目的推动印刷行业朝更高效的方向迈进,提高印刷设备的易操作性和视觉识别性。方法将Kano和FAST(Function Analysis System Technique)模型引入瓦楞纸激光打印设备设计的前期应用需求分析中,通过问卷的方法获取用户的基本要求,并划分为...目的推动印刷行业朝更高效的方向迈进,提高印刷设备的易操作性和视觉识别性。方法将Kano和FAST(Function Analysis System Technique)模型引入瓦楞纸激光打印设备设计的前期应用需求分析中,通过问卷的方法获取用户的基本要求,并划分为几个子类型,进而建立Kano的二维功能属性模型。采用FAST法建立功能树,辅助使用Kano模型,从而更精准地分析用户需求,并更好地根据其需求进行优化设计。结果综合运用设计原理,针对性地挖掘瓦楞纸激光印刷设备在造型识别性、操作易用性、生产安全性上存在的问题,进而输出更优解。结论该设计方法的引入有助于为同类型的印刷设备设计提供参考,并引起更多相关厂家的重视,推动印刷行业向更积极的方向发展。展开更多
来自人造卫星的信号是射电天文观测面临的主要射频干扰(radio frequency interference,RFI)之一,这些RFI会将天文信号掩埋,为天文信号的搜寻和分析带来困扰。为了缓减卫星对天文观测的影响,我们在之前的工作中为500 m口径球面射电望远镜...来自人造卫星的信号是射电天文观测面临的主要射频干扰(radio frequency interference,RFI)之一,这些RFI会将天文信号掩埋,为天文信号的搜寻和分析带来困扰。为了缓减卫星对天文观测的影响,我们在之前的工作中为500 m口径球面射电望远镜(Five-hundred-meter Aperture Spherical radio Telescope,FAST)开发了卫星电磁干扰监测软件,主要包括卫星数据库、观测模块和监测模块。近年来随着多个巨型卫星星座的规划发射以及望远镜观测模式的增多,卫星对射电天文观测的影响更为复杂,已有的软件已经不能满足实际的需要。为此,本文在单个卫星干扰分析的基础上提出了卫星星座的干扰评估方法,并对已有监测软件进行了升级,升级后卫星数据库覆盖更多的在轨卫星及星座信息且能够自动化更新,观测模块能够支持更多种观测模式下的卫星过境预测和干扰评估。在实际天文观测中,通过接在FAST接收机上的频谱仪数据对软件的干扰预测结果进行了实验验证,结果证明升级后的软件能够在多种观测模式下预测可能威胁的卫星以及对应的过境时间,为望远镜观测规划的调整、卫星干扰的规避和接收系统的保护提供重要的支撑。展开更多
Video analytics is an integral part of surveillance cameras. Comparedto video analytics, audio analytics offers several benefits, includingless expensive equipment and upkeep expenses. Additionally, the volume ofthe a...Video analytics is an integral part of surveillance cameras. Comparedto video analytics, audio analytics offers several benefits, includingless expensive equipment and upkeep expenses. Additionally, the volume ofthe audio datastream is substantially lower than the video camera datastream,especially concerning real-time operating systems, which makes it lessdemanding of the data channel’s bandwidth needs. For instance, automaticlive video streaming from the site of an explosion and gunshot to the policeconsole using audio analytics technologies would be exceedingly helpful forurban surveillance. Technologies for audio analytics may also be used toanalyze video recordings and identify occurrences. This research proposeda deep learning model based on the combination of convolutional neuralnetwork (CNN) and recurrent neural network (RNN) known as the CNNRNNapproach. The proposed model focused on automatically identifyingpulse sounds that indicate critical situations in audio sources. The algorithm’saccuracy ranged from 95% to 81% when classifying noises from incidents,including gunshots, explosions, shattered glass, sirens, cries, and dog barking.The proposed approach can be applied to provide security for citizens in openand closed locations, like stadiums, underground areas, shopping malls, andother places.展开更多
为确定500 m口径球面射电望远镜(Five-hundred-meter Aperture Spherical radio Telescope,FAST)与其周边公众移动通信(Public Mobile Telecommunications,PMT)系统的电磁兼容(electromagnetic compatibility,EMC)特性,本文综合论述了F...为确定500 m口径球面射电望远镜(Five-hundred-meter Aperture Spherical radio Telescope,FAST)与其周边公众移动通信(Public Mobile Telecommunications,PMT)系统的电磁兼容(electromagnetic compatibility,EMC)特性,本文综合论述了FAST宁静区内中国移动、中国联通和中国电信三大运营商所属PMT基站对其产生的电磁干扰。首先,从射电天文业务的频谱划分谈起,论述了射电天文业务干扰源类型,引出了其运行保护标准,进而针对FAST详细说明了FAST宁静区的用频法规和保护要求;其次,分析了ITU-R建议电波传播预测与干扰分析方法,并通过实地测量验证了该方法的适用性,进一步针对性地分析了PMT基站的电磁辐射传播特性,综合评估了FAST宁静区内PMT基站的干扰情况:FAST宁静区域90.24%的PMT基站在一定程度上均会对FAST产生干扰,而在所选分析条件下,仅有43.14%的数据符合FAST保护要求;最后,针对PMT基站干扰信号的抑制和消除,分析了常用的射电天文射频干扰抑制方法,同时为保障FAST免受PMT基站干扰,从FAST和PMT基站的角度出发论述了可行的用频防护措施,并基于实施难度、经济成本、策略收益和通信质量4类指标建立了防护方法的评估体系,对所提防护方法进行了实例说明。上述研究成果可为保障FAST的安全观测提供技术基础。展开更多
