Information (Switzerland)最新文献_第7页

Particle Swarm Optimization-Based Control for Maximum Power Point Tracking Implemented in a Real Time Photovoltaic System 基于粒子群优化的实时光伏系统最大功率点跟踪控制

Information (Switzerland) Pub Date : 2023-10-11 DOI: 10.3390/info14100556

Asier del Rio, Oscar Barambones, Jokin Uralde, Eneko Artetxe, Isidro Calvo

{"title":"Particle Swarm Optimization-Based Control for Maximum Power Point Tracking Implemented in a Real Time Photovoltaic System","authors":"Asier del Rio, Oscar Barambones, Jokin Uralde, Eneko Artetxe, Isidro Calvo","doi":"10.3390/info14100556","DOIUrl":"https://doi.org/10.3390/info14100556","url":null,"abstract":"Photovoltaic panels present an economical and environmentally friendly renewable energy solution, with advantages such as emission-free operation, low maintenance, and noiseless performance. However, their nonlinear power-voltage curves necessitate efficient operation at the Maximum Power Point (MPP). Various techniques, including Hill Climb algorithms, are commonly employed in the industry due to their simplicity and ease of implementation. Nonetheless, intelligent approaches like Particle Swarm Optimization (PSO) offer enhanced accuracy in tracking efficiency with reduced oscillations. The PSO algorithm, inspired by collective intelligence and animal swarm behavior, stands out as a promising solution due to its efficiency and ease of integration, relying only on standard current and voltage sensors commonly found in these systems, not like most intelligent techniques, which require additional modeling or sensoring, significantly increasing the cost of the installation. The primary contribution of this study lies in the implementation and validation of an advanced control system based on the PSO algorithm for real-time Maximum Power Point Tracking (MPPT) in a commercial photovoltaic system to assess its viability by testing it against the industry-standard controller, Perturbation and Observation (P&O), to highlight its advantages and limitations. Through rigorous experiments and comparisons with other methods, the proposed PSO-based control system’s performance and feasibility have been thoroughly evaluated. A sensitivity analysis of the algorithm’s search dynamics parameters has been conducted to identify the most effective combination for optimal real-time tracking. Notably, experimental comparisons with the P&O algorithm have revealed the PSO algorithm’s remarkable ability to significantly reduce settling time up to threefold under similar conditions, resulting in a substantial decrease in energy losses during transient states from 31.96% with P&O to 9.72% with PSO.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136214057","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 Survey of Machine Learning Assisted Continuous-Variable Quantum Key Distribution 机器学习辅助连续变量量子密钥分发研究进展

Information (Switzerland) Pub Date : 2023-10-10 DOI: 10.3390/info14100553

Nathan K. Long, Robert Malaney, Kenneth J. Grant

引用次数: 0

Top-Down Models across CPU Architectures: Applicability and Comparison in a High-Performance Computing Environment 跨CPU架构的自顶向下模型:在高性能计算环境中的适用性和比较

Information (Switzerland) Pub Date : 2023-10-10 DOI: 10.3390/info14100554

Fabio Banchelli, Marta Garcia-Gasulla, Filippo Mantovani

引用次数: 0

CapGAN: Text-to-Image Synthesis Using Capsule GANs 使用胶囊gan进行文本到图像的合成

Information (Switzerland) Pub Date : 2023-10-09 DOI: 10.3390/info14100552

Maryam Omar, Hafeez Ur Rehman, Omar Bin Samin, Moutaz Alazab, Gianfranco Politano, Alfredo Benso

{"title":"CapGAN: Text-to-Image Synthesis Using Capsule GANs","authors":"Maryam Omar, Hafeez Ur Rehman, Omar Bin Samin, Moutaz Alazab, Gianfranco Politano, Alfredo Benso","doi":"10.3390/info14100552","DOIUrl":"https://doi.org/10.3390/info14100552","url":null,"abstract":"Text-to-image synthesis is one of the most critical and challenging problems of generative modeling. It is of substantial importance in the area of automatic learning, especially for image creation, modification, analysis and optimization. A number of works have been proposed in the past to achieve this goal; however, current methods still lack scene understanding, especially when it comes to synthesizing coherent structures in complex scenes. In this work, we propose a model called CapGAN, to synthesize images from a given single text statement to resolve the problem of global coherent structures in complex scenes. For this purpose, skip-thought vectors are used to encode the given text into vector representation. This encoded vector is used as an input for image synthesis using an adversarial process, in which two models are trained simultaneously, namely: generator (G) and discriminator (D). The model G generates fake images, while the model D tries to predict what the sample is from training data rather than generated by G. The conceptual novelty of this work lies in the integrating capsules at the discriminator level to make the model understand the orientational and relative spatial relationship between different entities of an object in an image. The inception score (IS) along with the Fréchet inception distance (FID) are used as quantitative evaluation metrics for CapGAN. IS recorded for images generated using CapGAN is 4.05 ± 0.050, which is around 34% higher than images synthesized using traditional GANs, whereas the FID score calculated for synthesized images using CapGAN is 44.38, which is ab almost 9% improvement from the previous state-of-the-art models. The experimental results clearly demonstrate the effectiveness of the proposed CapGAN model, which is exceptionally proficient in generating images with complex scenes.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135095663","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

