Development of an intelligent agent system for collaborative mold production with RFID technology

This paper presents a multi-agents system called agent-based collaborative mold production (ACMP) system. ACMP supports the collaborative and autonomous mold manufacturing outsourcing processes. The mold manufacturing outsourcing processes involve not only many manufacturing sequences but also many collaboration partners. ACMP provides autonomous features to handle three major tasks in outsourcing. They are vendor selection, task selection, and real-time outsourcing task progress tracking. This research applies the analytic hierachy process (AHP) decision models to solve the vendor selection and task selection problems. In addition, radio frequency identification (RFID) technology is adopted to provide a real-time tracking capability for remote collaboration, control and monitoring among outsourcing partners.     

Research on the collaboration of service selection and resource scheduling for IoT simulation workflows

Cloud resources provide a promising way to efficiently perform the needed simulation tasks for a complex manufacturing process. Most of the existing work focuses only on how to effectively schedule computing resources to execute computing requirements of simulation workflows in Internet of Things (IoT) applications. Research on the scheduling of simulation workflows in consideration of task ordering, service selection, and resource allocation altogether has not been lacking. To fill in this void, this paper proposes a cloud-based 3-stage workflow scheduling model. Before scheduling computing resources to complete task requirements, the order of the tasks is determined and the services that can meet the task requirements are selected. In this model, the workload to satisfy task requirements is not fixed and takes on a different value depending upon the service selected with its unique complexity and accuracy. An optimization function that transforms and integrates makespan, cost, and accuracy in a unique way is proposed. For its solution, the relatively new symbiotic organisms search (SOS) algorithm is modified and two SOS-based optimization strategies are developed, i.e., joint optimization-based SOS (JOSOS) and split optimization-based SOS (SOSOS). The simulation results reveal that SOS-based algorithms, especially the SOSOS method, outperform all compared algorithms. Based on the proposed method, simulation services and computing resources can be rationally selected and scheduled to ensure the requirements of IoT applications.     

物联网设备识别及异常检测研究综述

随着物联网技术的发展,物联网设备广泛应用于生产和生活的各个领域,但也为设备资产管理和安全管理带来了严峻的挑战.首先,由于物联网设备类型和接入方式的多样性,网络管理员通常难以得知网络中的物联网设备类型及运行状态.其次,物联网设备由于其计算、存储资源有限,难以部署传统防御措施,正逐渐成为网络攻击的焦点.因此,通过设备识别了解网络中的物联网设备并基于设备识别结果进行异常检测,以保证其正常运行尤为重要.近几年来,学术界围绕上述问题开展了大量的研究.系统地梳理物联网设备识别和异常检测方面的相关工作.在设备识别方面,根据是否向网络中发送数据包,现有研究可分为被动识别方法和主动识别方法.针对被动识别方法按照识别方法、识别粒度和应用场景进行进一步的调研,针对主动识别方法按照识别方法、识别粒度和探测粒度进行进一步的调研.在异常检测方面,按照基于机器学习算法的检测方法和基于行为规范的规则匹配方法进行梳理.在此基础上,总结物联网设备识别和异常检测领域的研究挑战并展望其未来发展方向.     

多自由度机器人自适应滑模迭代学习跟踪控制

机械臂可代替人工完成繁重工作、降低生产风险、提高生产效率,被广泛应用于制造业和生产业中.工业生产的高质量需求对机械臂的操作精度提出了较高要求,考虑有色金属工厂中铸锭打磨的应用场景,该任务的作业轨迹均具有较强重复性.此外,实际应用环境复杂,存在如环境干扰及系统参数变化等多种不确定性,固定的控制参数难以保证系统持续稳定运行.基于上述考虑,针对多自由度机械臂系统,设计一种自适应滑模迭代学习跟踪控制方法,控制器包含参数自整定的比例-微分项、基于滑模的符号函数项和上一次迭代的控制输入,其中PD项的控制参数通过模糊逻辑系统实时调整,在保证控制系统正常运行的情况下提高系统的鲁棒性.同时,在理论上证明迭代域闭环系统的稳定性和跟踪误差的收敛性.最后通过仿真验证所提出控制方法的有效性和鲁棒性.     

