Hasil untuk "Automation"

Kevin A. Hoff, Masooda N. Bashir

Alec A. K. Nielsen, Bryan S. Der, Jonghyeon Shin et al.

W. V. D. Aalst, M. Bichler, Armin Heinzl

Daniel P. Tabor, L. Roch, S. Saikin et al.

Y. Altintas

L. Bainbridge

M. Arntz, Terry Gregory, Ulrich Zierahn

Ljubica Nedelkoska, G. Quintini

Jorge Ribeiro, Rui Lima, T. Eckhardt et al.

Abstract Taking into account the technological evolution of the last decades and the proliferation of information systems in society, today we see the vast majority of services provided by companies and institutions as digital services. Industry 4.0 is the fourth industrial revolution where technologies and automation are asserting themselves as major changes. Robotic Process Automation (RPA) has numerous advantages in terms of automating organizational and business processes. Allied to these advantages, the complementary use of Artificial Intelligence (AI) algorithms and techniques allows to improve the accuracy and execution of RPA processes in the extraction of information, in the recognition, classification, forecasting and optimization of processes. In this context, this paper aims to present a study of the RPA tools associated with AI that can contribute to the improvement of the organizational processes associated with Industry 4.0. It appears that the RPA tools enhance their functionality with the objectives of AI being extended with the use of Artificial Neural Network algorithms, Text Mining techniques and Natural Language Processing techniques for the extraction of information and consequent process of optimization and of forecasting scenarios in improving the operational and business processes of organizations.

James Bessen

Will new technologies cause industries to shed jobs, requiring novel policies to address mass unemployment? Sometimes productivity-enhancing technology increases industry employment instead. In manufacturing, jobs grew along with productivity for a century or more; only later did productivity gains bring declining employment. What changed? The elasticity of demand. Using data over two centuries for US textile, steel and auto industries, this paper shows that automation initially spurred job growth because demand was highly elastic. But demand later became satiated, leading to job losses. A simple model explains why this pattern might be common, suggesting that today’s technologies may cause some industries to decline and others to grow. Automation might not cause mass unemployment, but it may well require workers to make disruptive transitions to new industries, requiring new skills and occupations.

F. Tschang, Esteve Almirall Mezquita

There has been great concern in recent years that artificial intelligence (AI) may cause widespread unemployment, but proponents say that AI augments existing jobs. Both of these positions have sub...

Spencer Kohn, Ewart J. de Visser, E. Wiese et al.

With the rise of automated and autonomous agents, research examining Trust in Automation (TiA) has attracted considerable attention over the last few decades. Trust is a rich and complex construct which has sparked a multitude of measures and approaches to study and understand it. This comprehensive narrative review addresses known methods that have been used to capture TiA. We examined measurements deployed in existing empirical works, categorized those measures into self-report, behavioral, and physiological indices, and examined them within the context of an existing model of trust. The resulting work provides a reference guide for researchers, providing a list of available TiA measurement methods along with the model-derived constructs that they capture including judgments of trustworthiness, trust attitudes, and trusting behaviors. The article concludes with recommendations on how to improve the current state of TiA measurement.

Erin K. Chiou, John D. Lee

Objective This paper reviews recent articles related to human trust in automation to guide research and design for increasingly capable automation in complex work environments. Background Two recent trends—the development of increasingly capable automation and the flattening of organizational hierarchies—suggest a reframing of trust in automation is needed. Method Many publications related to human trust and human–automation interaction were integrated in this narrative literature review. Results Much research has focused on calibrating human trust to promote appropriate reliance on automation. This approach neglects relational aspects of increasingly capable automation and system-level outcomes, such as cooperation and resilience. To address these limitations, we adopt a relational framing of trust based on the decision situation, semiotics, interaction sequence, and strategy. This relational framework stresses that the goal is not to maximize trust, or to even calibrate trust, but to support a process of trusting through automation responsivity. Conclusion This framing clarifies why future work on trust in automation should consider not just individual characteristics and how automation influences people, but also how people can influence automation and how interdependent interactions affect trusting automation. In these new technological and organizational contexts that shift human operators to co-operators of automation, automation responsivity and the ability to resolve conflicting goals may be more relevant than reliability and reliance for advancing system design. Application A conceptual model comprising four concepts—situation, semiotics, strategy, and sequence—can guide future trust research and design for automation responsivity and more resilient human–automation systems.

