BOHR International Journal of Future Robotics and Artificial Intelligence最新文献

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Robotic Mushroom Harvesting by Employing Probabilistic Road Map and Inverse Kinematics 基于概率路线图和逆运动学的蘑菇采摘机器人

BOHR International Journal of Future Robotics and Artificial Intelligence Pub Date : 2021-02-23 DOI: 10.54646/bijfrai.001

M. Mohanan, Ambuja Salgaonkar

引用次数: 5

Evaluate Sustainability of Using Autonomous Vehicles for the Last-mile Delivery Industry 评估自动驾驶汽车在最后一英里送货行业的可持续性

BOHR International Journal of Future Robotics and Artificial Intelligence Pub Date : 1900-01-01 DOI: 10.54646/bijfrai.003

Linghang Huang

{"title":"Evaluate Sustainability of Using Autonomous Vehicles for the Last-mile Delivery Industry","authors":"Linghang Huang","doi":"10.54646/bijfrai.003","DOIUrl":"https://doi.org/10.54646/bijfrai.003","url":null,"abstract":"This research is aimed at confirming whether the autonomous vehicles (AV) for last-mile delivery is sustainable in terms of three aspects – social sustainability, environmental sustainability, and economic sustainability. The safety was solely considered for the social sustainability because of its importance of AV application for lastmile delivery. This study finds that it is relatively safe to use AVs for delivery because of the speed limit of actual society and the good road conditions provide the ground that AV runs safely for last-mile delivery in urban areas. Besides, AV has a special advantage when facing pandemic. For environmental sustainability, the emission problem is the main concern. It is concluded that AV has a significant advantage in emission reduction in terms of a series of emissions. This mainly results from the driving behaviors difference between AV and human vehicles. As for the economic sustainability of AV, this research adopted a quantitative way to illustrate because the cost of AV is essential to consider because of AV’s commercial nature. The research reveals the cost advantage of AV under different carrying capabilities.","PeriodicalId":139566,"journal":{"name":"BOHR International Journal of Future Robotics and Artificial Intelligence","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131433312","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

Deep Learning Analysis for Estimating Sleep Syndrome Detection Utilizing the Twin Convolutional Model FTC2 利用双卷积模型FTC2估计睡眠综合征检测的深度学习分析

BOHR International Journal of Future Robotics and Artificial Intelligence Pub Date : 1900-01-01 DOI: 10.54646/bijfrai.002

Tim Cvetko, Tinkara Robek

{"title":"Deep Learning Analysis for Estimating Sleep Syndrome Detection Utilizing the Twin Convolutional Model FTC2","authors":"Tim Cvetko, Tinkara Robek","doi":"10.54646/bijfrai.002","DOIUrl":"https://doi.org/10.54646/bijfrai.002","url":null,"abstract":"Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient's neurophysiological signals collected at sleep labs. This is a difficult, tedious and a time-consuming task. The limitations of manual sleep stage scor- ing have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diag- nosis and treatment of related sleep disorders. In this paper, we propose a novel method and a practical approach to predicting early onsets of sleep syndromes utilizing the Twin Convolutional Model FTC2, including restless leg syndrome, insomnia, based on an algorithm which is comprised of two modules. A Fast Fourier Transform is applied to 30 seconds long epochs of EEG recordings to provide localized time-frequency information, and a deep convolutional LSTM neural network is trained for sleep stage classification. Automating sleep stages detection from EEG data offers a great potential to tackling sleep irregularities on a daily basis. Thereby, a novel approach for sleep stage classification is pro- posed which combines the best of signal processing and statistics. In this study, we used the PhysioNet Sleep European Data Format (EDF) Database. The code evaluation showed impressive results, reaching accuracy of 90.43, precision of 77.76, recall of 93,32, F1-score of 89.12 with the final mean false error loss 0.09. All the source code is availlable at https://github.com/timothy102/eeg.","PeriodicalId":139566,"journal":{"name":"BOHR International Journal of Future Robotics and Artificial Intelligence","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132944107","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: 1