chain (ASC) becomes an effective approach that supports dynamic ent erprise alliance and realizes agile manufacturing, as for the enterprises to cap ture market opportunities rapidly and strengthen their anti-risk ability. As Ag ile Supply Chain System (ASCS) is dynamic and distributed, the realization of it ’s processes needs to be much flexible. Often in Agile Supply Chain System (ASCS ), there are many dynamic tasks, many changes of the processes, and cooperative workflows, which need the workflows able to be adjusted agilely and smoothly, no t much affecting others. This paper will propose a flexible workflow management for achieving the agility and compatibility. At the first of the paper, we will discuss the situation in which Agile Supply C hain System is applied, and then we will elaborate the characteristics of Agile Supply Chain System. So with that, the shortcomings of the process managements t hat are, at present, used in Supply Chain Systems will be displayed clearly. The n, the proposal will be given in the paper, to resolve the problems in the prese nt process management in ASCS. We will see supply chain systems implemented mult i-agent-based technology to adapt to the dynamic environment, however in many systems, coordination of the workflows is executed by the explicit messages comm unicated between the agents. This will lead to a tightly coupled system. To over come it, the paper suggests the workflow management in agent-based ASCS using t he Events-Triggering mechanism, instead of the explicit messages. We create Eve nts-Triggering contents and rules, having push-pull mechanism, event-state se t and triggering-conditions, and store them in the distributed database subject ed to the corresponding agents. Adjusting the Events-Triggering data will be us ed to adapt to the dynamic tasks and the changes of processes. In sum, we will p resent the methods of process modeling, the system structure, and the executing -mechanism of the flexible workflow management we propose in the paper. Also, t he paper outlines several exception-handling policies for the flexible workflow management, in succession. And at the final of the paper, we discuss the realiz ation of the flexible workflow management in Agile Supply Chain System. The flex ible workflow management will be implemented using CORBA services and Java techn ology for the distributed systems. CORBA is chosen as the specification of distr ibuted electronic data exchange, and Java as the construction tool of the system . The framework of the flexible workflow management constructed on them is showe d in the paper.

基于RIMER的台风灾害下城市供水系统失效情景推演研究

为分析台风登陆带来风雨潮灾害时城市供水系统Natech事件,根据城市供水系统原水系统、制水系统、输配水系统以及配电系统灾害要素之间复杂的交互关系,构建城市供水系统失效情景演化贝叶斯拓扑结构;收集94例相关灾情数据进行关联数据挖掘,利用修正规则库结果集置信度的RIMER优化算法对该供水系统失效情景进行推演分析;通过采取实例,即2022年台风“暹芭”登陆广东某沿海城市时的灾情信息对本文模型进行验证。研究结果表明:实例模型推演结果与实际情况较为吻合,本文模型能有效模拟城市供水系统面临台风耦合灾害时所存在的失效情景演化关系与风险诊断情况。研究结果可为促进城市供水应急管理的精准化和高效化发展提供参考方法。

热点事件网络谣言的内生逻辑与规制

热点事件极易滋生网络谣言。谣言的认定应注重两个区别,一是“科学质疑”与“非法造谣”,二是“善意传播”与“恶意传谣”。热点事件网络谣言的内生逻辑是吸粉引流的“流量驱动”,其行为的结构链条为:社会公共话题领域内的基础性热点事件+不良心理动机的虚假信息发布者+主动配合或被动接受的当事人+虚假信息制造与传播+有意或无意的网络受众+真相揭秘舆情反转。热点事件网络谣言的应对规制主要包括,对“人”采取网络禁言与失信惩戒措施,对“事”进行实体法与程序法的细化规定,有权机关与部门应坚守法治底线,构建法治化通报制度,完善奖惩激励机制。

论规章制定程序规定的责任条款——以环境规章制定责任为例

当前,在国务院制定的《规章制定程序条例》之中并无行政规章制定责任的相关规定,在各省的规章制定程序规定之中也鲜有相关行政立法责任的说明,这极不利于规章质量的提升以及行政问责制度的科学发展。在生态治理领域内,环境规章适用性与效能性问题的出现,持续影响着环境行政质量的改善。因此,要完善环境公共行政体系的建设势必要有效落实环境规章制定责任:通过行政立法技术的改良和归责理论的升级,在规章制定程序规定之中添加责任条款;探寻规章制定责任条款的价值内涵,准确引导各地方政府构建规章制定的事前责任模式以及集体责任模式。通过对上述两项行政立法责任模式理论风险的有效应对,完善规章立法责任模式的理论模型,逐步修正环境规章的安定性问题,使其更有效地发挥环境治理的积极作用是可行的路径。

