In this paper, an advance measuring algorithm was designed to measure acoustic emission (AE) signals in a high-frequency range based on a C-Sharp programming language for a large two-stroke marine diesel engine. To ve...
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In this paper, an advance measuring algorithm was designed to measure acoustic emission (AE) signals in a high-frequency range based on a C-Sharp programming language for a large two-stroke marine diesel engine. To verify the ability of this algorithm, an experiment was carried out to investigate the characteristics of the AE signal caused by the combustion process on the large two-stroke low-speed marine diesel engine in the frequency range 100-900 kHz. The results showed that the AE signals emitted from the combustion event at around the top dead center could be well-observed in this frequency range. These outcomes also proved that the AE signal could not propagate to other cylinders due to a big distance between the cylinders. However, the combustion process affected the remaining AE sources. Moreover, this technique could be applied to detect the firing state as well as the combustion time of each cylinder. (C) 2020 Elsevier Ltd. All rights reserved.
False alarms are a big challenge for intrusion detection systems (IDSs). A lot of approaches, especially machine learning based schemes, have been proposed to mitigate this issue by filtering out these false alarms. B...
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ISBN:
(纸本)9780769550220
False alarms are a big challenge for intrusion detection systems (IDSs). A lot of approaches, especially machine learning based schemes, have been proposed to mitigate this issue by filtering out these false alarms. But a fundamental problem is how to objectively evaluate an algorithm in terms of its ability to correctly identify false alarms and true alarms. To improve the utilization of various machine learning algorithms, intelligent false alarm reduction has been proposed that aims to select and apply an appropriate algorithm in an adaptive way. Traditional metrics (e. g., true positive rate, false positive rate) are mainly used in the algorithm selection and evaluation, however, no single metric seems sufficient and objective enough to measure the capability of an algorithm in reducing false alarms. The lack of an objective and single metric makes it difficult to further fine-tune and evaluate the performance of algorithms in reducing IDS false alarms. In this paper, we begin by describing the relationship between the process of intrusion detection and the process of false alarm detection (reduction). Then we provide an information-theoretic analysis of intelligent false alarm reduction and propose an objective and single metric to evaluate different algorithms in identifying IDS false alarms. We further evaluate our metric under three scenarios by comparing it with several existing metrics.
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