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1. 发明专利

AU2020102433A4 MACHINE LEARNING BASED FISH MONITORING MACHINE AND METHOD THEREOF 未知
公开(公告)号：AU2020102433A4
公开(公告)日：2020-11-05
申请号：AU2020102433
申请日：2020-09-25
申请人： BHOI AKASH KUMAR DR , G SAILAJA DR , JAGADEESH S DR , KALLI SIVA NAGIREDDY DR , MOHAMMED ARSHAD MR , MARYAM BIBI MRS , MUTHYALA RAJU DR , NARENDRA KUMAR B DR , PRASANNA KUMAR G DR , RAVIKUMAR K C DR , SATISH KUMAR P DR , SHAIK MAHABOOB DR , SRINU NAIK R DR , TULASIRAM KORRAPATI MR
发明人： MOHAMMED ARSHAD , G SAILAJA , KALLI , JAGADEESH S , RAVIKUMAR K C , NARENDRA KUMAR B , TULASIRAM KORRAPATI , MARYAM BIBI , SATISH KUMAR P , SRINU NAIK R , BHOI AKASH KUMAR , MUTHYALA RAJU , SHAIK MAHABOOB , PRASANNA KUMAR G
IPC分类号： A01K61/95 , A01K61/10 , G01J5/00
摘要： "MACHINE LEARNING BASED FISH MONITORING MACHINE AND METHOD THEREOF" Exemplary aspects of the present disclosure are directed towards the MACHINE LEARNING BASED FISH MONITORING MACHINE AND METHOD THEREOF consisting of Thermal Camera 101 and Camera-module 102 placed in each chute 107, Microcontroller 103, the plurality of RFID reading/writing devices 105 and plurality of RFID tags 106 implanted in fishes. The Microcontroller 103 capable of collecting both thermal and visual images and process them using trained Machine Learning Algorithms 104 to monitor, count and determine health conditions of fishes in a farm pond or cage culture. When fish enter the chute, Microcontroller 103 determines, the fish size, temperature, weight and any anomaly with the help of images and relevant Machine Learning Algorithms and data-set available. If found any abnormality, the fish is trapped in the chute and intimated to the user else updated data along with previous data with timestamp updated in RFID tags 106 and user interface. THERMAL IMAGING BASED FISH MONITORING MACHINE

2. 发明专利

AU2020104355A4 IoT and MACHINE LEARNING BASED POWER QUALITY IMPROVEMENT SYSTEM FOR MICRO-GRID 未知
公开(公告)号：AU2020104355A4
公开(公告)日：2021-03-25
申请号：AU2020104355
申请日：2020-12-28
申请人： B ANIL KUMAR MR , KHAN AMER ALI DR , MOHAMMED ARSHAD MR , MOHAMMED IMRAN SHARIEFF DR , MOHAMMED SAJID DR , N VASANTHA GOWRI DR , RAMAVATHU SRINU NAIK DR
发明人： N VASANTHA GOWRI , KHAN AMER ALI , B ANIL KUMAR , MOHAMMED IMRAN SHARIEFF , MOHAMMED SAJID , RAMAVATHU SRINU NAIK , MOHAMMED ARSHAD
IPC分类号： H02J1/02 , G05B19/042 , H02J13/00
摘要： "IoT and MACHINE LEARNING BASED POWER QUALITY IMPROVEMENT SYSTEM FOR MICRO-GRID" Exemplary aspects of the present disclosure are directed towards the IoT and Machine Learning Based Power Quality Improvement System for Micro-Grid consists of the plurality of Line Monitoring & Control System (LMCS) 101 connected to Central Monitoring & Control System (CMCS) 103 through Communication-Network 102. LMCS 101 is an integration of microcontroller 101a with advanced power-management chip 101b, two pair of three potential-transformers 101d and current-sensors 101c, Circuitry for Circuit-Breaker 10le control, and Capacitor-Banks 101f. LMCS 101 uses the Machine-Learning (ML) algorithm to predict power quality issues and operates respective Capacitor-Banks 101f for mitigating voltage and frequency profiles. All the power data received by CMCS 103 is normalized and fed to relevant ML algorithm to identify and predict, load, line stress and power quality issues and mitigate the problems. Based on mitigation CMCS 103 isolates the section if necessary by operating the concerned Circuit-Breaker 101e or switch on the concerned capacitor-banks. Page 1 *Acquire Current and Voltage magnitudes and respective wave forms through Power IC 101b. Assign time stamping to the acquired values along with LMS Compare waveforms of two sets of currents and voltages of that line and compute the change in currents and voltages Al AV and Power Factor. Using relevant machine learning algorithm, ascertain whether Al and AV values are in limits in comparison them with set values If anomaly is found in Al and AV, then feed the values directly to machine learning algorithm and predict the type of power quality issue alert the CMCS-MAIN and await for the mitigation strategy. Based on mitigation strategy, connect capacitors of the respective capacitor bank. Acquire Al and AV after energies the capacitor banks and fed the values to respective MLA for determining and predicting their safe values. If the PQ issues persisting, then increase the capacitor bank 10 lg values and again Acquire Al and AV If Al is high and persistent after the step 507, then ascertain the type of PQ issues and intimate the CMCS with time stamping & LMCS ID and upload power data and type of PQ issue. FIG 5 500 Process for Executed in LMS

