Actions Of Human Operator Research Articles

Trust in automation and the accuracy of human–algorithm teams performing one-to-one face matching tasks

The human face is commonly used for identity verification. While this task was once exclusively performed by humans, technological advancements have seen automated facial recognition systems (AFRS) integrated into many identification scenarios. Although many state-of-the-art AFRS are exceptionally accurate, they often require human oversight or involvement, such that a human operator actions the final decision. Previously, we have shown that on average, humans assisted by a simulated AFRS (sAFRS) failed to reach the level of accuracy achieved by the same sAFRS alone, due to overturning the system’s correct decisions and/or failing to correct sAFRS errors. The aim of the current study was to investigate whether participants’ trust in automation was related to their performance on a one-to-one face matching task when assisted by a sAFRS. Participants (n = 160) completed a standard face matching task in two phases: an unassisted baseline phase, and an assisted phase where they were shown the identification decision (95% accurate) made by a sAFRS prior to submitting their own decision. While most participants improved with sAFRS assistance, those with greater relative trust in automation achieved larger gains in performance. However, the average aided performance of participants still failed to reach that of the sAFRS alone, regardless of trust status. Nonetheless, further analysis revealed a small sample of participants who achieved 100% accuracy when aided by the sAFRS. Our results speak to the importance of considering individual differences when selecting employees for roles requiring human–algorithm interaction, including identity verification tasks that incorporate facial recognition technologies.

Artificial intelligence – Human intelligence conflict and its impact on process system safety

In the Industry 4.0 revolution, industries are advancing their operations by leveraging Artificial Intelligence (AI). AI-based systems enhance industries by automating repetitive tasks and improving overall efficiency. However, from a safety perspective, operating a system using AI without human interaction raises concerns regarding its reliability. Recent developments have made it imperative to establish a collaborative system between humans and AI, known as Intelligent Augmentation (IA). Industry 5.0 focuses on developing IA-based systems that facilitate collaboration between humans and AI. However, potential conflicts between humans and AI in controlling process plant operations pose a significant challenge in IA systems. Human-AI conflict in IA-based system operation can arise due to differences in observation, interpretation, and control action. Observation conflict may arise when humans and AI disagree with the observed data or information. Interpretation conflicts may occur due to differences in decision-making based on observed data, influenced by the learning ability of human intelligence (HI) and AI. Control action conflicts may arise when AI-driven control action differs from the human operator action. Conflicts between humans and AI may introduce additional risks to the IA-based system operation. Therefore, it is crucial to understand the concept of human-AI conflict and perform a detailed risk analysis before implementing a collaborative system. This paper aims to investigate the following: 1. Human and AI operations in process systems and the possible conflicts during the collaboration. 2. Formulate the concept of observation, interpretation, and action conflict in an IA-based system. 3. Provide a case study to identify the potential risk of human-AI conflict.

Towards utilising Artificial Intelligence for advanced reasoning and adaptability in human-robot collaborative workstations

Significant effort has been allocated recently by the research community for the seamless integration of Artificial Intelligence (AI) in modern human-robot collaborative (HRC) systems. Mainly in the context of human operator support and robotic object manipulation, the adoption of AI could become the main enabler towards the realisation of seamless HRC. This work presents the vision of the European funded project MASTERLY for an AI-based framework to enhance operator assistance and optimize robotic object manipulation. The proposed framework aims to contribute towards the realisation of intelligent, human-centric collaborative workstations focusing on two principal areas. The first area is human operator action recognition, which involves interpreting human actions to provide instructions and notifications to the operator only when it is considered necessary via Augmented Reality (AR) interfaces. The second area is the generation of a strategy for robotic manipulation of unknown objects. The proposed approach utilizes AI methods to identify optimal grasping points on unknown objects for efficient and precise robotic handling. The performance of the proposed framework is tested and validated in a case study from the elevators’ manufacturing industry, and more specifically, the assembly of the elevator's electrical panel.

До питання формалізації знань в управлінні виробництвом ентомофагів

The article is devoted to the issue of formalization of knowledge in the management of entomophages production. The analysis of scientific works concerning the chosen direction of researches is carried out in the work, their urgency is substantiated. Factors influencing the efficiency of entomophages production management as a complex biotechnical system of ergatic type are identified. The aim of the research was to formalize knowledge in the management of entomophages production to increase the efficiency of its operation. The object of research is the processes of entomophages production management. Research methods - system modeling, precedent-based management, data mining. A model for presenting knowledge for entomophages production management based on precedents in the form of an associative map using the FreeMind software environment has been developed. A method for management the production of entomophages based on the application of a precedent approach and data mining has been developed. A precedent knowledge base has been developed using the Microsoft Access software environment for systematization of information on human operator actions in entomophages production management processes. The functioning of the knowledge base is based on production rules. The expediency of formalization of knowledge in the management of entomophages production is scientifically substantiated, which creates the conditions for increasing the efficiency of production and the quality of entomological products. This is achieved by reducing the time for the formation of strategies for its management through the use of previous experience and structuring information on the actions of the operator-technologist in management processes in the form of a precedent knowledge base. Key words: management, entomophages production, precedent, knowledge base, data mining

