Pub Date : 2024-09-13DOI: 10.3390/electronics13183655
Miljana Shulajkovska, Maj Smerkol, Gjorgji Noveski, Marko Bohanec, Matjaž Gams
As urban populations rise globally, cities face increasing challenges in managing urban mobility. This paper addresses the question of identifying which modifications to introduce regarding city mobility by evaluating potential solutions using city-specific, subjective multi-objective criteria. The innovative AI-based recommendation engine assists city planners and policymakers in prioritizing key urban mobility aspects for effective policy proposals. By leveraging multi-criteria decision analysis (MCDA) and ±1/2 analysis, this engine provides a structured approach to systematically and simultaneously navigate the complexities of urban mobility planning. The proposed approach aims to provide an open-source interoperable prototype for all smart cities to utilize such recommendation systems routinely, fostering efficient, sustainable, and forward-thinking urban mobility strategies. Case studies from four European cities—Helsinki (tunnel traffic), Amsterdam (bicycle traffic for a new city quarter), Messina (adding another bus line), and Bilbao (optimal timing for closing the city center)—highlight the engine’s transformative potential in shaping urban mobility policies. Ultimately, this contributes to more livable and resilient urban environments, based on advanced urban mobility management.
{"title":"Artificial Intelligence-Based Decision Support System for Sustainable Urban Mobility","authors":"Miljana Shulajkovska, Maj Smerkol, Gjorgji Noveski, Marko Bohanec, Matjaž Gams","doi":"10.3390/electronics13183655","DOIUrl":"https://doi.org/10.3390/electronics13183655","url":null,"abstract":"As urban populations rise globally, cities face increasing challenges in managing urban mobility. This paper addresses the question of identifying which modifications to introduce regarding city mobility by evaluating potential solutions using city-specific, subjective multi-objective criteria. The innovative AI-based recommendation engine assists city planners and policymakers in prioritizing key urban mobility aspects for effective policy proposals. By leveraging multi-criteria decision analysis (MCDA) and ±1/2 analysis, this engine provides a structured approach to systematically and simultaneously navigate the complexities of urban mobility planning. The proposed approach aims to provide an open-source interoperable prototype for all smart cities to utilize such recommendation systems routinely, fostering efficient, sustainable, and forward-thinking urban mobility strategies. Case studies from four European cities—Helsinki (tunnel traffic), Amsterdam (bicycle traffic for a new city quarter), Messina (adding another bus line), and Bilbao (optimal timing for closing the city center)—highlight the engine’s transformative potential in shaping urban mobility policies. Ultimately, this contributes to more livable and resilient urban environments, based on advanced urban mobility management.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"5 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The existing collaborative channel perception suffers from unreasonable data fusion weight allocation, which mismatches the channel perception capability of the node devices. This often leads to significant deviations between the channel perception results and the actual channel state. To solve this issue, this paper integrates the data fusion algorithm from evidence fusion theory with data link channel state perception. It applies the data fusion advantages of evidence fusion theory to evaluate the traffic pulse statistical capability of network node devices. Specifically, the typical characteristic parameters describing the channel perception capability of node devices are regarded as evidence parameter sets under the recognition framework. By calculating the credibility and falsity of the characteristic parameters, the differences and conflicts between nodes are measured to achieve a comprehensive evaluation of the traffic pulse statistical capabilities of node devices. Based on this evaluation, the geometric mean method is adopted to calculate channel state perception weights for each node within a single-hop range, and a weight allocation strategy is formulated to improve the accuracy of channel state perception.
