Design of a PID optimized neural networks and PD fuzzy logic controllers for a two‐wheeled mobile robot

In this paper, two intelligent techniques for a two‐wheeled differential mobile robot are designed and presented: A smart PID optimized neural networks based controller (SNNPIDC) and a PD fuzzy logic controller (PDFLC). Basically, mobile robots are required to work and navigate under exigent circumstances where the environment is hostile, full of disturbances such as holes and stones. The robot navigation leads to an autonomous decision making to overcome an obstacle and/or to stop the engine to protect it. In fact, the actuators that drive the robot should in no way be damaged and should stop to change direction in case of insurmountable disturbances. In this context, two controllers are implemented and a comparative study is carried out to demonstrate the effectiveness of the proposed approaches. For the first one, neural networks are used to optimize the parameters of a PID controller and for the second a fuzzy inference system type Mamdani based controller is adopted. The goal is to implement control algorithms for safe robot navigation while avoiding damage to the motors. In these two control cases, the smart robot has to quickly perform tasks and adapt to changing environment conditions while ensuring stability and accuracy and must be autonomous with regards to decision making. Simulations results aren't done in real environments, but are obtained with the Matlab/Simulink environment in which holes and stones are modeled by different load torques and are applied as disturbances on the mobile robot environment. These simulation results and the robot performances are satisfactory and are compared to a PID controller in which parameters are tuned by the Ziegler–Nichols tuning method. The applied methods have proven to be highly robust.     

New computer vision based Snakes and Ladders algorithm for the safe trajectory of two axis CNC machines

Multi-axis machine production process optimization, automation and intelligence are the key codes of today’s scientific community. Rapid decision and intelligence are becoming more important for precise and safe virtual and real production. Multi-axis CNC production is a high speed machining process that demands less human intervention and high intelligence, to deal with any uncomfortable situation regarding collisions. Current CAM softwares as well as CNC machines are able to detect collisions but are unable to avoid these collisions automatically. This paper aims to make the CAD-CAM/CNC multi-axis safe trajectory generation process optimal, intelligent and automatic, using vision based image processing by the Snakes and Ladders game analogy. Applying the Snakes and Ladders analogy on machine virtual scene (trajectory preparation) and real scene (during production) images gives promising methodology for safe and efficient trajectory generation while avoiding collisions named Snakes and Ladders Analogy for Production Trajectory (SLAPT). Our Rectangular Enveloped Safe and Efficient Trajectory (RESET) algorithm, based on the same principle of the SLAPT methodology is also discussed in this paper. Results include some applications of algorithms on virtual and real machine scene images for the safe and optimized trajectory of tools. This paper focuses on intelligence and optimization of 2D non-functional transversal trajectories of 2-axis machines for production and preparation processes as an initial effort towards the complex safe trajectory generation process (mill-turn).     

Design and evaluation of virtual home objects with music interaction in smart homes

Well-designed virtual home objects and human-computer interactions (HCIs) can provide convenient ways to easily make use of home services for home inhabitants in smart homes. This work tries to design the virtual home objects with the ability of playing the music and implement a particular HCI interface accomplishing the music interaction with virtual home objects for deploying virtual home music services in smart homes. Each virtual home object is connected to a list of songs. When a home inhabitant holds a virtual home object and moves it as operating gestures upon the implemented HCI interface, the virtual home music service will be activated and the smart home will automatically play the favorite songs for its inhabitant. In this way, a highly interactive home music service for home inhabitants can be realized through the convenient operating interactions between the virtual home objects and the implemented HCI interface in smart homes.     

A framework to design a human-centred adaptive manufacturing system for aging workers