The scarcity of in-situ ocean observations poses a challenge for real-time information acquisition in the ocean.Among the crucial hydroacoustic environmental parameters,ocean sound velocity exhibits significant spatia...The scarcity of in-situ ocean observations poses a challenge for real-time information acquisition in the ocean.Among the crucial hydroacoustic environmental parameters,ocean sound velocity exhibits significant spatial and temporal variability and it is highly relevant to oceanic research.In this study,we propose a new data-driven approach,leveraging deep learning techniques,for the prediction of sound velocity fields(SVFs).Our novel spatiotemporal prediction model,STLSTM-SA,combines Spatiotemporal Long Short-Term Memory(ST-LSTM) with a self-attention mechanism to enable accurate and real-time prediction of SVFs.To circumvent the limited amount of observational data,we employ transfer learning by first training the model using reanalysis datasets,followed by fine-tuning it using in-situ analysis data to obtain the final prediction model.By utilizing the historical 12-month SVFs as input,our model predicts the SVFs for the subsequent three months.We compare the performance of five models:Artificial Neural Networks(ANN),Long ShortTerm Memory(LSTM),Convolutional LSTM(ConvLSTM),ST-LSTM,and our proposed ST-LSTM-SA model in a test experiment spanning 2019 to 2022.Our results demonstrate that the ST-LSTM-SA model significantly improves the prediction accuracy and stability of sound velocity in both temporal and spatial dimensions.The ST-LSTM-SA model not only accurately predicts the ocean sound velocity field(SVF),but also provides valuable insights for spatiotemporal prediction of other oceanic environmental variables.展开更多
Progress in the fast charging of high-capacity silicon monoxide(SiO)-based anode is currently hindered by insufficient conductivity and notable volume expansion.The construction of an interface conductive network effe...Progress in the fast charging of high-capacity silicon monoxide(SiO)-based anode is currently hindered by insufficient conductivity and notable volume expansion.The construction of an interface conductive network effectively addresses the aforementioned problems;however,the impact of its quality on lithium-ion transfer and structure durability is yet to be explored.Herein,the influence of an interface conductive network on ionic transport and mechanical stability under fast charging is explored for the first time.2D modeling simulation and Cryo-transmission electron microscopy precisely reveal the mitigation of interface polarization owing to a higher fraction of conductive inorganic species formation in bilayer solid electrolyte interphase is mainly responsible for a linear decrease in ionic diffusion energy barrier.Furthermore,atomic force microscopy and Raman shift exhibit substantial stress dissipation generated by a complete conductive network,which is critical to the linear reduction of electrode residual stress.This study provides insights into the rational design of optimized interface SiO-based anodes with reinforced fast-charging performance.展开更多
Applications of lithium-sulfur(Li-S)batteries are still limited by the sluggish conversion kinetics from polysulfide to Li_(2)S.Although various single-atom catalysts are available for improving the conversion kinetic...Applications of lithium-sulfur(Li-S)batteries are still limited by the sluggish conversion kinetics from polysulfide to Li_(2)S.Although various single-atom catalysts are available for improving the conversion kinetics,the sulfur redox kinetics for Li-S batteries is still not ultrafast.Herein,in this work,a catalyst with dual-single-atom Pt-Co embedded in N-doped carbon nanotubes(Pt&Co@NCNT)was proposed by the atomic layer deposition method to suppress the shuttle effect and synergistically improve the interconversion kinetics from polysulfides to Li_(2)S.The