Complex Human-machine Systems Research Articles

An Integrated CREAM for Human Reliability Analysis Based on Consensus Reaching Process under Probabilistic Linguistic Environment

Human reliability analysis (HRA) is widely used to evaluate the impact of human errors on various complex human–machine systems for enhancing their safety and reliability. Nevertheless, it is hard to estimate the human error probability (HEP) in reality due to the uncertainty of state assessment information and the complex relations among common performance conditions (CPCs). In this paper, we aim to present a new integrated cognitive reliability and error analysis method (CREAM) to solve the HRA problems under probabilistic linguistic environment. First, the probabilistic linguistic term sets (PLTSs) are utilized to handle the uncertain task state assessments provided by experts. Second, the minimum conflict consensus model (MCCM) is employed to deal with conflict task state assessment information to assist experts reach consensus. Third, the entropy weighting method is used to determine the relative objective weights of CPCs. Additionally, the CPC effect indexes are introduced to assess the overall effect of CPCs on performance reliability and obtain the HEP estimation. Finally, the reliability of the proposed CREAM is demonstrated via a healthcare practical case. The result shows that the new integrated CREAM can not only effectively represent experts’ uncertain task state assessments but also determine more reliable HEP estimation in HRA.

A systemic approach for stochastic reliability management in human–machine systems

In today’s complex engineered systems, comprising a multitude of interacting components, preserving system performance is of utmost importance. The challenge often lies in effectively prioritizing components with the highest potential to compromise system reliability, mainly when human interaction with technical artefacts is not negligible. This study proposes a systemic methodology for pragmatic reliability management within human–machine systems. The proposed approach combines a rule-based adaptation of the well-established Failure Mode, Effects, and Criticality Analysis (FMECA) with a probabilistic Fault Tree Analysis (FTA). Furthermore, the technical considerations are seamlessly integrated into a human-centric analysis, utilizing the Standardized Plant Analysis Risk – Human Reliability Analysis (SPAR-H). The proposed decision-support methodology is instantiated through Monte Carlo simulations to account for stochastic phenomena and uncertain operating conditions. The effectiveness and practicality of the proposed approach are elucidated through a case study involving a high-reliability system, specifically a high-mobility multi-wheeled vehicle. This study demonstrates the step-by-step application of the proposed approach and its implications in challenging operating scenarios, reaffirming its potential to enhance reliability management within human–machine systems.

Имитационное моделирование и методы определения уровня пожарной безопасности электроустановок на объектах АПК

Currently, problems of health protection and ensuring anthropogenic human safety are becoming relevant. Their solution is impossible without an effective system for monitoring the environment and predicting its dynamics. The peculiarity of such a system is the spatial heterogeneity and temporal nonstationarity of its inherent patterns, which generate complexity and uncertainty of the human-machine system being studied. The work examines two interrelated tasks: 1. Generalization of the main provisions of simulation modeling as a methodology for studying complex human-machine systems under a priori uncertainty; 2. Methods for determining the level of fire safety electrical installations at agricultural facilities. Keywords: SIMULATION MODELING, FIRE SAFETY, HUMAN-MACHINE SYSTEM

О НЕОБХОДИМОСТИ ПРИМЕНЕНИЯ СЛОЖНЫХ ЧЕЛОВЕКО-МАШИННЫХ СИСТЕМ ОПЕРАТИВНОГО УПРАВЛЕНИЯ ПРОИЗВОДСТВЕННЫМИ ПРОЦЕССАМИ

The issues of organization and management of complex human-machine systems are considered in the article via heating plant example. A phenomenological controlled indicator is introduced in order to consider the ratio of electricity generation and emissions of nitrogen oxides. Approximation model of the controlled indicator via modified model of logistic function has been developed.