Research on recommendation and interaction strategies based on resource similarity in the manufacturing ecosystem

Nowadays, the thriving of the manufacturing ecosystems (ME) driven by the increasing competition in industrial markets, the ubiquitous implementation of intelligent systems, and the more frequent collaboration among manufacturing enterprises. During the practice of the system upgrade, it is increasingly noted that the redundancy of manufacturing resources and the inefficiency in resource configuration are the major obstacles to achieving satisfying value-creation within ME, which also result in cumbersome decision making (DM) in the problems of requirement-service configuration (RSC) and collaborative production. To address these issues, the research on resource recommendation and interaction is carried out. Firstly, the resource similarity models for autonomous resource filtering brace the whole DM mechanism in RSC and push the most suitable resource to the host automatically. Then, the interaction model provides a self-organized production mode without human intervention. The blindness, lag, and unfairness in the manual communication is eliminated by the Machine to Machine (M2M) interaction and automatic coordination. Besides, an NLP-based machine learning algorithm is introduced for quantifying semantic distance and measuring the differences between orders. Composed by these models, a total solution named Industry-Chat (I-Chat) emerges. With the help of that, production resources can be scheduled and managed autonomously and the order-based production processes could be promoted seamlessly. Thus, an improved industrial ecosystem with automatic DM and self-organization for future intelligent manufacturing is realized. The practicability of the research is verified by a case study. The results show that the production cost is reduced by 12%, the resource utilization rate is improved and its economic value is demonstrated.     

A multi-agent and cloud-edge orchestration framework of digital twin for distributed production control

The demands for mass individualization and networked collaborative manufacturing are increasing, bringing significant challenges to effectively organizing idle distributed manufacturing resources. To improve production efficiency and applicability in the distributed manufacturing environment, this paper proposes a multi-agent and cloud-edge orchestration framework for production control. A multi-agent system is established both at the cloud and the edge to achieve the operation mechanism of cloud-edge orchestration. By leveraging Digital Twin (DT) technology and Industrial Internet of Things (IIoT), real-time status data of the distributed manufacturing resources are collected and processed to perform the decision-making and manufacturing execution by the corresponding agent with permission. Based on the generated data of distributed shop floors and factories, the cloud production line model is established to support the optimal configuration of the distributed idle manufacturing resources by applying a systematic evaluation method and digital twin technology, which reflects the actual manufacturing scenario of the whole production process. In addition, a rescheduling decision prediction model for distributed control adjustment on the cloud is developed, which is driven by Convolutional Neural Network (CNN) combined with Bi-directional Long Short-Term Memory (BiLSTM) and attention mechanism. A self-adaptive strategy that makes the real-time exceptions results available on the cloud production line for holistic rescheduling decisions is brought to make the distributed manufacturing resources intelligent enough to address the influences of different degrees of exceptions at the edge. The applicability and efficiency of the proposed framework are verified through a design case.     

基于深度学习的嵌入式目标追踪研究进展

作为计算机视觉领域的基本问题之一, 目标追踪具有广泛的应用场景. 随着硬件算力和深度学习方法的进步, 常规的深度学习目标追踪方法精度越来越高, 但其模型参数量庞大, 计算资源和能耗需求高. 近年来, 随着无人机和智能物联网应用的蓬勃发展, 如何在存储空间和算力有限、低功耗需求的嵌入式硬件环境中进行实时目标跟踪, 成为当前研究的热点. 本文对面向嵌入式应用的目标追踪方法进行了分析综述, 包括相关滤波结合深度学习的目标追踪方法、基于轻量神经网络的目标跟踪方法, 并总结了深度学习模型部署流程和无人机等领域的嵌入式目标追踪典型应用实例, 最后对未来研究重点进行了展望.     

