Reconfigurable Intelligent Surface (RIS) technology represents a significant advancement in wireless communication and signal transmission, operating in passive, active, and hybrid modalities. The hybrid mode combines the advantages of passive and active configurations, allowing RIS units to collaborate with active transceivers to reflect and amplify signals efficiently. However the optimization of hybrid RIS is settled in area of continuous research. This study proposes the maximization of Spectral Efficiency (SE) through optimized amplification gains and phase shifts of RIS using of Block Coordinate Descent (BCD) as a method by breaking down a complex task into simpler subproblems, thereby enhancing the overall optimization process. Evaluation demonstrates the efficacy of our approach in maximizing system’s (SE), enhancing Quality of Service (QoS), while considering RIS power budget constraints. The simulation results demonstrated that the proposed hybrid RIS model achieved an SNR improvement of at least 42% for 120 RIS elements compared to conventional schemes.
{"title":"Enhancing Spectral Efficiency with a novel hybrid Reconfigurable Intelligent Surface scheme","authors":"Maria-Garyfallio Volakaki , Grigorios Papaioannou , Demosthenes Vouyioukas","doi":"10.1016/j.fraope.2025.100234","DOIUrl":"10.1016/j.fraope.2025.100234","url":null,"abstract":"<div><div>Reconfigurable Intelligent Surface (RIS) technology represents a significant advancement in wireless communication and signal transmission, operating in passive, active, and hybrid modalities. The hybrid mode combines the advantages of passive and active configurations, allowing RIS units to collaborate with active transceivers to reflect and amplify signals efficiently. However the optimization of hybrid RIS is settled in area of continuous research. This study proposes the maximization of Spectral Efficiency (SE) through optimized amplification gains and phase shifts of RIS using of Block Coordinate Descent (BCD) as a method by breaking down a complex task into simpler subproblems, thereby enhancing the overall optimization process. Evaluation demonstrates the efficacy of our approach in maximizing system’s (SE), enhancing Quality of Service (QoS), while considering RIS power budget constraints. The simulation results demonstrated that the proposed hybrid RIS model achieved an SNR improvement of at least 42% for 120 RIS elements compared to conventional schemes.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"10 ","pages":"Article 100234"},"PeriodicalIF":0.0,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-14DOI: 10.1016/j.fraope.2025.100233
Ramyashri B. Ramteke , Gaurav O. Gajbhiye , Vijaya R. Thool
In today’s competitive environment, everyone is under psychological stress. Long-term exposure to stress can lead to serious issues such as high blood pressure, depression, violence, cardiac and brain damage, and even suicide. To live a healthy lifestyle, it is critical to monitor stress and its levels regularly. Existing methods detect stress specifically, whereas detection of multiple levels of stress has yet to be explored. To address this issue, the paper presents the lightweight ECG-stress-ScaloNet model, which employs an attentive convolutional neural network (CNN) to analyze short-term ECG scalogram images. In this work, a unique inception-attention block is created. The inception module captures multi-scale information; additionally, attention focuses on extracting meaningful features from multi-scale feature maps by utilizing cross-channel and spatial information. Two databases are used to evaluate the proposed ECG-stress-ScaloNet model. The first is Physionet driver stress and normal ECG data that is publicly available, and the second is self-created academic practical-viva stress and normal ECG data. The ECG-stress-ScaloNet outperforms the existing methods, with a test accuracy of 98.28% for the Physionet dataset and 95.71% for the self-created dataset. For the intended application, the ECG-stress-ScaloNet model is reliable and accurate since it has fewer learnable parameters and decreases computational complexity.
