Machine Learning-Driven Classification for Enhanced Rule Proposal Framework

In enterprise operations,maintaining manual rules for enterprise processes can be expensive,time-consuming,and dependent on specialized domain knowledge in that enterprise domain.Recently,rule-generation has been automated in enterprises,particularly through Machine Learning,to streamline routine tasks.Typically,these machine models are black boxes where the reasons for the decisions are not always transparent,and the end users need to verify the model proposals as a part of the user acceptance testing to trust it.In such scenarios,rules excel over Machine Learning models as the end-users can verify the rules and have more trust.In many scenarios,the truth label changes frequently thus,it becomes difficult for the Machine Learning model to learn till a considerable amount of data has been accumulated,but with rules,the truth can be adapted.This paper presents a novel framework for generating human-understandable rules using the Classification and Regression Tree(CART)decision tree method,which ensures both optimization and user trust in automated decision-making processes.The framework generates comprehensible rules in the form of if condition and then predicts class even in domains where noise is present.The proposed system transforms enterprise operations by automating the production of human-readable rules from structured data,resulting in increased efficiency and transparency.Removing the need for human rule construction saves time and money while guaranteeing that users can readily check and trust the automatic judgments of the system.The remarkable performance metrics of the framework,which achieve 99.85%accuracy and 96.30%precision,further support its efficiency in translating complex data into comprehensible rules,eventually empowering users and enhancing organizational decision-making processes.

Thinking process rules extraction for manufacturing process design

To realize the reuse of process design knowledge and improve the efficiency and quality of process design, a method for extracting thinking process rules for process design is proposed. An instance representation model of the process planning reflecting the thinking process of techni- cians is established to achieve an effective representation of the process documents. The related process attributes are extracted from the model to form the related events. The manifold learning algorithm and clustering analysis are used to preprocess the process instance data. A rule extraction mechanism of process design is introduced, which is based on the related events after dimension reduction and clustering, and uses the association rule mining algorithm to realize the similar process information extraction in the same cluster. Through the vectorization description of the related events, the final process design rules are formed. Finally, an example is given to evaluate the method of process design rules extraction.

Formalization of Rules for the Cooperation between Goal and Process

To meet the end-user's dynamically changed requirements for software and achieve the on-demand changes of operational process in time, based on the Goal model and the Process model, the user's personalized operations of the related work were simplified and integrated, and rules for the cooperation between Goal and Process which fit for software engineers were proposed and formalized. The user's personalized operations of the related work were analyzed to conclude the pattern of Goal modification. By the direct drive of Goal modification, the corresponding changes of Process were induced and specified. Then rules for the cooperation between Goal and Process were concluded and the formalization was achieved in Semantic Web Rule Language (SWRL). In this paper, reasoning support was provided for the model modification based on ontology and the dynamic evolution of Process model.

COMPUTER-AIDED BLOCK ASSEMBLY PROCESS PLANNING IN SHIPBUILD-ING BASED ON RULE-REASONING

Computer-aided block assembly process planning based on rule-reasoning are developed in order to improve the assembly efficiency and implement the automated block assembly process planning generation in shipbuilding. First, weighted directed liaison graph （WDLG） is proposed to represent the model of block assembly process according to the characteristics of assembly relation, and edge list （EL） is used to describe assembly sequences. Shapes and assembly attributes of block parts are analyzed to determine the assembly position and matched parts of parts used frequently. Then, a series of assembly rules are generalized, and assembly sequences for block are obtained by means of rule reasoning. Final, a prototype system of computer-aided block assembly process planning is built. The system has been tested on actual block, and the results were found to be quite efficiency. Meanwhile, the fundament for the automation of block assembly process generation and integration with other systems is established.

Basic rules for rheologic forging process of semisolid alloy

Semisolid mold forging is a major type of semisolid processing, which isdifferent from neither traditional mold forging nor traditional permanent casting. However,processing defects are often seen in work pieces because of lacking available rules for the processdesign and control. Some basic rules for the process design and control, simply named the shortestflowing length, pressure filling and the minimum uplifting mold pressure, are advanced in the paperbased on amount of researches and experiments. The equations to determine the major processparameters are given out such as the filling pressure, forming pressure and locking mold pressurefor the process design and control. The rules and equations are experimentally proved available andapplicable by several actual work pieces produced by the semisolid forging process.