Analyzing Standard Operating Procedures Using Model‐Based Systems Engineering Diagrams

AbstractStandard Operating Procedures (SOPs) are critical for the safe operations of complex, hazard‐sensitive systems. The SOPs are particularity important for dealing with non‐normal operations in which the human operator must intervene and/or provide instructions to the automation. Under these rare circumstances, the appropriate human operator actions are defined by SOPs. The SOPs must be completed within an Allowable Operational Time Window (AOTW) to avert a hazardous outcome. In many cases, the AOTW is not fixed, but exhibits variance due to complex non‐linear, plant dynamics. The Time on Procedure (ToP) is also subject to variability due to human factors such as experience, proficiency, fatigue, and the efficacy of the SOPs and the supporting automation user‐interface. For this reason, it is critical to evaluate the dynamic performance of the SOP in the context of the operations and determine the likelihood of the ToP exceeding the AOTW.This paper describes how to model SOPs and perform SOP analysis using LML/SysML Action/Activity Diagrams that can be found in most Model‐based System Engineering (MBSE) tools. The method enables the SOP designer to assess the performance of the SOP by accounting for human factors and operation dynamics. The ability to test the procedures in a MBSE tool can inform the system design and verify the procedure design early in the development life‐cycle. The implications and limitations of this approach are discussed.

Digital Twin in biomanufacturing: challenges and opportunities towards its implementation

The domain of industrial biomanufacturing is enthusiastically embracing the concept of Digital Twin, owing to its promises of increased process efficiency and resource utilisation. However, Digital Twin in biomanufacturing is not yet clearly defined and this sector of the industry is falling behind the others in terms of its implementation. On the other hand, some of the benefits of Digital Twin seem to overlap with the more established practices of process control and optimization, and the term is vaguely used in different scenarios. In an attempt to clarify this issue, we investigate this overlap for the specific case of fermentation operation, a central step in many biomanufacturing processes. Based on this investigation, a framework built upon a five-step pathway starting from a basic steady-state process model is proposed to develop a fully-fledged Digital Twin. For demonstration purposes, the framework is applied to a bench-scale second-generation ethanol fermentation process as a case study. It is proposed that the success or failure of a fully-fledged Digital Twin implementation is determined by key factors that comprise the role of modelling, human operator actions, and other propositions of economic value.

The autonomous intelligence challenge

The considerable development of modern technology during last decades has been accom-panied by improvements in associated adapted machines� performance. The improvement process has always been guided by the same rules of researching higher efficiency and more secure effects each time. This leads to a progressive transfer of human action to more adapted and more specific effecting objects, from simple tools for elementary actions to more sophisticated machines for quite complex tasks. Each step of this transfer of human operator action has been realized by delegating to the effecting machine efficiency, accuracy, power and safety�basically all of technical nature and linked to power flux, with the human operator still keeping the mastery of the action to fulfil his own goals.

Методологія досліджень та навчання в аеронавігаційній соціотехнічній системі

Мета: на основі формалізації професійних і непрофесійних факторів, конструювання моделей прийняття рішень людиною-оператором аеронавігаційної системи та розвитку польотної ситуації розробити методологію досліджень та навчання в аеронавігаційній соціотехнічній системі. Методи: системний аналіз, рефлексивна теорія, мережеве планування, дерево рішень, мережа Маркова, мережа GERT, нейронна мережа, експертний метод. Результати: Аеронавігаційна система представлена як складна соціотехнічна система. Визначено вплив професійних факторів (знань, навичок, вмінь, досвіду), а також факторів непрофесійного характеру (індивідуально-психологічних, психофізіологічних та соціально-психологічних) на прийняття рішень людиною-оператором аеронавігаційної системи. Отримано графічно-аналітичні, детерміновані, стохастичні, рефлекторні, нейромережеві моделі прийняття рішень людиною-оператором аеронавігаційної системи, та розвитку ситуаціях, які потребують польотної ситуації. Створено програмний комплекс "Підказка", який оперативно та своєчасно підбирає оптимальну стратегію завершення польоту в позаштатних виконання вимушеної посадки повітряного корабля. Розроблено методологію досліджень та навчання в аеронавігаційній соціотехнічній системі. Обговорення: результатом оцінки непрофесійних факторів є визначення соціально-психологічного впливу на прийняття рішень людиною-оператором шляхом виявлення переваг, діагностика індивідуально-психологічних якостей людини-оператора при розвитку польотної ситуації, моніторинг психофізіологічних показників людини-оператора (емоційного стану). Запропоновані моделі дозволять своєчасно діагностувати та прогнозувати можливі дії людини-оператора в очікуваних та неочікуваних умовах експлуатації повітряного корабля. Алгоритми та методологія аналізу розвитку ситуації під впливом прийняття рішень людиною-оператором можуть використовуватись в інших техногенних виробництвах.

