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2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)最新文献

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Remaining Useful Life Prediction Using an Ensemble Learning-Based Network for a Belt Conveyor System 基于集成学习网络的带式输送机剩余使用寿命预测
2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2023-02-20 DOI: 10.1109/ICAIIC57133.2023.10066971
Junhyung Jo, Zeu Kim, Y. Suh
{"title":"Remaining Useful Life Prediction Using an Ensemble Learning-Based Network for a Belt Conveyor System","authors":"Junhyung Jo, Zeu Kim, Y. Suh","doi":"10.1109/ICAIIC57133.2023.10066971","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10066971","url":null,"abstract":"The belt conveyor system is widely used in production and distribution industries because it is more cost-effective than manpower and can be used in a variety of ways. Prognostics of the belt conveyor system is the main activity to maintain efficiency. Lack of performance of the system is most often an error in which the system is no longer available to meet the desired performance which arises the entire system can be damaged and fatal industrial accidents may occur. In this paper, we present a model that predicts the remaining useful life of the head pulley, a key part of the belt conveyor system. The ensemble learning-based model to predict is composed of a deep learning-based representation model and boosting model. The model is trained using a combination of classification and regression rather than simple regression to predict the remaining useful life. The data used to train the model was collected by directly building a test bed with an environment similar to a belt conveyor system.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"75 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131787728","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
Temporal Attention Gate Network With Temporal Decomposition for Improved Prediction Accuracy of Univariate Time-Series Data 基于时间分解的时间注意门网络提高单变量时间序列数据的预测精度
2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2023-02-20 DOI: 10.1109/ICAIIC57133.2023.10067135
Sunghyun Sim, Dohee Kim, Seok Chan Jeong
{"title":"Temporal Attention Gate Network With Temporal Decomposition for Improved Prediction Accuracy of Univariate Time-Series Data","authors":"Sunghyun Sim, Dohee Kim, Seok Chan Jeong","doi":"10.1109/ICAIIC57133.2023.10067135","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067135","url":null,"abstract":"Time-series forecasting has widely been addressed in data science and various domains, but many limitations persist in terms of prediction accuracy. We propose a network architecture called temporal attention gate network (TAGNet) to improve the prediction accuracy of time-series prediction. TAGNet integrates new concepts of temporal filter and temporal attention gate. First, the temporal filter learns information embedded in time-series data by decomposing the input data through variational mode decomposition. Second, the temporal attention gate learns the relationship between the decomposed time-series signals and hidden states to learn their relationships. To verify the performance of the proposed TAGNet, a comparative experiment was conducted on three univariate time-series datasets. The results show that the prediction performance improves by 15% on average for short-, medium-, and long-term predictions compared with various deep learning methods.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117065884","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
Proposal of Docker and Kubernetes Direction through the Event Timeline of Kubernetes 通过Kubernetes的事件时间线提出Docker和Kubernetes方向的建议
2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2023-02-20 DOI: 10.1109/ICAIIC57133.2023.10066988
Seungchan Woo, Jong-Hyouk Lee
{"title":"Proposal of Docker and Kubernetes Direction through the Event Timeline of Kubernetes","authors":"Seungchan Woo, Jong-Hyouk Lee","doi":"10.1109/ICAIIC57133.2023.10066988","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10066988","url":null,"abstract":"Modern developers typically run their workloads through cloud-native environments such as Docker and Kubernetes. Docker is a platform that runs and manages containers. With the birth of Docker, interest in containers and technology has grown. As one of the container orchestration tools that control and manage containers running on multiple hosts, Kubernetes has a very large share and is used by many cloud companies, making it the standard for practical container orchestration tools. Therefore, in this paper, by analyzing the Kubernetes event timeline, we present the future direction of Kubernetes and Docker, which are key tools in the cloud-native environment.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126285019","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
A Data Augmentation Approach to 28GHz Path Loss Modeling Using CNNs 基于cnn的28GHz路径损耗建模的数据增强方法
2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2023-02-20 DOI: 10.1109/ICAIIC57133.2023.10067053
Bokyung Kwon, Youngbin Kim, Hyukjoon Lee
