On the representation of automation using a work domain analysis

Work domain analysis (WDA) has been applied extensively within cognitive engineering as an analytic framework for the evaluation of complex sociotechnical systems in support of design. However, the WDAs described in the literature have not explored the representation of automated system components, despite the documented problems associated with operator-automation interaction and the requirements for operator support in complex automated systems. The current research examines the application of WDA to model an example automated system – a camera – by representing the camera along with its automated components as separate systems using the abstraction hierarchy (AH). Additionally, we contrasted this modelling approach with the more typical approach of modelling automation within a cognitive work analysis (CWA) by performing a control task analysis using the decision ladder. The results of these analyses suggest that, similar to non-automated systems, considering a separate representation of an automated system within the WDA may provide useful system design guidance in terms of user support and information requirements. The comparison of the AH and the decision ladder (DL) models indicates that the models provide different information about the requirements and support different user needs: the AH provides information about specific subsystems and components of the automation that are used for control and how they support functions and purposes of the automation while the DL shows how the control tasks are achieved interleaving automated actions with those of the user. The information obtained from modelling automation with the AH is different from what would normally be obtained by performing only a control task analysis, yet is necessary for operator support.     

A brain-based system for adaptive automation

Adaptive automation refers to technology that can change its mode of operation dynamically. Further, both the technology and the operator can initiate changes in the level or mode of automation. One of the important issues surrounding this technology concerns the method for initiating changes in the state of automation. The present paper considers the potential of using brain activity to drive an adaptive automation system. Relevant research on EEG is presented followed by a review of several experiments in which EEG is used to trigger changes among system modes in an adaptive automation system. The system moderates operator task load based upon an index derived from a ratio of EEG power bands. The research shows that it may be feasible to build an adaptive automation system and use this index of brain activity to drive the system. The paper concludes with a discussion of several issues that still need to be addressed before this approach can move beyond the laboratory.     

Dynamic behavioural model for assessing impact of regeneration actions on system availability: Application to weapon systems

Mastering system availability all along the system life cycle is now a critical issue with regards to systems engineering. It is more true for military systems which operate in a battle context. Indeed as they must act in a hostile environment, they can become unavailable due to failures of or damage to the system. In both cases, system regeneration is required to restore its availability. Many approaches based on system modelling have been developed to assess availability. However, very few of them take battlefield damage into account and relevant methods for the model development are missing. In this paper, a modelling method for architecture of weapon system of systems that supports regeneration engineering is proposed. On the one hand, this method relies on a unified failure/damage approach to extend acknowledged availability models. It allows to integrate failures, damages, as well as the possibility of regeneration, into operational availability assessment. Architectures are modelled as a set of operational functions, supported by components that belong to platform (system). Modelling atoms (i.e. elementary units of modelling) for both the architecture components and functions are defined, based on state-space formalism. Monte Carlo method is used to estimate availability through simulation. Availability of the architecture is defined on the basis of the possible states of the required functions for a mission. The states of a function directly depend on the state of the corresponding components (i.e. the components that support the function). Aggregation rules define the state of the function knowing the states of each component. Aggregation is defined by means of combinatorial equations of the component states. The modelling approach is supported by means of stochastic activity network for the models simulation. Results are analysed in terms of graphs of availability for mission's days. Thus, given the simulation results, it is possible to plan combat missions based on criteria such as the number of platforms to be involved given functions required for the mission or the mean of regeneration to be deployed given the possible threats. Further, the simulation will help towards the design of improved architecture of system of systems which could focus on the factors affecting the availability.     

A performance evaluation of three inference engines as expert systems for failure mode identification in shafts

This paper aims to present performance evaluation of three different inference engines (rule based reasoning, fuzzy based reasoning and Bayesian based reasoning) for failure mode identification in shafts. This research was done with a focus on the validation cases and results after their use in failure cases from several industries where the three systems were tested under the same conditions.Each system was implemented using the same user interface and knowledge base, with different frameworks and techniques as follows: rule based inference reasoning (prolog, C#), Mamdani-fuzzy based reasoning (C, MATLAB®) and Bayesian based reasoning with a variable elimination algorithm (C, MATLAB®).The best performance was obtained using the Bayesian inference engine. The conditional probabilities give flexibility when evidence is not listed, while the fuzzy and classical IF-THEN systems depend on the rules in the inference engine.The process presented in this paper could be used for validation of any expert system or for comparison with other expert systems (inference engines) when the knowledge base is the same.     