A Bayesian-Influence Model for Error Probability Analysis of Combine Operations in Harvesting

Harvesting is one of the most important agricultural operations because it captures the value from the entire cropping season. In modern agriculture, grain harvesting has been mechanized through the combine harvester. A combine harvester enables highly productive crop harvesting. Combine harvesting performance depends on the highly variable skill of combine operators and associated operator error. An approach was developed to analyze the risk of the combine harvesting operation as it relates to operator error. Specifically, a risk analysis model was built based on a task analysis from operator interviews and estimates of the probability of operator error. This paper employs a Bayesian approach to assess risks in combine operation. This approach applies a Bayesian Belief Network to agriculture operations, which represents a new application for this risk analysis tool. Sensitivity analysis of different errors and operator skill levels was also performed. The preliminary results indicate that a reduction of human operator action errors can substantially improve the outcomes of the human-machine interaction.

Petri-Net-Based Modeling of Human Operator's Planning for the Evaluation of Task Performance Using the Example of Air Traffic Control

With respect to planning capabilities, a model is developed to be used for the evaluation of human operator's cognitive performance. Plans are goal-directed interaction sequences cognitively simulated before appearing in dynamic human–machine systems. In this context, human operator's actions have long-term effects as well as different consequences depending on the time of their activation. The proposed model allows the evaluation of cognitive task performance by comparing implemented decisions to the available options represented by plans. In this paper, situation operator modeling is applied to describe operator's behavior and the planning process based on the system modeled using colored Petri Nets. The Petri Net is applied for the prediction of future system states. The planning model uses a set of rules that describe the normative behavior of operators as definitions of problems and actions required to avoid these. The rules are derived systematically from the operator's objectives and from the available actions. The proposed method allows the calculation and, therefore, the prediction of future states as well as possible upcoming conflicts within the system. Corresponding rules may modify and, therefore, affect the generated plan. The method is demonstrated using a simplified air traffic control simulation. This method extends earlier methods published by the authors for the determination of available options based on Petri Nets. It can be concluded that the calculation of sequences of actions for interaction with dynamic systems with constant change over time is possible and realized for the first time.

Optimal control for a linear quadratic neuro Takagi--Sugeno fuzzy singular system using genetic programming

Optimal control for a linear neuro Takag--Sugeno fuzzy singular system with quadratic performance is obtained using genetic programming (gp). To obtain the optimal control, the solution of a matrix Riccati differential equation is computed by solving a differential algebraic equation using the gp approach. The obtained solution is equivalent or very close to the exact solution of the problem. The accuracy of the solution computed by the gp approach is qualitatively better than the traditional Runge--Kutta method. An illustrative numerical example is presented for the proposed method. References P. Balasubramaniam, J. Abdul Samath, N. Kumaresan and A. Vincent Antony Kumar, Solution of matrix Riccati differential equation for the linear quadratic singular system using neural networks, Appl. Math. Comput. 182(2):1832–1839, 2006. doi:10.1016/j.amc.2006.06.020 G. Da Prato and A. Ichikawa, Quadratic control for linear periodic systems, Appl. Math. Opt. 18:39–66, 1988. doi:10.1007%2FBF01443614 D. E. Goldberg, Genetic algorithms in search, optimization and machine learning , Addision Wesley, 1989. http://dl.acm.org/citation.cfm?id=534133 J. Jang, ANFIS: adaptive-network-based fuzzy inference systems, IEEE T. Syst. Man. Cyb. 23(3):665–685, 1993. doi:10.1109/21.256541 J. R. Koza, Genetic programming: on the programming of computers by means of natural selection . MIT Press, 1992. http://mitpress.mit.edu/books/genetic-programming M. O'Neill and C. Ryan, Evolutionary automatic programming in an arbitrary language, Genetic Programming, Vol. 4 , Kluwer Academic Publishers, 2003. http://www.springer.com/computer/ai/book/978-1-4020-7444-8 N. Kumaresan, Optimal control for stochastic linear quadratic singular periodic neuro Takagi–Sugeno fuzzy system with singular cost using ant colony programming, Appl. Math. Model. , 35:3797–3808, 2011. doi:10.1016/j.apm.2011.02.017 T. Takagi and M. Sugeno, Derivation of fuzzy control rules from human operator's actions, IFAC-IFIP-IFORS Symp., Fuzzy information, knowledge representation and decision analysis , 55–60, 1983. http://dl.acm.org/citation.cfm?id=577582 I. G.Tsoulos and I. E. Lagaris, Solving differential equations with genetic programming, Genet. Program. Evolv. M. , 7:33–54, 2006. doi:10.1007/s10710-006-7009-y S.-J. Wu, H.-H. Chiang, H.-T. Lin and T.-T. Lee, Neural-nerwork-based optimal fuzzy controller design for nonlinear systems, Fuzzy Set. Syst. , 154:182–207, 2005. doi:10.1016/j.fss.2005.03.011

