Different Types Of Automation Research Articles

Reducing Ship’s Energy Consumption through Accommodation and Cargo Spaces Lights Automation

The effective use of energy proposes using less energy to achieve the same goal. The International Maritime Organization started using criteria for ship’s energy efficiency in 2013, when the new International Convention for the Prevention of Pollution from Ships, Annex VI, was adopted. The purpose of the Annex is to improve the ships’ energy efficiency through the use of new technologies such as improved hull design, and new propulsion systems, including innovations in energy management. One of the topics for improving the car carriers’ energy efficiency is the constantly working lights in the superstructure and on the car decks. Lights’ energy consumption may be negligible on most ships, but on car carriers, the consumption is of greater magnitude. This article presents a survey of the improvement of energy consumption on board a car carrier during regular voyages as well as the cost-effectiveness of introducing light control automation. The authors of this article review an example of light automation set up to control the lights in a ship’s superstructure and on the car decks. The implementation of such a system and the different types of automation are also reviewed. This research is towards new regulations, established by Maritime Environment Protection Committee and implemented in shipping since 1 November 2022. Conclusions for practical use are extracted.

Effects of technological change and automation on industry structure and (wage-)inequality: insights from a dynamic task-based model

The advent of artificial intelligence is changing the task allocation of workers and machines in firms’ production processes with potentially wide ranging effects on workers and firms. We develop an agent-based simulation framework to investigate the consequences of different types of automation for industry output, the wage distribution, the labor share, and industry dynamics. It is shown how the competitiveness of markets, in particular barriers to entry, changes the effects that automation has on various outcome variables, and to which extent heterogeneous workers with distinct general skill endowments and heterogeneous firms featuring distinct wage offer rules affect the channels via which automation changes market outcomes.

Preface to the Special Issue on Human Factors and Advanced Vehicle Automation: Of Benefits, Barriers, and Bridges to Safe and Effective Implementation.

The aim of this special issue is to bring together the latest research related to driver interaction with various types of vehicle automation. Vehicle technology has undergone significant progress over the past decade, bringing new support features that can assist the driver and take on more and more of the driving responsibilities. This issue is comprised of eight articles from international research teams, focusing on different types of automation and different user populations, including driver support features through to highly automated driving systems. The papers comprising this special issue are clustered into three categories: (a) experimental studies of driver interactions with advanced vehicle technologies; (b) analysis of existing data sources; and (c) emerging human factors issues. Studies of currently available and pending systems highlight some of the human factors challenges associated with the driver-system interaction that are likely to become more prominent in the near future. Moreover, studies of more nascent concepts (i.e., those that are still a long way from production vehicles) underscore many attitudes, perceptions, and concerns that will need to be considered as these technologies progress. Collectively, the papers comprising this special issue help fill some gaps in our knowledge. More importantly, they continue to help us identify and articulate some of the important and potential human factors barriers, design considerations, and research needs as these technologies become more ubiquitous.

Playing with machines: Using machine learning to understand automated copyright enforcement at scale

This article presents the results of methodological experimentation that utilises machine learning to investigate automated copyright enforcement on YouTube. Using a dataset of 76.7 million YouTube videos, we explore how digital and computational methods can be leveraged to better understand content moderation and copyright enforcement at a large scale.We used the BERT language model to train a machine learning classifier to identify videos in categories that reflect ongoing controversies in copyright takedowns. We use this to explore, in a granular way, how copyright is enforced on YouTube, using both statistical methods and qualitative analysis of our categorised dataset. We provide a large-scale systematic analysis of removals rates from Content ID’s automated detection system and the largely automated, text search based, Digital Millennium Copyright Act notice and takedown system. These are complex systems that are often difficult to analyse, and YouTube only makes available data at high levels of abstraction. Our analysis provides a comparison of different types of automation in content moderation, and we show how these different systems play out across different categories of content. We hope that this work provides a methodological base for continued experimentation with the use of digital and computational methods to enable large-scale analysis of the operation of automated systems.