{"title":"A Data Augmentation Approach to 28GHz Path Loss Modeling Using CNNs","authors":"Bokyung Kwon, Youngbin Kim, Hyukjoon Lee","doi":"10.1109/ICAIIC57133.2023.10067053","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067053","url":null,"abstract":"Millimeter waves are easily influenced by the surrounding environment, making it difficult to predict path loss values for 28GHz communication systems. Recently, deep learning approaches have become popular mainly thanks to their superior performance in terms of prediction accuracy, generalizability as well as local adaptability. These deep learning approaches require a sufficient number of training data which often lacks variability with respect to the parameter values of base station configuration if not unavailable at all. This paper proposes to use the data augmentation approach to address these two issues by using a simulator to generate predicted data for the arbitrary values of base station parameters. It is shown that a Convolution Neural Network (CNN) trained with both measurement and augmented data outperforms a vanilla CNN model trained with measurement data only and that it can make accurate predictions for arbitrary base station configurations.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130947981","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
cGAN Model-Based Radio Frequency Interference Mitigation for Radio Astronomy Data 基于cGAN模型的射电天文数据射频干扰抑制
2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2023-02-20 DOI: 10.1109/ICAIIC57133.2023.10066995
I. Helmy, Wooyeol Choi
{"title":"cGAN Model-Based Radio Frequency Interference Mitigation for Radio Astronomy Data","authors":"I. Helmy, Wooyeol Choi","doi":"10.1109/ICAIIC57133.2023.10066995","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10066995","url":null,"abstract":"Radio astronomy is one of the essential branches of space sciences where astronomers explore the universe by collecting data using various tools. The radio telescope is one of the principal tools for receiving celestial objects' emissions. How-ever, radio frequency interference (RFI) detection, mitigation, and avoidance are some of the main challenges in astronomical radio data. Additionally, they are essential steps for selecting the best site to initiate the radio telescope. RFI mitigation is arduous because interference can take a wide range of forms and affects different scientific goals. The substantial challenges of handling large radio data volumes make it a good application of deep learning (DL). The research aims to mitigate the interference using a DL-based approach, specifically, conditional generative adversarial network (cGAN), because of its powerful ability to differentiate the interference and the clean data.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121845075","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: 2
Pallet Detection and Distance Estimation with YOLO and Fiducial Marker Algorithm in Industrial Forklift Robot 基于YOLO和基准标记算法的工业叉车机器人托盘检测与距离估计
2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2023-02-20 DOI: 10.1109/ICAIIC57133.2023.10066999
Eric Sean Kesuma, P. Rusmin, D. Maharani
{"title":"Pallet Detection and Distance Estimation with YOLO and Fiducial Marker Algorithm in Industrial Forklift Robot","authors":"Eric Sean Kesuma, P. Rusmin, D. Maharani","doi":"10.1109/ICAIIC57133.2023.10066999","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10066999","url":null,"abstract":"Utilising technology such as artificial intelligence and robotics potentially improves E-Commerce in efficiency. In this trends, the usage of autonomous forklifts in the warehouse to lift and arrange things should be implemented. The picking system in the warehouse needs pallet detection and tracking to carry out the things. This research will find the best performance of the YOLOv5 model and correct the distance estimation model to the fiducial marker. In this paper, we used the ArUco fiducial marker to mark the pallet target and estimate the pose and distance in real time. The insertion points of the pallet were also detected using the YOLOv5 algorithm to validate the pallet and get the coordinate variables of the holes. The YOLOv5n gives the best performance at 24 fps in real-time detection. Distance measurement from the marker detection had an average error of 2.28 cm with linear regression.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122437431","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
Philippine National Elections 2022: Voter Preferences and Topics of Discussion on Twitter 菲律宾国家选举2022:选民偏好和推特上的讨论主题
2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2023-02-20 DOI: 10.1109/ICAIIC57133.2023.10067082
Reina Erika Demillo, Geoffrey A. Solano, Nathaniel Oco