{"title":"Acute mental stress level detection: ECG-scalogram based attentive convolutional network","authors":"Ramyashri B. Ramteke , Gaurav O. Gajbhiye , Vijaya R. Thool","doi":"10.1016/j.fraope.2025.100233","DOIUrl":"10.1016/j.fraope.2025.100233","url":null,"abstract":"<div><div>In today’s competitive environment, everyone is under psychological stress. Long-term exposure to stress can lead to serious issues such as high blood pressure, depression, violence, cardiac and brain damage, and even suicide. To live a healthy lifestyle, it is critical to monitor stress and its levels regularly. Existing methods detect stress specifically, whereas detection of multiple levels of stress has yet to be explored. To address this issue, the paper presents the lightweight ECG-stress-ScaloNet model, which employs an attentive convolutional neural network (CNN) to analyze short-term ECG scalogram images. In this work, a unique inception-attention block is created. The inception module captures multi-scale information; additionally, attention focuses on extracting meaningful features from multi-scale feature maps by utilizing cross-channel and spatial information. Two databases are used to evaluate the proposed ECG-stress-ScaloNet model. The first is Physionet driver stress and normal ECG data that is publicly available, and the second is self-created academic practical-viva stress and normal ECG data. The ECG-stress-ScaloNet outperforms the existing methods, with a test accuracy of 98.28% for the Physionet dataset and 95.71% for the self-created dataset. For the intended application, the ECG-stress-ScaloNet model is reliable and accurate since it has fewer learnable parameters and decreases computational complexity.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"10 ","pages":"Article 100233"},"PeriodicalIF":0.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-12DOI: 10.1016/j.fraope.2025.100231
Jamilu Yahaya , Poom Kumam , Abdulmalik Usman Bello
Conjugate gradient methods play a crucial role in solving unconstrained optimization problems and have recently been extended to vector optimization problems (VOPs). This paper introduces four conjugate gradient methods for finding critical points of vector-valued functions with respect to the partial order induced by a closed, convex, and pointed cone with nonempty-interior, inspired by the Dai–Liao method. Initially, two Dai–Liao-type conjugate gradient methods are proposed. While these methods do not guarantee a descent direction, they are proven to converge under the assumption that a descent direction exists. These methods are further refined into modified versions that satisfy the sufficient descent condition. By employing the Wolfe line search, the sufficient descent condition is satisfied, and global convergence is achieved without requiring regular restarts or assumptions of convexity on the objective functions. Numerical experiments are conducted to demonstrate the effectiveness of the proposed methods with detailed implementation and results provided.
{"title":"Some descent Dai-Liao-type conjugate gradient methods for vector optimization","authors":"Jamilu Yahaya , Poom Kumam , Abdulmalik Usman Bello","doi":"10.1016/j.fraope.2025.100231","DOIUrl":"10.1016/j.fraope.2025.100231","url":null,"abstract":"<div><div>Conjugate gradient methods play a crucial role in solving unconstrained optimization problems and have recently been extended to vector optimization problems (VOPs). This paper introduces four conjugate gradient methods for finding critical points of vector-valued functions with respect to the partial order induced by a closed, convex, and pointed cone with nonempty-interior, inspired by the Dai–Liao method. Initially, two Dai–Liao-type conjugate gradient methods are proposed. While these methods do not guarantee a descent direction, they are proven to converge under the assumption that a descent direction exists. These methods are further refined into modified versions that satisfy the sufficient descent condition. By employing the Wolfe line search, the sufficient descent condition is satisfied, and global convergence is achieved without requiring regular restarts or assumptions of convexity on the objective functions. Numerical experiments are conducted to demonstrate the effectiveness of the proposed methods with detailed implementation and results provided.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"10 ","pages":"Article 100231"},"PeriodicalIF":0.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-08DOI: 10.1016/j.fraope.2025.100232
Ranen Sen, Saurabh Shukla, Shakti Singh
An effective method for controlling battery power in electric vehicles to improve their interaction with the electrical grid is presented in this research study. A simplistic approach is adopted to provide a viable solution for battery charging from the grid as well as feeding power to the grid whenever required. A rotor flux-oriented mechanical sensorless scheme is presented for induction motor driven electric vehicle. An extensive study is carried out for different operating modes of electrical vehicle viz. motoring and regeneration by utilizing same control mechanism. The motor is controlled by a 3 phase Voltage Source Converter (VSC) and the grid side converter is used for the DC bus voltage regulation. An aiding device for bidirectional power flow is a bidirectional buck-boost converter. The current multiplier approach (CMA) concept controls the bidirectional power flow between the single-phase grid source and the standard DC bus voltage that links to the induction motor driven EV via 3-phase VSC. Furthermore, this method enhances power quality by preserving a unity power factor (UPF) and lowering THD. In addition to outlining the mathematical model and system-wide power management strategy, the study is presented and evaluated using MATLAB/Simulink platform. This work is further validated on hardware setup developed in the laboratory. Real-time test results confirm the system's compliance with different regulatory guidelines viz. IEEE 519–2014 and highlight the enhanced performance of the proposed control strategy for wide range of vehicular operations.
