Hasil untuk "Automation"

R. Parasuraman, D. Manzey

Hye Kyung Jeon, Gwang Ha Kim

Accurate lesion size measurement is essential in endoscopic practice as it influences treatment strategies, surveillance decisions, and clinical outcomes, especially in colorectal polyps. Traditional measurement techniques, including visual estimation and biopsy forceps, have significant interobserver variability and procedural inefficiencies. Recent advancements in digital measurement technologies, including virtual scale endoscopy (VSE) and artificial intelligence (AI)-assisted virtual rulers, have addressed these limitations. VSE projects a virtual scale onto endoscopic images, enhancing measurement precision and reducing variability. Several studies have demonstrated its superior accuracy compared with conventional methods; however, limitations such as increased procedure time and operator training requirements persist. AI-assisted virtual rulers utilize deep learning algorithms to automate lesion size estimation, significantly improving reproducibility and diagnostic reliability. Although these technologies offer promising improvements, challenges remain, including real-time integration, standardization, and regulatory approval. Future research should focus on refining AI models, expanding validation studies, and optimizing their usability in routine practice. A hybrid approach that combines AI automation with real-time digital tools may enhance the precision and efficiency of endoscopic lesion assessment, ultimately improving patient outcomes.

Shu-Yu Liang, Run-Qiu Zhu, Hong Xia et al.

Laser micro-nano processing technologies have been developed to address challenges that are otherwise difficult to solve in industrial applications and diverse scientific fields. These technologies offer designable patterning, arraying capabilities, three-dimensional (3D) processing, and high precision. Recent advancements in laser technologies have demonstrated their effectiveness as powerful tools for micro-nano processing of optoelectronic materials. By utilizing various laser techniques—such as laser-induced polymerization, laser ablation, laser-induced transfer, laser-directed assembly, and laser-assisted crystallization—broad applications in image sensors, displays, solar cells, lasers, anti-counterfeiting, and information encryption have been enabled. This review comprehensively summarizes recent progress in the laser micro-nano processing of optoelectronic materials, including the technologies used for preparation, patterning, arraying, and modification. These laser fabrication methods uniquely provide capabilities such as annealing, phase transitions, and ion exchange in optoelectronic materials. We also discuss the perspectives and challenges for future developments, including the advantages, disadvantages, and potential applications of different laser micro-nano processing technologies. With the rapid advancements in laser micro-nanofabrication, we foresee significant growth in advanced, high-performance optoelectronic applications. This review aims to provide researchers with insights into the current state and future prospects of laser-based micro-nano processing, encouraging further exploration and innovation in this promising field.

Ruchira Ray, Leona Pang, Sanjana Srivastava et al.

Understanding the motivations underlying the human inclination to automate tasks is vital to developing truly helpful robots integrated into daily life. Accordingly, we ask: are individuals more inclined to automate chores based on the time they consume or the feelings experienced while performing them? This study explores these preferences and whether they vary across different social groups (i.e., gender category and income level). Leveraging data from the BEHAVIOR-1K dataset, the American Time-Use Survey, and the American Time-Use Survey Well-Being Module, we investigate the relationship between the desire for automation, time spent on daily activities, and their associated feelings - Happiness, Meaningfulness, Sadness, Painfulness, Stressfulness, or Tiredness. Our key findings show that, despite common assumptions, time spent does not strongly relate to the desire for automation for the general population. For the feelings analyzed, only happiness and pain are key indicators. Significant differences by gender and economic level also emerged: Women prefer to automate stressful activities, whereas men prefer to automate those that make them unhappy; mid-income individuals prioritize automating less enjoyable and meaningful activities, while low and high-income show no significant correlations. We hope our research helps motivate technologies to develop robots that match the priorities of potential users, moving domestic robotics toward more socially relevant solutions. We open-source all the data, including an online tool that enables the community to replicate our analysis and explore additional trends at https://robin-lab.cs.utexas.edu/why-automate-this/.

Md Mahadi Hassan, John Salvador, Akond Rahman et al.

LLMs show promise in code generation, yet their effectiveness for IT automation tasks, particularly for tools like Ansible, remains understudied. Existing benchmarks rely primarily on synthetic tasks that fail to capture the needs of practitioners who use IT automation tools, such as Ansible. We present ITAB (IT Automation Task Benchmark), a benchmark of 126 diverse tasks (e.g., configuring servers, managing files) where each task accounts for state reconciliation: a property unique to IT automation tools. ITAB evaluates LLMs' ability to generate functional Ansible automation scripts via dynamic execution in controlled environments. We evaluate 14 open-source LLMs, none of which accomplish pass@10 at a rate beyond 12%. To explain these low scores, we analyze 1,411 execution failures across the evaluated LLMs and identify two main categories of prevalent semantic errors: failures in state reconciliation related reasoning (44.87% combined from variable (11.43%), host (11.84%), path(11.63%), and template (9.97%) issues) and deficiencies in module-specific execution knowledge (24.37% combined from Attribute and parameter (14.44%) and module (9.93%) errors). Our findings reveal key limitations in open-source LLMs' ability to track state changes and apply specialized module knowledge, indicating that reliable IT automation will require major advances in state reasoning and domain-specific execution understanding.

