Pub Date : 2024-07-07DOI: 10.3390/technologies12070106
Francesco Zito, N. Giannoccaro, Roberto Serio, S. Strazzella
This article illustrates the development of SolarFertigation (SF), an IoT (Internet of Things) solution for precision agriculture. Contrary to similar systems on the market, SolarFertigation can monitor and optimize fertigation autonomously, based on the analysis of data collected through the cloud. The system is made up of two main components: the central unit, which enables the precise deployment and distribution of water and fertilizers in different areas of the agricultural field, and the sensor node, which oversees collecting environmental and soil data. This article delves into the evolution of the system, focusing on structural and architectural changes to develop an infrastructure suitable for implementing a predictive model based on artificial intelligence and big data. Aspects concerning both the sensor node, such as energy management, accuracy of solar radiation readings, and qualitative soil moisture measurements, as well as implementations to the hydraulic system and the control and monitoring system of the central unit, are explored. This article provides an overview of the results obtained from solar radiation and soil moisture measurements. In addition, the results of an experimental campaign, in which 300 salad plants were grown using the SolarFertigation system in a photovoltaic field, are presented. This study demonstrated the effectiveness and applicability of the system under real-world conditions and highlighted its potential in optimizing resources and increasing agricultural productivity, especially in agrivoltaic settings.
{"title":"Analysis and Development of an IoT System for an Agrivoltaics Plant","authors":"Francesco Zito, N. Giannoccaro, Roberto Serio, S. Strazzella","doi":"10.3390/technologies12070106","DOIUrl":"https://doi.org/10.3390/technologies12070106","url":null,"abstract":"This article illustrates the development of SolarFertigation (SF), an IoT (Internet of Things) solution for precision agriculture. Contrary to similar systems on the market, SolarFertigation can monitor and optimize fertigation autonomously, based on the analysis of data collected through the cloud. The system is made up of two main components: the central unit, which enables the precise deployment and distribution of water and fertilizers in different areas of the agricultural field, and the sensor node, which oversees collecting environmental and soil data. This article delves into the evolution of the system, focusing on structural and architectural changes to develop an infrastructure suitable for implementing a predictive model based on artificial intelligence and big data. Aspects concerning both the sensor node, such as energy management, accuracy of solar radiation readings, and qualitative soil moisture measurements, as well as implementations to the hydraulic system and the control and monitoring system of the central unit, are explored. This article provides an overview of the results obtained from solar radiation and soil moisture measurements. In addition, the results of an experimental campaign, in which 300 salad plants were grown using the SolarFertigation system in a photovoltaic field, are presented. This study demonstrated the effectiveness and applicability of the system under real-world conditions and highlighted its potential in optimizing resources and increasing agricultural productivity, especially in agrivoltaic settings.","PeriodicalId":504839,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141671473","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}
Pub Date : 2024-07-05DOI: 10.3390/technologies12070104
Huiyuan Bao, Md. Amirul Islam, Bidyut Baran Saha
This study utilizes waste Albizia lebbeck wood from a sawmill to prepare activated carbon adsorbents and explores their potential application in adsorption cooling systems with a novel hydrofluoroolefin (HFO) refrigerant characterized by a low global warming potential. Activated carbon was synthesized through a simple and green steam activation method, and the optimal carbon shows a specific surface area of 946.8 m2/g and a pore volume of 0.843 cm3/g. The adsorption isotherms of HFO-1234ze(E) (Trans-1,3,3,3-tetrafluoropropene) on the activated carbon were examined at 30, 40, and 50 °C up to 400 kPa using a customized constant-volume variable-pressure system, and significant adsorption of 1.041 kg kg−1 was achieved at 30 °C and 400 kPa. The experimental data were fitted using both the Dubinin–Astakhov and Tóth models, and both models provided excellent fit results. The D−A adsorption model simulated the net adsorption capacity at possible operating temperatures. The isosteric of adsorption was determined using the Clausius–Clapeyron and modified Dubinin–Astakhov equations. In addition, the specific cooling effect and coefficient of performance were also studied.
