Technologies最新文献_第5页

Multistage Malware Detection Method for Backup Systems 备份系统的多级恶意软件检测方法

Technologies Pub Date : 2024-02-05 DOI: 10.3390/technologies12020023

Pavel Novák, V. Oujezský, Patrik Kaura, T. Horvath, M. Holik

{"title":"Multistage Malware Detection Method for Backup Systems","authors":"Pavel Novák, V. Oujezský, Patrik Kaura, T. Horvath, M. Holik","doi":"10.3390/technologies12020023","DOIUrl":"https://doi.org/10.3390/technologies12020023","url":null,"abstract":"This paper proposes an innovative solution to address the challenge of detecting latent malware in backup systems. The proposed detection system utilizes a multifaceted approach that combines similarity analysis with machine learning algorithms to improve malware detection. The results demonstrate the potential of advanced similarity search techniques, powered by the Faiss model, in strengthening malware discovery within system backups and network traffic. Implementing these techniques will lead to more resilient cybersecurity practices, protecting essential systems from hidden malware threats. This paper’s findings underscore the potential of advanced similarity search techniques to enhance malware discovery in system backups and network traffic, and the implications of implementing these techniques include more resilient cybersecurity practices and protecting essential systems from malicious threats hidden within backup archives and network data. The integration of AI methods improves the system’s efficiency and speed, making the proposed system more practical for real-world cybersecurity. This paper’s contribution is a novel and comprehensive solution designed to detect latent malware in backups, preventing the backup of compromised systems. The system comprises multiple analytical components, including a system file change detector, an agent to monitor network traffic, and a firewall, all integrated into a central decision-making unit. The current progress of the research and future steps are discussed, highlighting the contributions of this project and potential enhancements to improve cybersecurity practices.","PeriodicalId":504839,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139804040","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

Energy Efficiency in Additive Manufacturing: Condensed Review 快速成型制造中的能效：简要回顾

Technologies Pub Date : 2024-02-05 DOI: 10.3390/technologies12020021

Ismail Fidan, Vivekanand Naikwadi, Suhas Alkunte, Roshan Mishra, Khalid Tantawi

{"title":"Energy Efficiency in Additive Manufacturing: Condensed Review","authors":"Ismail Fidan, Vivekanand Naikwadi, Suhas Alkunte, Roshan Mishra, Khalid Tantawi","doi":"10.3390/technologies12020021","DOIUrl":"https://doi.org/10.3390/technologies12020021","url":null,"abstract":"Today, it is significant that the use of additive manufacturing (AM) has growing in almost every aspect of the daily life. A high number of sectors are adapting and implementing this revolutionary production technology in their domain to increase production volumes, reduce the cost of production, fabricate light weight and complex parts in a short period of time, and respond to the manufacturing needs of customers. It is clear that the AM technologies consume energy to complete the production tasks of each part. Therefore, it is imperative to know the impact of energy efficiency in order to economically and properly use these advancing technologies. This paper provides a holistic review of this important concept from the perspectives of process, materials science, industry, and initiatives. The goal of this research study is to collect and present the latest knowledge blocks related to the energy consumption of AM technologies from a number of recent technical resources. Overall, they are the collection of surveys, observations, experimentations, case studies, content analyses, and archival research studies. The study highlights the current trends and technologies associated with energy efficiency and their influence on the AM community.","PeriodicalId":504839,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139864434","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

Angle Calculus-Based Thrust Force Determination on the Blades of a 10 kW Wind Turbine 基于角度计算的 10 千瓦风力涡轮机叶片推力确定方法

Technologies Pub Date : 2024-02-05 DOI: 10.3390/technologies12020022

J. R. Dorrego-Portela, A. E. Ponce-Martínez, Eduardo Pérez-Chaltell, Jaime Peña-Antonio, Carlos Alberto Mateos-Mendoza, J. Robles-Ocampo, P. Y. Sevilla-Camacho, Marcos Aviles, J. Rodríguez-Reséndíz

