用卷积神经网络调查占用情况

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Pub Date : 2019-11-13 DOI:10.1145/3360322.3360989

J. Park, Jianli Chen, Xin Jin, Z. Nagy

{"title":"用卷积神经网络调查占用情况","authors":"J. Park, Jianli Chen, Xin Jin, Z. Nagy","doi":"10.1145/3360322.3360989","DOIUrl":null,"url":null,"abstract":"In this paper, we implement a convolutional neural networks (CNN) based autoencoder to investigate occupancy profiles. We use the American time use survey (ATUS) data, which contains 191,558 schedules with binary occupancy information. Our results suggest that the trained filters provide an important insight of occupancy profiles (i.e., dominant and distinct patterns), and the latent space compresses the profiles with representative information.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Investigating occupancy profiles using convolutional neural networks\",\"authors\":\"J. Park, Jianli Chen, Xin Jin, Z. Nagy\",\"doi\":\"10.1145/3360322.3360989\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we implement a convolutional neural networks (CNN) based autoencoder to investigate occupancy profiles. We use the American time use survey (ATUS) data, which contains 191,558 schedules with binary occupancy information. Our results suggest that the trained filters provide an important insight of occupancy profiles (i.e., dominant and distinct patterns), and the latent space compresses the profiles with representative information.\",\"PeriodicalId\":128826,\"journal\":{\"name\":\"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3360322.3360989\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3360322.3360989","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 3

摘要

在本文中，我们实现了一个基于卷积神经网络(CNN)的自编码器来研究占用曲线。我们使用美国时间使用调查(ATUS)数据，其中包含191,558个带有二进制占用信息的时间表。我们的研究结果表明，训练后的过滤器提供了一个重要的占用概况(即占主导地位的和独特的模式)的洞察力，并且潜在空间压缩了具有代表性信息的概况。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Investigating occupancy profiles using convolutional neural networks

In this paper, we implement a convolutional neural networks (CNN) based autoencoder to investigate occupancy profiles. We use the American time use survey (ATUS) data, which contains 191,558 schedules with binary occupancy information. Our results suggest that the trained filters provide an important insight of occupancy profiles (i.e., dominant and distinct patterns), and the latent space compresses the profiles with representative information.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

自引率

0.00%

发文量