Leveraging Robust Artificial Intelligence for Mechatronic Product Development—A Literature Review

Mechatronic product development is a complex and multidisciplinary field that encompasses various domains, including, among others, mechanical engineering, electrical engineering, control theory and software engineering. The integration of artificial intelligence technologies is revolutionizing this domain, offering opportunities to enhance design processes, optimize performance, and leverage vast amounts of knowledge. However, human expertise remains essential in contextualizing information, considering trade-offs, and ensuring ethical and societal implications are taken into account. This paper therefore explores the existing literature regarding the application of artificial intelligence as a comprehensive database, decision support system, and modeling tool in mechatronic product development. It analyzes the benefits of artificial intelligence in enabling domain linking, replacing human expert knowledge, improving prediction quality, and enhancing intelligent control systems. For this purpose, a consideration of the V-cycle takes place, a standard in mechatronic product development. Along this, an initial assessment of the AI potential is shown and important categories of AI support are formed. This is followed by an examination of the literature with regard to these aspects. As a result, the integration of artificial intelligence in mechatronic product development opens new possibilities and transforms the way innovative mechatronic systems are conceived, designed, and deployed. However, the approaches are only taking place selectively, and a holistic view of the development processes and the potential for robust and context-sensitive artificial intelligence along them is still needed.

Web Intelligence with Enhanced Sunflower Optimization Algorithm for Sentiment Analysis

Exponential increase in the quantity of user generated content in websites and social networks have resulted in the emergence of web intelligence approaches.Several natural language processing(NLP)tools are commonly used to examine the large quantity of data generated online.Particularly,sentiment analysis(SA)is an effective way of classifying the data into different classes of user opinions or sentiments.The latest advances in machine learning(ML)and deep learning(DL)approaches offer an intelligent way of analyzing sentiments.In this view,this study introduces a web intelligence with enhanced sunflower optimization based deep learning model for sentiment analysis(WIESFO-DLSA)technique.The major intention of the WIESFO-DLSA technique is to identify the expressions or sentiments that exist in the social networking data.The WIESFO-DLSA technique initially performs pre-processing and word2vec feature extraction processes to generate a meaningful set of features.At the same time,bidirectional long short term memory(BiLSTM)model is applied for classification of sentiments into different class labels.Moreover,an enhanced sunflower optimization(ESFO)algorithm is exploited to optimally adjust the hyperparameters of the BiLSTM model.A wide range of simulation analyses is performed to report the better outcomes of the WISFO-DLSA technique and the experimental outcomes ensured its promising performance under several measures.

A Deep Learning-Based Computational Algorithm for Identifying Damage Load Condition: An Artificial Intelligence Inverse Problem Solution for Failure Analysis

In this work,we have developed a novel machine(deep)learning computational framework to determine and identify damage loading parameters(conditions)for structures and materials based on the permanent or residual plastic deformation distribution or damage state of the structure.We have shown that the developed machine learning algorithm can accurately and(practically)uniquely identify both prior static as well as impact loading conditions in an inverse manner,based on the residual plastic strain and plastic deformation as forensic signatures.The paper presents the detailed machine learning algorithm,data acquisition and learning processes,and validation/verification examples.This development may have significant impacts on forensic material analysis and structure failure analysis,and it provides a powerful tool for material and structure forensic diagnosis,determination,and identification of damage loading conditions in accidental failure events,such as car crashes and infrastructure or building structure collapses.

Advanced Data Collection and Analysis in Data‑Driven Manufacturing Process

The rapidly increasing demand and complexity of manufacturing process potentiates the usage of manufacturing data with the highest priority to achieve precise analyze and control,rather than using simplified physical models and human expertise.In the era of data-driven manufacturing,the explosion of data amount revolutionized how data is collected and analyzed.This paper overviews the advance of technologies developed for in-process manufacturing data collection and analysis.It can be concluded that groundbreaking sensoring technology to facilitate direct measurement is one important leading trend for advanced data collection,due to the complexity and uncertainty during indirect measurement.On the other hand,physical model-based data analysis contains inevitable simplifications and sometimes ill-posed solutions due to the limited capacity of describing complex manufacturing process.Machine learning,especially deep learning approach has great potential for making better decisions to automate the process when fed with abundant data,while trending data-driven manufacturing approaches succeeded by using limited data to achieve similar or even better decisions.And these trends can demonstrated be by analyzing some typical applications of manufacturing process.

