Why NUBISON AIoT

Technology Innovation

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Connectivity Innovation

SaaS-based device cloud connection technology
It is a revolutionary technology that isolates communication and protocol into "Thing Drivers" and may be used to services without development even if H/W is added or altered, in contrast to how it was previously run and controlled in combination with the system.

Previously, even simple H/W modifications required several months of development time and a large amount of manpower, resulting in enormous expenses. By linking directly with simple UI settings and script adjustments, you may run a real-time connection-based service reliably using "Thing Driver."

Moreover, "Thing Driver" is offered to function as a middleman for multiple service providers who demand H/W by constructing a cloud distribution system.

The H/W ecosystem may be expanded by making sensors that were previously only used in one sector available to other industries, and cost-effective services can be provided by increasing H/W manufacturer sales and employing existing H/W for service providers.

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Industrial AI Innovation

Overall Time Series Data AI Technology and Normal Spatialization-based Scoring Technique
We deliver unique solutions that can be implemented directly to the field while having universality utilizing AI's basic technologies that can be employed throughout the industry through "Overall Time Series Data AI Technology" and "Normal Spatialization-based Scoring Technique."

In the case of industrial AI, it was typical for developers to create models after becoming adequately acquainted with domain knowledge (specific skills and information in the industry) and identifying data features. It was difficult to maintain or improve performance in this scenario since development and learning had to be repeated when the environment changed.

To solve these challenges, we created a system that can be implemented immediately in the field using basic pre-processing technologies and models that can be utilized across the industry, and that can raise performance to a particular level in the future through re-learning. Preprocessing is performed instantly using "Overall time-series data AI technology," and the learning model is implemented using "normal spatialization-based scoring technique," allowing the status of equipment and facilities to be quantified.

These metrics have relevance since they quantify performance capacity as well as whether items and individuals are normal. You can identify the initial settings that can further increase the performance by associating the initial settings with the commodities generated by the equipment, and you can know the performance by quantifying the condition of things that fit the settings.

This approach predicts not only the amount but also the quality of the products produced, therefore it is used in AI throughout the industry, enabling swift business innovation.