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":null,"pages":null},"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":null,"pages":null},"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/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":null,"pages":null},"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/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.
{"title":"The Impact of Federated Learning on Improving the IoT-Based Network in a Sustainable Smart Cities","authors":"Muhammad Ali Naeem, Yahui Meng, Sushank Chaudhary","doi":"10.3390/electronics13183653","DOIUrl":"https://doi.org/10.3390/electronics13183653","url":null,"abstract":"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.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209023","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":null,"pages":null},"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/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":null,"pages":null},"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 : 2024-09-12DOI: 10.3390/electronics13183624
Yanfang Hou, Hui Tian
This paper develops a parameter tuning method for solving the set restabilization problem of perturbed Boolean control networks (BCNs). First, the absorbable attractor, which we previously proposed, is recalled. Based on the relationship between attractors, a necessary and sufficient restabilizability criterion is derived. This criterion is used to check whether a perturbed BCN can be stabilized to the original target set by modifying the least number of parameters to the old controller. Furthermore, a constructive method for fine-tuning the old controller is provided if the criterion condition derived above is satisfied. Compared with the existing relevant results, ours have clear advantages, since they can address the set restabilization problem of BCNs subject to multi-column function perturbations, which has not been solved yet. Finally, two examples are employed to show the effectiveness and advantages of our results.
{"title":"Set Restabilization of Perturbed Boolean Control Networks","authors":"Yanfang Hou, Hui Tian","doi":"10.3390/electronics13183624","DOIUrl":"https://doi.org/10.3390/electronics13183624","url":null,"abstract":"This paper develops a parameter tuning method for solving the set restabilization problem of perturbed Boolean control networks (BCNs). First, the absorbable attractor, which we previously proposed, is recalled. Based on the relationship between attractors, a necessary and sufficient restabilizability criterion is derived. This criterion is used to check whether a perturbed BCN can be stabilized to the original target set by modifying the least number of parameters to the old controller. Furthermore, a constructive method for fine-tuning the old controller is provided if the criterion condition derived above is satisfied. Compared with the existing relevant results, ours have clear advantages, since they can address the set restabilization problem of BCNs subject to multi-column function perturbations, which has not been solved yet. Finally, two examples are employed to show the effectiveness and advantages of our results.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208974","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 : 2024-09-12DOI: 10.3390/electronics13183640
Jiangang Zhu, Donglin Jing, Dapeng Gao
Object detection in aerial images has had a broader range of applications in the past few years. Unlike the targets in the images of horizontal shooting, targets in aerial photos generally have arbitrary orientation, multi-scale, and a high aspect ratio. Existing methods often employ a classification backbone network to extract translation-equivariant features (TEFs) and utilize many predefined anchors to handle objects with diverse appearance variations. However, they encounter misalignment at three levels, spatial, feature, and task, during different detection stages. In this study, we propose a model called the Staged Adaptive Alignment Detector (SAADet) to solve these challenges. This method utilizes a Spatial Selection Adaptive Network (SSANet) to achieve spatial alignment of the convolution receptive field to the scale of the object by using a convolution sequence with an increasing dilation rate to capture the spatial context information of different ranges and evaluating this information through model dynamic weighting. After correcting the preset horizontal anchor to an oriented anchor, feature alignment is achieved through the alignment convolution guided by oriented anchor to align the backbone features with the object’s orientation. The decoupling of features using the Active Rotating Filter is performed to mitigate inconsistencies due to the sharing of backbone features in regression and classification tasks to accomplish task alignment. The experimental results show that SAADet achieves equilibrium in speed and accuracy on two aerial image datasets, HRSC2016 and UCAS-AOD.
{"title":"Stage-by-Stage Adaptive Alignment Mechanism for Object Detection in Aerial Images","authors":"Jiangang Zhu, Donglin Jing, Dapeng Gao","doi":"10.3390/electronics13183640","DOIUrl":"https://doi.org/10.3390/electronics13183640","url":null,"abstract":"Object detection in aerial images has had a broader range of applications in the past few years. Unlike the targets in the images of horizontal shooting, targets in aerial photos generally have arbitrary orientation, multi-scale, and a high aspect ratio. Existing methods often employ a classification backbone network to extract translation-equivariant features (TEFs) and utilize many predefined anchors to handle objects with diverse appearance variations. However, they encounter misalignment at three levels, spatial, feature, and task, during different detection stages. In this study, we propose a model called the Staged Adaptive Alignment Detector (SAADet) to solve these challenges. This method utilizes a Spatial Selection Adaptive Network (SSANet) to achieve spatial alignment of the convolution receptive field to the scale of the object by using a convolution sequence with an increasing dilation rate to capture the spatial context information of different ranges and evaluating this information through model dynamic weighting. After correcting the preset horizontal anchor to an oriented anchor, feature alignment is achieved through the alignment convolution guided by oriented anchor to align the backbone features with the object’s orientation. The decoupling of features using the Active Rotating Filter is performed to mitigate inconsistencies due to the sharing of backbone features in regression and classification tasks to accomplish task alignment. The experimental results show that SAADet achieves equilibrium in speed and accuracy on two aerial image datasets, HRSC2016 and UCAS-AOD.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208976","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}
This research introduces a novel approach to crisis management by implementing a multi-agent algorithm within a strategic decision system. The proposed system harnesses multiple agents’ collective intelligence and adaptive capabilities to enhance decision-making processes during critical situations. The study first investigates the theoretical foundations of crisis management and multi-agent systems, emphasizing the need for an integrated approach that combines strategic decision-making with autonomous agents. Subsequently, the research presents the design and implementation of the multi-agent algorithm, outlining its ability to gather, process, and analyze diverse data sources in real time. The multi-agent algorithm is specifically tailored to adapt to dynamic crisis scenarios, ensuring a resilient decision-making framework. Experimental simulations present the implementation of a panic simulator and prediction of evacuation and intervention routes using multi-agent artificial intelligence algorithms. The results demonstrate the multi-agent algorithm-driven decision system’s superiority in response time, resource allocation, and overall crisis mitigation. Furthermore, the research explores the system’s scalability and adaptability to different crisis types, illustrating its potential applicability across diverse domains.