X-ray absorption near edge curves indicated the reversible conversion of Li_(2)Sx on the S/Pt&Co@NCNT electrode.Meanwhile,density functional theory demonstrated that the Pt&Co@NCNT promoted the free energy of the phase transition of sulfur species and reduced the oxidative decomposition energy of Li_(2)S.As a result,the batteries assembled with S/Pt&Co@NCNT electrodes exhibited a high capacity retention of 80%at 100 cycles at a current density of 1.3 mA cm^(−2)(S loading:2.5 mg cm^(−2)).More importantly,an excellent rate performance was achieved with a high capacity of 822.1 mAh g^(−1) at a high current density of 12.7 mA cm^(−2).This work opens a new direction to boost the sulfur redox kinetics for ultrafast Li-S batteries.展开更多
Auscultation is crucial for the diagnosis of respiratory system diseases.However,traditional stethoscopes have inherent limitations,such as inter-listener variability and subjectivity,and they cannot record respirator...Auscultation is crucial for the diagnosis of respiratory system diseases.However,traditional stethoscopes have inherent limitations,such as inter-listener variability and subjectivity,and they cannot record respiratory sounds for offline/retrospective diagnosis or remote prescriptions in telemedicine.The emergence of digital stethoscopes has overcome these limitations by allowing physicians to store and share respiratory sounds for consultation and education.On this basis,machine learning,particularly deep learning,enables the fully-automatic analysis of lung sounds that may pave the way for intelligent stethoscopes.This review thus aims to provide a comprehensive overview of deep learning algorithms used for lung sound analysis to emphasize the significance of artificial intelligence(AI)in this field.We focus on each component of deep learning-based lung sound analysis systems,including the task categories,public datasets,denoising methods,and,most importantly,existing deep learning methods,i.e.,the state-of-the-art approaches to convert lung sounds into two-dimensional(2D)spectrograms and use convolutional neural networks for the end-to-end recognition of respiratory diseases or abnormal lung sounds.Additionally,this review highlights current challenges in this field,including the variety of devices,noise sensitivity,and poor interpretability of deep models.To address the poor reproducibility and variety of deep learning in this field,this review also provides a scalable and flexible open-source framework that aims to standardize the algorithmic workflow and provide a solid basis for replication and future extension:https://github.com/contactless-healthcare/Deep-Learning-for-Lung-Sound-Analysis.展开更多
文摘目的推动印刷行业朝更高效的方向迈进,提高印刷设备的易操作性和视觉识别性。方法将Kano和FAST(Function Analysis System Technique)模型引入瓦楞纸激光打印设备设计的前期应用需求分析中,通过问卷的方法获取用户的基本要求,并划分为几个子类型,进而建立Kano的二维功能属性模型。采用FAST法建立功能树,辅助使用Kano模型,从而更精准地分析用户需求,并更好地根据其需求进行优化设计。结果综合运用设计原理,针对性地挖掘瓦楞纸激光印刷设备在造型识别性、操作易用性、生产安全性上存在的问题,进而输出更优解。结论该设计方法的引入有助于为同类型的印刷设备设计提供参考,并引起更多相关厂家的重视,推动印刷行业向更积极的方向发展。
文摘来自人造卫星的信号是射电天文观测面临的主要射频干扰(radio frequency interference,RFI)之一,这些RFI会将天文信号掩埋,为天文信号的搜寻和分析带来困扰。为了缓减卫星对天文观测的影响,我们在之前的工作中为500 m口径球面射电望远镜(Five-hundred-meter Aperture Spherical radio Telescope,FAST)开发了卫星电磁干扰监测软件,主要包括卫星数据库、观测模块和监测模块。近年来随着多个巨型卫星星座的规划发射以及望远镜观测模式的增多,卫星对射电天文观测的影响更为复杂,已有的软件已经不能满足实际的需要。为此,本文在单个卫星干扰分析的基础上提出了卫星星座的干扰评估方法,并对已有监测软件进行了升级,升级后卫星数据库覆盖更多的在轨卫星及星座信息且能够自动化更新,观测模块能够支持更多种观测模式下的卫星过境预测和干扰评估。在实际天文观测中,通过接在FAST接收机上的频谱仪数据对软件的干扰预测结果进行了实验验证,结果证明升级后的软件能够在多种观测模式下预测可能威胁的卫星以及对应的过境时间,为望远镜观测规划的调整、卫星干扰的规避和接收系统的保护提供重要的支撑。
基金funded by the project,“Design and implementation of real-time safety ensuring system in the indoor environment by applying machine learning techniques”.IRN:AP14971555.