L. L. Lo Bello, W. Steiner

Networks are a core element of many industrial and automation systems at present. Often these networks transport time- and safety-critical messages that control physical processes. Thus, timely and guaranteed delivery is an essential property of networks for such critical systems. Over the past decade, a variety of network solutions have evolved to satisfy said properties. However, these solutions are largely incompatible with each other and many system architects are forced to deploy different solutions in parallel due to their different capabilities. IEEE 802.1 Time-Sensitive Networking (TSN) is a standardization group that enhances IEEE networking standards, most prominently Ethernet-based networks, with said properties and has the unique potential to evolve as a cross-industry mainstream networking technology. In this survey paper, we give an overview of TSN in industrial communication and automation systems and discuss specific TSN standards and projects in detail as well as their applicability to various industries.

Saar Alon-Barkat, M. Busuioc

Artificial intelligence algorithms are increasingly adopted as decisional aides by public bodies, with the promise of overcoming biases of human decision-makers. At the same time, they may introduce new biases in the human–algorithm interaction. Drawing on psychology and public administration literatures, we investigate two key biases: overreliance on algorithmic advice even in the face of “warning signals” from other sources ( automation bias ), and selective adoption of algorithmic advice when this corresponds to stereotypes ( selective adherence ). We assess these via three experimental studies conducted in the Netherlands: In study 1 ( N = 605), we test automation bias by exploring participants’ adherence to an algorithmic prediction compared to an equivalent human-expert prediction. We do not find evidence for automation bias. In study 2 ( N = 904), we replicate these findings, and also test selective adherence . We find a stronger propensity for adherence when the advice is aligned with group stereotypes, with no significant differences between algorithmic and human-expert advice. In study 3 ( N = 1,345), we replicate our design with a sample of civil servants. This study was conducted shortly after a major scandal involving public authorities’ reliance on an algorithm with discriminatory outcomes (the “childcare benefits scandal”). The scandal is itself illustrative of our theory and patterns diagnosed empirically in our experiment, yet in our study 3, while supporting our prior findings as to automation bias, we do not find patterns of selective adherence. We suggest this is driven by bureaucrats’ enhanced awareness of discrimination and algorithmic biases in the aftermath of the scandal. We discuss the implications of our findings for public sector decision making in the age of automation. Overall, our study speaks to potential negative effects of automation of the administrative state for already vulnerable and disadvantaged citizens.

Luísa Nazareno, Danielle Schiff

Crispin R. Coombs, D. Hislop, Stanimira K. Taneva et al.

Abstract A significant recent technological development concerns the automation of knowledge and service work as a result of advances in Artificial Intelligence (AI) and its sub-fields. We use the term Intelligent Automation to describe this phenomenon. This development presents organisations with a new strategic opportunity to increase business value. However, academic research contributions that examine these developments are spread across a wide range of scholarly disciplines resulting in a lack of consensus regarding key findings and implications. We conduct the first interdisciplinary literature review that systematically characterises the intellectual state and development of Intelligent Automation technologies in the knowledge and service sectors. Based on this review, we provide three significant contributions. First, we conceptualise Intelligent Automation and its associated technologies. Second, we provide a business value-based model of Intelligent Automation for knowledge and service work and identify twelve research gaps that hinder a complete understanding of the business value realisation process. Third, we provide a research agenda to address these gaps.

E. Lucchi

Xingguo TAN, Chaomeng LI, Gaoming FENG et al.

To address the issue of low transmission efficiency in dual active bridge (DAB) converters during electric vehicle charging and discharging processes, a minimum current stress optimization control strategy combining the differential extremum method with segmented control is proposed. This strategy effectively optimizes current stress and suppresses backflow power under soft-switching constraints, thereby significantly improving transmission efficiency. Firstly, taking forward power transmission as an example, the conditions for achieving zero voltage soft-switching for all switches in two operation modes under extended phase shift (EPS) control are derived, and the mechanism of backflow power generation is analyzed, elucidating how reducing current stress contributes to its suppression. Subsequently, the optimization phase-shift combinations for minimum current stress are derived using the differential extremum method, and a segmented control scheme is implemented based on the soft-switching ranges of different modes. Finally, experimental results demonstrate that when the voltage conversion ratio is greater than 1, the proposed strategy achieves soft-switching for all switches across the full power range, while effectively reducing current stress and suppressing backflow power, leading to a significant improvement in transmission efficiency. However, when the voltage conversion ratio is less than 1, while current stress is still reduced, zero voltage switching cannot be achieved for all switches.