An improved multilayer perceptron approach for detecting sugarcane yield production in IoT based smart agriculture

Internet of Things (IoT) as one of most powerful technologies can provides precision management and intelligent navigation for managers and manufacturing plants’ Smart agriculture to deal a good strategy for improving agricultural productions and maximizing farm efficiency. Sugar production is subsidiary to many diverse and various parameters. Due to a diverse variety of parameters and the lengthy process in precision agriculture, the analytical prediction is difficult and impossible. In such situations, using intelligent systems such as machine learning may be proposed as an alternative solution. This paper proposed an improved Multilayer Perceptron (MLP) approach to predict the amount of sugar yield production in IoT agriculture. Experimental results show that the proposed MLP algorithm has maximum accuracy of 99%, precision of 95%, recall of 96% and Minimum Mean Absolute Error (MAE) of 0.04% and Root mean square error (RMSE) of 0.006% for detecting sugarcane yield production in IoT Agriculture.     

Cloud manufacturing ecosystem analysis and design

The manufacturing industry is facing the impact of a dynamic market and intensive competition. Many companies are looking for a new approach to improve their business activities in a collaborative business ecosystem with other stakeholders. Cloud computing enables the sharing of manufacturing resources and capabilities between different stakeholders to support business and physical production. The purpose of this research is to explore approaches moving towards a cloud manufacturing ecosystem and present possible implications for practice. To fulfil the research objectives, a multiple-case study was conducted within sheet metal manufacturing companies. Business and technology related requirements for cloud-based collaborative manufacturing portals were collected through interviews with industrial practitioners from the sheet metal manufacturing perspective. Based on analysis a prototype model of a CloudEcosystem was presented to demonstrate the essential features of the portals. This research found that there are three different portal types for cloud manufacturing ecosystems depending on the value chain configuration. Close to real-time information provided by cloud-based platforms can create manufacturing ecosystems where machine owners, product designers and customers may collaborate and compete simultaneously. The CloudEcosystem designed in this research can provide a practical tool and technical solution to help manufacturers think about moving towards cloud manufacturing ecosystems.     

基于模糊强化学习的空气压缩机群组协调预测控制

空气压缩机在提供动力的同时消耗着大量的电力能源,其节能增效备受关注.空气负荷需求受生产节奏、计划排程等影响呈现阶段性、间歇性的特点,导致空气压缩机组在大范围内变工况运行,人工调节速度慢,机组能耗高.针对此问题,以国内工业园区空气压缩机群组系统为背景,提出一种基于模糊强化学习的空气压缩机群组协调预测控制方法.首先利用工业现场的历史数据和设备运行机理建立基于模糊辨识的空气压缩机群组多工况模型;在此基础上,以最小化生产过程能耗为目标,结合生产工艺条件、设备安全等约束条件,建立基于模糊强化学习的空气压缩机组变负荷协调预测控制方法,保证系统在复杂工况下安全稳定运行;最后,将所提出方法应用于工业园区空气压缩仿真系统进行性能测试,取得较好的控制效果.     

A novel knowledge graph-based optimization approach for resource allocation in discrete manufacturing workshops

Dynamic personalized orders demand and uncertain manufacturing resource availability have become the research hotspots of intelligent resource optimization allocation. Currently, the data generated from the manufacturing industry are rapidly expanding. Such data are multi-source, heterogeneous and multi-scale. Transforming the data into knowledge to optimize the allocation between personalized orders and manufacturing resources is an effective strategy to improve the cognitive intelligent production level of enterprises. However, the manufacturing processes in resource allocation is diversity. There are many rules and constraints among the data. And the relationship among data is more complicated. There lacks a unified approach to information modeling and industrial knowledge generation from mining semantic information from massive manufacturing data. The research challenge is how to fully integrate the complex data of workshop resources and mine the implicit semantic information to form a viable knowledge-driven resource allocation optimization method. Such method can then efficiently provide the relevant engineering information needed for resource allocation. This research presented a unified knowledge graph-driven production resource allocation approach, allowing fast resource allocation decision-making for given order inserting tasks, subject to the resource machining information and the device evaluation strategy. The workshop resource knowledge graph (WRKG) model was presented to integrate the engineering semantic information in the machining workshop. A distributed knowledge representation learning algorithm was developed to mine the implicit resource information for updating the WRKG in real-time. Moreover, a three-staged resource allocation optimization method supported by the WRKG was proposed to output the device sets needed for a specific task. A case study of the manufacturing resource allocation process task in an aerospace enterprise was used to demonstrate the feasibility of the proposed approach.     