An Efficient Multiple Fault Detection and Isolation Scheme for Discrete Event System

The malfunction of sensors, actuators, and erroneous actions of human operators can have some disastrous consequences in high risk systems especially if these systems have multiple faults that can lead to undesirable shutdowns and consequently a plant output reduction. This paper introduced a new scheme to detect and isolate multiple faults in complex discrete event systems. This scheme comprises a set of discrete rules and an inference engine. The former characterizes the dynamic behavior of a process that is modeled by a Petri net and the latter infers the consequent part of the fired rules. The consequent parts are aggregated and compared with the original Petri net model of the process for detecting multiple faults. The proposed inference engine follows the forward reasoning strategy that fires transitions from the initial marking until the final marking is reached. Industrial processes are employed for testing the soundness of the proposed scheme.

Using the BCD model for risk analysis: An influence diagram based approach

In this paper, we are interested in addressing risk analysis. We propose an influence diagram-based approach that focuses on a Benefit, Cost, Deficit (BCD) model. The BCD model is proposed for studying the intentional deviant behaviors of human operators in a system. In this model, the consequences of human actions are analyzed with respect to three parameters: benefit, cost and deficit. Our approach aims to expand the BCD model by integrating factors, such as those related to the organization of the system in question, that influence human operator actions. In addition, the approach considers multiple criteria that are related, for example, to safety and productivity. To build a model that evaluates the risk induced by human actions in a system and analyzes the impact of the different factors, we use influence diagrams. Influence diagrams are probabilistic graphical models that can deal with uncertainty and with incomplete and imprecise information. Influence diagrams also represent the interdependencies between the different variables of the studied problem. In addition, contrary to Bayesian networks, influence diagrams can rank a set of actions by providing information on which action carries the greatest risk or the most benefits. We applied this approach to a case study of an industrial rotary press, but it can also be used in other problems and sectors.

Fault-diagnosis in discrete event systems: Improvements and new results

The malfunction of sensors, actuators, and erroneous actions of human operators can have some disastrous consequences in high risk systems especially if these systems have multiple faults that can lead to undesirable shutdowns and consequently mass reduction. A reduced interpreted Petri net (IPN) diagnoser has been devised only for safe Petri net models with an output function that associates an output vector to each net marking. The main drawback of this approach is that the Petri net model of the system to be monitored should be diagnosable i.e. all faults can be detected that limits its application on a set of diagnosable models. For non diagnosable Petri net model, the conventional diagnoser incidence matrix has columns with null or similar values that fail to detect a single fault. The conventional diagnoser also cannot detect multiple faults even for diagnosable models. This paper introduces a new local diagnoser to overcome such problems. It decomposes the central IPN-diagnoser into a set of local diagnosers that are linked with multi sessions of the process to be monitored. This decomposition should guarantee that the developed local diagnosers have incidence matrices that their columns are different from each other. For null values contained in the incidence matrix of a local diagnoser, this paper proposes a set of rules based on the synchronic composition idea to overcome this problem. This proposed scheme allows multiple faults detection and isolation in quick and accurate manner for all Petri net models. Industrial processes are employed for testing the soundness of the proposed scheme.