基于关联规则的航空运行不正常事件统计分析

为进一步妥善处理航空运行中发生的不正常事件,以2022年空管安全信息系统(ASIS)收集到的10995件不正常事件数据为研究对象,首先通过海恩法则统计得到不同严重程度的不正常事件比例,然后利用Apriori算法和关联规则对所发生的不正常事件进行关联性分析,确定致因并构建不同性质的事件分类。最后使用SPSS Modeler对统计的所有不正常事件数据进行建模,构建布尔矩阵并产生关联规则,分析出不正常事件致因和事件发生之间的关系。依据事件发生概率降序排列,逐一进行排查预警,提出相应的安全建议,降低不正常事件发生频率,对国内空管高质量运行与发展有着重要意义。

我国体育赛事转播权市场反垄断规制研究

体育赛事转播作为一种新的产业模式,为体育产业发展注入新的动能。随着体育赛事转播权市场的蓬勃发展,垄断早已潜滋暗长,这一问题将会影响我国体育赛事转播的可持续健康发展。运用文献资料、专家访谈和逻辑分析等方法,梳理了体育赛事转播权的交易模式与相关市场,分析了目前我国体育赛事转播权垄断基本形态及其规制的必要性,其垄断的基本形态主要是行政性垄断行为和经营者集中行为,对其反垄断规制存在相关市场界定、市场支配地位的认定以及企业合并的事先审查等困境,以此提出了要建立健全体育赛事转播权反垄断法律法规体系、在行政性垄断的体育赛事转播权领域引入规制改革、在自然垄断的体育赛事转播权领域引入反垄断法理念、在垄断竞争的体育赛事转播权领域细化反垄断法规则、引入行业监督机构参与经营者集中的反垄断调查等五个方面的我国体育赛事转播权市场反垄断规制策略。

WTT赛事新规程解析及成效研究

为大力发展世界乒乓球职业大联盟比赛(WTT),国际乒联全面创新了竞赛规程,给运动员及赛事本身带来重要影响。本文运用文献资料法、数理统计法、逻辑分析法,对WTT新规程的赛事体系、积分规则及比赛规则进行全面审视,分析新规程对WTT赛事及运动员带来的影响情况。结果表明:扩大赛事规模能够有效提升运动员的参赛人数和参赛频率;加大赛事积分分差有助于提高顶级运动员的参赛效率;差异化的赛制设置能够增加比赛结果的偶然性和爆冷率;大幅增加奖金可以进一步提升赛事的国际影响力。

基于ECA规则和动态污点分析的SQL注入攻击在线检测

SQL注入攻击是一种常见的针对Web应用程序漏洞的攻击形式。任何形式的SQL注入攻击最终都会改变原有SQL语句的逻辑结构,违背设计者的初衷。现有的SQL注入攻击检测方法存在检测代码不易被重用、不能被在线注入Web应用程序等不足。因此,提出一种基于ECA(Event Condition Action)规则和动态污点分析的在线检测SQL注入攻击的模型。首先,定义污点标记规则监视污点源函数以标记系统外部引入数据;然后,定义污点传播规则实时跟踪污点数据在应用内部的流向;接着,定义污点检查规则以拦截污点汇聚点函数的参数,并解析它可能携带的污点状态;最后,在原始的Web应用运行时加载ECA规则脚本达到在线检测SQL注入攻击的目的,Web应用无须重新编译、打包和部署。使用Byteman实现了所提模型。在两个不同的Web应用测试实验中,该模型可以识别绝大多数的SQL注入攻击样本,对于正常请求样本没有误报,检测准确率可达99.42%,优于基于支持向量机(SVM)和基于词频逆向文件频率(TF-IDF)的方法;与基于面向方面编程(AOP)的方法相比,该模型易于在Web应用启动后在线加载检测模块。实验结果表明所提模型能够在不修改应用程序执行引擎及源码的情况下,检测6种常见的SQL注入攻击类型,且具有在线检测的优点。

基于规则推理的足球视频任意球射门事件检测

足球视频事件检测对视频检索具有重要意义。然而,足球视频中事件较少,且主要发生在远镜头中,难以捕捉关键球员和关键动作,导致足球事件检测困难。近年来,基于深度学习的方法在足球视频事件检测上取得了一定的进展,但对事件的高层语义学习仍不够充分,检测结果有待进一步提高。如何提升足球视频事件检测的准确性是亟待解决的问题。以任意球射门事件为研究对象,提出了足球规则与深度学习相结合的事件检测模型。为了深入了解任意球射门事件的内在特性,人工总结了事件规则并在公共足球数据集上进行了验证,同时提出了规则的应用场景。针对足球视频中事件过少的问题,设计了基于规则的初始定位算法对视频进行预处理。通过多规则组合和应用,从原始视频中初步定位可能发生任意球射门事件的位置,并将其作为深度学习模型的输入进行进一步预测。在公共足球数据集上将所提模型与其他模型进行对比实验。结果表明,该模型取得了最好的效果,其精确率达到78%,召回率达到81.25%。相比其他模型,其精确率的提升尤为明显。可见,足球规则与深度学习相结合的任意球事件检测模型有效提升了任意球射门事件的检测性能,为足球视频中其他事件的检测提供了参考依据。