本研究利用锯木厂的废Albizia lebbeck木材制备活性炭吸附剂，并探索其在使用新型氢氟烯烃（HFO）制冷剂的吸附冷却系统中的潜在应用，该制冷剂具有全球变暖潜能值低的特点。活性炭是通过简单、绿色的蒸汽活化法合成的，最佳炭的比表面积为 946.8 m2/g，孔体积为 0.843 cm3/g。使用定制的恒容变压系统，在 30、40 和 50 °C 至 400 kPa 温度条件下考察了 HFO-1234ze(E)（反式-1,3,3,3-四氟丙烯）在活性炭上的吸附等温线，结果表明在 30 °C 和 400 kPa 温度条件下，活性炭对 HFO-1234ze(E) （反式-1,3,3,3-四氟丙烯）的吸附量达到了 1.041 kg kg-1。使用杜宾-阿斯塔霍夫模型和托特模型对实验数据进行了拟合，两个模型都提供了出色的拟合结果。D-A 吸附模型模拟了可能工作温度下的净吸附容量。利用克劳修斯-克拉皮隆方程和修正的杜宾-阿斯塔霍夫方程确定了吸附的等效性。此外，还研究了比冷却效应和性能系数。
{"title":"Adsorption of HFO-1234ze(E) onto Steam-Activated Carbon Derived from Sawmill Waste Wood","authors":"Huiyuan Bao, Md. Amirul Islam, Bidyut Baran Saha","doi":"10.3390/technologies12070104","DOIUrl":"https://doi.org/10.3390/technologies12070104","url":null,"abstract":"This study utilizes waste Albizia lebbeck wood from a sawmill to prepare activated carbon adsorbents and explores their potential application in adsorption cooling systems with a novel hydrofluoroolefin (HFO) refrigerant characterized by a low global warming potential. Activated carbon was synthesized through a simple and green steam activation method, and the optimal carbon shows a specific surface area of 946.8 m2/g and a pore volume of 0.843 cm3/g. The adsorption isotherms of HFO-1234ze(E) (Trans-1,3,3,3-tetrafluoropropene) on the activated carbon were examined at 30, 40, and 50 °C up to 400 kPa using a customized constant-volume variable-pressure system, and significant adsorption of 1.041 kg kg−1 was achieved at 30 °C and 400 kPa. The experimental data were fitted using both the Dubinin–Astakhov and Tóth models, and both models provided excellent fit results. The D−A adsorption model simulated the net adsorption capacity at possible operating temperatures. The isosteric of adsorption was determined using the Clausius–Clapeyron and modified Dubinin–Astakhov equations. In addition, the specific cooling effect and coefficient of performance were also studied.","PeriodicalId":504839,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141677157","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}
Pub Date : 2024-07-04DOI: 10.3390/technologies12070103
Mario Fiorino, Muddasar Naeem, Mario Ciampi, Antonio Coronato
Artificial intelligence has brought many innovations to our lives. At the same time, it is worth designing robust safety machine learning (ML) algorithms to obtain more benefits from technology. Reinforcement learning (RL) being an important ML method is largely applied in safety-centric scenarios. In such a situation, learning safety constraints are necessary to avoid undesired outcomes. Within the traditional RL paradigm, agents typically focus on identifying states associated with high rewards to maximize its long-term returns. This prioritization can lead to a neglect of potentially hazardous situations. Particularly, the exploration phase can pose significant risks, as it necessitates actions that may have unpredictable consequences. For instance, in autonomous driving applications, an RL agent might discover routes that yield high efficiency but fail to account for sudden hazardous conditions such as sharp turns or pedestrian crossings, potentially leading to catastrophic failures. Ensuring the safety of agents operating in unpredictable environments with potentially catastrophic failure states remains a critical challenge. This paper introduces a novel metric-driven approach aimed at containing risk in RL applications. Central to this approach are two developed indicators: the Hazard Indicator and the Risk Indicator. These metrics are designed to evaluate the safety of an environment by quantifying the likelihood of transitioning from safe states to failure states and assessing the associated risks. The fact that these indicators are characterized by a straightforward implementation, a highly generalizable probabilistic mathematical foundation, and a domain-independent nature makes them particularly interesting. To demonstrate their efficacy, we conducted experiments across various use cases, showcasing the feasibility of our proposed metrics. By enabling RL agents to effectively manage hazardous states, this approach paves the way for a more reliable and readily implementable RL in practical applications.
