Journal articles: 'Experiment automation'

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Sun, Huan, and Hong Li Liu. "Research and Construction of Automation Comprehensive Experimental Platform." Applied Mechanics and Materials 687-691 (November 2014): 134–37. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.134.

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According to the automation and related professional laboratory present situation, combining with the main courses of teaching content, an automation integrated innovation experimental teaching platform is designed based on the current situation of the laboratory for automation specialty and related majors. This platform integrates advanced theory and technology of sensor, measurement computer control,information engineering and communication engineering, which can complete the basic experiment of main courses, but also develop some comprehensive, professional design and innovative experiments. So the platform can maximize the effect of teaching, experiment and research.

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Gross, Thomas, Kevin Anderson, and Nolan Tsuchiya. "Programmable Automation Controller Mechatronic Experiment." IAES International Journal of Robotics and Automation (IJRA) 6, no. 1 (March 1, 2017): 39. http://dx.doi.org/10.11591/ijra.v6i1.pp39-48.

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This paper describes the use of the OPTO-22 Programmable Automation Controller (PAC) Learning center in the undergraduate control systems course at California State Polytechnic University at Pomona (Cal Poly Pomona). The OPTO-22 PAC System is an integrated system of hardware and software used for industrial control, remote monitoring, and data acquisition applications. The paper compares the pros and cons of using a PAC versus Programmable Logic Controller (PLC) or Field Programmable Gate Array (FPGA) systems. The paper introduces the flowchart based programming environment used in PACs. The paper includes an illustrative example of how the OPTO-22 PAC system can be interfaced to an industrial based Mechatronics pick-and-place robot station. This example details the input/output interfaces of the OPTO-22 PAC unit and the SUN Equipment Mecahtronics pick and place robot unit. Details of the flow chart programming and I/O interfacing protocols are given in the paper. The I/O configuration dialog in the OPTO-22 PAC development environment are also presented in this paper.

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Bass, Ellen J., Leigh A. Baumgart, and Kathryn Klein Shepley. "The Effect of Information Analysis Automation Display Content on Human Judgment Performance in Noisy Environments." Journal of Cognitive Engineering and Decision Making 7, no. 1 (August 10, 2012): 49–65. http://dx.doi.org/10.1177/1555343412453461.

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Displaying both the strategy that information analysis automation employs to makes its judgments and variability in the task environment may improve human judgment performance, especially in cases where this variability impacts the judgment performance of the information analysis automation. This work investigated the contribution of providing either information analysis automation strategy information, task environment information, or both, on human judgment performance in a domain where noisy sensor data are used by both the human and the information analysis automation to make judgments. In a simplified air traffic conflict prediction experiment, 32 participants made probability of horizontal conflict judgments under different display content conditions. After being exposed to the information analysis automation, judgment achievement significantly improved for all participants as compared to judgments without any of the automation’s information. Participants provided with additional display content pertaining to cue variability in the task environment had significantly higher aided judgment achievement compared to those provided with only the automation’s judgment of a probability of conflict. When designing information analysis automation for environments where the automation’s judgment achievement is impacted by noisy environmental data, it may be beneficial to show additional task environment information to the human judge in order to improve judgment performance.

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de Visser, Ewart, and Raja Parasuraman. "Adaptive Aiding of Human-Robot Teaming." Journal of Cognitive Engineering and Decision Making 5, no. 2 (June 2011): 209–31. http://dx.doi.org/10.1177/1555343411410160.

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In many emerging civilian and military operations, human operators are increasingly being tasked to supervise multiple robotic uninhabited vehicles (UVs) with the support of automation. As 100% automation reliability cannot be assured, it is important to understand the effects of automation imperfection on performance. In addition, adaptive aiding may help counter any adverse effects of static (fixed) automation. Using a high-fidelity multi-UV simulation involving both air and ground vehicles, two experiments examined the effects of automation reliability and adaptive automation on human-system performance with different levels of task load. In Experiment 1, participants performed a reconnaissance mission while assisted with an automatic target recognition (ATR) system whose reliability was low, medium, or high. Overall human-robot team performance was higher than with either human or ATR performance alone. In Experiment 2, participants performed a similar reconnaissance mission with no ATR, static automation, or with adaptive automation keyed to task load. Participant trust and self-confidence were higher and workload was lower for adaptive automation compared with the other conditions. The results show that human-robot teams can benefit from imperfect static automation even in high task load conditions and that adaptive automation can provide additional benefits in trust and workload.

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Wickens, Christopher D., Nicholas J. Fitzgerald, Benjamin A. Clegg, C. A. P. Smith, Dylan Orth, and Katie Kincaid. "Decision Aiding for Nautical Collision Avoidance: Trust, Dependence, and Implicit Understanding of the Decision Algorithm." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 1950–54. http://dx.doi.org/10.1177/1071181320641470.

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Two experiments examined imperfect automation decision aids for maritime collision avoidance. In Experiment 1, the algorithm was driven purely by safety, recommending turning your ship in the direction that produced the greatest separation from a hazard ship. In Experiment 2, the algorithm incorporated two additional factors known to influence ship collision avoidance maneuvers: efficiency and procedural adherence to established “rules of the road.” In both experiments, results revealed heavy but not total reliance on the aid. A strong influence of categorical rules of the road was indicated by compliance with recommendations in concurrence to those conventions, even as they reduced continuous parameters of safety, particularly in Experiment 2 when the rules were incorporated in the algorithm. This illustrates the powerful influence of categorical procedural algorithm elements over continuous quantitative ones in affecting automation compliance. Results also revealed the dissociation between rated trust in, and behavioral dependence on decision aiding automation.

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KAWAMOTO, Hiromichi, and Hitoshi SOMA. "Driving Simulator Experiment on Automation Surprise." Proceedings of the Transportation and Logistics Conference 2016.25 (2016): 1202. http://dx.doi.org/10.1299/jsmetld.2016.25.1202.

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Manzey, Dietrich, Juliane Reichenbach, and Linda Onnasch. "Human Performance Consequences of Automated Decision Aids." Journal of Cognitive Engineering and Decision Making 6, no. 1 (January 13, 2012): 57–87. http://dx.doi.org/10.1177/1555343411433844.

