Human Interface System Research Articles

Visual Style Elements in Human-System Interface Design for Nuclear Power Operations: Does Style Affect Performance and Preference?

As the world increasingly adopts renewable and sustainable energy systems, transitionary solutions include nuclear power, which currently provides 20% of the United States' electricity and is the largest single source of carbon-free electricity generation. Advanced reactors are a critical component of a carbon-free mixed energy portfolio that require careful design of first-of-a-kind control rooms. The application of Human Factors Engineering (HFE) is essential for scientific and iterative testing of novel human-system interface (HSI) concepts to ensure effective, efficient, and safe plant operations. Microworlds are simulators that use simplified physics models and control systems to distill nuclear power operations into essential functions. HFE scientists used the Rancor Microworld Simulator to obtain preference and performance metrics for novel and traditional static HSI design styles. Participants comprised advanced reactor company employees and nuclear industry consultants. A mixture of quantitative and qualitative data was captured. There was a preference for the basic graphical style that included high contrast and traditional color scheme elements. No single HSI design outperformed the others, and the participants did not perform better using their preferred HSI style. This experiment is the first in a series of HFE testing for HSIs in advanced reactor control room development. Clear user preferences emerged for elements within static displays. The cutting-edge neumorphic style was the least preferred. Future directions include tests of dynamic displays. HFE is used in evaluating and designing HSI devices that will improve the efficiency and safety of advanced nuclear power operations.

A Hybrid Architectural Model for Monitoring Production Performance in the Plastic Injection Molding Process

Monitoring production systems is a key element for identifying waste and production efficiency, and for this purpose, the calculation of the Key Performance Indicator (KPI) Overall Equipment Effectiveness (OEE) is validly recognized in the scientific literature. The collection and analysis of the cause of the interruption of the plants is particularly useful in this sense. The use of Internet of Things (IoT) technology in order to automate data collection for the purpose of calculating the OEE and the causes of interruption is effective. Furthermore, the existing literature lacks research studies that aim to improve the data quality of important process data that cannot be collected automatically. This study proposes the use of IoT technologies to request targeted and intelligent information inputs from the operators directly involved in the process, improving the completeness and accuracy of the information through the real-time and smart combination of manual and automated data. The Business Process Model and Notation (BPMN) methodology was used to analyze and redesign the collection data process and define the architectural model with a deep knowledge of the specific process. The proposed architecture, designed for application to a plastic injection molding production line, comprises several elements: the telemetry of the injection molding machine, an intervention request system, an intervention tracking system, and a human–system interface. Furthermore, a dashboard was developed using the Power BI software, 2.122.746.0 version, to analyze the information collected. Reducing the randomness of manual data makes it possible to direct production efficiency efforts more effectively, helping to reduce waste and production costs. Reducing production costs appears to be strongly linked to reducing environmental impacts, and future studies will be able to quantify the benefits obtained from the solution in terms of environmental impact.

Prediction of LOCA’s break size and location based on random forest and Multi Tasking Deep Neural Network

This work presents an approach for predicting break size and location in nuclear power plants (NPP) loss of coolant accidents (LOCA) with or without emergency shutdown of a nuclear reactor (SCRAM). To accomplish that, a multi-tasking deep neural network (MTDNN) model is proposed. The training and validation data sets were generated on a PWR simulator at the Human-Systems Interface Laboratory (LABIHS) at Instituto de Engenharia Nuclear (IEN), Brazil. For each simulation, the time behaviors of more than a hundred state variables were recorded and a random forest (RF) algorithm was applied to select the most relevant variables. Using the set of 15 variables selected, the MTDNN model was trained and applied to a test set, in which accuracy of 97.26 % for location and relative error of about 3.77 were obtained for break sizes between 0 and 1200 cm2.

Effects of Sleep Deprivation on Performance during a Change Signal Task with Adaptive Dynamics.