人工智能为我们的生活带来了许多创新。与此同时，为了从技术中获得更多益处，设计强大的安全机器学习（ML）算法也是值得的。强化学习（RL）作为一种重要的 ML 方法，主要应用于以安全为中心的场景。在这种情况下，有必要学习安全约束，以避免出现不期望的结果。在传统的强化学习范例中，代理通常专注于识别与高回报相关的状态，以最大化其长期回报。这种优先顺序可能会导致忽视潜在的危险情况。特别是，探索阶段可能会带来巨大风险，因为它需要采取可能产生不可预测后果的行动。例如，在自动驾驶应用中，RL 代理可能会发现产生高效率的路线，但却没有考虑到急转弯或人行横道等突发危险情况，从而可能导致灾难性故障。确保代理在不可预测的环境中运行的安全性，以及潜在的灾难性故障状态，仍然是一个严峻的挑战。本文介绍了一种新颖的度量驱动方法，旨在控制 RL 应用中的风险。这种方法的核心是两个已开发的指标：危险指标和风险指标。这些指标旨在通过量化从安全状态过渡到失效状态的可能性以及评估相关风险来评价环境的安全性。这些指标的特点是实施简单、具有高度通用性的概率数学基础以及与领域无关的性质，这使得它们特别有趣。为了证明这些指标的有效性，我们在各种使用案例中进行了实验，展示了我们提出的指标的可行性。通过让 RL 代理有效管理危险状态，这种方法为在实际应用中实现更可靠、更易于实施的 RL 铺平了道路。
{"title":"Defining a Metric-Driven Approach for Learning Hazardous Situations","authors":"Mario Fiorino, Muddasar Naeem, Mario Ciampi, Antonio Coronato","doi":"10.3390/technologies12070103","DOIUrl":"https://doi.org/10.3390/technologies12070103","url":null,"abstract":"Artificial intelligence has brought many innovations to our lives. At the same time, it is worth designing robust safety machine learning (ML) algorithms to obtain more benefits from technology. Reinforcement learning (RL) being an important ML method is largely applied in safety-centric scenarios. In such a situation, learning safety constraints are necessary to avoid undesired outcomes. Within the traditional RL paradigm, agents typically focus on identifying states associated with high rewards to maximize its long-term returns. This prioritization can lead to a neglect of potentially hazardous situations. Particularly, the exploration phase can pose significant risks, as it necessitates actions that may have unpredictable consequences. For instance, in autonomous driving applications, an RL agent might discover routes that yield high efficiency but fail to account for sudden hazardous conditions such as sharp turns or pedestrian crossings, potentially leading to catastrophic failures. Ensuring the safety of agents operating in unpredictable environments with potentially catastrophic failure states remains a critical challenge. This paper introduces a novel metric-driven approach aimed at containing risk in RL applications. Central to this approach are two developed indicators: the Hazard Indicator and the Risk Indicator. These metrics are designed to evaluate the safety of an environment by quantifying the likelihood of transitioning from safe states to failure states and assessing the associated risks. The fact that these indicators are characterized by a straightforward implementation, a highly generalizable probabilistic mathematical foundation, and a domain-independent nature makes them particularly interesting. To demonstrate their efficacy, we conducted experiments across various use cases, showcasing the feasibility of our proposed metrics. By enabling RL agents to effectively manage hazardous states, this approach paves the way for a more reliable and readily implementable RL in practical applications.","PeriodicalId":504839,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141678173","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}
Pub Date : 2024-07-04DOI: 10.3390/technologies12070102
Dmitrii V. Andreev, V. Andreev, Marina Konuhova, A. Popov
We propose a technique for the wafer-level testing of the gate dielectrics of metal–insulator–semiconductor (MIS) devices by the high-field injection of electrons into the dielectric using a mode of increasing injection current density up to a set level. This method provides the capability to control a change in the charge state of the gate dielectric during all the testing. The proposed technique makes it possible to assess the integrity of the thin dielectric and at the same time to control the charge effects of its degradation. The method in particular can be used for manufacturing processes to control integrated circuits (ICs) based on MIS structures. In the paper, we propose an advanced algorithm of the Bounded J-Ramp testing of the gate dielectric and receive its approval when monitoring the quality of the gate dielectrics of production-manufactured MIS devices. We found that the maximum value of positive charge obtained when tested by the proposed method was a value close to that obtained when the charge was injected into the dielectric under a constant current with a Bounded J value despite large differences in the rate of degradation of the dielectric.