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Two experiments are reported that investigate to what extent performance consequences of automated aids are dependent on the distribution of functions between human and automation and on the experience an operator has with an aid. In the first experiment, performance consequences of three automated aids for the support of a supervisory control task were compared. Aids differed in degree of automation (DOA). Compared with a manual control condition, primary and secondary task performance improved and subjective workload decreased with automation support, with effects dependent on DOA. Performance costs include return-to-manual performance issues that emerged for the most highly automated aid and effects of complacency and automation bias, respectively, which emerged independent of DOA. The second experiment specifically addresses how automation bias develops over time and how this development is affected by prior experience with the system. Results show that automation failures entail stronger effects than positive experience (reliably working aid). Furthermore, results suggest that commission errors in interaction with automated aids can depend on three sorts of automation bias effects: (a) withdrawal of attention in terms of incomplete cross-checking of information, (b) active discounting of contradictory system information, and (c) inattentive processing of contradictory information analog to a “looking-but-not-seeing” effect.

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Nejatimoharrami, Farzad, Andres Faina, and Kasper Stoy. "New Capabilities of EvoBot: A Modular, Open-Source Liquid-Handling Robot." SLAS TECHNOLOGY: Translating Life Sciences Innovation 22, no. 5 (January 31, 2017): 500–506. http://dx.doi.org/10.1177/2472630316689285.

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We introduce a robot developed to perform feedback-based experiments, such as droplet experiments, a common type of experiments in artificial chemical life research. These experiments are particularly well suited for automation because they often stretch over long periods of time, possibly hours, and often require that the human takes action in response to observed events such as changes in droplet size, count, shape, or clustering or declustering of multiple droplets. Our robot is designed to monitor long-term experiments and, based on the feedback from the experiment, interact with it. The combination of precise automation, accurately collected experiment data, and integrated analysis and modeling software makes real-time interaction with the experiment feasible, as opposed to traditional offline processing of experiments. Last but not least, we believe the low cost of our platform can promote artificial life research. Furthermore, prevalently, findings from an experiment will inspire redesign for novel experiments. In addition, the robot’s open-source software enables easy modification of experiments. We will cover two case studies for application of our robot in feedback-based experiments and demonstrate how our robot can not only automate these experiments, collect data, and interact with the experiments intelligently but also enable chemists to perform formerly infeasible experiments.

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Zhang, Yijing, Jinfei Ma, Chunyang Pan, and Ruosong Chang. "Effects of automation trust in drivers’ visual distraction during automation." PLOS ONE 16, no. 9 (September 14, 2021): e0257201. http://dx.doi.org/10.1371/journal.pone.0257201.

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With ongoing improvements in vehicle automation, research on automation trust has attracted considerable attention. In order to explore effects of automation trust on drivers’ visual distraction, we designed a three-factor 2 (trust type: high trust group, low trust group) × 2 (video entertainment: variety-show videos, news videos) × 3 (measurement stage: 1–3) experiment. 48 drivers were recruited in Dalian, China for the experiment. With a driving simulator, we used detection-response tasks (DRT) to measure each driver’s performance. Their eye movements were recorded, and automation-trust scale was used to divide participants into high trust group and low trust group. The results show that: (1) drivers in the high trust group has lower mental workload and paid more attention to visual non-driving-related tasks; (2) video entertainment also has an impact on distraction behavior, variety-show videos catch more attention than news videos. The findings of the present study indicate that drivers with high automation trust are more likely to be involved in non-driving-related visual tasks.

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Krasovskii, A. A. "Experiment automation using digital signal processing methods." Automation and Remote Control 71, no. 10 (October 2010): 2224–31. http://dx.doi.org/10.1134/s0005117910100255.

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Shaw Stewart, Patrick, and Jochen Mueller-Dieckmann. "Automation in biological crystallization." Acta Crystallographica Section F Structural Biology Communications 70, no. 6 (May 28, 2014): 686–96. http://dx.doi.org/10.1107/s2053230x14011601.

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Crystallization remains the bottleneck in the crystallographic process leading from a gene to a three-dimensional model of the encoded protein or RNA. Automation of the individual steps of a crystallization experiment, from the preparation of crystallization cocktails for initial or optimization screens to the imaging of the experiments, has been the response to address this issue. Today, large high-throughput crystallization facilities, many of them open to the general user community, are capable of setting up thousands of crystallization trials per day. It is thus possible to test multiple constructs of each target for their ability to form crystals on a production-line basis. This has improved success rates and made crystallization much more convenient. High-throughput crystallization, however, cannot relieve users of the task of producing samples of high quality. Moreover, the time gained from eliminating manual preparations must now be invested in the careful evaluation of the increased number of experiments. The latter requires a sophisticated data and laboratory information-management system. A review of the current state of automation at the individual steps of crystallization with specific attention to the automation of optimization is given.

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Zhao, Gang, and Shi Huan Zhai. "The Design of Innovative Control Technology Integrated Experiment System for Engineering Applications." Applied Mechanics and Materials 378 (August 2013): 413–17. http://dx.doi.org/10.4028/www.scientific.net/amm.378.413.

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The design of innovative control technology integrated experiment system for engineering applications, which completed integrated experiment system design based on modern automation control technology. It contents regular control, intelligent electrical appliances, programmable logic controller (PLC), inverter, industrial control computer automation technology card. This system represents modern control technology in electrical engineering application, and it can complete overview of the teaching experiment such as motor control, electric control technology, programmable control technology, man-machine interface configuration technology, PLC control technology based on personal computer soft logic, computer control technology and industrial control system design, and this work can expand for modern automation technology, a open and development of scientific research platform.

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Dolgov, Igor, and Jeremy D. Schwark. "Acquiescence Bias in Aided Visual Search." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 1123–27. http://dx.doi.org/10.1177/1541931218621257.