Knowledge Representation and Semantic Inference of Process Based on Ontology and Semantic Web Rule Language

The process inference cannot be achieved effectively by the traditional expert system,while the ontology and semantic technology could provide better solution to the knowledge acquisition and intelligent inference of expert system.The application mode of ontology and semantic technology on the process parameters recommendation are mainly investigated.Firstly,the content about ontology,semantic web rule language(SWRL)rules and the relative inference engine are introduced.Then,the inference method about process based on ontology technology and the SWRL rule is proposed.The construction method of process ontology base and the writing criterion of SWRL rule are described later.Finally,the results of inference are obtained.The mode raised could offer the reference to the construction of process knowledge base as well as the expert system's reusable process rule library.

Forward heuristic breadth-first reasoning based on rule match for biomass hybrid soft-sensor modeling in fermentation process

Biomass is a key parameter in fermentation process, directly influencing the performance of the fermentation system as well as the quality and yield of the targeted product. Hybrid soft-sensor modeling is a good method for on-line estimation of biomass. Structure of hybrid soft-sensor model is a key to improve the estimating accuracy. In this paper, a forward heuristic breadth-first reasoning approach based on rule match is proposed for constructing structure of hybrid model. First, strategy of forward heuristic reasoning about facts is introduced, which can reason complex hybrid model structure in the event of few known facts. Second, rule match degree is defined to obtain higher esti- mating accuracy. The experiment results of Nosiheptide fermentation process show that the hybrid modeling process can estimate biomass with higher accuracy by adding transcendental knowledge and partial mechanism to the process.

A Scheme for Mining State Association Rules of Process Object Based on Big Data

This paper devises a scheme which can discover the state association rules of process object. The scheme aims to dig the hidden close relationships of different links in process object. We adopt a method based on difference and extremum to compute the timing. Clustering is used to classifying the adjusted data, and the next is associating the clusters. Based on the rules of clusters, we produce the rules of links. Association degrees between each two links can be determined. It is easy to get association chains according to the degree. The state association rules that can be obtained in accordance with association rules are the final results. Some industry guidance can be directly summarized from the state association rules, and we can apply the guidance to improve the efficiency of production and operational in allied industries.

习近平法治思想融入高校思政课教学的路径探析

习近平法治思想是建党百年来提出的最全面、最系统、最科学的法治思想体系。习近平法治思想与高校思政课在教育目的、教学内容和教育规律上具有高度的耦合性,在教学实践中,应遵循学理性与政治性、知识性与价值性、灌输性与启发性相统一的原则,着力于扩大学生的学习内需、充盈教学内容的体系,选取新颖的教学方法、组建优质的教师团队、渲染浓厚的法治氛围等,充分发挥习近平法治思想融入高校思政课教学全过程的积极效用。

基于CART决策树的调度算法研究

以往的作业车间存在大量的离线加工数据。基于数据挖掘、调度规则和算法优化相关知识,提出基于贝叶斯优化的改进CART算法来对车间数据挖掘利用,根据车间数据的属性逐步划分节点,生成树状结构,剪枝,最后生成加工规则。通过不同数据算例的实验结果表明,经过贝叶斯优化后的CART算法相较于传统CART算法提高了对数据划分的能力并提升了生成的决策树的准确度。

论新质生产力基础上的“新质民主”与“新质法治”的辩证统一

从分析“新质生产力”的性质、内涵、特征、构成要素及意义出发,运用马克思主义立场、观点和方法,坚持习近平法治思想为指导,可以明确提出“新质生产力”的出现和发展可能引起“新质生产关系”的形成,继而通过“新质经济基础”推动上层建筑朝着“新质上层建筑”转变的重大法理判断。“新质生产力”所引发的社会变革是客观规律,民主与法治作为上层建筑的重要组成部分,也会随着“新质生产力”的不断发展而日益呈现“新质民主”与“新质法治”的特征。“全过程人民民主”是“新质民主”的重要标志,而数字法治、涉外法治构成了“新质法治”的核心要素。法学界要及早规划、超前研究,对“新质生产力”发展可能引发的包含了民主与法治等要素在内的上层建筑可能发生的深远影响,从法理上进行深入分析并拿出切实可行的应对方案。

