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The Impact of Weather Conditions on Renewable Energy Production: A Machine Learning Approach

The Impact of Weather Conditions on Renewable Energy Product...

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IEEE international conference on Big Data

作者： Recep Özbay Merve Çelebi Uraz Yavanoğlu Dept. Of Computer Forensics Graduate School of Informatics Gazi University Ankara Turkey Dept. Of Computer Education and Instructional Technologies Faculty of Education Hatay Mustafa Kemal University Hatay Turkey Dept. of Computer Engineering Faculty of Engineering Gazi University Ankara Turkey

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

In recent decades, obtaining energy from wind, solar, and hydro energy has brought hope for a sustainable future. Machine learning (ML) significantly impacts the field of energy systems. ML techniques have emerged as a powerful tool for analyzing and optimizing energy systems. This approach can potentially reduce the cost of analyzing a device by focusing solely on the data generated during its operation. This paper investigates the application of regression models to predict renewable energy production based on weather conditions, aiming to enhance the reliability and efficiency of renewable energy systems. Experiments are performed using Decision Tree (DT), k-nearest Neighbors (KNN), Multiple Linear (MLR), Polynomial (PR), Random Forest (RF), and Support Vector (SVR) Regression algorithms. The performances of ML models are compared based on the evaluations obtained through the Coefficient of Determination (R2), and error measures namely Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). RF is the most successful algorithm in predicting renewable energy production depending on weather conditions as it has the highest R2 value and the lowest error measures.

关键词： Radio frequency Renewable energy sources Costs Wind energy Biological system modeling measurement uncertainty Production Nearest neighbor methods Prediction algorithms Reliability

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An Attention Mechanism Neural Network for Spatiotemporal Network Traffic Data Completion

An Attention Mechanism Neural Network for Spatiotemporal Net...

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international joint conference on Neural Networks (IJCNN)

作者： Weikang Xiao Yi Xie School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Due to the high cost of network monitoring equipment and uncontrollable factors, network measurement data often contains missing values. These missing values can severely hinder the progress of many downstream tasks, such as anomaly detection and resource scheduling. Utilizing known network measurement traffic data to infer network-wide network traffic data represents a crucial approach to alleviate this issue. Recently, spatiotemporal completion methods have received much attention because they can use spatiotemporal information to improve the accuracy of the completion compared to traditional unilateral methods. However, these methods typically only focus on completing missing values in static network topologies and struggle to filter out negative spatiotemporal information. To address these limitations, we propose a new approach for spatiotemporal data completion of network traffic under dynamic network topology conditions. The proposed method utilizes a neural network with an attention mechanism to capture the spatiotemporal correlation of network traffic data while effectively filtering out irrelevant spatiotemporal information. The model first uses external attention to capture the dependency of missing values of nodes in the time dimension, then adaptively aggregates the spatial information of nodes via graph attention, and finally decodes the spatiotemporal features of each node to predict the true values through a fully connected layer. The model can be trained by randomly sampling subgraphs from the training set. It can make immediate inferences even in the presence of changes in network topology during testing, without requiring retraining. The experimental results suggest that our proposed method outperforms existing benchmark methods in both real network traffic spatiotemporal datasets and various missing scenarios.

关键词： Adaptation models Correlation Attention mechanisms Network topology Computational modeling Neural networks Telecommunication traffic

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County-Level Associations Between Social and Sleep Deprivation Conditioned by Regional Effects

County-Level Associations Between Social and Sleep Deprivati...

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IEEE international conference on Healthcare Informatics (ICHI)

作者： Swapna S. Gokhale Dept. of Computer Science & Engineering University of Connecticut Storrs CT

