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Auswahl der wissenschaftlichen Literatur zum Thema „Online diagnostic system“
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Zeitschriftenartikel zum Thema "Online diagnostic system"
Kozitsin, Viacheslav, Iurii Katser und Dmitry Lakontsev. „Online Forecasting and Anomaly Detection Based on the ARIMA Model“. Applied Sciences 11, Nr. 7 (02.04.2021): 3194. http://dx.doi.org/10.3390/app11073194.
Der volle Inhalt der QuelleHajar Mat Jani, und Rozita Yati Masri. „An Improved Online Mental Status Examination System and Mental Health Diagnostic System“. INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 3, Nr. 9 (31.10.2011): 66–75. http://dx.doi.org/10.4156/aiss.vol3.issue9.9.
Der volle Inhalt der QuelleGe Ming, Xu Yangsheng und Du Ruxu. „An Intelligent Online Monitoring and Diagnostic System for Manufacturing Automation“. IEEE Transactions on Automation Science and Engineering 5, Nr. 1 (Januar 2008): 127–39. http://dx.doi.org/10.1109/tase.2006.886833.
Der volle Inhalt der QuelleLoboda, Igor, Luis Angel Miró Zárate, Sergiy Yepifanov, Cristhian Maravilla Herrera und Juan Luis Pérez Ruiz. „Estimation of Gas Turbine Unmeasured Variables for an Online Monitoring System“. International Journal of Turbo & Jet-Engines 37, Nr. 4 (18.11.2020): 413–28. http://dx.doi.org/10.1515/tjj-2017-0065.
Der volle Inhalt der QuelleChaoui, Hicham, Asmae El Mejdoubi, Amrane Oukaour und Hamid Gualous. „Online System Identification for Lifetime Diagnostic of Supercapacitors With Guaranteed Stability“. IEEE Transactions on Control Systems Technology 24, Nr. 6 (November 2016): 2094–102. http://dx.doi.org/10.1109/tcst.2016.2520911.
Der volle Inhalt der QuelleAngeli, Chrissanthi, und Derek Atherton. „A Model-Based Method for an Online Diagnostic Knowledge-Based System“. Expert Systems 18, Nr. 3 (Juli 2001): 150–58. http://dx.doi.org/10.1111/1468-0394.00167.
Der volle Inhalt der QuelleYang, Cheng Gang, Chun Hui Zhang, Jing Yi Zhao und Shi Ming Zhao. „Research on Active Online Diagnosis Technology of Hydraulic System“. Applied Mechanics and Materials 599-601 (August 2014): 1032–35. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.1032.
Der volle Inhalt der QuelleQawqzeh, Yousuf, und Khalid Nazim Abdul Sattar. „Online Diagnostic Expert System for Detection of Breast Cancer in Saudi Arabia“. International Journal of Computer Applications 113, Nr. 6 (18.03.2015): 40–47. http://dx.doi.org/10.5120/19833-1686.
Der volle Inhalt der QuelleJing, Lu Yang, Tai Yong Wang, Dong Xiang Chen und Jing Xiang Fang. „Design and Implementation of Online Monitoring and Remote Diagnostic System for CNC Machine Tools“. Advanced Materials Research 819 (September 2013): 136–39. http://dx.doi.org/10.4028/www.scientific.net/amr.819.136.
Der volle Inhalt der QuelleFujimura, Tatsuhiro, Takayuki Okamura, Kazuki Furuya, Yosuke Miyazaki, Hitoshi Takenaka, Hiroki Tateishi, Tetsuro Oda et al. „Comparison of diagnostic performance in assessing the rewiring position into a jailed side branch between online 3D reconstruction systems version 1.1 and 1.2 derived from optical frequency domain imaging“. Cardiovascular Intervention and Therapeutics 35, Nr. 4 (09.11.2019): 336–42. http://dx.doi.org/10.1007/s12928-019-00629-2.
