Draft:V2M: Difference between revisions
copyvio and based on a press release anyway so not useful |
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{{AFC submission|d|npov|u=Analobova|ns=118|decliner=Marshelec|declinets=20230502040157|reason2=adv|small=yes|ts=20230331181029}} <!-- Do not remove this line! --> |
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{{AFC comment|1=Like before sources are press releases, interviews and/or based on what the company says about itself. Some other sources make no mention of V2M so not also not useful and this is entirely promotional. Please also see [[WP:COI]]. [[User:S0091|S0091]] ([[User talk:S0091|talk]]) 14:45, 14 November 2023 (UTC)}} |
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{{AFC comment|1=References appear to be [[churnalism]] around the corp and the launch. Another is an interview with the principal 🇺🇦 [[User:Timtrent|<span style="color:#800">Fiddle</span><sup><small>Timtrent</small></sup>]] [[User talk:Timtrent|<span style="color:#070">Faddle</span><sup><small>Talk to me</small></sup>]] 🇺🇦 12:59, 27 September 2023 (UTC)}} |
{{AFC comment|1=References appear to be [[churnalism]] around the corp and the launch. Another is an interview with the principal 🇺🇦 [[User:Timtrent|<span style="color:#800">Fiddle</span><sup><small>Timtrent</small></sup>]] [[User talk:Timtrent|<span style="color:#070">Faddle</span><sup><small>Talk to me</small></sup>]] 🇺🇦 12:59, 27 September 2023 (UTC)}} |
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| ⚫ | '''V2M''' is a technology company that specializes in the development of advanced methods using [[artificial intelligence]] (AI) and a multilayer [[neural network]] to detect faulty sound patterns in [[Vehicle|vehicles]]. The company's innovation allows it to diagnose vehicle faults even in the most challenging dynamic conditions and amidst excessive extraneous noise. V2M has a [[patent]] for its development, and its founder's scientific article "[[Acoustics|Acoustic]] Fault Trace as a Diagnostic Parameter of Modern Vehicles" was included in the scientific abstract and citation database [[Scopus]] in 2022....<ref>{{Cite journal |last=Bakulov |first=Petr |title=IEEE |url=https://ieeexplore.ieee.org/document/9744317 |access-date=2022-04-01 |website=IEEE Xplore|date=March 2022 |pages=1–4 |doi=10.1109/IEEECONF53456.2022.9744317 |s2cid=247858190 }}</ref> |
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| ⚫ | '''V2M''' is a technology company that specializes in the development of advanced methods using [[artificial intelligence]] (AI) and a multilayer [[neural network]] to detect faulty sound patterns in [[Vehicle|vehicles]]. The company's innovation allows it to diagnose vehicle faults even in the most challenging dynamic conditions and amidst excessive extraneous noise. V2M has a [[patent]] for its development, and its founder's scientific article "[[Acoustics|Acoustic]] Fault Trace as a Diagnostic Parameter of Modern Vehicles" was included in the scientific abstract and citation database [[Scopus]] in 2022...<ref>{{Cite journal |last=Bakulov |first=Petr |title=IEEE |url=https://ieeexplore.ieee.org/document/9744317 |access-date=2022-04-01 |website=IEEE Xplore|date=March 2022 |pages=1–4 |doi=10.1109/IEEECONF53456.2022.9744317 |s2cid=247858190 }}</ref> |
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== History == |
== History == |
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The platform collects and processes sound streams perceived by the acoustic sensors installed on the vehicle in real-time to diagnose moving elements. The [[control unit]] with pre-installed special software performs the processing. The software can diagnose several critical moving parts of a vehicle structure, including the [[engine]], [[power transmission]] details, [[Bearing (mechanical)|bearings]], [[axle]] shafts, [[Hinge|hinges]], attachments, [[Generator (computer programming)|generator]], [[air conditioning]] compressor, [[Starter (engine)|starter]], [[power steering]] pump, rollers, [[Idle (engine)|idle]] and [[tension]], suspension parts, and [[Actuator|actuators]] of the [[brake]] system. |
The platform collects and processes sound streams perceived by the acoustic sensors installed on the vehicle in real-time to diagnose moving elements. The [[control unit]] with pre-installed special software performs the processing. The software can diagnose several critical moving parts of a vehicle structure, including the [[engine]], [[power transmission]] details, [[Bearing (mechanical)|bearings]], [[axle]] shafts, [[Hinge|hinges]], attachments, [[Generator (computer programming)|generator]], [[air conditioning]] compressor, [[Starter (engine)|starter]], [[power steering]] pump, rollers, [[Idle (engine)|idle]] and [[tension]], suspension parts, and [[Actuator|actuators]] of the [[brake]] system. |
