无人机(UAV)因其低成本、高动态性与低部署性等优点被逐渐应用于城市巡防中。为提高异构无人机航迹规划的效率,首先建立了考虑无人机的任务执行率、航迹代价和撞击代价的多无人机任务规划模型。其次针对传统优化算法容易陷入局部最优解...无人机(UAV)因其低成本、高动态性与低部署性等优点被逐渐应用于城市巡防中。为提高异构无人机航迹规划的效率,首先建立了考虑无人机的任务执行率、航迹代价和撞击代价的多无人机任务规划模型。其次针对传统优化算法容易陷入局部最优解,均匀性差等问题,将差分策略和Levy飞行策略引入乌鸦搜索算法中对算法进行改进,提出基于Levy飞行策略的混合差分乌鸦搜索算法(LDCSA),将剪枝处理和Logistic混沌映射机制加入快速遍历随机树(rapidly-exploring random trees,RRT)算法中,并通过改进的RRT算法进行航迹初始化。最后建立了3维的城市模型进行仿真实验,将所提算法与粒子群(PSO)、模拟退火(SA)、乌鸦搜索(CSA)算法对比,仿真结果表明该算法能提高全局收敛性与鲁棒性、缩短收敛时间、提高无人机执行覆盖率和减少能耗,在解决多无人机航迹规划问题中更具有优势。展开更多
In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on ...In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.展开更多
Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kin...Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kinds of researches on forensic detection have been presented,and it provides less accuracy.This paper proposed a novel forgery detection technique in image frames of the videos using enhanced Convolutional Neural Network(CNN).In the initial stage,the input video is taken as of the dataset and then converts the videos into image frames.Next,perform pre-sampling using the Adaptive Rood Pattern Search(ARPS)algorithm intended for reducing the useless frames.In the next stage,perform preprocessing for enhancing the image frames.Then,face detection is done as of the image utilizing the Viola-Jones algorithm.Finally,the improved Crow Search Algorithm(ICSA)has been used to select the extorted features and inputted to the Enhanced Convolutional Neural Network(ECNN)classifier for detecting the forged image frames.The experimental outcome of the proposed system has achieved 97.21%accuracy compared to other existing methods.展开更多
Mammography is considered a significant image for accurate breast cancer detection.Content-based image retrieval(CBIR)contributes to classifying the query mammography image and retrieves similar mammographic images fr...Mammography is considered a significant image for accurate breast cancer detection.Content-based image retrieval(CBIR)contributes to classifying the query mammography image and retrieves similar mammographic images from the database.This CBIR system helps a physician to give better treatment.Local features must be described with the input images to retrieve similar images.Exist-ing methods are inefficient and inaccurate by failing in local features analysis.Hence,efficient digital mammography image retrieval needs to be implemented.This paper proposed reliable recovery of the mammographic image from the data-base,which requires the removal of noise using Kalman filter and scale-invariant feature transform(SIFT)for feature extraction with Crow Search Optimization-based the deep belief network(CSO-DBN).This proposed technique decreases the complexity,cost,energy,and time consumption.Training the proposed model using a deep belief network and validation is performed.Finally,the testing pro-cess gives better performance compared to existing techniques.The accuracy rate of the proposed work CSO-DBN is 0.9344,whereas the support vector machine(SVM)(0.5434),naïve Bayes(NB)(0.7014),Butterfly Optimization Algorithm(BOA)(0.8156),and Cat Swarm Optimization(CSO)(0.8852).展开更多
Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the crop.Plant diseases are one of the underlying causes in the decrease in the number of quantity and qu...Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the crop.Plant diseases are one of the underlying causes in the decrease in the number of quantity and quality of the farming crops.Recognition of diseases from the plant images is an active research topic which makes use of machine learning(ML)approaches.A novel deep neural network(DNN)classification model is proposed for the identification of paddy leaf disease using plant image data.Classification errors were minimized by optimizing weights and biases in the DNN model using a crow search algorithm(CSA)during both the standard pre-training and fine-tuning processes.This DNN-CSA architecture enables the use of simplistic statistical learning techniques with a decreased computational workload,ensuring high classification accuracy.Paddy leaf images were first preprocessed,and the areas indicative of disease were initially extracted using a k-means clustering method.Thresholding was then applied to eliminate regions not indicative of disease.Next,a set of features were extracted from the previously isolated diseased regions.Finally,the classification accuracy and efficiency of the proposed DNN-CSA model were verified experimentally and shown to be superior to a support vector machine with multiple cross-fold validations.展开更多