{"title":"Enhanced control strategy for grid fed battery assisted induction motor based electric vehicle","authors":"Ranen Sen, Saurabh Shukla, Shakti Singh","doi":"10.1016/j.fraope.2025.100232","DOIUrl":"10.1016/j.fraope.2025.100232","url":null,"abstract":"<div><div>An effective method for controlling battery power in electric vehicles to improve their interaction with the electrical grid is presented in this research study. A simplistic approach is adopted to provide a viable solution for battery charging from the grid as well as feeding power to the grid whenever required. A rotor flux-oriented mechanical sensorless scheme is presented for induction motor driven electric vehicle. An extensive study is carried out for different operating modes of electrical vehicle viz. motoring and regeneration by utilizing same control mechanism. The motor is controlled by a 3 phase Voltage Source Converter (VSC) and the grid side converter is used for the DC bus voltage regulation. An aiding device for bidirectional power flow is a bidirectional buck-boost converter. The current multiplier approach (CMA) concept controls the bidirectional power flow between the single-phase grid source and the standard DC bus voltage that links to the induction motor driven EV via 3-phase VSC. Furthermore, this method enhances power quality by preserving a unity power factor (UPF) and lowering THD. In addition to outlining the mathematical model and system-wide power management strategy, the study is presented and evaluated using MATLAB/Simulink platform. This work is further validated on hardware setup developed in the laboratory. Real-time test results confirm the system's compliance with different regulatory guidelines viz. IEEE 519–2014 and highlight the enhanced performance of the proposed control strategy for wide range of vehicular operations.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"10 ","pages":"Article 100232"},"PeriodicalIF":0.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-06DOI: 10.1016/j.fraope.2025.100229
Ramez Abdalla , Nermine Agban , Christian Lüddeke , Dan Sui , Philip Jaeger
Waterflooding optimization is a critical process for enhancing oil recovery in mature oil fields, where conventional approaches often rely on fixed injection rates over an extended period. However, this may not be the most efficient strategy due to reservoir heterogeneity and complexity. In this study, we propose a multi-agent physics informed reinforcement learning (MAPIRL) framework to optimize the waterflooding process. The MAPIRL approach utilizes a Markov decision process to formulate the optimization problem, where multiple RL agents are trained to interact with a reservoir simulation model and receive rewards for each action. The proposed approach uses an actor–critic RL architecture to train the agents to find the optimal strategy. The agents interact with the environment during several episodes until convergence is achieved. We evaluated the effectiveness of the MAPIRL approach based on the improvement in net present value (NPV), which reflects the economic benefits of the optimized waterflooding strategy. Then, we compared the MAPIRL approach with the multi-objective particle swarm optimization (MOPSO) algorithm. The comparison revealed that the MAPIRL approach outperformed the MOPSO algorithm in terms of net present value. In conclusion, the MAPIRL approach is a scientifically accurate method for optimizing waterflooding in mature oil fields, providing a more efficient and robust waterflooding strategy that reduces water consumption and associated costs while maximizing the economic benefits. The ability of the MAPIRL approach to optimize the waterflooding process with a high degree of complexity makes it a promising tool for the energy industry, and further research is needed to explore its potential for addressing other complex problems in this domain.
