The Additive Manufacturing Benchmark Test Series (AM Bench) provides rigorous measurement data for validating additive manufacturing (AM) simulations for a broad range of AM technologies and material systems. AM Bench...
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The Additive Manufacturing Benchmark Test Series (AM Bench) provides rigorous measurement data for validating additive manufacturing (AM) simulations for a broad range of AM technologies and material systems. AM Bench includes extensive in situ and ex situ measurements, simulation challenges for the AM modeling community, and a corresponding conference series. In 2022, the second round of AM Bench measurements, challenge problems, and conference were completed, focusing primarily upon laser powder bed fusion (LPBF) processing of metals, and both material extrusion processing and vat photopolymerization of polymers. In all, more than 100 people from 10 National Institute of Standards and Technology (NIST) divisions and 21 additional organizations were directly involved in the AM Bench 2022 measurements, data management, and conference organization. The international AM community submitted 138 sets of blind modeling simulations for comparison with the in situ and ex situ measurements, up from 46 submissions for the first round of AM Bench in 2018. Analysis of these submissions provides valuable insight into current AM modeling capabilities. The AM Bench data are permanently archived and freely accessible online. The AM Bench conference also hosted an embedded workshop on qualification and certification of AM materials and components.
In recent years, data spaces have emerged as a framework for secure data collaboration. data spaces are distributed data collaboration infrastructure systems that enable data collaboration among stakeholders. One of t...
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Proper edge 3-coloring of a cubic graph is an NP-complete problem, which is present in number of real-live problems - correct and efficient assigning of variables used in the program to registers of the system, schedu...
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Acute respiratory distress syndrome (ARDS) prognosis has become integral to modern critical care models aimed at determining expected patient outcomes, optimizing clinical pathways, and improving resource allocation. ...
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Due to the complexity and dynamic nature of large-scale microservice systems, manual troubleshooting is time-consuming and impractical. Therefore, automated Root Cause Analysis (RCA) is essential. However, existing RC...
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ISBN:
(数字)9798400712487
ISBN:
(纸本)9798400712487
Due to the complexity and dynamic nature of large-scale microservice systems, manual troubleshooting is time-consuming and impractical. Therefore, automated Root Cause Analysis (RCA) is essential. However, existing RCA approaches face significant challenges. (1) Multi-modal data (e.g. traces, logs, and metrics) record the status of microservice systems, but most existing RCA approaches rely on single-source data, failing to understand the system fully. (2) Existing RCA approaches ignore the services' anomaly state and their anomaly intensity. (3) The service-level RCAs lack detailed information for quick issue resolution. To tackle these challenges, we propose MRCA, a metric-level RCA approach using multi-modal data. Our key insight is that using multi-modal data allows for a comprehensive understanding of the system, enabling the localization of root causes across more anomaly scenarios. MRCA first utilizes traces and logs to obtain the ranking list of abnormal services based on reconstruction probability. It further builds causal graphs from services with high anomaly probability to discover the order in which abnormal metrics of different services occur. By incorporating a reward mechanism, MRCA terminates the excessive expansion of the causal graph and significantly reduces the time taken for causal analysis. Finally, MRCA can prune the ranking list based on the causal graph and identify metric-level root causes. Experiments on two widely-used microservice benchmarks demonstrate that MRCA outperforms state-of-the-art approaches in terms of both accuracy and efficiency.
Mask detection plays a major role in the prevention and control of epidemics after the COVID-19 outbreak as it is the most practical and effective method of prevention. For the appropriate employees, a great automatic...
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Emerging smart grid applications analyze large amounts of data collected from millions of meters and systems to facilitate distributed monitoring and real-time control tasks. However, current parallel data processing ...
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ISBN:
(纸本)9781538674628
Emerging smart grid applications analyze large amounts of data collected from millions of meters and systems to facilitate distributed monitoring and real-time control tasks. However, current parallel data processing systems are designed for common applications, unaware of the massive volume of the collected data, causing long data transfer delay during the computation and slow response time of smart grid systems. A promising direction to reduce delay is to jointly schedule computation tasks and data transfers. We identify that the smart grid data analytic jobs require the intermediate data among different computation stages to be transmitted orderly to avoid network congestion. This new feature prevents current scheduling algorithms from being efficient. In this work, an integrated computing and communication task scheduling scheme is proposed. The mathematical formulation of smart grid data analytic jobs scheduling problem is given, which is unsolvable by existing optimization methods due to the strongly coupled constraints. Several techniques are combined to linearize it for adapting the Branch and Cut method. Based on the topological information in the job graph, the Topology Aware Branch and Cut method is further proposed to speed up searching for optimal solutions. Numerical results demonstrate the effectiveness of the proposed method.
The data-cleaning approach applies the capabilities of large language models to reduce the noise in the extracted and received data from healthcare sources. The aim will be to clean the collected and extracted data by...
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Cloud service providers are currently storing large volumes of data on cloud. This requires verification mechanisms often employing cryptography by third party auditors. However, ensuring the security and privacy of e...
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Root cause analysis is crucial for cloud-native systems. However, existing supervised approaches ignore the potential of unlabeled data, which is frequent in the cloud-native root cause analysis scenarios. Moreover, t...
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ISBN:
(纸本)9798350344868;9798350344851
Root cause analysis is crucial for cloud-native systems. However, existing supervised approaches ignore the potential of unlabeled data, which is frequent in the cloud-native root cause analysis scenarios. Moreover, the class-imbalanced distribution of faults presents obstacles to applying semi-supervised learning. To overcome these limitations, we propose STRCA, a metrics-based semi-supervised self-training approach for root cause analysis. Furthermore, STRCA employs minority priority self-training, which selects pseudo-labels of high quality during generations. Additionally, the stepwise distribution alignment is introduced to rebalance the predicted distribution with gradually decreasing strength. These two strategies mitigate the class-imbalance of data in semi-supervised learning. Experiments on the public dataset show the effectiveness of STRCA with limited labels.
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