发布求助
文献互助 智能选刊 最新文献

2023 IEEE Conference on Artificial Intelligence (CAI)最新文献

筛选
英文 中文
AI for Expensive Optimization Problems in Industry 工业昂贵优化问题的人工智能
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00113
Niki van Stein, R. de Winter, Thomas Bäck, Anna V. Kononova
{"title":"AI for Expensive Optimization Problems in Industry","authors":"Niki van Stein, R. de Winter, Thomas Bäck, Anna V. Kononova","doi":"10.1109/CAI54212.2023.00113","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00113","url":null,"abstract":"The optimization of real-world engineering problems can be a challenging task, owing to the limited understanding of problem characteristics and the high cost of evaluating objectives and constraints in terms of computing time or licenses. This study proposes an AI-assisted optimization pipeline that addresses these challenges by using proxy functions in order to select and optimize the optimization algorithm and its hyper-parameters, thereby significantly accelerating the optimization process on the real (expensive) problem. These proxy functions are inexpensive to evaluate and are selected to exhibit similar landscape characteristics as the original problem. To obtain such proxy functions, we adopt an approach, which involves computing Exploratory Landscape Analysis (ELA) features to characterize the problem’s landscape. The ELA features are then used to identify an artificial function that replicates the original problem’s properties, which can then be employed as a low-cost proxy function for the hyper-parameter optimization of our pipeline. Several real-world industrial applications are discussed as use-case of our proposed approach.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"15 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":"130012886","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
Unfolding the Variability of Clinical Data in Parkinson Treatment using Multi-objective Analysis 运用多目标分析揭示帕金森治疗临床数据的可变性
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00058
Sanaz Mostaghim, Qihao Shan, Christiane Desel, Alexander Duscha, A. Haghikia, T. Hegelmaier
{"title":"Unfolding the Variability of Clinical Data in Parkinson Treatment using Multi-objective Analysis","authors":"Sanaz Mostaghim, Qihao Shan, Christiane Desel, Alexander Duscha, A. Haghikia, T. Hegelmaier","doi":"10.1109/CAI54212.2023.00058","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00058","url":null,"abstract":"The typical way to analyze clinical data is to use one performance metric and extract the most important features by performing dimensionality reduction mechanisms. In this paper, we identify several performance metrics describing data of patients with Parkinson’s disease and observe a large variability of their performance when we consider these metrics separately. None of the patients has the same performance in all parameters, some are better in one and worse in others. This feature is well-known in the context of multi-objective optimization. In this paper, we propose a clustering of data based on multi-objective analysis and perform a correlation-based feature selection with statistical testing to quantify and understand the variability in the clinical data.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"31 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":"124604266","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
Home Energy Management with V2X Capability using Reinforcement Learning 使用强化学习的V2X功能的家庭能源管理
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00046
Z. Tchir, M. Reformat, P. Musílek
{"title":"Home Energy Management with V2X Capability using Reinforcement Learning","authors":"Z. Tchir, M. Reformat, P. Musílek","doi":"10.1109/CAI54212.2023.00046","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00046","url":null,"abstract":"The increased demand for Smart Home control technologies and the rapid growth of AI-based approaches provide an opportunity to develop systems that significantly reduce homeowners’ electricity costs and decrease the inconvenience of power outages. Reinforcement Learning is an AI tool for training systems to perform complex tasks. The paper proposes an RL-based Home Energy Management System to optimally manage a user’s electricity cost while maximizing user comfort and convenience. The system can control the smart home in the presence of the uncertainty and variability of Solar power generation and a varying electricity demands of a homeowner.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"206 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":"121119344","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
Building Trust – The People’s Panel for AI 建立信任-人工智能的人民小组
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/cai54212.2023.00080
Keeley A. Crockett, A. Latham, Melissa Wood, Luke Abberley, M. Rawsthorne, Sam Attwood