烟丝加工过程中的破碎规律研究

为探索烟丝在加工过程中的破碎规律,设计不同切丝宽度和尺寸的烟丝,研究滚筒、流态化、机械输送等关键加工环节前后烟丝的结构变化规律。结果表明,随着烟丝加工进行,各规格烟丝不断破碎,烟丝的特征尺寸降低,破碎度升高,主要表现在>7.10 mm和4.50~7.10 mm等尺寸较长的烟丝质量分数不断降低,<2.00 mm的烟丝质量分数不断升高,不同处理间3.35~4.50 mm的烟丝质量分数变化差异最大。滚筒加工过程中烟丝质量分数的变化率整体较大,其中滚筒干燥过程最大,其次是振动输送、干燥后的提升输送和风送过程,风选和干燥前的提升等过程较小。加工后1.0 mm宽度烟丝、1.0 mm宽度断丝的烟丝、0.8 mm宽度烟丝和0.8 mm宽度断丝的烟丝特征尺寸变幅分别达到1.86 mm、1.09 mm、1.65 mm和1.08 mm,其破碎度分别达到33.51%、26.35%、33.39%和27.43%。综上,>7.10 mm和4.50~7.10 mm等特征尺寸较大及同一断丝或未断丝处理下宽度较窄的烟丝在加工过程中更易破碎,应根据加工的需求,关注特定尺寸的烟丝质量变化规律,在原料消耗允许的范围内,开展烟丝结构调控和工艺优化。

我国金融科技“监管沙盒”测试企业退出预备制度研究

测试企业进入金融科技“监管沙盒”正式参与测试后,基于其主观或客观原因而存在主动或被动退出沙盒测试的可能。从沙盒测试退出的实践需求来看,测试企业退出沙盒测试的预备行为亟需统一的制度规范。在充分借鉴澳大利亚相关制度设计经验的基础上,我国应从不同等级风险预防与预警规则设置、解除合作与交易协议及终止线上线下服务规则设计、建立测试企业相关技术与数据的有效处理制度以及制定测试企业退出前的牌照申请计划和准备规则等方面,为我国测试企业退出沙盒测试的预备行为提供合理有效的制度供给。

构建医院“三重一大”集体决策流程评分体系研究

本研究以文献研究、专家咨询为基础,通过对上海市8所市级医院开展深度访谈,全面梳理“三重一大”决策流程各关键环节和特色经验,最终聚焦“前期论证”“沟通酝酿”“协调督办”等9个主要环节构建评分体系,确定25项评分指标,实现“三重一大”集体决策流程的量化与评估。通过评分积累,有针对性地解决个性化科室问题与共性化薄弱环节;通过持续梳理改进、良性循环,强化“三重一大”过程管理和反馈机制建设,推进民主科学决策规范化开展。

基于层次分析和关联规则的陆路交通工程间接勘察技术方案决策方法

勘察技术方案的决策是开展陆路交通工程勘察的首要工作,然而在不同的勘察阶段,由于成本、效率、效果等需求不同,对勘察技术及手段的要求也不同。现阶段勘察技术的选取长期单纯地依赖从业者经验,导致其无法满足信息化、智能化勘察的需求;同时,实用的新型勘察技术由于缺乏科学有效的决策机制,导致工程实践中无法及时融入。因此,需要建立科学的决策机制,结合新技术,制定更加精准、高效的勘察方案。将层次分析法和关联规则进行融合,提出了一种基于两者的间接勘察技术方案决策方法。首先,根据勘察技术本身具备的特性和目标地区地层参数进行定量分析,确定主要的勘察类别;然后,利用层次分析法选取该类别中满足勘察准确性、技术可靠性、经济合理性的最佳勘察技术;最后,运用关联规则围绕最佳勘察技术补齐在应用中存在的短板,综合新型勘察技术的应用实践结果,构建间接勘察技术组合方案。通过本决策方法的应用,极大程度地减小了勘察技术选择的随意性,据此建立的间接勘察技术组合方案符合现场需求和实践经验,证明了本方法的可行性。

