System logs record detailed information about system operation and areimportant for analyzing the system's operational status and performance. Rapidand accurate detection of system anomalies is of great significan...System logs record detailed information about system operation and areimportant for analyzing the system's operational status and performance. Rapidand accurate detection of system anomalies is of great significance to ensure system stability. However, large-scale distributed systems are becoming more andmore complex, and the number of system logs gradually increases, which bringschallenges to analyze system logs. Some recent studies show that logs can beunstable due to the evolution of log statements and noise introduced by log collection and parsing. Moreover, deep learning-based detection methods take a longtime to train models. Therefore, to reduce the computational cost and avoid loginstability we propose a new Word2Vec-based log unsupervised anomaly detection method (LogUAD). LogUAD does not require a log parsing step and takesoriginal log messages as input to avoid the noise. LogUAD uses Word2Vec togenerate word vectors and generates weighted log sequence feature vectors withTF-IDF to handle the evolution of log statements. At last, a computationally effi-cient unsupervised clustering is exploited to detect the anomaly. We conductedextensive experiments on the public dataset from Blue Gene/L (BGL). Experimental results show that the F1-score of LogUAD can be improved by 67.25%compared to LogCluster.展开更多
In order to optimize the wood internal quality detection and evaluation system and improve the comprehensive utilization rate of wood,this paper invented a set of log internal defect detection and visualization system...In order to optimize the wood internal quality detection and evaluation system and improve the comprehensive utilization rate of wood,this paper invented a set of log internal defect detection and visualization system by using the ultrasonic dry coupling agent method.The detection and visualization analysis of internal log defects were realized through log specimen test.The main conclusions show that the accuracy,reliability and practicability of the system for detecting the internal defects of log specimens have been effectively verified.The system can make the edge of the detected image smooth by interpolation algorithm,and the edge detection algorithm can be used to detect and reflect the location of internal defects of logs accurately.The content mentioned above has good application value for meeting the requirement of increasing demand for wood resources and improving the automation level of wood nondestructive testing instruments.展开更多
One particular challenge for large‑scale software systems is anomaly detection.System logs are a straightforward and common source of information for anomaly detection.Existing log‑based anomaly detectors are unusable...One particular challenge for large‑scale software systems is anomaly detection.System logs are a straightforward and common source of information for anomaly detection.Existing log‑based anomaly detectors are unusable in real‑world industrial systems due to high false‑positive rates.In this paper,we incorporate human feedback to adjust the detection model structure to reduce false positives.We apply our approach to two industrial large‑scale systems.Results have shown that our approach performs much better than state‑of‑the-art works with 50%higher accuracy.Besides,human feedback can reduce more than 70%of false positives and greatly improve detection precision.展开更多
Neutron well logging,using instruments equipped with neutron source and detectors(e.g.,^(3)He-tubes,Nal,BGO),plays a key role in lithological differentiation,porosity determination,and fluid property evaluation in the...Neutron well logging,using instruments equipped with neutron source and detectors(e.g.,^(3)He-tubes,Nal,BGO),plays a key role in lithological differentiation,porosity determination,and fluid property evaluation in the petroleum industry.The growing trend of multifu nctional neutron well logging,which enables simultaneous extraction of multiple reservoir characteristics,requiring high-performance detectors capable of withstanding high-temperature downhole conditions,limited space,and instrument vibrations,while also detecting multiple particle types.The Cs_(2)LiYCl_(6):Ce^(3+)(CLYC)elpasolite scintillator demonstrates excellent temperature resistance and detection efficiency,making it become a promising candidate for leading the development of the novel neutron-based double-particle logging technology.This study employed Monte Carlo simulations to generate equivalent gamma spectra and proposed a pulse shape discrimination simulation method based on theoretical analysis and probabilistic iteration.The performance of CLYC was compared to that of common detectors in terms of physical properties and detection efficiency.A double-particle pulsed neutron detection system for porosity determination was established,based on the count ratio of equivalent gamma rays from the range of 2.95-3.42 MeVee energy bins.Results showed that CLYC can effectively replace ^(3)He-tubes for porosity measurement,providing consistent responses.This study offers numerical simulation support for the design of future neutron well logging tools and the application of double-particle detectors in logging systems.展开更多
基金funded by the Researchers Supporting Project No.(RSP.2021/102)King Saud University,Riyadh,Saudi ArabiaThis work was supported in part by the National Natural Science Foundation of China under Grant 61802030+2 种基金Natural Science Foundation of Hunan Province under Grant 2020JJ5602the Research Foundation of Education Bureau of Hunan Province under Grant 19B005the International Cooperative Project for“Double First-Class”,CSUST under Grant 2018IC24.
