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Support for development using machine learning.

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last updated:Jan 07, 2026

ネオリウム・テクノロジー
ネオリウム・テクノロジー
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Achieve optimization of the development process and improvement of analysis accuracy using big data (time series, images)!

The benefits of introducing machine learning into the development process are numerous. By optimizing the design and development processes, efficiency is improved, and defects can be prevented through enhanced predictive accuracy. Furthermore, it reduces the number of prototypes, cutting costs, and allows for process adaptability through real-time feedback. It also achieves a reduction in defective products, strengthens quality control, improves production efficiency through automation, and accelerates simulations. Additionally, by collaborating with digital twins, it enables the optimization of the entire manufacturing process.

    Analysis and prediction systemBig DataOther Smart Grid
ML_NeoriumTechnology_EngineeringService_Image.png

Support for development using machine learning.

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At Neorium Technology, we provide technical support for incorporating machine learning into our customers' development processes. Customers may have various challenges, such as "how can the big data collected from experimental validation be utilized in machine learning?" and "what do we need to start machine learning?" In this information, we offer engineering services related to nonlinear control target modeling and image recognition system construction using deep learning. The catalog and videos briefly introduce examples using the Transformer, which is also famous for ChatGPT, as well as examples of image recognition.

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Applications/Examples of results

Development of an image recognition system using deep learning - Developed a system that learns from traffic-related videos using deep learning to distinguish between vehicles, humans, and other objects. Development of a time series prediction model using deep learning - Developed a multi-input multi-output nonlinear dynamic model using deep learning (LSTM model). Hyperparameter optimization of neural networks using machine learning - Applied Bayesian optimization to determine the hyperparameters of neural networks (such as the number of hidden layers and the number of neurons in hidden layers). Additionally, we incorporate machine learning into the development of predictive maintenance algorithms for equipment and support for the development of control target models to meet our customers' needs.

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Technical Documentation: Data-Driven Control - Application Example to Electric Power Steering PI Controller

Technical Documentation: Data-Driven Control - Application Example to Electric Power Steering PI Controller

TECHNICAL
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Technical Document: Machine Learning (Transformer) - An Example of Surrogate Modeling for Automotive Parts

Technical Document: Machine Learning (Transformer) - An Example of Surrogate Modeling for Automotive Parts

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Model-Based Development Support Service

Model-Based Development Support Service

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Technical Documentation: Machine Learning (Deep Learning) Image Recognition Examples and Electric Throttle Valve Control Examples

Technical Documentation: Machine Learning (Deep Learning) Image Recognition Examples and Electric Throttle Valve Control Examples

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ネオリウム・テクノロジー

ネオリウム・テクノロジー

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Neolium Technology responds to the diverse needs of customers involved in model-based development and consistently provides high-quality technology. In particular, we have a wealth of experience in professional services (model development, contracting, consulting, technical seminars) and product services (sales of control design support software and technical support) in the fields of control, optimization, machine learning, and system modeling. If you have any issues with model development, please feel free to consult with us.

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