With the rapid development of the Internet of Things(IoT),the automation of edge-side equipment has emerged as a significant trend.The existing fault diagnosismethods have the characteristics of heavy computing and st...With the rapid development of the Internet of Things(IoT),the automation of edge-side equipment has emerged as a significant trend.The existing fault diagnosismethods have the characteristics of heavy computing and storage load,and most of them have computational redundancy,which is not suitable for deployment on edge devices with limited resources and capabilities.This paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge distillation.First,we offer a clustering-based self-knowledge distillation approach(Cluster KD),which takes the mean value of the sample diagnosis results,clusters them,and takes the clustering results as the terms of the loss function.It utilizes the correlations between faults of the same type to improve the accuracy of the teacher model,especially for fault categories with high similarity.Then,the double knowledge distillation framework uses ordinary knowledge distillation to build a lightweightmodel for edge-side deployment.We propose a two-stage edge-side fault diagnosismethod(TSM)that separates fault detection and fault diagnosis into different stages:in the first stage,a fault detection model based on a denoising auto-encoder(DAE)is adopted to achieve fast fault responses;in the second stage,a diverse convolutionmodel with variance weighting(DCMVW)is used to diagnose faults in detail,extracting features frommicro andmacro perspectives.Through comparison experiments conducted on two fault datasets,it is proven that the proposed method has high accuracy,low delays,and small computation,which is suitable for intelligent edge-side fault diagnosis.In addition,experiments show that our approach has a smooth training process and good balance.展开更多
Precision medicine is revolutionizing global healthcare by enabling personalized diagnostics,disease prediction,and tailored treatment strategies.While the integration of genomics and data science holds immense potent...Precision medicine is revolutionizing global healthcare by enabling personalized diagnostics,disease prediction,and tailored treatment strategies.While the integration of genomics and data science holds immense potential to optimize precision therapeutic outcomes,a critical challenge lies in translating gene sequencing data into actionable insights for in vitro diagnostics.This bottleneck is largely attributed to the limitations of edge-side intelligent processing and automation.Despite advancements in gene sequencing technologies and bioinformatics tools,the workflow from sample collection to diagnostic report generation remains fragmented,inefficient,and lacks of intelligence.To address these challenges,we introduce an embodied LLM NGS sequencer on the edge for real-time,on-site smart genetic diagnostics.This instrument integrates a streamlined and comprehensive pipeline with deep learning networks for primary data analysis,machine learning for secondary data processing,and a large language model(LLM)optimized for tertiary data interpretation.The LLM is enhanced through quantization and compression,facilitating deployment on FPGA/GPU to accelerate diagnostic workflows.Experimental results showcased the superior performance by achieving a 13.72%increase in throughput,a 99.50%Q30%,and enable smart diagnostic on the edge with the performance up to 75 tokens/s.This work enables immediate,on-site DNA analysis,hence dramatically improving precision medicine’s accessibility and efficiency,and significantly advances diagnostic accuracy,automation,establishing a robust platform for AI-driven personalized medicine and setting a new benchmark for the future of healthcare delivery.展开更多
基金supported by the National Key R&D Program of China(2019YFB2103202).
文摘With the rapid development of the Internet of Things(IoT),the automation of edge-side equipment has emerged as a significant trend.The existing fault diagnosismethods have the characteristics of heavy computing and storage load,and most of them have computational redundancy,which is not suitable for deployment on edge devices with limited resources and capabilities.This paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge distillation.First,we offer a clustering-based self-knowledge distillation approach(Cluster KD),which takes the mean value of the sample diagnosis results,clusters them,and takes the clustering results as the terms of the loss function.It utilizes the correlations between faults of the same type to improve the accuracy of the teacher model,especially for fault categories with high similarity.Then,the double knowledge distillation framework uses ordinary knowledge distillation to build a lightweightmodel for edge-side deployment.We propose a two-stage edge-side fault diagnosismethod(TSM)that separates fault detection and fault diagnosis into different stages:in the first stage,a fault detection model based on a denoising auto-encoder(DAE)is adopted to achieve fast fault responses;in the second stage,a diverse convolutionmodel with variance weighting(DCMVW)is used to diagnose faults in detail,extracting features frommicro andmacro perspectives.Through comparison experiments conducted on two fault datasets,it is proven that the proposed method has high accuracy,low delays,and small computation,which is suitable for intelligent edge-side fault diagnosis.In addition,experiments show that our approach has a smooth training process and good balance.
基金the Shenzhen Science and Technology Program under Grant KQTD20200820113051096,Grant JCYJ20220818100217038the Science and Technology Innovation Key R&D Program of Chongqing.
文摘Precision medicine is revolutionizing global healthcare by enabling personalized diagnostics,disease prediction,and tailored treatment strategies.While the integration of genomics and data science holds immense potential to optimize precision therapeutic outcomes,a critical challenge lies in translating gene sequencing data into actionable insights for in vitro diagnostics.This bottleneck is largely attributed to the limitations of edge-side intelligent processing and automation.Despite advancements in gene sequencing technologies and bioinformatics tools,the workflow from sample collection to diagnostic report generation remains fragmented,inefficient,and lacks of intelligence.To address these challenges,we introduce an embodied LLM NGS sequencer on the edge for real-time,on-site smart genetic diagnostics.This instrument integrates a streamlined and comprehensive pipeline with deep learning networks for primary data analysis,machine learning for secondary data processing,and a large language model(LLM)optimized for tertiary data interpretation.The LLM is enhanced through quantization and compression,facilitating deployment on FPGA/GPU to accelerate diagnostic workflows.Experimental results showcased the superior performance by achieving a 13.72%increase in throughput,a 99.50%Q30%,and enable smart diagnostic on the edge with the performance up to 75 tokens/s.This work enables immediate,on-site DNA analysis,hence dramatically improving precision medicine’s accessibility and efficiency,and significantly advances diagnostic accuracy,automation,establishing a robust platform for AI-driven personalized medicine and setting a new benchmark for the future of healthcare delivery.