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This study looked for evidence of acquiescence bias, a tendency to agree rather than disagree, in human-automation interaction. This bias has not previously been identified in automation use and its existence could help explain operator mistakes made when interacting with automated decision aids. In the first two experiments, it was as beneficial to agree with the aid in one condition and it was beneficial to disagree with the aid in the other. The third experiment gave participants a 50% accurate aid, which offered them no meaningful help. Experiments 1A and 1B revealed that participants performed better when it was beneficial to agree rather than beneficial to disagree with the aid. Experiment 2 revealed that the aid influenced participants’ search strategies even though they were told that the aid was as helpful as a coin flip. The findings suggest that participants display acquiescence bias when making decisions with an automated aid.

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Ruff, Heath, Gloria Calhoun, Elizabeth Frost, Kyle Behymer, and Jessica Bartik. "Comparison of Adaptive, Adaptable, and Hybrid Automation for Surveillance Task Completion in a Multi-Task Environment." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 155–59. http://dx.doi.org/10.1177/1541931218621036.

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Increasing applications of automation in system designs raise issues on how to achieve an ideal balance of automation with human interaction for optimal operator situation awareness and performance. This experiment examined four automation control schemes applied to surveillance tasks performed in a multi-task simulation environment. Participants completed trials with four level of automation (LOA) control schemes: fixed, performance-based adaptive, adaptable (participant controls LOA), and hybrid (adaptive and adaptable). Results showed that task accuracy was better when participants controlled LOA. Participants also preferred adaptable automation.

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Moray, Neville, Toshiuki Inagaki, and Makoto Itoh. "Allocation of Function, Adaptive Automation, and Fault Management." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 42, no. 3 (October 1998): 277–79. http://dx.doi.org/10.1177/154193129804200320.

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Sheridan's “Levels of Automation” were explored in an experiment on fault management of a continuous process control task which included situation adaptive automation. Levels of automation with more or less automation autonomy, and different levels of advice to the operator were compared, with automatic diagnosis whose reliability varied. The efficiency of process control and of fault management were explored under human control and automation in fault management, and aspects of the task in which human or automation were the more efficient defined. The results are related to earlier work on trust and self confidence in allocation of function by Lee, Moray, and Muir.

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Dikmen, Murat, Yeti Li, Philip Farrell, Geoffrey Ho, Shi Cao, and Catherine Burns. "The Effects of Automation and Role Allocation on Team Performance." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 235–39. http://dx.doi.org/10.1177/1071181319631501.

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An experiment was conducted to investigate the effects of automation and role allocation on performance in a simulated picture compilation task with fourteen two-person student teams. In the absence of automation support, the system integrated sensor information. In the presence of automation support, the system both integrated sensor information and identified contacts. Roles were assigned either based on warfare domain or geographical sectors. Results showed that human-automation system performance was similar in two automation conditions, but participants were slower in classifying tracks and overall classified fewer tracks when the automation was present. We conclude that working with automation may lead to degraded team performance due to complacency and additional task complexity.

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Xavier, E. M. A., F. J. Ariza-López, and M. A. Ureña-Cámara. "WEB SERVICE FOR POSITIONAL QUALITY ASSESSMENT: THE WPS TIER." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W5 (August 20, 2015): 257–62. http://dx.doi.org/10.5194/isprsannals-ii-3-w5-257-2015.

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In the field of spatial data every day we have more and more information available, but we still have little or very little information about the quality of spatial data. We consider that the automation of the spatial data quality assessment is a true need for the geomatic sector, and that automation is possible by means of web processing services (WPS), and the application of specific assessment procedures. In this paper we propose and develop a WPS tier centered on the automation of the positional quality assessment. An experiment using the NSSDA positional accuracy method is presented. The experiment involves the uploading by the client of two datasets (reference and evaluation data). The processing is to determine homologous pairs of points (by distance) and calculate the value of positional accuracy under the NSSDA standard. The process generates a small report that is sent to the client. From our experiment, we reached some conclusions on the advantages and disadvantages of WPSs when applied to the automation of spatial data accuracy assessments.

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Kingsley Ogueri Chilakpu. "Automation of bio-digester stirring system using locally available materials." Global Journal of Engineering and Technology Advances 5, no. 2 (November 30, 2020): 005–13. http://dx.doi.org/10.30574/gjeta.2020.5.2.0082.

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An anaerobic bio-digester with electronically controlled stirring system was fabricated and tested using locally available materials. The experiment was conducted on a laboratory scale in Federal University of Technology Owerri. Cow dung obtained from an abattoir in Owerri and household biodegradable waste randomly collected from residential homes and eateries were used as the feedstock for this work. The feedstock was pre-fermented for a period of 10 days in an airtight bag before it was mixed with water in the ratio of 1:2 to form slurry that was fed into the digester. An Arduino system controlled timer with LED display screen was designed to control the on/off signal of a 2Hp electric motor powering the stirring arms. The programmable stirring timer device was set to run the electric motor for 30minutes at an interval of four hours to allow for even distribution of nutrients and microbes in the bio-digester. A control experiment was also carried out with non-automated stirring system. . It was observed that gas production in the control experiment reached its peak in fourteen days and the period was mired by fluctuating and less volume of gas production when compared to the main experiment where the gas production increased appreciably and steadily with maximum volume recorded on the tenth and eleventh day as contained in the results obtained. Model equations were generated for the two experiments and the wide variation in values of determinant factors (R2) in the two experiments is an indication that the automated stirring system with R2 = 0.97 performed better than the manually timed stirred trials with R2=0.88.

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Liu, Wen Lai. "Research on a Novel Automation Software Reliability Detection Method." Applied Mechanics and Materials 556-562 (May 2014): 2928–31. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2928.

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The reliability detection method for the traditional large-scale automation software is based on the module design principle of the automation software which detects the reliability features one by one. It does not consider the concurrent reliable chain problems for the automation software which cause the low detection accuracy. The paper proposes a novel automation software system reliability detection method based on the path-based interfaces. The detection model integrates the features of the automation software. The established stochastic points process and state probability transition diagram overcome the shortcomings of the traditional large-scale automation software reliability detection methods. The experiment results illustrate the improved methods can increase the detection accuracy of the large-scale automation software which can be widely applied.

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Bartlett, Megan L., and Jason S. McCarley. "Benchmarking Aided Decision Making in a Signal Detection Task." Human Factors: The Journal of the Human Factors and Ergonomics Society 59, no. 6 (March 15, 2017): 881–900. http://dx.doi.org/10.1177/0018720817700258.