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The [[hardware]] complex includes at least three [[Acoustic sensor|acoustic sensors]], [[control unit]], and a connection kit<ref>{{Cite web |last=Taimur |date=2023-03-11 |title=This Novel Tech Listens To Your Car To Diagnose Issues |url=https://wonderfulengineering.com/this-novel-tech-listens-to-your-car-to-diagnose-issues/ |access-date=2023-03-31 |website=Wonderful Engineering |language=en-US}}</ref> |
The [[hardware]] complex includes at least three [[Acoustic sensor|acoustic sensors]], [[control unit]], and a connection kit<ref>{{Cite web |last=Taimur |date=2023-03-11 |title=This Novel Tech Listens To Your Car To Diagnose Issues |url=https://wonderfulengineering.com/this-novel-tech-listens-to-your-car-to-diagnose-issues/ |access-date=2023-03-31 |website=Wonderful Engineering |language=en-US}}</ref> |
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V2M developed an [[algorithm]] that checks the [[Sensor|sensors]] periodically to ensure safe operation and diagnose any problems. The algorithm enables the addition of new [[features]] that enhance user experience, such as [[automatic parking]] and [[traffic jam]] assistance. With this innovation, V2M can detect failures in advance<ref>{{Cite web |date=2023-03-08 |title=AI-Based Self-Diagnostic Solution for Vehicles Makes One Hear Malfunctions to Improve Safety |url=https://techacute.com/v2m-ai-based-self-diagnostic-solution/ |website=Tech Acute}}</ref> |
V2M developed an [[algorithm]] that checks the [[Sensor|sensors]] periodically to ensure safe operation and diagnose any problems. The algorithm enables the addition of new [[features]] that enhance user experience, such as [[automatic parking]] and [[traffic jam]] assistance. With this innovation, V2M can detect failures in advance<ref>{{Cite web |date=2023-03-08 |title=AI-Based Self-Diagnostic Solution for Vehicles Makes One Hear Malfunctions to Improve Safety |url=https://techacute.com/v2m-ai-based-self-diagnostic-solution/ |website=Tech Acute}}</ref> |
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The last [[version]] of software opens up the realm of predictive diagnostics. This means a [[solution]] not just addressing current issues; it is foreseeing and preventing potential ones.<ref>{{Cite news |date=2023-11-07 |title=Tech Acute |work=V2M’s Cutting-Edge Neural Networks Transform Automotive Fault Diagnosis |url=https://techacute.com/v2ms-cutting-edge-neural-networks-transform-automotive-fault-diagnosis/}}</ref> |
The last [[version]] of software opens up the realm of predictive diagnostics. This means a [[solution]] not just addressing current issues; it is foreseeing and preventing potential ones.<ref>{{Cite news |date=2023-11-07 |title=Tech Acute |work=V2M’s Cutting-Edge Neural Networks Transform Automotive Fault Diagnosis |url=https://techacute.com/v2ms-cutting-edge-neural-networks-transform-automotive-fault-diagnosis/}}</ref> |
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V2M as a [[methodology]] exhibits versatility and readiness for diverse [[applications]], spanning industries such as mineral resource extraction, specialized machinery, and commercial [[Vehicle fleet|vehicle fleets]]. It finds utility in scenarios where the auditory dimension serves as an indicative [[element]] of [[mechanical]] or operational irregularities, facilitating successful implementation across various sectors. |
V2M as a [[methodology]] exhibits versatility and readiness for diverse [[applications]], spanning industries such as mineral resource extraction, specialized machinery, and commercial [[Vehicle fleet|vehicle fleets]]. It finds utility in scenarios where the auditory dimension serves as an indicative [[element]] of [[mechanical]] or operational irregularities, facilitating successful implementation across various sectors.<ref>{{Cite web |last=Топорова |first=Светлана |date=2023-07-26 |title=Уйти из науки и построить стартап с перспективой сотрудничества с Tesla: интервью с Петром Бакуловым, основателем V2M — Светлана Топорова на vc.ru |url=https://vc.ru/u/2136172-svetlana-toporova/773005-uyti-iz-nauki-i-postroit-startap-s-perspektivoy-sotrudnichestva-s-tesla-intervyu-s-petrom-bakulovym-osnovatelem-v2m |access-date=2023-11-14 |website=vc.ru}}</ref> |
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== References == |
== References == |
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Revision as of 14:45, 14 November 2023
| Submission declined on 14 November 2023 by S0091 (talk). This submission appears to read more like an advertisement than an entry in an encyclopedia. Encyclopedia articles need to be written from a neutral point of view, and should refer to a range of independent, reliable, published sources, not just to materials produced by the creator of the subject being discussed. This is important so that the article can meet Wikipedia's verifiability policy and the notability of the subject can be established. If you still feel that this subject is worthy of inclusion in Wikipedia, please rewrite your submission to comply with these policies.