Customer Shopping Behavior Analysis Using RFID and Machine Learning Models 基于RFID和机器学习模型的顾客购物行为分析

Information (Switzerland) Pub Date : 2023-10-08 DOI: 10.3390/info14100551

Ganjar Alfian, Muhammad Qois Huzyan Octava, Farhan Mufti Hilmy, Rachma Aurya Nurhaliza, Yuris Mulya Saputra, Divi Galih Prasetyo Putri, Firma Syahrian, Norma Latif Fitriyani, Fransiskus Tatas Dwi Atmaji, Umar Farooq, Dat Tien Nguyen, Muhammad Syafrudin

{"title":"Customer Shopping Behavior Analysis Using RFID and Machine Learning Models","authors":"Ganjar Alfian, Muhammad Qois Huzyan Octava, Farhan Mufti Hilmy, Rachma Aurya Nurhaliza, Yuris Mulya Saputra, Divi Galih Prasetyo Putri, Firma Syahrian, Norma Latif Fitriyani, Fransiskus Tatas Dwi Atmaji, Umar Farooq, Dat Tien Nguyen, Muhammad Syafrudin","doi":"10.3390/info14100551","DOIUrl":"https://doi.org/10.3390/info14100551","url":null,"abstract":"Analyzing customer shopping habits in physical stores is crucial for enhancing the retailer–customer relationship and increasing business revenue. However, it can be challenging to gather data on customer browsing activities in physical stores as compared to online stores. This study suggests using RFID technology on store shelves and machine learning models to analyze customer browsing activity in retail stores. The study uses RFID tags to track product movement and collects data on customer behavior using receive signal strength (RSS) of the tags. The time-domain features were then extracted from RSS data and machine learning models were utilized to classify different customer shopping activities. We proposed integration of iForest Outlier Detection, ADASYN data balancing and Multilayer Perceptron (MLP). The results indicate that the proposed model performed better than other supervised learning models, with improvements of up to 97.778% in accuracy, 98.008% in precision, 98.333% in specificity, 98.333% in recall, and 97.750% in the f1-score. Finally, we showcased the integration of this trained model into a web-based application. This result can assist managers in understanding customer preferences and aid in product placement, promotions, and customer recommendations.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135199489","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

BibRank: Automatic Keyphrase Extraction Platform Using Metadata BibRank:基于元数据的自动关键词提取平台

Information (Switzerland) Pub Date : 2023-10-07 DOI: 10.3390/info14100549

Abdelrhman Eldallal, Eduard Barbu

引用次数: 0

A Deep Learning Methodology for Predicting Cybersecurity Attacks on the Internet of Things 预测物联网网络安全攻击的深度学习方法

Information (Switzerland) Pub Date : 2023-10-07 DOI: 10.3390/info14100550

Omar Azib Alkhudaydi, Moez Krichen, Ans D. Alghamdi

{"title":"A Deep Learning Methodology for Predicting Cybersecurity Attacks on the Internet of Things","authors":"Omar Azib Alkhudaydi, Moez Krichen, Ans D. Alghamdi","doi":"10.3390/info14100550","DOIUrl":"https://doi.org/10.3390/info14100550","url":null,"abstract":"With the increasing severity and frequency of cyberattacks, the rapid expansion of smart objects intensifies cybersecurity threats. The vast communication traffic data between Internet of Things (IoT) devices presents a considerable challenge in defending these devices from potential security breaches, further exacerbated by the presence of unbalanced network traffic data. AI technologies, especially machine and deep learning, have shown promise in detecting and addressing these security threats targeting IoT networks. In this study, we initially leverage machine and deep learning algorithms for the precise extraction of essential features from a realistic-network-traffic BoT-IoT dataset. Subsequently, we assess the efficacy of ten distinct machine learning models in detecting malware. Our analysis includes two single classifiers (KNN and SVM), eight ensemble classifiers (e.g., Random Forest, Extra Trees, AdaBoost, LGBM), and four deep learning architectures (LSTM, GRU, RNN). We also evaluate the performance enhancement of these models when integrated with the SMOTE (Synthetic Minority Over-sampling Technique) algorithm to counteract imbalanced data. Notably, the CatBoost and XGBoost classifiers achieved remarkable accuracy rates of 98.19% and 98.50%, respectively. Our findings offer insights into the potential of the ML and DL techniques, in conjunction with balancing algorithms such as SMOTE, to effectively identify IoT network intrusions.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135252049","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

