2023 15th International Conference on Computer and Automation Engineering (ICCAE)最新文献_第3页

Performance Analysis of SWIPT Cooperative-NOMA Over Rayleigh Fading Channel 瑞利衰落信道下SWIPT合作- noma性能分析

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111338

B. Chaudhary, R. Mishra

引用次数: 0

Design and Implementation of Modbus RTU/TCP to Profibus Gateway Using Raspberry Pi Modbus RTU/TCP到Profibus网关的树莓派设计与实现

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111395

Dorsa Zaheri, M. H. Refan

引用次数: 0

A Dataset and Multi-task Multi-view Approach for Question-Answering with the Dual Perspectives of Text and Knowledge 基于文本和知识双视角的数据集多任务多视图问答方法

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111327

MS Adithya, M. Ahmed, Mihir Madhusudan Kestur, A. S. Chaithanya, Bhaskarjyothi Das

{"title":"A Dataset and Multi-task Multi-view Approach for Question-Answering with the Dual Perspectives of Text and Knowledge","authors":"MS Adithya, M. Ahmed, Mihir Madhusudan Kestur, A. S. Chaithanya, Bhaskarjyothi Das","doi":"10.1109/ICCAE56788.2023.10111327","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111327","url":null,"abstract":"Question-answering (QA) systems are important tools for extracting information from large datasets and providing accurate and relevant answers to user queries. Two of the most widely studied and built QA systems are Natural Language Question Answering (NLQA) and Knowledge Graph Question Answering (KGQA). NLQA relies on sequence learning algorithms, which have limitations on the length of input they can handle, while KGQA relies on the Subject-Predicate-Object (SPO) tuple representation of data, which may not always be available in the knowledge graph. In this paper, we present a novel approach for addressing these challenges by utilizing the structural information from the Knowledge Graph (KG) and the semantic information from the Natural Language Context. Due to the lack of a dataset to enable this approach, we propose the creation of a multi-view dataset - MTL-QA, specifically designed for multi-task learning. We also present a multi-task learning approach to jointly train NLQA and KGQA models and demonstrate the effectiveness on the proposed MTL-QA dataset.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114669301","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

Design of Picoammeter for Test and Measurement 测试测量用皮安计的设计

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111445

M. A. Latina, Leonardo D. Valiente, Glendel A. Carmen, Maria Isabel Y. Lim, Kimberly O. Chua

引用次数: 0

A Survey on Mapping of Urban Green Spaces within Remote Sensing Data Using Machine Learning & Deep Learning Techniques 利用机器学习和深度学习技术在遥感数据中绘制城市绿地的研究

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111467

Smita Sunil Burrewar, M. Haque, Tanwir Uddin Haider

{"title":"A Survey on Mapping of Urban Green Spaces within Remote Sensing Data Using Machine Learning & Deep Learning Techniques","authors":"Smita Sunil Burrewar, M. Haque, Tanwir Uddin Haider","doi":"10.1109/ICCAE56788.2023.10111467","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111467","url":null,"abstract":"For environmental protection, urban planning, monitoring, and management of the urban ecosystem, mapping urban green spaces is a crucial undertaking. A vital source of information for United Nations Sustainable Development Goal 11.7 could come from urban green space mapping. The standard method of mapping urban green spaces requires field measurements and takes a lot of time. It is also important to update urban green space maps periodically because urban green spaces can change quickly over time due to development. With the advent of high-resolution satellite sensors like Sentinel-1 and Sentinel-2, a large number of remote sensing images may be gathered, providing quick and precise information over urban areas. This work intends to offer a new perspective on how crowd sourced geospatial big data and remote sensing may be combined to enhance the mapping of urban green spaces, including time optimization and accurate information through machine learning and deep learning. For the revitalization of cities, this data will be valuable. Remote sensing imagery data can be classified using machine learning techniques like Support vector machines (SVM), Random forests (RF), and Naive Bayes (NB). In deep learning techniques such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), K Nearest Neighbor (KNN), Generative Adversarial Networks (GAN), and Recurrent Neural Networks (RNN) can be used to classify remote sensing images.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124218255","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

Greenhouse Networked Control System Design Methodology to Minimize Cost & Downtime 减少成本和停机时间的温室网络控制系统设计方法

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111465

Mohammed A. Ba Humaish, T. Refaat, H. Amer, Hassan M. F. El-Menier, R. Daoud, Nora A. Ali

{"title":"Greenhouse Networked Control System Design Methodology to Minimize Cost & Downtime","authors":"Mohammed A. Ba Humaish, T. Refaat, H. Amer, Hassan M. F. El-Menier, R. Daoud, Nora A. Ali","doi":"10.1109/ICCAE56788.2023.10111465","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111465","url":null,"abstract":"Smart greenhouses enable the cultivation of various crops in different environmental conditions. Wireless Sensor Networks (WSNs) and Networked Control Systems (NCSs) could be utilized within greenhouses to access, control and monitor different environmental parameters. This paper studies a greenhouse with a special focus on the wireless NCS. It first shows how to use the 2.4GHz frequency band while minimizing Access Point (AP) cost and maintaining an acceptable Packet Loss Rate (PLR). Second, fault-tolerant AP architectures are studied, and a metric is proposed which incorporates AP failure and repair rates, as well as NCS information efficiency to help management select an appropriate architecture, to minimize profit loss. This metric is shown to produce some counter-intuitive results. Finally, Riverbed simulations are used to obtain the minimum sensor transmission power to produce the minimum acceptable PLR in the context of the 2.4GHz band. This power is much lower than that for the 5GHz band; this will prolong sensor battery lifetime.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129653532","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