Der volle Inhalt der QuelleDissertationen zum Thema "Online diagnostic system"
Cirhanová, Iva. „Zpracování dat z online diagnostického systému“. Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-443756.
Der volle Inhalt der QuelleKříž, Petr. „Online vibrační diagnostika vřetene frézovacího stroje DATRON“. Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-402508.
Der volle Inhalt der QuelleNAIK, SAURABH. „ONLINE DOCUMENTATION AND DIAGNOSTIC SYSTEM FOR THE BEARCAT CUB“. University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1100799703.
Der volle Inhalt der QuelleZářecký, Tomáš. „Online diagnostika obráběcích strojů“. Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-417767.
Der volle Inhalt der QuelleLi, Zhongliang. „Data-driven fault diagnosis for PEMFC systems“. Thesis, Aix-Marseille, 2014. http://www.theses.fr/2014AIXM4335/document.
Der volle Inhalt der QuelleAiming at improving the reliability and durability of Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems and promote the commercialization of fuel cell technologies, this thesis work is dedicated to the fault diagnosis study for PEMFC systems. Data-driven fault diagnosis is the main focus in this thesis. As a main branch of data-driven fault diagnosis, the methods based on pattern classification techniques are firstly studied. Taking individual fuel cell voltages as original diagnosis variables, several representative methodologies are investigated and compared from the perspective of online implementation.Specific to the defects of conventional classification based diagnosis methods, a novel diagnosis strategy is proposed. A new classifier named Sphere-Shaped Multi-class Support Vector Machine (SSM-SVM) and modified diagnostic rules are utilized to realize the novel fault recognition. While an incremental learning method is extended to achieve the online adaptation.Apart from the classification based diagnosis approach, a so-called partial model-based data-driven approach is introduced to handle PEMFC diagnosis in dynamic processes. With the aid of a subspace identification method (SIM), the model-based residual generation is designed directly from the normal and dynamic operating data. Then, fault detection and isolation are further realized by evaluating the generated residuals.The proposed diagnosis strategies have been verified using the experimental data which cover a set of representative faults and different PEMFC stacks. The preliminary online implementation results with an embedded system are also supplied
Rázgová, Benedikta. „Online diagnostika obráběcího stroje MCV 754“. Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-417766.
Der volle Inhalt der QuelleSojer, Dominik [Verfasser]. „Synthesis of Online Diagnostic Techniques for Embedded Systems / Dominik Sojer“. München : Verlag Dr. Hut, 2013. http://d-nb.info/1042308551/34.
Der volle Inhalt der QuelleShahin, Kamrul. „Modèle graphique probabiliste appliqué au diagnostic de l'état de santé des systèmes, au pronostic et à l'estimation de la durée de vie résiduelle“. Electronic Thesis or Diss., Université de Lorraine, 2020. http://www.theses.fr/2020LORR0129.
Der volle Inhalt der QuelleThis thesis contributes to prognosis and health management for assessing health condition of complex systems. In the context of operational management and operational safety of systems, we propose to investigate how Dynamic Probabilistic Graphical Modelling (DPGM) can be used to diagnose the current health state of systems, prognostic the future health state, and the evolution of degradation, as well as estimate its remaining useful life based on its operating conditions. System degradation is generally unknown and requires shutting down the system to be observed. However, this is difficult or even impossible during system operation. Though, a set of observable quantities on a system or component can characterise the level of degradation and help to estimate the remaining useful life of components and systems. The DPGM provides an approach suitable for modelling the evolution of the health state of systems and components. The aim of this thesis is to transpose and capitalize on the experience of these previous works in a prognostic context on the basis of a more efficient DPGM taking into account the available knowledge on the system. We extend the classical HMM family models to the IOHMM to allow the time propagation of uncertainty to address prognostic problems. This research includes the extension of learning and inference algorithms. Variants of the HMM model are proposed to incorporate the operating environment into the prognosis. The aim of this thesis is to contribute to solving the following scientific locks: - Considering the state of health whatever the complexity of the system by a stochastic model and learning the model parameters from the available measurements on the system. - Establish a diagnosis of the state of health of the system and the prognosis of its evolution by integrating several operational conditions. - Estimate the remaining useful life of components and structured systems with series and parallel components. This is a major challenge because the prognosis of the degradation of system components makes it possible to define strategies for either control or maintenance in relation to the residual life of the system. This allows the reduction of the probability of occurrence of a shutdown due to a system malfunction either by adjusting the degradation speed to fit in with a preventive maintenance plan or by proactively planning maintenance interventions
Barnard, Jakobus Petrus. „Empirical state space modelling with application in online diagnosis of multivariate non-linear dynamic systems“. Thesis, Stellenbosch : Stellenbosch University, 1999. http://hdl.handle.net/10019.1/51258.