智能交互系统中人机组队协同工效提升的模型构建

目的 智能交互系统在城市救援及日常生活中应用越来越广泛,探讨了人机组队作业中人机协同工效提升的内在机制和规律,以构建人机组队协同的相容性理论模型并应用于复杂系统中人机组队的设计和优化。方法 采用跨学科交叉分析和文献调研分析的方法,分别对认知心理学、工程心理学和计算机科学等不同领域文献进行充分检索和分析,系统梳理了从智能体的界面设计及自动化水平、操作员状态与情境感知能力、人机关系及交互方式等对人机协同工效的影响因素;并通过共享心理模型、自然主义决策模型、多重表征模型尝试对人机如何协同的机制进行探讨和分析,指出了目前研究中存在的问题和不足。在此基础上提出较为系统的智能交互系统中人机组队相容性理论模型。结果 该模型通过对人、智能体、环境和任务特征进行分析,并从物理相容性、认知相容性和情感相容性三个角度,对人机组队协同工效提升的内在规律和机制进行深入探讨,有效提升人机组队协同的系统工效和用户体验,避免安全事故的发生。结论 智能交互系统中人机组队的相容性模型可用于未来人机协同工效提升的理论和实践研究,也可用于未来智能人机交互系统,特别是智能化时代人机组队的设计和优化,满足众多复杂的人机系统(如商用飞机驾驶舱、核电站中央控制室)人机高效协同设计的需求,为建立自然高效人机协同作业的交互模式提供科学依据。     

Actant model of an extraction plant

Facing a growing complexity of industrial plants, we recognise the need for qualitative modelling methods capturing functional and causal complexity in a human-centred way. The present paper presents actant modelling as a functional modelling method rooted in linguistics and semiotics. Actant modelling combines actant models from linguistics with multilevel flow modelling (MFM). Thus the semantics of MFM functions is developed further and given an interpretation in terms of actant functions. The present challenge is to provide coherence between seemingly different categories of knowledge. Yet the gap between functional and causal modelling methods can be bridged. Actant modelling provides an open and provisional, but in no way exhaustive or final answer as to how teleological concepts like goals and functions relate to causal concepts. As the main focus of the paper an actant model of an extraction plant is presented. It is shown how the actant model merges functional and causal knowledge in a natural way.     

Understanding reliance on automation: effects of error type,error distribution,age and experience

An obstacle detection task supported by ‘imperfect’ automation was used with the goal of understanding the effects of automation error types and age on automation reliance. Sixty younger and sixty older adults interacted with a multi-task simulation of an agricultural vehicle (i.e. a virtual harvesting combine). The simulator included an obstacle detection task and a fully manual tracking task. A micro-level analysis provided insight into the way reliance patterns change over time. The results indicated that there are distinct patterns of reliance that develop as a function of error type. A prevalence of automation false alarms led participants to under-rely on the automation during alarm states while over-relying on it during non-alarm states. Conversely, a prevalence of automation misses led participants to over-rely on automated alarms and under-rely on the automation during non-alarm states. Older adults adjusted their behaviour according to the characteristics of the automation similar to younger adults, although it took them longer to do so. The results of this study suggest that the relationship between automation reliability and reliance depends on the prevalence of specific errors and on the state of the system. Understanding the effects of automation detection criterion settings on human–automation interaction can help designers of automated systems to make predictions about human behaviour and system performance as a function of the characteristics of the automation.     

Modelling and simulation of automated manufacturing systems for evaluation of complex schedules

Automated manufacturing systems have been studied widely in terms of scheduling. As technology evolves, the behaviour of tools in automated manufacturing systems has become complicated. Therefore, mathematical approaches to the analysis of complex schedules no longer reflect reality. In this paper, we propose a systematic way of conducting simulation experiments to evaluate the complex operating schedules of automated manufacturing systems. A simulation model is based on a timed Petri net to take advantage of its mathematical strength. Since a Petri net cannot itself have token firing rules, we introduce additional states called operational states. Operational states are not directly related to a Petri net, and are only used for decision making. In addition, a decision function that is responsible for the conflict resolution of a Petri net model and an operational state transition function are introduced. The parallel simulation concept is also suggested by dividing a Petri net into several independent decision sub-nets. A multi-cluster tool system for semiconductor manufacturing is analysed as an application.     