The so-called smart manufacturing systems (SMS) combine smart manufacturing technologies, cyber-physical infrastructures, and data control to realize predictive and adaptive behaviours. In this context, industrial research focused mainly on improving the manufacturing system performance, almost neglecting human factors (HF) and their relation to the production systems. However, in order to create an effective smart factory context, human performance should be included to drive smart system adaptation in efficient and effective way, also by exploiting the linkages between tangible and intangible entities offered by Industry 4.0. Furthermore, modern companies are facing another interesting trend: aging workers. The age of workers is generally growing up and, consequently, the percentage of working 45–64 years old population with different needs, capabilities, and reactions, is increasing. This research focuses on the design of human-centred adaptive manufacturing systems (AMS) for the modern companies, where aging workers are more and more common. In particular, it defines a methodology to design AMS able to adapt to the aging workers’ needs considering their reduced workability, due to both physical and cognitive functional decrease, with the final aim to improve the human-machine interaction and the workers’ wellbeing. The paper finally presents an industrial case study focusing on the woodworking sector, where an existing machine has been re-designed to define a new human-centred AMS. The new machine has been engineered and prototyped by adopting cyber-physical systems (CPS) and pervasive technologies to smartly adapt the machine behaviour to the working conditions and the specific workers’ skills, tasks, and cognitive-physical abilities, with the final aim to support aging workers. The achieved benefits were expressed in terms of system usability, focusing on human-interaction quality.     

Task mapper and application-aware virtual machine scheduler oriented for parallel computing

We design a task mapper TPCM for assigning tasks to virtual machines, and an application-aware virtual machine scheduler TPCS oriented for parallel computing to achieve a high performance in virtual computing systems. To solve the problem of mapping tasks to virtual machines, a virtual machine mapping algorithm (VMMA) in TPCM is presented to achieve load balance in a cluster. Based on such mapping results, TPCS is constructed including three components: a middleware supporting an application-driven scheduling, a device driver in the guest OS kernel, and a virtual machine scheduling algorithm. These components are implemented in the user space, guest OS, and the CPU virtualization subsystem of the Xen hypervisor, respectively. In TPCS, the progress statuses of tasks are transmitted to the underlying kernel from the user space, thus enabling virtual machine scheduling policy to schedule based on the progress of tasks. This policy aims to exchange completion time of tasks for resource utilization. Experimental results show that TPCM can mine the parallelism among tasks to implement the mapping from tasks to virtual machines based on the relations among subtasks. The TPCS scheduler can complete the tasks in a shorter time than can Credit and other schedulers, because it uses task progress to ensure that the tasks in virtual machines complete simultaneously, thereby reducing the time spent in pending, synchronization, communication, and switching. Therefore, parallel tasks can collaborate with each other to achieve higher resource utilization and lower overheads. We conclude that the TPCS scheduler can overcome the shortcomings of present algorithms in perceiving the progress of tasks, making it better than schedulers currently used in parallel computing.     

Multi-agent reinforcement learning for online scheduling in smart factories

Rapid advances in sensing and communication technologies connect isolated manufacturing units, which generates large amounts of data. The new trend of mass customization brings a higher level of disturbances and uncertainties to production planning. Traditional manufacturing systems analyze data and schedule orders in a centralized architecture, which is inefficient and unreliable for the overdependence on central controllers and limited communication channels. Internet of things (IoT) and cloud technologies make it possible to build a distributed manufacturing architecture such as the multi-agent system (MAS). Recently, artificial intelligence (AI) methods are used to solve scheduling problems in the manufacturing setting. However, it is difficult for scheduling algorithms to process high-dimensional data in a distributed system with heterogeneous manufacturing units. Therefore, this paper presents new cyber-physical integration in smart factories for online scheduling of low-volume-high-mix orders. First, manufacturing units are interconnected with each other through the cyber-physical system (CPS) by IoT technologies. Attributes of machining operations are stored and transmitted by radio frequency identification (RFID) tags. Second, we propose an AI scheduler with novel neural networks for each unit (e.g., warehouse, machine) to schedule dynamic operations with real-time sensor data. Each AI scheduler can collaborate with other schedulers by learning from their scheduling experiences. Third, we design new reward functions to improve the decision-making abilities of multiple AI schedulers based on reinforcement learning (RL). The proposed methodology is evaluated and validated in a smart factory by real-world case studies. Experimental results show that the new architecture for smart factories not only improves the learning and scheduling efficiency of multiple AI schedulers but also effectively deals with unexpected events such as rush orders and machine failures.     