Der volle Inhalt der QuelleENGLISH ABSTRACT: System identification has been sufficiently formalized for linear systems, but not for empirical identification of non-linear, multivariate dynamic systems. Therefore this dissertation formalizes and extends non-linear empirical system identification for the broad class of nonlinear multivariate systems that can be parameterized as state space systems. The established, but rather ad hoc methods of time series embedding and nonlinear modeling, using multilayer perceptron network and radial basis function network model structures, are interpreted in context with the established linear system identification framework. First, the methodological framework was formulated for the identification of non-linear state space systems from one-dimensional time series using a surrogate data method. It was clearly demonstrated on an autocatalytic process in a continuously stirred tank reactor, that validation of dynamic models by one-step predictions is insufficient proof of model quality. In addition, the classification of data as either dynamic or random was performed, using the same surrogate data technique. The classification technique proved to be robust in the presence of up to at least 10% measurement and dynamic noise. Next, the formulation of a nearly real-time algorithm for detection and removal of radial outliers in multidimensional data was pursued. A convex hull technique was proposed and demonstrated on random data, as well as real test data recorded from an internal combustion engine. The results showed the convex hull technique to be effective at a computational cost two orders of magnitude lower than the more proficient Rocke and Woodruff technique, used as a benchmark, and incurred low cost (0.9%) in terms of falsely identifying outliers. Following the identification of systems from one-dimensional time series, the methodological framework was expanded to accommodate the identification of nonlinear state space systems from multivariate time series. System parameterization was accomplished by combining individual embeddings of each variable in the multivariate time series, and then separating this combined space into independent components, using independent component analysis. This method of parameterization was successfully applied in the simulation of the abovementioned autocatalytic process. In addition, the parameterization method was implemented in the one-step prediction of atmospheric N02 concentrations, which could become part of an environmental control system for Cape Town. Furthermore, the combination of the embedding strategy and separation by independent component analysis was able to isolate some of the noise components from the embedded data. Finally the foregoing system identification methodology was applied to the online diagnosis of temporal trends in critical system states. The methodology was supplemented by the formulation of a statistical likelihood criterion for simultaneous interpretation of multivariate system states. This technology was successfully applied to the diagnosis of the temporal deterioration of the piston rings in a compression ignition engine under test conditions. The diagnostic results indicated the beginning of significant piston ring wear, which was confirmed by physical inspection of the engine after conclusion of the test. The technology will be further developed and commercialized.