Critical scenarios derivation methodology for mechatronic systems

This paper deals with safety in design of mechatronic systems. We propose a method based on a qualitative analysis of a Petri net model of the system. It allows deriving feared scenarios by determining the sequences of actions and state changes leading to the feared state in which the passenger's safety is no longer guaranteed. The Petri net model of the system takes into account normal behaviour, failures and reconfiguration mechanisms. Our approach uses linear logic as formal framework and is based on a backward and a forward reasoning. It derives feared scenarios as causal relationships between normal states and the feared one.     

Transfer Function Measurement Using Analog Modeling Techniques

Analog models provide a cheap and effective method for identification and checkout of a wide range of physical systems via the transfer function concept. This paper reviews the excitation of series, parallel, and pi mode analog models by deterministic stimuli. Errors between the system and model responses may be displayed on a cathode-ray tube, or on an instrument measuring mean-error modulus. Novel methods of tuning the model coefficients by observing the error displays are described and evaluated experimentally. The checkout strategy is particularly suitable for use by technicians since reliable repeatable models are obtained independent of the level of operator understanding of the system under test. These models may then be used as input data for quality control of production items and for preventive maintenance schemes during the life of the system under test. A low-order model is shown to be perfectly adequate for dynamic performance checkout of a complex noisy nonlinear system. Intermediate access points then permit elemental modeling down to replaceable units. For adequate confidence in system dynamic performance, checkout tolerances placed on the model coefficients must be conditional and a suitable tolerance flow chart is included. Although presented on the basis of manual instrumentation, the methodology is suitable for automation and is applicable to self-adaptive systems in which system dynamics change slowly with time.     

基于推理机制的图形界面理论探究

目的图形界面作为信息传达的窗口是最先通过人眼到达大脑皮层的,其速度远远超越了文字,如何更快更有效地获取信息已成为界面可用性研究中重要的衡量准则,且该研究有利于揭示人类智能的内在潜力和人类心智的发展潜能。方法基于形象推理研究抽象推理,通过对心智逻辑和心智模型两种推理机制的探索,采用任务推理实验研究了图形界面设计的内在推理规律。结果以反应时和错误率作为实验指标,证实用户在不同的界面架构下使用的推理机制不同,且推理结果受认知负荷的影响。结论发现用户在同父或同子关系的信息架构下,认知倾向于心智模型的推理方式;在不同父子关系的信息架构下,认知倾向于心智逻辑的推理方式;且图形认知负荷越高,推理过程越慢,错误率越高。     

分数阶具有不确定项和外扰的超混沌金融系统的滑模同步

本文利用滑模控制与比例积分滑模控制技巧研究了分数阶具有不确定项和外扰的一类超混沌金融系统的同步问题,运用分数阶微积分设计出滑模函数,通过设计适应规则构造出适应控制器,得到了分数阶不确定超混沌金融系统取得滑模同步和积分滑模同步的两个充分性条件．Matlab 数值仿真验证了理论结果．文中滑模函数的设计、控制器的构造及适应规则的选取对研究整数阶超混沌金融系统的滑模同步具有可移植性,所使用的方法为研究分数阶混沌系统提供了思路,同时分数阶系统的相关结果可以移植到整数阶系统的同步问题．     

Automation-induced complacency for monitoring highly reliable systems: the role of task complexity,system experience,and operator trust

The increase in quantity and complexity of advanced automated systems has generated new concerns surrounding automation-induced complacency, or the difficulties operators have monitoring the status of automated systems. The present investigation consists of two studies that assessed the impact of system reliability, monitoring complexity, operator trust, and system experience on automation-induced complacency. In both studies, participants operated a manually controlled flight task while monitoring several simulated aircraft displays for failures. The ability of operators to detect a single automation failure over three experimental sessions was also assessed. Results indicated that realistic levels of system reliability severely impaired an operator's ability to monitor effectively. Further, as system experience increased, operator monitoring performance declined. The results also indicated that the complexity of the monitoring task heavily influenced operator monitoring, with poorer performance associated with more cognitively demanding tasks. Finally, results from both studies indicated that operator trust increased and monitoring performance decreased as a function of increasing system reliability. These results suggest that for highly reliable systems, increasing task complexity and extensive experience may severely impair an operator's ability to monitor for unanticipated system states.     

