This study proposes a system for biometric access control utilising the improved Cultural Chicken Swarm Optimization(CCSO)technique.This approach mitigates the limitations of conventional Chicken Swarm Optimization(CS...This study proposes a system for biometric access control utilising the improved Cultural Chicken Swarm Optimization(CCSO)technique.This approach mitigates the limitations of conventional Chicken Swarm Optimization(CSO),especially in dealing with larger dimensions due to diversity loss during solution space exploration.Our experimentation involved 600 sample images encompassing facial,iris,and fingerprint data,collected from 200 students at Ladoke Akintola University of Technology(LAUTECH),Ogbomoso.The results demonstrate the remarkable effectiveness of CCSO,yielding accuracy rates of 90.42%,91.67%,and 91.25%within 54.77,27.35,and 113.92 s for facial,fingerprint,and iris biometrics,respectively.These outcomes significantly outperform those achieved by the conventional CSO technique,which produced accuracy rates of 82.92%,86.25%,and 84.58%at 92.57,63.96,and 163.94 s for the same biometric modalities.The study’s findings reveal that CCSO,through its integration of Cultural Algorithm(CA)Operators into CSO,not only enhances algorithm performance,exhibiting computational efficiency and superior accuracy,but also carries broader implications beyond biometric systems.This innovation offers practical benefits in terms of security enhancement,operational efficiency,and adaptability across diverse user populations,shaping more effective and resource-efficient access control systems with real-world applicability.展开更多
Backbreak is one of the undesirable phenomena in open-pit mines and causes several adverse hazards,such as lanslide,rock falling off and bench instability.Backbreak is influenced by many factors,such as rock propertie...Backbreak is one of the undesirable phenomena in open-pit mines and causes several adverse hazards,such as lanslide,rock falling off and bench instability.Backbreak is influenced by many factors,such as rock properties,blasting design and local geology,so it is very difficult to assess and evaluate backbreak accurately.Therefore,controlling and accurate prediction of backbreak distance are crucial tasks to reduce hazards in open-pit mines.For this,soft computing-based techniques are considered to be an effective means,as they can integrate various sophisticated factors into a function to predict and evaluate backbreak distance.So,in this study,support vector regression(SVR)based techniques and three different types of bio-inspired meta-heuristic(BIMH)algorithms,such as chicken swarm optimization(CSO),whale optimization algorithm(WOA)and seagull optimization al gorithm(SOA),are used to develop backbreak distance prediction models.The support vector regression is used as a regression tool and BIMH algorithms are used to optimize the hyper-parameters in the support vector regression.Four different types of evaluation metrics are utilized to assess the model performance,namely co efficient of determination(R^(2)),mean square error(MSE),mean absolute error(MAE)and variance account for(VAF).An integrated evaluation system is adopted to provide overall performance for each backbreak prediction scenario.It can be indicated that CSO-SVR based backbreak prediction models can procure the best compre hensive performance and also show the best calculation efficiency.Detailed results include R^(2),VAF,MSE and MAEequal to 0.99475,0.034,99.477 and 0.1553 for a testing set and 0.97450,0.1633,97.466,and 0.1914 for a training set which can be said to be an excellent prediction result.By doing this,the hazard risk induced by backbreak can be indirectly assessed.In addition,it is also found that some superior performance can be obtained in some evaluation metrics compared with previous studies which utilized the same backbreak dataset for prediction.展开更多
With the continuous growth of the tertiary industry and residential loads,balancing the power supply and consumption during peak demand time has become a critical issue.Some studies try to alleviate peak load by incre...With the continuous growth of the tertiary industry and residential loads,balancing the power supply and consumption during peak demand time has become a critical issue.Some studies try to alleviate peak load by increasing power generation on the supply side.Due to the short duration of peak load,this may cause redundant installation capacity.Alternatively,others attempt to shave peak demand by installing energy storage facilities.However,the aforementioned research did not consider interruptible load regulation when optimizing system operations.In fact,regulating interruptible load has great potential for reducing system peak load.In this paper,an interruptible load scheduling model considering the user subsidy rate is first proposed to reduce system peak load and operational costs.This model has fully addressed the constraints of minimum daily load reduction and user interruption load time.After that,by taking a community in Shanghai as an example,the improved chicken swarm optimization algorithm is applied to solve the interruptible load scheduling scheme.Finally,the simulation results validate the efficacy of the proposed optimization algorithm and indicate the significant advantages of the proposed model in alleviating the peak load and reducing operational costs.展开更多
基金supported by Ladoke Akintola University of Technology,Ogbomoso,Nigeria and the University of Zululand,South Africa.