{"title":"Angle Calculus-Based Thrust Force Determination on the Blades of a 10 kW Wind Turbine","authors":"J. R. Dorrego-Portela, A. E. Ponce-Martínez, Eduardo Pérez-Chaltell, Jaime Peña-Antonio, Carlos Alberto Mateos-Mendoza, J. Robles-Ocampo, P. Y. Sevilla-Camacho, Marcos Aviles, J. Rodríguez-Reséndíz","doi":"10.3390/technologies12020022","DOIUrl":"https://doi.org/10.3390/technologies12020022","url":null,"abstract":"In this article, the behavior of the thrust force on the blades of a 10 kW wind turbine was obtained by considering the characteristic wind speed of the Isthmus of Tehuantepec. Analyzing mechanical forces is essential to efficiently and safely design the different elements that make up the wind turbine because the thrust forces are related to the location point and the blade rotation. For this reason, the thrust force generated in each of the three blades of a low-power wind turbine was analyzed. The angular position (θ) of each blade varied from 0° to 120°, the blades were segmented (r), and different wind speeds were tested, such as cutting, design, average, and maximum. The results demonstrate that the thrust force increases proportionally with increasing wind speed and height, but it behaves differently on each blade segment and each angular position. This method determines the angular position and the exact blade segment where the smallest and the most considerable thrust force occurred. Blade 1, positioned at an angular position of 90°, is the blade most affected by the thrust force on P15. When the blade rotates 180°, the thrust force decreases by 9.09 N; this represents a 66.74% decrease. In addition, this study allows the designers to know the blade deflection caused by the thrust force. This information can be used to avoid collision with the tower. The thrust forces caused blade deflections of 10% to 13% concerning the rotor radius used in this study. These results guarantee the operation of the tested generator under their working conditions.","PeriodicalId":504839,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139802847","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

Parametric Metamodeling Based on Optimal Transport Applied to Uncertainty Evaluation 基于最优传输的参数元模型应用于不确定性评估

Technologies Pub Date : 2024-02-02 DOI: 10.3390/technologies12020020

S. Torregrosa, David Muñoz, Vincent Herbert, F. Chinesta

引用次数: 0

Parametric Metamodeling Based on Optimal Transport Applied to Uncertainty Evaluation 基于最优传输的参数元模型应用于不确定性评估

Technologies Pub Date : 2024-02-02 DOI: 10.3390/technologies12020020

S. Torregrosa, David Muñoz, Vincent Herbert, F. Chinesta

引用次数: 0

An Optimum Load Forecasting Strategy (OLFS) for Smart Grids Based on Artificial Intelligence 基于人工智能的智能电网最佳负荷预测策略（OLFS）

Technologies Pub Date : 2024-02-01 DOI: 10.3390/technologies12020019

A. H. Rabie, Ahmed I. Saleh, Said H. Abd Elkhalik, Ali E. Takieldeen

{"title":"An Optimum Load Forecasting Strategy (OLFS) for Smart Grids Based on Artificial Intelligence","authors":"A. H. Rabie, Ahmed I. Saleh, Said H. Abd Elkhalik, Ali E. Takieldeen","doi":"10.3390/technologies12020019","DOIUrl":"https://doi.org/10.3390/technologies12020019","url":null,"abstract":"Recently, the application of Artificial Intelligence (AI) in many areas of life has allowed raising the efficiency of systems and converting them into smart ones, especially in the field of energy. Integrating AI with power systems allows electrical grids to be smart enough to predict the future load, which is known as Intelligent Load Forecasting (ILF). Hence, suitable decisions for power system planning and operation procedures can be taken accordingly. Moreover, ILF can play a vital role in electrical demand response, which guarantees a reliable transitioning of power systems. This paper introduces an Optimum Load Forecasting Strategy (OLFS) for predicting future load in smart electrical grids based on AI techniques. The proposed OLFS consists of two sequential phases, which are: Data Preprocessing Phase (DPP) and Load Forecasting Phase (LFP). In the former phase, an input electrical load dataset is prepared before the actual forecasting takes place through two essential tasks, namely feature selection and outlier rejection. Feature selection is carried out using Advanced Leopard Seal Optimization (ALSO) as a new nature-inspired optimization technique, while outlier rejection is accomplished through the Interquartile Range (IQR) as a measure of statistical dispersion. On the other hand, actual load forecasting takes place in LFP using a new predictor called the Weighted K-Nearest Neighbor (WKNN) algorithm. The proposed OLFS has been tested through extensive experiments. Results have shown that OLFS outperforms recent load forecasting techniques as it introduces the maximum prediction accuracy with the minimum root mean square error.","PeriodicalId":504839,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139827270","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