Robust I-PD Controller Design with Case Studies on Boiler Steam Drum and Bioreactor 鲁棒I-PD控制器设计——以锅炉汽包和生物反应器为例

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111205

G. Raja

引用次数: 1

An Efficient Influence Maximization Technique Based on Betweenness Centrality Measure and Clustering Coefficient (Bet-Clus) 一种基于中间性中心性测度和聚类系数的影响最大化方法

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111177

Rahul Saxena, M. Jadeja, Pranshu Vyas

{"title":"An Efficient Influence Maximization Technique Based on Betweenness Centrality Measure and Clustering Coefficient (Bet-Clus)","authors":"Rahul Saxena, M. Jadeja, Pranshu Vyas","doi":"10.1109/ICCAE56788.2023.10111177","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111177","url":null,"abstract":"Graph theory has found its rigorous applicability in defining the social network phenomenas. Influence maximization in real world graphical networks have been an area of keen interest to researcher. Determining the initial seed set population for an influence spread model is found to be an N-P complete problem. Many optimization and heuristic algorithms have been proposed to solve the problem in the past. However, all the proposed solutions are constrained in terms of the scalability, their correctness etc. In this article, a graph theoretical approach based metric has been defined to effectively identify the initial seed set population for the network. The seed nodes are selected based on their clustering coefficient and betweenness centrality scores on a ranking basis. The top ’k’ ranked nodes are selected as seed nodes (information carriers) in the network. Using Independent Cascade (IC) diffusion model, the network coverage is calculated. The proposed method attains higher network coverage in comparison to the base IC model. Also, the method’s performance is compared and found to be superior when the seed nodes are selected based on other centrality measures and graph measures. The results have been evaluated over four different benchmark networks- CORA, Citeseer, PubMed (Citation Networks) and Amazon Computers Network (Product Network).","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125689153","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

Coverage Prediction and REM Construction for 5G Networks in Band n78 n78频段5G网络覆盖预测与REM构建

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111476

Carla E. García, Insoo Koo

{"title":"Coverage Prediction and REM Construction for 5G Networks in Band n78","authors":"Carla E. García, Insoo Koo","doi":"10.1109/ICCAE56788.2023.10111476","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111476","url":null,"abstract":"Currently, a great number of commercial fifth-generation (5G) networks are deployed on the mid-band, especially in the range between 3.3 GHz and 3.8 GHz, denominated Band n78. Therefore, a radio environment map (REM) construction is a meaningful task for a network operator to indicate the service areas of 5G cellular systems, improve network planning, and handle mobility. Thus, we propose a novel approach to predict the coverage of outdoor-to-indoor propagation for 5G mid-band operational networks, based on the extremely randomized trees regressor (ERTR) algorithm. Then, we construct a REM to improve the visualization of the results and easily detect coverage holes and traffic hotspots.For this purpose, we utilize a dataset of channel measurements in a building of Sapienza University of Rome, Italy. Furthermore, we use three error metrics: relative error, mean absolute error (MAE), and root mean square error (RMSE) to validate our proposed ERTR-based scheme. For comparison purposes, we evaluate the performance of five additional machine learning (ML) regression algorithms. Satisfactorily, the proposed ERTR technique outperforms the comparative approaches by improving the accuracy of coverage prediction in all scenarios.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116560662","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

MGRU-M1dCNN: A Hybrid Model for Stock Price Prediction MGRU-M1dCNN:股票价格预测的混合模型

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI: 10.1109/ICCAE56788.2023.10111233

Anika Kanwal, Siva Chandrasekaran

{"title":"MGRU-M1dCNN: A Hybrid Model for Stock Price Prediction","authors":"Anika Kanwal, Siva Chandrasekaran","doi":"10.1109/ICCAE56788.2023.10111233","DOIUrl":"https://doi.org/10.1109/ICCAE56788.2023.10111233","url":null,"abstract":"Predicting the stocks price is both intriguing and difficult due to the nonlinear and noisy stock’s data. Stock investors’ investment risk can reduce and their return on investment can be significantly increased by properly predicting the change in stock prices. It is challenging for researchers to forecast stock prices using an individual stock forecasting model. Therefore, in this research we proposes MGRU-M1dCNN, a hybrid model to predict the stock opening price of the next day. This model is consisted of multiple gated recurrent unit (MGRU) and Multiple single dimensional convolutional neural networks (M1dCNN). MGRU employs to extract the valuable feature patterns from opening price of stocks. M1dCNN is used to extract the spatial features of the inputs received from MGRU to predict the stock prices. The outcomes demonstrate that this technique performs best, with the least Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) which are 0.854 and 0.456. When compared to other GRU based models (GRU-CNN, GRU-DNN, and GRU), the MGRU-M1dCNN model is better suited for stock price forecasting and for providing investors a trustworthy means to choose which stocks to buy.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123986515","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