2023 IEEE Conference on Artificial Intelligence (CAI)最新文献_第5页

RippleGo - An AI-based Voyage Planner for US Inland Waterways RippleGo -一个基于人工智能的美国内河航道航行计划器

2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/cai54212.2023.00162

D. Sathiaraj, Andrew Smith, Eric Rohli, Cathy Hsieh, Arthur R. Salindong, Nicholas Woolsey, Andres Tec

{"title":"RippleGo - An AI-based Voyage Planner for US Inland Waterways","authors":"D. Sathiaraj, Andrew Smith, Eric Rohli, Cathy Hsieh, Arthur R. Salindong, Nicholas Woolsey, Andres Tec","doi":"10.1109/cai54212.2023.00162","DOIUrl":"https://doi.org/10.1109/cai54212.2023.00162","url":null,"abstract":"RippleGo (https://www.ripplego.com) is an AI-based Software-as-a-Service application that makes voyages along the US Inland Waterways (IWS) safe and efficient. These voyages require enormous planning and data collection processes. Existing mariner data is available in disparate locations and lacks predictive or forecasting information. This makes a mariner’s voyage planning processes manual, ad-hoc, and present-minded. RippleGo utilizes two AI-based predictive technologies. The first technology is a deep learning based algorithm to forecast river levels. Advanced knowledge of river levels help in planning cargo loads and safely navigating under bridges and through locks. The second AI technology is a machine learning based technology that predicts time taken to travel from one point to any other point along the waterways. Advanced information on travel time will enable mariners to provide accurate ETAs to customers and port terminals and improve supply chain reliability. RippleGo combines the two methodologies to provide efficient voyage plans with better situational awareness, safety alerts (through Bridge Air Gap (BAG) and Under Keel Clearances (UKC)), improved reliability of delivery, and better utilization of water transportation ports and terminals.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"21 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120857239","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

RL-based Scheduling of an AAM Traffic Network 基于rl的AAM交通网络调度

2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00045

Arinc Tutku Altun, Yan Xu, G. Inalhan, Michael W. Hardt

引用次数: 0

Ensemble Stacking with the Multi-Layer Perceptron Neural Network Meta-Learner for Passenger Train Delay Prediction 基于多层感知器神经网络元学习器的集成叠加旅客列车延误预测

2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/cai54212.2023.00017

Veronica A. Boateng, Bo Yang

{"title":"Ensemble Stacking with the Multi-Layer Perceptron Neural Network Meta-Learner for Passenger Train Delay Prediction","authors":"Veronica A. Boateng, Bo Yang","doi":"10.1109/cai54212.2023.00017","DOIUrl":"https://doi.org/10.1109/cai54212.2023.00017","url":null,"abstract":"Accurately predicting delays is crucial for improving passenger service quality and railway traffic management. The import of artificial neural networks into train delay prediction helps to improve prediction accuracy. In this paper, we propose a novel stacking ensemble regression model (ST-NN) that uses MultiLayer Perceptron’s (MLP) neural networks as single learners and MLP as the meta-learner, which enhances the accuracy of Passenger Train arrival delay prediction time in minutes. We evaluated the model performance using Amtrak data to compare with other combinations of stacking ensembles and single learners including, Decision Trees (DT), Random Forest (RF), Gradient Boosting Machines (GBM), XGBoost (XGB), LightGBM (LGBM), regression algorithms, Artificial Neural Network (ANN), and MLP to determine enhanced model accuracy of our model. The experiments demonstrate that our ST-NN regression model significantly improves model evaluation indicators by producing a 63.4-82.37% decrease in Mean Absolute Error. Furthermore, the accuracy outperforms the best benchmark models regarding train delay prediction.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131959886","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

Machine Excellence Tradeoffs to Ethical and Legal Perspectives 机器卓越权衡伦理和法律的观点

2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/cai54212.2023.00109

J. Tørresen, Diana Saplacan, Adel Baselizadeh, T. Mahler

{"title":"Machine Excellence Tradeoffs to Ethical and Legal Perspectives","authors":"J. Tørresen, Diana Saplacan, Adel Baselizadeh, T. Mahler","doi":"10.1109/cai54212.2023.00109","DOIUrl":"https://doi.org/10.1109/cai54212.2023.00109","url":null,"abstract":"We appreciate well-functioning technology being able to also personalize its services. However, to protect privacy and avoid a potential misuse of personal data, we are encouraged to limit the amount of personal data we share through apps and Internet services. While some services do not really need all the data they ask us to provide, others depend on it to provide the best possible performance of its service. That regards systems that apply data in machine learning for tasks like medical diagnostics. Especially deep learning algorithms perform better by using a large amount of data and are now able to benefit from the large amount as well with limited training time given access to high-performance computing resources. This paper address and discuss the tradeoffs like the one we have between data sharing minimalization for increased privacy and data maximization for machine learning systems. Perspectives related to ethics, legal, and social issues are considered in the paper. There is no single conclusion on the challenge, but attention to it can increase the awareness that the best balance differs depending on the application addressed.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134415354","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

