Cyber-Physical systems connect physical infrastructure and things to the internet and to one another by integrating sensing, computation, control, and networking. One of the main optimization objectives for the effect...
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This research study provides an overview of the QR-based Trash Management System, which emerges as a technology- driven process that aims at optimizing processes within the waste management process within urban enviro...
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This research study proposes a novel approach for behavioral tracking and anomaly detection in digital systems by using AI-driven models, particularly for applications in signal processing and digital computer environ...
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C2000 TM 32-bit microcontrollers excel in real-time control, optimizing closed-loop performance for various applications such as industrial motor drives, solar inverters, digital power systems, and sensing. The lineup...
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Traditional fault diagnosis methods are often inefficient and costly, hindered by limited computing power that compromises accuracy. Their lack of flexibility also poses challenges in establishing effective multi-devi...
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Traffic accidents pose a major risk to human life and have a considerable economic impact worldwide. In this study, we present the RAPID framework, an innovative approach that integrates Artificial Intelligence (AI) a...
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Designing machine learning applications can be challenging, especially software architectures for handling real-time sensor data processed by compute- and software-intensive machine learning applications. This paper a...
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ISBN:
(纸本)9798350366266;9798350366259
Designing machine learning applications can be challenging, especially software architectures for handling real-time sensor data processed by compute- and software-intensive machine learning applications. This paper answers the two research questions: "Which parts of a machine learning pipeline do novel software architectures and framework optimize?" and the associated question of "Which parts do the architect then need to focus on?". The presented experiences and experimental results suggest that novel software architectures and frameworks optimize the learning and classification part of the pipelines. Therefore, the architect in particular needs to focus on data distribution and preprocessing as these parts were observed to have an overlooked computational cost and complexity. These results are important for software architects to become better at architecting machine learning-based systems.
This paper proposes an architecture of an ARM-based remote video electronic information monitoring system. The system converts the embedded Linux terminal system memory allocation problem into a 0-1 knapsack problem. ...
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Implementing aerial transportation systems in real-world scenarios is becoming feasible. Due to the ability to dynamically adjust the distance between quadrotor and cargo, the aerial transportation system with variabl...
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High Performance computing (HPC) systems generate a large amount of unstructured/alphanumeric log messages that capture the health state of their components. Due to their design complexity, HPC systems often undergo f...
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ISBN:
(纸本)9798350347937
High Performance computing (HPC) systems generate a large amount of unstructured/alphanumeric log messages that capture the health state of their components. Due to their design complexity, HPC systems often undergo failures that halt applications (e.g., weather prediction, aerodynamics simulation) execution. However, existing failure prediction methods, which typically seek to extract some information theoretic features, fail to scale both in terms of accuracy and prediction speed, limiting their adoption in real-time production systems. In this paper, differently from existing work and inspired by current transformer-based neural networks which have revolutionized the sequential learning in the natural language processing (NLP) tasks, we propose a novel scalable log-based, self-supervised model (i.e., no need for manual labels), called time Machine1, that predicts (i) forthcoming log events (ii) the upcoming failure and its location and (iii) the expected lead time to failure. time Machine is designed by combining two stacks of transformer-decoders, each employing the self-attention mechanism. The first stack addresses the failure location by predicting the sequence of log events and then identifying if a failure event is part of that sequence. The lead time to predicted failure is addressed by the second stack. We evaluate time Machine on four realworld HPC log datasets and compare it against three state-of-the-art failure prediction approaches. Results show that time Machine significantly outperforms the related works on Bleu, Rouge, MCC, and F1-score in predicting forthcoming events, failure location, failure lead-time, with higher prediction speed.
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