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2022 5th International Conference on Artificial Intelligence for Industries (AI4I)最新文献

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Key Elements to Contextualize AI-Driven Condition Monitoring Systems towards Their Risk-Based Evaluation 情境化人工智能驱动状态监测系统的关键要素，以实现基于风险的评估
2022 5th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2022-09-01 DOI: 10.1109/AI4I54798.2022.00017
M. Dadfarnia, M. Sharp
{"title":"Key Elements to Contextualize AI-Driven Condition Monitoring Systems towards Their Risk-Based Evaluation","authors":"M. Dadfarnia, M. Sharp","doi":"10.1109/AI4I54798.2022.00017","DOIUrl":"https://doi.org/10.1109/AI4I54798.2022.00017","url":null,"abstract":"Industrial users can be justifiably hesitant in adopting Condition Monitoring Systems (CMSs) unless evidence indicates benefits from their use. Measuring a CMS’s ability to prevent losses is difficult and lacks standard procedures. The increasing availability of closed-box Artificial Intelligence (AI)- driven CMSs exacerbates the hesitancy as predicting their impacts is more challenging. This paper details three key elements critical to evaluating CMS impact:(1) the Application Area, (2) the Risk Management Processes, and (3) the Monitoring Mechanism. This paper discusses these elements in their capacity to contextualize a CMS’s role within an asset’s risk management processes, which can lead to justifying CMS use.","PeriodicalId":345427,"journal":{"name":"2022 5th International Conference on Artificial Intelligence for Industries (AI4I)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129852139","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
AI and K-12 Forum 人工智能和K-12论坛
2022 5th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2022-09-01 DOI: 10.1109/ai4i54798.2022.00027
Ganesh Mani, Jim Bologna, Phillip C.-Y. Sheu
{"title":"AI and K-12 Forum","authors":"Ganesh Mani, Jim Bologna, Phillip C.-Y. Sheu","doi":"10.1109/ai4i54798.2022.00027","DOIUrl":"https://doi.org/10.1109/ai4i54798.2022.00027","url":null,"abstract":"This brief report describes the forum with a focus on AI and K–12, which was part of the larger AI for Industries conference. Several contemporary learning resources and methods were presented and discussed.","PeriodicalId":345427,"journal":{"name":"2022 5th International Conference on Artificial Intelligence for Industries (AI4I)","volume":"111 3S 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126322242","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
Towards AI Platforms for Stationary Retail 面向文具零售的人工智能平台
2022 5th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2022-09-01 DOI: 10.1109/AI4I54798.2022.00012
Tim Schopf, Kilian Dresse, F. Matthes
{"title":"Towards AI Platforms for Stationary Retail","authors":"Tim Schopf, Kilian Dresse, F. Matthes","doi":"10.1109/AI4I54798.2022.00012","DOIUrl":"https://doi.org/10.1109/AI4I54798.2022.00012","url":null,"abstract":"A major challenge for stationary retail is the increasing digitization of business. While online retailers can increase their sales through data-driven AI applications, brick-and-mortar retailers are left behind because they have less data available due to their traditional physical stores. To bridge the gap between physical stores and online stores, in this paper, an AI platform that connects the digital world with stationary retail is proposed. A digital twin, which represents instances of physical stores in digital form, builds the core of the AI platform. The AI platform can enable digital business models, as well as sales and operations processes that have not been possible to date.","PeriodicalId":345427,"journal":{"name":"2022 5th International Conference on Artificial Intelligence for Industries (AI4I)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124303432","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
Post-Fault Power Grid Voltage Prediction via 1D-CNN with Spatial Coupling 基于空间耦合的一维cnn故障后电网电压预测
2022 5th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2022-09-01 DOI: 10.1109/AI4I54798.2022.00016
Carson Hu, Guang Lin, Bao Wang, Meng Yue, Jack Xin
{"title":"Post-Fault Power Grid Voltage Prediction via 1D-CNN with Spatial Coupling","authors":"Carson Hu, Guang Lin, Bao Wang, Meng Yue, Jack Xin","doi":"10.1109/AI4I54798.2022.00016","DOIUrl":"https://doi.org/10.1109/AI4I54798.2022.00016","url":null,"abstract":"We propose a one-dimensional convolutional neural network (1D-CNN) with spatial coupling for post-fault power grid voltage prediction. Our proposed deep learning framework was inspired by the celebrated Prony’s method in classical signal processing. Our spatio-temporal model significantly outperforms existing benchmarks, including long short-term memory model, and is applicable to other strong transients in power industries.","PeriodicalId":345427,"journal":{"name":"2022 5th International Conference on Artificial Intelligence for Industries (AI4I)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122197934","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
Virtual Commissioning Simulation as OpenAI Gym - A Reinforcement Learning Environment for Control Systems 基于OpenAI Gym的虚拟调试仿真——控制系统的强化学习环境