Studying History Education of High School in Taiwan by Causal Layered Analysis

After the 21st century,high school history learning will focus on teachers promoting the twelve-year state education.In recent years,in line with the changes in the new 108-year social curriculum,supporting strategies have been proposed:such as literacy orientation,inquiry and practice,learning process archives,and the structural direction of the controversial Chinese history into East Asian history.Historical learning has indeed had a great impact on the people’s national spiritual education and the development of historical consciousness in Taiwan’s education policy.This is the reason Taiwan’s Ministry of Education strives to improve students’historical literacy and connotation application abilities.When developing a learning policy,both external and internal learning factors need to be considered.The external aspect deals with the reasons for learning:Is learning for the purpose of using or accumulating historical wisdom in daily life to learn from the past and the present,on the other hand,to test the content of the course and the degree of absorption;or is it specifically for exams or other enlightenment purposes.The internal aspect involves those most affected by the policy:students and teachers.After studying and observing high school history learning policies for decades,some alternative future visions for history learning were found in the method of reflection on future research-the conclusion is that history is interestingly revitalized,and the preferred future is thematic history.According to the famous futurology scholar Sohail Inayatuallah’s proposal:the causal layering model.It helps understand how Taiwan’s historical policies operate.And how teachers and students on the front line respond to changes and take future actions.The key is to change the future:in the process of building an alternative future,whether the internal and external mix has changed or whether you want to try new things and expand your horizons.In fact,the difficulty of teaching lies in students’cooperation and conscious learning.Therefore,in the analysis of learning through alternative futures,is it possible to distinguish between internal and external situations and methods such as:1.Internal:Is education centered on teachers?Or is it student-centered?2.External:Does the Ministry of Education prioritize testing,or encourage teachers to adopt interactive communication and integrate education into the curriculum?Therefore,what is the function and inspiration of studying high school history and life?If thematic history teaching is used:teachers can use thematic learning methods to help students focus on causal relationships,the causes of turning points,or the evolution process of the beginning and end of events.This is more advantageous for testing based on the application topic,and it is easy to test how much understanding and understanding of history?Has an activating effect.By studying history in high school,using the“CLA(Causal layered analysis)”method of future studies,you can enter the stage of worldview exploration with the goal of improving professional depth and emotional level,and use it in your own understanding and utilization of history.Based on research,some insights into the prospects and thinking of learning history in high schools are provided:1.Facing the impact of declining birthrate,Taiwan needs a macro perspective to improve its future competitiveness and look forward to a new perspective on world history,using futuristic cause-and-effect level analysis to combine world changes with daily life applications.2.The study of history in high schools should go into a systematic construction:understand its cause-and-effect relationships and global trends,so teachers play a professional and future role in controlling the use of new information and technology.3.In the future,humans may develop more“intelligent”needs.As a reference from history or to explore the preferred path for the future,there will also be a greater need to innovate and meet challenges.4.Studying high school history has entered the professional field.Through self-exploration,it can be transformed into life affairs and establish the concept and value of lifelong learning.5.In studying the“history of high school learning”,have new prospects for the future of education.Through professional knowledge such as“trend theory and causal hierarchy analysis”of futurology,pursue new horizons and visions,making future education full of hope and possibility.

视频侦查技术关键及其发展展望

视频侦查技术的核心是通过视频图像的提取、查看、分析和研判来获取侦查线索、固定涉案证据。多年来经过在实战应用中不断地发展创新,视频侦查技术形成了自己独特的关键技术体系:视频信息分析解读技术是侦查应用和证据固定的基础和核心;视频证据固定保全技术的规范是审判中心主义的客观要求,可以获取视频侦查记录报告、视频检验鉴定报告或视频数据关联报告;低质量视频图像的增强恢复技术专业性强,应用范围窄,技术成熟度高,然而它不断面临新的挑战。目前,视频数据的智能应用在大数据背景下变得越来越重要,仍需进一步突破视频自动识别技术的应用范畴,建立完善多层次的视频数据综合应用体系,打造适应不同业务需要的视频数据实战应用模型。