我们提出了一种对金属-绝缘体-半导体（MIS）器件的栅极电介质进行晶圆级测试的技术，方法是通过向电介质中注入高场电子的方式，将注入电流密度提高到设定水平。这种方法能够在所有测试过程中控制栅极电介质电荷状态的变化。所提出的技术可以评估薄介质的完整性，同时控制其降解的电荷效应。该方法尤其可用于制造工艺，以控制基于 MIS 结构的集成电路 (IC)。在本文中，我们提出了一种先进的栅极电介质 Bounded J-Ramp 测试算法，并在监控生产制造的 MIS 器件的栅极电介质质量时得到了认可。我们发现，尽管电介质的劣化率存在很大差异，但采用所提出的方法进行测试时获得的正电荷最大值与在恒定电流下以有界 J 值向电介质注入电荷时获得的正电荷最大值相近。
{"title":"Technique of High-Field Electron Injection for Wafer-Level Testing of Gate Dielectrics of MIS Devices","authors":"Dmitrii V. Andreev, V. Andreev, Marina Konuhova, A. Popov","doi":"10.3390/technologies12070102","DOIUrl":"https://doi.org/10.3390/technologies12070102","url":null,"abstract":"We propose a technique for the wafer-level testing of the gate dielectrics of metal–insulator–semiconductor (MIS) devices by the high-field injection of electrons into the dielectric using a mode of increasing injection current density up to a set level. This method provides the capability to control a change in the charge state of the gate dielectric during all the testing. The proposed technique makes it possible to assess the integrity of the thin dielectric and at the same time to control the charge effects of its degradation. The method in particular can be used for manufacturing processes to control integrated circuits (ICs) based on MIS structures. In the paper, we propose an advanced algorithm of the Bounded J-Ramp testing of the gate dielectric and receive its approval when monitoring the quality of the gate dielectrics of production-manufactured MIS devices. We found that the maximum value of positive charge obtained when tested by the proposed method was a value close to that obtained when the charge was injected into the dielectric under a constant current with a Bounded J value despite large differences in the rate of degradation of the dielectric.","PeriodicalId":504839,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141677900","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}
Pub Date : 2024-07-03DOI: 10.3390/technologies12070100
Amit Kaimkuriya, Balaguru Sethuraman, M. Gupta
Fatigue refers to the progressive and localized structural damage that occurs when a material is subjected to repeated loading and unloading, typically at levels below its ultimate strength. Several failure mechanisms have been observed in practical scenarios, encompassing high-cycle, low-cycle, thermal, surface, corrosion, and fretting fatigue. Fatigue, connected to the failure of numerous engineered products, stands out as a prevalent cause of structural failure in service. Conducting research on the advancement and application of fatigue analysis technologies is crucial because fatigue analysis plays a critical role in determining the service life of components and mitigating the risk of failure. This study compiles data from a wide range of sources and offers a thorough summary of the state of fatigue analysis. It focuses on the effects of different parameters, including hardness, temperature, residual stresses, and hardfacing, on the fatigue life of different materials and their alloys. The fatigue life of alloys is typically high at low temperatures, but it is significantly reduced at high temperatures or under high-stress conditions. One of the main causes of lower fatigue life is residual stress. High-temperature conditions and hardfacing processes cause the development of tensile residual stresses, which in turn decreases fatigue life. But, if the hardness of the material significantly increases due to hardfacing, then the fatigue life also increases. This manuscript focuses on reviewing the research on fatigue-life prediction methods, shortcomings, and recommendations.