Crow Search Algorithm(CSA)is a swarm-based single-objective optimizer proposed in recent years,which tried to inspire the behavior of crows that hide foods in different locations and retrieve them when needed.The orig...Crow Search Algorithm(CSA)is a swarm-based single-objective optimizer proposed in recent years,which tried to inspire the behavior of crows that hide foods in different locations and retrieve them when needed.The original version of the CSA has simple parameters and moderate performance.However,it often tends to converge slowly or get stuck in a locally optimal region due to a missed harmonizing strategy during the exploitation and exploration phases.Therefore,strategies of mutation and crisscross are combined into CSA(CCMSCSA)in this paper to improve the performance and provide an efficient optimizer for various optimization problems.To verify the superiority of CCMSCSA,a set of comparisons has been performed reasonably with some well-established metaheuristics and advanced metaheuristics on 15 benchmark functions.The experimental results expose and verify that the proposed CCMSCSA has meaningfully improved the convergence speed and the ability to jump out of the local optimum.In addition,the scalability of CCMSCSA is analyzed,and the algorithm is applied to several engineering problems in a constrained space and feature selection problems.Experimental results show that the scalability of CCMSCSA has been significantly improved and can find better solutions than its competitors when dealing with combinatorial optimization problems.The proposed CCMSCSA performs well in almost all experimental results.Therefore,we hope the researchers can see it as an effective method for solving constrained and unconstrained optimization problems.展开更多
针对基于差分隐私的K-means聚类存在数据效用差的问题,基于乌鸦搜索和轮廓系数提出了一个隐私保护的聚类算法(privacy preserving clustering algorithm based on crow search, CS-PCA)。该算法一方面利用轮廓系数对每次迭代中每个簇的...针对基于差分隐私的K-means聚类存在数据效用差的问题,基于乌鸦搜索和轮廓系数提出了一个隐私保护的聚类算法(privacy preserving clustering algorithm based on crow search, CS-PCA)。该算法一方面利用轮廓系数对每次迭代中每个簇的聚类效果进行评估,根据聚类效果添加不同数量的噪声,并利用聚类合并思想降低噪声对聚类的影响;另一方面利用乌鸦搜索对差分隐私的K-means隐私保护聚类算法中初始质心的选择进行优化,防止算法陷入局部最优。实验结果表明,CS-PCA算法的聚类有效性更高,并且同样适用于大规模数据。从整体上看,随着隐私预算的不断增大,CS-PCA算法的F-measure值分别比DP-KCCM和PADC算法高了0~281.3312%和4.5876%~470.3704%。在相同的隐私预算下,CS-PCA算法在绝大多数情况下聚类结果可用性优于对比算法。展开更多
Mammography is considered a significant image for accurate breast cancer detection.Content-based image retrieval(CBIR)contributes to classifying the query mammography image and retrieves similar mammographic images fro...Mammography is considered a significant image for accurate breast cancer detection.Content-based image retrieval(CBIR)contributes to classifying the query mammography image and retrieves similar mammographic images from the database.This CBIR system helps a physician to give better treatment.Local features must be described with the input images to retrieve similar images.Exist-ing methods are inefficient and inaccurate by failing in local features analysis.Hence,efficient digital mammography image retrieval needs to be implemented.This paper proposed reliable recovery of the mammographic image from the data-base,which requires the removal of noise using Kalmanfilter and scale-invariant feature transform(SIFT)for feature extraction with Crow Search Optimization-based the deep belief network(CSO-DBN).This proposed technique decreases the complexity,cost,energy,and time consumption.Training the proposed model using a deep belief network and validation is performed.Finally,the testing pro-cess gives better performance compared to existing techniques.The accuracy rate of the proposed work CSO-DBN is 0.9344,whereas the support vector machine(SVM)(0.5434),naïve Bayes(NB)(0.7014),Butterfly Optimization Algorithm(BOA)(0.8156),and Cat Swarm Optimization(CSO)(0.8852).展开更多