{"title":"Building Trust – The People’s Panel for AI","authors":"Keeley A. Crockett, A. Latham, Melissa Wood, Luke Abberley, M. Rawsthorne, Sam Attwood","doi":"10.1109/cai54212.2023.00080","DOIUrl":"https://doi.org/10.1109/cai54212.2023.00080","url":null,"abstract":"This paper describes The People’s Panel for AI – a mechanism to build public trust in AI products and services from conceptualization to deployment. To increase public awareness of how AI and data-driven systems are affecting the lives of ordinary people, a series of Artificial Intelligence Roadshows were delivered in community centers. Community members were recruited to the People’s Panel and completed two days of training about key aspects of data, AI and ethics, including learning a technique for exploring ethical aspects of new technologies (consequence scanning). As part of a pilot study, four People’s Panel sessions were held where tech businesses and researchers pitched their ideas and discussed questions and concerns of the panel members. Through participating in the panel, panel members reported an increase in confidence in being able to question businesses and businesses heard a diverse stakeholder voice on the ethical impacts of their products / services, leading to change.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"223 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":"115751786","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
Multi-scale inverse design of optical metasurfaces using physics-informed computational intelligence 基于物理信息计算智能的光学超表面多尺度反设计
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00098
Marshall Lindsay, Andy G. Varner, S. Kovaleski, Charlie T. Veal, Derek Anderson, Stanton R. Price, S. R. Price
{"title":"Multi-scale inverse design of optical metasurfaces using physics-informed computational intelligence","authors":"Marshall Lindsay, Andy G. Varner, S. Kovaleski, Charlie T. Veal, Derek Anderson, Stanton R. Price, S. R. Price","doi":"10.1109/CAI54212.2023.00098","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00098","url":null,"abstract":"Interest in inverse design for the efficient and accurate design of optical devices has increased in recent years. In the case of complex optical problems which span several orders of magnitude, inverse design is an especially difficult problem. In this paper we propose a multi-scale inverse design process which leverages machine learning tools to encode the numerical simulation of optical wave propagation and material wave modulation directly as layers of a neural network. This requires consideration of both the near field electromagnetic response with respect to metasurface (material) devices, as well as far field effects as the wave propagates through space. The end result is the efficient modeling and optimization spanning several orders of magnitude.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"47 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":"115775266","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
Augmenting Vision Queries with RADAR for BEV Detection in Autonomous Driving 基于雷达增强视觉查询的自动驾驶车辆检测
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00031
Apoorv Singh
{"title":"Augmenting Vision Queries with RADAR for BEV Detection in Autonomous Driving","authors":"Apoorv Singh","doi":"10.1109/CAI54212.2023.00031","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00031","url":null,"abstract":"In order to build an autonomous driving platform at scale, we need to have an affordable sensor stack that provides holistic scene information with just enough information for estimating right depth and semantics of the dynamic scene. Cameras - RADARs came out to be the only combination of the sensor stack to fulfill above two conditions, since LiDARs are too expensive and other sensors like Ultra-sound sensors have extremely short range. However, there is a limited work around radar fused with vision, compared to LiDAR fused with vision work. In this paper we target to fuse RADAR detections to the vision’s object-proposals in the transformers-based state-of-the-art Vision-only networks. Vision-only networks are hypothesized to classify objects very well but they lack behind in depth estimation of the detected objects. In this paper, we hypothesize that adding in radar detections as a query in a transformers decoder along with the pre-learned vision queries from the training data-set can help improving overall recall as well as depth and velocity estimates of the detections.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"84 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":"126242037","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
Multi-Channel 3D Deep Learning Architectures for Evaluation of Prostate Lesion Detection 用于前列腺病变检测评估的多通道3D深度学习架构
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00071
Dimitrios I. Zaridis, E. Mylona, N. Tachos, K. Marias, M. Tsiknakis, D. Fotiadis
{"title":"Multi-Channel 3D Deep Learning Architectures for Evaluation of Prostate Lesion Detection","authors":"Dimitrios I. Zaridis, E. Mylona, N. Tachos, K. Marias, M. Tsiknakis, D. Fotiadis","doi":"10.1109/CAI54212.2023.00071","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00071","url":null,"abstract":"The localization of prostate cancer on MR images is of paramount importance for accurate diagnosis and treatment. In the present study, transformations of 3 Deep Learning segmentation models from 2D to 3D space are proposed to segment prostate lesions in MR images. The 3D Use-Net model is the best model outperforming the second by 10% Dice Score and 1.79mm Hausdorff Distance.","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":"128059558","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
