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Efficiency of SRM can be in excess of 95% in specific operating modes. Commonly around 93 to 95%.

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Very good content sir..

😀 *Promo SM*

SynRM : New - Geração = Sistem's : Elétro = Magnétic = Moto = Perpétuo = Motor = Elétric = Funciona = Rotação = Permanente = Sempre : Syncro= Rotação = Motor ; Fantástic ! Motor = Elétric ! Para = Sempre = Funcionar ! 🏆🏆🏆🥇🥇🥇.

Hi, can yo fix the problem of 'currency not set' it appears when I want to by a membership.

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Good job sir ji

Thank u for ur efforts to share knowledge.Pls do not add music.Also pls focus on multimeter to show readings.

sir, SRM with out sensor how we will know of the rotor...

i am confusing about SYRM rotor material... you said in video (2:99) synrm rotor made up of laminations.... is it correct explanation... i need clarity about rotor of SYNRM material...

switched reluctance motor rotor is made up of steel plate..... see 2:52 minute....

One on the right is a real challenge in the material of the rotor.

Load capability CAPACITY??

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Bruh, Hyperloop content in 2023? Musk admitted in his own Biography that it's nothing but an evil attempt at sabotaging US high speed rail.

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It won't 😂

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useless without animation

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💕 Promo SM

Please remove englishwording on vedios

Why don't you use animation my man... This way of presentation is so much outdated consider the complexity of the matter and the ease of making animation video nowadays.

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A SynRM rotor is a set of powerful permanent magnets that required rare earth elements. An SRM rotor is just made of iron.

Sir what will be the price of the course if we want to purchase it?

You solved my query.

Your contact please

Good information

Very important topic . well done Sir. With present 440 v ac motor wings, by parallelling the diff wining coils of three phses , cant be the operating voltage brought them .

your sound system background and also eco breaks your information.subject not clear sir please

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Informative

Impact Maximization via Hebbian Learning is an approach for AGI and ASI. The approach posits 3 main points 1) Making an impact is the objective function of all life forms n so the objective function of AGI is to maximise impact. Living thing have more impact potential than non-living things and highly intelligent beings haveeven more potential for impact. ANything we do is a kind of impact whether is self-preservation, procreation, meme propogation, DDAO and other derived objectives, and so maximising impact is what an AGI system should do. 2)Impact maximization can happen this way - If an agent relaxes(suspends output action) while perceiving something impactful and action when it perceives a lack of impact/novelity/interesting thing, so as to bring about a change in the environment, it will be more efficient in maximising impact compared to a dumb agent acting randomly. Also,Impact is a very subjective concept: what is impact for one may not be for someone else, what is impactful today for you may not be so tomorrow. hence we cant have a solid mathematical framework for impact based on a measure of activity or entropy or anything else. hebbian rule automatically results in impact maximising behaviour as, when presented with the same input again and again (usually associated with a lack of activity or impact) the same pathways get excited leading to quicker flow of activation to the o/p region whereas hen something new is perceived, activation travels through a newer pathway and hence takes longer to go to output therefore temporarily suspending o/p action. So conversely, its is not an impactful input that makes the system relax, but what makes the system relax is what is considered impactful for the system at that moment and vice versa. 3) emergence of various modules and useful and complex intelligent information processing and behavior, in a large neural network using just a variation of the hebbian rule for synaptic weight updation in addition with formulas/rules for learning rate, threshold,activation loss, neurogenesis and death updation. People talk about evolution and forming of regions taking millions of years and so we would need the same tim3efrme for this pproach, but thats wrong on 2 accounts. a) we can start with 30 billion neurons and 150 trillion synapses on day 1 ( which didnt happen in nature) and on day 1 its an impact maximising system with just hebbian rule. b) the formation of different region via evolution is not an accidental change that got permanent, the regions were shaped by biological rules such as hebbian and other homeostatic factors. So no random/accident component here thats required to form the different regions in the brain, just based on what its exposed to, the brain region can form so many different associations, representations, and other desired qualities such as remembering, abstraction, etc as demonstrated here. The approach involves building prototypes and incrementally adding input and output devices while increasing the size of the neural network to get to high levels of intelligence with the same underlying framework and learning rules. superintelligencetoken.wordpress.com/2021/11/13/our-approach/

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