{"title":"Effect of Physical Parameters on Fatigue Life of Materials and Alloys: A Critical Review","authors":"Amit Kaimkuriya, Balaguru Sethuraman, M. Gupta","doi":"10.3390/technologies12070100","DOIUrl":"https://doi.org/10.3390/technologies12070100","url":null,"abstract":"Fatigue refers to the progressive and localized structural damage that occurs when a material is subjected to repeated loading and unloading, typically at levels below its ultimate strength. Several failure mechanisms have been observed in practical scenarios, encompassing high-cycle, low-cycle, thermal, surface, corrosion, and fretting fatigue. Fatigue, connected to the failure of numerous engineered products, stands out as a prevalent cause of structural failure in service. Conducting research on the advancement and application of fatigue analysis technologies is crucial because fatigue analysis plays a critical role in determining the service life of components and mitigating the risk of failure. This study compiles data from a wide range of sources and offers a thorough summary of the state of fatigue analysis. It focuses on the effects of different parameters, including hardness, temperature, residual stresses, and hardfacing, on the fatigue life of different materials and their alloys. The fatigue life of alloys is typically high at low temperatures, but it is significantly reduced at high temperatures or under high-stress conditions. One of the main causes of lower fatigue life is residual stress. High-temperature conditions and hardfacing processes cause the development of tensile residual stresses, which in turn decreases fatigue life. But, if the hardness of the material significantly increases due to hardfacing, then the fatigue life also increases. This manuscript focuses on reviewing the research on fatigue-life prediction methods, shortcomings, and recommendations.","PeriodicalId":504839,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141681097","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}
Pub Date : 2024-07-03DOI: 10.3390/technologies12070101
Panagiotis Christakakis, Garyfallia Papadopoulou, Georgios Mikos, Nikolaos Kalogiannidis, D. Ioannidis, D. Tzovaras, E. Pechlivani
In recent years, the integration of smartphone technology with novel sensing technologies, Artificial Intelligence (AI), and Deep Learning (DL) algorithms has revolutionized crop pest and disease surveillance. Efficient and accurate diagnosis is crucial to mitigate substantial economic losses in agriculture caused by diseases and pests. An innovative Apple® and Android™ mobile application for citizen science has been developed, to enable real-time detection and identification of plant leaf diseases and pests, minimizing their impact on horticulture, viticulture, and olive cultivation. Leveraging DL algorithms, this application facilitates efficient data collection on crop pests and diseases, supporting crop yield protection and cost reduction in alignment with the Green Deal goal for 2030 by reducing pesticide use. The proposed citizen science tool involves all Farm to Fork stakeholders and farm citizens in minimizing damage to plant health by insect and fungal diseases. It utilizes comprehensive datasets, including images of various diseases and insects, within a robust Decision Support System (DSS) where DL models operate. The DSS connects directly with users, allowing them to upload crop pest data via the mobile application, providing data-driven support and information. The application stands out for its scalability and interoperability, enabling the continuous integration of new data to enhance its capabilities. It supports AI-based imaging analysis of quarantine pests, invasive alien species, and emerging and native pests, thereby aiding post-border surveillance programs. The mobile application, developed using a Python-based REST API, PostgreSQL, and Keycloak, has been field-tested, demonstrating its effectiveness in real-world agriculture scenarios, such as detecting Tuta absoluta (Meyrick) infestation in tomato cultivations. The outcomes of this study in T. absoluta detection serve as a showcase scenario for the proposed citizen science tool’s applicability and usability, demonstrating a 70.2% accuracy (mAP50) utilizing advanced DL models. Notably, during field testing, the model achieved detection confidence levels of up to 87%, enhancing pest management practices.
{"title":"Smartphone-Based Citizen Science Tool for Plant Disease and Insect Pest Detection Using Artificial Intelligence","authors":"Panagiotis Christakakis, Garyfallia Papadopoulou, Georgios Mikos, Nikolaos Kalogiannidis, D. Ioannidis, D. Tzovaras, E. Pechlivani","doi":"10.3390/technologies12070101","DOIUrl":"https://doi.org/10.3390/technologies12070101","url":null,"abstract":"In recent years, the integration of smartphone technology with novel sensing technologies, Artificial Intelligence (AI), and Deep Learning (DL) algorithms has revolutionized crop pest and disease surveillance. Efficient and accurate diagnosis is crucial to mitigate substantial economic losses in agriculture caused by diseases and pests. An innovative Apple® and Android™ mobile application for citizen science has been developed, to enable real-time detection and identification of plant leaf diseases and pests, minimizing their impact on horticulture, viticulture, and olive cultivation. Leveraging DL algorithms, this application facilitates efficient data collection on crop pests and diseases, supporting crop yield protection and cost reduction in alignment with the Green Deal goal for 2030 by reducing pesticide use. The proposed citizen science tool involves all Farm to Fork stakeholders and farm citizens in minimizing damage to plant health by insect and fungal diseases. It utilizes comprehensive datasets, including images of various diseases and insects, within a robust Decision Support System (DSS) where DL models operate. The DSS connects directly with users, allowing them to upload crop pest data via the mobile application, providing data-driven support and information. The application stands out for its scalability and