Hasil untuk "Automation"

Sebastian Raisch, Sebastian Krakowski

Taking three recent business books on artificial intelligence (AI) as a starting point, we explore the automation and augmentation concepts in the management domain. Whereas automation implies that...

D. Otasek, J. Morris, Jorge Bouças et al.

Cytoscape is one of the most successful network biology analysis and visualization tools, but because of its interactive nature, its role in creating reproducible, scalable, and novel workflows has been limited. We describe Cytoscape Automation (CA), which marries Cytoscape to highly productive workflow systems, for example, Python/R in Jupyter/RStudio. We expose over 270 Cytoscape core functions and 34 Cytoscape apps as REST-callable functions with standardized JSON interfaces backed by Swagger documentation. Independent projects to create and publish Python/R native CA interface libraries have reached an advanced stage, and a number of automation workflows are already published.

D. Acemoglu, P. Restrepo

David H. Autor

M. Wollschlaeger, T. Sauter, J. Jasperneite

Thuy Duong Oesterreich, Frank Teuteberg

M. Arntz, Terry Gregory, Ulrich Zierahn

John D. Lee, Katrina A. See

R. Parasuraman, T. Sheridan, C. Wickens

R. Parasuraman, V. Riley

K. Slinkard, V. L. Singleton

A fully automated-continuous flow 40-sample/ hour procedure was adapted from the Singleton-Rossi method of analysis for total phenols in wine and other plant extracts. It was compared with small-volume manual and semiautomated versions of this analysis. The agreement in mg of gallic acid equivalent phenol (GAE) per liter among a series of dry wines was excellent by all three procedures. The coefficients of variation in replicate analyses averaged 5.8% for the manual, 6.2% for the semi-automated and 2.2% for the automated procedure. This greater reproducibility, plus savings of about 70% in labor and up to 40% in reagents, makes the automated procedure attractive for laboratories doing enough total phenol analyses to recoup the cost of the automating equipment. For continuous flow, color development with the Folin-Ciocalteu reagent in alkaline solution must be hastened by heating compared to slower room temperature development for the manual methods. Heating of sugar-containing samples in the alkaline solution gives interference presumably from endiol formation. Examples are given of corrections which were used successfully to estimate the true phenol content of sweet wines.

D. Acemoglu, P. Restrepo

We argue theoretically and document empirically that aging leads to greater (industrial) automation, because it creates a shortage of middle-aged workers specializing in manual production tasks. We show that demographic change is associated with greater adoption of robots and other automation technologies across countries and with more robotics-related activities across U.S. commuting zones. We also document more automation innovation in countries undergoing faster aging. Our directed technological change model predicts that the response of automation technologies to aging should be more pronounced in industries that rely more on middle-aged workers and those that present greater opportunities for automation and that productivity should improve and the labor share should decline relatively in industries that are more amenable to automation. The evidence supports all four of these predictions.

R. Syed, S. Suriadi, M. Adams et al.

Through the application of Robotic Process Automation (RPA) organisations aim to increase their operational efficiency. In RPA, robots, or ‘bots’ for short, represent software agents capable of interacting with software systems by mimicking user actions, thus alleviating the workload of the human workforce. RPA has already seen significant uptake in practice; solution technologies are offered by multiple vendors. Contrasting with this early practical adoption is the hitherto relative lack of attention to RPA in the academic literature. As a consequence, RPA lacks the sound theoretical foundations that allow for objective reasoning around its application and development. This, in turn, hinders initiatives for achieving meaningful advances in the field. This paper presents a structured literature review that identifies a number of contemporary, RPA-related themes and challenges for future research.

Paulinus Tobechukwu Ogunjiofor, Amit Joshi

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Hao Wen, Yuanchun Li, Guohong Liu et al.

