Representation Learning of Complex Assemblies, An Effort to Improve Corporate Scope 3 Emissions Calculation

• URL: http://arxiv.org/abs/2409.03769v1
• Date: Wed, 21 Aug 2024 06:21:31 GMT
• Title: Representation Learning of Complex Assemblies, An Effort to Improve Corporate Scope 3 Emissions Calculation
• Authors: Ajay Chatterjee, Srikanth Ranganathan,
• Abstract summary: Governments, corporations, and citizens alike must accurately assess the climate impact of manufacturing goods and providing services. Process life cycle analysis (pLCA) are used to evaluate the climate impact of production, use, and disposal. We propose a semi-supervised learning-based framework to identify substitute parts.
• Score: 0.276240219662896
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Climate change is a pressing global concern for governments, corporations, and citizens alike. This concern underscores the necessity for these entities to accurately assess the climate impact of manufacturing goods and providing services. Tools like process life cycle analysis (pLCA) are used to evaluate the climate impact of production, use, and disposal, from raw material mining through end-of-life. pLCA further enables practitioners to look deeply into material choices or manufacturing processes for individual parts, sub-assemblies, assemblies, and the final product. Reliable and detailed data on the life cycle stages and processes of the product or service under study are not always available or accessible, resulting in inaccurate assessment of climate impact. To overcome the data limitation and enhance the effectiveness of pLCA to generate an improved environmental impact profile, we are adopting an innovative strategy to identify alternative parts, products, and components that share similarities in terms of their form, function, and performance to serve as qualified substitutes. Focusing on enterprise electronics hardware, we propose a semi-supervised learning-based framework to identify substitute parts that leverages product bill of material (BOM) data and a small amount of component-level qualified substitute data (positive samples) to generate machine knowledge graph (MKG) and learn effective embeddings of the components that constitute electronic hardware. Our methodology is grounded in attributed graph embeddings and introduces a strategy to generate biased negative samples to significantly enhance the training process. We demonstrate improved performance and generalization over existing published models.

Related papers

• Measuring the Recyclability of Electronic Components to Assist Automatic Disassembly and Sorting Waste Printed Circuit Boards [4.0998481751764]
  We focus on the measurement of recyclability of waste electronic components (WECs) from waste printed circuit boards (WPCBs) using mathematical innovation model. This innovative approach evaluates both the recyclability and recycling difficulties of WECs, integrating an AI model for improved disassembly and sorting.
  arXiv  Detail & Related papers  (2024-06-24T12:33:56Z)
• Evaluating the Energy Efficiency of Few-Shot Learning for Object Detection in Industrial Settings [6.611985866622974]
  This paper presents a finetuning approach to adapt standard object detection models to downstream tasks. Case study and evaluation of the energy demands of the developed models are presented. Finally, this paper introduces a novel way to quantify this trade-off through a customized Efficiency Factor metric.
  arXiv  Detail & Related papers  (2024-03-11T11:41:30Z)
• An explainable machine learning-based approach for analyzing customers' online data to identify the importance of product attributes [0.6437284704257459]
  We propose a game theory machine learning (ML) method that extracts comprehensive design implications for product development. We apply our method to a real-world dataset of laptops from Kaggle, and derive design implications based on the results.
  arXiv  Detail & Related papers  (2024-02-03T20:50:48Z)
• Client-side Gradient Inversion Against Federated Learning from Poisoning [59.74484221875662]
  Federated Learning (FL) enables distributed participants to train a global model without sharing data directly to a central server. Recent studies have revealed that FL is vulnerable to gradient inversion attack (GIA), which aims to reconstruct the original training samples. We propose Client-side poisoning Gradient Inversion (CGI), which is a novel attack method that can be launched from clients.
  arXiv  Detail & Related papers  (2023-09-14T03:48:27Z)
• Efficiency Pentathlon: A Standardized Arena for Efficiency Evaluation [82.85015548989223]
  Pentathlon is a benchmark for holistic and realistic evaluation of model efficiency. Pentathlon focuses on inference, which accounts for a majority of the compute in a model's lifecycle. It incorporates a suite of metrics that target different aspects of efficiency, including latency, throughput, memory overhead, and energy consumption.
  arXiv  Detail & Related papers  (2023-07-19T01:05:33Z)
• A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact [62.997667081978825]
  This study seeks to address the demands of high-performance machine learning models with environmental sustainability. Traditional machine learning algorithms, such as Decision Trees and Random Forests, demonstrate robust efficiency and performance. However, superior outcomes were obtained with optimised configurations, albeit with a commensurate increase in resource consumption.
  arXiv  Detail & Related papers  (2023-07-01T15:18:00Z)
• ALP: Action-Aware Embodied Learning for Perception [60.64801970249279]
  We introduce Action-Aware Embodied Learning for Perception (ALP) ALP incorporates action information into representation learning through a combination of optimizing a reinforcement learning policy and an inverse dynamics prediction objective. We show that ALP outperforms existing baselines in several downstream perception tasks.
  arXiv  Detail & Related papers  (2023-06-16T21:51:04Z)
• Machine Learning Approaches in Agile Manufacturing with Recycled Materials for Sustainability [2.132096006921048]
  This research addresses environmental sustainability in materials science via decision support in agile manufacturing using recycled and reclaimed materials. We propose to use data-driven methods in AI by applying machine learning models for predictive analysis to guide decision support in manufacturing.
  arXiv  Detail & Related papers  (2023-03-15T00:39:31Z)
• Striving for data-model efficiency: Identifying data externalities on group performance [75.17591306911015]
  Building trustworthy, effective, and responsible machine learning systems hinges on understanding how differences in training data and modeling decisions interact to impact predictive performance. We focus on a particular type of data-model inefficiency, in which adding training data from some sources can actually lower performance evaluated on key sub-groups of the population. Our results indicate that data-efficiency is a key component of both accurate and trustworthy machine learning.
  arXiv  Detail & Related papers  (2022-11-11T16:48:27Z)
• Making Machine Learning Datasets and Models FAIR for HPC: A Methodology and Case Study [0.0]
  The FAIR Guiding Principles aim to improve the findability, accessibility, interoperability, and reusability of digital content by making them both human and machine actionable. These principles have not yet been broadly adopted in the domain of machine learning-based program analyses and optimizations for High-Performance Computing. We design a methodology to make HPC datasets and machine learning models FAIR after investigating existing FAIRness assessment and improvement techniques.
  arXiv  Detail & Related papers  (2022-11-03T18:45:46Z)
• DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks [88.62288327934499]
  We propose a novel augmentation method with language models trained on the linearized labeled sentences. Our method is applicable to both supervised and semi-supervised settings.
  arXiv  Detail & Related papers  (2020-11-03T07:49:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.