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Proceedings - IEEE international conference on Mobile data Management

Proceedings - IEEE International Conference on Mobile Data M...

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22nd IEEE international conference on Mobile data Management, MDM 2021

ISBN: (纸本)9781665428453

The proceedings contain 46 papers. The topics discussed include: towards predicting vehicular data consumption;multi-inhabitant and eXplainable activity recognition in smart homes;measuring heart rate and heart rate variability with smartphone camera;OSMRunner : a system for exploring and fixing OSM Connectivity;dynamic rate control for topic-based pub/sub systems;dual sequence transformer for query-based interactive recommendation;efficient spatio-textual similarity join processing on NUMA systems;crime monitor: monitoring criminals from trajectory data;machine learning meets big spatial data (revised);improving persistence based trajectory simplification;and 2SRS: a two-sided recommender system to connect local businesses to bus passengers.

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Optimization of Photovoltaic Power Forecasting: A Comparative Study of Deep learning Architectures, Optimization Techniques, and Evaluation Metrics

Optimization of Photovoltaic Power Forecasting: A Comparativ...

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learning and Technology conference (L&T)

作者： Husein Mauladdawilah Mohammed Balfaqih Zain Balfagih E.J. Gago M.C. Pegalajar School of Civil Engineering University of Granada Granada Spain Department of Computer and Network Engineering University of Jeddah Jeddah Saudi Arabia Computer Science Department Effat University Jeddah Saudi Arabia Department of Computer Science and Artificial Intelligence University of Granada Granada Spain

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

This study presents a systematic evaluation of deep learning architectures for photovoltaic (PV) power forecasting, comparing nine model configurations across three architectures (MLP, LSTM, CNN) and three optimizers (Adam, RMSprop, Adagrad). Using six years of hourly data from a 350kWp grid-connected PV system in Scotland, we demonstrate that architectural choice and optimizer selection significantly impact forecasting accuracy. The LSTM-RMSprop configuration achieved superior performance with RMSE of 2.651 kWh and MAE of 1.197 kWh, showing a 90.29% coefficient of determination (R 2 ). This outperforms both CNN (RMSE: 2.767-2.902 kWh) and MLP architectures (RMSE: 3.104-3.115 kWh) across all optimizers. Our main contributions include: (1) comprehensive optimization of model architectures through hyperparameter evaluation, revealing optimal configurations for each model type; (2) systematic evaluation of optimizer impact, demonstrating RMSprop's superiority for LSTM with improved accuracy across architectures; (3) detailed error analysis showing model stability across different conditions, with NRMSE ranging from 31.15% to 39.07%; and (4) practical insights into computational requirements, where CNN architectures achieve fastest training times (2.3-3.8 minutes/epoch) compared to LSTM (3.9-5.9 minutes/epoch) and MLP (36.54-46.72 minutes/epoch). Results demonstrate that LSTM architectures with appropriate optimization can outperform simpler models in PV power forecasting, providing valuable guidance for practical implementations.

关键词： Photovoltaic systems Training Analytical models Accuracy Computational modeling Computer architecture Predictive models Forecasting Optimization Long short term memory

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Possibilities for Correcting SQL Exercises Automatically 2nd

Possibilities for Correcting SQL Exercises Automatically

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22nd international conference on Interactive Collaborative learning (ICL) / 48th IGIP international conference on engineering Pedagogy - Impact of the 4th Industrial Revolution on engineering Education

作者： Baksa-Hasko, Gabriella Corvinus Univ Budapest Budapest Hungary

ISBN: (纸本)9783030402716;9783030402709

In this study, we will present a method for evaluating DDL exercises in an exam situation. In relational database systems, we have not just our data in the database but the metadata about our objects as well in so-called data dictionary tables and views. If we give DDL exercises in an exam and students have to run their statements in a system, not just write them, then we can check the objects they made.

关键词： SQL data dictionary Evaluation

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Preliminary Investigation into Machine-Learned 2D automated Video Surveillance Systems

Preliminary Investigation into Machine-Learned 2D Automated ...