3. 发明专利

AU2020103211A4 MACHINE LEARNING BASED RODENT DETECTION AND CONTROL DEVICE 未知
公开(公告)号：AU2020103211A4
公开(公告)日：2021-01-28
申请号：AU2020103211
申请日：2020-11-04
申请人： BHOI AKASH KUMAR DR , BHOI PRIYA BRATA DR , JHA AJEYA DR , KHANDELWAL BIDITA DR , MOHAMMED ARSHAD MR , PADHEE SUMAN KUMAR DR , PANIGRAHI RANJIT DR , PRAMANIK MOUMITA DR , PRIYADARSHI NEERAJ DR , SHAIK MAHABOOB DR , WALI VEERESH S DR
发明人： PADHEE SUMAN KUMAR , BHOI PRIYA BRATA , WALI VEERESH S , PRAMANIK MOUMITA , PANIGRAHI RANJIT , KHANDELWAL BIDITA , JHA AJEYA , PRIYADARSHI NEERAJ , BHOI AKASH KUMAR , SHAIK MAHABOOB , MOHAMMED ARSHAD
IPC分类号： A01M29/16 , G06N20/00
摘要： "MACHINE LEARNING BASED RODENT DETECTION AND CONTROL DEVICE " Exemplary aspects of the present disclosure are directed towards the Machine Learning Based Rodent Detection and Control Device, consisting of a LASER MAPPING DEVICE (LMD) 101 capable of mapping planner surface in three dimension (3-D), RODENT MONITORING DEVICE (RMD) 102 capable of detecting rodents based on thermal signatures and Seismic Signals, and Plurality of RODENT CONTROL DEVICE (RCD) 103 capable of driving away the rodent by playing higher order prey's Bio- Acoustic sound and Seismic signal. Microcontroller 101a integrated with LMD 101, RMD 102 and RCD 103 executes relevant machine learning algorithms (MLA) for detecting rodents in a farm field based on rodent's seismic activities, thermal and video images and generates Acoustic and Seismic signal to thrive away the rodents. The higher order pray acoustic and seismic signals are mimicked and played so as to scare the rodent. 100 MACHINE LEARNING BASED RODENT DETECTION AND CONTROL DEVISE

4. 发明专利

AU2020103212A4 IoT AND MACHINE LEARNING-BASED POWER DISTRIBUTION MANAGEMENT SYSTEM 未知
公开(公告)号：AU2020103212A4
公开(公告)日：2021-01-14
申请号：AU2020103212
申请日：2020-11-04
申请人： AZAM FAROOQUE MR , BHOI AKASH KUMAR DR , KUMAR SANJEET DR , MOHAMMED ARSHAD MR , PARVATHANENI NAGA SRINIVASU DR , PATEL NIL MR , PATTNAYAK PARTHASARATHI DR , PRIYADARSHI NEERAJ DR , SHAIK MAHABOOB DR , SHERPA KARMA SONAM DR , SINGH VINOD KUMAR DR
发明人： BHOI AKASH KUMAR , PRIYADARSHI NEERAJ , SHERPA KARMA SONAM , KUMAR SANJEET , SINGH VINOD KUMAR , PATTNAYAK PARTHASARATHI , PARVATHANENI NAGA SRINIVASU , AZAM FAROOQUE , PATEL NIL , SHAIK MAHABOOB , MOHAMMED ARSHAD
IPC分类号： G05B19/042 , G05B15/00 , G06N20/10
摘要： "IoT AND MACHINE LEARNING-BASED POWER DISTRIBUTION MANAGEMENT SYSTEM" Exemplary aspects of the present disclosure are directed towards the IoT and Machine Learning Based Power Distribution Management System consists of the plurality of Transformer Management System (TMS) 101 connected to Smart Power Distribution System (SPDS-MAIN) 103 through Communication Network 102. Transformer Management System (TMS) 101 is an integration of low-cost microcontroller 101a with advanced power management chip 101b, six potential transformers 101d, six current sensors 101c. Circuit Breaker 10le, Temperature sensor 101f, and vibrations sensor 101g. TMS 101 uses the Random Forest-based Machine Learning (ML) algorithm to determine internal and external Faults and operates circuit bakers 101e. All the power data of the plurality of Transformer Management System (TMS) 101 transmitted to SPDS MAIN 103 for analyzing the data to identify and predict faults, load variations, transformer efficiency, losses, and oil temperature variation and isolates the section. 101a 101b 101c MICRO ADE7816 CT2 BREAKERS 101f 101 TRANSFORMER MONITORING SYSTEM (TMS)

