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
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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 8 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 13 months ago. |
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.
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).
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.
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.
Submission declined on 14 November 2023 by
S0091 (
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:
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.
Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
|
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 8 months ago.
|
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 13 months ago. |
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.
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).
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.
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.
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