文摘This study proposes a system for biometric access control utilising the improved Cultural Chicken Swarm Optimization(CCSO)technique.This approach mitigates the limitations of conventional Chicken Swarm Optimization(CSO),especially in dealing with larger dimensions due to diversity loss during solution space exploration.Our experimentation involved 600 sample images encompassing facial,iris,and fingerprint data,collected from 200 students at Ladoke Akintola University of Technology(LAUTECH),Ogbomoso.The results demonstrate the remarkable effectiveness of CCSO,yielding accuracy rates of 90.42%,91.67%,and 91.25%within 54.77,27.35,and 113.92 s for facial,fingerprint,and iris biometrics,respectively.These outcomes significantly outperform those achieved by the conventional CSO technique,which produced accuracy rates of 82.92%,86.25%,and 84.58%at 92.57,63.96,and 163.94 s for the same biometric modalities.The study’s findings reveal that CCSO,through its integration of Cultural Algorithm(CA)Operators into CSO,not only enhances algorithm performance,exhibiting computational efficiency and superior accuracy,but also carries broader implications beyond biometric systems.This innovation offers practical benefits in terms of security enhancement,operational efficiency,and adaptability across diverse user populations,shaping more effective and resource-efficient access control systems with real-world applicability.
基金the State Key Laboratory of Precision Blasting and Hubei Key Laboratory of Blasting Engineering,Jianghan University in China(No.PBSKL2023A12)the Distinguished Youth Science Foundation of Hunan Province of China(No.2022JJ10073)The first author is supported by China Scholarship Council(No.202006370006).
文摘Backbreak is one of the undesirable phenomena in open-pit mines and causes several adverse hazards,such as lanslide,rock falling off and bench instability.Backbreak is influenced by many factors,such as rock properties,blasting design and local geology,so it is very difficult to assess and evaluate backbreak accurately.Therefore,controlling and accurate prediction of backbreak distance are crucial tasks to reduce hazards in open-pit mines.For this,soft computing-based techniques are considered to be an effective means,as they can integrate various sophisticated factors into a function to predict and evaluate backbreak distance.So,in this study,support vector regression(SVR)based techniques and three different types of bio-inspired meta-heuristic(BIMH)algorithms,such as chicken swarm optimization(CSO),whale optimization algorithm(WOA)and seagull optimization al gorithm(SOA),are used to develop backbreak distance prediction models.The support vector regression is used as a regression tool and BIMH algorithms are used to optimize the hyper-parameters in the support vector regression.Four different types of evaluation metrics are utilized to assess the model performance,namely co efficient of determination(R^(2)),mean square error(MSE),mean absolute error(MAE)and variance account for(VAF).An integrated evaluation system is adopted to provide overall performance for each backbreak prediction scenario.It can be indicated that CSO-SVR based backbreak prediction models can procure the best compre hensive performance and also show the best calculation efficiency.Detailed results include R^(2),VAF,MSE and MAEequal to 0.99475,0.034,99.477 and 0.1553 for a testing set and 0.97450,0.1633,97.466,and 0.1914 for a training set which can be said to be an excellent prediction result.By doing this,the hazard risk induced by backbreak can be indirectly assessed.In addition,it is also found that some superior performance can be obtained in some evaluation metrics compared with previous studies which utilized the same backbreak dataset for prediction.
文摘With the continuous growth of the tertiary industry and residential loads,balancing the power supply and consumption during peak demand time has become a critical issue.Some studies try to alleviate peak load by increasing power generation on the supply side.Due to the short duration of peak load,this may cause redundant installation capacity.Alternatively,others attempt to shave peak demand by installing energy storage facilities.However,the aforementioned research did not consider interruptible load regulation when optimizing system operations.In fact,regulating interruptible load has great potential for reducing system peak load.In this paper,an interruptible load scheduling model considering the user subsidy rate is first proposed to reduce system peak load and operational costs.This model has fully addressed the constraints of minimum daily load reduction and user interruption load time.After that,by taking a community in Shanghai as an example,the improved chicken swarm optimization algorithm is applied to solve the interruptible load scheduling scheme.Finally,the simulation results validate the efficacy of the proposed optimization algorithm and indicate the significant advantages of the proposed model in alleviating the peak load and reducing operational costs.