Risk evolution model of marine traffic via STPA method and MC simulation: A case of MASS along coastal setting

Marine traffic safety for Maritime Autonomous Surface Ship (MASS)is affected by the maritime environment and complex human-machine technical systems, and it is necessary to reveal its risk evolution characteristics involving new technology. The novel risk evolution model based on the system-theoretic process analysis (STPA)method for specific scenarios is established to determine the risk mechanism in random processes. First, by combining Markov chain (MC) with cloud model based on the information transmission path, the Markov process hypothesis is proposed to devise a coupling effect model of system components and external interference environment. Second, operation and control modes for MASS under different scenarios based on STPA are constructed and analyzed, which are based on clarifications regarding two models for the parallel control of MASS. Third, a systemic risk model using safety control methodology is constructed considering the operation scenario of MASS. Last and not least, the risk performance in the MASS navigation process is simulated to reveal the emergence characteristics of ship navigation process risk after applying specific scenarios under three modes. Application examples show that the process risk emergence of MASS navigation is characterized by the dependence on safety information transmission path. Different transmission paths lead to inconsistent risk evolution results. The randomness and complexity of external environmental disturbances are the main factors involved in the formation of navigation risks in MASS.

Features of simulation of operator control of unmanned aerial vehicle and its target load

The article is devoted to the formulation of the problem of synthesis of the control system of an unmanned aerial vehicle (UAV) with the participation of a human operator. An analysis is made of various approaches to the synthesis of systems, models of a human operator as a link in a tracking system, mathematical models and information support for complex human-machine systems, competencies required by UAV operators. The system considers a human operator as a dynamic link that performs compensatory monitoring of the relevant information that is presented on the monitor of the corresponding operator controlling the UAV or its target load in accordance with the task. On the basis of the analysis, a generalized scheme of the UAV control system and its target load is substantiated, a mathematical model of a human operator is studied in the form of a transfer function consisting of five elementary links that take into account the dynamic characteristics of the central nervous system, the delay of the neuromuscular system, the ability of the human operator to smooth random fluctuations of the information presented on the monitor screen received from the telemetry system. As a typical example of a human-machine control system, the ground control point of the multifunctional unmanned aerial complex “Busel” is considered, which includes two workplaces for the UAV control operator and the target load control operator, which is considered equipment designed to monitor objects located on the earth’s surface

A model to evaluate the Human Error Probability in inspection tasks of a production system

Investigating the dynamics of human-machine interaction and its impacts on production performance is a key issue in the context of Industry 4.0 because the concept of the “operator 4.0”, i.e., an operator integrated into a cyber-physical system, implies the need to manage complex human-machine systems. One of the most concerned fields on this topic is human reliability analysis, as the Human Error Probability (HEP) estimation by considering different work environment aspects. To this concern, the present work's purpose consists of assessing human error's impact on a manufacturing system by considering the different Performance Shaping Factors (PSFs) that affect the HEP. To this end, an analytical model has been developed to evaluate the human error in an inspection task to be accomplished in a full-real industrial case study. The HEP was estimated as a function of PSFs, including three different dimensions (i.e., task error proneness, operator's capabilities and characteristics of the work environment in the production system). It was found that the most impactful PSF affecting HEP depends on the working environment conditions in the production system. In this regard, the model shows that assuming an equal variation in the attributes related to all dimensions, the changes in working environment conditions from a physical and psychological point of view generate the most significant reduction in HEP. Consistently with these results, the average costs for reducing HEP by improving the working environment conditions are significantly higher than the average costs incurred to reduce the HEP considering the task error proneness and operator's capabilities.

Modeling Pilot Flight Performance in a Cognitive Architecture: Model Demonstration

Cognitive architecture models can support the simulation and prediction of human performance in complex human-machine systems. In the current work, we demonstrate a pilot model that can perform and simulate taxiing and takeoff tasks. The model was built in Queueing Network-Adaptive Control of Thought Rational (QN-ACTR) cognitive architecture and can be connected to flight simulators such as X-Plane to generate various data, including performance, mental workload, and situation awareness. The model results are determined in combination by the declarative knowledge chunks, production rules, and a set of parameters. Currently, the model can generate flight operation behavior similar to human pilots. We will collect human pilot data to examine further and validate model assumptions and parameter values. Once validated, such models can support interface evaluation and competency-based pilot training, providing a theory-based predictive approach complementary to human-in-the-loop experiments for aviation research and development.

