After conducting a concise analysis of the current landscape of listed companies and industrial transformation in Hubei Province, data from listed companies across 12 prefecture-level cities in this province spanning ...
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With the advancement of equipment and technology in tobacco enterprises, the construction of warehouse and distribution centers has become a primary focus on utilizing advanced equipment to enhance automation logistic...
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In the era of Industry 4.0 and beyond, intelligent and reliable models are vital for processes and assets. Models in smart manufacturing involve combining knowledge-based and data-driven methods with discrete and cont...
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In the era of Industry 4.0 and beyond, intelligent and reliable models are vital for processes and assets. Models in smart manufacturing involve combining knowledge-based and data-driven methods with discrete and continuous modelling components. Formalism choice determines models' strengths and weaknesses in accuracy, efficiency, robustness, and explainability. Hybrid models seem to be the only way to address the complexity of modern industrial systems with respect to different and conflicting quality criteria. This study focuses on three paradigms: Petri nets, cellular automata, and neural network driven deep learning. We create four hybrids: Petri nets controlling deep neural networks, and vice versa;cellular automata controlling deep neural networks, and vice versa. These hybrids combine explainable discrete models with continuous black-box models, enhancing either explainability with robustness or elevating accuracy with efficiency. The flexibility of these and similar hybrids enable enhancement of the scope and quality of modeling and simulation in smart manufacturing.
Traffic prediction is essential for intelligent transportation systems and smart city applications, yet existing spatio-temporal models face limitations. These include inadequate spatial feature extraction, neglect of...
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Log files generated by software systems can be utilized as a valuable resource in data-driven approaches to improve the system health and stability. These files often contain valuable information about runtime executi...
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ISBN:
(纸本)9798350381566;9798350381559
Log files generated by software systems can be utilized as a valuable resource in data-driven approaches to improve the system health and stability. These files often contain valuable information about runtime execution, and their effective monitoring requires analyzing an increasingly large volume of data logs. In this paper, a graph mining technique for log parsing is presented, which is source agnostic to the system. This means that the technique can function regardless of the source of the logs, making it more scalable and reusable. Unlike the existing approaches that rely heavily on domain knowledge and regular expression patterns, the proposed approach uses graph models and semantic analysis to detect data patterns with minimal user input. This makes it easy to implement it in a variety of scenarios where application-based logs may differ significantly. The proposed parsing technique is evaluated over seven datasets. It achieves the best performance on the Thunderbird dataset, where the technique takes 3.87 seconds for 2000 logs, while obtaining precision, recall and F1 measure higher than 0.99.
作者：
Rakov, Dmitry
Department of Technological Processes and Systems Control Moscow Russia
Decision making by extracting the knowledge underlying Big data is considered. Big data and morphological synthesis of engineeringsystems can be interrelated in the context of using Big data to improve and develop in...
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Tool wear monitoring (TWM) based on data-driven methods is essential to ensure product quality and overall efficiency. Machine learning (ML) models, unlike physics-based approaches, are practical for online monitoring...
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Automation in manufacturingsystems is rapidly changing, shifting from rigid solutions towards the use of smart technologies that better support manufacturers in meeting market demand. The implementation of these new ...
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In today's digital landscape, ensuring the security of data transmitted and stored in the cloud is paramount. Traditional encryption methods face challenges in scalability and susceptibility to quantum threats. Th...
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This paper explores hackathons as an evaluative tool and their contribution to students' learning. Mixed-data collection methods/qualitative and quantitative data collection were used by conducting a survey among ...
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ISBN:
(纸本)9783031533815;9783031533822
This paper explores hackathons as an evaluative tool and their contribution to students' learning. Mixed-data collection methods/qualitative and quantitative data collection were used by conducting a survey among students and semi-structured interviews among hackathon juries and organizers. Results have demonstrated that hackathons can be used as evaluative tools alongside exams. From a student's perspective, hackathons supply positive motivation and are less stressful while enabling students to demonstrate their knowledge in a work-like environment. From the organizer's perspective, hackathons require more effort in terms of organization whereas less effort is needed in handling of evaluation and grading. This work provides guidelines to address potential challenges and organize a hackathon as an exam replacement or part of the exam. This study advocates that the evaluation steps described in this paper could constitute a solid basis to develop a formal evaluation strategy to be applied to similar future events. The paper provides preliminary results that were collected in a rigorous manner. Future work should target a larger sample size to draw more firm conclusions and generalize to wide academic settings of software engineering programmes.
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