Ankur Tomar, Hengyue Liang, Indranil Bhattacharya et al.

The emergence of AI-driven web automation through Large Language Models (LLMs) offers unprecedented opportunities for optimizing digital workflows. However, deploying such systems within industry's real-world environments presents four core challenges: (1) ensuring consistent execution, (2) accurately identifying critical HTML elements, (3) meeting human-like accuracy in order to automate operations at scale and (4) the lack of comprehensive benchmarking data on internal web applications. Existing solutions are primarily tailored for well-designed, consumer-facing websites (e.g., Amazon.com, Apple.com) and fall short in addressing the complexity of poorly-designed internal web interfaces. To address these limitations, we present Cybernaut, a novel framework to ensure high execution consistency in web automation agents designed for robust enterprise use. Our contributions are threefold: (1) a Standard Operating Procedure (SOP) generator that converts user demonstrations into reliable automation instructions for linear browsing tasks, (2) a high-precision HTML DOM element recognition system tailored for the challenge of complex web interfaces, and (3) a quantitative metric to assess execution consistency. The empirical evaluation on our internal benchmark demonstrates that using our framework enables a 23.2% improvement (from 72% to 88.68%) in task execution success rate over the browser_use. Cybernaut identifies consistent execution patterns with 84.7% accuracy, enabling reliable confidence assessment and adaptive guidance during task execution in real-world systems. These results highlight Cybernaut's effectiveness in enterprise-scale web automation and lay a foundation for future advancements in web automation.

M. Vagia, A. Transeth, S. Fjerdingen

Yuanxin Chai, Liguo Miao, Jinghu Tang et al.

To solve the problem of insufficient melt pool width feature extraction accuracy caused by splash, arc light, and other interferences in the metal deposition process, a melt pool width extraction method based on the variable step size erosion model is proposed according to the characteristics of the spatial distribution of the melt pool size features. To achieve accurate measurement of the melt pool width, the melt pool image is first denoised using mathematical morphology and then segmented roughly using manual thresholding. Subsequently, the melt pool contour is iterated using an erosion model to obtain precise point localization information after fine segmentation, followed by the calculation of the melt pool width. Comparison experiments demonstrate that the method exhibits excellent accuracy and robustness in extracting melt pool width, while also showcasing high efficiency in fulfilling the requirements for closed-loop control. These findings lay the groundwork for the closed-loop control of the melt pool size.

Muhammad Ali Musarat, Abdul Mateen Khan, Wesam Salah Alaloul et al.

As construction projects increase in complexity, there are growing challenges with conventional monitoring methods in terms of efficiency, safety, and competitiveness. Traditional supervision techniques are labour-intensive, intermittent, and prone to errors. Hence, this study statistically evaluates the potential advantages of photogrammetry, sensors, and algorithms to enable continuous automated monitoring. The results of this survey were analysed to compare manual and automated monitoring systems. The outcome shows that the Malaysian construction industry is aware of Automated Monitoring Innovations for Efficient and Safe Construction Practices. The top ranked factor was Photogrammetry which had a relative importance index (RII) of 0.821 for straightforward site monitoring and 0.812 for accelerated 3D BIM modelling. The RII for sensors to track labourers, apparatus, and progress in real time was 0.82, while the RII for hazard anticipation was 0.796. Automation achieved a reduction in fatigue by 0.784, labour intensity by 0.792, and time demands by 0.768, as measured by the RII. A conceptual framework was developed that incorporates measurable improvements in schedules, safety, and quality control. Automated solutions, as opposed to human examinations which are prone to error, provided exhaustive geographical data and ongoing surveillance notwithstanding obstacles pertaining to cost, cybersecurity, privacy, and integration. Construction monitoring must incorporate new technology, strategic change management, data investments, and supporting regulations to increase profitability, safety, and efficiency as competition and complexity increase.