Mobile task automation is an attractive technique that aims to enable voice-based hands-free user interaction with smartphones. However, existing approaches suffer from poor scalability due to the limited language understanding ability and the non-trivial manual efforts required from developers or endusers. The recent advance of large language models (LLMs) in language understanding and reasoning inspires us to rethink the problem from a model-centric perspective, where task preparation, comprehension, and execution are handled by a unified language model. In this work, we introduce AutoDroid, a mobile task automation system capable of handling arbitrary tasks on any Android application without manual efforts. The key insight is to combine the commonsense knowledge of LLMs and domain-specific knowledge of apps through automated dynamic analysis. The main components include a functionality-aware UI representation method that bridges the UI with the LLM, exploration-based memory injection techniques that augment the app-specific domain knowledge of LLM, and a multi-granularity query optimization module that reduces the cost of model inference. We integrate AutoDroid with off-the-shelf LLMs including online GPT-4/GPT-3.5 and on-device Vicuna, and evaluate its performance on a new benchmark for memory-augmented Android task automation with 158 common tasks. The results demonstrated that AutoDroid is able to precisely generate actions with an accuracy of 90.9%, and complete tasks with a success rate of 71.3%, outperforming the GPT-4-powered baselines by 36.4% and 39.7%.

Francisco Javier Folgado, David Calderón, Isaías González et al.

Industry 4.0 is a new paradigm that is transforming the industrial scenario. It has generated a large amount of scientific studies, commercial equipment and, above all, high expectations. Nevertheless, there is no single definition or general agreement on its implications, specifically in the field of automation and supervision systems. In this paper, a review of the Industry 4.0 concept, with equivalent terms, enabling technologies and reference architectures for its implementation, is presented. It will be shown that this paradigm results from the confluence and integration of both existing and disruptive technologies. Furthermore, the most relevant trends in industrial automation and supervision systems are covered, highlighting the convergence of traditional equipment and those characterized by the Internet of Things (IoT). This paper is intended to serve as a reference document as well as a guide for the design and deployment of automation and supervision systems framed in Industry 4.0.

H. Chi, C. Wu, N. Huang et al.

Network automation has been bred by the deployment of 5G based Industrial Internet-of-Things (IIoT) in Industry 4.0, and further approaching pervasive AI and human-robot-interaction/-collaboration toward 6G based Industry 5.0. Hitherto, to the best of the authors knowledge, research efforts are still required to provide a comprehensive review of the state-of-the-art network automation technologies for IIoT in 5G based Industry 4.0 and summary of challenges for next-generation network automation regarding the stricter network requirements of 6G based Industry 5.0. Therefore, in this article, we conduct a comprehensive overview of the state-of-the-art network automation technologies, standardizations, and corresponding impact on IIoT of Industry 4.0. We also forecast the next-generation network automation development toward 6G based Industry 5.0. This article provides blueprint of the next-generation network automation, meanwhile conducting comprehensive overview of the SoA network automation technologies in Industry 4.0, which gains high referable value for the researchers in the relative domain.

Enoch Oluwademilade Sodiya, Uchenna Joseph Umoga, Olukunle Oladipupo Amoo et al.

This comprehensive review explores the profound impact of artificial intelligence (AI) on warehouse automation, providing an in-depth examination of various AI-driven systems. As industries increasingly embrace automation to enhance efficiency and streamline operations, the integration of AI technologies into warehouse management systems has become pivotal, reshaping the landscape of logistics and supply chain management. AI-driven warehouse automation systems leverage advanced algorithms to optimize various aspects of warehouse operations, from inventory management to order fulfillment. Machine learning algorithms play a key role in demand forecasting, allowing warehouses to predict and adapt to changing customer needs. Computer vision technologies enhance robotic vision, facilitating tasks such as item recognition, pick-and-place operations, and quality control. These advancements significantly contribute to increased accuracy, speed, and cost-effectiveness in warehouse processes. The review provides a detailed examination of the applications of AI in warehouse automation, encompassing autonomous mobile robots (AMRs), robotic arms, and automated guided vehicles (AGVs). AMRs equipped with AI algorithms navigate warehouse environments autonomously, optimizing pick routes and adapting to changes in the warehouse layout. Robotic arms, enhanced by AI, enable precise and adaptable material handling, contributing to the efficiency of tasks like packing and palletizing. AGVs, guided by AI, ensure seamless material transport within warehouses, enhancing overall operational agility. Recent trends in AI-driven warehouse automation systems underscore the dynamic evolution of this field. Edge computing solutions empower these systems to process data locally, reducing latency and enhancing real-time decision-making. Reinforcement learning algorithms enable robotic systems to learn and adapt their behavior based on changing environmental conditions, contributing to continuous improvement and efficiency gains. In conclusion, this review illuminates the pivotal role of AI in transforming warehouse automation systems, revolutionizing the way logistics and supply chain operations are conducted. The collaborative synergy between AI and warehouse automation promises to drive unprecedented advancements in efficiency, accuracy, and adaptability within the evolving landscape of modern warehouses.