interoperability, enabling the continuous integration of new data to enhance its capabilities. It supports AI-based imaging analysis of quarantine pests, invasive alien species, and emerging and native pests, thereby aiding post-border surveillance programs. The mobile application, developed using a Python-based REST API, PostgreSQL, and Keycloak, has been field-tested, demonstrating its effectiveness in real-world agriculture scenarios, such as detecting Tuta absoluta (Meyrick) infestation in tomato cultivations. The outcomes of this study in T. absoluta detection serve as a showcase scenario for the proposed citizen science tool’s applicability and usability, demonstrating a 70.2% accuracy (mAP50) utilizing advanced DL models. Notably, during field testing, the model achieved detection confidence levels of up to 87%, enhancing pest management practices.","PeriodicalId":504839,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141681869","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}
Pub Date : 2024-07-02DOI: 10.3390/technologies12070099
Bayron Jesit Ospina Cifuentes, Álvaro Suárez, Vanessa García Pineda, Ricardo Alvarado Jaimes, Alber Oswaldo Montoya Benitez, Juan David Grajales Bustamante
The distributed structure of traditional networks often fails to promptly and accurately provide the computational power required for artificial intelligence (AI), hindering its practical application and implementation. Consequently, this research aims to analyze the use of AI in software-defined networks (SDNs). To achieve this goal, a systematic literature review (SLR) is conducted based on the PRISMA 2020 statement. Through this review, it is found that, bottom-up, from the perspective of the data plane, control plane, and application plane of SDNs, the integration of various network planes with AI is feasible, giving rise to Intelligent Software Defined Networking (ISDN). As a primary conclusion, it was found that the application of AI-related algorithms in SDNs is extensive and faces numerous challenges. Nonetheless, these challenges are propelling the development of SDNs in a more promising direction through the adoption of novel methods and tools such as route optimization, software-defined routing, intelligent methods for network security, and AI-based traffic engineering, among others.
传统网络的分布式结构往往无法及时准确地提供人工智能（AI）所需的计算能力，阻碍了人工智能的实际应用和实施。因此，本研究旨在分析人工智能在软件定义网络（SDN）中的应用。为实现这一目标，我们根据 PRISMA 2020 声明进行了系统性文献综述（SLR）。通过综述发现，自下而上，从 SDN 的数据平面、控制平面和应用平面的角度来看，将各种网络平面与人工智能集成是可行的，从而产生了智能软件定义网络（ISDN）。研究得出的主要结论是，人工智能相关算法在 SDN 中的应用非常广泛，并面临诸多挑战。不过，通过采用路由优化、软件定义路由、网络安全智能方法和基于人工智能的流量工程等新方法和工具，这些挑战正推动 SDN 向着更有前途的方向发展。
{"title":"Analysis of the Use of Artificial Intelligence in Software-Defined Intelligent Networks: A Survey","authors":"Bayron Jesit Ospina Cifuentes, Álvaro Suárez, Vanessa García Pineda, Ricardo Alvarado Jaimes, Alber Oswaldo Montoya Benitez, Juan David Grajales Bustamante","doi":"10.3390/technologies12070099","DOIUrl":"https://doi.org/10.3390/technologies12070099","url":null,"abstract":"The distributed structure of traditional networks often fails to promptly and accurately provide the computational power required for artificial intelligence (AI), hindering its practical application and implementation. Consequently, this research aims to analyze the use of AI in software-defined networks (SDNs). To achieve this goal, a systematic literature review (SLR) is conducted based on the PRISMA 2020 statement. Through this review, it is found that, bottom-up, from the perspective of the data plane, control plane, and application plane of SDNs, the integration of various network planes with AI is feasible, giving rise to Intelligent Software Defined Networking (ISDN). As a primary conclusion, it was found that the application of AI-related algorithms in SDNs is extensive and faces numerous challenges. Nonetheless, these challenges are propelling the development of SDNs in a more promising direction through the adoption of novel methods and tools such as route optimization, software-defined routing, intelligent methods for network security, and AI-based traffic engineering, among others.","PeriodicalId":504839,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141684124","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}
Pub Date : 2024-07-01DOI: 10.3390/technologies12070098
P. Netinant, Siwakron Phonsawang, Meennapa Rukhiran
Reliable and cost-efficient license plate recognition (LPR) systems enhance security, traffic management, and automated toll collection in real-world applications. This study addresses optimal unique configurations for enhancing LPR system accuracy and reliability by evaluating the impact of camera angle, object velocity, and distance on the efficacy of real-time LPR systems. The Internet of Things (IoT) LPR framework is proposed and utilized on single-board computer (SBC) technology, such as the Raspberry Pi 4 platform, with a high-resolution webcam using advanced OpenCV and OCR–Tesseract algorithms applied. The research endeavors to simulate common deployment scenarios of the real-time LPR system and perform thorough testing by leveraging SBC computational capabilities and the webcam’s imaging capabilities. The testing process is not just comprehensive, but also meticulous, ensuring the system’s reliability in various operational settings. We performed extensive experiments with a hundred repetitions at diverse angles, velocities, and distances. An assessment of the data’s precision, recall, and F1 score indicates the accuracy with which Thai license plates are identified. The results show that camera angles close to 180° significantly reduce perspective distortion, thus enhancing precision. Lower vehicle speeds (<10 km/h) and shorter distances (<10 m) also improve recognition accuracy by reducing motion blur and improving image clarity. Images captured from shorter distances (approximately less than 10 m) are more accurate for high-resolution character recognition. This study substantially contributes to SBC technology utilizing IoT-based real-time LPR systems for practical, accurate, and cost-effective implementations.