Комплексный анализ эффективности системы «Оператор - машина» на основе методов искусственного интеллекта

Methodological approaches to the analysis of the effectiveness of complex human-machine systems (SMS) based on the use of methods of heuristic self-organization and artificial neural networks are considered. Modeling of the system taking into account a large number of factors and several output variables that characterize the MFM is based on obtaining multilayer perceptrons with the exception of factors with low sensitivity

Personality risk factors in assessing the reliability of the performance of operating personnel

IntroductionThe development of complex human-machine systems has led to greater demands on operators’ skills, and has increased the importance of human error (Pribytkova et al, 2012; Vondráčková et al, 2017; Jian Ai Yeow, 2014). For this reason performance reliability, defined as operators’ capacity to conduct essential work processes in a high-quality and timely manner (Bodrov, Orlov, 1998) has become a topical subject.ObjectivesThis study concerns an investigation of subjective predictors of operators’ reliability, namely personality risk factors (supported by the RFBR #19-013-00799).MethodsSubjects: 67 operators and 69 engineers at a hydro-power station. Personality traits were assessed using Sobchik’s verbatim Russian translation of the MMPI (Sobchik, 1990). Performance reliability was assessed using simple and complex sensorimotor reaction tests as standard procedures for the pre-shift assessment of operators.ResultsIn the operators’ group significant correlations (Spearman’s test) were found between the level of quality of complex sensorimotor reactions and the level of such personal traits as impulsiveness and individualism: a higher manifestation of these traits was associated with a higher level of mistakes in conducting the pre-shift psychophysiological test (p<0,05). With the engineers there was a significant link between the higher speed of simple sensorimotor reactions and higher optimism scores.ConclusionsThe results suggest that a tendency to behave spontaneously, and orientation to one’s own needs, could be risk factors in terms of operator reliability. They also reveal the specifics of reliability predictors in different professions at the power plant.

Development of a new method of management of ergo-technical system on the basis of its dynamics monitoring

This paper explores the state assessment and management of complex human-machine systems that ensure that results are quickly obtained with the desired accuracy. The problem is solved by monitoring the real parameters of the process and comparing them with the results of the simulation, taking into account their fuzziness, incompleteness and lack of definition. The proposed method of solving control problems can be used in a number of subject areas (robotics, aero-space monitoring, etc.).

Artificial Intelligence's Societal Impacts, Governance, and Ethics

AI’s societal impacts and governance challenges carry high stakes but are deeply uncertain, due to uncertainties in both the characteristics and capabilities of the technologies and in how they will be developed, deployed, and used in social and political context. We provide an introduction and synthesis of discussions at the 2019 Summer Institute on AI and Society, an intense interdisciplinary exploration of these issues. The summer institute used an experimental model to promote informed discussions among a diverse international group of researchers, professionals, and students: one day of focused briefings, followed by two days of collaborative group work on projects proposed and selected by participants via a real-time decision process. The workgroups that emerged from this process examined a wide range of questions: embedded values in automated mobility systems; the concept of meaningful human control in complex human-machine decision systems; the prospect of AI systems replacing policy analysis in government decision-making; how AI challenges and changes conventional understandings of agency; the feasibility and requirements for AI to drive large-scale societal progress; and two distinct perspectives on the multiple sets of proposed AI ethical principles and attempts to make these operational: one a practical attempt to develop and specify such an operationalization, and one a critique of an existing operationalization recently proposed by an EU expert group. Workgroup discussions offered new insights on the risks posed to corollary or implicit values in present institutions or systems that may be inherent in automating or codifying decisions; the frequency with which seemingly prosaic matters of AI system design directly implicate deep political and moral issues; the value of a dialectical or adversarial approach to assessing impacts of AI systems or governance efforts; and the acute need for methods to assess and manage medium-term AI developments and impacts.