文摘System logs record detailed information about system operation and areimportant for analyzing the system's operational status and performance. Rapidand accurate detection of system anomalies is of great significance to ensure system stability. However, large-scale distributed systems are becoming more andmore complex, and the number of system logs gradually increases, which bringschallenges to analyze system logs. Some recent studies show that logs can beunstable due to the evolution of log statements and noise introduced by log collection and parsing. Moreover, deep learning-based detection methods take a longtime to train models. Therefore, to reduce the computational cost and avoid loginstability we propose a new Word2Vec-based log unsupervised anomaly detection method (LogUAD). LogUAD does not require a log parsing step and takesoriginal log messages as input to avoid the noise. LogUAD uses Word2Vec togenerate word vectors and generates weighted log sequence feature vectors withTF-IDF to handle the evolution of log statements. At last, a computationally effi-cient unsupervised clustering is exploited to detect the anomaly. We conductedextensive experiments on the public dataset from Blue Gene/L (BGL). Experimental results show that the F1-score of LogUAD can be improved by 67.25%compared to LogCluster.
文摘In order to optimize the wood internal quality detection and evaluation system and improve the comprehensive utilization rate of wood,this paper invented a set of log internal defect detection and visualization system by using the ultrasonic dry coupling agent method.The detection and visualization analysis of internal log defects were realized through log specimen test.The main conclusions show that the accuracy,reliability and practicability of the system for detecting the internal defects of log specimens have been effectively verified.The system can make the edge of the detected image smooth by interpolation algorithm,and the edge detection algorithm can be used to detect and reflect the location of internal defects of logs accurately.The content mentioned above has good application value for meeting the requirement of increasing demand for wood resources and improving the automation level of wood nondestructive testing instruments.
基金ZTE Industry-University-Institute Cooperation Funds under Grant No.20200492.
文摘One particular challenge for large‑scale software systems is anomaly detection.System logs are a straightforward and common source of information for anomaly detection.Existing log‑based anomaly detectors are unusable in real‑world industrial systems due to high false‑positive rates.In this paper,we incorporate human feedback to adjust the detection model structure to reduce false positives.We apply our approach to two industrial large‑scale systems.Results have shown that our approach performs much better than state‑of‑the-art works with 50%higher accuracy.Besides,human feedback can reduce more than 70%of false positives and greatly improve detection precision.
基金the support of the National Natural Science Foundation of China(42174147,42474155)the Scientific and Technological Innovation Projects of Laoshan Laboratory(LSKJ20220347)。
文摘Neutron well logging,using instruments equipped with neutron source and detectors(e.g.,^(3)He-tubes,Nal,BGO),plays a key role in lithological differentiation,porosity determination,and fluid property evaluation in the petroleum industry.The growing trend of multifu nctional neutron well logging,which enables simultaneous extraction of multiple reservoir characteristics,requiring high-performance detectors capable of withstanding high-temperature downhole conditions,limited space,and instrument vibrations,while also detecting multiple particle types.The Cs_(2)LiYCl_(6):Ce^(3+)(CLYC)elpasolite scintillator demonstrates excellent temperature resistance and detection efficiency,making it become a promising candidate for leading the development of the novel neutron-based double-particle logging technology.This study employed Monte Carlo simulations to generate equivalent gamma spectra and proposed a pulse shape discrimination simulation method based on theoretical analysis and probabilistic iteration.The performance of CLYC was compared to that of common detectors in terms of physical properties and detection efficiency.A double-particle pulsed neutron detection system for porosity determination was established,based on the count ratio of equivalent gamma rays from the range of 2.95-3.42 MeVee energy bins.Results showed that CLYC can effectively replace ^(3)He-tubes for porosity measurement,providing consistent responses.This study offers numerical simulation support for the design of future neutron well logging tools and the application of double-particle detectors in logging systems.
