数字化工厂中的产品虚拟制造

介绍了虚拟制造的概念和特点，以及数字化工厂软件eMPOwer，并利用该软件实现产品在数字化工厂中虚拟制造的过程。通过实例说明这项先进技术能成功地应用于产品制造的模拟，有效地解决了制造中的实际问题。     

水电工程施工区水泥混凝土路面设计方法探讨

根据现行水电工程施工区水泥混凝土路面设计规范,以水泥混凝土面板疲劳强度作为控制指标,比较了两种设计方法的计算结果,发现按照《公路水泥混凝土路面设计规范》计算所得的水泥混凝土路面结构厚度较厚。通过分析这两种设计方法的差异,提出《厂矿道路设计规范》更适合用来指导水电工程施工区水泥混凝土路面的设计,同时也提出该规范还需进一步完善,以考虑最重轴载对施工区的极限破坏作用。     

计算机本科专业软件实习工厂的构建研究

本文针对目前中国计算机专业本科教育中生产实习受客观因素所限,造成学生实验动手和科技创新能力不足的问题,提出利用软件实习工厂进行生产实习的方法,将生产实习投入到一个实际项目的开发与建设上,真实模拟软件公司的生产过程,并探讨其建设的总体设计思想和结构模型。     

Network Intrusion Detection in Internet of Blended Environment Using Ensemble of Heterogeneous Autoencoders (E-HAE)

Contemporary attackers, mainly motivated by financial gain, consistently devise sophisticated penetration techniques to access important information or data. The growing use of Internet of Things (IoT) technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation, as it facilitates multiple new attack vectors to emerge effortlessly. As such, existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems. To address this problem, we designed a blended threat detection approach, considering the possible impact and dimensionality of new attack surfaces due to the aforementioned convergence. We collectively refer to the convergence of different technology sectors as the internet of blended environment. The proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational autoencoder. An extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02% detection accuracy. Furthermore, performance of the proposed approach was compared with various single model (autoencoder)-based network intrusion detection approaches: autoencoder, variational autoencoder, convolutional variational autoencoder, and long short-term memory variational autoencoder. The proposed model outperformed all compared models, demonstrating F1-score improvements of 4.99%, 2.25%, 1.92%, and 3.69%, respectively.     

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL) can be employed to identify anonymous intrusions. Therefore, the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection (HGSODL-ID) model for the IIoT environment. The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format. The HGSO algorithm is employed for Feature Selection (HGSO-FS) to reduce the curse of dimensionality. Moreover, Sparrow Search Optimization (SSO) is utilized with a Graph Convolutional Network (GCN) to classify and identify intrusions in the network. Finally, the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model. The proposed HGSODL-ID model was experimentally validated using a benchmark dataset, and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.     

基于无线传感器网络的工厂安防系统设计

工厂具有占地面积大、功能区域复杂、人员流动大的特点.因此对于对安防技术有较高的要求;无线传感器网络是具有感知能力、计算能力、无线通讯能力的传感器节点组成的智能网络,可以很好地监测参数变化,实现复杂的大规模环境监测和目标追踪任务,由此设计一套基于WSN的身份识别的智能安防管理系统;对整个系统的安全要求、设计方法、功能实现、程序流程、软硬件实现方法进行了分析研究,结果表明由无线传感器网络构成的系统可以高效的实现工厂安防任务.     

飞轮通用测试软件平台的设计与实现

针对原有飞轮测试设备复用性差、维护成本高、难以保证多套产品同步自动测试的问题,设计开发了针对多型号飞轮的通用自动化测试系统;通过深入分析,提炼出多个产品在测试过程中功能的一致性和接口的差异性,采用分层架构、抽象工厂的设计模式以及配置文件的方法,解决了测试软件的通用性问题;提出了基于测试流程表的自动测试解决方案,能够适应多种测试流程的要求,扩展性强;采用混合多线程并行处理架构,解决了资源冲突,完善了多台飞轮的并行测试;该测试软件已在多型号飞轮的批产任务中使用,稳定可靠、适应性强,提高了设备使用率三倍以上,大大提升了飞轮批产测试效率。     

基于AVR单片机温室智能控制系统的设计

设计了一种用于植物工厂的温室智能控制系统．采取AVR单片机完成下位机设计,通过RS-485总线与上位机通信,并定义了通信协议,利用VC6．0开发人机界面．将该系统用于对温室环境因子的监控,用户可以根据植物实际生长需要选择适当的控制参数,实现对温室环境的管理．     

纺织工厂数字化技术平台及其发展

针对纺织行业“信息孤岛”现象严重，探讨了纺织工厂数字化技术平台建设的相关问题。文中简要讨论了工厂数字化技术平台的建设目标及层次性、企业的应用集成和网络化制造等相关技术，并指出了平台建设对从整体上进一步提高系统建设投资回报率的重要性。     

纺织工厂数字化技术平台及其发展（二）

针对纺织行业“信息孤岛”现象严重，探讨了纺织工厂数字化技术平台建设的相关问题。文中简要讨论了工厂数字化技术平台的建设目标及层次性，企业的应用集成和网络化制造等相关技术，并指出了平台建设对从整体上进一步系统建设投资回报率的重要性。