Selecting manufacturing partners in push and pull-type smart collaborative networks

The idea of Collaborative Manufacturing, also known as the Production Networks or Social Manufacturing, has been around for more than 25 years. It is a production concept based on non-hierarchical collaboration among enterprises often referred as Virtual Enterprise (VE). Despite many scientific research and projects with this topic, it is difficult to find an example of fully operational non-hierarchical production network anywhere in the world. However, that fact could be changed very soon. Namely, the new industrial revolution, called Industry 4.0, encourages industrial enterprises to adopt information-communication technology (ICT) and Internet of Things (IoT) into their production systems, thus creating Cyber-Physical Production System (CPPS). From the aspect of production networks, CPPS represents crucial infrastructure, or a missing link between enterprises. Now, with CPPS in place, non-hierarchical networking and collaboration becomes possible through Smart Collaborative Production Networks. In this research, the concept of information system for Smart Collaborative Production Networks was developed and called ‘VENTIS’. Although the idea of the concept is to manage the collaboration inside Virtual Enterprise, in this research, a special focus has been put on manufacturing planning phase in which optimization problem known as the Partner Selection Problem (PSP) occurs. Since the PSP in manufacturing phase is far more complex than partner selection during the collaborative product development phase, new research premises regarding the Virtual Enterprise type have been set. Two types of Virtual Enterprise business models – Push-type and Pull-type – have been defined in this research. If VE is Push-type, HUMANT algorithm is used to solve PSP that occurs in that case. If VE is Pull-type, a special procedure, inspired by phenomenological reduction, has been established in which set of a priori created VEs is compared with theoretically ‘the best’ VE and theoretically ‘the worst’ VE. Enterprises’ data of production network from Dalmatia (Split-Dalmatia County, Croatia) is used as a Case Study to present ‘VENTIS’ concept and to present the procedure for creation of sustainable Virtual Enterprise.     

Quality-enabled decentralized IoT architecture with efficient resources utilization

The Internet of Things (IoT) is a paradigm aimed at connecting everyday objects to the internet. IoT applications include smart cities, healthcare, agriculture, as well as the industry and manufacturing. The ability to monitor and control the physical world using information technology creates many opportunities. However, it also comes with some costs. The exponential growth of connected devices, the heterogeneity of IoT use cases, and the diversity of the network technologies yield a concern regarding IoT sustainability. With this work, we aim to contribute to this concern. In doing so, we introduce a novel representation model that is destined for (i) monitoring the IoT environment at runtime, (ii) expressing the overall quality of the system, and (iii) helping to utilize the available resources efficiently. We also define a feature set that describes the best the expectations of decentralized IoT platforms. Furthermore, we describe a quality-enabled decentralized IoT architecture too that incorporates the specified feature set as well as our representation model. Such solutions are necessary to improve and maintain IoT of the future and all its application domains, including the Industrial Internet of Things (IIoT). With the presented research, we aim to encourage the efficient utilization of resources and simplify the production of next-generation IoT solutions.     

基于物联网设备指纹的情境认证方法

针对物联网设备中因非法设备接入带来的远程控制安全问题，提出一种基于设备指纹的情境认证方法。首先，通过提出的对交互流量中单个字节的分析技术，提取物联网设备指纹；其次，提出认证的流程框架，根据设备指纹在内的六种情境因素进行身份认证，设备认证通过才可允许访问；最后，对物联网设备进行实验，提取相关设备指纹特征，结合决策树分类算法，从而验证情境认证方法的可行性。实验中所提方法的分类准确率达90%，另外10%误判率为特殊情况但也符合认证要求。实验结果表明基于物联网设备指纹的情境认证方法可以确保只有可信的物联网终端设备接入网络。     

Data fusion and transfer learning empowered granular trust evaluation for Internet of Things