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

Insufficient sleep leads to numerous negative health out-comes, and yet, approximately one-third of the adult U.S. population receives less than the recommended amount of sleep for optimal health. Consequently, sleep deprivation is now recognized as a public health crisis. Strategies to encourage optimal sleep must consider the social, environmental’ and cultural factors linked to insufficient sleep, although studies exploring these links are limited. This paper utilizes machine learning to address a gap in epidemiological modeling by conducting a joint county-level analysis of PLACES data and social deprivation measures across four U.S. regions. The innovation of this research stems from the use of random forest models, which include spatial autocorrelation structures within the modeling framework, commonly applied in ecology and earth science. Our findings reveal that incorporating spatial structures enhances the models' explainability by approximately 20%. Social deprivation measures can account for roughly 70% of the prevalence of sleep deficiency in the South and Midwest, but only about 55% in the Northeast and West, indicating that additional factors, such as the cultural glorification of sleep deprivation and urban lifestyles with lengthy commutes, may influence these regions. An analysis combining all four regions indi-cates that unemployment and educational attainment, rather than poverty as previously suggested, are the primary social deprivation factors affecting sleep duration. These insights could inform intervention strategies to promote optimal sleep, aligning with the objectives of Healthy People 2030.

关键词： Technological innovation Biological system modeling Sociology Length measurement Cultural differences Unemployment Statistics

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Improving routing efficiency in vehicular AD Hoc Networks

Improving routing efficiency in vehicular AD Hoc Networks

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international conference on Control, Decision and Information Technologies (CoDIT)

作者： W. Akermi N. Alyaoui A. B. Guiloufi K. Chabbir N. Nasri Automatic Industrial Systems Department Applied Science and Technology Gafsa University Gafsa Tunisia Laboratory of Modeling Analysis and Control of Systems (MACS) National Engineering School of Gabes (ENIG) Gabes University Gabes Tunisia Computer Science and Telecommunications Department Higher Institute of Applied Science and Technology Gafsa University Gafsa Tunisia Preparatory Institute for Engineering Studies of Gabes Laboratory of Modeling Analysis and Control of Systems (MACS) National Engineering School of Gabes (ENIG) Gabes University Gabes Tunisia Computer Science Department-Gafsa Gafsa University Gafsa Tunisia Laboratory of Smart Systems for Engineering and E-Health Based on Technologies of Image and Telecommunications (SETIT) Sfax University Sfax Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Vehicular networks, also known as Vehicular Ad Hoc Networks (VANETs), have become a crucial technology for vehicle communication, providing wireless connectivity among vehicles and between vehicles and infrastructure. However, the dynamic and complex nature of these networks presents unique challenges in routing and resource management. In this study, we explore the use of artificial intelligence (AI) to improve the performance of vehicular networks, focusing on two widely used routing protocols: Optimized Link State Routing (OLSR) and Destination-Sequenced Distance Vector (DSDV).We aim to evaluate the performance of these traditional routing protocols within the context of vehicular networks by integrating machine learning techniques such as neural network regression (MLRegression). Our goal is to understand how the integration of artificial intelligence can enhance the prediction and optimization of vehicular network performance. Using MLRegression, we aim to develop accurate prediction models for performance metrics such as latency, throughput, packet delivery rate, and more, based on network parameters and environmental conditions. This comparative evaluation, which involves incorporating an AI-based approach, will allow us to better understand the strengths and weaknesses of each protocol and provide recommendations for improving vehicular networks.

关键词： measurement Wireless communication Adaptation models Adaptive systems Vehicular ad hoc networks Routing Throughput Routing protocols Vectors Vehicle dynamics

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modeling Poisoning Attacks Against Machine Learning Components of Intrusion Detection systems

Modeling Poisoning Attacks Against Machine Learning Componen...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Egor A. Ichetovkin Igor V. Kotenko Laboratory of Computer Security Problems St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS) Saint-Petersburg Russia

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

To solve intrusion detection problems in computer networks, intrusion detection systems with machine learning components are now increasingly being used, which provide fairly high detection efficiency. However, the benefit of using machine learning can be negated if poisoning attacks are carried out against machine learning components. Data poisoning attacks involve manipulating the training data used to train machine learning models, causing the models to produce incorrect or biased outputs. In the context of intrusion detection systems, this can lead to false negatives (failing to detect actual intrusions) or false positives (flagging normal behavior as suspicious). The novelty of the research lies in the proposed approach to modeling poisoning attacks against machine learning components of intrusion detection systems, which differs by assessing the dependence of the metrics of completeness, accuracy and F-measure on the influence of poisoning on machine learning components. Poisoning attacks were carried out by adding malicious traffic to normal traffic while training machine learning components. The results of evaluating the effectiveness of intrusion detection systems with machine learning components under the influence of poisoning attacks are presented.