Augmented cognition, which refers to real-time modifications to a human-system interface to improve performance and includes dynamic task environments with automated adaptations, can serve to protect against performance impairment under challenging work conditions. However, the effectiveness of augmented cognition as a countermeasure for performance impairment due to sleep loss is unknown. Here, in a controlled laboratory study, an adaptive version of a Change Signal task was administered repeatedly to healthy adults randomized to 62 h of total sleep deprivation (TSD) or a rested control condition. In the computerized task, a left- or right-facing arrow was presented to start each trial. In a subset of trials, a second arrow facing the opposite direction was presented after a delay. Subjects were to respond within 1000 ms of the trial start by pressing the arrow key corresponding to the single arrow (Go trials) or to the second arrow when present (Change trials). The Change Signal Delay (CSD)-i.e., the delay between the appearance of the first and second arrows-was shortened following incorrect responses and lengthened following correct responses so that subsequent Change trials became easier or harder, respectively. The task featured two distinct CSD dynamics, which produced relatively stable low and high error rates when subjects were rested (Low and High Error Likelihood trials, respectively). During TSD, the High Error Likelihood trials produced the same, relatively high error rate, but the Low Error Likelihood trials produced a higher error rate than in the rested condition. Thus, sleep loss altered the effectiveness of the adaptive dynamics in the Change Signal task. A principal component analysis revealed that while subjects varied in their performance of the task along a single dominant dimension when rested, a second inter-individual differences dimension emerged during TSD. These findings suggest a need for further investigation of the interaction between augmented cognition approaches and sleep deprivation in order to determine whether and how augmented cognition can be relied upon as a countermeasure to performance impairment in operational settings with sleep loss.

A Quantitative Analysis of Chemical Plant Safety Based on Bayesian Network

Once a chemical production accident occurs in a chemical plant, it often causes serious economic losses, casualties, and environmental damage. Statistics show that many major accidents in the production and storage of chemicals are mainly caused by human factors. This article considers the influence of the human factor and proposes a quantitative analysis model of a chemical plant based on a Bayesian network. The model takes into account the main human factors in seven aspects: organization, information, job design, human system interface, task environment, workplace design, and operator characteristics. The Bayesian network modeling method and simulation were used to predict the safety quantitative value and safety level of the chemical plant. Using this model, we can quickly calculate the safe quantitative ratio of each factor in the chemical plant. Through the safety quantitative value, safety level, and sensitivity analysis, the safety hazards of chemical companies can be discovered. Immediate improvements of potential safety hazards in chemical plants are very effective in preventing major safety accidents. This model provides an effective method for chemical park managers to monitor and manage chemical plants based on quantitative safety data.

User involvement in design of a safety-critical operating panel system

The design of complex systems requires expertise of various kinds. In particular, expert users with operative experience can have much to say, even though their voices are always not been heard during design. In this paper, we provide an analysis of a real-life example, that is, the involvement of end-users in the design of a human-system interface for safety-critical operations of a nuclear power plant during a period extending over three decades. During this period, growing safety regulations and technological advances have been key motivators to design changes. At the same time, increasing expertise of operators makes them more proficient to comment those changes and engage in their implementation. In presenting empirical insights from several phases of the control room life cycle, we discuss the benefits, as well as the challenges, of user involvement in the design of complex systems and reflect on those in the light of our example case. Generally, we found that the user-designer interaction is most often built around concrete design artifacts such as mock-ups and prototypes and takes place later phases of design. Furthermore, user involvement often lacks rigor and depth that would help to comprehend the work activity and enable critical reflection of this generic understanding to foresee the overall vision for future work. A close look at historical turns of user involvement in one particular example provides insights that enable us to develop a general account of human-centered design in safety-critical domain.

Preliminary Siting, Operations, and Transportation Considerations for Licensing Fission Batteries in the United States

