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无损检测 2010年第9期32卷 691-699,703页

作者： Henry Scholz CGNPC Inspection Technology Company, China(CITEC) Jinhong Liu Peter Bieder Fraunhofer Institute for Nondestructive Testing Dresden Germany(IZFP-D) arxes information design berlin gmbh Germany(arXes) CGNPC Inspection Technology Company China(CITEC)

许多管道和管道系统从外部仅为部分可达甚至完全不可达,如独立管道、水下管道或埋地管道。因此,如果不解决独立问题或去除管道覆盖物,就无法实现管道裂纹或腐蚀的检测,然而解决可达问题或去除覆盖物时需要花费不菲的费用甚至根本不能实... 详细信息

许多管道和管道系统从外部仅为部分可达甚至完全不可达,如独立管道、水下管道或埋地管道。因此,如果不解决独立问题或去除管道覆盖物,就无法实现管道裂纹或腐蚀的检测,然而解决可达问题或去除覆盖物时需要花费不菲的费用甚至根本不能实现。由此对长距离检测系统产生了需求。对于光杆和管的局部腐蚀检测,业界已经研制成功了基于超声的检测系统[1-3]。然而,目前由于外形尺寸以及费用等,这些系统并不适用于以SHM为目的的检测。为了完成用大量探头单元涵盖一个完整的产业厂房的检测,有必要研发新型的检测硬件、探头技术以及数据评估技术。

关键词：导波检测工业管道监测

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Controlling and regulation of integrated aquaponic production systems – An approach for a management execution system (MES)

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information Processing in Agriculture 2019年第3期6卷 326-334页

作者： Olaf Witzel Stefan Wilm Divas Karimanzira Daniela Baganz Automation&Software Gunther Tausch gmbh Bassower Str.217034 NeubrandenburgGermany Gesellschaft fur informationsverarbeitung Fertigungssteuerung und Qualita tssicherungIFQ GmbH WismarAlter Holzhafen 17a23966 WismarGermany Department of Surface and Maritime Systems Fraunhofer I0SB-ASTIlmenauAm Vogelherd 5098689 IlmenauGermany Department of Biology and Ecology of Fishes Leibniz-Institute of Freshwater Ecology and Inland FisheriesBerlinGermany

Manufacturing execution systems(MESs)play a significant role in the manufacturing *** is there to link between the Enterprise Resource Planning(ERP)systems and the plant equipment control or Supervisory Control and Data Acquisition(SCADA)*** this paper the MES of the INAPRO aquaponics system which was developed to support and advise the aquaponics managers in operating the complex aquaponic farms,will be *** important feature of the INAPRO aquaponics system is to minimize fresh waterdesign of the fish and crop mixture,considering the fish to crop ratio,when to sow the crops *** to monitor the system to see whether it performing as designed or ***,the MES has a view to show the designed system with all the material flow(water,energy and nutrients)balances and also how the system will be performing for a given predictive *** the future developments of the system,the operator can taking corrective measures to make sure that the system is behaving as *** example of water balance of a system with 40 m3 fish tanks coupled with a hydroponic NFT system with 1,000 m2 which can produce five tons of Tilapia and 75 tons of tomato yearly is given.

关键词： Aquaponics Manufacturing execution system Predictive analytics

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Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients

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Journal of Intensive Medicine 2021年第2期1卷 110-116页

作者： Jonathan Montomoli Luca Romeo Sara Moccia Michele Bernardini Lucia Migliorelli Daniele Berardini Abele Donati Andrea Carsetti Maria Grazia Bocci Pedro David Wendel Garcia Thierry Fumeaux Philippe Guerci Reto Andreas Schüpbach Can Ince Emanuele Frontoni Matthias Peter Hilty RISC-19-ICU Investigators Department of Anaesthesia and Intensive Care Infermi HospitalAUSL della RomagnaRimini 47923Italy Department of information Engineering UniversitàPolitecnica delle MarcheAncona 60131Italy The BioRobotics Institute and Department of Excellence in Robotics and AI Scuola Superiore Sant’AnnaPisa 56127Italy Anesthesia and Intensive Care Unit Azienda Ospedaliero Universitaria Ospedali RiunitiAncona 60126Italy Department of Biomedical Sciences and Public Health UniversitàPolitecnica delle MarcheAncona 60126Italy Department of Anaesthesia and Intensive Care Fondazione Policlinico Universitario A.Gemelli IRCCSUniversitàCattolica del Sacro CuoreRome 00168Italy Institute of Intensive Care Medicine University Hospital of ZurichZurich 8091Switzerland Swiss Society of Intensive Care Medicine Basel 4001Switzerland Department of Anesthesiology and Critical Care Medicine University Hospital of NancyNancy 54511France Department of Intensive Care Erasmus MC University Medical Center RotterdamRotterdam3015 GDNetherlands 不详

Background:Accurate risk stratification of critically ill patients with coronavirus disease 2019(COVID-19)is essential for optimizing resource allocation,delivering targeted interventions,and maximizing patient survival *** learning(ML)techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical ***:We retrieved data on patients with COVID-19 admitted to an intensive care unit(ICU)between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit(RISC-19-ICU)*** applied the Extreme Gradient Boosting(XGBoost)algorithm to the data to predict as a binary out-come the increase or decrease in patients’Sequential Organ Failure Assessment(SOFA)score on day 5 after ICU *** model was iteratively cross-validated in different subsets of the study ***:The final study population consisted of 675 *** XGBoost model correctly predicted a decrease in SOFA score in 320/385(83%)critically ill COVID-19 patients,and an increase in the score in 210/290(72%)*** area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model(0.86 vs.0.69,P<0.01[paired t-test with 95%confidence interval]).Conclusions:The XGBoost model predicted the change in SOFA score in critically ill COVID-19 patients admitted to the ICU and can guide clinical decision support systems(CDSSs)aimed at optimizing available resources.

关键词： Machine learning Extreme gradient boosting(XGBoost) COVID-19 Multiple organ failure Clinical decision support system(CDSS) Organ dysfunction score

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