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Objective: A series of experiments examined human operators’ strategies for interacting with highly (93%) reliable automated decision aids in a binary signal detection task. Background: Operators often interact with automated decision aids in a suboptimal way, achieving performance levels lower than predicted by a statistically ideal model of information integration. To better understand operators’ inefficient use of decision aids, we compared participants’ automation-aided performance levels with the predictions of seven statistical models of collaborative decision making. Method: Participants performed a binary signal detection task that asked them to classify random dot images as either blue or orange dominant. They made their judgments either unaided or with assistance from a 93% reliable automated decision aid that provided either graded (Experiments 1 and 3) or binary (Experiment 2) cues. We compared automation-aided performance with the predictions of seven statistical models of collaborative decision making, including a statistically optimal model and Robinson and Sorkin’s contingent criterion model. Results and Conclusion: Automation-aided sensitivity hewed closest to the predictions of the two least efficient collaborative models, well short of statistically ideal levels. Performance was similar whether the aid provided graded or binary judgments. Model comparisons identified potential strategies by which participants integrated their judgments with the aid’s. Application: Results lend insight into participants’ automation-aided decision strategies and provide benchmarks for predicting automation-aided performance levels.

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Boubin, Jayson G., Christina F. Rusnock, and Jason M. Bindewald. "Quantifying Compliance and Reliance Trust Behaviors to Influence Trust in Human-Automation Teams." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 750–54. http://dx.doi.org/10.1177/1541931213601672.

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Automation is utilized heavily in many domains to increase productivity. With new, more complex automation, like the self-driving car, humans will be required to forego direct task performance in favor of maintaining a supervisory role over automation systems. While the use of these systems generally results in greater performance than humans performing alone, humans are reluctant to adopt these superior systems due to a lack of trust. The United States Department of Defense is investigating trust in automation in order to influence the rate of adoption of automation technology. Studying trust in automation systems requires a mechanism for quantifying and measuring trust. This paper proposes a method for measuring human trust behaviors with regard to human-automation systems through response rates of compliance and reliance. Using behavioral data from a human-subjects experiment involving automated agents, we create a system dynamics model which relates trust to other system level variables. Using this trust model, engineers will be able to study trust in human-automation team scenarios in order to design automation systems with higher rates of adoption.

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Kim, D. J., and Z. Fisk. "A LabVIEW based template for user created experiment automation." Review of Scientific Instruments 83, no. 12 (December 2012): 123705. http://dx.doi.org/10.1063/1.4770121.

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Morar, Natan, and Chris Baber. "Joint Human-Automation Decision Making in Road Traffic Management." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 385–89. http://dx.doi.org/10.1177/1541931213601578.

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In this paper we explore automation bias in terms of joint decision making between humans and automation. In an experiment, participants made decisions, and indicated the reason for this decision, in a road traffic monitoring task with the aid of automation of varying reliability (i.e., 25% or 81%). Reliability level had a clear impact on the user’s behavior: at low reliability, participants ignored automation suggestion and rely on their own decision making, whereas in the high reliability condition, participants tended to accept the automation suggestion (even if this was incorrect). Overall, performance is higher as a result of the human intervention that would be expected from automation alone, i.e., accuracy is in the region of 87-96% on all conditions. Performance is affected by the order in which the human and automation give their answers and how much detail they are required to provide. We consider these results in terms of a theory of joint decision making. [156 words]

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Volz, Katherine, Euijung Yang, Rachel Dudley, Elizabeth Lynch, Maria Dropps, and Michael C. Dorneich. "An Evaluation of Cognitive Skill Degradation in Information Automation." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (September 2016): 191–95. http://dx.doi.org/10.1177/1541931213601043.

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The purpose of this research is to investigate long term effects of cognitive skill degradation through the use of automation. Even though advanced studies have looked into information automation (IA) in aviation, the amount of empirical data on the effects of these systems on the retention of cognitive skills is less deeply examined. Measurement and analysis of the effects of IA on cognitive performance is an important first step in understanding cognitive skill degradation, which should be considered during the design of these systems. The use of an automation aid is expected to result in a high level of performance degradation over time. Participants were randomly placed into three experimental groups (manual, alternating, or automation) and asked to perform flight planning calculations as an experiment task. Participants performed the task five times, once every two weeks. The manual group used the manual method throughout the experiment, the alternating group switched between the manual and automated method every trial. The automation group used the manual method for the first trial, the automated method for the three consecutive trials and then went back to using the manual method during the last trial. The automation group showed the most performance degradation and highest workload, while the alternating group presented reduced performance degradation and workload, and the manual group showed the least performance degradation and workload. This work provides the foundation for the design of guidelines and recommendations for IA systems in order to prevent cognitive skill degradation.

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Mosier, Kathleen L., Linda J. Skitka, Mark D. Burdick, and Susan T. Heers. "Automation Bias, Accountability, and Verification Behaviors." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 40, no. 4 (October 1996): 204–8. http://dx.doi.org/10.1177/154193129604000413.

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Automated procedural and decision aids may in some cases have the paradoxical effect of increasing errors rather than eliminating them. Results of recent research investigating the use of automated systems have indicated the presence automation bias, a term describing errors made when decision makers rely on automated cues as a heuristic replacement for vigilant information seeking and processing (Mosier & Skitka, in press). Automation commission errors, i.e., errors made when decision makers take inappropriate action because they over-attend to automated information or directives, and automation omission errors, i.e., errors made when decision makers do not take appropriate action because they are not informed of an imminent problem or situation by automated aids, can result from this tendency. A wide body of social psychological research has found that many cognitive biases and resultant errors can be ameliorated by imposing pre-decisional accountability, which sensitizes decision makers to the need to construct compelling justifications for their choices and how they make them. To what extent these effects generalize to performance situations has yet to be empirically established. The two studies presented represent concurrent efforts, with student and “glass cockpit” pilot samples, to determine the effects of accountability pressures on automation bias and on verification of the accurate functioning of automated aids. Students (Experiment 1) and commercial pilots (Experiment 2) performed simulated flight tasks using automated aids. In both studies, participants who perceived themselves “accountable” for their strategies of interaction with the automation were significantly more likely to verify its correct functioning, and committed significantly fewer automation-related errors than those who did not report this perception.