5. 发明专利

AU2021101556A4 HYDEL POWER GENERATION FROM MULTI STOREY BUILDING SEWERAGE 未知
公开(公告)号：AU2021101556A4
公开(公告)日：2021-06-03
申请号：AU2021101556
申请日：2021-03-26
申请人： BIREDDY SRINIVASA REDDY MR , G SREENIVASULU REDDY MR , J DHANRAJ PAMAR DR , JAVED SYED DR , K SARUPYA SANTHOSH MR , KHAN AMER ALI MR , MAAZ AHMED MR , MOHAMMED ARSHAD MR , MOHAMMED SAJID DR , MIRZANA ISHRAT MEERA DR , NBV LAKSHMI KUMARI MRS , SHAIK MAHABOOB DR
发明人： MOHAMMED ARSHAD , KHAN AMER ALI , MAAZ AHMED , J DHANRAJ PAMAR , G SREENIVASULU REDDY , K SARUPYA SANTHOSH , BIREDDY SRINIVASA REDDY , JAVED SYED , MIRZANA ISHRAT MEERA , NBV LAKSHMI KUMARI , MOHAMMED SAJID , SHAIK MAHABOOB
IPC分类号： E03F5/10 , E03F3/02 , G05B13/02 , G05B19/048 , H02J3/00 , H02K7/18
摘要： "HYDEL POWER GENERATION FROM MULTI STOREY BUILDING SEWERAGE" Exemplary aspects of the present disclosure are directed towards the " HYDEL POWER GENERATION FROM MULTI STOREY BUILDING SEWERAGE" consisting of Waste Water Collection Arrangement (WWCA) 001, Water-Level Monitoring Device (WLMD) 100, and an Electricity-Generating Unit (EGU) 200. Waster-Water Collection Arrangement (WWCA) 001 comprises Plurality of sub-storage system 002 collecting sewerage/ waste-water, Filter arrangement 003 and Storage-Tank 004. Water Level Monitoring Device (WLMD) 100 encompassing Microcontroller 103, Water-level sensors 103a, and Outlet-Value control 103b. Electricity Generating Unit (EGU) 200 comprises Horizontal-Turbine 201, an Electrical Generator 202, and an Inlet-Arrangement 203 and Outlet-arrangement 204. Microcontroller 101a executes the relevant machine learning algorithm in coordination with Water-level sensors 103a identifies the exact amount of water in the Storage-tank 004 and sub-storage tanks 002. Based on the prediction system, microcontroller 103 opens the Outlet-Value control 103b, thereby releasing the water through Inlet-Arrangement 203 on to Horizontal-Turbine 201 to rotate the Electrical-Generator 202. The electricity produced is stored/utilized for the building lighting-system. Acquire Water level in sub-storage tanks 002 and 004 using sensors 103a Execute Relevent machine learning algorithm on water level sensor 103a data and accertain maximum waste-water flow rate and there by maximum power 302 that can be generated. Execute Relevent machine learning algorithm on water level sensor 103a data 303 and accertain the optimal timings to which the Outlet-Value control 103b of enneerned tankscn he nnernt 7 Either the predicted optimal timing or power level reaches threshold value 304 then turn on the Outlet-Value control 103b of concerned tanks. 305 Measure the energy produced through the Electricity Generating Unit (EGU) Intimate the user about the predicted and actual energy generated 307 Close the Outlet-Value control 103b either optimal timing or the water level reaches threshold value, and intimate the user FIG3 300 Process Executed Water-Level Monitoring Device (WLMD)