{"title":"Multi agent physics informed reinforcement learning for waterflooding optimization","authors":"Ramez Abdalla , Nermine Agban , Christian Lüddeke , Dan Sui , Philip Jaeger","doi":"10.1016/j.fraope.2025.100229","DOIUrl":"10.1016/j.fraope.2025.100229","url":null,"abstract":"<div><div>Waterflooding optimization is a critical process for enhancing oil recovery in mature oil fields, where conventional approaches often rely on fixed injection rates over an extended period. However, this may not be the most efficient strategy due to reservoir heterogeneity and complexity. In this study, we propose a multi-agent physics informed reinforcement learning (MAPIRL) framework to optimize the waterflooding process. The MAPIRL approach utilizes a Markov decision process to formulate the optimization problem, where multiple RL agents are trained to interact with a reservoir simulation model and receive rewards for each action. The proposed approach uses an actor–critic RL architecture to train the agents to find the optimal strategy. The agents interact with the environment during several episodes until convergence is achieved. We evaluated the effectiveness of the MAPIRL approach based on the improvement in net present value (NPV), which reflects the economic benefits of the optimized waterflooding strategy. Then, we compared the MAPIRL approach with the multi-objective particle swarm optimization (MOPSO) algorithm. The comparison revealed that the MAPIRL approach outperformed the MOPSO algorithm in terms of net present value. In conclusion, the MAPIRL approach is a scientifically accurate method for optimizing waterflooding in mature oil fields, providing a more efficient and robust waterflooding strategy that reduces water consumption and associated costs while maximizing the economic benefits. The ability of the MAPIRL approach to optimize the waterflooding process with a high degree of complexity makes it a promising tool for the energy industry, and further research is needed to explore its potential for addressing other complex problems in this domain.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"10 ","pages":"Article 100229"},"PeriodicalIF":0.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-03DOI: 10.1016/j.fraope.2025.100226
Sadiq Ali , Nabeel Ali Khan
We present a robust non-stationary signal analysis tool that is applied to accurate parameter estimation of multi-sensor non-stationary signals. This tool analyzes multi-sensor signals in a joint Time–frequency and Spatial-Frequency (JTFSF) domain using quadratic and linear representations. It is illustrated that multi-source signals with overlapping Time-Frequency signatures become non-overlapping in JTFSF domains thus allowing more refined signal analysis than conventional Time-Frequency representations. The JTFSF domain representation is applied to solve the parameters (Instantaneous Frequency) estimation problem of closely placed components in the Time-Frequency domain by employing joint Time–Frequency and Spatial filtering. It is illustrated that the proposed tool achieves superior performance than the state of art methods in terms of accuracy.
{"title":"Analyzing non-stationary signals: A joint time-frequency and spatial-frequency approach","authors":"Sadiq Ali , Nabeel Ali Khan","doi":"10.1016/j.fraope.2025.100226","DOIUrl":"10.1016/j.fraope.2025.100226","url":null,"abstract":"<div><div>We present a robust non-stationary signal analysis tool that is applied to accurate parameter estimation of multi-sensor non-stationary signals. This tool analyzes multi-sensor signals in a joint Time–frequency and Spatial-Frequency (JTFSF) domain using quadratic and linear representations. It is illustrated that multi-source signals with overlapping Time-Frequency signatures become non-overlapping in JTFSF domains thus allowing more refined signal analysis than conventional Time-Frequency representations. The JTFSF domain representation is applied to solve the parameters (Instantaneous Frequency) estimation problem of closely placed components in the Time-Frequency domain by employing joint Time–Frequency and Spatial filtering. It is illustrated that the proposed tool achieves superior performance than the state of art methods in terms of accuracy.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"10 ","pages":"Article 100226"},"PeriodicalIF":0.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-03DOI: 10.1016/j.fraope.2025.100230
Eda Gizem Koçyiğit , M. Iqbal Jeelani , Khalid Ul Islam Rather
This paper presents a novel approach to estimating the population mean by introducing a modified class of ratio estimators that effectively use auxiliary variables. Specifically, the coefficient of skewness (Sk) and quartile deviation (QD) are utilized within three distinct sampling methods: simple random sampling (SRS), ranked set sampling (RSS), and median ranked set sampling (MRSS). The estimators can improve accuracy and precision by incorporating these known auxiliary variables. The study investigates the estimators' mean square error (MSE) and bias, analyzing their performance up to the first degree of approximation. Through simulation and empirical studies, the results demonstrate the superior performance of the proposed estimators compared to existing methods.