{"title":"Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion","authors":"Zhiyong Zhao, Zhongyang Mao, Zhilin Zhang, Yaozong Pan, Jianwu Xu","doi":"10.3390/electronics13183643","DOIUrl":"https://doi.org/10.3390/electronics13183643","url":null,"abstract":"The existing collaborative channel perception suffers from unreasonable data fusion weight allocation, which mismatches the channel perception capability of the node devices. This often leads to significant deviations between the channel perception results and the actual channel state. To solve this issue, this paper integrates the data fusion algorithm from evidence fusion theory with data link channel state perception. It applies the data fusion advantages of evidence fusion theory to evaluate the traffic pulse statistical capability of network node devices. Specifically, the typical characteristic parameters describing the channel perception capability of node devices are regarded as evidence parameter sets under the recognition framework. By calculating the credibility and falsity of the characteristic parameters, the differences and conflicts between nodes are measured to achieve a comprehensive evaluation of the traffic pulse statistical capabilities of node devices. Based on this evaluation, the geometric mean method is adopted to calculate channel state perception weights for each node within a single-hop range, and a weight allocation strategy is formulated to improve the accuracy of channel state perception.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"25 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13DOI: 10.3390/electronics13183656
Zejun Huang, Hao Bai, Min Xu, Yuchao Hou, Ruotian Yao, Yipeng Liu, Qi Guo, Chunming Tu
To resolve the issue of the difficultly in effectively balancing the output performance improvement, cost reduction, and efficiency improvement of a medium-voltage modular multilevel converter (MMC), a novel MMC (NMMC) topology based on high- and low-frequency hybrid modulation is proposed in this study. Each arm of the NMMC contains a high-frequency sub-module composed of a heterogeneous cross-connect module (HCCM) and N − 1 low-frequency sub-modules composed of half-bridge converters. The high-frequency bridge arm of the HCCM in this study adopts SiC MOSFET devices, while the commutation bridge arm and low-frequency sub-module of the HCCM adopt Si IGBT devices. For the NMMC topology, this study adopts a high/low-frequency hybrid modulation strategy, which gives full play to the advantages of low switching loss in SiC MOSFET devices and low on-state loss in Si IGBT devices. In addition, a specific capacitor voltage balance strategy is proposed for the HCCM, and the working state of the HCCM is analyzed in detail. Furthermore, the feasibility and effectiveness of the proposed topology, modulation strategy, and voltage balancing strategy are verified by experiments. Finally, the proposed topology is compared with the existing MMC topology in terms of device cost and operating loss, which proves that the proposed topology can better balance the cost and efficiency indicators of the device.
为了解决中压模块化多电平转换器（MMC）在提高输出性能、降低成本和提高效率之间难以有效平衡的问题，本研究提出了一种基于高低频混合调制的新型 MMC（NMMC）拓扑结构。NMMC 的每个臂包含一个由异质交叉连接模块 (HCCM) 组成的高频子模块和 N - 1 个由半桥转换器组成的低频子模块。本研究中 HCCM 的高频桥臂采用了 SiC MOSFET 器件，而 HCCM 的换向桥臂和低频子模块则采用了 Si IGBT 器件。对于 NMMC 拓扑，本研究采用了高/低频混合调制策略，充分发挥了 SiC MOSFET 器件开关损耗低和 Si IGBT 器件导通损耗低的优势。此外，还针对 HCCM 提出了具体的电容器电压平衡策略，并详细分析了 HCCM 的工作状态。此外，还通过实验验证了所提出的拓扑结构、调制策略和电压平衡策略的可行性和有效性。最后，将所提出的拓扑结构与现有的 MMC 拓扑结构在器件成本和工作损耗方面进行了比较，证明所提出的拓扑结构能更好地平衡器件的成本和效率指标。
{"title":"A Novel Modular Multilevel Converter Topology with High- and Low-Frequency Modules and Its Modulation Strategy","authors":"Zejun Huang, Hao Bai, Min Xu, Yuchao Hou, Ruotian Yao, Yipeng Liu, Qi Guo, Chunming Tu","doi":"10.3390/electronics13183656","DOIUrl":"https://doi.org/10.3390/electronics13183656","url":null,"abstract":"To resolve the issue of the difficultly in effectively balancing the output performance improvement, cost reduction, and efficiency improvement of a medium-voltage modular multilevel converter (MMC), a novel MMC (NMMC) topology based on high- and low-frequency hybrid modulation is proposed in this study. Each arm of the NMMC contains a high-frequency sub-module composed of a heterogeneous cross-connect module (HCCM) and N − 1 low-frequency sub-modules composed of half-bridge converters. The high-frequency bridge arm of the HCCM in this study adopts SiC MOSFET devices, while the commutation bridge arm and low-frequency sub-module of the HCCM adopt Si IGBT devices. For the NMMC topology, this study adopts a high/low-frequency hybrid modulation strategy, which gives full play to the advantages of low switching loss in SiC MOSFET devices and low on-state loss in Si IGBT devices. In addition, a specific capacitor voltage balance strategy is proposed for the HCCM, and the working state of the HCCM is analyzed in detail. Furthermore, the feasibility and effectiveness of the proposed topology, modulation strategy, and voltage balancing strategy are verified by experiments. Finally, the proposed topology is compared with the existing MMC topology in terms of device cost and operating loss, which proves that the proposed topology can better balance the cost and efficiency indicators of the device.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"19 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13DOI: 10.3390/electronics13183650