6kV变电所绝缘监视装置监控单元设计的研究

本设计研究的主要内容为6 k V变电所绝缘监视装置监控系统设计,它是6 k V变电所微机远程监控的一个重要组成部分。系统采用主从式微机网络形式的通信方式,即用一台高性能的P C机(上位机)对整个系统进行管理,而用多台前端机(单片机智能控制单元)执行分布式检测与控制任务。     

智能家居虚拟场景设计与实现

面向智能家居应用,以Unity 3D为虚拟现实开发平台,给出了用虚拟智能化家庭场景实现虚拟场景家电设备及环境监测设备的模型设计。平台中的虚拟人可以在虚拟场景中自由漫游,并且通过虚拟人手中的终端设备,实时家电控制并获得环境监测设备的实时数据,使得家庭控制更加生动形象,实现虚拟与现实的融合。虚拟场景的设计有助于智能家居的宣传、培训和展示,同时让用户通过远程操作,也可以体验到真实的智能家居系统,零距离展示智能家居实验室的科研成果。     

Javacard CPU的设计与实现

支持Javacard技术标准是智能卡的发展方向，目前的Javacard系统大多是采用软件虚拟机的方式来解释执行或者通过just-in-time方式执行Java指令，系统软件平台本身占用了大量的资源，且执行效率不高。解决这些问题的方法就是实现硬件Javacard指令处理器。该文给出了一种基于微码的Javacard指令处理器的FPGA设计和实现，以Javacard CPU为核心搭建Javacard CPU测试平台，并将其集成在一块FPGA上实现。     

面向长作业环境中的云调度策略

随着云计算的普及,大量的数据处理选择云服务来完成。现有算法较少考虑异构型系统中虚拟机计算能力的不同,导致某些任务等待时间过长。提出了虚拟机负载大小实时调整的算法。对云计算中资源虚拟化特征,给出一种评估虚拟机计算能力的方法。根据虚拟机能力和运行过程中的状态变化,自适应进行任务量大小调整,满足实时要求。通过任务调度,协调任务完成时间,保持各虚拟机负载的动态均衡,缩短长作业的总执行时间,提高了系统的吞吐量和整体服务能力,提升了效益。实验结果表明,本文算法能自适应地调整任务量大小,进行调度,以维持虚拟机负载均衡。     

Remote real-time CNC machining for web-based manufacturing

Today's machining shop floors, characterized by large variety of products in small batch sizes, require dynamic control and real-time monitoring capabilities that are responsive and adaptive to the rapid changes of production capability and functionality. It is especially true when the shop floors are combined with the e-manufacturing concept. However, a highly efficient infrastructure that can integrate the pieces of automated equipment together and link them to the e-manufacturing is still missing. The objective of this research is to develop an appropriate methodology with open architecture for real-time monitoring and remote control of networked CNC machines. A framework named Wise-ShopFloor (Web-based-integrated sensor-driven e-ShopFloor) is designed for this purpose. Within the context, this paper presents a new enabling technology to bring traditional CNC machine tools on-line with combined monitoring and control capability. Issues such as architecture design, methodology development, and prototype implementation are addressed through a milling machine case study. It is expected that the developed technology can be readily applied to real shop floor environments with increased productivity, flexibility, and responsiveness.     

Toward an execution system for self-healing workflows in cyber-physical systems

Cyber-physical systems (CPS) represent a new class of information system that also takes real-world data and effects into account. Software-controlled sensors, actuators and smart objects enable a close coupling of the cyber and physical worlds. Introducing processes into CPS to automate repetitive tasks promises advantages regarding resource utilization and flexibility of control systems for smart spaces. However, process execution systems face new challenges when being adapted for process execution in CPS: the automated processing of sensor events and data, the dynamic invocation of services, the integration of human interaction, and the synchronization of the cyber and physical worlds. Current workflow engines fulfill these requirements only to a certain degree. In this work, we present PROtEUS—an integrated system for process execution in CPS. PROtEUS integrates components for event processing, data routing, dynamic service selection and human interaction on the modeling and execution level. It is the basis for executing self-healing model-based workflows in CPS. We demonstrate the applicability of PROtEUS within two case studies from the Smart Home domain and discuss its feasibility for introducing workflows into cyber-physical systems.     

基于微内核的虚拟机I/O安全机制

NOVA等微内核虚拟化架构解决了宏内核平台可信计算基体积和攻击面过大的问题, 但其仍缺乏虚拟机分等级保护和I/O资源访问控制等安全机制. 本文提出了安全域的概念, 并将虚拟机划分至不同的安全域, 进而建立可定制的I/O资源访问控制机制. 通过将访问控制模块添加至I/O资源访问的关键代码路径, 实现了不同安全域的I/O资源访问控制. 实验表明, 该机制提高了数据的隔离性与安全性, 仅对计算密集型、I/O密集型任务造成了较小的性能损耗.     