Dynamic safety assessment: Scenario identification via a possibilistic clustering approach

Dynamic safety and reliability methodologies aim at quantitatively describing the integrated dynamic response of the systems/components/operating crew during an accident by combining the models of the underlying process dynamics and human operator actions with the stochastic processes governing the failure, repair and state transitions of components and systems. The amount of information produced by such dynamic analyses, in terms of scenarios and probability distributions of the occurring events, is very broad and usually calls for a significant effort in the post-processing phase. In an attempt to retrieve and organize this information, the present paper presents an approach for identifying and grouping the scenarios resulting from a dynamic system safety analysis. The aim is to single out the principal patterns of system evolution with respect not only to the final system states but also to the time of events and to the process evolution. Due to the burden of the analysis, this is often overlooked in dynamic safety analyses, which mainly focus on the system states at the end of the scenarios with little consideration given to the actual evolution of the system towards these states. Monte Carlo simulation is exploited for generating stochastic scenarios that are then grouped by combining information from the end state, the sequence of events and the physical behavior of the process variables. The grouping is based on possibilistic clustering classification. The approach has been tested on scenarios produced by dynamic simulation of a chemical batch reactor of literature in which a highly exothermic process is worked out.

107 作業者と操作機器との相互関係を考慮した特殊車両設計シミュレーション(OS1-2 デジタル設計・製造支援II)

An intensive SD-simulation method for special vehicle design considering human operator actions has been developed. Basic motions necessary for the vehicle handling such as "stable-riding, carefully-sitting, field view -checking, etc" are presented as examples of the most important factors for efficient design scheme. A rich human-body model with 44-DOF has been introduced. The model is utilized for formulation the concept of SecurePath as well as Secure View estimating adequate vehicle-related human actions in uncertain field environment.

Predictive modeling of human operators using parametric and neuro-fuzzy models by means of computer-based identification experiment

Driving a car and piloting an airplane are the most common examples for manual control of complicated processes. Human operators are known to be nonlinear, adaptive, time varying and intelligent controllers. In some cases, the human operator may or may not be well trained or an expert, showing different dynamics from operator to operator as in driving example. Therefore, it is very difficult to obtain mathematical models of human operators in a human-in-the-loop-manual control tasks. The goal of this research is to find a simple dynamic model for the prediction of the human operator actions in a manual control system. A computer-based experiment has been designed using the system identification theory to collect data from human operators. The autoregressive with exogenous inputs (ARX), as a parametric model and the adaptive-network-based fuzzy inference system (ANFIS), as an intelligent modeling approach that has the advantages of both neural networks and fuzzy logic, have been investigated and compared for simple and fast implementation to predict the response of human operators. ANFIS, having only 32 rules, provided much better prediction results than ARX model.

Failure Diagnosis on Discrete Event Systems

The modern technology advances to a point where it is possible and extensively desirable to improve reliability and the technical process safety. This is achieved by computer implanted FDI procedures (Fault Detection and Isolation). However, the malfunction of actuators, sensors and of the process components, as well as erroneous actions of human operators can have some disastrous consequences in high risk systems such as: Spatial engines (Astronomy), aircrafts (Aviation), nuclear reactors and chemical plants. Thus, each failure or fault can lead to shutdowns or a rupture of service and consequently a plant output reduction. There is an improvement of consciousness and attitude to possible disaster provoked by failures that could enable a failure tolerating system development. Such system must maintain a optimal performance during normal operating conditions and must handle encountered critical situations during which the system’s conditions are abnormal that is by performing of detection and diagnosis procedures and reconfiguration according to accurate software programs. In this study, we focus on the diagnosis of the flexible manufacturing systems which are described by a model based on the Petri nets. The basic idea consists of residuals generators resulting from the equation of marking evolution of the process and having appropriated structures to facilitate fault isolation.

Limits on surveillance: Frictions, fragilities and failures in the operation of camera surveillance

Public video surveillance tends to be discussed in either utopian or dystopian terms: proponents maintain that camera surveillance is the perfect tool in the fight against crime, while critics argue that the use of security cameras is central to the development of a panoptic, Orwellian surveillance society. This paper provides an alternative, more nuanced view. On the basis of an empirical case study, the paper explores how camera surveillance applications do not simply augment surveillance capacities, but rather have to deal with considerable uncertainties in the process of producing a continuous, effective, all‐seeing gaze. The case study shows that the actions of human operators and the operation of camera technologies each place limits on the execution of electronic visual surveillance, instead of efficiently enhancing the powers of the surveilling gaze. The analysis suggests that the effects of video surveillance are rather ambivalent and uncertain, thus showing that public camera systems are not simply beneficial or malign.

COPILOT—A PC-based expert system for reactor operational assistance using a Bayesian diagnostic module

Abstract This paper describes an expert system for nuclear plant operational assistance built around the idea of Bayesian diagnosis. A key feature of the system is its ability to handle uncertainties, human operator actions, and time-dependent or trajectory-type input information. The hierarchical structure of a sample knowledge base is shown for a typical pressurized water reactor, and numerical examples are presented.