{"title":"A new class of ratio estimators under different sampling techniques","authors":"Eda Gizem Koçyiğit , M. Iqbal Jeelani , Khalid Ul Islam Rather","doi":"10.1016/j.fraope.2025.100230","DOIUrl":"10.1016/j.fraope.2025.100230","url":null,"abstract":"<div><div>This paper presents a novel approach to estimating the population mean by introducing a modified class of ratio estimators that effectively use auxiliary variables. Specifically, the coefficient of skewness (<em>S<sub>k</sub></em>) and quartile deviation (QD) are utilized within three distinct sampling methods: simple random sampling (SRS), ranked set sampling (RSS), and median ranked set sampling (MRSS). The estimators can improve accuracy and precision by incorporating these known auxiliary variables. The study investigates the estimators' mean square error (MSE) and bias, analyzing their performance up to the first degree of approximation. Through simulation and empirical studies, the results demonstrate the superior performance of the proposed estimators compared to existing methods.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"10 ","pages":"Article 100230"},"PeriodicalIF":0.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-30DOI: 10.1016/j.fraope.2025.100227
Lukas M.N. Gabriel , John A. Adebisi , Leokadia N.P. Ndjuluwa , Dickson K. Chembe
This study investigates the deployment of smart grid technologies in electricity distribution networks for possible improvement of energy reliability and continuity of energy supply to consumers. Recently energy distribution companies have been observed to have been challenged with power disruptions, which mostly experienced during the rainy season. These disruptions are as results of growing demand, aging infrastructures, lack of advanced technologies and dependability on major utility companies for power supply hence this study. This investigative research used both qualitative and quantitative methodologies to identify potential opportunities and challenges associated with smart grid technologies deployment into distribution networks using Central North Regional Electricity Distributor (CENORED) and Namibia Power Corporation (NAMPOWER) as a case study for the possibility of smart technologies deployment to enhance energy distribution network. Questionnaires and interviews were administered for engineers and stakeholder with over 80 % respondents in agreement that the deployment of Distributed Energy Resources (DERs) and Demand Side Response (DSR) can improve network reliability while 100 % respondents aligned that Fault Location, Isolation and Service Restoration technology (FLISR) can help them improve the performance of their network. CENORED network was modeled using ETAP software for performance evaluation. Result from this study shows that DERs, FLISR and DSR possess a potential to improve distribution network reliability and possible implementation.
{"title":"Investigation of smart grid technologies deployment for energy reliability enhancement in electricity distribution networks","authors":"Lukas M.N. Gabriel , John A. Adebisi , Leokadia N.P. Ndjuluwa , Dickson K. Chembe","doi":"10.1016/j.fraope.2025.100227","DOIUrl":"10.1016/j.fraope.2025.100227","url":null,"abstract":"<div><div>This study investigates the deployment of smart grid technologies in electricity distribution networks for possible improvement of energy reliability and continuity of energy supply to consumers. Recently energy distribution companies have been observed to have been challenged with power disruptions, which mostly experienced during the rainy season. These disruptions are as results of growing demand, aging infrastructures, lack of advanced technologies and dependability on major utility companies for power supply hence this study. This investigative research used both qualitative and quantitative methodologies to identify potential opportunities and challenges associated with smart grid technologies deployment into distribution networks using Central North Regional Electricity Distributor (CENORED) and Namibia Power Corporation (NAMPOWER) as a case study for the possibility of smart technologies deployment to enhance energy distribution network. Questionnaires and interviews were administered for engineers and stakeholder with over 80 % respondents in agreement that the deployment of Distributed Energy Resources (DERs) and Demand Side Response (DSR) can improve network reliability while 100 % respondents aligned that Fault Location, Isolation and Service Restoration technology (FLISR) can help them improve the performance of their network. CENORED network was modeled using ETAP software for performance evaluation. Result from this study shows that DERs, FLISR and DSR possess a potential to improve distribution network reliability and possible implementation.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"10 ","pages":"Article 100227"},"PeriodicalIF":0.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143372279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-29DOI: 10.1016/j.fraope.2025.100225
Afolabi I. Awodeyi, Omolegho A. Ibok, Idama Omokaro, Jones U. Ekwemuka, Michael O. Ighofiomoni
Facial recognition systems are increasingly used across various applications; however, their performance often degrades in challenging conditions such as poor lighting and occlusions. Preprocessing techniques play a critical role in improving input image quality, enhancing feature extraction, and ultimately boosting recognition accuracy. This study evaluates advanced preprocessing methods, including edge detection using the Canny detector and illumination normalization through histogram equalization and gamma correction, which are integrated into a preprocessing pipeline. A detailed comparative analysis demonstrates significant recognition rate improvements under low-light and occluded scenarios, supported by quantitative evidence. Additionally, computational efficiency is evaluated, highlighting the applicability of these methods for large-scale and real-time systems. The results affirm that effective preprocessing strengthens the performance and reliability of facial recognition systems, making them suitable for real-world applications where conditions are often unpredictable.