Johannes Betz, Melina Lutwitzi, Steven Peters

The aim of this paper is to investigate the relationship between operational design domains (ODD), automated driving SAE Levels, and Technology Readiness Level (TRL). The first highly automated vehicles, like robotaxis, are in commercial use, and the first vehicles with highway pilot systems have been delivered to private customers. It has emerged as a crucial issue that these automated driving systems differ significantly in their ODD and in their technical maturity. Consequently, any approach to compare these systems is difficult and requires a deep dive into defined ODDs, specifications, and technologies used. Therefore, this paper challenges current state-of-the-art taxonomies and develops a new and integrated taxonomy that can structure automated vehicle systems more efficiently. We use the well-known SAE Levels 0-5 as the "level of responsibility", and link and describe the ODD at an intermediate level of abstraction. Finally, a new maturity model is explicitly proposed to improve the comparability of automated vehicles and driving functions. This method is then used to analyze today's existing automated vehicle applications, which are structured into the new taxonomy and rated by the new maturity levels. Our results indicate that this new taxonomy and maturity level model will help to differentiate automated vehicle systems in discussions more clearly and to discover white fields more systematically and upfront, e.g. for research but also for regulatory purposes.

Lola Burgueño, Davide Di Ruscio, Houari Sahraoui et al.

Model-Driven Engineering (MDE) provides a huge body of knowledge of automation for many different engineering tasks, especially those involving transitioning from design to implementation. With the huge progress made in Artificial Intelligence (AI), questions arise about the future of MDE, such as how existing MDE techniques and technologies can be improved or how other activities that currently lack dedicated support can also be automated. However, at the same time, it has to be revisited where and how models should be used to keep the engineers in the loop for creating, operating, and maintaining complex systems. To trigger dedicated research on these open points, we discuss the history of automation in MDE and present perspectives on how automation in MDE can be further improved and which obstacles have to be overcome in both the medium and long-term.

Ben Armstrong, Valerie K. Chen, Alex Cuellar et al.

Common narratives about automation often pit new technologies against workers. The introduction of advanced machine tools, industrial robots, and AI have all been met with concern that technological progress will mean fewer jobs. However, workers themselves offer a more optimistic, nuanced perspective. Drawing on a far-reaching 2024 survey of more than 9,000 workers across nine countries, this paper finds that more workers report potential benefits from new technologies like robots and AI for their safety and comfort at work, their pay, and their autonomy on the job than report potential costs. Workers with jobs that ask them to solve complex problems, workers who feel valued by their employers, and workers who are motivated to move up in their careers are all more likely to see new technologies as beneficial. In contrast to assumptions in previous research, more formal education is in some cases associated with more negative attitudes toward automation and its impact on work. In an experimental setting, the prospect of financial incentives for workers improve their perceptions of automation technologies, whereas the prospect of increased input about how new technologies are used does not have a significant effect on workers' attitudes toward automation.

Zhengang Zhao, Jiankun Wang, Qingchan Liu et al.

The micro-direct methanol fuel cell (μDMFC) has the advantages of high energy density, high conversion efficiency, and simple structure, which brought vast application prospects in portable devices. However, some shortcomings still exist, such as low catalyst utilization and power density. This paper proposes a new cathode electrode structure for the μDMFC. The structure consists of a multi-walled carbon nanotube layer and a cathode double microporous layer (CD-MPL) prepared from carbon powder. The outer microporous layer (OMPL) is composed of multi-walled carbon nanotubes (MWCNTs), Nafion solution, and carbon powder, and the inner microporous layer (IMPL) is composed of carbon powder and polytetrafluoroethylene (PTFE). The experimental results show that the maximum power density of the μDMFC with a CD-MPL (CD-μDMFC) is 42.8 mW/cm2, which is 31.6% higher than that of the μDMFC with a cathode single microporous layer (CS-μDMFC). The pore size distribution of the OMPL of the CNT is measured by the mercury intrusion method. It can be seen that the distribution of pore size is wider and there are more pores with larger pore sizes, which are more conducive to the utilization of catalysts. The discharge experiment of the cell shows that the CD-μDMFC shows high discharge performance and fuel utilization at different concentrations. The double microporous layer (MPL) structure increases the porosity and pore range, broadens the three-phase interface for the reaction, and allows the catalyst to have more attachment sites. The existence of MWCNTs improves the conductivity and mass transfer capacity of the cathode.

Minan Tang, Kai Liang, Jiandong Qiu

Abstract The proportion of insulators in aerial power patrol images is small and the background of overhead lines is complex, often leading to incomplete and inaccurate detection of insulators. Therefore, an algorithm for detecting insulator targets based on multi‐feature fusion is developed in this study. Firstly, a dynamic threshold oriented fast and rotated brief algorithm is proposed, which uses the bag‐of‐words dictionary model to determine local shape features of the image, applies gradient weighting to the global texture feature vector extracted by the histogram of oriented gradients algorithm and performs radial gradient transformations to get the improved HOG of features. Secondly, the feature vectors are fused serially, the learning machine is trained and the parameters of the support vector machine are optimized using the quantum particle swarm optimization algorithm. Finally, the target area is pre‐divided by the selective search algorithm, and the area is classified by the learning machine. The experimental results show that the proposed feature extraction method can describe the image details more accurately than the existing methods, and the average accuracy of the feature extraction classifier can reach 93.7%, which helps to overcome the incomplete detection problem of insulator detection at the aerial work site.