The next step in mobile communication technology,known as 5G,is set to go live in a number of countries in the near future.New wireless applica-tions have high data rates and mobility requirements,which have posed a c...The next step in mobile communication technology,known as 5G,is set to go live in a number of countries in the near future.New wireless applica-tions have high data rates and mobility requirements,which have posed a chal-lenge to mobile communication technology researchers and designers.5G systems could benefit from the Universal Filtered Multicarrier(UFMC).UFMC is an alternate waveform to orthogonal frequency-division multiplexing(OFDM),infiltering process is performed for a sub-band of subcarriers rather than the entire band of subcarriers Inter Carrier Interference(ICI)between neighbouring users is reduced via the sub-bandfiltering process,which reduces out-of-band emissions.However,the UFMC system has a high Peak-to-Average Power Ratio(PAPR),which limits its capabilities.Metaheuristic optimization based Selective mapping(SLM)is used in this paper to optimise the UFMC-PAPR.Based on the cognitive behaviour of crows,the research study suggests an innovative metaheuristic opti-mization known as Crow Search Algorithm(CSA)for SLM optimization.Com-pared to the standard UFMC,SLM-UFMC system,and SLM-UFMC with conventional metaheuristic optimization techniques,the suggested technique sig-nificantly reduces PAPR.For the UFMC system,the suggested approach has a very low Bit Error Rate(BER).展开更多
The generation of electricity based on renewable energy sources,parti-cularly Photovoltaic(PV)system has been greatly increased and it is simply insti-gated for both domestic and commercial uses.The power generated fr...The generation of electricity based on renewable energy sources,parti-cularly Photovoltaic(PV)system has been greatly increased and it is simply insti-gated for both domestic and commercial uses.The power generated from the PV system is erratic and hence there is a need for an efficient converter to perform the extraction of maximum power.An improved interleaved Single-ended Primary Inductor-Converter(SEPIC)converter is employed in proposed work to extricate most of power from renewable source.This proposed converter minimizes ripples,reduces electromagnetic interference due tofilter elements and the contin-uous input current improves the power output of PV panel.A Crow Search Algo-rithm(CSA)based Proportional Integral(PI)controller is utilized for controlling the converter switches effectively by optimizing the parameters of PI controller.The optimized PI controller reduces ripples present in Direct Current(DC)vol-tage,maintains constant voltage at proposed converter output and reduces over-shoots with minimum settling and rise time.This voltage is given to single phase grid via 1�Voltage Source Inverter(VSI).The command pulses of 1�VSI are produced by simple PI controller.The response of the proposed converter is thus improved with less input current.After implementing CSA based PI the efficiency of proposed converter obtained is 96%and the Total Harmonic Distor-tion(THD)is found to be 2:4%.The dynamics and closed loop operation is designed and modeled using MATLAB Simulink tool and its behavior is performed.展开更多
Frequency control of an interconnected power system in the presence of wind integration is complex since wind speed/power variations also affect system frequency in addition to load perturbations.Therefore,improving e...Frequency control of an interconnected power system in the presence of wind integration is complex since wind speed/power variations also affect system frequency in addition to load perturbations.Therefore,improving existing control schemes is necessary to maintain a stable frequency in such complex power system scenarios.In this paper,a new 2-degree of freedom combined proportional-integral and derivative control scheme is applied to a wind integrated interconnected power system.In designing the controller,several inputs used for a secondary frequency control loop are considered along with the merits of the existing controllers.The combined controller provides better control action than existing controllers in the presence of wind as is evidenced by the wide variety of results pre-sented.For tuning of the controller gains,a crow search optimization algorithm(CRSOA)is used.Results are obtained via the MATLAB/Simulink software.展开更多