{"title":"Effective preprocessing techniques for improved facial recognition under variable conditions","authors":"Afolabi I. Awodeyi, Omolegho A. Ibok, Idama Omokaro, Jones U. Ekwemuka, Michael O. Ighofiomoni","doi":"10.1016/j.fraope.2025.100225","DOIUrl":"10.1016/j.fraope.2025.100225","url":null,"abstract":"<div><div>Facial recognition systems are increasingly used across various applications; however, their performance often degrades in challenging conditions such as poor lighting and occlusions. Preprocessing techniques play a critical role in improving input image quality, enhancing feature extraction, and ultimately boosting recognition accuracy. This study evaluates advanced preprocessing methods, including edge detection using the Canny detector and illumination normalization through histogram equalization and gamma correction, which are integrated into a preprocessing pipeline. A detailed comparative analysis demonstrates significant recognition rate improvements under low-light and occluded scenarios, supported by quantitative evidence. Additionally, computational efficiency is evaluated, highlighting the applicability of these methods for large-scale and real-time systems. The results affirm that effective preprocessing strengthens the performance and reliability of facial recognition systems, making them suitable for real-world applications where conditions are often unpredictable.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"10 ","pages":"Article 100225"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143372280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-26DOI: 10.1016/j.fraope.2025.100214
Qi Wu , Yong Zhang , Tingting Ru , Chenxiao Cai
This paper investigates the temperature control problem in hydrogen fuel cells based on the improved sliding mode control method, specifically within the context of multirotor drone applications. The study focuses on constructing a control-oriented nonlinear thermal model, which serves as a foundation for the subsequent development of a practical temperature regulation approach. Initially, a novel sliding mode control strategy is proposed, which significantly enhances the precision and stability of temperature control by reducing the impact of sensor errors and environmental disturbances. Subsequently, the effectiveness and robustness of this control method under various dynamic loads and environmental conditions are demonstrated. The simulation results demonstrate that the improved sliding mode controller is effective in managing and regulating the fuel cell temperature, ensuring optimal performance and stability.
{"title":"Improved sliding mode temperature control of hydrogen fuel cells for multirotor drones","authors":"Qi Wu , Yong Zhang , Tingting Ru , Chenxiao Cai","doi":"10.1016/j.fraope.2025.100214","DOIUrl":"10.1016/j.fraope.2025.100214","url":null,"abstract":"<div><div>This paper investigates the temperature control problem in hydrogen fuel cells based on the improved sliding mode control method, specifically within the context of multirotor drone applications. The study focuses on constructing a control-oriented nonlinear thermal model, which serves as a foundation for the subsequent development of a practical temperature regulation approach. Initially, a novel sliding mode control strategy is proposed, which significantly enhances the precision and stability of temperature control by reducing the impact of sensor errors and environmental disturbances. Subsequently, the effectiveness and robustness of this control method under various dynamic loads and environmental conditions are demonstrated. The simulation results demonstrate that the improved sliding mode controller is effective in managing and regulating the fuel cell temperature, ensuring optimal performance and stability.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"10 ","pages":"Article 100214"},"PeriodicalIF":0.0,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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