{"title":"Crowd Panic Behavior Simulation Using Multi-Agent Modeling","authors":"Cătălin Dumitrescu, Valentin Radu, Radu Gheorghe, Alina-Iuliana Tăbîrcă, Maria-Cristina Ștefan, Liliana Manea","doi":"10.3390/electronics13183622","DOIUrl":"https://doi.org/10.3390/electronics13183622","url":null,"abstract":"This research introduces a novel approach to crisis management by implementing a multi-agent algorithm within a strategic decision system. The proposed system harnesses multiple agents’ collective intelligence and adaptive capabilities to enhance decision-making processes during critical situations. The study first investigates the theoretical foundations of crisis management and multi-agent systems, emphasizing the need for an integrated approach that combines strategic decision-making with autonomous agents. Subsequently, the research presents the design and implementation of the multi-agent algorithm, outlining its ability to gather, process, and analyze diverse data sources in real time. The multi-agent algorithm is specifically tailored to adapt to dynamic crisis scenarios, ensuring a resilient decision-making framework. Experimental simulations present the implementation of a panic simulator and prediction of evacuation and intervention routes using multi-agent artificial intelligence algorithms. The results demonstrate the multi-agent algorithm-driven decision system’s superiority in response time, resource allocation, and overall crisis mitigation. Furthermore, the research explores the system’s scalability and adaptability to different crisis types, illustrating its potential applicability across diverse domains.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208975","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 : 2024-09-11DOI: 10.3390/electronics13183606
Junxin Shen, Shuilan Zhou, Fanghao Xiao
Exploring the data quality problems in the context of federated cooperation and adopting corresponding governance countermeasures can facilitate the smooth progress of federated cooperation and obtain high-performance models. However, previous studies have rarely focused on quality issues in federated cooperation. To this end, this paper analyzes the quality problems in the federated cooperation scenario and innovatively proposes a “Two-stage” data quality governance framework for the federated collaboration scenarios. The first stage is mainly local data quality assessment and optimization, and the evaluation is performed by constructing a metrics scoring formula, and corresponding optimization measures are taken at the same time. In the second stage, the outlier processing mechanism is introduced, and the Data Quality Federated Averaging (Abbreviation DQ-FedAvg) aggregation method for model quality problems is proposed, so as to train high-quality global models and their own excellent local models. Finally, experiments are conducted in real datasets to compare the model performance changes before and after quality governance, and to validate the advantages of the data quality governance framework in a federated learning scenario, so that it can be widely applied to various domains. The governance framework is used to check and govern the quality problems in the federated learning process, and the accuracy of the model is improved.
{"title":"Research on Data Quality Governance for Federated Cooperation Scenarios","authors":"Junxin Shen, Shuilan Zhou, Fanghao Xiao","doi":"10.3390/electronics13183606","DOIUrl":"https://doi.org/10.3390/electronics13183606","url":null,"abstract":"Exploring the data quality problems in the context of federated cooperation and adopting corresponding governance countermeasures can facilitate the smooth progress of federated cooperation and obtain high-performance models. However, previous studies have rarely focused on quality issues in federated cooperation. To this end, this paper analyzes the quality problems in the federated cooperation scenario and innovatively proposes a “Two-stage” data quality governance framework for the federated collaboration scenarios. The first stage is mainly local data quality assessment and optimization, and the evaluation is performed by constructing a metrics scoring formula, and corresponding optimization measures are taken at the same time. In the second stage, the outlier processing mechanism is introduced, and the Data Quality Federated Averaging (Abbreviation DQ-FedAvg) aggregation method for model quality problems is proposed, so as to train high-quality global models and their own excellent local models. Finally, experiments are conducted in real datasets to compare the model performance changes before and after quality governance, and to validate the advantages of the data quality governance framework in a federated learning scenario, so that it can be widely applied to various domains. The governance framework is used to check and govern the quality problems in the federated learning process, and the accuracy of the model is improved.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209058","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}
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