View source for Samsung electronics co., ltd. (20240320402). METHOD, DEVICE, AND SYSTEM FOR ESTIMATING THRESHOLD VALUE OF KERNEL DENSITY FUNCTION WITH RESPECT TO DEFECT OF PRODUCT simplified abstract

=METHOD, DEVICE, AND SYSTEM FOR ESTIMATING THRESHOLD VALUE OF KERNEL DENSITY FUNCTION WITH RESPECT TO DEFECT OF PRODUCT=


==Organization Name==

[[:Category:samsung electronics co., ltd.|samsung electronics co., ltd.]]


[[Category:samsung electronics co., ltd.]]

==Inventor(s)==

[[:Category:Sangjune Bae of Suwon-si (KR)|Sangjune Bae of Suwon-si (KR)]][[Category:Sangjune Bae of Suwon-si (KR)]]

[[:Category:Taeyoon An of Suwon-si (KR)|Taeyoon An of Suwon-si (KR)]][[Category:Taeyoon An of Suwon-si (KR)]]

[[:Category:Joohyung You of Suwon-si (KR)|Joohyung You of Suwon-si (KR)]][[Category:Joohyung You of Suwon-si (KR)]]

[[:Category:In Huh of Suwon-si (KR)|In Huh of Suwon-si (KR)]][[Category:In Huh of Suwon-si (KR)]]

[[:Category:Moonhyun Cha of Suwon-si (KR)|Moonhyun Cha of Suwon-si (KR)]][[Category:Moonhyun Cha of Suwon-si (KR)]]

[[:Category:Jaemyung Choe of Suwon-si (KR)|Jaemyung Choe of Suwon-si (KR)]][[Category:Jaemyung Choe of Suwon-si (KR)]]

==METHOD, DEVICE, AND SYSTEM FOR ESTIMATING THRESHOLD VALUE OF KERNEL DENSITY FUNCTION WITH RESPECT TO DEFECT OF PRODUCT - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240320402 titled 'METHOD, DEVICE, AND SYSTEM FOR ESTIMATING THRESHOLD VALUE OF KERNEL DENSITY FUNCTION WITH RESPECT TO DEFECT OF PRODUCT

'''Simplified Explanation:'''
The patent application describes a method, device, and system for estimating threshold values of a kernel density function related to defects in a product. This involves bootstrapping sampling, determining optimal kernel bandwidths, estimating threshold values for the tail region of the kernel density function, and quantifying uncertainty of the threshold values.

'''Key Features and Innovation:'''
* Method for estimating threshold values of a kernel density function with respect to defects in a product.
* Utilizes bootstrapping sampling and optimal kernel bandwidth estimation.
* Provides quantitative values to quantify uncertainty of the threshold values.
* Tailors bandwidth estimation methods based on the number of sample data sets.
* Enhances accuracy in identifying defects in products.

'''Potential Applications:'''
The technology can be applied in quality control processes in various industries such as manufacturing, healthcare, and finance to detect and address defects in products.

'''Problems Solved:'''
* Improves the accuracy of estimating threshold values for defects in products.
* Enhances the efficiency of quality control processes.
* Provides a systematic approach to quantifying uncertainty in defect detection.

'''Benefits:'''
* Increased accuracy in identifying defects.
* Enhanced quality control processes.
* Improved decision-making based on quantified uncertainty.
* Cost-effective defect detection methods.

'''Commercial Applications:'''
The technology can be utilized in industries such as manufacturing, healthcare, and finance for quality control, defect detection, and process optimization, leading to improved product quality and customer satisfaction.

'''Questions about Kernel Density Function Estimation:'''
1. How does the method of bootstrapping sampling contribute to estimating threshold values in the kernel density function?
2. What are the potential implications of using different bandwidth estimation methods on the accuracy of defect detection in products?

'''Frequently Updated Research:'''
Researchers are continuously exploring new methods and algorithms to enhance the accuracy and efficiency of defect detection in products using kernel density function estimation. Stay updated on the latest advancements in this field to leverage the most effective techniques for quality control processes.


==Original Abstract Submitted==

provided are a method, a device, and a system for estimating threshold values of a kernel density function with respect to defects of a product. the method includes a bootstrapping sampling operation, estimating optimal kernel bandwidths for sample data sets by using a bandwidth estimation method selected according to a number of sample data from among a plurality of bandwidth estimation methods, estimating threshold values corresponding to a tail region of the kernel density function based on the optimal kernel bandwidths, and providing a quantitative value for quantifying uncertainty of the threshold values based on the plurality of threshold values.



[[Category:G06F30/27]]
[[Category:CPC_G06F30/27]]