The quality of use case submissions will be evaluated for inclusion in the Working Group's Technical Report based the application area, relevant AI technologies, credible reference sources (see References section), and the following characteristics:
Data Focus & Learning: Use cases for AI system which utilizes Machine Learning, and those that use a fixed a priori knowledge base.
Level of Autonomy: Use cases demonstrating several degrees (dependent, autonomous, human/critic in the loop, etc.) of AI system autonomy.
Verifiability & Transparency: Use cases demonstrating several types and levels of verifiability and transparency, including approaches for explainable AI, accountability, etc.
Impact: Use cases demonstrating the impact of AI systems to society, environment, etc.
Architecture: Use cases demonstrating several architectural paradigms for AI systems (e.g., cloud, distributed AI, crowdsourcing, swarm intelligence, etc.)
Use Case Name:
Machine Learning Driven Analysis of Batch Process Operation Data to Identify Causes for Poor Batch Performance
Provide insight to the operation team to improve the productivity of batch manufacturing through machine learning on historical operation data
Short Description (up to 150 words)
An approach was developed that can use machine learning models to identify issues in batch manufacturing.
Batch operation is generally quite complex involving dynamics in the operation and interplay of various process variables. Due to this, sometimes, few batches end up running slower than nominal batch time and few batches also yield bad quality end products resulting in significant production loss. Additionally, often in the industrial context, data size and variety are limited and to develop a robust machine learning model from limited available data sets is a challenging task. Due to transient nature of batch operation data, the traditional PCA algorithm fails in analyzing the batch data and hence MPCA was applied as logical extension of PCA algorithm. As MPCA naturally considers the dynamics in the data and inter-correlations among the process variables, it provides a valuable insight on the batch data. The approach was successfully demonstrated on milk pasteurization process data where only 4 batches were provided for modelling. Using such 4 seed batches, the algorithm synthetically creates 50 batches of data and introduction of anomalies in some batches. Concept of design of experiments and stochastic perturbations are used in synthetic generation of the data set. The work was able to successfully build a robust MPCA model with such data and isolate the bad batches of data from good batches of the data. Additionally, through contribution plots, the algorithm identifies when a certain batch drifted from nominal operation and which variables are the root causes for the bad batch operation.
Peer-reviewed scientific/technical publications on AI applications (e.g. ).
Patent documents describing AI solutions (e.g. , ).
Technical reports or presentations by renowned AI experts (e.g. )
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:
 B. Du Boulay. "Artificial Intelligence as an Effective Classroom Assistant". IEEE Intelligent Systems, V 31, p.76-81. 2016.
 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.
 M.R. Sumner, B.J. Newendorp and R.M. Orr. "Structured dictation using intelligent automated assistants". N US 9,865,280, 2018.
 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