Towards artificial general intelligence (agi) in the internet of things (iot): Opportunities and challenges

F Dou, J Ye, G Yuan, Q Lu, W Niu, H Sun… - arxiv preprint arxiv …, 2023 - arxiv.org
Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and
execute tasks with human cognitive abilities, engenders significant anticipation and intrigue …

[HTML][HTML] A review of modeling bioelectrochemical systems: engineering and statistical aspects

S Luo, H Sun, Q **, R **, Z He - Energies, 2016 - mdpi.com
Bioelectrochemical systems (BES) are promising technologies to convert organic
compounds in wastewater to electrical energy through a series of complex physical …

Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective

SK Kang, R **, X Deng, RS Kenett - Journal of Intelligent Manufacturing, 2023 - Springer
Abstract In Industry 4.0, smart manufacturing is facing its next stage, cybermanufacturing,
founded upon advanced communication, computation, and control infrastructure …

A weighted information-gain measure for ordinal classification trees

G Singer, R Anuar, I Ben-Gal - Expert Systems with Applications, 2020 - Elsevier
This paper proposes an ordinal decision-tree model, which applies a new weighted
information-gain ratio (WIGR) measure for selecting the classifying attributes in the tree. The …

Inn: An interpretable neural network for ai incubation in manufacturing

X Chen, Y Zeng, S Kang, R ** - ACM Transactions on Intelligent …, 2022 - dl.acm.org
Both artificial intelligence (AI) and domain knowledge from human experts play an important
role in manufacturing decision making. Smart manufacturing emphasizes a fully automated …

[КНИГА][B] Industrial Statistics: A Computer-Based Approach with Python

RS Kenett, S Zacks, P Gedeck - 2023 - books.google.com
This innovative textbook presents material for a course on industrial statistics that
incorporates Python as a pedagogical and practical resource. Drawing on many years of …

Logistic regression for crystal growth process modeling through hierarchical nonnegative garrote-based variable selection

H Sun, X Deng, K Wang, R ** - Iie Transactions, 2016 - Taylor & Francis
Single-crystal silicon ingots are produced from a complex crystal growth process. Such a
process is sensitive to subtle process condition changes, which may easily become failed …

Ensemble active learning by contextual bandits for AI incubation in manufacturing

Y Zeng, X Chen, R ** - ACM Transactions on Intelligent Systems and …, 2023 - dl.acm.org
An Industrial Cyber-physical System (ICPS) provides a digital foundation for data-driven
decision-making by artificial intelligence (AI) models. However, the poor data quality (eg …

Adaptive and nonlinear control of discharge pressure for variable displacement axial piston pumps

J Koivumäki, J Mattila - Journal of Dynamic Systems …, 2017 - asmedigitalcollection.asme.org
This paper proposes, for the first time without using any linearization or order reduction, an
adaptive and model-based discharge pressure control design for the variable displacement …

Distributed data filtering and modeling for fog and networked manufacturing

Y Li, L Wang, X Chen, R ** - IISE Transactions, 2024 - Taylor & Francis
Abstract Fog Manufacturing applies both Fog and Cloud Computing collaboratively in Smart
Manufacturing to create an interconnected network through sensing, actuation, and …