文摘Video analytics is an integral part of surveillance cameras. Comparedto video analytics, audio analytics offers several benefits, includingless expensive equipment and upkeep expenses. Additionally, the volume ofthe audio datastream is substantially lower than the video camera datastream,especially concerning real-time operating systems, which makes it lessdemanding of the data channel’s bandwidth needs. For instance, automaticlive video streaming from the site of an explosion and gunshot to the policeconsole using audio analytics technologies would be exceedingly helpful forurban surveillance. Technologies for audio analytics may also be used toanalyze video recordings and identify occurrences. This research proposeda deep learning model based on the combination of convolutional neuralnetwork (CNN) and recurrent neural network (RNN) known as the CNNRNNapproach. The proposed model focused on automatically identifyingpulse sounds that indicate critical situations in audio sources. The algorithm’saccuracy ranged from 95% to 81% when classifying noises from incidents,including gunshots, explosions, shattered glass, sirens, cries, and dog barking.The proposed approach can be applied to provide security for citizens in openand closed locations, like stadiums, underground areas, shopping malls, andother places.
文摘为确定500 m口径球面射电望远镜(Five-hundred-meter Aperture Spherical radio Telescope,FAST)与其周边公众移动通信(Public Mobile Telecommunications,PMT)系统的电磁兼容(electromagnetic compatibility,EMC)特性,本文综合论述了FAST宁静区内中国移动、中国联通和中国电信三大运营商所属PMT基站对其产生的电磁干扰。首先,从射电天文业务的频谱划分谈起,论述了射电天文业务干扰源类型,引出了其运行保护标准,进而针对FAST详细说明了FAST宁静区的用频法规和保护要求;其次,分析了ITU-R建议电波传播预测与干扰分析方法,并通过实地测量验证了该方法的适用性,进一步针对性地分析了PMT基站的电磁辐射传播特性,综合评估了FAST宁静区内PMT基站的干扰情况:FAST宁静区域90.24%的PMT基站在一定程度上均会对FAST产生干扰,而在所选分析条件下,仅有43.14%的数据符合FAST保护要求;最后,针对PMT基站干扰信号的抑制和消除,分析了常用的射电天文射频干扰抑制方法,同时为保障FAST免受PMT基站干扰,从FAST和PMT基站的角度出发论述了可行的用频防护措施,并基于实施难度、经济成本、策略收益和通信质量4类指标建立了防护方法的评估体系,对所提防护方法进行了实例说明。上述研究成果可为保障FAST的安全观测提供技术基础。
基金supported by the National Natural Science Foundation of China(Grant No.42004030)Basic Scientific Fund for National Public Research Institutes of China(Grant No.2022S03)+1 种基金Science and Technology Innovation Project(LSKJ202205102)funded by Laoshan Laboratory,and the National Key Research and Development Program of China(2020YFB0505805).
文摘The scarcity of in-situ ocean observations poses a challenge for real-time information acquisition in the ocean.Among the crucial hydroacoustic environmental parameters,ocean sound velocity exhibits significant spatial and temporal variability and it is highly relevant to oceanic research.In this study,we propose a new data-driven approach,leveraging deep learning techniques,for the prediction of sound velocity fields(SVFs).Our novel spatiotemporal prediction model,STLSTM-SA,combines Spatiotemporal Long Short-Term Memory(ST-LSTM) with a self-attention mechanism to enable accurate and real-time prediction of SVFs.To circumvent the limited amount of observational data,we employ transfer learning by first training the model using reanalysis datasets,followed by fine-tuning it using in-situ analysis data to obtain the final prediction model.By utilizing the historical 12-month SVFs as input,our model predicts the SVFs for the subsequent three months.We compare the performance of five models:Artificial Neural Networks(ANN),Long ShortTerm Memory(LSTM),Convolutional LSTM(ConvLSTM),ST-LSTM,and our proposed ST-LSTM-SA model in a test experiment spanning 2019 to 2022.Our results demonstrate that the ST-LSTM-SA model significantly improves the prediction accuracy and stability of sound velocity in both temporal and spatial dimensions.The ST-LSTM-SA model not only accurately predicts the ocean sound velocity field(SVF),but also provides valuable insights for spatiotemporal prediction of other oceanic environmental variables.
