Mathematical model and discrete artificial Bee Colony algorithm for distributed integrated process planning and scheduling

With the development of the globalization of economy and manufacturing industry, distributed manufacturing mode has become a hot topic in current production research. In the context of distributed manufacturing, one job has different process routes in different workshops because of heterogeneous manufacturing resources and manufacturing environments in each factory. Considering the heterogeneous process planning problems and shop scheduling problems simultaneously can take advantage of the characteristics of distributed factories to finish the processing task well. Thus, a novel network-based mixed-integer linear programming (MILP) model is established for distributed integrated process planning and scheduling problem (DIPPS). The paper designs a new encoding method based on the process network and its OR-nodes, and then proposes a discrete artificial bee colony algorithm (DABC) to solve the DIPPS problem. The proposed DABC can guarantee the feasibility of individuals via specially-designed mapping and switching operations, so that the process precedence constraints contained by the network graph can be satisfied in the entire procedure of the DABC algorithm. Finally, the proposed MILP model is verified and the proposed DABC is tested through some open benchmarks. By comparing with other powerful reported algorithms and obtaining new better solutions, the experiment results prove the effectiveness of the proposed model and DABC algorithm successfully.     

Building a right digital twin with model engineering

In recent years, the concept of digital twin (DT) is attracting more and more attention from researchers and engineers. But there is still no consensus on what a right DT is. On one hand, some common models are renamed as DTs. On the other hand, some DTs extremely pursue ‘the same’ as physical objects, which bring unnecessary complexities to them. In this paper, we try to answer two questions from the point of view of model engineering: how to define a right digital twin, and how to build a right digital twin. The concept and related technologies of model engineering are introduced. Some basic principles and a set of metrics for a right DT are given. An evolutionary concurrent modeling method for DT (ECoM4DT) is proposed not only inheriting the theory from classic M&S methods but also highlighting the characteristics of DT compared with traditional models to systemically guide the DT modeling process.     

Improving human robot collaboration through Force/Torque based learning for object manipulation

Human–Robot Collaboration (HRC) is a term used to describe tasks in which robots and humans work together to achieve a goal. Unlike traditional industrial robots, collaborative robots need to be adaptive; able to alter their approach to better suit the situation and the needs of the human partner. As traditional programming techniques can struggle with the complexity required, an emerging approach is to learn a skill by observing human demonstration and imitating the motions; commonly known as Learning from Demonstration (LfD). In this work, we present a LfD methodology that combines an ensemble machine learning algorithm (i.e. Random Forest (RF)) with stochastic regression, using haptic information captured from human demonstration. The capabilities of the proposed method are evaluated using two collaborative tasks; co-manipulation of an object (where the human provides the guidance but the robot handles the objects weight) and collaborative assembly of simple interlocking parts. The proposed method is shown to be capable of imitation learning; interpreting human actions and producing equivalent robot motion across a diverse range of initial and final conditions. After verifying that ensemble machine learning can be utilised for real robotics problems, we propose a further extension utilising Weighted Random Forest (WRF) that attaches weights to each tree based on its performance. It is then shown that the WRF approach outperforms RF in HRC tasks.     

RFID芯片在物联网应用中的设计与研究

文章就政府主管部门在物联网应用环境下,在物品监管、证件关联和失控溯源等管控过程中的RFID芯片的信息安全问题进行了研究,特别是针对工作在915．0MHz频段的RFID芯片的安全机制,分析了在保证实时掌握监管物品本身的状态、随时检查与之对应的监管证件和文书状态、实时绑定监管物品的信息与物联网视频监控的管控要求下的信息安全机制,并提出了可资借鉴的信息安全解决方案。     

云存储安全增强系统的设计与实现

云存储是一种新型的网络存储形式,并逐步为大家所接受,企业和个人用户都开始使用云存储作为其网络存储媒介。目前很多著名的IT企业都推出了云存储服务,其中Amazon公司推出的SimpleStorageService（S3）就是商用云存储服务的典型代表。随着云存储的广泛使用,云存储中数据的安全问题,如数据泄漏和数据篡改,也成了用户广泛关注的问题。文章基于Amazons3的云存储服务,设计并实现了一款云存储安全增强系统,对用户上传至Amazons3的数据进行加密保护,使得文件以密文形式存储于Amazons3,可以有效防止数据在传输和存储过程中的泄漏;同时系统还对从s3下栽的文件进行完整性校验,检测其内容与上传时是否一致,以防止文件被篡改;最后,系统还提供了多用户访问控制支持,多个用户可以共享同一个S3账号,同时保证各自存储的内容互相隔离,禁止一个用户非授权访问其他用户存储的文件。     

引入关联分析的用户行为信任评估在云计算平台中的研究

关联规则是数据挖掘的重要方法。随着国内外云计算应用及研究的不断推进,云计算的安全性成为云计算的核心问题之一。通过进行用户的状态行为关联分析,提取信任信息,结合可信云的思想,提出一个云计算下基于信任的防御系统模型。     

防爆变频器在煤矿应用中的关键技术

分析了防爆变频器在煤矿井下使用过程中存在的电压等级、散热、电磁兼容性、逆变器转换效率等问题,详细介绍了解决这些问题的方法,即通过采用隔爆兼本质安全型技术和热管散热技术解决变频器的防爆和散热问题,使防爆变频器具有性能高、功能强、噪音低等特点;通过改变变频器的电路拓扑结构、改进控制策略等技术手段解决变频器的电磁兼容性和能耗问题,使防爆变频器满足煤矿生产的现代化要求。     

矿用救生舱环境监控系统软件的研究

给出了矿用救生舱环境监控系统的组成,主要介绍了该系统软件的功能设计、总体架构及界面设计方案。该系统软件可实时显示各传感器的运行状态和检测数据、舱内气体余量、电源电量、照明灯状态、空气净化状态、自动控制状态、舱门状态等,人机交互界面友好,操作简便。该软件经长期连续运行实验,工作正常,达到了设计要求。     

大采高采煤机挡矸装置的改进设计

分析了传统的大采高采煤机挡矸装置的结构原理、技术特点及存在的问题,提出了改进设计并进行了仿真分析。通过增加油缸数量、加强加厚顶护板、将刚性连接改为柔性连接、增设分流集流阀等手段,实现了对挡板的动态调节和支护,有效地提高了挡矸装置的可靠性和采煤机的开采效率。仿真结果表明,改进后的挡矸装置受力更均匀,挡板能承受的力更大。     

煤矿井下采掘设备隔爆兼本安型电气控制箱的本安设计

针对煤矿井下采掘设备防爆兼本安型电气控制箱内本安电路与非本安电路并存、非本安电路的能量可能会窜入本安电路而影响本安电路防爆及本安性能的问题,从结构设计和电气隔离两方面介绍了煤矿井下采掘设备隔爆兼本安型电气控制箱的本安设计方法,给出了电气间隙、爬电距离、绝缘及耐压、接线端子、本安布线、电源变压器、本安隔离栅印制板等结构布置方法以及RS485通信隔离电路、模拟量隔离电路、压频变换电路、电阻信号隔离电路、开关量隔离电路、继电器隔离电路等电气隔离措施。应用表明,采用该方法设计的煤矿井下采掘设备隔爆兼本安型电气控制箱安全可靠,维护简便,能够满足煤矿井下的防爆要求。