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Ruiz-Lendínez, Juan José, Francisco Javier Ariza-López, and Manuel Antonio Ureña-Cámara. "Expert Knowledge as Basis for Assessing an Automatic Matching Procedure." ISPRS International Journal of Geo-Information 10, no. 5 (May 2, 2021): 289. http://dx.doi.org/10.3390/ijgi10050289.

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The continuous development of machine learning procedures and the development of new ways of mapping based on the integration of spatial data from heterogeneous sources have resulted in the automation of many processes associated with cartographic production such as positional accuracy assessment (PAA). The automation of the PAA of spatial data is based on automated matching procedures between corresponding spatial objects (usually building polygons) from two geospatial databases (GDB), which in turn are related to the quantification of the similarity between these objects. Therefore, assessing the capabilities of these automated matching procedures is key to making automation a fully operational solution in PAA processes. The present study has been developed in response to the need to explore the scope of these capabilities by means of a comparison with human capabilities. Thus, using a genetic algorithm (GA) and a group of human experts, two experiments have been carried out: (i) to compare the similarity values between building polygons assigned by both and (ii) to compare the matching procedure developed in both cases. The results obtained showed that the GA—experts agreement was very high, with a mean agreement percentage of 93.3% (for the experiment 1) and 98.8% (for the experiment 2). These results confirm the capability of the machine-based procedures, and specifically of GAs, to carry out matching tasks.

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Varnon, Christopher A., and Charles I. Abramson. "The Propeller Experiment Controller: Low-Cost Automation for Classroom Experiments in Learning and Behavior." Comprehensive Psychology 2 (April 6, 2012): 07.08.IT.2.2. http://dx.doi.org/10.2466/07.08.it.2.2.

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Alam, M. Rabiul, and Alam S. M. Jahangir. "Data Automation System by Using Computer Controlled Stepper Motor." Applied Mechanics and Materials 197 (September 2012): 482–88. http://dx.doi.org/10.4028/www.scientific.net/amm.197.482.

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Computer Controller has found their place in many different applications. A Controller can be a good choice whenever controlled movement is required. This paper presents the usage of the applications of stepper motors in different areas including printers, plotters, high-end office equipment, hard disk drives, medical equipment, fax machines, robotic, washing machine, power generator automation, satellite automation, sensing automation, bio-sensing systems, earthquake measurement any automotive and many more. Practical experiment shows that when Controller is connected with parallel port, it can be controlled by the computer.

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Foroughi, Cyrus K., Ericka Rovira, Kaley Rose, DaShawn Davis, Jaritzel J. Jurado, Jongbum Chae, Rich Pak, Noelle L. Brown, Ciara Sibley, and Joseph T. Coyne. "How do Individual Differences in Attention Control and Working Memory Capacity Relate to Monitoring Automation?" Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 1409–13. http://dx.doi.org/10.1177/1071181319631196.

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With the proliferation of automated tasks, software, and systems, humans are moving from an active participant in the function of a task to a passive monitor of an automated system that is completing that task. Unfortunately, humans are not well-suited for monitoring roles and there is a need to better understand the factors involved when humans successfully identify when an automated system fails. The goal for this research was to determine whether individual differences in attention control (as measured by the anti-saccade task) and working memory capacity (as measured by the shortened operation span) related to an individual’s ability to detect automation failures. In experiment 1, there was a significant positive relationship ( r = .31) between scores on the anti-saccade task and the number of automation failures that participants detected. In experiment 2, there was a significant positive relationship ( r = .32) between scores on the shortened operation span and the number of automation failures that participants’ detected. The results suggest that certain individuals are better suited for detecting automation failures. Selecting for these individuals may be a fruitful endeavor as automated systems continue to grow across society.

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Pan, Wei Jun, Wen Bo Wang, Dan Wu, and Chen Yu Huang. "Research of Human Factors under Automatic ATC System." Applied Mechanics and Materials 513-517 (February 2014): 2799–803. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2799.

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This paper is to assess the impact of ATC automation on controller workload, situation awareness, teamwork and mutual trust, and solve the relationship between human and ATC automation system collaboration. Based on experiment statistics, the method of mathematical statistics has been used to carry on overall analysis, significance analysis and correlation analysis. As a result, in the new generation ATC automation system, the overall level of controllers workload, situation awareness, teamwork and mutual trust is relatively higher than that under traditional condition, along with the increase of the controller work experience, the teamwork consciousness and situation awareness are stronger, the workload is lower, the ATC automation system of mutual trust is higher. Teamwork, workload and mutual trust have a significant positive correlation.

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Mazur-Milecka, Magdalena, and Antoni Nowakowski. "Comparison of Tracking Methods in Respect of Automation of an Animal Behavioral Test." Metrology and Measurement Systems 18, no. 1 (January 1, 2011): 91–104. http://dx.doi.org/10.2478/v10178-011-0009-0.

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Comparison of Tracking Methods in Respect of Automation of an Animal Behavioral TestAutomation in experiments carried out on animals is getting more and more important in research. Computers take over laborious and time-consuming activities like recording and analysing images of the experiment scene. The first step in an image analysis is finding and distinguishing between the observed animals and then tracking all objects during the experiment. In this paper four tracking methods are presented. Quantitative and qualitative figures of merit are applied to confront those methods. The comparison takes into consideration the level of correct object recognition during different disturbances, the speed of computation, requirements as to the frame rate and image illumination, quality of recovering from occluded situations and others.

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Jia, Yang, Qiang Fu, Du Shi Ma, and Ming Yang Zhu. "Power Distribution Automation System in Green Power Engineering." Applied Mechanics and Materials 340 (July 2013): 1034–38. http://dx.doi.org/10.4028/www.scientific.net/amm.340.1034.

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Not only distribution automation system but the principle of the existing means of communication are studied systematically and distribution automation communication system model based on IP network is provided. Backbone network is set between master station in the control center and substation sub-station. Communication between electronic station and terminal connections rely on the branch network. Simulation experiment shows the test of data traffic and network delay of IP communications network. In the actual network environment the data refresh meet the application requirements. So the program on the improvement of distribution automation communication in this article is feasible.