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international Carpathian Control conference (ICCC)

作者： Adam Surówka Faculty of Electrical and Computer Engineering Cracow University of Technology Kraków PL

The purpose of this study is to explore the possibility of using 2D human pose estimation algorithms in automated video surveillance systems. The proposed concept boils down to develop an intelligent application that can handle the incoming results and even prevent the occurrence of critical events in the monitored areas. The so-called critical events represent the acts of vandalism, crime, burglary, theft, fights, etc. The core aspect of the described solution is the use of conventional 2D imaging devices, i.e. CCTV cameras, IP webcams or even mobile phone cameras and an AI (artificial intelligence) neural network trained on a set of images. That makes the proposed solution relatively cheap, universal and easy to install across a wide range of target environments without the need for additional sensors. In the paper the authors highlights the architecture of the system, presents details of the video data acquisition stage followed by a description of the 2D human pose estimation toolkit. It is then complemented by summarizing the data extraction stage from pre-recorded clips for machine learning.

关键词： Webcams Pose estimation data acquisition Machine learning Video surveillance Mobile handsets IP networks

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AI-Driven Analysis and Prediction of Energy Consumption in NYC's Municipal Buildings

AI-Driven Analysis and Prediction of Energy Consumption in N...

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international conference on Software engineering Research, Management and Applications (SERA)

作者： Hossein Jamali Sergiu M. Dascalu Frederick C. Harris Department of Computer Science and Engineering University of Nevada Reno Reno NV USA

ISBN: (数字)9798350391343

ISBN: (纸本)9798350391350

Municipal buildings are major energy consumers in urban areas, contributing significantly to greenhouse gas emissions and climate change. Understanding and predicting their energy consumption patterns is crucial for informing energy policy and planning decisions. This study presents an investigation into the energy consumption patterns of municipal buildings in New York City, employing a suite of artificial intelligence (AI) techniques. Utilizing a robust dataset, we apply a range of machine learning models, including linear regression, random forest regressor, gradient boosting regressor, and neural networks, to predict energy consumption patterns. Our findings reveal that the random forest regressor model outperforms other models, achieving a mean squared error of 134.63. This underscores the potential of AI in providing accurate predictions of energy consumption, which can inform energy policy and planning decisions. However, the interpretability of these models remains a significant challenge, highlighting the need for further research into methods for enhancing the transparency and explainability of AI models. This study contributes to the burgeoning field of AI and energy consumption, offering valuable insights for policymakers, researchers, and practitioners. It underscores the potential of AI in transforming our understanding of energy consumption patterns, while also highlighting the challenges that need to be addressed to fully harness the power of AI in this domain.

关键词： Energy consumption Buildings Urban areas Neural networks Linear regression Predictive models data models

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22nd IEEE international conference on Communication Technology, ICCT 2022

作者： Shi, Kaibo Li, Bo Wang, Yonghao Ran, Bo Kong, Peng Huang, Xiao Ma, Dong Liang, Fei School of Physics and Electronic-Electrical Engineering Ningxia University Yinchuan750021 China Yinchuan750021 China

ISBN: (纸本)9781665470674

Mobile content service has been experiencing an explosive traffic growth in radio access networks. Due to the frequent downloads of popular content requested by multiple mobile users, most of the data traffic is caused by repetitive data transfers. Proactive content caching is an effective way to reduce the burden of backhaul link traffic and to improve the user experience. Content popularity is an important factor which influences active caching. However, content popularity is often unknown in advance. Therefore, predicting content popularity is an important challenge for content caching. It is studied that the problem of cache content popularity prediction in a unmanned aerial vehicle(UAV) assisted wireless cache network(UA-WCN) which includes a base station(BS) and multiple UAVs. A model framework and strategy for content popularity prediction based on the comprehensive characteristics of users and content are proposed to solve the popularity prediction problem. Firstly, user preference learning is applied based on considering that users prefer request the contents they are interested in. The users is classified by using some of the physiological characteristics of the user. Then, the weight of the user's preference for the content is learned by using Follow The Regularized Leader(FTRL) algorithm. Finally, the gradient descent algorithm is used to learn the local model parameters. The feasibility of the model is verified by the simulation results. And the proposed popularity prediction strategy can not only accurately predict the popularity of content, but also greatly reduce the computational complexity. © 2022 IEEE.

