AGNISTOK 2023-24

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Implementation andPerformance importanceof loaded distributedenergy system.14-Prof. Trupti TembhekarElectrical Department
Kalman Filter & Machine LearningA Kalman Filter is an algorithm that takes data inputs from multiple sources andestimates unknown variables, despite a potentially high level of signal noise. Oftenused in navigation and control technology, the Kalman Filter has the advantage ofbeing able to predict unknown values more accurately than if individual predictionsare made using singular methods of measurement. Kalman Filters use a two-stepprocess for estimating unknown variables. The algorithm works by first estimating thecurrent state variables, and measures their uncertainties. Then, the algorithmupdates the estimates using a weighted average, wherein more weight is attributedto estimates with higher levels Of uncertainty. Because the filter takes in informationfrom multiple sources, both current state and predicted state, the filter is able toprovide a level of accuracy higher than if estimates were made given only one of themultiple sources.One of the most common uses for the Kalman Filter is in navigation and positioningtechnology. Imagine a car with a GPS transmitter is traveling down a mountain road.A Kalman Filter can be applied to take in the GPS data from the car, however GPSdevices are not always entirely accurate. So, the Kalman Filter can take in speedand velocity data to adjust the rate of change in the cars position over time.Naturally, given the laws of physics, the level of variable uncertainty is lower whenthe car is traveling faster, and vice versa. All of this information is used to predictwhere the car will be, and then the process is repeated with updated information asthe car travels down the road. Because the Kalman Filter is recursive, it doesn't needto know the entirety of the cars position and speed data, but rather just the lastknown position and speed.Hidden Markov Model (HMM) is a statistical Markov model in which the model statesare hidden. It is important to understand that the state of the model, and not theparameters of the model, are hidden.15
Page 1 and 2: NAGAR YUWAK SHIKSHAN SANSTHA'SY E S
Page 3 and 4: DEPARTMENT OFELECTRICAL ENGINEERING
Page 5 and 6: व दे वां भूदे
Page 7 and 8: MESSAGE FROM REGISTRARDR. S.V.Praya
Page 9 and 10: MESSAGE FROM FACULTY-INCHARGEThe ma
Page 11 and 12: MESSAGE FROM EXECUTIVE EDITORSSHRUT
Page 13 and 14: INDEX01. Theme Story09.Creativity S
Page 15: TECHNICAL SECTIONTechNosightSV I K
Page 19 and 20: The ElectricRevolutionThe rapidly e
Page 21 and 22: FACTSABOUTEVThey use electric motor
Page 23 and 24: 24 NOV, 2023TECHNICAL INTERVIEWINTR
Page 25 and 26: We are a little different!Often, wh
Page 27 and 28: What are we?Maybe we are too small,
Page 29 and 30: DEALING WITH FAILURE“Failing to a
Page 31 and 32: जी-20 और भारतजी
Page 33 and 34: कहानीये कहान
Page 35 and 36: व हणजे काय ?व ह
Page 37 and 38: शौयसुट या सार
Page 39 and 40: Sanskrit Section"The world is one F
Page 41 and 42: शीषक): व ुत् या
Page 43 and 44: ुसुभा षतउ मं
Page 45 and 46: Vidhi DukreEL 2nd YrShriya Karanjek
Page 47 and 48: Dnyanata WakodeEL 1st yrDnyaneshwar
Page 49 and 50: Imagine. Make. Create. Organisekala
Page 51 and 52: BY:Sayli Bobde3rd year (Electrical)
Page 53 and 54: CHETAN WADIKARNAVYA KASHIMKAAIDS 2N
Page 55 and 56: Sumanti MantriEL 1ST YRAnurag Muley
Page 57 and 58: DESIGNING SECTIONSakshi SukhadeveDe
Page 59 and 60: AchievemntsFACULTY ACHIEVEMENTSD R
Page 61 and 62: THIRD YEARSECTION ‘A’SECTION
Page 63 and 64: TEACHING FACULTYNON-TEACHING FACULT

AGNISTOK 2023-24

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