Nuclear energy is currently in the spotlight as a future energy source all over the world amid the global warming crisis. In the current state of miniaturization, through the development of advanced reactors, such as small modular reactors (SMRs) and micro-reactors, a fission battery is inspired by the idea that nuclear energy can be used by ordinary people using the “plug-and-play” concept, such as chemical batteries. As for design requirements, fission batteries must be economical, standardized, installed, unattended, and reliable. Meanwhile, the commercialization of reactors is regulated by national bodies, such as the United States (U.S.) Nuclear Regulatory Commission (NRC). At an international level, the International Atomic Energy Agency (IAEA) oversees the safe and peaceful use of nuclear power. However, regulations currently face a significant gap in terms of their applicability to advanced non-light water reactors (non-LWRs). Therefore, this study investigates the regulatory gaps in the licensing of fission batteries concerning safety in terms of siting, autonomous operation, and transportation, and suggests response strategies to supplement them. To figure out the applicability of the current licensing framework to fission batteries, we reviewed the U.S. NRC Title 10, Code of Federal Regulations (CFR), and IAEA INSAG-12. To address siting issues, we explored the non-power reactor (NPR) approach for site restrictions and the permit-by-rule (PBR) approach for excessive time burdens. In addition, we discussed how the development of an advanced human-system interface augmented with artificial intelligence and monitored by personnel for fission batteries may enable successful exemptions from the current regulatory operation staffing requirements. Finally, we discovered that no transportation regulatory challenge exists.

Operation validation system to prevent human errors in nuclear power plants

The importance of human and organizational factors in the nuclear industry has been emphasized, and along these lines there have been various improvements to human–system interfaces (HSIs). Operator support systems are one of the key HSIs that can contribute to reduce human errors. The system proposed in this work, called the concealed intelligent assistant (CIA), is designed as an operator support system using artificial intelligence algorithms. The CIA system validates operator actions and notifies operators of human errors to prevent adverse effects on plant integrity. The operation validation system consists of two-step filtering, namely through a procedural compliance check module and a comparison of safety impact evaluation module. Both modules employ a prediction algorithm with deep neural networks, where the former predicts the results of operators’ decision-making, and the latter implements plant parameter prediction strategies. The CIA system minimizes any increase in the cognitive burden on operators because it provides additional information only when a mistake is actually expected to adversely affect the integrity of the power plant.

Interpersonal Interface System of Multimedia Intelligent English Translation Based on Deep Learning

Artificial intelligence is a very challenging science, and people who are engaged in this work must understand computer knowledge, psychology, and philosophy. Artificial intelligence includes a wide range of sciences; it is composed of different fields, such as machine learning and computer vision. In recent years, with the rise and joint drive of technologies such as the Internet, big data, the Internet of Things, and voice recognition, the rapid development of artificial intelligence technology has presented new features such as deep learning, cross-border integration, and human-machine collaboration. Intelligent English translation is an innovation and experiment of the English language industry in the field of science and technology. Machine translation is the use of computer programmes to translate natural language into a specific natural language. This interdisciplinary subject includes three subjects: artificial intelligence, computational linguistics, and mathematical logic. Machine translation is becoming more and more popular with the public, and its advantages are mainly that machine translation is much cheaper than manual translation. From this study, it can be seen that the accuracy rate of the traditional ICTCLAS method is 76.40%, while the accuracy rate of the research method in this article is 94.58%, indicating that the research method used in this article is better. Machine translation greatly reduces the cost of translation because there are very few processes that require human participation, and the translation is basically done by computers. It is also an important research content in the field of English major research. Due to the wide range of language cultures and the influence of local culture, thought, and semantic environment, traditional human translation methods have many shortcomings. Nowadays, the demand for translation is unsatisfactory. Based on this, this paper analyzes the application status of intelligent translation, the prospect of intelligent translation, and realizes the optimization design of the human interface system for the intelligent translation of English. Through experiments, it is found that the multimedia intelligent English translation system based on deep learning not only improves the accuracy of English translation but also greatly improves the efficiency of people learning English.

Nuclear Operating Experience Review and User Needs Assessment for a Risk-informed Predictive Maintenance Human System Interface

The U.S. commercial nuclear industry is facing increasing economic pressure due to low-cost alternatives such as natural gas combined-cycle combustion power plants and renewable energy technologies such as wind and solar. More efficient and intelligent operations and maintenance strategies can yield substantial economic gains through a proposed risk-informed predictive maintenance strategy. The strategy leverages plant process, maintenance, and vibration data with advanced data analytics to monitor and predict condition degradations in support of preventative maintenance. This paper documents the operating experience review of the existing monitoring and diagnostic process. A semi-structured series of interviews assessed current practices, tools, shortcomings, and anlaysts’ needs to support the development of a human system interface (HSI) for the advanced data analytics approach. The results of the interviews are reported as feature requirements for the proposed HSI system which is actively under development as part of this ongoing project.