基金the National Natural Science Foundation of China(Nos.22209095 and 22238004).
文摘Progress in the fast charging of high-capacity silicon monoxide(SiO)-based anode is currently hindered by insufficient conductivity and notable volume expansion.The construction of an interface conductive network effectively addresses the aforementioned problems;however,the impact of its quality on lithium-ion transfer and structure durability is yet to be explored.Herein,the influence of an interface conductive network on ionic transport and mechanical stability under fast charging is explored for the first time.2D modeling simulation and Cryo-transmission electron microscopy precisely reveal the mitigation of interface polarization owing to a higher fraction of conductive inorganic species formation in bilayer solid electrolyte interphase is mainly responsible for a linear decrease in ionic diffusion energy barrier.Furthermore,atomic force microscopy and Raman shift exhibit substantial stress dissipation generated by a complete conductive network,which is critical to the linear reduction of electrode residual stress.This study provides insights into the rational design of optimized interface SiO-based anodes with reinforced fast-charging performance.
基金supported by the National Natural Science Foundation of China(22208039)the Basic Scientific Research Project of the Educational Department of Liaoning Province(LJKMZ20220878)+1 种基金and the Dalian Science and Technology Talent Innovation Support Plan(2022RQ036)supported by the Natural Science and Engineering Research Council of Canada(NSERC),the Canada Research Chair Program(CRC),the Canada Foundation for Innovation(CFI),and Western University。
文摘Applications of lithium-sulfur(Li-S)batteries are still limited by the sluggish conversion kinetics from polysulfide to Li_(2)S.Although various single-atom catalysts are available for improving the conversion kinetics,the sulfur redox kinetics for Li-S batteries is still not ultrafast.Herein,in this work,a catalyst with dual-single-atom Pt-Co embedded in N-doped carbon nanotubes(Pt&Co@NCNT)was proposed by the atomic layer deposition method to suppress the shuttle effect and synergistically improve the interconversion kinetics from polysulfides to Li_(2)S.The X-ray absorption near edge curves indicated the reversible conversion of Li_(2)Sx on the S/Pt&Co@NCNT electrode.Meanwhile,density functional theory demonstrated that the Pt&Co@NCNT promoted the free energy of the phase transition of sulfur species and reduced the oxidative decomposition energy of Li_(2)S.As a result,the batteries assembled with S/Pt&Co@NCNT electrodes exhibited a high capacity retention of 80%at 100 cycles at a current density of 1.3 mA cm^(−2)(S loading:2.5 mg cm^(−2)).More importantly,an excellent rate performance was achieved with a high capacity of 822.1 mAh g^(−1) at a high current density of 12.7 mA cm^(−2).This work opens a new direction to boost the sulfur redox kinetics for ultrafast Li-S batteries.
基金This work is supported by the National Key Research and Development Program of China(2022YFC2407800)the General Program of National Natural Science Foundation of China(62271241)+1 种基金the Guangdong Basic and Applied Basic Research Foundation(2023A1515012983)the Shenzhen Fundamental Research Program(JCYJ20220530112601003).
文摘Auscultation is crucial for the diagnosis of respiratory system diseases.However,traditional stethoscopes have inherent limitations,such as inter-listener variability and subjectivity,and they cannot record respiratory sounds for offline/retrospective diagnosis or remote prescriptions in telemedicine.The emergence of digital stethoscopes has overcome these limitations by allowing physicians to store and share respiratory sounds for consultation and education.On this basis,machine learning,particularly deep learning,enables the fully-automatic analysis of lung sounds that may pave the way for intelligent stethoscopes.This review thus aims to provide a comprehensive overview of deep learning algorithms used for lung sound analysis to emphasize the significance of artificial intelligence(AI)in this field.We focus on each component of deep learning-based lung sound analysis systems,including the task categories,public datasets,denoising methods,and,most importantly,existing deep learning methods,i.e.,the state-of-the-art approaches to convert lung sounds into two-dimensional(2D)spectrograms and use convolutional neural networks for the end-to-end recognition of respiratory diseases or abnormal lung sounds.Additionally,this review highlights current challenges in this field,including the variety of devices,noise sensitivity,and poor interpretability of deep models.To address the poor reproducibility and variety of deep learning in this field,this review also provides a scalable and flexible open-source framework that aims to standardize the algorithmic workflow and provide a solid basis for replication and future extension:https://github.com/contactless-healthcare/Deep-Learning-for-Lung-Sound-Analysis.