Purpose-Breast cancer is one of the most common malignant tumors in women,which badly have an effect on women’s physical and psychological health and even danger to life.Nowadays,mammography is considered as a fundam...Purpose-Breast cancer is one of the most common malignant tumors in women,which badly have an effect on women’s physical and psychological health and even danger to life.Nowadays,mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer.Though,due to the intricate formation of mammogram images,it is reasonably hard for practitioners to spot breast cancer features.Design/methodology/approach-Breast cancer is one of the most common malignant tumors in women,which badly have an effect on women’s physical and psychological health and even danger to life.Nowadays,mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer.Though,due to the intricate formation of mammogram images,it is reasonably hard for practitioners to spot breast cancer features.Findings-The performance analysis was done for both segmentation and classification.From the analysis,the accuracy of the proposed IAP-CSA-based fuzzy was 41.9%improved than the fuzzy classifier,2.80%improved than PSO,WOA,and CSA,and 2.32%improved than GWO-based fuzzy classifiers.Additionally,the accuracy of the developed IAP-CSA-fuzzy was 9.54%better than NN,35.8%better than SVM,and 41.9%better than the existing fuzzy classifier.Hence,it is concluded that the implemented breast cancer detection model was efficient in determining the normal,benign and malignant images.Originality/value-This paper adopts the latest Improved Awareness Probability-based Crow Search Algorithm(IAP-CSA)-based Region growing and fuzzy classifier for enhancing the breast cancer detection of mammogram images,and this is the first work that utilizes this method.展开更多
文摘无人机(UAV)因其低成本、高动态性与低部署性等优点被逐渐应用于城市巡防中。为提高异构无人机航迹规划的效率,首先建立了考虑无人机的任务执行率、航迹代价和撞击代价的多无人机任务规划模型。其次针对传统优化算法容易陷入局部最优解,均匀性差等问题,将差分策略和Levy飞行策略引入乌鸦搜索算法中对算法进行改进,提出基于Levy飞行策略的混合差分乌鸦搜索算法(LDCSA),将剪枝处理和Logistic混沌映射机制加入快速遍历随机树(rapidly-exploring random trees,RRT)算法中,并通过改进的RRT算法进行航迹初始化。最后建立了3维的城市模型进行仿真实验,将所提算法与粒子群(PSO)、模拟退火(SA)、乌鸦搜索(CSA)算法对比,仿真结果表明该算法能提高全局收敛性与鲁棒性、缩短收敛时间、提高无人机执行覆盖率和减少能耗,在解决多无人机航迹规划问题中更具有优势。
基金supported by CNPC-CZU Innovation Alliancesupported by the Program of Polar Drilling Environmental Protection and Waste Treatment Technology (2022YFC2806403)。
文摘In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.
文摘Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kinds of researches on forensic detection have been presented,and it provides less accuracy.This paper proposed a novel forgery detection technique in image frames of the videos using enhanced Convolutional Neural Network(CNN).In the initial stage,the input video is taken as of the dataset and then converts the videos into image frames.Next,perform pre-sampling using the Adaptive Rood Pattern Search(ARPS)algorithm intended for reducing the useless frames.In the next stage,perform preprocessing for enhancing the image frames.Then,face detection is done as of the image utilizing the Viola-Jones algorithm.Finally,the improved Crow Search Algorithm(ICSA)has been used to select the extorted features and inputted to the Enhanced Convolutional Neural Network(ECNN)classifier for detecting the forged image frames.The experimental outcome of the proposed system has achieved 97.21%accuracy compared to other existing methods.
文摘Mammography is considered a significant image for accurate breast cancer detection.Content-based image retrieval(CBIR)contributes to classifying the query mammography image and retrieves similar mammographic images from the database.This CBIR system helps a physician to give better treatment.Local features must be described with the input images to retrieve similar images.Exist-ing methods are inefficient and inaccurate by failing in local features analysis.Hence,efficient digital mammography image retrieval needs to be implemented.This paper proposed reliable recovery of the mammographic image from the data-base,which requires the removal of noise using Kalman filter and scale-invariant feature transform(SIFT)for feature extraction with Crow Search Optimization-based the deep belief network(CSO-DBN).This proposed technique decreases the complexity,cost,energy,and time consumption.Training the proposed model using a deep belief network and validation is performed.Finally,the testing pro-cess gives better performance compared to existing techniques.The accuracy rate of the proposed work CSO-DBN is 0.9344,whereas the support vector machine(SVM)(0.5434),naïve Bayes(NB)(0.7014),Butterfly Optimization Algorithm(BOA)(0.8156),and Cat Swarm Optimization(CSO)(0.8852).