AFRIKAANSE OPSOMMING: Stelselidentifikasie is weI genoegsaam ten opsigte van lineere stelsels geformaliseer, maar nie ten opsigte van die identifikasie van nie-lineere, multiveranderlike stelsels nie. In hierdie tesis word nie-lineere, empiriese stelselidentifikasie gevolglik ten opsigte van die wye klas van nielineere, multiveranderlike stelsels, wat geparameteriseer kan word as toestandveranderlike stelsels, geformaliseer en uitgebrei. Die gevestigde, maar betreklik ad hoc metodes vir tydreeksontvouing en nie-lineere modellering (met behulp van multilaag-perseptron- en radiaalbasisfunksie-modelstrukture) word in konteks met die gevestigde line ere stelselidentifikasieraamwerk vertolk. Eerstens is die metodologiese raamwerk vir die identifikasie van nie-lineere, toestandsveranderlike stelsels uit eendimensionele tydreekse met behulp van In surrogaatdatametode geformuleer. Daar is duidelik by wyse van 'n outokatalitiese proses in 'n deurlopend geroerde tenkreaktor getoon dat die bevestiging van dinamiese modelle deur middel van enkelstapvoorspellings onvoldoende bewys van die kwaliteit van die modelle is. Bykomend is die klassifikasie van tydreekse as 6f dinamies Of willekeurig, met behulp van dieselfde surrogaattegniek gedoen. Die klassifikasietegniek het in die teenwoordigheid van tot minstens 10% meetgeraas en dinamiese geraas robuust vertoon. / Vervolgens is die formulering van In bykans intydse algoritme vir die opspoor en verwydering van radiale uitskieters in multiveranderlike data aangepak. 'n Konvekse hulstegniek is V:oorgestel en op ewekansige data, sowel as op werklike toetsdata wat van 'n binnebrandenjin opgeneem is, gedemonstreer. Volgens die resultate was die konvekse hulstegniek effektief teen 'n rekenkoste twee grootte-ordes kleiner as die meer vermoende Rocke en Woodrufftegniek, wat as meetstandaard beskou is. Die konvekse hulstegniek het ook 'n lae loopkoste (0.9%) betreffende die valse identifisering van uitskieters behaal. Na aanleiding van die identifisering van stelsels uit eendimensionele tydreekse, is die metodologiese raamwerk uitgebiei om die identifikasie van nie-lineere, toestandsveranderlike stelsels uit multiveranderlike data te omvat. Stelselparameterisering is bereik deur individuele ontvouings van elke veranderlike in die multidimensionele tydreeks met die skeiding van die gesamenlike ontvouingsruimte tot onafhanklike komponente saam te span. Sodanige skeiding is deur middel van onafhanklike komponentanalise behaal. Hierdie metode van parameterisering is suksesvc1 op die simulering van bogenoemde outokatalitiese proses toegepas. Die parameteriseringsmetode is bykomend in die enkelstapvoorspelling van atmosferiese N02-konsentrasies ingespan en sal moontlik deel van 'n voorgestelde omgewingsbestuurstelsel vir Kaapstad uitmaak. Die kombinasie van die ontvouingstrategie en skeiding deur onafhanklike komponentanalise was verder ook in staat om van die geraaskomponente in die data uit te lig. Ten slotte is die voorafgaande tegnologie vir stelselidentifikasie op die lopende diagnose van tydsgebonde neigings in kritiese stelseltoestande toegepas. Die metodologie is met die formulering van 'n statistiese waarskynlikheidsmaatstaf vir die gelyktydige vertolking van multiveranderlike stelseltoestande aangevul. Hierdie tegnologie is suksesvol op die diagnose van die tydsgebonde verswakking van die suierringe in 'n kompressieontstekingenj in tydens toetstoestande toegepas. Die diagnostiese resultate het die aanvang van beduidende slytasie in die suierringe aangedui, wat later tydens fisiese inspeksie van die enjin met afloop van die toets, bevestig is. Die tegnologie sal verder ontwikkel en markgereed gemaak word.
Hashemi, Seyed Reza. „An Intelligent Battery Managment System For Electric And Hybrid Electric Aircraft“. University of Akron / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=akron1615732366021405.
Der volle Inhalt der QuelleBücher zum Thema "Online diagnostic system"
Murphy, Margaret. An online CAL system for teaching infection/diagnosis of bacterial pathogens commonly found in the intestinal tract. [s.l: The Author], 2004.
Den vollen Inhalt der Quelle findenAn introduction to the Tennessee Valley Authority online diagnostic monitoring system project. [Knoxville, Tenn.?]: Tennessee Valley Authority, Power Business Operations/Research and Development, 1989.