Miniaturized and Automated Synthesis of Biomolecules-Overview and Perspectives.

Chemical synthesis is performed by reacting different chemical building blocks with defined stoichiometry, while meeting additional conditions, such as temperature and reaction time. Such a procedure is especially suited for automation and miniaturization. Life sciences lead the way to synthesizing millions of different oligonucleotides in extremely miniaturized reaction sites, e.g., pinpointing active genes in whole genomes, while chemistry advances different types of automation. Recent progress in matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) imaging could match miniaturized chemical synthesis with a powerful analytical tool to validate the outcome of many different synthesis pathways beyond applications in the life sciences. Thereby, due to the radical miniaturization of chemical synthesis, thousands of molecules can be synthesized. This in turn should allow ambitious research, e.g., finding novel synthesis routes or directly screening for photocatalysts. Herein, different technologies are discussed that might be involved in this endeavor. A special emphasis is given to the obstacles that need to be tackled when depositing tiny amounts of materials to many different extremely miniaturized reaction sites.

Effects of Automation Type on Human Performance in Proofreading Tasks

Automation is ubiquitous and indispensable in modern working environments. It is adopted and used in not only advanced industrial- and technology-oriented operations, but also ordinary home or office computational functions. In general, automated systems aim to improve overall work efficiency and productivity of labor-intensive tasks by decreasing the risk of errors, and cognitive and physical workloads. The systems offer the support for diverse decision-making processes as well. However, the benefits of automation are not consistently achieved and depend on the types and features of automation (Onnasch, Wickens, Li, & Manzey, 2014; Parasuraman, Sheridan, & Wickens, 2000). Possible negative side effects have been reported. Sometimes, automation may lead to multi-tasking environments, which allows operators to be distractive with several tasks. It ultimately prolongs task completion time and causes to neglect monitoring and follow-up steps of the pre-processing tasks (Endsley, 1996). Furthermore, the operators who excessively depend on automation are easily deteriorated in skill acquisition, which is necessary for the emergency or manual operations. Thus, inconsistent performance in automation is a major issue in successful adoption and trust in automation (Jeong, Park, Park, & Lee, 2017). This paper presents an experimental study that investigates the main features and causes of the inconsistency in task performance in different types of automation. Automated proofreading tasks were used in this study, which is one of the most common types of automation we experience in daily life. Based on the similar algorithm of the auto-correct function in Microsoft Word, a custom-built program of five proofreading tasks, including one non-automated and four automated proofreading tasks, were developed using Visual Studio 2015 C#. In the non-automated task used as a reference for individual difference, participants were asked to manually find a typographical error in a sentence. In the automated tasks, auto-correcting functions are provided in two levels (i.e., low and high) of automation and two statuses (i.e., routine and failure of automation). The type of automation is defined as the combinations of a status and a level. Participants identified typographical errors by only an underlined word at the low-level automation, whereas an underlined word with a possible substituting word was given at the high-level. Additionally, in the routine automation status, a correct substituting word is provided. On the other hand, a grammatically incorrect word is given in the failed automation status. Nineteen participants (11 females and 8 males; age mean = 33.8, standard deviation = 19.1) took part in this study. Results of statistical analyses show a clear advantage in high-routine automation, in terms of both task completion time and accuracy. While task performances of high & routine automation types are quite obvious in both task completion time and accuracy, those in the failed automation types are mixed and indistinguishable. Different levels and statues of failed automation do not much influence task performance. Moreover, task completion time and mental demand are strongly correlated, and the accuracy rate and perceived trust show a strong positive correlation. The approaches and outcomes of the current study can provide some insights into the human-automation interaction systems that support human performance and safety, such as in-vehicle warning systems and automated vehicle controls.