基于AIGC协同工业设计流程的气道廓清辅具设计研究与实践

目的设计领域引入蓬勃发展的AIGC技术以期提高效率,在此基础上结合大数据技术和双钻模型,以提高AIGC应用于复杂产品工业设计的质量。方法提出了一种AIGC协同的“1+2”钻工业设计流程模型,通过三次发散、三次收敛来提高AIGC协同工业设计的流程效率与方案质量。首先,在ChatGPT和Python大数据爬取辅助下发散需求问题,在Python高频词分析、竞品分析、专家评审下收敛设计目标;其次,运用Midjourney的Stop参数模糊生图进行创意发散,通过对项目人员问卷调查收敛设计方向;最后,联合使用DALL·E、StableDiffusion和Midjourney发散设计细节,通过焦点小组收敛以确定最终方案并建模渲染。结果实现全面、专业、准确的长期卧床老人气道廓清的需求分析,且辅具设计方案的在用户友好程度、造型美观度、技术可行性和功能可用性方面获得了设计与工程双方专业人员的认可。结论AIGC、大数据与传统工业设计方法相结合的“1+2”钻设计流程,能够通过人工控制AIGC的参考内容、处理AIGC的生成内容,以及阶段性专家评估有效地提升AIGC协同的工业设计方案质量,为规范化的AIGC协同工业设计流程提供思路。

基于云边协同的智能制造系统中的实时数据处理与分析研究

旨在探讨基于云边协同的智能制造系统中的实时数据处理与分析的方法与应用。随着制造业的数字化转型,实时数据处理与分析对提高生产效率和质量至关重要。作为一种新兴的计算范式,云边协同允许将数据处理任务分布到边缘设备,以满足实时性要求。提出了一种基于云边协同的框架,通过实验验证了其有效性。结果表明,所提出的框架能显著提高数据处理效率,满足实时性需求,为智能制造系统的数字化转型提供了有力支持。

压缩空气储能盐穴储气库技术及其智能建造工艺技术研究

当前,盐穴型储气库建造技术已较为成熟,并朝着智能化方向发展。根据盐穴储气库技术现状,对盐穴储气库当前建造技术进行分析;从全生命周期管理角度,将盐穴储气库智能建造划分为智能选址、智能设计、智能施工、智能运维4个阶段,并对每个阶段涉及的关键技术进行研究;提出盐穴储气库智能建造工艺技术框架及其具体内容;最后,从系统、技术、理论、模型4个方面,提出并总结盐穴储气库智能建造未来研究重点。相关技术在应城300 MW级压缩空气储能示范工程上得到有效应用。

基于MobileNetV3网络的龋病和根尖周炎根尖片的诊断

目的研究深度学习技术智能诊断龋齿和根尖周炎的效果,初步探讨深度学习在口腔疾病诊断中的应用价值。方法以2298张包含健康牙齿、龋病、根尖周炎的根尖片数据集为研究对象,随机划分为1573张训练集图像,233张验证集图像以及492张测试集图像。通过多种神经网络对比验证,选择性能较好的MobileNetV3网络模型应用于牙病诊断,并通过调整网络超参数优化模型。采用精确率、准确率、召回率和F1分数评估模型识别龋齿和根尖周炎的能力,并使用类激活热力图对网络模型性能进行可视化分析。结果基于MobileNetV3网络模型的牙齿病变检测算法对健康牙齿、龋病和根尖周炎进行分类的精确率、召回率和准确率分别为99.42%、99.73%和99.60%,F1分数为99.57%,达到了较为理想的智能诊断效果。可视化类激活热力图也显示出网络模型能够较为准确地提取牙科病变的特征。结论基于MobileNetV3网络模型的牙齿病变检测算法能够排除图像质量和人为因素的干扰,具有较高的诊断准确率,可满足口腔医学教学和临床应用需求。

基于失效模式与效应分析法的智能手术室药房管理体系的构建与应用

目的 建立智能手术室药房管理体系,促进手术室药品的规范化管理,为医疗机构提供借鉴与参考。方法 基于失效模式与效应分析(FMEA)法对苏州大学附属第二医院手术室药房工作流程进行风险识别及评估,依据风险优先指数(RPN)值确定需要采取措施进行改善的失效模式,分析失效原因,拟定干预措施并对改进效果进行评价。结果 在手术室药房工作流程中共发现12个需优先改善的失效模式,将智能药柜及相应管理体系投入使用后,RPN值共下降337,下降幅度达67.8%;麻醉药品管理水平、患者用药及时性和医务人员满意度均显著提高。结论 运用FMEA法能够能有效识别手术室药房环节的风险,智能手术室药房管理体系显著提高了手术室药品管理水平和医疗服务质量。