Zimu Zheng

The scale of the global edge AI market continues to grow. The current technical challenges that hinder the large-scale replication of edge AI are mainly small samples on the edge and heterogeneity of edge data. In addition, edge AI customers often have requirements for data security compliance and offline autonomy of edge AI services. Based on the lifelong learning method in the academic world, we formally define the problem of edge-cloud collaborative lifelong learning for the first time, and release the industry's first open-source edge-cloud collaborative lifelong learning. Edge-cloud collaborative lifelong learning adapts to data heterogeneity at different edge locations through (1) multi-task transfer learning to achieve accurate prediction of "thousands of people and thousands of faces"; (2) incremental processing of unknown tasks, the more systems learn and the smarter systems are with small samples, gradually realize AI engineering and automation; (3) Use the cloud-side knowledge base to remember new situational knowledge to avoid catastrophic forgetting; (4) The edge-cloud collaborative architecture enables data security compliance and edge AI services to be offline autonomy while applying cloud resources. This work hopes to help fundamentally solve the above-mentioned challenges of edge-cloud collaborative machine learning.

Kevin Pu, Jim Yang, Angel Yuan et al.

Knowledge workers frequently encounter repetitive web data entry tasks, like updating records or placing orders. Web automation increases productivity, but translating tasks to web actions accurately and extending to new specifications is challenging. Existing tools can automate tasks that perform the same logical trace of UI actions (e.g., input text in each field in order), but do not support tasks requiring different executions based on varied input conditions. We present DiLogics, a programming-by-demonstration system that utilizes NLP to assist users in creating web automation programs that handle diverse specifications. DiLogics first semantically segments input data to structured task steps. By recording user demonstrations for each step, DiLogics generalizes the web macros to novel but semantically similar task requirements. Our evaluation showed that non-experts can effectively use DiLogics to create automation programs that fulfill diverse input instructions. DiLogics provides an efficient, intuitive, and expressive method for developing web automation programs satisfying diverse specifications.

Noah Goodall

Introduction: This paper reviewed current driving automation (DA) and baseline human-driven crash databases and evaluated their comparability. Method: Five sources of DA crash data and three sources of human-driven crash data were reviewed for consistency of inclusion criteria, scope of coverage, and potential sources of bias. Alternative methods to determine vehicle automation capability using vehicle identification number (VIN) from state-maintained crash records were also explored. Conclusions: Evaluated data sets used incompatible or nonstandard minimum crash severity thresholds, complicating crash rate comparisons. The most widely-used standard was "police-reportable crash," which itself has different reporting thresholds among jurisdictions. Although low- and no-damage crashes occur at greater frequencies and have more statistical power, they were not consistently reported for automated vehicles. Crash data collection can be improved through collection of driving automation exposure data, widespread collection of crash data form electronic data recorders, and standardization of crash definitions. Practical Applications: Researchers and DA developers may use this analysis to conduct more thorough and accurate evaluations of driving automation crash rates. Lawmakers and regulators may use these findings as evidence to enhance data collection efforts, both internally and via new rules regarding electronic data recorders.

Katja Mann, Lukas Püttmann

Abstract We provide a new measure of automation based on patents and study its employment effects. Classifying all U.S. patents granted between 1976 and 2014 as automation or nonautomation patents, we document a strong rise in the number and share of automation patents. We link patents to their industries of use and to commuting zones. To estimate the effect of automation, we use an instrumental variables strategy that relies on innovations developed independently from U.S. labor market trends. We find that automation technology has a positive effect on employment in local labor markets, driven by job growth in the service sector.

Kenneth Evans

Lidan Xiang, Ximin Li, Hao Liu et al.

As a classic algorithm for realizing robot local path planning, the dynamic window approach (DWA) uses an objective function to choose the optimal velocity commands. However, the path generated by the DWA in the complex environment is not smooth. Therefore, this paper proposes an improved DWA algorithm to make the path of the robot more smoother when avoiding obstacles. At the same time, in view of the condition that the weight coefficients of the evaluation function of the original DWA remain unchanged, this paper will add a fuzzy controller to realize the weight coefficients adaptation, so as to adapt to a more complex environment, and the generated path can be smoother.