2021 Swedish Artificial Intelligence Society Workshop (SAIS)最新文献_第2页

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2021 Swedish Artificial Intelligence Society Workshop (SAIS) Pub Date : 2021-06-14 DOI: 10.1109/sais53221.2021.9483950

引用次数: 0

Robot First Aid: Autonomous Vehicles Could Help in Emergencies 机器人急救:自动驾驶汽车可以在紧急情况下提供帮助

2021 Swedish Artificial Intelligence Society Workshop (SAIS) Pub Date : 2021-06-14 DOI: 10.1109/SAIS53221.2021.9483964

M. Cooney, Felipe Valle, A. Vinel

引用次数: 1

Towards a Machine Learning Framework for Drill Core Analysis 面向岩心分析的机器学习框架

2021 Swedish Artificial Intelligence Society Workshop (SAIS) Pub Date : 2021-06-14 DOI: 10.1109/SAIS53221.2021.9484025

C. Günther, N. Jansson, M. Liwicki, F. Liwicki

{"title":"Towards a Machine Learning Framework for Drill Core Analysis","authors":"C. Günther, N. Jansson, M. Liwicki, F. Liwicki","doi":"10.1109/SAIS53221.2021.9484025","DOIUrl":"https://doi.org/10.1109/SAIS53221.2021.9484025","url":null,"abstract":"This paper discusses existing methods for geological analysis of drill cores and describes the research and development directions of a machine learning framework for such a task. Drill core analysis is one of the first steps of the mining value chain. Such analysis incorporates a high complexity of input features (visual and compositional) derived from multiple sources and commonly by multiple observers. Especially the huge amount of visual information available from the drill core can provide valuable insights, but due to the complexity of many geological materials, automated data acquisition is difficult. This paper (i) describes the difficulty of drill core analysis, (ii) discusses common approaches and recent machine learning-based approaches to address the issues towards automation, and finally, (iii) proposes a machine learning-based framework for drill core analysis which is currently in development. The first major component, the registration of the drill core image for further processing, is presented in detail and evaluated on a dataset of 180 drill core images. We furthermore investigate the amount of labelled data required to automate the drill core analysis. As an interesting outcome, already a few labelled images led to an average precision (AP) of around 80%, which indicates that the manual drill core analysis can be made more efficient with the support of a Machine Learning/labeling workflow.","PeriodicalId":334078,"journal":{"name":"2021 Swedish Artificial Intelligence Society Workshop (SAIS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121278711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying regions most likely to contribute to an epidemic outbreak in a human mobility network 在人员流动网络中确定最有可能导致流行病爆发的区域

2021 Swedish Artificial Intelligence Society Workshop (SAIS) Pub Date : 2021-06-14 DOI: 10.1109/SAIS53221.2021.9483971

A. Bridgwater, András Bóta

{"title":"Identifying regions most likely to contribute to an epidemic outbreak in a human mobility network","authors":"A. Bridgwater, András Bóta","doi":"10.1109/SAIS53221.2021.9483971","DOIUrl":"https://doi.org/10.1109/SAIS53221.2021.9483971","url":null,"abstract":"The importance of modelling the spreading of infectious diseases as part of a public health strategy has been highlighted by the ongoing coronavirus pandemic. This includes identifying the geographical areas or travel routes most likely to contribute to the spreading of an outbreak. These areas and routes can then be monitored as part of an early warning system, be part of intervention strategies, e.g. lockdowns, aiming to mitigate the spreading of the disease or be a focus of vaccination campaigns.In this paper we present our work in developing a network-based infection model between the municipalities of Sweden in order to identify the areas most likely to contribute to an epidemic. We first construct a human mobility model based on the well-known radiation model, then we employ a network-based compartmental model to simulate epidemic outbreaks with various parameters. Finally, we adopt the influence maximization problem known in network science to identify the municipalities having the largest impact on the spreading of infectious diseases.We only present the first part of our work in this paper. In the future, we plan to investigate the robustness of our model in identifying high-risk areas by simulating outbreaks with various parameters. We also plan to extend our work to selecting the most likely infection paths contributing to the spreading of infectious diseases.","PeriodicalId":334078,"journal":{"name":"2021 Swedish Artificial Intelligence Society Workshop (SAIS)","volume":"446 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122148298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Smart Sewage Water Management and Data Forecast 智能污水管理和数据预测

2021 Swedish Artificial Intelligence Society Workshop (SAIS) Pub Date : 2021-06-14 DOI: 10.1109/SAIS53221.2021.9484017

Qinghua Wang, Viktor Westlund, Jonas Johansson, M. Lindgren

引用次数: 4

[SAIS 2021 Front cover] [SAIS 2021封面]

2021 Swedish Artificial Intelligence Society Workshop (SAIS) Pub Date : 2021-06-14 DOI: 10.1109/sais53221.2021.9484003

引用次数: 0