关键词： Training measurement Machine learning algorithms Intrusion detection Training data Machine learning Data models

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Utilizing Non Linear modeling Techniques for Fugitive Gas Emissions Data in Environmental Monitoring Applications

Utilizing Non Linear Modeling Techniques for Fugitive Gas Em...

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Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), international conference on

作者： Anees Ul Husnain Norrima Binti Mokhtar Mohamad Amir Bin Mohamad Mansor Department of Electrical Engineering Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

Air pollution is a pressing issue with implications for climate change and public health. Monitoring and estimating fugitive emissions is crucial for taking appropriate measures to mitigate this crisis. This study investigated the performance of different predictive models in estimating gas concentrations as part of air quality monitoring systems. This study considers Multilayer Perceptron (MLP) and Radial Basis Function (RBF) for the experiment data collected from previous studies. The architecture of these models was optimized considering each parameter in every model for the missing points in the gas concentrations data. The performance was evaluated for estimation accuracy and error measurement. Comparative analysis based on performance indicators demonstrates that the RBF model is the most accurate and best fitted model overall for emission estimation with the lowest RMSE of 0.0281 and the highest R2 of 0.9959.

关键词： Atmospheric modeling Computational modeling Neurons Estimation computer architecture Machine learning Predictive models Multilayer perceptrons Data models Pollution measurement

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19th international conference on computer-Aided Architectural Design Futures, CAAD Futures 2021

19th International Conference on Computer-Aided Architectura...

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19th international conference on computer-Aided Architectural Design Futures, CAAD Futures 2021

ISBN: (纸本)9789811912795

The proceedings contain 33 papers. The special focus in this conference is on computer-Aided Architectural Design Futures. The topics include: Interior Design Network of Furnishing and Color Pairing with Object Detection and Color Analysis Based on Deep Learning;social Distancing and Behavior modeling with Agent-Based Simulation;art Places and Their Impact on Property Prices of Condominiums in Singapore;Rethinking computer-Aided Architectural Design (CAAD) – From Generative Algorithms and Architectural Intelligence to Environmental Design and Ambient Intelligence;screen Techniques:: Oscilloscopes and the Embodied Instrumentality of Early Graphic Displays;synthesizing Point Cloud Data Set for Historical Dome systems;designing with Your Eyes: Active and Two-Way Employment of Our Visual Sense;Learning Geometric Transformations for Parametric Design: An Augmented Reality (AR)-Powered Approach;embedded Intelligent Empathy: A Systematic Review Towards a Conceptual Framework;Risk Assessment for Performance Driven Building Design with BIM-Based Parametric Methods;An Info-Biological Theory Approach to computer-Aided Architectural Design: UnSSEUS;measurement of Spatial Openness of Indoor Space Using 3D Isovists Methods and Fibonacci Lattices;gradient Acoustic Surfaces: The Design, Simulation, Robotic Fabrication of a Prototype Wall;CDPR Studio: A Parametric Design Tool for Simulating Cable-Suspended Parallel Robots;PRINT in PRINT: A Nested Robotic Fabrication Strategy for 3D Printing Dissolvable Formwork of a Stackable Column;comprehending Algorithmic Design;Adaptive Toolpath: Enhanced Design and Process Control for Robotic 3DCP;building Archetype Characterization Using K-Means Clustering in Urban Building Energy Models;structural Design Recommendations in the Early Design Phase Using Machine Learning;type Investigation in the Form of High-Rise Building Using Deep Neural Network.

