Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.

Vibrating Particles System Algorithm for Solving Classification Problems

Big data is a term that refers to a set of data that,due to its largeness or complexity,cannot be stored or processed with one of the usual tools or applications for data management,and it has become a prominent word in recent years for the massive development of technology.Almost immediately thereafter,the term“big data mining”emerged,i.e.,mining from big data even as an emerging and interconnected field of research.Classification is an important stage in data mining since it helps people make better decisions in a variety of situations,including scientific endeavors,biomedical research,and industrial applications.The probabilistic neural network(PNN)is a commonly used and successful method for handling classification and pattern recognition issues.In this study,the authors proposed to combine the probabilistic neural network(PPN),which is one of the data mining techniques,with the vibrating particles system(VPS),which is one of the metaheuristic algorithms named“VPS-PNN”,to solve classi-fication problems more effectively.The data set is eleven common benchmark medical datasets from the machine-learning library,the suggested method was tested.The suggested VPS-PNN mechanism outperforms the PNN,biogeography-based optimization,enhanced-water cycle algorithm(E-WCA)and the firefly algorithm(FA)in terms of convergence speed and classification accuracy.

Using Data Mining to Find Patterns in Ant Colony Algorithm Solutions to the Travelling Salesman Problem

Travelling Salesman Problem(TSP) is a classical optimization problem and it is one of a class of NP-Problem.The purposes of this work is to apply data mining methodologies to explore the patterns in data generated by an Ant Colony Algorithm(ACA) performing a searching operation and to develop a rule set searcher which approximates the ACA′s searcher.An attribute-oriented induction methodology was used to explore the relationship between an operations′ sequence and its attributes and a set of rules has been developed.At the end of this paper,the experimental results have shown that the proposed approach has good performance with respect to the quality of solution and the speed of computation.

Understanding the Artisanal Mining of the Pissy Granite Quarry Using the Problem-in-Context Framework (PiC)

The Pissy granite quarry of Ouagadougou in Burkina Faso exists since 2006, and it is a source of financial incomes for many poor families working there. The Problem-In-Context framework (PiC), lead the current study to understand better the quarry exploitation and structuration. Different motivations guide the quarry actors. Through those motivations, different solutions were suggested, mainly the shutdown of the quarry as the best way to decrease the impacts of the quarry exploitation on the environment, particularly regarding the air pollution and the loss of biodiversity in the area. The Pissy granite quarry is not recognized by the country’s legislation and this study is presenting the site organization. They are three main actors on the site and the tertiary actors are the ones having more incomes comparing to the primary and secondary actors of the chain. There is a need to evaluate the shell life of the quarry if the shutdown is not the final option chosen by the country. This evaluation should be a way to restructure the field and to have more incomes from it. Many standards exist in the country to guide the quarries and mines exploitation but they do not have impact on the Pissy granite quarry because of its non-reorganization.

煤矿安全知识问答系统的答案生成模型研究

随着国家和煤矿行业对煤矿应急管理要求的逐步提高,对煤矿安全知识的学习也提出了更高的要求,因此建立一种煤矿安全知识智能问答模型。有效学习煤矿安全知识,对于确保煤矿企业工作人员的人身安全和预防煤矿安全事故的发生至关重要。首先,基于RoBERTa-wwm算法自动生成问答对数据,获取并分析煤矿安全知识原始文本数据,定义问题类型并标注问答对;然后,结合RoBERTa-wwm与UniLM,采用点互信息与邻接熵发现新词扩充领域词典,提出问答对自动生成算法,同时构建煤矿安全培训知识问答对数据集,解决煤矿安全知识系统问答对数据集问题;最后,引入问题相似度机制,针对无法回答问题和无关问题提出答案生成策略,构建基于问题相似度机制的答案生成模型,使其只关注可回答问题,从而提升模型的推理能力。实验结果表明,所提出的煤矿安全知识问答系统答案生成模型可有效识别无法回答和无关的问题,能够为煤矿企业工作人员提供知识支持,最大程度地提升煤矿企业工作人员安全培训学习效果。