DeepRainX: Integrated Image Nowcast Based on Deep Learning And Physical Models DeepRainX:基于深度学习和物理模型的集成图像临近预报
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00050
Hidetomo Sakaino, Dwi Fetiria Ningrum, Alivanh Insisiengmay, Louie Zamora, Natnapat Gaviphat
{"title":"DeepRainX: Integrated Image Nowcast Based on Deep Learning And Physical Models","authors":"Hidetomo Sakaino, Dwi Fetiria Ningrum, Alivanh Insisiengmay, Louie Zamora, Natnapat Gaviphat","doi":"10.1109/CAI54212.2023.00050","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00050","url":null,"abstract":"This paper proposes an integrated image nowcasting model, DeepRainX, based on Deep Learning (DL), physical models, and multiple Optical Flow (OF) models using radar image sequences. The spatio-temporal DL model is employed to predict images within short future timeframes. DL is robust against radar clutter noise and no OF estimation at the initial stage. However, DL predicts deteriorated image sequences 1 hr ahead of time. Therefore, multiple OFs are used to estimate motions from two DL output images 1 hr ahead of time. The estimated motions and the final output from DL serve as inputs to the advection equation and the Navier-Stokes equation to predict longer future image sequences after that time. Using heavy rainfall events, i.e., typhoons, the proposed DeepRainX outperforms the two worlds leading nowcast methods, i.e., Rainymotion and DL-based DGMR, in terms of accuracy in estimating precipitation amounts.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"424 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":"127012072","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
The Right to Be Forgotten in Artificial Intelligence: Issues, Approaches, Limitations and Challenges 人工智能中的被遗忘权:问题、方法、限制和挑战
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00085
J. Lobo, S. Gil-Lopez, J. Del Ser
{"title":"The Right to Be Forgotten in Artificial Intelligence: Issues, Approaches, Limitations and Challenges","authors":"J. Lobo, S. Gil-Lopez, J. Del Ser","doi":"10.1109/CAI54212.2023.00085","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00085","url":null,"abstract":"The Right To Be Forgotten is widely conceived as a fundamental principle of the human being. It has become a subject of capital importance in domains where sensitive information is collected from individuals, requiring the provision of monitoring, governance and audit tools to control where such information is used. Artificial Intelligence models are not an exception to this statement: since they are learned from data, this fundamental right should allow individuals to have their personal information erased from AI-based systems. However, the application of this right is not straightforward: what does erasing mean in the context of a model learned from data? Is it just a matter of removing the concerned data and retraining the models? This manuscript provides a brief overview of these and more issues, proposing a desiderata for technical advances noted in this direction, and outlining research directions for prospective studies.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"17 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":"131969746","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
Adaptive Duty Cycle Control for Optimal Battery Energy Storage System Charging by Reinforcement Learning 基于强化学习的电池储能系统最优充电自适应占空比控制
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00013
Richard Wiencek, Sagnika Ghosh
{"title":"Adaptive Duty Cycle Control for Optimal Battery Energy Storage System Charging by Reinforcement Learning","authors":"Richard Wiencek, Sagnika Ghosh","doi":"10.1109/CAI54212.2023.00013","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00013","url":null,"abstract":"This paper works on adaptive duty cycle control of a Solar power system using a Reinforcement Learning approach for optimizing the charging of a 12 V 30 Ah Battery Energy Storage System (BESS). The Twin-Delayed Deep Deterministic (TD3) algorithm is used to train an agent that adaptively controls the duty cycle of a Pulse-Width Modulation (PWM) signal to maintain the output voltage of the Photovoltaic (PV) system in the optimal range for charging the BESS. Results from MATLAB Simulink show that the TD3 algorithm optimizes the charging of the BESS when compared to other techniques, such as a PID controller, achieving an SOC of around 50.33% from an initial value of 50% with low noise for 10 seconds, whereas the PID controller achieved around 50.06% with high voltage spikes. This work opens avenues for expanding the system to include other renewable energy sources and their interactions for improving power distribution.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"186 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":"133699589","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信