Insights on AI, LISA and Data CAMP

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Federated Learning: Concepts and Applications

11/28/2023

AI machine learning relies on vast amounts of data. To achieve this, you start by gathering substantial data in a...

Embracing the Future: Why Data CAMP Outshines Traditional SCADA

11/28/2023

4 Limitations of Traditional SCADA Software: What is SCADA Software? Supervisory Control and Data Acquisition...

How Homomorphic Encryption Safeguards Data in the Cloud Environment

11/28/2023

With the increasing demand for AI technology based on cloud and big data, there is a growing need for more advanced...

Navigating the Future: Unleashing the Potential of Digital Twin Technology in Manufacturing

11/21/2023

The concept of Digital Twin has recently emerged as a core technological trend in the manufacturing industry. As the...

Mastering Real-Time Anomaly Detection with LISA: Your Gateway to Industrial Excellence

11/01/2023

As the global manufacturing industry accelerates its transition towards smart factories, there is a growing interest...

Why Data CAMP Is the Ultimate Choice for Digital Twins

10/26/2023

The idea of a digital twin is to address real-world problems by rendering the physical world visible within a digital...

A successful case of reducing ‘false-positives’ by applying a deep learning model to detect ‘type 1 errors’

Challenge If the test standard is set too sensitively to achieve 0% ‘false-negative’, ‘false-positive (type 1 error)’ increases. Inefficiency due to random sampling and visual inspection to detect false-positives Approach LISA's type 1 error inference...

Success case of monitoring ‘data drift’ and performing predictive maintenance with a data quality index (DQI) model

Challenge Consistent optical images must be taken at all times to properly train the model and increase the accuracy of quality control However, if the camera angle shifts or the lighting deteriorates, causing improper image capture, defect inspection fails...

Configuring an AI model pipeline to detect robotic grasping anomalies in real time

Challenge Robot drops battery, causing downtime Difficulty utilizing existing machine learning solutions due to 'class imbalance' problem Difficulty treating footage with no battery for the robot to grab in the first place as separate normal data Approach...

How the Anomaly Detector model was used to automate the quality control process using only normal data

Challenge Pouch-type batteries have flexible surface geometry, complicating defect identification. This makes it challenging to use existing rule-based inspections and deep learning model-based inspections like classification and segmentation Finding skilled...

Implementing Quality Control in Multi-Product Small-Batch Production through the Utilization of Data CAMP’s ‘RECIPE’ feature

1. Challenge Managing Varied Quality Standards for Each Model without a Centralized History View The latest trend in manufacturing revolves around the "low-volume production of various products" to cater to the diversification of consumer preferences. The diverse...