A group decision making framework based on fuzzy VIKOR approach for machine tool selection with linguistic information

Computer numerical control (CNC) machines are used for repetitive, difficult and unsafe manufacturing tasks that require a high degree of accuracy. However, when selecting an appropriate CNC machine, multiple criteria need to be considered by multiple decision makers. In this study, a multi-criteria group decision making (MCGDM) technique based on the fuzzy VIKOR method is developed to solve a CNC machine tool selection problem. Linguistic variables represented by triangular fuzzy numbers are used to reflect decision maker preferences for the criteria importance weights and the performance ratings. After the individual preferences are aggregated or after the separation values are computed, they are then defuzzified. In this paper, two algorithms based on a fuzzy linguistic approach are developed. Based on these two algorithms and the VIKOR method, a general MCGDM framework is proposed. A CNC machine tool selection example illustrates the application of the proposed approach. A comparative study of the two algorithms using the above case study information highlighted the need to combine the ranking results, as both algorithms have distinct characteristics.     

数控机床加工系统远程在线监测方案设计

本文以数控机床为对象,设计了一个基于网络技术和虚拟仪器技术的数控机床加工系统远程监测系统,并将当前流行的视频监控技术应用到该在线监测系统中来,重点研究了刀具状态的监测方法。     

基于CRIO的数控机械在线监测诊断系统设计

目前提出的数控机械在线监测诊断系统温度采集准确度较低,导致诊断过程稳定性及准确性较差,为此基于CRIO设计了一种新的数控机械在线监测诊断系统,以期更好地分析数控机械的运行状态;通过数控机床在线监测模块、远程设备管理模块及故障诊断模块组成系统总结构,硬件设备包括NTI CRIO驱动器、新型伺服电动机、采集卡、传感器以及PC上位机,设计多参数在线监测模块、网络控制模块、设备管理模块和故障诊断模块;在采集信号中提取数控机械运行状态数据,通过格拉布斯准则估算标准偏差,去掉误差数据后,输出有效数据,完成数据的采集及存储,实现数控机械在线监测诊断系统设计;实验结果表明,基于CRIO的数控机械在线监测诊断系统的温度采集准确率为99%,振动信号采集准确率为95%,可以有效提高采集精准度,增强数控机械在线监测诊断系统的准确性及稳定性。     

A modular-architecture controller for CNC systems based on open-source electronics

Open control architectures regain relevance with the new revolution of open source electronics. This paper presents the general ideas, examples of implementation and latest advances of a new, open architecture controller for CNC systems based on open source electronics. The multiprocessor and distributed architecture of this controller leverages the power of platforms like Arduino or TI Launchpad to realize CNC systems of increased computational resources, closed-loop position of the tool, smoother motions and higher feeds. Additionally, this work demonstrates the first steps in the development of virtual machine as a new software component of the architecture. A “tight binding” between the real and virtual machines will delineate the path for realistic, machine monitoring, remote operation and process planning environment.     

一种数字家庭环境下的本体服务映射模型

当前的数字家庭系统大多使用预定义的服务调用模式,无法从语义层面上理解服务的含义并会导致系统可伸缩性差等问题.为了解决这些问题并借鉴语义网的经验,提出一种数字家庭环境下的本体服务映射模型(OSMM).OSMM以家庭环境本体为基础,以Jess推理机为推理核心,使用决策语义输入,输出映射后的实际物理设备调用接口.在虚拟数字家庭仿真实验平台(VSM)中的仿真表明,OSMM能够很好地理解决策语义并输出合适的映射结果,并具有低延时的特点,能够满足一般数字家庭决策系统的需求.     

Design of suspension-based and collocated dual stage actuated suspensions

Dual stage actuators have recently been implemented in hard disk drive suspensions in order to increase the track density of hard disk drives. This paper investigates the lateral deflection (lateral stroke), stress distribution and design characteristics of a suspension-based and a collocated flexure-based dual stage actuated suspension design using finite element analysis. Design parameters for each suspension design are examined and guidelines for improved lateral deflection characteristics are proposed.