煤矿开采过程中采煤工艺与技术探讨

通过对煤矿开采过程中常用的采煤工艺进行研究,系统总结了长壁、综采和短壁工作面采煤技术的应用与发展。同时,分析了当前采煤技术存在的效率低、矿工安全保障和环保方面的问题,并提出了一系列解决措施。通过采用先进的机械设备、优化工作面设计、强化培训与管理、加强采空区治理、完善瓦斯防治、建立科学的事故应急处理体系及引入环保技术,以期提高采煤效率,确保矿工的生命安全,促进煤炭行业的可持续发展。

Data Mining and Machine Learning Methods Applied to A Numerical Clinching Model

Numerical mechanical models used for design of structures and processes are very complex and high-dimensionally parametrised.The understanding of the model characteristics is of interest for engineering tasks and subsequently for an efficient design.Multiple analysis methods are known and available to gain insight into existing models.In this contribution,selected methods from various fields are applied to a real world mechanical engineering example of a currently developed clinching process.The selection of introduced methods comprises techniques of machine learning and data mining,in which the utilization is aiming at a decreased numerical effort.The methods of choice are basically discussed and references are given as well as challenges in the context of meta-modelling and sensitivities are shown.An incremental knowledge gain is provided by a step-bystep application of the numerical methods,whereas resulting consequences for further applications are highlighted.Furthermore,a visualisation method aiming at an easy design guideline is proposed.These visual decision maps incorporate the uncertainty coming from the reduction of dimensionality and can be applied in early stage of design.

协作问题解决中平等参与对大学生认知投入的影响机制研究

协作问题解决能力被视为智能时代学习者的关键素养之一,要求每位共同体成员投入必要的认知努力。尽管该领域的相关研究已广泛开展,但对于协作问题解决中认知投入的过程机理,特别是成员平等参与如何影响认知投入的作用机制尚未充分阐释。以某高校178位大学生作为研究对象,探究协作问题解决中不同平等参与程度的小组在认知投入方面的差异。通过分析小组绩效和参与均衡度,将小组划分为四种类型,并采用内容分析及过程挖掘技术比较各类小组在认知投入水平、过程模式和典型序列上的差异。结果显示,高绩效小组倾向于观点整合,而高均衡小组则更多地展现出同伴支持。小组绩效与参与均衡度的交互作用表明,在参与均衡的小组中,与任务相关的知识水平是促进认知投入的关键因素;而在参与不均衡的小组中,主导者的角色则对认知投入有显著影响,其中“引导者”角色相较于“独行者”更能促进良好的认知投入。研究结果丰富了智能时代下协作问题解决中认知投入过程机制的理解,也为针对不同类型小组的有效学习干预提供了理论依据。

风障设置角度对上隅角低氧气体赋存规律的影响

风障角度的变化会对上隅角低氧气体的赋存状态产生重要影响。研究以山西某矿100510工作面为工程背景,利用Ansys Fluent软件构建工作面气体运移模型,对不同风障角度条件下上隅角低氧气体的赋存状态进行模拟,并通过现场实测数据进行验证。研究结果表明:设置风障能提高上隅角区域的风速,有利于解决上隅角低氧问题;在开切眼内距回风巷道5 m位置,风障的最佳角度为60°时,上隅角低氧问题的治理效果最好。研究成果有助于井下作业过程中解决上隅角低氧问题,确保安全生产。