Review Essay: Technology and the Professions: Utopian and Dystopian Futures

This essay is inspired by and consists of a critique of ‘The Future of the Professions: How Technology Will Transform the Work of Human Experts’ (OUP, 2015) by Richard Susskind and Daniel Susskind. They start from the question ‘how do we share practical expertise in society?’ and argue that ‘in a post-professional society, we predict that practical expertise will be available online,’ leading to their key moral question ‘who should own and control [this] practical expertise’? Their conclusion is optimistic, at least for the sharing of expertise, if not for traditional professions. While I ought to be enthusiastic, and I agree with much of the analysis that leads to it, I reach more pessimistic conclusions, including much more dystopian options for the future for most professionals. This article explains why I have a more dystopian view. In order to consider the Susskinds’ arguments, this review distinguishes the three different types of automation that are relevant to professionalism, and in each case the likelihood that it will be ‘liberated’ or made part of a commons: representing expert domain information; representing expertise; and applying expertise to individual situations. We also need to distinguish at least three types of the programmatic application of expertise: where ‘knowledge engineers’ embody expertise in programs; embedded knowledge; and machine-generated expertise; plus a related ‘communities of experience’. These distinctions allow somewhat different conclusions to be reached than those reached by the Susskinds on future modes of professionalism, and their relationships to expertise. My conclusions is that most of the encapsulated expertise is going to be locked up in (new) private hands unless there are enormous efforts by civil society, universities, and governments. On this semi-optimistic scenario, public bodies, members of the public, and parts of the professions might maintain sufficient commons to support the continuation of professional expertise. By making these issues central to their book, the Susskinds have taken a courageous step, for which future discussions of technology and professions will be indebted.

Expertise Development With Different Types of Automation: A Function of Different Cognitive Abilities.

I explored whether different cognitive abilities (information-processing ability, working-memory capacity) are needed for expertise development when different types of automation (information vs. decision automation) are employed. It is well documented that expertise development and the employment of automation lead to improved performance. Here, it is argued that a learner's ability to reason about an activity may be hindered by the employment of information automation. Additional feedback needs to be processed, thus increasing the load on working memory and decelerating expertise development. By contrast, the employment of decision automation may stimulate reasoning, increase the initial load on information-processing ability, and accelerate expertise development. Authors of past research have not investigated the interrelations between automation assistance, individual differences, and expertise development. Sixty-one naive learners controlled simulated air traffic with two types of automation: information automation and decision automation. Their performance was captured across 16 trials. Well-established tests were used to assess information-processing ability and working-memory capacity. As expected, learners' performance benefited from expertise development and decision automation. Furthermore, individual differences moderated the effect of the type of automation on expertise development: The employment of only information automation increased the load on working memory during later expertise development. The employment of decision automation initially increased the need to process information. These findings highlight the importance of considering individual differences and expertise development when investigating human-automation interaction. The results are relevant for selecting automation configurations for expertise development.

The optimal number, type and location of devices in automation of electrical distribution networks

This paper presents the mixed integer linear programming based model for determining optimal number, type and location of remotely controlled and supervised devices in distribution networks in the presence of distributed generators. The proposed model takes into consideration a number of different devices simultaneously (remotely controlled circuit breakers/reclosers, sectionalizing switches, remotely supervised and local fault passage indicators) along with the following: expected outage cost to consumers and producers due to momentary and long-term interruptions, automated device expenses (capital investment, installation, and annual operation and maintenance costs), number and expenses of crews involved in the isolation and restoration process. Furthermore, the other possible benefits of each of automated device are also taken into account (e.g., benefits due to decreasing the cost of switching operations in normal conditions). The obtained numerical results emphasize the importance of consideration of different types of automation devices simultaneously. They also show that the proposed approach have a potential to improve the process of determining of the best automation strategy in real life distribution networks.