Den vollen Inhalt der Quelle findenDaroff, Robert B., Joseph Jankovic, Gerald Fenichel und Walter G. Bradley. Neurology in Clinical Practice e-dition: Text with Continually Updated Online Reference, 2-Volume Set. 5. Aufl. Butterworth-Heinemann, 2007.
Den vollen Inhalt der Quelle findenClinical Gastroenterology and Hepatology e-dition: Text with Continually Updated Online Reference. Mosby, 2005.
Den vollen Inhalt der Quelle findenLee, Christine U. C., und James Glockner. Mayo Clinic Body MRI Case Review. Oxford University Press, 2014. http://dx.doi.org/10.1093/med/9780199915705.001.0001.
Der volle Inhalt der QuelleCrash Course (US): Neurology: With STUDENT CONSULT Online Access (Crash Course). Mosby, 2005.
Den vollen Inhalt der Quelle findenNeuropathology of Neurodegenerative Diseases Book and Online: A Practical Guide. University of Cambridge ESOL Examinations, 2014.
Den vollen Inhalt der Quelle findenNon-Neoplastic Pathology of the Gastrointestinal Tract with Online Resource: A Practical Guide to Biopsy Diagnosis. University of Cambridge ESOL Examinations, 2020.
Den vollen Inhalt der Quelle findenSteketee, Gail, und Randy O. Frost. Treatment for Hoarding Disorder. Oxford University Press, 2013. http://dx.doi.org/10.1093/med:psych/9780199334940.001.0001.
Der volle Inhalt der QuelleWilkinson, Michael A. Authoritarian Liberalism and the Transformation of Modern Europe. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198854753.001.0001.
Der volle Inhalt der QuelleBuchteile zum Thema "Online diagnostic system"
Zagorecki, Adam, Piotr Orzechowski und Katarzyna Hołownia. „Online Diagnostic System Based on Bayesian Networks“. In Artificial Intelligence in Medicine, 145–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38326-7_22.
Der volle Inhalt der QuelleKong, Detong, Wei Liu, Zhanli Liu und Hongwei Wang. „A Remote Online Condition Monitoring and Intelligent Diagnostic System for Wind Turbine“. In Intelligent Robotics and Applications, 505–16. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65289-4_48.
Der volle Inhalt der QuelleSafi, Asad, und Anabel Martin-Gonzalez. „Online Breast Cancer Diagnosis System“. In Intelligent Computing Systems, 108–15. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30447-2_9.
Der volle Inhalt der QuellePanda, Meenakshi, Bhabani S. Gouda und Trilochan Panigrahi. „Distributed Online Fault Diagnosis in Wireless Sensor Networks“. In Lecture Notes in Networks and Systems, 197–221. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9574-1_9.
Der volle Inhalt der QuelleMao, Yiming, Bin Xu, Jifan Yu, Yifan Fang, Jie Yuan, Juanzi Li und Lei Hou. „Learning Behavior-Aware Cognitive Diagnosis for Online Education Systems“. In Communications in Computer and Information Science, 385–98. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5943-0_31.
Der volle Inhalt der QuelleO’Keeffe, James, Danesh Tarapore, Alan G. Millard und Jon Timmis. „Towards Fault Diagnosis in Robot Swarms: An Online Behaviour Characterisation Approach“. In Towards Autonomous Robotic Systems, 393–407. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64107-2_31.
Der volle Inhalt der QuelleZhang, Honghui. „A Study on Online Fault Diagnosis Technology for Shield Core Components“. In Advances in Intelligent, Interactive Systems and Applications, 533–39. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-02804-6_70.
Der volle Inhalt der QuelleZhang, Peng-Xian, Zhi-Fen Zhang und Jian-Hong Chen. „Online Diagnosis of Joints Quality in Resistance Spot Welding for Sedan Body“. In Advances in Intelligent Systems and Computing, 263–72. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18997-0_22.
