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.     

基于云计算技术的信息安全试验系统设计与实现

在云计算领域,信息安全显得尤为重要,是一个长期的研究课题。文章利用云计算开源平台,设计一套信息安全试验系统,利用该系统,将重要信息置于云端,客户端通过虚拟机实现远程调用,可以大大提高运行的安全性和健壮性,同时也能大大提升工作效率。     

煤矿监控图像增强算法的分析与实现

针对煤矿井下粉尘多、光照差的恶劣环境使得矿井监控图像偏暗、对比度低、视觉效果差的特点,提出了一种基于小波变换和模糊理论的图像增强算法。该算法选择小波变换为工具分解图像,应用新的模糊隶属度和增强算子对高频信息进行模糊处理,利用直方图均衡化对低频信息进行处理,最后对图像进行重构。处理结果较好地增强了图像细节信息,从整体上改善了图像效果。     

经济型矿用无极绳绞车控制装置设计

介绍了经济型矿用无极绳绞车控制装置的组成、工作原理、硬件及软件设计。该装置以主控制箱为核心,可实时检测速度信号和绞车运行信息,并可将信息实时显示在触摸显示屏上,具有绞车启停、正反向控制、调速和自动加减速控制、机头机尾过卷、启车预警、岔道告警等功能;当绞车出现故障时能够立即停车,确保绞车安全运行。该装置既可以作为无极绳绞车的软启或直启控制系统,又可以作为无极绳绞车变频控制系统。     

钱家营煤矿矿井综合自动化系统的改造

针对钱家营矿综合自动化系统存在仅以信息集成为主、服务器配置落后、无法实现智能调度、集中监控等问题,提出了系统改造原则,详细阐述了系统环网、调控中心和软件平台的改造与实施。改造后系统在注重信息采集和处理的同时,也注重对信息进行综合处理及生产和安全自动化系统的联合监测监控,实现了矿井综合自动化系统的五融合。     

一种测量工业环境感应传导干扰的方法

针对采用干扰测量仪或频谱分析仪只能测量工业环境中干扰源的辐射干扰强度的问题,提出了一种测量工业环境感应传导干扰的方法。该方法采用HBYV非双绞2芯线作为干扰接收体,采用数字示波器记录干扰波形的最大幅值和单个脉冲的最大强度,通过与标准干扰发生器感应到HBYV非双绞2芯线上的干扰信号大小进行比较,确定干扰强度。以瞬变脉冲群干扰强度和浪涌干扰强度的测量为例,详细介绍了该方法的具体实现。     

攻击型无人机切换控制系统容错算法研究

攻击型无人机单侧发射或投放重型武器时,可将飞行控制系统看作是一个切换控制系统。针对常见的常值型和时变型两种执行器故障,提出了一种自适应观测器设计方法,同时结合状态反馈控制设计了容错控制器。通过Lyapunov函数推出了观测器的误差是关于故障界输入状态实际稳定的充分条件,通过平均驻留时间方法证明了切换控制系统是全局输入状态实际稳定的。某型攻击型无人机仿真实例表明,此方法能在准确估计故障大小的基础上,有效保持切换飞行控制系统的性能,应用前景十分广阔。     

Automatic recognition fingerspelling gestures in multiple languages for a communication interface for the disabled

This paper presents the design and evaluation of a multi-lingual fingerspelling recognition module that is designed for an information terminal. Through the use of multimodal input and output methods, the information terminal acts as a communication medium between deaf and blind people. The system converts fingerspelled words to speech and vice versa using fingerspelling recognition, fingerspelling synthesis, speech recognition and speech synthesis in Czech, Russian, and Turkish languages. We describe an adaptive skin color based fingersign recognition system with a close to real-time performance and present recognition results on 88 different letters signed by five different signers, using above four hours of training and test videos.     

Multi-agent systems for protecting critical infrastructures: A survey

Multi-agent systems have emerged as a very significant platform in provisioning distributed and collaborative services to critical applications. Such applications require ubiquitous agent presence in the environment for monitoring, collecting data, communication, and subsequent data analysis, where the sensitivity of the application's nature cannot be understated. Recent advances in the field of autonomous, ubiquitous, intelligent and distributed computing have led to corresponding developments in the use of collaborating multi-agents to protect critical infrastructures. Such systems have witnessed crucial demand for deployment in diverse application scenarios such as E-commerce, E-health, Network Intrusion Detection, Telematics and Transport Systems, Environmental Monitoring, as well as for distributed information processing in general. Critical infrastructures have longed for a distributed system in place for their uninterrupted and accurate operations. Multi-agents have provided one such approach towards addressing the issue of protecting such infrastructures through collaborative and distributed information processing. In this paper, a state-of-the-art on the use of multi-agent based systems for protecting five most common critical infrastructures, is presented.     

PUF-enhanced offline RFID security and privacy

RFID (Radio Frequency IDentification) based communication solutions have been widely used nowadays for mobile environments such as access control for secure system, ticketing systems for transportation, and sport events. These systems usually depend on readers that are not continuously connected to a secure backend system. Thus, the readers should be able to perform their duties even in offline mode, which generally requires the management by the readers of the susceptible data. The use of RFID may cause several security and privacy issues such as traceability of tag owner, malicious eavesdropping and cloning of tags. Besides, when a reader is compromised by an adversary, the solution to resolve these issues getting worse. In order to handle these issues, several RFID authentication protocols have been recently proposed; but almost none of them provide strong privacy for the tag owner. On the other hand, several frameworks have been proposed to analyze the security and privacy but none of them consider offline RFID system.Motivated by this need, in this paper, we first revisit Vaudenay's model, extend it by considering offline RFID system and introduce the notion of compromise reader attacks. Then, we propose an efficient RFID mutual authentication protocol. Our protocol is based on the use of physically unclonable functions (PUFs) which provide cost-efficient means to the fingerprint chips based on their physical properties. We prove that our protocol provides destructive privacy for tag owner even against reader attacks.