An Optimum Load Forecasting Strategy (OLFS) for Smart Grids Based on Artificial Intelligence 基于人工智能的智能电网最佳负荷预测策略（OLFS）

Technologies Pub Date : 2024-02-01 DOI: 10.3390/technologies12020019

A. H. Rabie, Ahmed I. Saleh, Said H. Abd Elkhalik, Ali E. Takieldeen

{"title":"An Optimum Load Forecasting Strategy (OLFS) for Smart Grids Based on Artificial Intelligence","authors":"A. H. Rabie, Ahmed I. Saleh, Said H. Abd Elkhalik, Ali E. Takieldeen","doi":"10.3390/technologies12020019","DOIUrl":"https://doi.org/10.3390/technologies12020019","url":null,"abstract":"Recently, the application of Artificial Intelligence (AI) in many areas of life has allowed raising the efficiency of systems and converting them into smart ones, especially in the field of energy. Integrating AI with power systems allows electrical grids to be smart enough to predict the future load, which is known as Intelligent Load Forecasting (ILF). Hence, suitable decisions for power system planning and operation procedures can be taken accordingly. Moreover, ILF can play a vital role in electrical demand response, which guarantees a reliable transitioning of power systems. This paper introduces an Optimum Load Forecasting Strategy (OLFS) for predicting future load in smart electrical grids based on AI techniques. The proposed OLFS consists of two sequential phases, which are: Data Preprocessing Phase (DPP) and Load Forecasting Phase (LFP). In the former phase, an input electrical load dataset is prepared before the actual forecasting takes place through two essential tasks, namely feature selection and outlier rejection. Feature selection is carried out using Advanced Leopard Seal Optimization (ALSO) as a new nature-inspired optimization technique, while outlier rejection is accomplished through the Interquartile Range (IQR) as a measure of statistical dispersion. On the other hand, actual load forecasting takes place in LFP using a new predictor called the Weighted K-Nearest Neighbor (WKNN) algorithm. The proposed OLFS has been tested through extensive experiments. Results have shown that OLFS outperforms recent load forecasting techniques as it introduces the maximum prediction accuracy with the minimum root mean square error.","PeriodicalId":504839,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139887348","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

Comprehensive Study of Compression and Texture Integration for Digital Imaging and Communications in Medicine Data Analysis 数字成像和医学通信数据分析中的压缩和纹理整合综合研究

Technologies Pub Date : 2024-01-24 DOI: 10.3390/technologies12020017

A. Shakya, Anurag Vidyarthi

{"title":"Comprehensive Study of Compression and Texture Integration for Digital Imaging and Communications in Medicine Data Analysis","authors":"A. Shakya, Anurag Vidyarthi","doi":"10.3390/technologies12020017","DOIUrl":"https://doi.org/10.3390/technologies12020017","url":null,"abstract":"In response to the COVID-19 pandemic and its strain on healthcare resources, this study presents a comprehensive review of various techniques that can be used to integrate image compression techniques and statistical texture analysis to optimize the storage of Digital Imaging and Communications in Medicine (DICOM) files. In evaluating four predominant image compression algorithms, i.e., discrete cosine transform (DCT), discrete wavelet transform (DWT), the fractal compression algorithm (FCA), and the vector quantization algorithm (VQA), this study focuses on their ability to compress data while preserving essential texture features such as contrast, correlation, angular second moment (ASM), and inverse difference moment (IDM). A pivotal observation concerns the direction-independent Grey Level Co-occurrence Matrix (GLCM) in DICOM analysis, which reveals intriguing variations between two intermediate scans measured with texture characteristics. Performance-wise, the DCT, DWT, FCA, and VQA algorithms achieved minimum compression ratios (CRs) of 27.87, 37.91, 33.26, and 27.39, respectively, with maximum CRs at 34.48, 68.96, 60.60, and 38.74. This study also undertook a statistical analysis of distinct CT chest scans from COVID-19 patients, highlighting evolving texture patterns. Finally, this work underscores the potential of coupling image compression and texture feature quantification for monitoring changes related to human chest conditions, offering a promising avenue for efficient storage and diagnostic assessment of critical medical imaging.","PeriodicalId":504839,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139601068","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 Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision 机器视觉和机器人视觉中用于物体检测、语义分割和人类动作识别的机器学习和深度学习综述