{"title":"Philippine National Elections 2022: Voter Preferences and Topics of Discussion on Twitter","authors":"Reina Erika Demillo, Geoffrey A. Solano, Nathaniel Oco","doi":"10.1109/ICAIIC57133.2023.10067082","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067082","url":null,"abstract":"Studies have shown how social networking sites have been used in the political landscape as a tool to disseminate information, influence people in their political views and voting decisions, and even predict election results. This study analyzes voter preferences and identifies the topics of discussion on 2022 election-related tweets using sentiment analysis and topic modelling. Naive Bayes and Support Vector Machine are used for the sentiment analysis classifier models and Biterm Topic Modeling for identifying the most discussed topics. The results of sentiment analysis show that the Naive Bayes classifier gained a higher accuracy score of 73% than Support Vector Machine with 69%. By focusing on the leading presidential candidates, the sentiment classification revealed that Leni Robredo obtained higher positive sentiment rating than Bongbong Marcos, and is the most tweeted candidate. Significant issues regarding the candidates and the elections are determined from the topic models.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122484602","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
Forecasting Solar Energy Production using a Hybrid GCN-BiLSTM Model 利用混合GCN-BiLSTM模型预测太阳能发电量
2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2023-02-20 DOI: 10.1109/ICAIIC57133.2023.10067088
Radityo Fajar Pamungkas, Ida Bagus Krishna Yoga Utama, Muhammad Miftah Faridh, Md Morshed Alam, ByungDeok Chung, Y. Jang
{"title":"Forecasting Solar Energy Production using a Hybrid GCN-BiLSTM Model","authors":"Radityo Fajar Pamungkas, Ida Bagus Krishna Yoga Utama, Muhammad Miftah Faridh, Md Morshed Alam, ByungDeok Chung, Y. Jang","doi":"10.1109/ICAIIC57133.2023.10067088","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067088","url":null,"abstract":"Under increasing levels of renewable energy source (RES) penetration, unpredictability and uncertainty are emerging drivers of power imbalances. Forecasting is frequently used to anticipate renewable energy power generation. Forecast errors, on the other hand, significantly negatively impact power system performance. This research describes a deep learning technique based on spatiotemporal analysis for accurately forecasting solar power generation. Solar power generation output from seven PV sites is predicted using a hybrid graph convolutional network (GCN) module, bidirectional long short-term memory (BiLSTM) module, and attention layer. Our model effectively captures comprehensive spatiotemporal correlations on real-world solar power generation datasets and surpasses several existing methods.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121259583","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
GNN Link Prediction for Time-Triggered Systems 时间触发系统的GNN链路预测
2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2023-02-20 DOI: 10.1109/ICAIIC57133.2023.10066960
Carlos Lua, Ye Zhang, Omar Hekal, Daniel Onwuchekwa, R. Obermaisser
{"title":"GNN Link Prediction for Time-Triggered Systems","authors":"Carlos Lua, Ye Zhang, Omar Hekal, Daniel Onwuchekwa, R. Obermaisser","doi":"10.1109/ICAIIC57133.2023.10066960","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10066960","url":null,"abstract":"Research on graph neural networks (GNNs) has increasingly gained popularity recently. GNN is considered a powerful tool for solving machine learning tasks that require dealing with irregular topologies such as graph data. Meanwhile, solving the scheduling problems for time-triggered systems has been debated for a long time. Even though several algorithms were proposed to solve this problem, none considered exploiting GNN partially or wholly, solving time-triggered scheduling. In this work, we propose an approach for dynamic adaptation in time-triggered systems using GNN. We use GNNs to solve scheduling problems for time-triggered systems by transforming job allocation probelms to link prediction tasks. The preliminary results show that GNNs have a promising potential to perform job allocation problems in time-triggered systems.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127080682","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
Identification of Misogyny on Social Media in Indonesian Using Bidirectional Encoder Representations From Transformers (BERT) 利用《变形金刚》的双向编码器表征识别印尼社交媒体上的厌女症(BERT)
2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2023-02-20 DOI: 10.1109/ICAIIC57133.2023.10067106
Bagas Tri Wibowo, Dade Nurjanah, Hani Nurrahmi
{"title":"Identification of Misogyny on Social Media in Indonesian Using Bidirectional Encoder Representations From Transformers (BERT)","authors":"Bagas Tri Wibowo, Dade Nurjanah, Hani Nurrahmi","doi":"10.1109/ICAIIC57133.2023.10067106","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067106","url":null,"abstract":"Misogyny is a behavior that hates or dislikes women Text classification can be used to identify misogyny text. One text classification method currently popular and proven to have good performance is the Bidirectional Encoder From Transformers (BERT). Fine-tuning is a method to transfer knowledge from a trained model to a new model to complete a new task. This study focuses on building a misogyny identification model with IndoBert pre-trained model provided by IndoNLU. The identification of Misogyny model obtained the best results with an accuracy value of 83.74% and by using K-fold cross-validation, the average validation value is 77.86%.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127897659","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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