Building the Black Box: Cyberneticians and Complex Systems

In the 1950s and 1960s, cyberneticians defined and utilized a concept previously described by electronic engineers: the black box. They were interested in how it might aid them, as both a metaphor and as a physical or mathematical model, in their analysis of complex human-machine systems. The black box evolved as they applied it in new ways, across a range of scientific fields, from an unnamed concept involving inputs and outputs, to digital representations of the human brain, to white boxes that might be used to replicate black boxes. The diversity of understandings of the black box reflected the diversity of scientific perspectives and goals brought under the label of cybernetics. In this paper, I examine how cyberneticians drew upon the black box in their personal writings and publications. My goal is to unpack what the black box meant to these theorists as a starting framework from which we may understand the initial shape of the black box.

A Systematic Review of Physiological Measures of Mental Workload.

Mental workload (MWL) can affect human performance and is considered critical in the design and evaluation of complex human-machine systems. While numerous physiological measures are used to assess MWL, there appears no consensus on their validity as effective agents of MWL. This study was conducted to provide a comprehensive understanding of the use of physiological measures of MWL and to synthesize empirical evidence on the validity of the measures to discriminate changes in MWL. A systematical literature search was conducted with four electronic databases for empirical studies measuring MWL with physiological measures. Ninety-one studies were included for analysis. We identified 78 physiological measures, which were distributed in cardiovascular, eye movement, electroencephalogram (EEG), respiration, electromyogram (EMG) and skin categories. Cardiovascular, eye movement and EEG measures were the most widely used across varied research domains, with 76%, 66%, and 71% of times reported a significant association with MWL, respectively. While most physiological measures were found to be able to discriminate changes in MWL, they were not universally valid in all task scenarios. The use of physiological measures and their validity for MWL assessment also varied across different research domains. Our study offers insights into the understanding and selection of appropriate physiological measures for MWL assessment in varied human-machine systems.

МАТЕМАТИЧЕСКИЕ МОДЕЛИ И МЕТОДЫ РЕШЕНИЯ ЗАДАЧ ИНФОРМАЦИОННОГО ОБЕСПЕЧЕНИЯ, УПРАВЛЕНИЯ И ОЦЕНКИ КАЧЕСТВА РАБОТЫ ОПЕРАТОРОВ В СЛОЖНЫХ ЧЕЛОВЕКО-МАШИННЫХ СИСТЕМАХ

The article characterizes the general working conditions of the man-operator (MO) in the complex human-machine systems (CHMS). Specific examples of CHMS are given. It is shown that work of MO demands the following terms: storing instructions for necessary actions in memory; perception and judgment of large volumes of information; operational recognition of dangerous situations; acceptance and timely implementation of management and technical decisions; estimates of their implementation effects. Features of CHMS real-time management are specified; original mathematical models for the MO solutions acceptance and implementation are offered. Variants of MO sensor systems for ensuring operator with necessary information are considered. The key psychophysiological parameters influencing the information perception and usage by MO are discussed: attention indicators, volumes of short-term and long-term memory, speeds of percepting different types of information, time expenditure on acceptance of one- and multicriteria solutions, durations of “motor reactions”; opportunities of side-by-side execution by MO of several works types, concerned with CHCM management. Variants of operator testing by specified parameters are analyzed. The expediency of accumulation and retrospective analysis of testing results for individual MOs or MO groups has been justified. The opportunities for MO training including use of the adaptive modes are described. The information models of MO activity in terms of CHMS and models of dynamic management of information volumes demonstrated to MO. Practical opportunities and restrictions on continuous monitoring and/or periodic control of MO in the course of work are analyzed. There are offered the mathematical models for optimizing volumes of control, considering positive and negative effects. Possible approaches to a posteriori estimates of the quality of MO activity are analyzed; particular and integral criteria for such estimates are considered. Possible methods for MO activity quality management are specified.