2022 5th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2022-09-01 DOI: 10.1109/AI4I54798.2022.00023
Florian Jaensch, Lars Klingel, A. Verl
{"title":"Virtual Commissioning Simulation as OpenAI Gym - A Reinforcement Learning Environment for Control Systems","authors":"Florian Jaensch, Lars Klingel, A. Verl","doi":"10.1109/AI4I54798.2022.00023","DOIUrl":"https://doi.org/10.1109/AI4I54798.2022.00023","url":null,"abstract":"Manual development of control systems’ software is time-consuming and error-prone. Thus, high costs are already incurred in this phase of mechatronic system development. Virtual prototypes have so far only been used for testing purposes, such as virtual commissioning, but not for the automated creation of the control. A good test environment can also be extended to a learning environment with appropriate trial and error based algorithms. This work shows an approach to extend an industrial software tool for virtual commissioning as a standardized OpenAI gym environment. Thereby, established reinforcement learning algorithms can be used more easily and a step towards an industrial application of self-learning control systems can be made. The goal of this work is to provide industry and research with a platform for easy entry into the field of reinforcement learning.","PeriodicalId":345427,"journal":{"name":"2022 5th International Conference on Artificial Intelligence for Industries (AI4I)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134117967","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
Autonomous Load Carrier Approaching Based on Deep Reinforcement Learning with Compressed Visual Information 基于压缩视觉信息的深度强化学习的自主负载载体逼近
2022 5th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2022-09-01 DOI: 10.1109/AI4I54798.2022.00019
Simon Hadwiger, Tobias Meisen
{"title":"Autonomous Load Carrier Approaching Based on Deep Reinforcement Learning with Compressed Visual Information","authors":"Simon Hadwiger, Tobias Meisen","doi":"10.1109/AI4I54798.2022.00019","DOIUrl":"https://doi.org/10.1109/AI4I54798.2022.00019","url":null,"abstract":"In intralogistics, a large number of tasks are already fully automated. This holds true especially for tasks where strictly predefined positions and paths are specified and implementable. Challenges still exist when this is not the case and a more dynamic environment is present. One example of such a dynamic environment is the approaching and lifting of freely positioned pallet-like carriers with forklifts. In this work, we propose a method for approaching and picking up pallet-like carriers with a forklift based on data from an RGB camera. Unlike previous work, our method does not require an estimation of the pose of the load carrier. In order to control the forklift, we use a soft actor critical reinforcement learning agent. The required input consists of the bounding box of the load carrier in combination with the current speed and steering of the forklift. Our simulation experiments show that this compressed visual information is sufficient to successfully approach load carriers while reducing training time and network size. In a next step, we are going to apply the presented result on a real-world scenario and investigate its transferability.","PeriodicalId":345427,"journal":{"name":"2022 5th International Conference on Artificial Intelligence for Industries (AI4I)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115254747","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
Enhancing Zero-Shot Many to Many Voice Conversion via Self-Attention VAE with Structurally Regularized Layers 基于结构正则化层的自关注VAE增强零射多对多语音转换
2022 5th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2022-09-01 DOI: 10.1109/AI4I54798.2022.00022
Ziang Long, Yunling Zheng, Meng Yu, Jack Xin
{"title":"Enhancing Zero-Shot Many to Many Voice Conversion via Self-Attention VAE with Structurally Regularized Layers","authors":"Ziang Long, Yunling Zheng, Meng Yu, Jack Xin","doi":"10.1109/AI4I54798.2022.00022","DOIUrl":"https://doi.org/10.1109/AI4I54798.2022.00022","url":null,"abstract":"Variational auto-encoder (VAE) is an effective neural network architecture to disentangle a speech utterance into speaker identity and linguistic content latent embeddings, then generate an utterance for a target speaker from that of a source speaker. This is possible by concatenating the identity embedding of the target speaker and the content embedding of the source speaker uttering a desired sentence. In this work, we propose to improve VAE models with self-attention and structural regularization (RGSM). Specifically, we found a suitable location of VAE’s decoder to add a self-attention layer for incorporating non-local information in generating a converted utterance and hiding the source speaker’s identity. We applied relaxed groupwise splitting method (RGSM) to regularize network weights and remarkably enhance generalization performance. In experiments of zero-shot many-to-many voice conversion task on VCTK data set, with the self-attention layer and relaxed group-wise splitting method, our model achieves a gain of speaker classification accuracy on unseen speakers by 28.3% while slightly improved conversion voice quality in terms of MOSNet scores. Our encouraging findings point to future research on integrating more variety of attention structures in VAE framework while controlling model size and overfitting for advancing zero-shot many-to-many voice conversions1.1The work was partially supported by NSF grants DMS-1854434 and DMS-1952644 at UC Irvine.","PeriodicalId":345427,"journal":{"name":"2022 5th International Conference on Artificial Intelligence for Industries (AI4I)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121439694","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
