Wafer Reflectance Prediction for Complex Etching Process Based on <i>K</i>-Means Clustering and Neural Network

Xiong, WenQing; Qiao, Yan; Bai, Liping; Ghahramani, Mohammadhossein; Wu, Naiqi; Hsieh, PinHui; Liu, Bin

doi:10.1109/tsm.2021.3068974

Cited by 11 publications

(4 citation statements)

References 22 publications

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“…However, PCA-based approaches project features to another space based on a linear combination of original features. Therefore they cannot be interpreted in the original feature space [13], [17]. Moreover, most PCA-related work has considered linear PCA, which is not efficient in exploring non-linear patterns [14]- [16].…”

Section: Related Workmentioning

confidence: 99%

Data-driven Predictive Analysis for Smart Manufacturing Processes Based on a Decomposition Approach

Ghahramani¹

2021

Preprint

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Section: Related Workmentioning

confidence: 99%

Data-driven Predictive Analysis for Smart Manufacturing Processes Based on a Decomposition Approach

Ghahramani¹

2021

Preprint

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“…Developing new enabling technologies can help alleviate the situation. Artificial intelligence (AI) has made its way into different areas, e.g., engineering [4], [5], urban planning [6], and management [7]. AI-based models are incorporated in different fields of study, such as manufacturing, to solve various problems, such as process planning, route optimization, and extracting insights from sensor data [8]- [11].…”

Section: Introductionmentioning

confidence: 99%

IoT-Based Route Recommendation for an Intelligent Waste Management System

Ghahramani

Zhou

Mölter

et al. 2022

IEEE Internet Things J.

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The Internet of Things (IoT) is a paradigm characterized by a network of embedded sensors and services. These sensors are incorporated to collect various information, track physical conditions, e.g., waste bins' status, and exchange data with different centralized platforms. The need for such sensors is increasing; however, the proliferation of technologies comes with various challenges. For example, how can IoT and its associated data be used to enhance waste management? In smart cities, an efficient waste management system is crucial. Artificial intelligence (AI) and IoT-enabled approaches can empower cities to manage the waste collection. This work proposes an intelligent approach to route recommendation in an IoT-enabled waste management system given spatial constraints. It performs a thorough analysis based on AI-based methods and compare their corresponding results. Our solution is based on a multiple-level decision-making process in which bins' status and coordinates are taken into account to address the routing problem. Such AI-based models can help engineers design a sustainable infrastructure system.

show abstract

“…Developing new enabling technologies can help alleviate the situation. Artificial Intelligence (AI) has made its way into different areas, e.g., engineering [4], [5], urban planning [6], and management [7]. AI-based models are incorporated in different fields of study, such as manufacturing, to solve various problems like process planning, route optimization, and extracting insights from sensor data [8]- [11].…”

Section: Introductionmentioning

confidence: 99%

IoT-based Route Recommendation for an Intelligent Waste Management System

Ghahramani,

Zhou,

Molter

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The Internet of Things (IoT) is a paradigm characterized by a network of embedded sensors and services. These sensors are incorporated to collect various information, track physical conditions, e.g., waste bins' status, and exchange data with different centralized platforms. The need for such sensors is increasing; however, proliferation of technologies comes with various challenges. For example, how can IoT and its associated data be used to enhance waste management? In smart cities, an efficient waste management system is crucial. Artificial Intelligence (AI) and IoT-enabled approaches can empower cities to manage the waste collection. This work proposes an intelligent approach to route recommendation in an IoT-enabled waste management system given spatial constraints. It performs a thorough analysis based on AI-based methods and compare their corresponding results. Our solution is based on a multiple-level decision-making process in which bins' status and coordinates are taken into account to address the routing problem. Such AI-based models can help engineers design a sustainable infrastructure system.

show abstract

Wafer Reflectance Prediction for Complex Etching Process Based on K-Means Clustering and Neural Network

Cited by 11 publications

References 22 publications

Data-driven Predictive Analysis for Smart Manufacturing Processes Based on a Decomposition Approach

Data-driven Predictive Analysis for Smart Manufacturing Processes Based on a Decomposition Approach

IoT-Based Route Recommendation for an Intelligent Waste Management System

IoT-based Route Recommendation for an Intelligent Waste Management System

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