ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications
   12/06/2019

Editor's comments and enhancements are shown in green. [ Reviewed]

The quality of use case submissions will be evaluated for inclusion in the Working Group's Technical Report based on the application area, relevant AI technologies, credible reference sources (see References section), and the following characteristics:

  • [1] Data Focus & Learning: Use cases for AI system which utilizes Machine Learning, and those that use a fixed a priori knowledge base.
  • [2] Level of Autonomy: Use cases demonstrating several degrees (dependent, autonomous, human/critic in the loop, etc.) of AI system autonomy.
  • [3] Verifiability & Transparency: Use cases demonstrating several types and levels of verifiability and transparency, including approaches for explainable AI, accountability, etc.
  • [4] Impact: Use cases demonstrating the impact of AI systems to society, environment, etc.
  • [5] Architecture: Use cases demonstrating several architectural paradigms for AI systems (e.g., cloud, distributed AI, crowdsourcing, swarm intelligence, etc.)
  • [6] Functional aspects, trustworthiness, and societal concerns
  • [7] AI life cycle components include acquire/process/apply.
These characteristics are identified in red in the use case.

No. 33 ID: Use Case Name: Automated defect classification on product surfaces
Application
Domain
Manufacturing
Deployment
Model
On premise system
StatusPoC
ScopeImage Analytics for water taps in sanitary industries.
Objective(s)Image analytics using a combination of feature extraction and classification of defects on shining surfaces in sanitary industries.
Short
Description
(up to
150 words)
A vision system that inspects and identifies the defects on water taps in sanitary industries. The system uses a combination of features for an automatic defect classification on product surfaces. All defects (15 types are identified) are classified into two major categories, real-defects and pseudo-defects. The pseudo-defects cause no quality problem; while the real-defects are critical as they might malfunction the final products.
The AI system uses Support Vector Machine (SVM) classifier along with the combined features to identify the defect types. With the vision system in place, the quality control process is fully automated without any human intervention.
Complete Description The proposed vision system has two parts: the hardware part and the software part. The hardware captures the images of product surfaces under a constant illuminating condition. The software is developed to perform image processing tasks and identify defects on product surfaces.

The steps of proposed system include image acquisition, preprocessing, segmentation, feature extraction, classification and post-processing. The system presents two software components: Feature Extraction and Classifier Design. These two modules are implemented independently which can be developed in offline platform and can be integrated into vision system and work online.

As a first step, the feature extraction is critical and guides the extent to which a classifier can distinguish the defects from one class to another. A combination of features is used like geometry (shape, texture), and statistical features of the segmented images. In the second step, a support vector machine classification model is trained to identify the defect types. The classification results obtained by combining Gabor features, Statistical features, and grayscale features showed comparable performances with human evaluations.

Overall, the vision system is modularized with capabilities to self-learn and future extensions.

StakeholdersSanitary Industries
Stakeholders'
Assets, Values
Systems'
Threats &
Vulnerabilities
Incorrect AI System use (AI system affecting quality control); New Security Threats.
Performance
Indicators (KPIs)
Seq. No. Name Description Reference to mentioned
use case objectives
1 Classification Ratio Real to Pseudo wrong classification Establishes the quality of identification
AI Features Task(s)Recognition
Method(s)Classification; Feature Extraction
HardwareIP Camera and Work Station
Topology
Terms &
Concepts Used
Classification, Feature Extraction, Defect Identification
Standardization
Opportunities
Requirements
  1. Quality acceptance criterion from AI systems: What is the acceptable standard for AI output related to quality? How that can be independently validated?
  2. Standards for dealing with AI failures: How/Can standards facilitate dealing with AI failures, w.r.t., quality, productivity criteria?
Challenges
& Issues
Real time implementation, accurately identify the nature of defects
Societal Concerns Description Promoting sustainable industries, and investing in scientific research and innovation, are all important ways to facilitate sustainable development.
SDGs to
be achieved
Industry, Innovation, and Infrastructure
Data Characteristics
Description
Source
Type
Volume (size)
Velocity
Variety
Variability
(rate of change)
Quality
Scenario Conditions
No. Scenario
Name
Scenario
Description
Triggering Event Pre-condition Post-Condition






Training Scenario Name:
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Specification of training data
Scenario Name Evaluation
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Input of Evaluation
Output of Evaluation
Scenario Name Execution
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Input of Execution
Output of Execution
Scenario Name Retraining
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Specification of retraining data
References
No. Type Reference Status Impact of
use case
Originator
Organization
Link
1 Publication B. Kuhlenkötter, X. Zhang, C. Krewet, Quality Control in Automated Manufacturing Processes – Combined Features for Image Processing Acta Polytechnica Vol. 46 No. 5/2006. Published Use case taken from this reference Czech Technical University Link

  • Peer-reviewed scientific/technical publications on AI applications (e.g. [1]).
  • Patent documents describing AI solutions (e.g. [2], [3]).
  • Technical reports or presentations by renowned AI experts (e.g. [4])
  • High quality company whitepapers and presentations
  • Publicly accessible sources with sufficient detail

    This list is not exhaustive. Other credible sources may be acceptable as well.

    Examples of credible sources:

    [1] B. Du Boulay. "Artificial Intelligence as an Effective Classroom Assistant". IEEE Intelligent Systems, V 31, p.76-81. 2016.

    [2] S. Hong. "Artificial intelligence audio apparatus and operation method thereof". N US 9,948,764, Available at: https://patents.google.com/patent/US20150120618A1/en. 2018.

    [3] M.R. Sumner, B.J. Newendorp and R.M. Orr. "Structured dictation using intelligent automated assistants". N US 9,865,280, 2018.

    [4] J. Hendler, S. Ellis, K. McGuire, N. Negedley, A. Weinstock, M. Klawonn and D. Burns. "WATSON@RPI, Technical Project Review".
    URL: https://www.slideshare.net/jahendler/watson-summer-review82013final. 2013