Daniel Ajiga, Patrick Azuka Okeleke, Samuel Olaoluwa Folorunsho et al.

Software automation is revolutionizing industrial operations by significantly enhancing efficiency, productivity, and operational reliability. This review explores how automation technologies are transforming industrial sectors, focusing on their impact on improving various aspects of industrial operations. Automation software facilitates the streamlining of repetitive and time-consuming tasks by replacing manual processes with automated systems. This shift not only accelerates operational workflows but also reduces the likelihood of human error, leading to more consistent and reliable outcomes. Key areas where software automation has made substantial contributions include production line management, inventory control, and quality assurance. In production environments, automation software optimizes manufacturing processes by integrating realtime data analysis and machine learning algorithms. This enables predictive maintenance, where potential equipment failures are identified before they occur, minimizing downtime and extending machinery lifespan. Additionally, automated inventory management systems improve stock control by tracking inventory levels and adjusting orders dynamically, ensuring that resources are efficiently allocated and reducing excess inventory. Quality assurance is another critical area where software automation excels. Automated inspection systems use advanced sensors and imaging technologies to detect defects and ensure product standards are met. This realtime monitoring capability allows for immediate corrective actions, reducing waste and enhancing product quality. The integration of automation software in industrial operations also fosters improved datadriven decisionmaking. By leveraging data analytics and reporting tools, businesses can gain insights into operational performance, identify inefficiencies, and make informed decisions to enhance productivity and profitability. However, implementing software automation requires careful consideration of system integration, employee training, and change management. Addressing these challenges is crucial for maximizing the benefits of automation and ensuring a smooth transition from traditional practices. In conclusion, software automation is a powerful catalyst for improving industrial operations and efficiency. Its ability to enhance productivity, reduce errors, and optimize resource management positions it as a key driver of industrial innovation and competitiveness. Continued advancements in automation technologies promise further improvements in operational excellence and overall industrial performance.

Chanathip Pornprasit, C. Tantithamthavorn

Context: The rapid evolution of Large Language Models (LLMs) has sparked significant interest in leveraging their capabilities for automating code review processes. Prior studies often focus on developing LLMs for code review automation, yet require expensive resources, which is infeasible for organizations with limited budgets and resources. Thus, fine-tuning and prompt engineering are the two common approaches to leveraging LLMs for code review automation. Objective: We aim to investigate the performance of LLMs-based code review automation based on two contexts, i.e., when LLMs are leveraged by fine-tuning and prompting. Fine-tuning involves training the model on a specific code review dataset, while prompting involves providing explicit instructions to guide the model's generation process without requiring a specific code review dataset. Method: We leverage model fine-tuning and inference techniques (i.e., zero-shot learning, few-shot learning and persona) on LLMs-based code review automation. In total, we investigate 12 variations of two LLMs-based code review automation (i.e., GPT- 3.5 and Magicoder), and compare them with the Guo et al.'s approach and three existing code review automation approaches. Results: The fine-tuning of GPT 3.5 with zero-shot learning helps GPT-3.5 to achieve 73.17% -74.23% higher EM than the Guo et al.'s approach. In addition, when GPT-3.5 is not fine-tuned, GPT-3.5 with few-shot learning achieves 46.38% - 659.09% higher EM than GPT-3.5 with zero-shot learning. Conclusions: Based on our results, we recommend that (1) LLMs for code review automation should be fine-tuned to achieve the highest performance; and (2) when data is not sufficient for model fine-tuning (e.g., a cold-start problem), few-shot learning without a persona should be used for LLMs for code review automation.