Muhammad Arslan Ghaffar, Lei Peng, Muhammad Umer Aslam, Muhammad Adeel, Salim Dassari
In recent years, the delivery of medical supplies has faced significant challenges due to natural disasters and recurrent public health emergencies. Addressing the need for improved logistics operations during such crises, this article presents an innovative approach, namely integrating vehicle and unmanned aerial vehicle (UAV) logistics to enhance the efficiency and resilience of medical supply chains. Our study introduces a dual-mode distribution framework which employs the density-based spatial clustering of applications with noise (DBSCAN) algorithm for efficiently clustering demand zones unreachable by conventional vehicles, thereby identifying areas requiring UAV delivery. Furthermore, we categorize the demand for medical supplies into two distinct sets based on vehicle accessibility, optimizing distribution routes via both UAVs and vehicles. Through comparative analysis, our findings reveal that the artificial bee colony (ABC) algorithm significantly outperforms the genetic algorithm in terms of solving efficiency, iteration counts, and delivery speed. However, the ABC algorithm’s tendency toward early local optimization and rapid convergence leads to potential stagnation in local optima. To mitigate this issue, we incorporate a simulated annealing technique into the ABC framework, culminating in a refined optimization approach which successfully overcomes the limitations of premature local optima convergence. The experimental results validate the efficacy of our enhanced algorithm, demonstrating reduced iteration counts, shorter computation times, and substantially improved solution quality over traditional logistic models. The proposed method holds promise for significantly improving the operational efficiency and service quality of the healthcare system’s logistics during critical situations.
{"title":"Vehicle-UAV Integrated Routing Optimization Problem for Emergency Delivery of Medical Supplies","authors":"Muhammad Arslan Ghaffar, Lei Peng, Muhammad Umer Aslam, Muhammad Adeel, Salim Dassari","doi":"10.3390/electronics13183650","DOIUrl":"https://doi.org/10.3390/electronics13183650","url":null,"abstract":"In recent years, the delivery of medical supplies has faced significant challenges due to natural disasters and recurrent public health emergencies. Addressing the need for improved logistics operations during such crises, this article presents an innovative approach, namely integrating vehicle and unmanned aerial vehicle (UAV) logistics to enhance the efficiency and resilience of medical supply chains. Our study introduces a dual-mode distribution framework which employs the density-based spatial clustering of applications with noise (DBSCAN) algorithm for efficiently clustering demand zones unreachable by conventional vehicles, thereby identifying areas requiring UAV delivery. Furthermore, we categorize the demand for medical supplies into two distinct sets based on vehicle accessibility, optimizing distribution routes via both UAVs and vehicles. Through comparative analysis, our findings reveal that the artificial bee colony (ABC) algorithm significantly outperforms the genetic algorithm in terms of solving efficiency, iteration counts, and delivery speed. However, the ABC algorithm’s tendency toward early local optimization and rapid convergence leads to potential stagnation in local optima. To mitigate this issue, we incorporate a simulated annealing technique into the ABC framework, culminating in a refined optimization approach which successfully overcomes the limitations of premature local optima convergence. The experimental results validate the efficacy of our enhanced algorithm, demonstrating reduced iteration counts, shorter computation times, and substantially improved solution quality over traditional logistic models. The proposed method holds promise for significantly improving the operational efficiency and service quality of the healthcare system’s logistics during critical situations.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"24 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13DOI: 10.3390/electronics13183644
Sang-Ha Sung, Soongoo Hong, Jong-Min Kim, Do-Young Kang, Hyuntae Park, Sangjin Kim
As the population ages, Alzheimer’s disease (AD) and Parkinson’s disease (PD) are increasingly common neurodegenerative diseases among the elderly. Human voice signals contain various characteristics, and the voice recording signals with time-series properties include key information such as pitch, tremor, and breathing cycle. Therefore, this study aims to propose an algorithm to classify normal individuals, Alzheimer’s patients, and Parkinson’s patients using these voice signal characteristics. The study subjects consist of a total of 700 individuals, who provided data by uttering 40 predetermined sentences. To extract the main characteristics of the recorded voices, a Mel–spectrogram was used, and these features were analyzed using a Convolutional Neural Network (CNN). The analysis results showed that the classification based on DenseNet exhibited the best performance. This study suggests the potential for classification of cognitive impairment through voice signal analysis.
