Android智能求救系统的设计与实现

针对人们遇到突发事件得不到及时救助的难题,设计并实现一种基于Android的智能求救系统.该系统由穿戴式智能手环和Android客户端组成,融合蓝牙4.0和移动通信技术.通过智能手环激活手机应用程序,利用移动通信技术发送求救信息,使用百度地图进行定位、兴趣点搜索、路径规划.利用蓝牙4.0技术设计智能手环,降低了功耗和成本;基于百度地图API开发客户端,提供了准确、及时的位置服务.实验结果证明,该系统实时可靠,提供了求救和自救双重保障,可以有效减少突发事故造成的伤亡,对生命安全保障具有重要意义.     

Industry 4.0 integration with socio-technical systems theory: A systematic review and proposed theoretical model

Industry 4.0 promises the fourth industrial revolution by integration of cyber and physical worlds through technology. Industry 4.0 implementation will result in human interaction with technical system in a specialised manner. Therefore, Industry 4.0 will also be a socio (human related) and technical (nonhuman related) system in pursuit of a common goal. The purpose of this study is to suggest a mechanism to include Socio-Technical Systems Theory perspective while designing architecture for integration while implementing Industry 4.0. Building on the previous literatures on Socio-Technical Systems Theory and Industry 4.0, the article proposes bringing the two approaches together and presents a framework for integration mechanism. Successful implementation of Industry 4.0 warrants vertical, horizontal and end-to-end integration. This study suggests a design mechanism for three types of integration mechanism in Industry 4.0 by considering the socio-technical systems impact on people, infrastructure, technology, processes, culture and goals. Further, the integration is also suggested for analysis on the impact of stakeholders, economic situation and regulatory frameworks around which the operating organizations are operating. This is the first paper to propose the consideration of Socio-Technical Systems theory while designing the horizontal, vertical and end-to-end integration for sustainable implementation of Industry 4.0.     

Cyber-based design for additive manufacturing using artificial neural networks for Industry 4.0

Additive Manufacturing (AM) requires integrated networking, embedded controls and cloud computing technologies to increase their efficiency and resource utilisation. However, currently there is no readily applicable system that can be used for cloud-based AM. The objective of this research is to develop a framework for designing a cyber additive manufacturing system that integrates an expert system with Internet of Things (IoT). An Artificial Neural Network (ANN) based expert system was implemented to classify input part designs based on CAD data and user inputs. Three ANN algorithms were trained on a knowledge base to identify optimal AM processes for different part designs. A two-stage model was used to enhance the prediction accuracy above 90% by increasing the number of input factors and datasets. A cyber interface was developed to query AM machine availability and resource capability using a Node-RED IoT device simulator. The dynamic AM machine identification system developed using an application programme interface (API) that integrates inputs from the smart algorithm and IoT interface for real-time predictions. This research establishes a foundation for the development of a cyber additive design for manufacturing system which can dynamically allocate digital designs to different AM techniques over the cyber network.     

Redundant configuration of robotic assembly lines with stochastic failures

One of the main challenges in the operation of robotic assembly lines is the occurrence of failures. Due to the connection of the stations via a material handling system, failures at one station often result in throughput losses. To some extent, these throughput losses can be reduced by installing buffers between the stations. However, the installation of buffers requires considerable investments and scarce factory space. Due to the advances of manufacturing technologies that form the foundation of ‘Industry 4.0’, new solutions to reduce failure-related throughput losses open up. One solution is a redundant configuration, in which downstream (backup) stations automatically take over the operations of failed stations during repair time. The throughput loss in these situations depends on the allocation of operations and the assignment of backup stations. Existing approaches in the literature that consider redundancies in the configuration of automated lines neglect the resulting production rate. Instead, the lines’ level of redundancy is used as a surrogate measure for optimisation. We present a genetic algorithm for the redundant configuration of robotic assembly lines with stochastic failures to maximise the production rate of the line. In a numerical analysis, it is demonstrated that this approach allows for productivity improvements.     