电针治疗周围性面瘫相关随机对照试验的腧穴谱及刺激参数研究

目的:基于数据挖掘方法,总结并分析随机对照试验中电针治疗周围性面瘫(PFP)的腧穴谱及刺激参数,为规范电针治疗PFP提供参考依据。方法:通过检索国家知识基础设施数据库(CNKI)、中文科技期刊数据库(CCD)、中国学术期刊数据库(CSPD)、中国生物医学文献数据库(CBM)、PubMed、Cochrane Library和Web of Science等数据库,按照纳入标准和排除标准筛选电针治疗PFP的临床研究文献,提取文献基本资料、腧穴选择、腧穴归经、电针刺激频率、刺激强度、刺激波形、刺激时长、疗程、结局指标、安全性评价和有效率等信息,建立数据库,采用频数分析方法总结文献中选取的腧穴和电针参数的使用规律。结果:共纳入186篇文献,电针治疗PFP使用频率最高的腧穴为地仓穴,其次为阳白穴、颊车穴、太阳穴、下关穴、四白穴、翳风穴、合谷穴和牵正穴等,腧穴归经以足阳明胃经、经外奇穴、足少阳胆经和手阳明大肠经为主;电针刺激参数中波形多以疏密波为主;刺激时间以30 min为主,刺激强度多以患者面部肌肉跳动且能耐受为度;刺激频率以1 Hz和30~40次/min居多;观察指标以House-Brackmann面神经功能分级为主。结论:电针治疗PFP的随机对照试验研究显示,其腧穴选择相对一致,但在临床试验中缺乏统一的电针刺激参数,且大量研究并未提及PFP的分期选穴及电针的分期治疗,结局指标不规范,安全性评价较少,缺乏严谨性。目前缺乏明确指南来规范指导电针治疗PFP的临床实践,亟需大样本、多中心临床试验来评估电针治疗PFP不同分期的选穴及具体操作参数,并根据PFP的分期及证型制定合理规范的标准以指导临床应用,使临床操作规范化,临床治疗科学化,临床疗效最大化。

高效推进“十四五”末全面完成安徽省废弃矿山生态修复任务之路径

安徽省是矿业大省,长时间高强度的矿业活动对生态环境造成了损害。“十三五”以来安徽省投入大量人力、物力、财力对全省废弃矿山开展生态修复工作,生态效益、社会效益显著。本文对“十四五”2021—2023年6月间安徽省废弃矿山生态修复工作进行了全面评估,总结了取得的成效,分析了废弃矿山生态修复工作面临的问题,提出了2023—2025年下一步工作方向和实施路径。

可视化实时协作场景中群体深度学习发生的多维特征挖掘研究——基于三组不同协作习惯的个案分析

随着对教育互动的研究越来越强调“情境依存性”,关注复杂交互场景下互动数据的多维分析有助于还原群体学习复杂过程、揭示特定规律、优化特定服务。研究通过对三组不同协作成效的个案在可视化实时协作场景中的互动数据进行协作关系、协作行为和协作时序的多维分析发现,群体深度学习发生的特征表现为:(1)有多个核心成员且分层领导的组织关系,更有利于促使小组在协作前期的资料搜集和推进方案想法或成果形成的阶段积极展开交互反馈;(2)小组在协作过程中基于“内容建构”的集中性反馈和基于“案例共享、信息搜集、想法拓展”的实质性反馈对小组协作深度影响大于围绕“线索提示、任务明晰”的流程性反馈;(3)小组协作习惯主要存在“问题导向、资料导向、任务导向”三种典型模式,对小组协作深度的促进依次减弱。该研究为高校教学多元交互场景中新协作样态下的群体深度学习发生机理揭示、评价方法和促进策略优化等提供了理论与实践基础以及后续建议。

煤矿智能化建设中存在的问题及其对策研究

在对全国4省(区)20个单位煤矿智能化建设调研的基础上,全面深入地分析了当前煤矿智能化建设工作中存在的发展不平衡等突出问题。认为,这些问题既有政策、法规及标准规范层面,也有智能技术及智能装备层面,还有管理层面,需要予以高度重视。同时,提出加强政府引导和政策支持、加强智能化关键技术攻关、及时修订标准规范、加强政策引领提升煤炭科技创新驱动能力、做好煤矿智能化建设顶层设计、加强数据综合分析平台建设和加大相关人才培养力度等对策建议。