基于混合智能的电火花加工电参数学习模型的研究

提出了一个基于混合智能的电火花加工电参数学习模型,它模仿熟练操作者的决策过程,由于工艺数据库、加工规则库、学习模块和推理模块组成。在学习模块中利用遗传算法从工艺数据库中抽取出反映电参数和加工结果之间关系的模糊产生式规则,存储在规则库中。推理模块基于这些规则利用模糊拄是对新加的要求提供合适的电参数。     

Approaches to mitigating complexity-driven issues in commercial autoflight systems

There appears to be broad consensus in the aviation community that increased automation in the cockpit has changed the task of flying commercial aircraft. The changes have been both beneficial, through the increase of capabilities and efficiencies, and detrimental, as indicated by accidents implicating automation as a contributory factor. It is hypothesized that the constraining factor on automation design has changed from technological to human. The evolutionary growth of the automation has increased complexity which is thought to have led to the lack of a global model of the automation upon which the training material and operator feedback can be designed.Based on these analyses, three classes of complexity mitigation management techniques are explored. The first is to train pilots to understand and work within the current automation system. The second is to enhance feedback to allow more effective monitoring of aircraft systems, and to allow a reduction in the apparent order of the system to the pilot. Finally, a modified development process is suggested which explicitly considers the pilot in early design stages. It is believed that a process-oriented solution will be necessary for future automation systems. This process uses an explicit automation model as a basis for training material and for software requirement specification.     

Historical review and update to the state of the art of automation for plasma coating processes

Automation is the technique by which man is removed from the process and the resulting system operates automatically. A properly designed automated system will best take advantage of the man-machine combination using the machine for tedious or repetitive tasks and will use the man for those functions for which it is too difficult or costly to teach the computer or controller. Automated plasma coating facilities have been used historically to remove the operator from direct involvement in the coating process for several purposes, i.e. safety, process consistency and increased productivity. There is some natural overlap in these areas and the decision to automate a process can be motivated by all three concerns and perhaps even others such as available space or the search for prestige.Each of these areas is discussed with special interest given to increased productivity. Some specific systems are reviewed which illustrate the present state of the art of automation for the plasma spray process including both part handling and control systems.     

A formal control-theoretic model of a human–automation interactive manufacturing system control

This paper describes a human–automation interactive manufacturing system and presents a formal model for describing and controlling the system. The model presented in this paper considers a system from two perspectives: structural and operational perspectives. Human and an automated controller are considered agents that cooperate to achieve given goals by executing assigned tasks. A human–automation interaction is described with a particular communication model between two agents that exchanges messages. A system control schema and human tasks are modelled separately and then integrated in a formal manner using a modified communicating finite state machine framework. An interface model that coordinates the message exchanges between two agents is then introduced. An automated shop floor control system with a human material handler is modelled with the proposed framework and a simple illustrative example is provided.     

Development of a Structured Methodology for Knowledge Elicitation: Applications to Flexible Manufacturing Systems

While automation in the discrete parts manufacturing industry has increased productivity and improved product quality it has also significantly altered the role played by the operator of the system. The operators' role has not only become more critical to the effective functioning of the system but also causes mental overload and imposes greater demands on humans' information processing capabilities. This paper examines methods by which the mental overload experienced by operators can be alleviated while sustaining improvements in productivity and product quality. Additionally, the possibility of reducing mental overload by training and aiding the operator and effecting a more optimal allocation of function using dynamic allocation of function schemes is reviewed. It is concluded that a prerequisite to using the above methods to reduce mental overload is the ability to build appropriate decision support systems. A methodology that allows the development of such decision support systems is described and results of a study to evaluate the methodology are presented.     

Similarities and differences between human–human and human–automation trust: an integrative review

The trust placed in diagnostic aids by the human operator is a critical psychological factor that influences operator reliance on automation. Studies examining the nature of human interaction with automation have revealed that users have a propensity to apply norms of human–human inter-personal interaction to their interaction with ‘intelligent machines’. Nevertheless, there exist subtle differences in the manner in which humans perceive and react to automated aids compared to human team-mates. In the present paper, the concept of trust in human–automation dyads is compared and contrasted with that of human–human dyads. A theoretical framework that synthesizes and describes the process of trust development in humans vs automated aids is proposed and implications for the design of decision aids are provided. Potential implications of this research include the improved design of decision support systems by incorporating features into automated aids that elicit operator responses mirroring responses in human–human inter-personal interaction. Such interventions will likely facilitate better quantification and prediction of human responses to automation, while improving the quality of human interaction with non-human team-mates.