New flexibility drivers for manufacturing,supply chain and service operations

Increasing product proliferation, customisation, competition and customer expectations, as well as supply side disruptions, pose significant challenges to firm operations. Such challenges require improved efficiency and resilience in manufacturing, service and supply chain systems. New and innovative flexibility concepts and models offer a prospective route to such operational improvements. Several emerging issues in flexibility, such as risk and uncertainty management, environmental sustainability, optimal strategies under competition, optimal operations with strategic consumer behaviours are being examined in this regard. This overview provides a concise review of these critical research issues, and discusses related papers featured in this special issue. Four major flexibility drivers are classified: disruption risks, resilience and the ripple effect in the supply chain; digitalisation, smart operations and e-supply chains; sustainability and closed-loop supply chains; and supplier integration and behavioural flexibility.     

The link between Industry 4.0 and lean manufacturing: mapping current research and establishing a research agenda

In recent years, Industry 4.0 has emerged as one of the most discussed concepts and has gained significant popularity in both academia and the industrial sector. Both Industry 4.0 and lean manufacturing utilise decentralised control and aim to increase productivity and flexibility. However, there have been few studies investigating the link between these two domains. This article explores this novel area and maps the current literature. This is achieved through a systematic literature review methodology, investigating literature published up to and including August 2017. This article identifies four main research streams concerning the link between Industry 4.0 and lean manufacturing, and a research agenda for future studies is proposed.     

State-of-the-art survey on digital twin implementations

Digital twin (DT) has garnered attention in both industry and academia. With advances in big data and internet of things (IoTs) technologies, the infrastructure for DT implementation is becoming more readily available. As an emerging technology, there are both potential and challenges. DT is a promising methodology to leverage the modern data explosion to aid engineers, managers, healthcare experts and politicians in managing production lines, patient health and smart cities by providing a comprehensive and high fidelity monitoring, prognostics and diagnostics tools. New research and surveys into the topic are published regularly, as interest in this technology is high although there is a lack of standardization to the definition of a DT. Due to the large amount of information present in a DT system and the dual cyber and physical nature of a DT, augmented reality (AR) is a suitable technology for data visualization and interaction with DTs. This paper seeks to classify different types of DT implementations that have been reported, highlights some researches that have used AR as data visualization tool in DT, and examines the more recent approaches to solve outstanding challenges in DT and the integration of DT and AR.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00375-w     

用EVC4.0开发高效的Windows CE应用程序

本文介绍怎样开发高效的Windows CE应用程序.概述了消息的接收和分发机制,讨论了一些引起Windows CE程序开发不同于其他Windows 平台的独特特性.     

基于统计过程控制的水清洗控制系统

为了将水清洗机接入工业网络并实现智能自动化控制,本文提出了一种基于统计过程控制(SPC)可重构程序的水清洗控制系统.该系统根据水清洗设备的参数和组件特性构建了该设备的控制协议;根据协议指令制定了指令判断模块;根据SPC理论设计了过程控制模块.控制协议让该系统的清洗程序具有重构和联网的功能;指令判断模块为重构后的指令提供了安全性保障;过程控制模块让该系统的清洗过程具备动态调整清洗组件的功能.这些功能使得该设备可以实现智能自动化控制.通过测试,该系统比原有系统的清洗次数平均减少了15%,水清洗液的使用率提升了约5%,并扩展了3项功能,提高了设备的利用率和智能化水平,最终满足节能省水、多用途和联网的需求.     

A wireless reconfigurable modular manipulator and its control system

As the core of Industry 4.0, the intelligent manufacturing technology requires robotic arms to be networked, customized and flexible. Traditional industrial robots have a large number of electrical cables. The end effectors cannot be easily replaced. In this paper, a reconfigurable modular arm with quick replacement of tools and its neural adaptive control system are developed. It consists of an anthropomorphic 7 degree-of-freedom (DOF) manipulator, a reconfigurable connection mechanism (RCM) and a wireless controller. Based on the modular design ideas, each joint is integrated with a motor, a harmonic reducer, two encoders and a servo controller to achieve high torque capacity but keep light weight. Shape Memory Alloy (SMA) wires and steel spheres are used in the RCM to provide mechanical and electrical connections between the arm and the end effector for rapid replacement. The central controller communicates with each servo controller through wireless communication links. Furthermore, the neural adaptive control method compensating position and force tracking errors caused by the model uncertainty and time delay is addressed. Finally, the prototype is fabricated and experiments are carried out. The developed arm has high position accuracy, force control accuracy, and reliable reconfigurable capability.