关键词： Forecasting

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A Comparison of Various Machine learning Algorithms on ISAR Image Classification of Complex Targets with Varying Levels of Gaussian Noise 22

A Comparison of Various Machine Learning Algorithms on ISAR ...

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22nd international conference on Electromagnetics in Advanced Applications, ICEAA 2021

作者： Stewart-Burger, C.D. Ludick, D.J. Potgieter, M. Stellenbosch University Dept. Electrical and Electronic Engineering 7600 South Africa Defence and Security Csir Pretoria South Africa

ISBN: (纸本)9781665413862

This paper investigates the ability to classify complex targets using inverse synthetic aperture radar (ISAR) images with varying noise levels. ISAR is an imaging technique used to generate high resolution two-dimensional images of radar targets. ISAR images contain more information about targets than one-dimensional data, such as high- resolution range (HRR) profiles [1]. ISAR images therefore have useful applications in target classification. © 2021 IEEE.

关键词： Image classification

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot learning

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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IEEE international conference on Industrial Informatics (IndIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

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Load Modeling for Cryptocurrency Mining Devices Using System Identification and Machine learning

Load Modeling for Cryptocurrency Mining Devices Using System...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Ghali Ahmad Md Shafiullah Nezar M. AL-Yazidi Mohamed Zaery Mujahed Al-Dhaifallah M. A. Abido Faris Aljehani Control & Instrumentation Engineering Department King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia IRC for Sustainable Energy Systems King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia K.A.CARE Energy Research & Innovation Center King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia IRC for Smart Mobility and Logistics King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Electrical Engineering Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Saudi Arabia Dema Energy Dhahran Saudi Arabia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

This paper presents a novel flexible load model tailored for cryptocurrency mining applications, specifically designed to dynamically adjust Application Specific Integrated Circuits-based mining operations based on real-time power availability. The model allows miners to use excess power during peak generation and reduce their usage during low power generation, all in support of maintaining a stable grid. The integration of the System Identification technique and some machine learning methods, such as Linear Regression, Support Vector Machine, and Neural Network Models, are used to identify and verify the proposed model against the genuine load data. Values of R 2 ranging from 0.9898 to 0.9953 point toward a very good agreement between the simulated and actual load profiles. The Neural Network Model presents the lowest RMSE, MSE, MAE, and WIE, which means that the model accurately captures the actual behavior of the load. This approach strengthens the efficiency and accuracy of mining processes and is quite compatible with renewable power generation and, therefore, the effective use of energy. This research forms the reference platform for adaptive load management for such intensive applications to provide a viable approach to renewable energy integration into high-power utilization systems.

关键词： Renewable energy sources Adaptation models Machine learning Load management data models Cryptocurrency System identification Integrated circuit modeling Power generation Load modeling

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Digital Twin Enabled Flight Control System Testing: A Physical-Virtual Mapping Experiment

Digital Twin Enabled Flight Control System Testing: A Physic...

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IEEE international conference on Industrial Informatics (IndIN)

作者： Cheng Ren Cailian Chen Shanying Zhu Yehan Ma Xinping Guan Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai P. R. China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Flight control system testing (FCST) is one of the most important testing during aircraft final assembly, however, traditional testing method highly rely on manual labor, resulting in low testing quality and efficiency. Targeting at improving the testing quality and efficiency, in this paper, we apply digtial twin (DT) technology in the FCST process for the first time. A virtual experimental platform named DT-FCST is constructed which is identical to a real wing experimental platform, supporting testing elements management and physical-virtual mapping experiments. The development process of the DT-FCST platform is detailed. Several experiments are conducted to demonstrate the effectiveness of DT technology. Based on motion scripts integrating real-time data from the cockpit, the cockpit control sticks and flight control surfaces are driven to achieve consistency between physical and virtual motions. With the help of DT technology, the entire movement process of control sticks and flight control surfaces are mapped with high fidelity in the DT-FCST platform, greatly enhancing the testing efficiency and monitoring quality.

关键词： System testing Process control Manuals Real-time systems Aircraft Informatics Surface treatment Monitoring Testing Aerospace control

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