{"title":"Evaluating Factors Shaping Real-Time Internet-of-Things-Based License Plate Recognition Using Single-Board Computer Technology","authors":"P. Netinant, Siwakron Phonsawang, Meennapa Rukhiran","doi":"10.3390/technologies12070098","DOIUrl":"https://doi.org/10.3390/technologies12070098","url":null,"abstract":"Reliable and cost-efficient license plate recognition (LPR) systems enhance security, traffic management, and automated toll collection in real-world applications. This study addresses optimal unique configurations for enhancing LPR system accuracy and reliability by evaluating the impact of camera angle, object velocity, and distance on the efficacy of real-time LPR systems. The Internet of Things (IoT) LPR framework is proposed and utilized on single-board computer (SBC) technology, such as the Raspberry Pi 4 platform, with a high-resolution webcam using advanced OpenCV and OCR–Tesseract algorithms applied. The research endeavors to simulate common deployment scenarios of the real-time LPR system and perform thorough testing by leveraging SBC computational capabilities and the webcam’s imaging capabilities. The testing process is not just comprehensive, but also meticulous, ensuring the system’s reliability in various operational settings. We performed extensive experiments with a hundred repetitions at diverse angles, velocities, and distances. An assessment of the data’s precision, recall, and F1 score indicates the accuracy with which Thai license plates are identified. The results show that camera angles close to 180° significantly reduce perspective distortion, thus enhancing precision. Lower vehicle speeds (<10 km/h) and shorter distances (<10 m) also improve recognition accuracy by reducing motion blur and improving image clarity. Images captured from shorter distances (approximately less than 10 m) are more accurate for high-resolution character recognition. This study substantially contributes to SBC technology utilizing IoT-based real-time LPR systems for practical, accurate, and cost-effective implementations.","PeriodicalId":504839,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141716735","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}
Pub Date : 2024-06-12DOI: 10.3390/technologies12060089
H. Adamas-Pérez, M. Ponce-Silva, J. D. Mina-Antonio, Abraham Claudio-Sánchez, Omar Rodríguez-Benítez, Ó. M. Rodríguez-Benítez
This paper aims to propose a new sizing approach to reduce the footprint and optimize the performance of an LCL filter implemented in photovoltaic systems using grid-connected single-phase microinverters. In particular, the analysis is carried out on a single-phase full-bridge inverter, assuming the following two conditions: (1) a unit power factor at the connection point between the AC grid and the LCL filter; (2) a control circuit based on unipolar sinusoidal pulse width modulation (SPWM). In particular, the ripple and harmonics of the LCL filter input current and the current injected into the grid are analyzed. The results of the Simulink simulation and the experimental tests carried out confirm that it is possible to considerably reduce filter volume by optimizing each passive component compared with what is already available in the literature while guaranteeing excellent filtering performance. Specifically, the inductance values were reduced by almost 40% and the capacitor value by almost 100%. The main applications of this new design methodology are for use in single-phase microinverters connected to the grid and for research purposes in power electronics and optimization.