{"title":"Cognitive Impairment Classification Prediction Model Using Voice Signal Analysis","authors":"Sang-Ha Sung, Soongoo Hong, Jong-Min Kim, Do-Young Kang, Hyuntae Park, Sangjin Kim","doi":"10.3390/electronics13183644","DOIUrl":"https://doi.org/10.3390/electronics13183644","url":null,"abstract":"As the population ages, Alzheimer’s disease (AD) and Parkinson’s disease (PD) are increasingly common neurodegenerative diseases among the elderly. Human voice signals contain various characteristics, and the voice recording signals with time-series properties include key information such as pitch, tremor, and breathing cycle. Therefore, this study aims to propose an algorithm to classify normal individuals, Alzheimer’s patients, and Parkinson’s patients using these voice signal characteristics. The study subjects consist of a total of 700 individuals, who provided data by uttering 40 predetermined sentences. To extract the main characteristics of the recorded voices, a Mel–spectrogram was used, and these features were analyzed using a Convolutional Neural Network (CNN). The analysis results showed that the classification based on DenseNet exhibited the best performance. This study suggests the potential for classification of cognitive impairment through voice signal analysis.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"156 1 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13DOI: 10.3390/electronics13183654
Patricia Callejo, Marco Gramaglia, Rubén Cuevas, Ángel Cuevas, Michael Carl Tschantz
The ubiquity and pervasiveness of mobile network technologies has made them so deeply ingrained in our everyday lives that by interacting with them for very simple purposes (e.g., messaging or browsing the Internet), we produce an unprecedented amount of data that can be analyzed to understand our behavior. While this practice has been extensively adopted by telcos and big tech companies in the last few years, this condition, which was unimaginable just 20 years ago, has only been mildly exploited to fight the COVID-19 pandemic. In this paper, we discuss the possible alternatives that we could leverage in the current mobile network ecosystem to provide regulators and epidemiologists with the right understanding of our mobility patterns, to maximize the efficiency and extent of the introduced countermeasures. To validate our analysis, we dissect a fine-grained dataset of user positions in two major European countries severely hit by the pandemic. The potential of using these data, harvested employing traditional mobile network technologies, is unveiled through two exemplary cases that tackled macro and microscopic aspects.
