| 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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| 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 specializing in the development of advanced methods utilizing artificial intelligence (AI) and multilayer neural networks to detect faulty sound patterns in vehicles. The company's innovative approach enables the diagnosis of vehicle faults even in challenging dynamic conditions and amidst excessive extraneous noise. V2M holds a patent for its development, and its founder, Peter Bakulov, has contributed to the field with his scientific article "Acoustic Fault Trace as a Diagnostic Parameter of Modern Vehicles[1]," which was included in the scientific abstract and citation database Scopus in 2022.
History edit
Founded in 2012 by Peter Bakulov, a former professor at MADI with extensive experience in the automotive industry, V2M aimed to address the issue of vehicle malfunctions that could lead to accidents. Bakulov recognized the potential of recognizing vehicle noises to detect and prevent malfunctions[2]. In 2016, V2M developed a laboratory sample solution to tackle this problem. After five years of development, the company successfully completed a prototype, validated by Bakulov's PhD thesis. V2M's first test vehicle, a Tesla Model 3 Standard Range Plus[3], was acquired to install the prototype[4], showcasing the company's potential for partnerships, particularly with technologically advanced entities like Tesla.
To support its growth as a startup, V2M participated in the acceleration program of Starta Ventures. In early 2022, the company secured $100,000 in investment through a SAFE (Simple Agreement for Future Equity).
Developments edit
V2M has developed an AI technology-based platform that utilizes acoustic sensors, a control unit, and specialized server software to detect vehicle malfunctions through sound analysis. The platform collects and processes sound streams in real-time to diagnose various critical components of a vehicle, including the engine, transmission, bearings, and suspension parts. With an algorithm that periodically checks sensors for safe operation[5] and enables the addition of new features, V2M's technology offers predictive diagnostics, foreseeing and preventing potential failures.
Applications edit
Beyond automotive applications, V2M's methodology demonstrates versatility and readiness for diverse industries, such as mineral resource extraction, specialized machinery, and commercial vehicle fleets. The technology's ability to detect mechanical or operational irregularities based on auditory cues makes it applicable across various sectors.
References edit
- ^ Bakulov, Petr (2022-04-01). "Acoustic Fault Trace as a Diagnostic Parameter of Modern Vehicles". 2022 Systems of Signals Generating and Processing in the Field of on Board Communications. IEEE. pp. 1–4. doi:10.1109/IEEECONF53456.2022.9744317. ISBN 978-1-6654-0635-2.
- ^ admin (2023-04-10). "Acoustic based vehicle diagnostic system". Telematics Wire. Retrieved 2024-03-20.
- ^ Davies, Param (2021-10-24). "This Is Why The Tesla Model 3 Is The World's Best-Selling EV". HotCars. Retrieved 2024-03-20.
- ^ "V2M tech is designed to catch car problems – by listening for them". New Atlas. 2023-03-03. Retrieved 2024-03-20.
- ^ Driving-Tests.org. "2023 Driving Statistics: The Ultimate List of Driving Stats". driving-tests.org. Retrieved 2024-03-20.
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