Engineering framework for agent-based manufacturing control

MAS (multi-agent systems) and HMS (holonic manufacturing systems) are enabling the vision of the Plug & Play Factory and paving the way for future autonomous production systems. This paper reviews the state of the art in implementations of agent-based manufacturing systems, and identifies the lack of engineering tools as a technological gap for widespread industrial adoption of the paradigm. The lack of tools limits the implementation of agent-based manufacturing systems within reach of only a handful of domain experts. One of the current challenges for the design and implementation of intelligent agents is the simulation and visualization of the agent societies. This issue is significant as soon as the software agent is embedded into a mechatronic device or machine resulting in a physical intelligent agent with 3D-mechanical restrictions. These mechanical restrictions must be considered in the negotiations between agents in order to coordinate the execution of physical operations. This paper presents an engineering framework that contributes towards overcoming the identified technology gap. The framework consists of a comprehensive set of software tools that facilitate the creation, simulation and visualization of agent societies. The 3D framework is innovative in fully emulating the deployed agents, recreating multi-agent negotiations and societies that coordinate and execute control of assembly operations. The documented research describes the methodology for the 3D representation of individual physical agents, the related identified objects present in the interaction protocols, and the assembly features and clustering algorithms.     

Automated generation of multiphysics simulation models to support multidisciplinary design optimization

To ensure a consistent design representation for serving multidisciplinary analysis, this research study proposes an intelligent modeling system to automatically generate multiphysics simulation models to support multidisciplinary design optimization processes by using a knowledge based engineering approach. A key element of this system is a multiphysics information model (MIM), which integrates the design and simulation knowledge from multiple engineering domains. The intelligent modeling system defines classes with attributes to represent various aspects of physical entities. Moreover, it uses functions to capture the non-physical information, such as control architecture, simulation test maneuvers and simulation procedures. The challenge of system coupling and the interactions among the disciplines are taken into account during the process of knowledge acquisition. Depending on the domain requirements, the intelligent modeling system extracts the required knowledge from the MIM and uses this first to instantiate submodels and second to construct the multiphysics simulation model by combining all submodels. The objective of this research is to reduce the time and effort for modeling complex systems and to provide a consistent and concurrent design environment to support multidisciplinary design optimization. The development of an unstable and unmanned aerial vehicle, a multirotor UAV, is selected as test case. The intelligent modeling system is demonstrated by modeling thirty-thousand multirotor UAV designs with different topologies and by ensuring the automatic development of a consistent control system dedicated for each individual design. Moreover, the resulting multiphysics simulation model of the multirotor UAV is validated by comparing with the flight data of an actual quadrotor UAV. The results show that the multiphysics simulation model matches test data well and indicate that high fidelity models can be generated with the automatic model generation process.     

On the selection of variables for qualitative modelling of dynamical systems

Behavioural modelling of physical systems from observations of their input/output behaviour is an important task in engineering. Such models are needed for fault monitoring as well as intelligent control of these systems. The paper addresses one subtask of behavioural modelling, namely the selection of input variables to be used in predicting the behaviour of an output variable. A technique that is well suited for qualitative behavioural modelling and simulation of physical systems is Fuzzy Inductive Reasoning (FIR), a methodology based on General System Theory. Yet, the FIR modelling methodology is of exponential computational complexity, and therefore, it may be useful to consider other approaches as booster techniques for FIR. Different variable selection algorithms: the method of the unreconstructed variance for the best reconstruction, methods based on regression coefficients (OLS, PCR and PLS) and other methods as Multiple Correlation Coefficients (MCC), Principal Components Analysis (PCA) and Cluster analysis are discussed and compared to each other for use in predicting the behaviour of a steam generator. The different variable selection algorithms previously named are then used as booster techniques for FIR. Some of the used linear techniques have been found to be non-effective in the task of selecting variables in order to compute a posterior FIR model. Methods based on clustering seem particularly well suited for pre-selecting subsets of variables to be used in a FIR modelling and simulation effort.     

Visualization resources and strategies for remote subsea exploration

Common resources and strategies are described for graphics and imaging applications in remote subsea exploration. What is meant by resources are the hardware, software, and human assets that constitute sea-going and shore-based systems; strategies encompass the architectural, engineering, and practical aspects of making such a visualization environment operational and productive. Emphasis is placed on current applications within the oceanographic community for search/survey/mapping (towed, unmanned systems), remotely operated vehicles and submersibles (man-in-the-loop systems), and autonomous underwater robots (intelligent systems). For these applications, a common goal is the acquisition and processing of underwater remote-sensor data to create a model of the subsea terrain. Visualization tools offer an important means of conveying the information contained in such a model. Dominant requirements within this context are the management, processing, and presentation of high-bandwidth, multisensor data, including optical and acoustic imagery, laser and sonar bathymetry, and other physical data sets. Specific visualization tools are used for image processing, volumetric modeling, terrain visualization, real-time operator displays, and mapping and geographic information systems as well as for scientific and engineering research and development.     