Fast All-day 3D Object Detection Based on Multi-sensor Fusion 基于多传感器融合的全天快速三维目标检测

2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00038

Liang Xiao, Q. Zhu, Tongtong Chen, Dawei Zhao, Erke Shang, Yiming Nie

{"title":"Fast All-day 3D Object Detection Based on Multi-sensor Fusion","authors":"Liang Xiao, Q. Zhu, Tongtong Chen, Dawei Zhao, Erke Shang, Yiming Nie","doi":"10.1109/CAI54212.2023.00038","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00038","url":null,"abstract":"Realtime 3D object detection in all-day conditions is a challenging task for autonomous vehicles. Various image and point cloud based object detection methods have been proposed. Image based detectors are sensitive to illumination changes and cannot get accurate 3D information. Point cloud based detectors are less convenient for acceleration and deployment on commonly used hardware due to the unstructured nature of point cloud data, making it prohibitive for mobile platforms with limited computing resources in large-scale outdoor scenes. Frustum based 3D object detector first detects 2D objects in the image and then extracts frustum point cloud according to the cross-calibration parameters. Small-scale frustum point clouds can be used for 3D object detection, hence substantially accelerating the computation. However, when objects are missed in the first stage image based detector, the whole algorithm will fail to detect them. In this paper, we extended the frustum based 3D object detector by leveraging more sensor modalities. Our method combines two frustum based 3D object detecting branches in which visible light image and thermal image are used for 2D ROI extracting respectively. After obtaining 3D object proposals from the two branches, 3D non-maximum suppression is conducted to get the final detections. Experiments tested on our experimental autonomous vehicle show that our proposed method is capable of detecting 3D objects fast in various complex environments.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122688201","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

Copyright Page 版权页

2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/cai54212.2023.00003

引用次数: 0

Can AI have a personality? 人工智能可以有个性吗?

2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00097

Umarpreet Singh, Parham Aarabhi

引用次数: 0

Finger Versus Wrist Photoplethysmography Signals: Implications for Wearable Blood Pressure Monitoring 手指与手腕光电脉搏波信号:可穿戴式血压监测的意义

2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00060

Wenlong Wu, Yun Zou, Chunlong Tu, Guosong Gao, Zhenru Chen

{"title":"Finger Versus Wrist Photoplethysmography Signals: Implications for Wearable Blood Pressure Monitoring","authors":"Wenlong Wu, Yun Zou, Chunlong Tu, Guosong Gao, Zhenru Chen","doi":"10.1109/CAI54212.2023.00060","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00060","url":null,"abstract":"Blood pressure (BP) is a crucial indicator of cardiovascular health and provides essential information about the heart. Traditional cuff-based BP measurement with equipment like sphygmomanometers is uncomfortable and discontinued. Cuffless BP monitoring using photoplethysmography (PPG) signals has become an area of interest in the research community, particularly with the advent of wearables such as the Apple Watch and Xiaomi Band. While most wearable PPG signals are obtained from the wrist, the most widely used benchmark dataset, the MIMIC dataset, collects PPG signals from the finger. In this study, we collected PPG signals from both finger and wrist locations from 71 participants and compared their similarities and differences. Our results show that the quality of finger PPG signals is superior to that of wrist PPG signals. Additionally, transferring PPG signals from the finger to the wrist is more successful than transferring signals from the wrist to the finger. These findings make it possible to use the publicly available MIMIC dataset to predict BP from smartwatches. This study bridges the gap between academia and industry, and is an important step towards developing continuous BP monitoring for better management of cardiac health.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116778889","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

Domain generalization via feature disentanglement with reconstruction for pathology image segmentation 基于特征解缠和重构的病理图像分割领域泛化

2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00073

Yu-Hsuan Lin, H. Tsai, Meng-Ru Shen, P. Chung

引用次数: 0

Forecasting Partial Discharges of Cable Joints using Weather data 利用天气数据预测电缆接头的局部放电

2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/cai54212.2023.00021

Raymon van Dinter, S. Rieken, P. Leduc, Gerdtinus Netten, B. Tekinerdogan, C. Catal

引用次数: 0