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This draft has not been edited in over six months and qualifies to be deleted per CSD G13. Declined by S0091 6 months ago. Last edited by S0091 6 months ago. Reviewer: Inform author.
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| Submission declined on 27 September 2023 by Timtrent (talk). This draft's references do not show that the subject qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are: Declined by Timtrent 7 months ago.
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| Submission declined on 2 May 2023 by Marshelec (talk). This submission does not appear to be written in the formal tone expected of an encyclopedia article. Entries should be written from a neutral point of view, and should refer to a range of independent, reliable, published sources. Please rewrite your submission in a more encyclopedic format. Please make sure to avoid peacock terms that promote the subject. This submission appears to read more like an advertisement than an entry in an encyclopedia. Encyclopedia articles need to be written from a neutral point of view, and should refer to a range of independent, reliable, published sources, not just to materials produced by the creator of the subject being discussed. This is important so that the article can meet Wikipedia's verifiability policy and the notability of the subject can be established. If you still feel that this subject is worthy of inclusion in Wikipedia, please rewrite your submission to comply with these policies. Declined by Marshelec 12 months ago. | |
Comment: Like before sources are press releases, interviews and/or based on what the company says about itself. Some other sources make no mention of V2M so not also not useful and this is entirely promotional. Please also see WP:COI. S0091 (talk) 14:45, 14 November 2023 (UTC)
- Comment: References appear to be churnalism around the corp and the launch. Another is an interview with the principal 🇺🇦 FiddleTimtrent FaddleTalk to me 🇺🇦 12:59, 27 September 2023 (UTC)
- Comment: The article needs rewriting in an encyclopedic style. Too much of the content reads like a promotional advertisement. In addition, the prose generally needs review throughout. For example, the first sentence is 56 words long and does not provide an easy-to-read introduction to the subject. Marshelec (talk) 04:01, 2 May 2023 (UTC)
V2M is a technology company that specializes in the development of advanced methods using artificial intelligence (AI) and a multilayer neural network to detect faulty sound patterns in vehicles. The company's innovation allows it to diagnose vehicle faults even in the most challenging dynamic conditions and amidst excessive extraneous noise. V2M has a patent for its development, and its founder's scientific article "Acoustic Fault Trace as a Diagnostic Parameter of Modern Vehicles" was included in the scientific abstract and citation database Scopus in 2022....[1]
History
Founded in 2012 by Peter Bakulov, a former professor at MADI with 13 years of experience in the automotive industry, V2M was established to prevent malfunctions that could lead to accidents[2]. Official statistics indicate that in the United States, 2% of road traffic accidents can be attributed to vehicle malfunctions as the primary causative factor. Over several years of data analysis, it becomes evident that early identification and prevention of vehicle malfunctions have the potential to save an estimated 300 lives annually[3].
Bakulov realized that by recognizing the noise a vehicle was making, malfunctions could be detected and prevented. In 2016, the V2M team developed a laboratory sample solution to address this problem.