基于先验算法的服装生产流水线设计优化

针对服装生产线员工变动频繁、技能熟练度不一致以及设备布局受限等造成的流水线部件逆流和不平衡问题,提出了一种基于先验算法的服装生产工序组合关联规则挖掘方法,以优化流水线设计。根据服装工艺和生产特点提出了工序编码方法,通过改进Apriori算法来挖掘用于流水排布的生产工序关联规则,建立了以逆流情况和流水线平衡为约束的评判指标,通过工序内容与评判指标进行流水线优化排布。结果表明:优化后的流水线完全避免了零部件在路径上的传递损失,流水线的排布效率提高了20%,线内工位平均均衡比相较于原有产线提高了20%,排布内容更切合工厂生产实际环境和经验要求。使用该方法挖掘工序关联组合具有较好的可靠性,工序组合的应用有利于提升流水线排布的实际意义。

区块链技术下运动员个人信息治理研究

基于区块链技术架构及特征,探索区块链技术应用于运动员个人信息治理的价值、挑战及其应对。研究认为,区块链技术下运动员个人信息治理能够克服传统互联网模式下运动员个人信息储存工作繁复、利用不充分、保护滞后等问题。同时,也面临与“知情-同意”机制相冲突,与个人信息处理必要规则相违背,与运动员行使删除权、更正权与信息可携带权存在冲突,民事责任主体确定困难等挑战。对此,应当优化改进智能合约,实现运动员的“知情-同意”权;区分区块链信息治理的不同环节,分别进行“必要处理”考察;推动区块链技术创新,消除与其运动员删除权、更正权与信息可携带权的冲突;积极发挥联盟链作用,通过其多中心化特征确定民事责任主体。

基于医保全程审核系统的设计与应用

目的最大限度避免医保违规,保障患者顺利实现医保结算,解决传统审核系统无法满足医院对患者全部诊疗救治过程监管的问题。方法系统采用面向对象方法,利用Java编程语言和Oracle数据库,实现对患者入院后的医疗活动(如病案首页、住院病历、检验检查报告单、体温单、长期和临时医嘱、费用等)进行全过程审核、定时审核、出院审核等,并探讨系统的应用成效。结果系统上线后,不合理的诊疗行为可以及时反馈给医护人员,实现对患者门诊、住院所有诊疗活动的审核。疑似违规次数由系统上线前的(9.10±0.23)万条下降至(3.30±0.08)万条,拒付金额由(982.00±3.16)万元下降至(351.00±1.69)万元,每份病历平均审核时间从(10.30±0.12)min缩短至(3.20±0.11)min,患者满意度从96.20%±0.12%提升至98.60%±0.10%,且差异均有统计学意义(P<0.001)。结论医保全程审核系统对于规范诊疗行为、优化审核流程、推动医保工作的精细化管理,有着十分重要的意义,值得推广应用。

基于Petri网的面向公共过程的协同业务过程验证方法

如何在保护隐私性的前提下高效地验证协同业务过程的正确性,是业务过程管理领域中一个重要的研究问题。为此,提出一种基于Petri网的面向公共过程的协同业务过程正确性验证方法。首先,采用Petri网对组织的私有过程和协同业务过程进行建模;然后,基于私有过程的控制流,定义了基本块结构(包含顺序、选择、并发、循环及同步链接结构)并提出对应抽取规则集;最后,将私有过程的控制流转换为通信进程代数(ACP),利用ACP进程的等式理论证明抽取前后模型的正确性一致。通过大量实验,并与现有典型验证方法进行对比分析,结果表明:相较于现有的方法,所提方法可以在保护组织隐私性的前提下极大地提高协同业务过程正确性的验证效率。

基于时空间行为的深港跨界生活圈研究

通过明确跨界生活圈的概念,针对研究对象、范围划定和影响因素3个核心问题展开理论探讨,进而构建跨界生活圈的引力模型和时空间行为模式。以深圳和香港地区为例,将深港跨界生活圈的演进历程划分为历史变迁、跨界萌芽、跨界探索和深度融合4个阶段,从交通出行、人员往来、文化交流等方面剖析深港跨界生活圈的时空间行为特征,从空间演化、规则对接和柔性规划3个方面探讨深港跨界生活圈的发展趋势,以为其他跨界生活圈规划的编制提供思路借鉴。