Concepts,methods, and languages for building timely intelligent systems

We describe the ABE/RT toolkit—a set of design, development, and experimentation tools for building time-stressed intelligent systems-and its use for the Lockheed Pilot's Associate application. We use the termtimely systems to refer to systems with hard real-time requirements for interacting with a human operator or other agents with similar time-scales. The ABE/RT methodology is based on a philosophy of rigorous engineering design in which the application developer works to guarantee the system's timeliness by identifying the various events which require timely responses, determining the worst-case frequencies of these events and the deadlines and durations of the tasks that respond to the events, and then verifying that the run-time system has enough processing resources to complete all mandatory taks by their deadlines. We believe this is the only way in the near-term to build complex real-time intelligent systems that will be reliable enough for critical applications with demanding users. The ABE/RT Toolkit contains a set of languages for specifying the structure and behavior of timely systems, together with tools to simulate those models, log and analyze data collected during simulation runs, predict an application's performance on a specified target hardware architecture, and deploy the application on the target architecture.This research was partially funded by the Defense Advanced Research Projects Agency, 1400 Wilson Blvd., Arlington, VA 22209, under contracts F30602-85-C-0135 and F33615-85-C-3804, administered by the Air Force Systems Command, Rome Air Development Center and the Air Force Cockpit Technology Directorate, Wright Research and Development Center, respectively. Use of this material, including copying, by the U.S. government is permitted in accordance with the terms of those contracts.     

Developments in interdisciplinary simulation and design software for mechanical systems

Software for interdisciplinary simulation and design of mechanical systems is emerging as a broadly applicable computer-aided engineering (CAE) tool. The emergence of powerful graphics based workstations, affordable minisuper computers, and network computing systems enhances the potential of this class of advanced CAE tools for use in a broad range of industrial applications. This paper analyzes the potential that exists for creating advanced CAE tools by (1) defining requirements for an advanced interdisciplinary simulation and design software system, including system executives, application program integration, support utilities, user interfaces, data base management systems, and computer environment; (2) giving an overview of interdisciplinary systems that are currently in use or in development; and (3) reviewing important features of existing interdisciplinary software systems, with respect to requirements for an advanced CAE system.     

Intelligent query answering by knowledge discovery techniques

Knowledge discovery facilitates querying database knowledge and intelligent query answering in database systems. We investigate the application of discovered knowledge, concept hierarchies, and knowledge discovery tools for intelligent query answering in database systems. A knowledge-rich data model is constructed to incorporate discovered knowledge and knowledge discovery tools. Queries are classified into data queries and knowledge queries. Both types of queries can be answered directly by simple retrieval or intelligently by analyzing the intent of query and providing generalized, neighborhood or associated information using stored or discovered knowledge. Techniques have been developed for intelligent query answering using discovered knowledge and/or knowledge discovery tools, which includes generalization, data summarization, concept clustering, rule discovery, query rewriting, deduction, lazy evaluation, application of multiple-layered databases, etc. Our study shows that knowledge discovery substantially broadens the spectrum of intelligent query answering and may have deep implications on query answering in data- and knowledge-base systems     

大型水利工程建设期智慧应用探索

针对大型水利工程建设过程中协调困难、现场安全与质量难以监管到位、进度因素复杂难以控制等难题,围绕“安全、质量、成本、进度”等工程控制管理体系的建立与落地,结合工程全生命周期管理要求,对工程建设期智慧应用进行总体规划,建立统一的数据、技术及应用标准规范,利用云计算、流程引擎、物联网、大数据、BIM和GIS等关键信息技术,通过搭建所有参建单位使用的统一协同平台,部署覆盖工地现场管控的自动感知与智能预警,汇聚数据资源池,可视化数据综合分析等,实现工程建设管理流程与信息技术的深度融合,为工程的建设管理提供管理手段与辅助决策支撑工具。目前已在珠江三角洲水资源配置工程建设管理中实际应用,可有效落实对工程建设管理各环节的精细化管理,保障工程建设管理的标准化、规范化,为行业大型水利工程提供智慧水利建设的典型解决方案和实践案例。     

基于LabVIEW和MATLAB的数字孪生供热系统平台的设计与仿真

数字孪生技术充分利用物理结构、传感器更新、设备运行历史等数据通过集成多领域、多物理量、多可能性的模拟过程，在虚拟空间中进行镜像，以此表达相对应的实物装置的整个生命周期过程。从智慧供热的发展历史来看，依托现代工业系统理念，提出了基于"数字孪生"的智能供热系统结构。首先介绍了数字孪生的基本结构，给出了数字孪生的构建方式基于虚拟仪器Labview的大数据采集、处理、归档、仿真；然后以采集的数据为基础，得到供热系统的孪生模型，叙述了数字孪生技术解决的关键问题；最后，通过Labview仿真平台调用Matlab中神经网络智能算法，得到基于大数据采集以及经过智能算法优化后的参数，同时系统将参数反馈给物理实体设备，从而完成孪生模型的仿真、优化、反馈过程。通过热网系统优化仿真案例验证了Labview和Matlab混合编程在建立的孪生供热平台上、应用的有效性。