After five years, the company successfully completed the prototype, which was confirmed by the founder's PhD thesis. The company purchased its first test vehicle, a Tesla Model 3 Standard Range Plus, to install the prototype.[4]. The Tesla Model 3 holds the distinction of being the world's highest-selling electric vehicle.[5] Beyond its automotive endeavors, Tesla is recognized as both an automotive and technological company, which augments V2M as a company potential for partnerships. They plan to purchase two internal combustion engine vehicles and one hybrid vehicle to demonstrate that the product is suitable for every type of engine.
To develop V2M as a startup, the team participated in the acceleration of Starta Ventures[6]. In early 2022, the company raised $100,000 in investment (per SAFE).
Developments
V2M developed an AI technology-based platform to detect vehicle malfunctions using the sound of the car, providing emergency information through an app from acoustic sensors, control unit, and server with special software[7].
The platform collects and processes sound streams perceived by the acoustic sensors installed on the vehicle in real-time to diagnose moving elements. The control unit with pre-installed special software performs the processing. The software can diagnose several critical moving parts of a vehicle structure, including the engine, power transmission details, bearings, axle shafts, hinges, attachments, generator, air conditioning compressor, starter, power steering pump, rollers, idle and tension, suspension parts, and actuators of the brake system.
The hardware complex includes at least three acoustic sensors, control unit, and a connection kit[8]
V2M developed an algorithm that checks the sensors periodically to ensure safe operation and diagnose any problems. The algorithm enables the addition of new features that enhance user experience, such as automatic parking and traffic jam assistance. With this innovation, V2M can detect failures in advance[9]
The last version of software opens up the realm of predictive diagnostics. This means a solution not just addressing current issues; it is foreseeing and preventing potential ones.[10]
V2M as a methodology exhibits versatility and readiness for diverse applications, spanning industries such as mineral resource extraction, specialized machinery, and commercial vehicle fleets. It finds utility in scenarios where the auditory dimension serves as an indicative element of mechanical or operational irregularities, facilitating successful implementation across various sectors.[11]
References
- ^ Bakulov, Petr (March 2022). "IEEE". IEEE Xplore: 1–4. doi:10.1109/IEEECONF53456.2022.9744317. S2CID 247858190. Retrieved 2022-04-01.
- ^ admin (2023-03-28). "Acoustic based vehicle diagnostic system". Telematics Wire. Retrieved 2023-03-31.
- ^ Driving-Tests.org. "2023 Driving Statistics: The Ultimate List of Driving Stats". driving-tests.org. Retrieved 2023-11-14.
- ^ "V2M tech is designed to catch car problems – by listening for them". New Atlas. 2023-03-03. Retrieved 2023-03-31.
- ^ Davies, Param (2021-10-24). "This Is Why The Tesla Model 3 Is The World's Best-Selling EV". HotCars. Retrieved 2023-11-14.
- ^ "V2M (Road) Company Profile: Valuation & Investors | PitchBook". pitchbook.com. Retrieved 2023-03-31.
- ^ Emir, Can (2023-03-07). "This novel tech actually 'listens' to your car to diagnose issues". interestingengineering.com. Retrieved 2023-03-31.
- ^ Taimur (2023-03-11). "This Novel Tech Listens To Your Car To Diagnose Issues". Wonderful Engineering. Retrieved 2023-03-31.
- ^ "AI-Based Self-Diagnostic Solution for Vehicles Makes One Hear Malfunctions to Improve Safety". Tech Acute. 2023-03-08.
- ^ "Tech Acute". V2M’s Cutting-Edge Neural Networks Transform Automotive Fault Diagnosis. 2023-11-07.
- ^ Топорова, Светлана (2023-07-26). "Уйти из науки и построить стартап с перспективой сотрудничества с Tesla: интервью с Петром Бакуловым, основателем V2M — Светлана Топорова на vc.ru". vc.ru. Retrieved 2023-11-14.
- in-depth (not just brief mentions about the subject or routine announcements)
- reliable
- secondary
- strictly independent of the subject
Make sure you add references that meet all four of these criteria before resubmitting. Learn about mistakes to avoid when addressing this issue. If no additional references exist, the subject is not suitable for Wikipedia.