{"title":"Analyzing Mobility Patterns at Scale in Pandemic Scenarios Leveraging the Mobile Network Ecosystem","authors":"Patricia Callejo, Marco Gramaglia, Rubén Cuevas, Ángel Cuevas, Michael Carl Tschantz","doi":"10.3390/electronics13183654","DOIUrl":"https://doi.org/10.3390/electronics13183654","url":null,"abstract":"The ubiquity and pervasiveness of mobile network technologies has made them so deeply ingrained in our everyday lives that by interacting with them for very simple purposes (e.g., messaging or browsing the Internet), we produce an unprecedented amount of data that can be analyzed to understand our behavior. While this practice has been extensively adopted by telcos and big tech companies in the last few years, this condition, which was unimaginable just 20 years ago, has only been mildly exploited to fight the COVID-19 pandemic. In this paper, we discuss the possible alternatives that we could leverage in the current mobile network ecosystem to provide regulators and epidemiologists with the right understanding of our mobility patterns, to maximize the efficiency and extent of the introduced countermeasures. To validate our analysis, we dissect a fine-grained dataset of user positions in two major European countries severely hit by the pandemic. The potential of using these data, harvested employing traditional mobile network technologies, is unveiled through two exemplary cases that tackled macro and microscopic aspects.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"21 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13DOI: 10.3390/electronics13183646
Chieh-Huang Chen, Ying-Lei Lin, Ping-Feng Pai
The oriental lily ‘Casa Blanca’ is one of the most popular and high-value flowers. The period for keeping these flowers refrigerated is limited. Therefore, forecasting the prices of oriental lilies is crucial for determining the optimal planting time and, consequently, the profits earned by flower growers. Traditionally, the prediction of oriental lily prices has primarily relied on the experience and domain knowledge of farmers, lacking systematic analysis. This study aims to predict daily oriental lily prices at wholesale markets in Taiwan using many-to-many Long Short-Term Memory (MMLSTM) models. The determination of hyperparameters in MMLSTM models significantly influences their forecasting performance. This study employs Optuna, a hyperparameter optimization technique specifically designed for machine learning models, to select the hyperparameters of MMLSTM models. Various modeling datasets and forecasting time windows are used to evaluate the performance of the designed many-to-many Long Short-Term Memory with Optuna (MMLSTMOPT) models in predicting daily oriental lily prices. Numerical results indicate that the developed MMLSTMOPT model achieves highly satisfactory forecasting accuracy with an average mean absolute percentage error value of 12.7%. Thus, the MMLSTMOPT model is a feasible and promising alternative for forecasting the daily oriental lily prices.
{"title":"Forecasting Flower Prices by Long Short-Term Memory Model with Optuna","authors":"Chieh-Huang Chen, Ying-Lei Lin, Ping-Feng Pai","doi":"10.3390/electronics13183646","DOIUrl":"https://doi.org/10.3390/electronics13183646","url":null,"abstract":"The oriental lily ‘Casa Blanca’ is one of the most popular and high-value flowers. The period for keeping these flowers refrigerated is limited. Therefore, forecasting the prices of oriental lilies is crucial for determining the optimal planting time and, consequently, the profits earned by flower growers. Traditionally, the prediction of oriental lily prices has primarily relied on the experience and domain knowledge of farmers, lacking systematic analysis. This study aims to predict daily oriental lily prices at wholesale markets in Taiwan using many-to-many Long Short-Term Memory (MMLSTM) models. The determination of hyperparameters in MMLSTM models significantly influences their forecasting performance. This study employs Optuna, a hyperparameter optimization technique specifically designed for machine learning models, to select the hyperparameters of MMLSTM models. Various modeling datasets and forecasting time windows are used to evaluate the performance of the designed many-to-many Long Short-Term Memory with Optuna (MMLSTMOPT) models in predicting daily oriental lily prices. Numerical results indicate that the developed MMLSTMOPT model achieves highly satisfactory forecasting accuracy with an average mean absolute percentage error value of 12.7%. Thus, the MMLSTMOPT model is a feasible and promising alternative for forecasting the daily oriental lily prices.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"47 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13DOI: 10.3390/electronics13183648
Soichiro Kiyoki, Shigeo Yoshida, Mostafa A. Rushdi
In wind turbines, to investigate the cause of failures and evaluate the remaining lifetime, it may be necessary to measure their loads. However, it is often difficult to do so with only strain gauges in terms of cost and time, so a method to evaluate loads by utilizing only simple measurements is quite useful. In this study, we investigated a method with machine learning to estimate hub center loads, which is important in terms of preventing damage to equipment inside the nacelle. Traditionally, measuring hub center loads requires performing complex strain measurements on rotating parts, such as the blades or the main shaft. On the other hand, the tower is a stationary body, so the strain measurement difficulty is relatively low. We tackled the problem as follows: First, machine learning models that predict the time history of hub center loads from the tower top loads and operating condition data were developed by using aeroelastic analysis. Next, the accuracy of the model was verified by using measurement data from an actual wind turbine. Finally, individual pitch control, which is one of the applications of the time history of hub center loads, was performed using aeroelastic analysis, and the load reduction effect with the model prediction values was equivalent to that of the conventional method.