Learning Causal Graphs in Manufacturing Domains using Structural Equation Models 利用结构方程模型学习制造领域的因果图
2022 5th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2022-09-01 DOI: 10.1109/AI4I54798.2022.00010
Maximilian Kertel, S. Harmeling, M. Pauly
{"title":"Learning Causal Graphs in Manufacturing Domains using Structural Equation Models","authors":"Maximilian Kertel, S. Harmeling, M. Pauly","doi":"10.1109/AI4I54798.2022.00010","DOIUrl":"https://doi.org/10.1109/AI4I54798.2022.00010","url":null,"abstract":"Many production processes are characterized by numerous and complex cause-and-effect relationships. Since they are only partially known they pose a challenge to effective process control. In this work we present how Structural Equation Models can be used for deriving cause-and-effect relationships from the combination of prior knowledge and process data in the manufacturing domain. Compared to existing applications, we do not assume linear relationships leading to more informative results.","PeriodicalId":345427,"journal":{"name":"2022 5th International Conference on Artificial Intelligence for Industries (AI4I)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130138918","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
Efficient DER Voltage Control Using Ensemble Deep Reinforcement Learning 基于集成深度强化学习的DER电压控制
2022 5th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2022-09-01 DOI: 10.1109/AI4I54798.2022.00021
James Obert, R. Trevizan, A. Chavez
{"title":"Efficient DER Voltage Control Using Ensemble Deep Reinforcement Learning","authors":"James Obert, R. Trevizan, A. Chavez","doi":"10.1109/AI4I54798.2022.00021","DOIUrl":"https://doi.org/10.1109/AI4I54798.2022.00021","url":null,"abstract":"To meet the challenges oflow-carbon power generation, distributed energy resources (DERs) such as solar and wind power generators are now widely integrated into the power grid. Because of the autonomous nature of DERs, ensuring properly regulated output voltages of the individual sources to the grid distribution system poses a technical challenge to grid operators. Stochastic, model-free voltage regulations methods such as deep reinforcement learning (DRL) have proven effective in the regulation of DER output voltages; however, deriving an optimal voltage control policy using DRL over a large state space has a large computational time complexity. In this paper we illustrate a computationally efficient method for deriving an optimal voltage control policy using a parallelized DRL ensemble. Additionally, we illustrate the resiliency of the control ensemble when random noise is introduced by a cyber adversary.","PeriodicalId":345427,"journal":{"name":"2022 5th International Conference on Artificial Intelligence for Industries (AI4I)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114718298","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
Real Time Analysis on Bus Passenger for Unmanned Door Operation using Overhead Fisheye Cameras 基于头顶鱼眼摄像机的无人驾驶客车乘客实时分析
2022 5th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2022-09-01 DOI: 10.1109/ai4i54798.2022.00014
Masayuki Yamazaki, Kei Tsuji, Eigo Mori
{"title":"Real Time Analysis on Bus Passenger for Unmanned Door Operation using Overhead Fisheye Cameras","authors":"Masayuki Yamazaki, Kei Tsuji, Eigo Mori","doi":"10.1109/ai4i54798.2022.00014","DOIUrl":"https://doi.org/10.1109/ai4i54798.2022.00014","url":null,"abstract":"In this study, we propose a robust method for ensuring safe unmanned bus door operation using commonly available surveillance cameras. Bus door operation is a critical function of bus services because passengers can be injured unless they work appropriately. Our method leverages only image processing technologies and is composed of an object detector, an optical flow analyzer, and a pose estimator. We carefully combined the modules so that they judged the readiness of the door operation accurately and in real time. We confirmed that our proposed method can ensure safe bus door operation using only two industry fisheye surveillance cameras installed on a modified Toyota Coaster microbus. Our system runs at 10 fps using a commonly available GPU desktop.","PeriodicalId":345427,"journal":{"name":"2022 5th International Conference on Artificial Intelligence for Industries (AI4I)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128803174","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
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