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Problem-solving using AI ◎ AI determines whether sounds are normal or abnormal! We are advancing the development of abnormal sound detection that responds to "Did something happen?!" by collecting various environmental sounds and everyday sounds on-site in collaboration with several companies and training the collected sound data. By using the AI core model Auto_Encoder to learn normal sounds, we enable the detection of abnormal sounds (non-stationary sound detection, anomaly detection). We detect abnormal sounds from various sources such as machine sounds, factory sounds, sounds from the human body, environmental sounds, and more, including machine tools, manufacturing lines, sounds inside the body, roads, and residential areas. We have also made "visualization of abnormal sounds" possible using mobile tablet devices. Steps to solve problems like the above: 1) Create an AI inference model on our learning server 2) Implement the AI inference model in the 'AI-NETWORK TERMINAL II' 3) On-site operation In this way, we can consistently execute everything from deep learning model development to on-site implementation. We possess unique novelty, originality, and innovation.
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Free membership registrationProblem-solving using AI ◎ AI determines whether sounds are normal or abnormal! We are advancing the development of abnormal sound detection that responds to "Did something happen?!" by collecting various environmental and living sounds on-site in collaboration with several companies and training the collected sound data. By using the AI core model Auto_Encoder to learn normal sounds, we enable the detection of abnormal sounds (non-stationary sound detection, anomaly sound detection). We conduct abnormal sound detection from various machine sounds, factory sounds, human body sounds, and environmental sounds, such as those from machine tools, manufacturing lines, inside the body, roads, and residential areas. We have also made "visualization of abnormal sounds" possible using mobile tablet devices. Steps to solve problems like the above: 1) Create an AI inference model on our learning server 2) Implement the AI inference model in the 'AI-NETWORK TERMINAL II' 3) On-site operation In this way, we can consistently execute everything from deep learning model development to on-site implementation. We possess unique novelty, originality, and innovation.
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Free membership registrationIdeal for industrial voice network broadcasting (VoIP)! Our "VOICE-IP TERMINAL II" is designed entirely in hardware. It features extremely fast startup since it does not use a CPU. Even if the power is turned off and on during broadcasting, it will resume broadcasting operations within seconds. Additionally, there is no damage to the device. Naturally, since it does not implement an OS or application software, there are absolutely no issues such as hacking (rewriting or takeover) or software bugs. The high reliability is a point of pride for our devices. Device Overview - Supports one-way communication (broadcast) in a 1-to-N configuration. - Can transmit to a maximum of 60 receivers (up to 60 units). - The device can be configured as a transmitter or receiver, along with IP address settings, using the included setup application. - Information set once will not be lost even when the power is turned off. It can also be rewritten multiple times. - Equipped with LINE-IN/OUT terminals. Connect the audio source device to the LINE-IN terminal and an amplifier-equipped speaker to the LINE-OUT terminal to start network broadcasting immediately. Custom solutions for two-way communication and more are available. Please contact us directly.
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