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ブレインズテクノロジー

EstablishmentAugust 8, 2008
capital11000Ten thousand
number of employees43
addressTokyo/Minato-ku/4th Floor, Shinagawa Center Building, 3-23-17 Takanawa
phone03-6455-7023
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last updated:Jun 10, 2021
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Work analysis and improvement tool "Impulse"

A work analysis and improvement tool that combines AI (machine learning) and skeletal detection technology for visualizing and monitoring human tasks such as assembly work.

The Impulse work analysis and improvement tool captures human work using a camera and automatically analyzes the quality, efficiency, and load of the work by recognizing information such as the movements of objects and human skeletal structures with AI. Impulse was released in 2014 ahead of the market with the aim of practical application of machine learning technology, and it is an anomaly detection solution that has been "refined in the field" by numerous customers. In addition to having data scientists with advanced analytical skills, it aims to be a platform that can be used by a wider range of users, equipped with architecture and features that facilitate the easy introduction and operation of machine learning technology. Its most notable feature, auto-modeling (Patent No. 6315528), automatically classifies the characteristics of complex and vast data and provides functionality to derive the optimal analysis model through simulations using standard algorithms. <Data Analysis Process> - Data organization - Policy determination - Feature selection - Tuning - Model operation

  • Surveillance Camera System
  • Image Processing Software
  • Other assembly machines

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Anomaly Detection Solution "Impulse"

An anomaly detection solution that supports the operation of over 21,000 machine learning models for more than 100 customers, primarily in manufacturing, plants, and IT.

"Impulse" is a platform that utilizes machine learning-based anomaly detection technology as its core, addressing the diverse challenges and data analysis needs of various fields. It supports the advancement of many workplaces through the detection of equipment failure signs using vast amounts of sensor data output from IoT devices, defect detection, quality inspection using still images and videos, and work analysis. Users can create and operate machine learning models while interacting with AI, making it possible for a wide range of users, not just data scientists, to incorporate machine learning technology into their operations.

  • Business Intelligence and Data Analysis

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