基于数据驱动中医证治规律研究的核心问题及解决策略

辨证论治是中医核心诊疗思维,是决定临床疗效的关键。如今,基于临床数据研究是探索中医证治规律的主要方法,但未真正而全面地剖析出“病-证-方-药-效”关键因素的内在关系,导致研究结果的临床价值较低。因此,笔者系统梳理了电子病历与临床研究匹配性差、数据治理影响数据准确性、数据分析方法难以发掘中医证治规律等核心问题,并在数据驱动的背景下,建立中医临床科研大数据平台、开发以人工智能为核心的数据治理与分析技术,从而实现临床科研一体化,为中医证治规律研究提供新思路与方法,推动中医药的发展。

国内外典型露天矿山坍塌事故案例分析与启示

露天矿山的安全风险相对井工矿要低一些,但存在排土场坍塌事故隐患。以全球紧急灾难数据库中录入的2013-2022年矿山坍塌事故数据为基础,选取了5个国内外典型露天矿山坍塌事故案例进行研究,简要介绍了事故基本情况,总结了5起事故的共性原因。在此基础上,深入分析了事故发生背后的深层次原因,并提出加强矿山立法制修订工作、强化矿山企业主体责任落实、提高监管监察水平、提升强化技术保安能力等进一步完善我国露天矿山安全生产管理的发展建议。

Data mining for classification of power quality problems using WEKA and the effect of attributes on classification accuracy

There is growing interest in power quality issues due to wider developments in power delivery engineering.In order to maintain good power quality,it is necessary to detect and monitor power quality problems.The power quality monitoring requires storing large amount of data for analysis.This rapid increase in the size of databases has demanded new technique such as data mining to assist in the analysis and understanding of the data.This paper presents the classification of power quality problems such as voltage sag,swell,interruption and unbalance using data mining algorithms:J48,Random Tree and Random Forest decision trees.These algorithms are implemented on two sets of voltage data using WEKA software.The numeric attributes in first data set include 3-phase RMS voltages at the point of common coupling.In second data set,three more numeric attributes such as minimum,maximum and average voltages,are added along with 3-phase RMS voltages.The performance of the algorithms is evaluated in both the cases to determine the best classification algorithm,and the effect of addition of the three attributes in the second case is studied,which depicts the advantages in terms of classification accuracy and training time of the decision trees.

矿井通风系统的常见问题及对策

随着采煤深度的增加,井下通风系统在各种因素的干扰下,浮现出许多问题,制定相应的解决措施可以提高矿井通风的质量。影响通风系统的因素主要包括通风方式、通风网络、通风动力及通风设施,对其进行分析,可以得知,常见的问题有设施故障频率高、通风效率较低、缺乏通风监督和管理模式落后等,针对这些问题,提出了解决策略,并探讨了通风系统的运行效果,以期提高矿井通风系统的稳定性和高效性,满足井下作业对新鲜风流的需求。

煤矿通风瓦斯安全现存问题及解决对策

保证生产安全是煤矿企业安全管理的首要任务,也是应对瓦斯、粉尘等问题的关键。通风安全和管理标准受自然、人为、技术等多重因素影响,引发诸多安全隐患。需以实际案例为基础,深入分析研究煤矿通风瓦斯安全问题,采取多项措施有效预防安全事故,降低通风瓦斯风险,助力煤矿实现可持续发展。

金属矿山安全生产现状及问题与对策初探

为了提升金属矿山安全生产水平,本文主要对金属矿山安全生产现状及问题与对策进行研究,文中先分析了金属矿山安全生产现状,之后分析了存在的问题,并且提出了有效的措施,包括增加安全投入,提升生产的安全性;利用先进的科技;强化立法;强化教育培训;注重尾矿库安全管理;构建矿山系统安全管理体系;健全监管制度,希望可以为有关人员提供参考。