{"title":"Estimation of Hub Center Loads for Individual Pitch Control for Wind Turbines Based on Tower Loads and Machine Learning","authors":"Soichiro Kiyoki, Shigeo Yoshida, Mostafa A. Rushdi","doi":"10.3390/electronics13183648","DOIUrl":"https://doi.org/10.3390/electronics13183648","url":null,"abstract":"In wind turbines, to investigate the cause of failures and evaluate the remaining lifetime, it may be necessary to measure their loads. However, it is often difficult to do so with only strain gauges in terms of cost and time, so a method to evaluate loads by utilizing only simple measurements is quite useful. In this study, we investigated a method with machine learning to estimate hub center loads, which is important in terms of preventing damage to equipment inside the nacelle. Traditionally, measuring hub center loads requires performing complex strain measurements on rotating parts, such as the blades or the main shaft. On the other hand, the tower is a stationary body, so the strain measurement difficulty is relatively low. We tackled the problem as follows: First, machine learning models that predict the time history of hub center loads from the tower top loads and operating condition data were developed by using aeroelastic analysis. Next, the accuracy of the model was verified by using measurement data from an actual wind turbine. Finally, individual pitch control, which is one of the applications of the time history of hub center loads, was performed using aeroelastic analysis, and the load reduction effect with the model prediction values was equivalent to that of the conventional method.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"18 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13DOI: 10.3390/electronics13183652
Dawid Ostaszewicz, Krzysztof Rogowski
In this paper, genetic algorithms are applied to fine-tune the parameters of a system model characterized by unknown transfer functions utilizing the Strejc method. In this method, the high-order plant dynamic is approximated by the reduced-order multiple inertial transfer function. The primary objective of this research is to optimize the parameter values of the Strejc model using genetic algorithms to obtain the optimal value of the integral quality indicator for the model and step responses which fit the plant response. In the analysis, various structures of transfer functions will be considered. For fifth-order plants, different structures of a transfer function will be employed: second-order inertia and multiple-inertial models of different orders. The genotype structure is composed in such a way as to ensure the convergence of the method. A numerical example demonstrating the utility of the method of high-order plants is presented.
{"title":"Application of Genetic Algorithms for Strejc Model Parameter Tuning","authors":"Dawid Ostaszewicz, Krzysztof Rogowski","doi":"10.3390/electronics13183652","DOIUrl":"https://doi.org/10.3390/electronics13183652","url":null,"abstract":"In this paper, genetic algorithms are applied to fine-tune the parameters of a system model characterized by unknown transfer functions utilizing the Strejc method. In this method, the high-order plant dynamic is approximated by the reduced-order multiple inertial transfer function. The primary objective of this research is to optimize the parameter values of the Strejc model using genetic algorithms to obtain the optimal value of the integral quality indicator for the model and step responses which fit the plant response. In the analysis, various structures of transfer functions will be considered. For fifth-order plants, different structures of a transfer function will be employed: second-order inertia and multiple-inertial models of different orders. The genotype structure is composed in such a way as to ensure the convergence of the method. A numerical example demonstrating the utility of the method of high-order plants is presented.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"246 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13DOI: 10.3390/electronics13183653
Muhammad Ali Naeem, Yahui Meng, Sushank Chaudhary
The caching mechanism of federated learning in smart cities is vital for improving data handling and communication in IoT environments. Because it facilitates learning among separately connected devices, federated learning makes it possible to quickly update caching strategies in response to data usage without invading users’ privacy. Federated learning caching promotes improved dynamism, effectiveness, and data reachability for smart city services to function properly. In this paper, a new caching strategy for Named Data Networking (NDN) based on federated learning in smart cities’ IoT contexts is proposed and described. The proposed strategy seeks to apply a federated learning technique to improve content caching more effectively based on its popularity, thereby improving its performance on the network. The proposed strategy was compared to the benchmark in terms of the cache hit ratio, delay in content retrieval, and energy utilization. These benchmarks evidence that the suggested caching strategy performs far better than its counterparts in terms of cache hit rates, the time taken to fetch the content, and energy consumption. These enhancements result in smarter and more efficient smart city networks, a clear indication of how federated learning can revolutionize content caching in NDN-based IoT.
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