Computer Vision Tasks for Ambient Intelligence in Children’s Health 儿童健康环境智能的计算机视觉任务

Information (Switzerland) Pub Date : 2023-10-06 DOI: 10.3390/info14100548

Danila Germanese, Sara Colantonio, Marco Del Coco, Pierluigi Carcagnì, Marco Leo

引用次数: 0

Towards a Conceptual Framework for Data Management in Business Intelligence 商业智能中数据管理的概念框架

Information (Switzerland) Pub Date : 2023-10-06 DOI: 10.3390/info14100547

Ramakolote Judas Mositsa, John Andrew Van der Poll, Cyrille Dongmo

{"title":"Towards a Conceptual Framework for Data Management in Business Intelligence","authors":"Ramakolote Judas Mositsa, John Andrew Van der Poll, Cyrille Dongmo","doi":"10.3390/info14100547","DOIUrl":"https://doi.org/10.3390/info14100547","url":null,"abstract":"Business intelligence (BI) refers to technologies, tools, and practices for collecting, integrating, analyzing, and presenting large volumes of information to enable improved decision-making. A modern BI architecture typically consists of a data warehouse made up of one or more data marts that consolidate data from several operational databases. BI further incorporates a combination of analytics, data management, and reporting tools, together with associated methodologies for managing and analyzing data. An important goal of BI initiatives is to improve business decision-making for organizations to increase revenue, improve operational efficiency, and gain a competitive advantage. In this article, we analyze qualitatively various prominent business intelligence (BI) frameworks in the literature and develop a comprehensive BI framework from these. Through the technique of qualitative propositions, we identify the properties, respective advantages, and possible disadvantages of the said BI frameworks to develop a comprehensive framework aimed mainly at data management, incorporating the advantages and eliminating the disadvantages of the individual frameworks. The BI landscape is vast, so as a limitation, we note that the new framework is conceptual; hence, no implementation or any quantitative measurement is performed at this stage. That said, our work exhibits originality since it combines numerous BI frameworks into a comprehensive framework, thereby contributing to conceptual BI framework development. As part of future work, the new framework will be formally specified, followed by a practical phase, namely, conducting case studies in the industry to assist companies in their BI applications.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"299 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135351352","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 New Social Media Analytics Method for Identifying Factors Contributing to COVID-19 Discussion Topics 一种新的社交媒体分析方法，用于识别促成COVID-19讨论主题的因素

Information (Switzerland) Pub Date : 2023-10-05 DOI: 10.3390/info14100545

Fahim Sufi

{"title":"A New Social Media Analytics Method for Identifying Factors Contributing to COVID-19 Discussion Topics","authors":"Fahim Sufi","doi":"10.3390/info14100545","DOIUrl":"https://doi.org/10.3390/info14100545","url":null,"abstract":"Since the onset of the COVID-19 crisis, scholarly investigations and policy formulation have harnessed the potent capabilities of artificial intelligence (AI)-driven social media analytics. Evidence-driven policymaking has been facilitated through the proficient application of AI and natural language processing (NLP) methodologies to analyse the vast landscape of social media discussions. However, recent research works have failed to demonstrate a methodology to discern the underlying factors influencing COVID-19-related discussion topics. In this scholarly endeavour, an innovative AI- and NLP-based framework is deployed, incorporating translation, sentiment analysis, topic analysis, logistic regression, and clustering techniques to meticulously identify and elucidate the factors that are relevant to any discussion topics within the social media corpus. This pioneering methodology is rigorously tested and evaluated using a dataset comprising 152,070 COVID-19-related tweets, collected between 15th July 2021 and 20th April 2023, encompassing discourse in 58 distinct languages. The AI-driven regression analysis revealed 37 distinct observations, with 20 of them demonstrating a higher level of significance. In parallel, clustering analysis identified 15 observations, including nine of substantial relevance. These 52 AI-facilitated observations collectively unveil and delineate the factors that are intricately linked to five core discussion topics that are prevalent in the realm of COVID-19 discourse on Twitter. To the best of our knowledge, this research constitutes the inaugural effort in autonomously identifying factors associated with COVID-19 discussion topics, marking a pioneering application of AI algorithms in this domain. The implementation of this method holds the potential to significantly enhance the practice of evidence-based policymaking pertaining to matters concerning COVID-19.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135483294","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