Human-System Interface with Explanation of Actions for Autonomous Anti-UAV Systems

Research on explanation is currently of intense interest as documented in the DARPA 2021 investments reported by the USA Department of Defense. An emerging theme for explanation techniques research is their application to the improvement of human-system interfaces for autonomous anti-drone or C-UAV defense systems. In the present paper a novel proposal based on natural language processing technology concerning explanatory discourse using relations is briefly described. The proposal is based on the use of relations pertaining to the possible malicious actions of an intruding alien drone swarm and the defense decisions proposed by an autonomous anti-drone system. The aim of such an interface is to facilitate the supervision that a user must exercise on an autonomous defense system in order to minimize the risk of wrong mitigation actions and unnecessary spending of ammunition.

Developing an Integrated Energy System Interface for Electricity-Hydrogen Hybrid Nuclear Operations

Nuclear power plants are looking towards integrated energy systems to address the challenges faced by increasing competition from renewable energy and cheap natural gas in wholesale electricity markets. Electricity-hydrogen hybrid operations is one potential technology being explored. As part of this investigation a human factors team was integrated into the overall engineering project to develop a human system interface (HSI) for a novel system to extract steam for a coupled hydrogen production process. This paper presents the process used to perform the nuclear specific human factors engineering required to develop the HSI for this novel and unprecedented system. Furthermore, the early integration of the human factors team and the meaningful improvements to the engineering of the system itself in addition to the successful development of the HSI for this particular application are described. Lastly, the HSI developed is presented to demonstrate the culmination of the process and disseminate a potential HSI design for electricity-hydrogen hybrid operations that may be useful for others exploring similar integrated energy systems concepts.

Rancor Hybrid Energy System Microworld

Transitioning from fossil fuels to renewable energy sources will require a diverse energy portfolio to ensure a reliable and resilient electrical grid. Renewable sources are proliferating but are intermittent and low periods of low production must be offset by other energy generators. When renewable demand is high baseload generators must scale back or utilize energy for other sources such as hydrogen production. Hybrid energy systems such as nuclear thermolysis hydrogen production could play a critical role for our energy future. Hydrogen is critical for manufacturing fertilizer as well as other industrial processes. Here we describe the development of a human system interface for a micro-reactor thermal storage hydrogen production system. The development of the interface parallels the engineering of a physical test-loop known as the Thermal Energy Delivery System (TEDS) at Idaho National Laboratory.

Use of HFE Guidance for the Review of Nuclear Power Plants

The U.S. Nuclear Regulatory Commission (NRC) evaluates the human factors engineering (HFE) of nuclear power plant design and operations to protect public health and safety. The HFE safety reviews encompass both the design process and its products. The NRC staff performs the reviews using the detailed guidance contained in two key documents: the HFE Program Review Model (NUREG-0711) and the Human-System Interface Design Review Guidelines (NUREG-0700). This paper will describe these two documents and the method used to develop them. As the NRC is committed to the periodic update and improvement of the guidance to ensure that they remain state-of-the-art design evaluation tools, we will discuss the topics being addressed in support of future updates as well.

The application of cloud model combined with nonlinear fuzzy analytic hierarchy process for the safety assessment of chemical plant production process

According to statistics, majority of chemical accidents were caused by human factors during the production and storage of chemicals. From the perspective of human factors, a safety assessment method for chemical plant production process based on cloud model combined with nonlinear fuzzy analytic hierarchy process is proposed in this paper. The method comprehensively considers the safety influencing elements of human factors in seven aspects: organization, information, job design, human system interface, task environment, workplace design, and operator characteristics. We use the analytic hierarchy process to establish a hierarchical structure system. Then use the triangular fuzzy numbers to quantify the indicators and the logarithmic fuzzy preference programming method to calculate the index weights. Finally, the cloud model is applied to determine the safety level of the chemical plant production process. The method is simulated and verified simultaneously. The simulation results showed that the method is reliable, practical and scientific as a safety assessment method for chemical production.