Technologies Pub Date : 2024-01-23 DOI: 10.3390/technologies12020015

Nikoleta Manakitsa, George S. Maraslidis, L. Moysis, G. Fragulis

{"title":"A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision","authors":"Nikoleta Manakitsa, George S. Maraslidis, L. Moysis, G. Fragulis","doi":"10.3390/technologies12020015","DOIUrl":"https://doi.org/10.3390/technologies12020015","url":null,"abstract":"Machine vision, an interdisciplinary field that aims to replicate human visual perception in computers, has experienced rapid progress and significant contributions. This paper traces the origins of machine vision, from early image processing algorithms to its convergence with computer science, mathematics, and robotics, resulting in a distinct branch of artificial intelligence. The integration of machine learning techniques, particularly deep learning, has driven its growth and adoption in everyday devices. This study focuses on the objectives of computer vision systems: replicating human visual capabilities including recognition, comprehension, and interpretation. Notably, image classification, object detection, and image segmentation are crucial tasks requiring robust mathematical foundations. Despite the advancements, challenges persist, such as clarifying terminology related to artificial intelligence, machine learning, and deep learning. Precise definitions and interpretations are vital for establishing a solid research foundation. The evolution of machine vision reflects an ambitious journey to emulate human visual perception. Interdisciplinary collaboration and the integration of deep learning techniques have propelled remarkable advancements in emulating human behavior and perception. Through this research, the field of machine vision continues to shape the future of computer systems and artificial intelligence applications.","PeriodicalId":504839,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139605148","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

Machine Learning Approaches to Predict Major Adverse Cardiovascular Events in Atrial Fibrillation 预测心房颤动主要不良心血管事件的机器学习方法

Technologies Pub Date : 2024-01-23 DOI: 10.3390/technologies12020013

Pedro Moltó-Balado, Sílvia Reverté-Villarroya, Victor Alonso-Barberán, Cinta Monclús-Arasa, M. T. Balado-Albiol, Josep Clua-Queralt, J. Clua-Espuny

{"title":"Machine Learning Approaches to Predict Major Adverse Cardiovascular Events in Atrial Fibrillation","authors":"Pedro Moltó-Balado, Sílvia Reverté-Villarroya, Victor Alonso-Barberán, Cinta Monclús-Arasa, M. T. Balado-Albiol, Josep Clua-Queralt, J. Clua-Espuny","doi":"10.3390/technologies12020013","DOIUrl":"https://doi.org/10.3390/technologies12020013","url":null,"abstract":"The increasing prevalence of atrial fibrillation (AF) and its association with Major Adverse Cardiovascular Events (MACE) presents challenges in early identification and treatment. Although existing risk factors, biomarkers, genetic variants, and imaging parameters predict MACE, emerging factors may be more decisive. Artificial intelligence and machine learning techniques (ML) offer a promising avenue for more effective AF evolution prediction. Five ML models were developed to obtain predictors of MACE in AF patients. Two-thirds of the data were used for training, employing diverse approaches and optimizing to minimize prediction errors, while the remaining third was reserved for testing and validation. AdaBoost emerged as the top-performing model (accuracy: 0.9999; recall: 1; F1 score: 0.9997). Noteworthy features influencing predictions included the Charlson Comorbidity Index (CCI), diabetes mellitus, cancer, the Wells scale, and CHA2DS2-VASc, with specific associations identified. Elevated MACE risk was observed, with a CCI score exceeding 2.67 ± 1.31 (p < 0.001), CHA2DS2-VASc score of 4.62 ± 1.02 (p < 0.001), and an intermediate-risk Wells scale classification. Overall, the AdaBoost ML offers an alternative predictive approach to facilitate the early identification of MACE risk in the assessment of patients with AF.","PeriodicalId":504839,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139604515","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