Tactical Driving Behavior With Different Levels of Automation

This paper investigated how different types of automation affect tactical driving behavior, depending on trust in the system. Previous research indicates that drivers wait for automation to act, delegating the monitoring of traffic situations. This would be especially true for those who have more trust in automation. Behavioral and gaze data from 30 participants driving an advanced simulator were recorded in four driving conditions, namely, manual driving, intentional car following, adaptive cruise control (ACC), and ACC with adaptive steering. Measures of trust in the systems were recorded with a questionnaire. Three fairly common traffic events requiring a driver response were analyzed. Trust in automation was high among the participants, and no associations between trust levels and behavior could be found. Drivers seem to make informed choices on when to let the automation handle a situation and when to switch it off manually or via the vehicle controls. If drivers did not expect the system to be able to handle the situation, they usually resumed control before the automation reached its limits. If the automation was expected to be able to deal with the situation, control was usually not resumed. In addition, situations were dealt with in a tactically different manner with automation than without. Controlling the car with automation systems is thus accepted by drivers as being a different undertaking than driving in manual mode.

US venture capital in robotics

PurposeThis paper seeks to report on the US venture capital (VC) industry's data findings on robotics.Design/methodology/approachAn analysis of unique reports by the National Venture Capital Association and three interviews with US VC firms are presented.FindingsSince robotics spreads across many different types of automation and industries, companies looking for venture capitalists may fare better by grouping within the industry category that they serve rather than by classifying themselves under the sole “robotic” industrial product classification.Originality/valueThis paper researches the terminology in‐depth and data surrounding US published reports on robotics and VC. A unique report by the National Venture Capital Association was compiled for the purpose of this paper. The original responses of three interviews with representatives from US VC firms are also published here.

Extending the decision field theory to model operators' reliance on automation in supervisory control situations

Appropriate trust in and reliance on automation are critical for safe and efficient system operation. This paper fills an important research gap by describing a quantitative model of trust in automation. We extend decision field theory (DFT) to describe the multiple sequential decisions that characterize reliance on automation in supervisory control situations. Extended DFT (EDFT) represents an iterated decision process and the evolution of operator preference for automatic and manual control. The EDFT model predicts trust and reliance, and describes the dynamic interaction between operator and automation in a closed-loop fashion: the products of earlier decisions can transform the nature of later events and decisions. The simulation results show that the EDFT model captures several consistent empirical findings, such as the inertia of trust and the nonlinear characteristics of trust and reliance. The model also demonstrates the effects of different types of automation on trust and reliance. It is possible to expand the EDFT model for multioperator multiautomation situations

Automation for Biomass Production within Advanced Life Support Systems

Advanced life support systems for manned long-duration space missions include four major subsystems: crew, biomass production, food processing and nutrition, and waste processing and resource recovery. Crew time requirements are a concern, especially in the potentially labor intensive operation of the biomass production subsystem. Different types of automation (including mechanization and robotics) are needed in order to reduce crew time. Automation is an important aspect of advanced biomass production systems within controlled environment life support systems. A biomass production subsystem model was developed to investigate the effects of different crop and automation scenarios on crew time requirements.

Automation and Crew Performance: The Importance of Who and What

In the past, “automation” in the cockpit has been associated with both an increase and a reduction in workload, performance, and crew communications. A reason for these apparently contradictory results could be that different types of automation were studied, for example, automation that required little vs. considerable interaction with a machine. The current study investigated whether automation of different task domains for different crewmembers has differential effects. Sixteen pilots formed two-pilot crews. The crews each completed four experimental flights in a PC-based simulator, encountering different levels of automation. Systematically, an automated system for the pilot (a simple autopilot) and one for the copilot (a navigation computer) were made available or were unavailable, alone or in combination. The results showed that the automation manipulations improved task performance only when they were combined. Additionally, the use of the navigation computer reduced communications within the cockpit, while the autopilot was associated with larger contributions by the pilot. Further, mental workload and frustration were lower when the navigation computer was available. Also, perceived effort was lower with the autopilot on. The results of the current study suggest a more detailed picture of automation effects than previously known. Additionally, the results suggest that different types of automation can interact to create unexpected effects that may not be uncovered if one studies automated systems individually.