人工智能技术在失效分析领域的应用

本文探讨了人工智能技术,尤其是机器学习和自然语言处理技术在失效分析领域的应用与发展趋势。失效分析是确保设备可靠性和安全性的重要手段,广泛应用于航空航天、汽车制造、电子设备等领域。传统的失效分析方法通常依赖专家经验,而人工智能技术凭借其强大的数据处理能力,与传统方法相结合,极大地提升了分析的精度和效率。在失效模式诊断方面,人工智能技术能够快速准确地识别各种故障模式,并提供精确的诊断结果;在失效原因诊断中,人工智能通过整合多种数据来源,揭示复杂的失效因素和潜在的因果关系,提升了诊断的可靠性;在失效预测方面,机器学习技术能够精确预测材料的寿命和强度,减少实验时间和成本;在失效预防方面,人工智能技术提供了新的思路,能够有效降低失效发生的风险,减少产品维护成本。本文还展望了人工智能技术在失效分析领域的发展前景,提出了在数据质量提升、模型优化、跨学科合作以及伦理与安全等方面的挑战与建议。

基于机器视觉的咖啡生豆快速检测与分级研究

目的建立基于机器视觉的咖啡生豆快速检测分级方法,实现咖啡生豆的快速无损等级评估。方法本研究提取咖啡生豆图像预处理后的形状和颜色共13种特征,首先通过特征分布图分析与主成分分析找到13类缺陷豆的显著特征,并确定其显著特征值范围来判定缺陷豆,然后对正常豆按粒度大小判断其等级,最后在MATLAB App Designer平台上设计了咖啡生豆快速检测与分级界面。结果利用机器视觉技术能很好地识别各个类别咖啡生豆,检测一级咖啡生豆准确率为94.77%、二级为93.20%、三级为95.85%,13类缺陷豆平均准确率为82.25%,咖啡生豆检测平均准确率达到91.52%,检测300 g咖啡生豆总用时25.3156 s。结论本方法提高了咖啡生豆分级的智能化水平,分级过程平稳且快速,为今后咖啡生豆在线检测分选提供了技术支持。

基于弗雷歇距离的GIS隔离开关卡涩缺陷识别及量化方法

电力工业上GIS(gas insulatedswitchgear)隔离开关的多种机械故障多是由传动机构及导电部结构应力变化导致,而这种应力变化的早期体现形式主要是卡涩缺陷。对GIS隔离开关卡涩缺陷的识别及量化对避免设备发生严重故障具有重要意义。然而,GIS隔离开关面临因工业现场开关机械状态的离散化导致的难以对卡涩缺陷进行识别和量化的问题,限制了运维人员对GIS隔离开关机械状态的感知能力与故障预警能力。文中以GIS隔离开关正常机械状态下的驱动电机功率曲线作为标准曲线,采用多窗口斜率的特征点识别方法,结合开关运行物理过程分析将电机功率曲线进行分段处理。在开关动作各分段内,采用弗雷歇距离分析对GIS隔离开关的卡涩缺陷进行识别,并相应地以标准曲线与动作曲线的弗雷歇距离作为量化依据。最后,通过ZF12B⁃126型GIS隔离开关实验,验证了文中所提出技术路线的有效性,实验证明在卡涩缺陷发展初期,文中采取的方法可以灵敏的对卡涩缺陷进行识别。

基于改进STPA-DEMATEL的智能航电系统致因要素分析

针对智能航电系统在非线性耦合运行场景下产生的预期功能安全(safety of the intended functionality,SOTIF)问题,提出一种将系统理论过程分析(systematic theory process analysis,STPA)与决策试验与评价实验法(decision-making trial and evaluation laboratory,DEMATEL)相结合的致因分析框架。首先,在定义系统级危险的基础上构建安全控制结构,识别其不安全控制行为并提取与智能化缺陷相关的STPA致因要素。接下来,引入毕达哥拉斯模糊加权平均算子和闵可夫斯基距离对传统DEMATEL方法进行优化,专家根据控制反馈回路对致因要素进行评价并计算其中心度与原因度。最后,分析STPA致因要素与SOTIF致因属性之间的映射关系,给出关键致因要素的风险减缓措施。以单一飞行员驾驶(single-pilot operation,SPO)模式下的虚拟驾驶员助理系统为例说明了所提方法的可行性与有效性。研究结果表明,改进的STPA-DEMATEL方法可以有效识别关键致因要素,且能够克服专家评价的模糊性与不确定性,为智能航电系统的安全性设计提供了参考依据。

