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	<title>industrial monitoring &#8211; BIOENGINEER.ORG</title>
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		<title>Dual-path AI framework spots hidden anomalies in industrial time series data</title>
		<link>https://bioengineer.org/dual-path-ai-framework-spots-hidden-anomalies-in-industrial-time-series-data/</link>
		
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		<pubDate>Sun, 11 Oct 2026 10:25:50 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Anomaly Detection]]></category>
		<category><![CDATA[benchmark evaluation]]></category>
		<category><![CDATA[cybersecurity]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[dual-path separation]]></category>
		<category><![CDATA[frequency domain]]></category>
		<category><![CDATA[industrial monitoring]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[multi-view fusion]]></category>
		<category><![CDATA[nonstationary signals]]></category>
		<category><![CDATA[time series analysis]]></category>
		<category><![CDATA[wavelet transform]]></category>
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					<description><![CDATA[Researchers at Guangdong University of Technology have developed DP-MVAD, a dual-path multi-view framework that disentangles trends from perturbations and fuses temporal, spectral, and wavelet views to detect time series anomalies earlier and more accurately.]]></description>
		
		
		
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