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Trifonov, Roumen, Radoslav Yoshinov, Slavcho Manolov, Georgi Tsochev, and Galya Pavlova. "Artificial Intelligence methods suitable for Incident Handling Automation." MATEC Web of Conferences 292 (2019): 01044. http://dx.doi.org/10.1051/matecconf/201929201044.

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The Faculty of Computer Systems and Technology at Technical University – Sofia undertook analyses and experiments on the use of Artificial Intelligence methods in the field of Information Security. In the course of the study a Cyber Defence related to the classification consisting of three phases has been proposed: Operative Cyber Intelligence, Tactical Cyber Intelligence and Incident Handling. It has been found that there is no universal Artificial Intelligence method effective for all phases mentioned above and for all applications. In each case, a set of criteria should be developed to select (and then experiment) an appropriate method (or combination of methods). A selection of methods for the first two stages of Cyber Defence was described in previous work. In the present paper are considered some of the considerations related to effective use of appropriate methods for the Incident Handling phase.

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Andrews, J. J., and J. S. Bobowski. "Automation of the Cavendish torsion-balance experiment to measure G." European Journal of Physics 40, no. 3 (March 6, 2019): 035001. http://dx.doi.org/10.1088/1361-6404/ab01a8.

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Borisyak, M., F. Ratnikov, D. Derkach, and A. Ustyuzhanin. "Towards automation of data quality system for CERN CMS experiment." Journal of Physics: Conference Series 898 (October 2017): 092041. http://dx.doi.org/10.1088/1742-6596/898/9/092041.

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Галуза, Алексей Анатольевич, Иван Викторович Коленов, and Алла Ивановна Беляева. "Software and hardware platform for developing laboratory experiment automation systems." Eastern-European Journal of Enterprise Technologies 5, no. 9(65) (October 30, 2013): 11–16. http://dx.doi.org/10.15587/1729-4061.2013.18446.

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Du, Na, Qiaoning Zhang, and X. Jessie Yang. "Evaluating effects of automation reliability and reliability information on trust, dependence and dual-task performance." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 174. http://dx.doi.org/10.1177/1541931218621041.

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The use of automated decision aids could reduce human exposure to dangers and enable human workers to perform more challenging tasks. However, automation is problematic when people fail to trust and depend on it appropriately. Existing studies have shown that system design that provides users with likelihood information including automation certainty, reliability, and confidence could facilitate trust- reliability calibration, the correspondence between a person’s trust in the automation and the automation’s capabilities (Lee & Moray, 1994), and improve human–automation task performance (Beller et al., 2013; Wang, Jamieson, & Hollands, 2009; McGuirl & Sarter, 2006). While revealing reliability information has been proposed as a design solution, the concrete effects of such information disclosure still vary (Wang et al., 2009; Fletcher et al., 2017; Walliser et al., 2016). Clear guidelines that would allow display designers to choose the most effective reliability information to facilitate human decision performance and trust calibration do not appear to exist. The present study, therefore, aimed to reconcile existing literature by investigating if and how different methods of calculating reliability information affect their effectiveness at different automation reliability. A human subject experiment was conducted with 60 participants. Each participant performed a compensatory tracking task and a threat detection task simultaneously with the help of an imperfect automated threat detector. The experiment adopted a 2×4 mixed design with two independent variables: automation reliability (68% vs. 90%) as a within- subject factor and reliability information as a between-subjects factor. Reliability information of the automated threat detector was calculated using different methods based on the signal detection theory and conditional probability formula of Bayes’ Theorem (H: hits; CR: correct rejections, FA: false alarms; M: misses): Overall reliability = P (H + CR | H + FA + M + CR). Positive predictive value = P (H | H + FA); negative predictive value = P (CR | CR + M). Hit rate = P (H | H + M), correct rejection rate = P (CR | CR + FA). There was also a control condition where participants were not informed of any reliability information but only told the alerts from the automated threat detector may or may not be correct. The dependent variables of interest were participants’ subjective trust in automation and objective measures of their display-switching behaviors. The results of this study showed that as the automated threat detector became more reliable, participants’ trust in and dependence on the threat detector increased significantly, and their detection performance improved. More importantly, there were significant differences in participants’ trust, dependence and dual-task performance when reliability information was calculated by different methods. Specifically, when overall reliability of the automated threat detector was 90%, revealing positive and negative predictive values of the automation significantly helped participants to calibrate their trust in and dependence on the detector, and led to the shortest reaction time for detection task. However, when overall reliability of the automated threat detector was 68%, positive and negative predictive values didn’t lead to significant difference in participants’ compliance on the detector. In addition, our result demonstrated that the disclosure of hit rate and correct rejection rate or overall reliability didn’t seem to aid human-automation team performance and trust-reliability calibration. An implication of the study is that users should be made aware of system reliability, especially of positive/negative predictive values, to engender appropriate trust in and dependence on the automation. This can be applied to the interface design of automated decision aids. Future studies should examine whether the positive and negative predictive values are still the most effective pieces of information for trust calibration when the criterion of the automated threat detector becomes liberal.

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Cai, Xinyu, Natalya Garnova, Alla Filippova, and Sergey Glushkov. "Intelligent Automation of Student Performance Assessment based on Cloud Services." International Journal of Emerging Technologies in Learning (iJET) 16, no. 02 (January 26, 2021): 149. http://dx.doi.org/10.3991/ijet.v16i02.18827.

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The purpose of the study is to analyze the modern approach to the student performance assessment system and propose options for its improvement by automating the student result processing system. An experiment to analyze the implementation of an automated student result processing system based on the Power BI service developed by Microsoft and compare the results of the control and experimental groups was conducted at Sechenov University and the Capital University of Economics and Business. In total, 12 departments took part in the experiment (there were 6 departments that used the Power BI service and 6 departments that relied on the electronic university journal). The group that worked with Microsoft Power BI received visual results of student performance, which graphically displayed the dynamics. In turn, the group that created analytics with the help of the electronic university journal could not see most indicators of the student performance dynamics; in addition, it took 6 times more time to create such incomplete analytics compared to the analysis performed in the Power BI service. The practice of other educational institutions and organizations has shown that automation tools are being actively implemented at universities; however, the experience of using Microsoft Power BI in the educational environment is quite limited due to its recent introduction.