Widget-based Alarm Recognition and a Management Support Interface System in Nuclear Power Plants

As small modular reactors (SMRs) and various computerized operating support systems have been developed, visual display unit(VDU)-based multifunctional consoles have been proposed. The more a console depends on VDUs, the more human system interface (HSI) designers need to consider the decrease in the human performance caused by the effect of navigation tasks. This paper proposes a widget-based alarm recognition and management support interface system to increase the operator’s ability to recognize alarms and to manage alarm-related tasks by reducing additional and excessive navigation tasks. With a small display screen, the system provides operators with quick and accurate access to alarm-related information and allows them to make corrective responses.

ANALISIS PERBANDINGAN PELACAKAN OBJEK MENGGUNAKAN OPTICAL FLOW DAN BACKGROUND ESTIMATION PADA KAMERA BERGERAK

Object tracking is one of computer vision. Can be developed into various based applications like human computers interface, video compression and security system. Object tracking is used to identified objects within background and identify the number of objects that a cross. Algorithm for this object tracking use optical flow method and background estimation. Testing is carried out using a moving camera placed in a car. It's using parameter values for each Algorithm. The test is used three videos from the Matlab. Simulink Profile Report that optical flow method had Recorded Total Time better than the background estimation with 100 seconds duration. The optical flow Testing method successfully identified the car object. And background testing didn't succeed in identified and to differentiate an object to it’s background. The object test recorded from the distance with a camera, to examined how many the background was and the speed of cars.

Human Performance Benefits of The Automation Transparency Design Principle : Validation and Variation.

Test the automation transparency design principle using a full-scope nuclear power plant simulator. Automation transparency is a long-held human factors design principle espousing that the responsibilities, capabilities, goals, activities, and/or effects of automation should be directly observable in the human-system interface. The anticipated benefits of transparency include more effective reliance, more appropriate trust, better understanding, and greater user satisfaction. Transparency has enjoyed a recent upsurge in use in the context of human interaction with agent-oriented automation. Three full-scope nuclear power plant simulator studies were conducted with licensed operating crews. In the first two experiments, transparency was implemented for interlocks, controllers, limitations, protections, and automatic programs that operate at the local component level of the plant. In the third experiment, procedure automation assumed control of plant operations and was represented in dedicated agent displays. Results from Experiments 1 and 2 appear to validate the human performance benefits of automation transparency for automation at the component level. However, Experiment 3 failed to replicate these findings for automation that assumed control for executing procedural actions. Automation transparency appears to yield expected benefits for component-level automation, but caution is warranted in generalizing the design principle to agent-oriented automation. The automation transparency design principle may offer a powerful means of compensating for the detrimental impacts of hidden automation influence at the component level of complex systems. However, system developers should exercise caution in assuming that the principle extends to agent-oriented automation.

Exploring The Use of Cognitive Models for Nuclear Power Plant Human-System Interface Evaluation

Many of the United States’ commercial fleet of nuclear power plants (NPPs) are approaching the end of their operating licenses. To extend the life of these plants, advanced human-system interface (HSI) technologies are being researched to address aging and reliability concerns with existing legacy systems. Human factors engineering (HFE) a critical role in ensuring these technologies are designed in a way that does not introduce new failure modes and promotes optimal human-system performance. An important focus of HFE in NPP modernization is early involvement to inform design of the HSI. This includes traditional formative evaluations, which are done to collect design feedback. While these evaluations are useful, they are limited in providing convincing quantitative data for efficiency of use. This paper discusses the use of cognitive models to provide quantitative data early in the HSI design process. A comparison is made of three open-source and readily accessible cognitive modeling tools.

Small Modular Reactor Human-System Interface and Control Room Layout Design

The NuScale Small Modular Reactor (SMR) is premised on well-established nuclear technology principles with a focus on integration of components, simplification or elimination of systems, automation, and use of passive safety features. Traditional nuclear power plants have in some cases operated up to four modules from a single control room. Due to the unique nontraditional operating characteristics of this technology a state-of-the art control room design was needed to ensure proper staffing totals for monitoring and control of multiple modules (twelve) from a single control room. To accomplish this, the human system interface and control room layout must translate the functional and task requirements needed for safe operation of the plant into the detailed design of workstations, alarms, controls, navigation, and other needs of the control room operations staff.