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MUIR: Mamba for Underwater Image Rendering

MUIR: Mamba for Underwater Image Rendering

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Machine Learning and Intelligent systems Engineering (MLISE), international conference on

作者： Liyuan Chen Weijia Li Qingxia Yang Lihan Tong Erkang Chen Bin Huang RuiWen Li College of Ocean Information Engineering Jimei University Xiamen China College of Computer Engineering Jimei University Xiamen China

ISBN: (数字)9798350375077

ISBN: (纸本)9798350375084

Underwater image rendering techniques are designed to generate images with a distinct underwater style from arbitrary clean images. These techniques are widely used in underwater image generation, virtual games, and camera filters. Recently, a novel image processing architecture based on the state space model (SSM), named VMamba, has been proposed, which effectively reduces computational complexity to linear while preserving a global receptive field. This paper explores the potential of VMamba for underwater image rendering, focusing on computational efficiency and model performance. To this end, we incorporate the VSS Block, the core module of VMamba, into the underwater image rendering network and propose MUIR. Specifically, we use real underwater images to provide the light field style and clean images to supply the image content with depth information. Additionally, we design the VMUNet architecture, which utilizes the VSS Block for feature fusion after each layer of down sampling. To the best of our knowledge, this is the first underwater image rendering model based on the Mamba architecture. Experiments demonstrate that our method, with a lightweight architecture of 5.06GMac FLOPs and 7.16M Params, achieves visual effects and quantitative metrics comparable to or surpassing previous state-of-the-art methods.

关键词： measurement Image quality Image synthesis Computational modeling computer architecture Machine learning Rendering (computer graphics)

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Learning a Robust Minutiae Extractor via an Ensemble of Expert Models

Learning a Robust Minutiae Extractor via an Ensemble of Expe...

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IEEE international joint conference on Biometrics (IJCB)

作者： Arhan A. Mulay Steven A. Grosz Anil K. Jain Michigan State University East Lansing MI

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

Recognizing the limitations of manual methods in creating ground truth minutiae sets for fingerprint recognition, we propose an ensemble method that leverages the strengths of two well-known SDKs: Innovatrics ANSI&ISO v2.4.10 and Verifinger v12.4, referred to as experts. By combining the predictions of these two expert systems, our method aims to learn a robust ground truth minutiae set, serving as a foundation for training fingerprint-matching models. We introduce four ensemble configurations using union and intersection operations to capture a comprehensive set of minutiae, addressing the limitations inherent in any single extractor. Using the ensemble minutiae ground truth, we trained fingerprint recognition models using two distinct architectures: U-Net and Vision Transformers (ViT). These were evaluated using authentication scores and statistics were computed against a hand-marked ground truth dataset. Our experiment results are encouraging across both architectures, particularly for difficult test sets like NIST SD 302. Training with a ViT architecture led to a 1.59% improvement in matching accuracy, increasing from 92.79% for the model trained on Innovatrics ground truth minutiae to 94.38% for the model trained on the ensemble ground truth. The U-Net architecture performance improvement was 1.74%, from 92.00% to 93.74%. These are both significant improvements from the results obtained from the Innovatrics and Verifinger SDKs. These results demonstrate the effectiveness of ensemble ground truth minutiae in enhancing the performance of fingerprint recognition systems.

关键词： Training Accuracy Computational modeling computer architecture Fingerprint recognition Predictive models NIST Transformers Ensemble learning Expert systems

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Assessment of static BH characteristics of electrical sheets using a dc measuring set-up 16

Assessment of static BH characteristics of electrical sheets...

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16th international conference on Engineering of Modern Electric systems (EMES)

作者： Andrei, Paul Cristian Marin, Mihai Eugen Marinescu, Stelian Grumeza, Catalin Ionut Maricaru, Mihai Univ Politehn Bucuresti Elect Engn Dept Bucharest Romania ICPE Bucharest Romania

ISBN: (纸本)9781665449953

This paper presents an original system for determining the static BH relationship for electrical sheets. The set-up is remote controlled and the measuring process is automatic, managed by a computer connected microcontroller. Two dc currents are increasingly adjusted to get a null magnetic flux in the air gap found in the median yoke of the measuring device, obtaining several current pairs. Afterwards, one solves an inverse magnetic field problem, using the dc currents pairs as input data in order to compute the BH relationship of the studied sample.

关键词： static BH relationship dc measurement

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