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Lu, Yidu, and Nadine Sarter. "Feedback on system or operator performance: Which is more useful for the timely detection of changes in reliability, trust calibration and appropriate automation usage?" Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 312–16. http://dx.doi.org/10.1177/1071181319631345.

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Creating safe human-machine systems requires that operators can quickly notice changes in system reliability in the interest of trust calibration and proper automation usage. Operators’ readiness to trust a system is determined not only by the performance of the automation but also by their confidence in their own abilities. This study therefore compared the usefulness of feedback on the performance of either agent. The experiment required two groups of ten participants each to perform an automation-assisted target identification task with “Automation Performance Feedback” (APF) or “Operator Performance Feedback” (OPF). Four different scenarios differed with respect to the degree and duration of changes in system reliability. Findings indicate that APF was more effective for supporting timely adjustments of perceived system reliability, especially with large and long reliability changes. Subjective trust ratings and performance were not affected, however, suggesting that these two factors are closely linked and more relevant for automation reliance.

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Yu, Ying, Yu Wang, Ming Chen, and Xiao Yang Zhang. "Real Physical Model Marking Digital Control Experiments." Applied Mechanics and Materials 590 (June 2014): 920–24. http://dx.doi.org/10.4028/www.scientific.net/amm.590.920.

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Digital control is an important professional course for most engineering students. This course is not pure theoretical, but practical for its close relationship with practical industrial automation systems design and implementation. Experiment plays an important role in the whole course learning. Most of traditional experiments for digital control are pure simulation. Students cannot grasp knowledge and technical ability in this way. A real physical model is introduced in this paper. How the real physical model experiments was built up and integrated into the digital control course at CDHAW China. The lab architecture, experiments proposed, extending experiments were discussed.

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Hussein, Aya, Sondoss Elsawah, and Hussein Abbass. "A System Dynamics Model for Human Trust in Automation under Speed and Accuracy Requirements." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 822–26. http://dx.doi.org/10.1177/1071181319631167.

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Research shows that human trust in automation is a key predictor of human reliance on the automation. Several models have been proposed to capture the interplay between trust and reliance and their combined impacts on task performance. Whereas some models assume that trust is affected by automation reliability, others assume that trust is affected by automation speed. In fact, both speed and reliability can be crucial for mission performance, therefore, these models do not represent the interrelationships among automation speed, automation reliability, human decision making, and subsequent effects on mission performance. To address this gap, we propose a system dynamics model which incorporates both the speed and reliability of automation and their combined effects on trust. Our model explicitly represents the speed-accuracy compromise adopted by the subjects to weigh the perceived relative importance of these aspects while evaluating the reliance decision. The model is calibrated and evaluated using data collected from a human experiment in which 33 subjects interacted with an automated aid for swarm supervision in a foraging mission. The simulation results show that the model can closely replicate and predict the experimental data in terms of the reliance rate and the number of targets collected. Model limitations and further efforts for model extension are discussed.

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Cymek, Dietlind Helene. "Redundant Automation Monitoring: Four Eyes Don’t See More Than Two, if Everyone Turns a Blind Eye." Human Factors: The Journal of the Human Factors and Ergonomics Society 60, no. 7 (June 25, 2018): 902–21. http://dx.doi.org/10.1177/0018720818781192.

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Background: In safety-critical and highly automated environments, more than one person typically monitors the system in order to increase reliability. Objective: We investigate whether the anticipated advantage of redundant automation monitoring is lost due to social loafing and whether individual performance feedback can mitigate this effect. Method: In two experiments, participants worked on a multitasking paradigm in which one task was the monitoring and cross-checking of an automation. Participants worked either alone or with a team partner on this task. The redundant group was further subdivided. One subgroup was instructed that only team performance would be evaluated, whereas the other subgroup expected to receive individual performance feedback after the experiment. Results: Compared to participants working alone, those who worked collectively but did not expect individual feedback performed significantly less cross-checks and found 25% fewer automation failures. Due to this social loafing effect, even the combined team performance did not surpass the performance of participants working alone. However, when participants expected individual performance feedback, their monitoring behavior and failure detection performance was similar to participants working alone and a team advantage became apparent. Conclusion: Social loafing in redundant automation monitoring can negate the expected gain, if individual performance feedback is not provided. Application: These findings may motivate safety experts to evaluate whether their implementation of human redundancy is vulnerable to social loafing effects.

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Tamura, Yusuke, Mami Egawa, Shiro Yano, Takaki Maeda, Motoichiro Kato, and Hajime Asama. "Activeness Improves Cognitive Performance in Human-Machine Interaction." Journal of Advanced Computational Intelligence and Intelligent Informatics 17, no. 3 (May 20, 2013): 425–32. http://dx.doi.org/10.20965/jaciii.2013.p0425.

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In human-machine interaction, automation brings both advantages and potentially unpredictable disadvantages to human cognitive performance. In this study, we hypothesized that active behavior improves cognitive performance in human-machine interaction, and verified this hypothesis through three experiments. Experiment 1 examined the relationship between activeness and reaction time in a target-search task. Experiments 2 and 3 analyzed the factors that improved cognitive performance. Experimental results demonstrated that activeness positively affects cognitive performance and suggested that predictability associated with activeness plays a key role in improving cognitive performance.

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Wickens, Christopher D., Roger Marsh, Mireille Raby, Susan Straus, Russell S. Cooper, Charles L. Hulin, and Fred Switzer. "Aircrew Performance as a Function of Automation and Crew Composition: A Simulator Study." Proceedings of the Human Factors Society Annual Meeting 33, no. 13 (October 1989): 792–96. http://dx.doi.org/10.1177/154193128903301305.

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In an experiment designed to examine the effect of crew composition and automation level on flight performance, fifty pilot-copilot crews flew a simulated instrument flight mission between three Michigan cities. Half of the crews were of homogeneous composition (both low or both high time), while half were heterogeneous consisting of one senior high time member and one junior low time member. Within each group, roughly half flew xxx with automated flight control and the other half flew manually. The flight was disrupted by periodic instrument failures. Results indicated that automation improved flight performance and lowered workload. While there was no overall difference in performance between homogeneous and heterogeneous crews, the latter group appeared to benefit more from the advantages that automation had to offer. The results are discussed in terms of the effect of automation on cockpit authority gradients, the role of flight experience, and of crew communications.

