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Professional Services: IQ/OQ Validation

Professional Services: IQ/OQ Validation

We provide transducer calibration services along with IQ/OQ validation packages!

In medical device and pharmaceutical companies, software validation is essential to comply with FDA 21 CFR Part 820 and ISO 13485. To support IQ/OQ software validation, our services have been providing on-site IQ/OQ validation and documentation services for many years. This service, offered by the equipment manufacturer, is designed to ensure that our testing equipment meets specifications and can generate valid results. 【Features】 ■ Installation Qualification (IQ) - Installation checklist ■ Operational Qualification (OQ) - Transducer validation - Software functionality check - Calculation validation *For more details, please refer to the PDF document or feel free to contact us.

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  • Other contract services
  • Other services

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Verification of DeepSeek-R1-Distill-Llama-70B

Verification of DeepSeek-R1-Distill-Llama-70B

Thorough examination of the cost, performance, and practicality of DeepSeek-R1-Distill-Llama-70B!

DeepSeek-R1-Distill-Llama-70B is an AI model based on Llama 3.3 70B that utilizes knowledge distillation technology. It excels particularly in Japanese language processing and has strengths in the legal field and business efficiency, making it expected to be utilized in business applications. However, what is the actual cost performance? Is there value in adopting it compared to other AI models? In this article, we will thoroughly examine: ■ The performance and features of DeepSeek-R1-Distill-Llama-70B ■ A comparison with competing AIs such as GPT-4 and LLaMA 70B ■ Cost-effectiveness in business applications ■ The advantages and disadvantages of adoption and suitable use cases We will clarify whether this AI model is truly a "usable" option for companies. *For more details, you can view the related links. Please feel free to contact us for more information.

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Knowledge of Visual Inspection: Verification in Deep Learning (Object Recognition)

Knowledge of Visual Inspection: Verification in Deep Learning (Object Recognition)

I will explain how to proceed with validation in AI (deep learning).

One common question from companies considering the introduction of image recognition and inspection using deep learning is, "How many images do we need to prepare for inspection?" The conclusion is that it cannot be definitively stated. This is because the amount of data required varies significantly depending on the complexity of the object's appearance (color, shape, angle, etc.) and the changing features. In this article, we have organized the basic verification process as follows: 1. Capture images of the product to be inspected and collect approximately a few hundred images (for example, around 200). 2. Select half of those images and provide "annotations" for the object. 3. Use the annotated data as training data for the AI and validate it with the remaining images (testing for recognition). 4. If there are misrecognitions or missed recognitions, increase the number of images or review the annotations and retry. 5. Repeat this "data augmentation → training → validation → readjustment" process until satisfactory accuracy is achieved. *For more details, please refer to the related link (blog).

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Verification! Is it true that the dimensional accuracy has an error of about 5 to 10%?

Verification! Is it true that the dimensional accuracy has an error of about 5 to 10%?

Q: Is it true that the dimensional accuracy has an error of about 5-10%? 【Electroless Nickel Plating】

Q: Is it true that the dimensional accuracy has an error of about 5 to 10%? 【Electroless Nickel Plating】 A: In electroless nickel plating, it is possible to manage the plating thickness within ±5 to 10% for simple-shaped workpieces. Therefore, if the target plating thickness is 5 μm, it can be managed within 4.50 to 5.50 μm.

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