Vincent Martineau , Michael Morin , Jonathan Gaudreault , Philippe Thomas , Hind Bril El-Haouzi , Mohammed Khachan
{"title":"一张图像值10,000点:用于木材生产预测的神经网络架构和替代日志表示","authors":"Vincent Martineau , Michael Morin , Jonathan Gaudreault , Philippe Thomas , Hind Bril El-Haouzi , Mohammed Khachan","doi":"10.1016/j.compind.2023.103964","DOIUrl":null,"url":null,"abstract":"<div><p><span><span>Predicting the lumber products that can be obtained from a log allows for better allocation of resources and improves operations planning. Although sawing simulators make it possible to anticipate the production associated with a log, they do not allow processing many logs quickly. It was shown that machine learning<span> can be used in place of a simulator. However, prediction quality is still lacking and information rich log representations are seldomly used in the literature for machine learning purposes We compare several log representations that can be used (industry know-how-based features, 2D projections, and 3D point clouds) and several neural network architectures able to process these log representations (multilayer </span></span>perceptron<span>, residual network and </span></span><em>PointNet</em>). We also propose a new way to implement a loss function that improves prediction of sparse object count in regression. This new approach achieves a 15% improvement of F1 score compared to previous approaches.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":null,"pages":null},"PeriodicalIF":8.2000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An image is worth 10,000 points: Neural network architectures and alternative log representations for lumber production prediction\",\"authors\":\"Vincent Martineau , Michael Morin , Jonathan Gaudreault , Philippe Thomas , Hind Bril El-Haouzi , Mohammed Khachan\",\"doi\":\"10.1016/j.compind.2023.103964\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span><span>Predicting the lumber products that can be obtained from a log allows for better allocation of resources and improves operations planning. Although sawing simulators make it possible to anticipate the production associated with a log, they do not allow processing many logs quickly. It was shown that machine learning<span> can be used in place of a simulator. However, prediction quality is still lacking and information rich log representations are seldomly used in the literature for machine learning purposes We compare several log representations that can be used (industry know-how-based features, 2D projections, and 3D point clouds) and several neural network architectures able to process these log representations (multilayer </span></span>perceptron<span>, residual network and </span></span><em>PointNet</em>). We also propose a new way to implement a loss function that improves prediction of sparse object count in regression. This new approach achieves a 15% improvement of F1 score compared to previous approaches.</p></div>\",\"PeriodicalId\":55219,\"journal\":{\"name\":\"Computers in Industry\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.2000,\"publicationDate\":\"2023-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers in Industry\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0166361523001148\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers in Industry","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0166361523001148","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
An image is worth 10,000 points: Neural network architectures and alternative log representations for lumber production prediction
Predicting the lumber products that can be obtained from a log allows for better allocation of resources and improves operations planning. Although sawing simulators make it possible to anticipate the production associated with a log, they do not allow processing many logs quickly. It was shown that machine learning can be used in place of a simulator. However, prediction quality is still lacking and information rich log representations are seldomly used in the literature for machine learning purposes We compare several log representations that can be used (industry know-how-based features, 2D projections, and 3D point clouds) and several neural network architectures able to process these log representations (multilayer perceptron, residual network and PointNet). We also propose a new way to implement a loss function that improves prediction of sparse object count in regression. This new approach achieves a 15% improvement of F1 score compared to previous approaches.
期刊介绍:
The objective of Computers in Industry is to present original, high-quality, application-oriented research papers that:
• Illuminate emerging trends and possibilities in the utilization of Information and Communication Technology in industry;
• Establish connections or integrations across various technology domains within the expansive realm of computer applications for industry;
• Foster connections or integrations across diverse application areas of ICT in industry.