6. 发明专利

AU2020102642A4 Machine Learning-based Smart Workout Mirror and Method Thereof 未知
公开(公告)号：AU2020102642A4
公开(公告)日：2020-11-26
申请号：AU2020102642
申请日：2020-10-08
申请人： BHOI AKASH DR , BHOI PRIYA BRATA DR , MOHAMMED ARSHAD MR , MALLICK PRADEEP KUMAR DR , MISHRA SHRUTI DR , NAGA SRINIVASU PARVATHANENI DR , PANIGRAHI RANJIT DR , PRIYADARSHI NEERAJ DR , SATAPATHY SANDEEP KUMAR DR , SHAIK MAHABOOB DR
发明人： BHOI AKASH , MALLICK PRADEEP KUMAR , PANIGRAHI RANJIT , PRIYADARSHI NEERAJ , SATAPATHY SANDEEP KUMAR , MISHRA SHRUTI , NAGA SRINIVASU PARVATHANENI , BHOI PRIYA BRATA , SHAIK MAHABOOB , MOHAMMED ARSHAD
IPC分类号： G16H20/30 , A61B5/11 , G16H50/30
摘要： "Machine Learning-based Smart Workout Mirror and Method Thereof" Exemplary aspects of the present disclosure are directed towards the "Machine Learning-based Smart Workout Mirror and Method thereof' consisting of Mirror Arrangement 001, In-Mirror Monitoring Device (IMD) 100, and a plurality of Wearable Band (WB) 200. Mirror Arrangement 001 comprising of Two-way mirror 002, Display LCD-panel 003. In-Mirror Monitoring Device (IMD) 100 encompassing of Microcontroller 103, Camera 101, Thermal camera 102, GPRS-module 103a and Wi-Fi Dongle 103b. Microcontroller 101a on request from a user through Mobile Application 300 plays the concerned workout video 400 on the Display LCD-panel 003. In coordination with Camera 101, Microcontroller 103 using ML-algorithm identifies the user posture and match with the posture images displayed on Display LCD-panel 003 and finds anomaly in posture.Then Microcontroller 103 communicate with concerned Wearable Band (WB) 200 and vibrate till the user posture matches the played video 400. In coordination with Thermal-Camera 102, Microcontroller 103 using ML-algorithm identifies calories burnt. Date CAl Steps MIRROR 003 ACTUAL FIG 1 Machine Learning-Based Smart Workout Mirror

7. 发明专利

AU2020102473A4 MACHINE LEARNING AND IOT -BASED SMART SELF ADJUSTING POTTY SEAT 未知
公开(公告)号：AU2020102473A4
公开(公告)日：2020-11-12
申请号：AU2020102473
申请日：2020-09-28
申请人： BHOI AKASH KUMAR DR , BHOI PRIYA BRATA DR , BISOY SUKANT KISHORO DR , MOHAMMED ARSHAD MR , MALLICK PRADEEP KUMAR DR , PANDA AMIYA RANJAN DR , PANDA MOHIT RANJAN DR , PANIGRAHI RANJIT DR , PRIYADARSHI NEERAJ DR , SHAIK MAHABOOB DR
发明人： BHOI AKASH KUMAR , MALLICK PRADEEP KUMAR , PANIGRAHI RANJIT , BISOY SUKANT KISHORO , BHOI PRIYA BRATA , PANDA AMIYA RANJAN , PANDA MOHIT RANJAN , PRIYADARSHI NEERAJ , SHAIK MAHABOOB , MOHAMMED ARSHAD
IPC分类号： A47K13/28 , A47K13/10
摘要： "MACHINE LEARNING AND IOT -BASED SMART SELF ADJUSTING POTTY SEAT" Exemplary aspects of the present disclosure are directed towards the "MACHINE LEARNING AND IOT -BASED SMART SELF ADJUSTING POTTY SEAT" consisting of Central Imaging System 101, Height Adjusting System 102 and Potty seat Adjusting System 103. Central Imaging System 101 comprising of microcontroller 101a, camera 101b, GPRS module 101c and flush valve control 101d. In coordination, microcontroller 101a identifies the user and adjust the seat height using linear motors 102 and seat width using linear motor 103b. If the user database not found, then using image processing and Machine Learning Algorithms, microcontroller 101a predicts the user height and waist size and adjust the potty seat height and breadth accordingly. Central Imaging System 101 confirm that user completed the excrement discharge and open the flush valve controller 101d.

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