Dissociation Between Mental Workload, Performance, and Task Awareness in Pilots of High Performance Aircraft

The purpose of this study was to demonstrate how the inclusion of a tactical task goal awareness measure complemented mental workload and performance measures in a simulated air combat mission. It was hypothesized that the evaluation of the tactical task goal awareness could provide additional information concerning the cognitive demands a pilot is exposed to during a complex air combat task. A test setting was developed to test this hypothesis in a virtual flight training device. To highlight the impact of task complexity, high performance aircraft pilots' heart rate (HR), interbeat-interval (IBI), and performance in two simple flying tasks were first compared. Then, a similar comparison, complemented with the tactical task goal awareness measure, was made with two complex flying tasks. It was found that when the pilot's awareness of the tactical goals was low, a combination of low performance and low mental workload occurred. It was concluded that when the pilots' performance is evaluated on a complex air combat task, the awareness of the tactical goals, performance, and mental workload should be studied together as the pilot's awareness can explain some of HR/IBI responses that could be otherwise misinterpreted. More generally, mental workload, performance, and task goal awareness should all be considered when the operator's performance in any complex human-machine system is assessed.

An Agent-Based Reliability and Performance Modeling Approach for Multistate Complex Human-Machine Systems With Dynamic Behavior

A complex human-machine system (CHMS) consists of heterogeneous components with extensive human-machine interactions. CHMSs are typical multistate systems with the ability to adapt to disturbances such as machine failures. These characteristics must be considered comprehensively to accurately evaluate the reliability and performance of a CHMS. However, the existing literature scarcely considers both the reliability and performance simultaneously. In this paper, we propose an agent-based approach to model and evaluate a CHMS. First, a general agent-based modeling framework for a CHMS is generated by analyzing the structure and operations of a CHMS. Then, a dual-clock mechanism is introduced to describe the behaviors of the machine failures and human errors. Two environmental disturbance modeling methods are proposed based on the state transitions of the agent and random events. The methods to model the repair and reconfiguration behaviors are presented based on the contract network. A Monte Carlo-based method is developed to evaluate the reliability and performance of the CHMS simultaneously. Finally, a deck scheduling process for an aircraft carrier is used as a case study to verify the approach. The results show that the reliability and performance of a CHMS can be effectively evaluated.

Management of complex human-machine systems with an extended intellectual component during the operation of thermal power plants under conditions of non-linear dynamics of the effects of unaccounted factors leading to emergency disturbances

Management of complex human-machine systems with an extended intellectual component during the operation of thermal power plants under conditions of non-linear dynamics of the effects of unaccounted factors leading to emergency disturbances

Reflective agent as a partner of a human operator in the management of complex human-machine systems

Reflective agent as a partner of a human operator in the management of complex human-machine systems

The Underpinnings of Workload in Unmanned Vehicle Systems

This paper identifies and characterizes factors that contribute to operator workload in unmanned vehicle systems. Our objective is to provide a basis for developing models of workload for use in design and operation of complex human–machine systems. In 1986, Hart developed a foundational conceptual model of workload, which formed the basis for arguably the most widely used workload measurement technique—the NASA Task Load Index. Since that time, however, there have been many advances in models and factor identification as well as workload control measures. Additionally, there is a need to further inventory and describe factors that contribute to human workload in light of technological advances, including automation and autonomy. Thus, we propose a conceptual framework for the workload construct and present a taxonomy of factors that can contribute to operator workload. These factors, referred to as workload drivers, are associated with a variety of system elements including the environment, task, equipment, and operator. In addition, we discuss how workload moderators, such as automation and interface design, can be manipulated in order to influence operator workload. We contend that workload drivers, workload moderators, and the interactions among drivers and moderators all need to be accounted for when building complex human–machine systems.