In the Internet of Things (IoT), a huge amount of valuable data is generated by various IoT applications. As the IoT technologies become more complex, the attack methods are more diversified and can cause serious damages. Thus, establishing a secure IoT network based on user trust evaluation to defend against security threats and ensure the reliability of data source of collected data have become urgent issues, in this paper, a Data Fusion and transfer learning empowered granular Trust Evaluation mechanism (DFTE) is proposed to address the above challenges. Specifically, to meet the granularity demands of trust evaluation, time–space empowered fine/coarse grained trust evaluation models are built utilizing deep transfer learning algorithms based on data fusion. Moreover, to prevent privacy leakage and task sabotage, a dynamic reward and punishment mechanism is developed to encourage honest users by dynamically adjusting the scale of reward or punishment and accurately evaluating users’ trusts. The extensive experiments show that: (i) the proposed DFTE achieves high accuracy of trust evaluation under different granular demands through efficient data fusion; (ii) DFTE performs excellently in participation rate and data reliability.     

应用云计算架构六螺旋协同创新平台

为了“产、学、研”更好、更快的协同创新,以云计算、三螺旋和协同创新为理论基础,提出应用云计算技术架构六螺旋协同创新平台的设想,并对协同创新平台的五层体系结构和部分功能模块进行了详细设计。此平台能整合各种科技资源、人力资源和服务资源,还能使“政、企、校、研、中、金”六螺旋可以在自主式、交互式的环境中获取最新的信息、需求和供给,实现跨部门合作、跨学科合作、协同创新。对于稳固产学研交流合作渠道,促进高校、科研单位成果产业化,加快中小企业信息化建设和技术升级,实现协同创新等有重要意义。     

面向动车组高级修的工业物联网信息系统框架

物联网是信息系统及互联网向物理世界的延伸,其数据具有海量、高维和时空相关的特点.提出了面向动车组检修的工业物联网架构,分析了物联网技术在车间生产线和材料仓储配送情景下的应用模式.同时,针对物联网数据的特性,给出了它的数据仓库建模及多维数据统计分析方法.指出物联网为生产决策及监控提供关键支撑,从而起到改善生产组织和优化资源配置的作用.     

Intelligent condition monitoring using fuzzy inductive learning

Extensive research has been performed for developing knowledge based intelligent monitoring systems for improving the reliability of manufacturing processes. Due to the high expense of obtaining knowledge from human experts, it is expected to develop new techniques to obtain the knowledge automatically from the collected data using data mining techniques. Inductive learning has become one of the widely used data mining methods for generating decision rules from data. In order to deal with the noise or uncertainties existing in the data collected in industrial processes and systems, this paper presents a new method using fuzzy logic techniques to improve the performance of the classical inductive learning approach. The proposed approach, in contrast to classical inductive learning method using hard cut point to discretize the continuous-valued attributes, uses soft discretization to enable the systems have less sensitivity to the uncertainties and noise. The effectiveness of the proposed approach has been illustrated in an application of monitoring the machining conditions in uncertain environment. Experimental results show that this new fuzzy inductive learning method gives improved accuracy compared with using classical inductive learning techniques.     

一个协作式虚拟学习环境的设计与实现

当今网络技术以及虚拟现实技术将会把计算机辅助学习带入一个新的领域———协作式虚拟学习环境。文中根据建构主义和认知科学理论提出了一个基于Web的协作式虚拟学习系统的设计方法,并以一种工业产品作为学习对象开发了一个原型系统,旨在通过先进的学习理念和计算机技术提高学习者的认知效率。通过实践测试和评估,证明效果良好。     

一种基于遗传算法的多站点协同计算卸载算法

边缘计算为资源受限的物联网IoT设备扩展计算资源、增强存储容量,可以改善IoT应用程序的执行性能。在IoT环境中,大多数应用都将以分布式架构的形式部署在各站点中,站点之间需要协作完成任务。为了解决物联网环境中多站点协同计算的代价优化问题,提出了一种基于遗传算法的多站点协同计算卸载算法GAMCCO。该算法将应用程序抽象为任务依赖关系图模型,分析各任务之间的依赖关系,将多站点协同计算卸载的问题建模为代价模型,并利用遗传算法寻找最小代价的卸载方案。实验与评估结果表明,所提出的GAMCCO算法可以有效减少IoT应用的时延,同时降低终端设备的能耗。