基于PDCA循环的地理空间情报流程研究

[研究目的]地理空间情报质量及体系运行效率是影响其应用的重要因素,建立合理的地理空间情报流程模型对有效开展研究及发挥其价值有重要意义。[研究方法]基于PDCA循环的研究方法,建立以情报需求为中心的情报流程理论模型,将情报流程划分为情报需求明确、情报计划、情报生产、情报检查和情报增值五个阶段,对地理空间情报生产运行过程进行分析。[研究结论]随着各阶段情报工作持续开展,新的经验和成果不断促进已有情报环节修正,各环节不停运转、循环往复、螺旋上升,不断增强地理空间情报决策支持的科学性和有效性,提升情报体系运行效率,实现地理空间情报工作的不断发展与完善。

基于组合的情报分析构件化研究

[目的/意义]数智化技术在情报工作中的引入丰富了情报分析的参与要素,并对情报人员提出了更多的专业要求,构件视角下的情报分析不仅有助于规范各要素的工作职责,而且可以支持所有要素在标准流程下统一协调。[方法/过程]首先,根据软件构件模型对情报构件的内外部设计进行概念化表示;其次,引入MVC设计模式与以目标为中心的流程思想规范情报构件的组合原则及工作模式;最后,以构件为组成单位进行情报分析模型的示例开发,从组合逻辑上证明构件化分析的灵活性和规范性。[结果/结论]在组合原则和工作模式的指导下,不同功能的情报构件可以通过外部接口的连接完成同一层次下的组合。基于构件组合的情报分析模型的开发示例证明了情报构件化分析不仅能保证情报产品高质量的生产,还提升了情报分析的灵活性和规范性。

美国在1979年伊斯兰革命中的情报失误及其启示

[研究目的]研究1979年伊斯兰革命过程中美国情报工作的失误表现与成因能够为中国在对外事务研判和情报分析等方面提供更多借鉴和参考。[研究方法]主要使用文献分析法对美国相关外交档案展开梳理,从情报周期的各个阶段展开分析,系统梳理事前威胁研判、情报搜集和情报分析、情报分发等多个环节的失误成因和内在的逻辑关系。[研究结论]美国在这次伊朗国内政局变动中存在情报失误的情况,启示我们应提升战略情报预警能力,及时研判风险等级,情报消费者也应该注重保持中立,不断优化情报分发机制,才能避免类似失误。

基于多摄像头边云协同视频请求的智能监控预警方法研究

多摄像头边云协同视频已被广泛运用于智能化生活的各个领域,采用边云协同模式已然成为摄像头视频智能监控的数据处理的发展趋势。为了实现对多摄像头边云协同的视频请求任务的高精度检测,此次研究提出在基于多摄像头边云协同请求下的智能监控预警方法。采用边缘计算和云计算的紧密协同处理,在边云协同的数据协同能力下以较低成本完成对数据的高效处理。在多摄像头请求下的边云协同视频的智能监控预警,对视频数据进行处理与分析,提出利用网状关联分析的智能预警模型进行判定。对单摄像头与多摄像头下智能监控预警方法检测的准确率进行分析。得出该方法的检测准确率最高,且每项的检测准确率均在97%左右,证明了其在智能监控预警中的可靠性与有效性。

智能制造系统可靠性与风险评估模型

针对智能制造系统的可靠性与风险评估问题,提出一种基于改进失效模式与影响分析(FMEA)的智能制造系统可靠性与风险评估模型。从创新运用组合权重、逼近理想解排序法思想和模糊多准则妥协解排序法的角度对传统FMEA模型进行改进;基于逼近理想解排序法思想得到专家权重,减少了专家团队对失效模式风险因子分析过程中的个体差异;使用模糊层次分析法和熵权法分别计算风险因子的主观和客观权重,减少了风险因子确定的主观性。最后,运用围绕中心点划分(PAM)聚类算法对改进模型得到的结果进行分析,并应用于智能制造系统风险评估中,确定了智能制造系统中各失效模式的重要程度,通过比较分析验证了改进模型的有效性。