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Chen, Bo, Chuan Bao Jia, and Ji Cai Feng. "Active Visual Sensor Based Weld Seam Tracking for Underwater Wet Welding." Advanced Materials Research 717 (July 2013): 588–91. http://dx.doi.org/10.4028/www.scientific.net/amr.717.588.

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Weld automation is the development trend of underwater welding, and underwater weld seam tracking is one of the key technologies in weld automation. This paper used active visual sensor to automatically monitor the weld seam in underwater wet weld process, and image processing algorithms were developed to automatically obtain the weld torch deviation, then the weld torch was adjusted automatically according to the deviation obtained by the image, experiment results showed that this method could be used in underwater wet welding.

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Naujoks, Frederik, Christian Purucker, and Alexandra Neukum. "Secondary task engagement and vehicle automation – Comparing the effects of different automation levels in an on-road experiment." Transportation Research Part F: Traffic Psychology and Behaviour 38 (April 2016): 67–82. http://dx.doi.org/10.1016/j.trf.2016.01.011.

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Li, Shan Shan, Peng Wang, Xu Han, Jin Feng Liu, and Xiao Gang Zhou. "Investigation of Ventilation Monitoring and Controlling System for Supercavitation Experiment in the Water-Tunnel Based on Configuration Software." Applied Mechanics and Materials 556-562 (May 2014): 3219–22. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3219.

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The supercavitation weapon is a new concept weapon moving at a high speed underwater, which is devised applying the characteristic of supercavitation reducing the resistance of water, and it may be widely used in the future. The present ventilating system has many shortcomings, such as less automation and numerous data cannot be measured and recorded timely. In this paper, the automatic monitor and control ventilating system of supercavitation water-tunnel was developed using configuration software and PLC. All parameters can be measured, recorded and displayed automatically via using of the system. It has many advantages such as higher automation, convenience, and easy maintenance.

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Du, Na, Kevin Y. Huang, and X. Jessie Yang. "Not All Information Is Equal: Effects of Disclosing Different Types of Likelihood Information on Trust, Compliance and Reliance, and Task Performance in Human-Automation Teaming." Human Factors: The Journal of the Human Factors and Ergonomics Society 62, no. 6 (July 26, 2019): 987–1001. http://dx.doi.org/10.1177/0018720819862916.

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Objective The study examines the effects of disclosing different types of likelihood information on human operators’ trust in automation, their compliance and reliance behaviors, and the human-automation team performance. Background To facilitate appropriate trust in and dependence on automation, explicitly conveying the likelihood of automation success has been proposed as one solution. Empirical studies have been conducted to investigate the potential benefits of disclosing likelihood information in the form of automation reliability, (un)certainty, and confidence. Yet, results from these studies are rather mixed. Method We conducted a human-in-the-loop experiment with 60 participants using a simulated surveillance task. Each participant performed a compensatory tracking task and a threat detection task with the help of an imperfect automated threat detector. Three types of likelihood information were presented: overall likelihood information, predictive values, and hit and correct rejection rates. Participants’ trust in automation, compliance and reliance behaviors, and task performance were measured. Results Human operators informed of the predictive values or the overall likelihood value, rather than the hit and correct rejection rates, relied on the decision aid more appropriately and obtained higher task scores. Conclusion Not all likelihood information is equal in aiding human-automation team performance. Directly presenting the hit and correct rejection rates of an automated decision aid should be avoided. Application The findings can be applied to the design of automated decision aids.

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Parasuraman, Raja, Mustapha Mouloua, and Robert Molloy. "Effects of Adaptive Task Allocation on Monitoring of Automated Systems." Human Factors: The Journal of the Human Factors and Ergonomics Society 38, no. 4 (December 1996): 665–79. http://dx.doi.org/10.1518/001872096778827279.

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The effects of adaptive task allocation on monitoring for automation failure during multi task flight simulation were examined. Participants monitored an automated engine status task while simultaneously performing tracking and fuel management tasks over three 3D-min sessions. Two methods of adaptive task allocation, both involving temporary return of the automated engine status task to the human operator (“human control”), were examined as a possible countermeasure to monitoring inefficiency. For the model-based adaptive group, the engine status task was allocated to all participants in the middle of the second session for 10 min, following which it was again returned to automation control. The same occurred for the performance-based adaptive group, but only if an individual participant's monitoring performance up to that point did not meet a specified criterion. For the nonadaptive control groups, the engine status task remained automated throughout the experiment. All groups had low probabilities of detection of automation failures for the first 40 min spent with automation. However, following the lO-min intervening period of human control, both adaptive groups detected significantly more automation failures during the subsequent blocks under automation control. The results show that adaptive task allocation can enhance monitoring of automated systems. Both model-based and performance-based allocation improved monitoring of automation. Implications for the design of automated systems are discussed.

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Waggoner, Philip D., Ryan Kennedy, Hayden Le, and Myriam Shiran. "Big Data and Trust in Public Policy Automation." Statistics, Politics and Policy 10, no. 2 (December 18, 2019): 115–36. http://dx.doi.org/10.1515/spp-2019-0005.

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Abstract Big data is everywhere, both in and out of public policy. Though a rich data source, what is the impact of big data beyond the research community? We suggest such that invoking big data-related terms acts as a heuristic for assumed algorithmic quality. Such an assumption leads to greater trust in automation in public policy decision-making. We test this “big-data-effect” expectation using four tests including a conjoint experiment embedded in a recently fielded survey experiment. We find strong evidence that indeed, big data-related terms act as powerful signals of assumed quality where respondents consistently prefer algorithms with bigger data behind them, absent any mention of predictive accuracy or definitions of key terms (e.g. “training features”). As we expect this big-data-effect is likely not beholden to public policy, we encourage more research in this vein to deepen an understanding of the influence of big data on modern society.

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Journal articles on the topic 'Experiment automation'

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