Der volle Inhalt der QuelleZhang, Zhaolin, und Yugang Fan. „Online Modeling Method of Fault Diagnosis Based on CNN and OS-ELM“. In Advances in Intelligent Systems and Computing, 495–503. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0238-5_50.
Der volle Inhalt der QuelleZhu, Qinghui, und Zhikui Wang. „Software Design of Online Monitoring System of Large Linear Vibrating Screen Fault Diagnosis“. In Communications in Computer and Information Science, 293–300. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3415-7_24.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Online diagnostic system"
Pan, Min-Chun, und Po-Ching Li. „Remote online machine fault diagnostic system“. In NDE for Health Monitoring and Diagnostics, herausgegeben von Tribikram Kundu. SPIE, 2004. http://dx.doi.org/10.1117/12.537722.
Der volle Inhalt der QuelleIqbal, Muhammad Nadeem, Luo Yuan Xin, Waheed Ur Rehman, Allah Rakhio, Shahneel Siddique, Danyal Zahid, Wakeel Yasin und Ammar Bin Waqar. „Diagnostic tool and remote online diagnostic system for Euro standard vehicles“. In 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC). IEEE, 2017. http://dx.doi.org/10.1109/itoec.2017.8122328.
Der volle Inhalt der QuelleLeffler, Jan, Ondrej Rauner und Pavel Trnka. „Comparison of two different approaches to the design of an online diagnostic system“. In 2020 International Conference on Diagnostics in Electrical Engineering (Diagnostika). IEEE, 2020. http://dx.doi.org/10.1109/diagnostika49114.2020.9214610.
Der volle Inhalt der QuelleMuangprathub, Jirapond, und Veera Boonjing. „Online Thai medical diagnostic system using case-based reasoning“. In 2014 International Computer Science and Engineering Conference (ICSEC). IEEE, 2014. http://dx.doi.org/10.1109/icsec.2014.6978179.
Der volle Inhalt der QuelleFaizi, Rdouan, Sanaa El Fkihi und Abdellatif El Afia. „AN ONLINE DIAGNOSTIC SYSTEM TO ASSESS ENGLISH GRAMMAR SKILLS“. In 13th International Technology, Education and Development Conference. IATED, 2019. http://dx.doi.org/10.21125/inted.2019.1878.
Der volle Inhalt der QuelleMeher-Homji, Cyrus B. „A Feasibility Study of the Application of Artificial Intelligence Techniques for Turbomachinery Diagnostics“. In ASME 1985 International Gas Turbine Conference and Exhibit. American Society of Mechanical Engineers, 1985. http://dx.doi.org/10.1115/85-gt-102.
Der volle Inhalt der QuelleFrederking, T., und R. Gadow. „Novel Concept for Totally Integrated Automation (TIA) Using Siemens Bus System and WinCC Online Control“. In ITSC 2000, herausgegeben von Christopher C. Berndt. ASM International, 2000. http://dx.doi.org/10.31399/asm.cp.itsc2000p0963.
Der volle Inhalt der QuelleJani, Hajar Mat. „Benefiting from online mental status examination system and mental health diagnostic system“. In 2010 3rd International Conference on Information Sciences and Interaction Sciences (ICIS). IEEE, 2010. http://dx.doi.org/10.1109/icicis.2010.5534712.
Der volle Inhalt der QuelleYin, Jungang, Runqi Wu, Jian Yang und Xiangdong Xu. „Online Diagnostic Ultra-Wideband Antenna System in High Voltage Polymeric Insulator“. In 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting. IEEE, 2019. http://dx.doi.org/10.1109/apusncursinrsm.2019.8888521.
Der volle Inhalt der QuelleCai, Yujun, und Fang Yuan. „Design of Online Diagnostic System for Physical Parameters of Wind Turbine“. In 8th International Conference on Management and Computer Science (ICMCS 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/icmcs-18.2018.53.
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