{"title":"A New LCL Filter Design Method for Single-Phase Photovoltaic Systems Connected to the Grid via Micro-Inverters","authors":"H. Adamas-Pérez, M. Ponce-Silva, J. D. Mina-Antonio, Abraham Claudio-Sánchez, Omar Rodríguez-Benítez, Ó. M. Rodríguez-Benítez","doi":"10.3390/technologies12060089","DOIUrl":"https://doi.org/10.3390/technologies12060089","url":null,"abstract":"This paper aims to propose a new sizing approach to reduce the footprint and optimize the performance of an LCL filter implemented in photovoltaic systems using grid-connected single-phase microinverters. In particular, the analysis is carried out on a single-phase full-bridge inverter, assuming the following two conditions: (1) a unit power factor at the connection point between the AC grid and the LCL filter; (2) a control circuit based on unipolar sinusoidal pulse width modulation (SPWM). In particular, the ripple and harmonics of the LCL filter input current and the current injected into the grid are analyzed. The results of the Simulink simulation and the experimental tests carried out confirm that it is possible to considerably reduce filter volume by optimizing each passive component compared with what is already available in the literature while guaranteeing excellent filtering performance. Specifically, the inductance values were reduced by almost 40% and the capacitor value by almost 100%. The main applications of this new design methodology are for use in single-phase microinverters connected to the grid and for research purposes in power electronics and optimization.","PeriodicalId":504839,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141350678","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}
Pub Date : 2024-06-08DOI: 10.3390/technologies12060088
A. H. K. Asadi, Mohsen Eskandari, Hadi Delavari
The application of arresters is critical for the safe operation of electric grids against lightning. Arresters limit the consequences of lightning-induced over-voltages. However, surge arrester protection in electric grids is challenging due to the intrinsic complexities of distribution grids, including overhead lines and power components such as transformers. In this paper, an optimal arrester placement technique is developed by proposing a multi-objective function that includes technical, safety and risk, and economic indices. However, an effective placement model demands a comprehensive and accurate modeling of an electric grid’s components. In this light, appropriate models of a grid’s components including an arrester, the earth, an oil-immersed transformer, overhead lines, and lightning-induced voltage are developed. To achieve accurate models, high-frequency transient mathematical models are developed for the grid’s components. Notably, to have an accurate model of the arrester, which critically impacts the performance of the arrester placement technique, a new arrester model is developed and evaluated based on real technical data from manufacturers such as Pars, Tridelta, and Siemens. Then, the proposed model is compared with the IEEE, Fernandez, and Pinceti models. The arrester model is incorporated in an optimization problem considering the performance of the over-voltage protection and the risk, technical, and economic indices, and it is solved using the particle swarm optimization (PSO) and Monte Carlo (MC) techniques. To validate the proposed arrester model and the placement technique, real data from the Chopoghloo feeder in Bahar, Hamedan, Iran, are simulated. The feeder is expanded over three different geographical areas, including rural, agricultural plain, and mountainous areas.
{"title":"Accurate Surge Arrester Modeling for Optimal Risk-Aware Lightning Protection Utilizing a Hybrid Monte Carlo–Particle Swarm Optimization Algorithm","authors":"A. H. K. Asadi, Mohsen Eskandari, Hadi Delavari","doi":"10.3390/technologies12060088","DOIUrl":"https://doi.org/10.3390/technologies12060088","url":null,"abstract":"The application of arresters is critical for the safe operation of electric grids against lightning. Arresters limit the consequences of lightning-induced over-voltages. However, surge arrester protection in electric grids is challenging due to the intrinsic complexities of distribution grids, including overhead lines and power components such as transformers. In this paper, an optimal arrester placement technique is developed by proposing a multi-objective function that includes technical, safety and risk, and economic indices. However, an effective placement model demands a comprehensive and accurate modeling of an electric grid’s components. In this light, appropriate models of a grid’s components including an arrester, the earth, an oil-immersed transformer, overhead lines, and lightning-induced voltage are developed. To achieve accurate models, high-frequency transient mathematical models are developed for the grid’s components. Notably, to have an accurate model of the arrester, which critically impacts the performance of the arrester placement technique, a new arrester model is developed and evaluated based on real technical data from manufacturers such as Pars, Tridelta, and Siemens. Then, the proposed model is compared with the IEEE, Fernandez, and Pinceti models. The arrester model is incorporated in an optimization problem considering the performance of the over-voltage protection and the risk, technical, and economic indices, and it is solved using the particle swarm optimization (PSO) and Monte Carlo (MC) techniques. To validate the proposed arrester model and the placement technique, real data from the Chopoghloo feeder in Bahar, Hamedan, Iran, are simulated. The feeder is expanded over three different geographical areas, including rural, agricultural plain, and mountainous areas.","PeriodicalId":504839,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369392","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}
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