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1. 发明专利

AU2021102724A4 A METHOD FOR MODULAR MULTILEVEL CONVERTER (INVERTER) OPEN-CIRCUIT FAULT IDENTIFICATION AND POSITIONING 未知
公开(公告)号：AU2021102724A4
公开(公告)日：2021-07-15
申请号：AU2021102724
申请日：2021-05-21
申请人： ANKAIAH BURRI MR , H ANANDA M MR , KUMAR MAHESH MR , N LATHA MS , OOMMEN SUJO MS , V DIVYA B MS
发明人： OOMMEN SUJO , ANKAIAH BURRI , KUMAR MAHESH , H ANANDA M , N LATHA , V DIVYA B
IPC分类号： G01R31/27 , G01R31/28 , G01R31/317
摘要： A METHOD FOR MODULAR MULTILEVEL CONVERTER (INVERTER) OPEN CIRCUIT FAULT IDENTIFICATION AND POSITIONING Aspects of the present disclosure relate to method (100) for identifying and positioning open 5 circuit faults of multilevel inverter. The Modular Multilevel Converter (MMC) is a power electronic structure used for high voltage adjustable speed drives applications as well as power transmission applications and high-voltage direct current. MMC structure presents many advantages such as modularity, the absence of a high voltage DC bus and very low switching frequency. An ESO based fault detection and location (114) method (100) is 10 proposed, which features a low computation burden and relatively quick fault detection and location (114). In this method the voltage difference (106) between the ESO and theoretical voltages is used for setting (108) plurality of sampling points against the output current. From these sampling points the information is acquired (110). The proposed fault diagnosis strategy features a low computation burden and relatively fast speed in fault detection and 15 location (114) as it uses machine learning models based on multivariate gaussian distribution. (FIG. 1 will be the reference figure) - 13 - aippiican name: rage i 01 100 acquiring theoretical full ann voltage by a 102 calculator acquiring ESO full arm voltage by an ESO 104 calculating the difference between the theoretical full am voltage and ESO full arm voltage setting plurality of sampling point 108 acquiring current sampling information 110 identifying the open-eircuit fault 112 finding the location of the open-circuit fault using f' 114 a fault detection and location (FDL) method FIG. 1 Flow diagram of method for identifying and positioning open-circuit faults of multilevel inverter.

2. 发明专利

AU2021103167A4 An artificial intelligence and deep learning method for detecting impressions using internet of things 未知
公开(公告)号：AU2021103167A4
公开(公告)日：2021-10-14
申请号：AU2021103167
申请日：2021-06-07
申请人： AGGARWAL AMAN PROF , ARORA RENUKA MRS , BARAK DHEERDHWAJ DR , DANDEVA DIVYE MR , KUMAR MAHESH MR , KUMAR VINIT MR , RANI PRIYANKA MRS , SETIA UPASNA MRS , SHARMA NEETU DR , SINGH KHUSHWANT MR , SINGH NARENDER MR , SINGH YUDHVIR PROF
发明人： SINGH YUDHVIR , AGGARWAL AMAN , SHARMA NEETU , SINGH NARENDER , KUMAR MAHESH , ARORA RENUKA , SETIA UPASNA , RANI PRIYANKA , SINGH KHUSHWANT , DANDEVA DIVYE , BARAK DHEERDHWAJ , KUMAR VINIT
IPC分类号： G06K9/00 , G06N20/00 , H04L29/08
摘要： An artificial intelligence and deep learning method for detecting impressions using internet of things The present invention relates to a method of artificial intelligence and deep learning in analysis and recognition of fingerprints and facial impressions using IOT. In this invention, a method for finding the latent facial using an algorithm and maximizing the efficiency is proposed to solve the problem of recognition of different types of fingerprints and expressions. This invented method is useful for enhancing both latent fingerprints and recognition of facial impressions using Internet of things with its usage and augmentation in their pattern prediction and regression. Following invention is described in detail with the help of Figures of sheet 1 showing an overview of the research approach with the figures used. Figure 1. Latent images corresponding to quality indices of (a) 1 (lowest quality), (b) 30, (c) 50, (d) 80, and (e) 100 (highest quality) Daba ndusties Vehl --- Fi 2 Dtcothuhdasein------------------------------------gIOgaget-----------------s---- Fdnigur2:teciontrghatba esion agt

3. 发明专利

AU2021103225A4 EMBANKMENT DAM ANALYSIS TO REDUCE FLOOD IMPACT USING SENSOR MODELLING AND GIS TECHNIQUES 未知
公开(公告)号：AU2021103225A4
公开(公告)日：2021-08-19
申请号：AU2021103225
申请日：2021-06-09
申请人： SITENDER MR , ANAND RISHABH MR , BARAK DHEERDHWAJ DR , DALAL SACHIN MR , KUMAR DEEPAK MR , KUMAR MAHESH MR , KUMAR NITISH MR , KUMAR VINIT MR , PANDEY MURALIDHAR MR , QUADIR ABDUL MR , SHEHZAD MOHAMMED MR , SINGH KHUSHWANT MR , SINGH YUDHVIR PROF , VERMA NISHANT MR
发明人： DALAL SACHIN , VERMA NISHANT , QUADIR ABDUL , SINGH YUDHVIR , KUMAR VINIT , BARAK DHEERDHWAJ , SINGH KHUSHWANT , SITENDER , KUMAR NITISH , KUMAR DEEPAK , KUMAR MAHESH , PANDEY MURALIDHAR , SHEHZAD MOHAMMED , ANAND RISHABH
IPC分类号： G06F30/17 , E02D17/20 , G06F30/23 , G06F30/27
摘要： Embankment Dam Analysis To Reduce Flood Impact Using Sensor Modelling and Gis Techniques The present invention relates to a method of dams in analysis. We propose an objective quality measure for dam disaster, with Internet of things and cloud computing it will have sensors and monitoring and under that used to distinguish good quality, which do not require any human intervention, and to compensate for the subjective nature of value determination by different examiners. We investigate At present, the researches on stability of Embankment dam are mostly concentrated on single working condition and single method. However, the Embankment dam is a complex structure. On the one hand, there are many external environmental factors that affect its stability. And it is often a multi-case coupled condition in natural environment. The single working condition is unable to describe the actual condition of the project. On the other hand, Embankment particles couldn't be completely regarded as soil particles. It will cause differences by using soil slope stability analysis methods. However, there were fewer studies on physical and mechanical properties of Embankment particles. In addition, in stability studies, there were many analyses on the influence of seepage, but few analyses of the dynamic effects such as earthquakes. (a) Video monitoring Upstream Hydrostatic stress rmna Dry beach length Subsidence monitoring Water level monitoring monitoring monitoring Horizontal displacement Intemal stress monitoring monitoring Fracture monitoring Residue Internal Dam foundation field deformatio stress monitoring monitoring (b) l'"levelIpr"alarm Thepre-alarmsystem Saretyractor for tailings dam based on the loT, CC, and Randomreliability numerical simulation Interval reliability Output Cloud j- k5 Download z- s computing .no Z , Upload platform Lumit staeequaiion Internal .. stress sensor Upload L Phreatic lne Wate 'Iw *,monitoring Pre-ssure Displacement and sensor deformation sensor Numericalsimulation Internet of things Cloud computing Figure 3: A Pre Alarm Set up for Tailings Dams with Numerical Simulation with Internet of things and Cloud Computing

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