文摘Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the crop.Plant diseases are one of the underlying causes in the decrease in the number of quantity and quality of the farming crops.Recognition of diseases from the plant images is an active research topic which makes use of machine learning(ML)approaches.A novel deep neural network(DNN)classification model is proposed for the identification of paddy leaf disease using plant image data.Classification errors were minimized by optimizing weights and biases in the DNN model using a crow search algorithm(CSA)during both the standard pre-training and fine-tuning processes.This DNN-CSA architecture enables the use of simplistic statistical learning techniques with a decreased computational workload,ensuring high classification accuracy.Paddy leaf images were first preprocessed,and the areas indicative of disease were initially extracted using a k-means clustering method.Thresholding was then applied to eliminate regions not indicative of disease.Next,a set of features were extracted from the previously isolated diseased regions.Finally,the classification accuracy and efficiency of the proposed DNN-CSA model were verified experimentally and shown to be superior to a support vector machine with multiple cross-fold validations.
基金Natural Science Foundation of Zhejiang Province(LZ22F020005)National Natural Science Foundation of China(42164002,62076185 and,U1809209)National Key R&D Program of China(2018YFC1503806).
文摘Crow Search Algorithm(CSA)is a swarm-based single-objective optimizer proposed in recent years,which tried to inspire the behavior of crows that hide foods in different locations and retrieve them when needed.The original version of the CSA has simple parameters and moderate performance.However,it often tends to converge slowly or get stuck in a locally optimal region due to a missed harmonizing strategy during the exploitation and exploration phases.Therefore,strategies of mutation and crisscross are combined into CSA(CCMSCSA)in this paper to improve the performance and provide an efficient optimizer for various optimization problems.To verify the superiority of CCMSCSA,a set of comparisons has been performed reasonably with some well-established metaheuristics and advanced metaheuristics on 15 benchmark functions.The experimental results expose and verify that the proposed CCMSCSA has meaningfully improved the convergence speed and the ability to jump out of the local optimum.In addition,the scalability of CCMSCSA is analyzed,and the algorithm is applied to several engineering problems in a constrained space and feature selection problems.Experimental results show that the scalability of CCMSCSA has been significantly improved and can find better solutions than its competitors when dealing with combinatorial optimization problems.The proposed CCMSCSA performs well in almost all experimental results.Therefore,we hope the researchers can see it as an effective method for solving constrained and unconstrained optimization problems.
文摘针对基于差分隐私的K-means聚类存在数据效用差的问题,基于乌鸦搜索和轮廓系数提出了一个隐私保护的聚类算法(privacy preserving clustering algorithm based on crow search, CS-PCA)。该算法一方面利用轮廓系数对每次迭代中每个簇的聚类效果进行评估,根据聚类效果添加不同数量的噪声,并利用聚类合并思想降低噪声对聚类的影响;另一方面利用乌鸦搜索对差分隐私的K-means隐私保护聚类算法中初始质心的选择进行优化,防止算法陷入局部最优。实验结果表明,CS-PCA算法的聚类有效性更高,并且同样适用于大规模数据。从整体上看,随着隐私预算的不断增大,CS-PCA算法的F-measure值分别比DP-KCCM和PADC算法高了0~281.3312%和4.5876%~470.3704%。在相同的隐私预算下,CS-PCA算法在绝大多数情况下聚类结果可用性优于对比算法。
文摘Mammography is considered a significant image for accurate breast cancer detection.Content-based image retrieval(CBIR)contributes to classifying the query mammography image and retrieves similar mammographic images from the database.This CBIR system helps a physician to give better treatment.Local features must be described with the input images to retrieve similar images.Exist-ing methods are inefficient and inaccurate by failing in local features analysis.Hence,efficient digital mammography image retrieval needs to be implemented.This paper proposed reliable recovery of the mammographic image from the data-base,which requires the removal of noise using Kalmanfilter and scale-invariant feature transform(SIFT)for feature extraction with Crow Search Optimization-based the deep belief network(CSO-DBN).This proposed technique decreases the complexity,cost,energy,and time consumption.Training the proposed model using a deep belief network and validation is performed.Finally,the testing pro-cess gives better performance compared to existing techniques.The accuracy rate of the proposed work CSO-DBN is 0.9344,whereas the support vector machine(SVM)(0.5434),naïve Bayes(NB)(0.7014),Butterfly Optimization Algorithm(BOA)(0.8156),and Cat Swarm Optimization(CSO)(0.8852).
文摘The next step in mobile communication technology,known as 5G,is set to go live in a number of countries in the near future.New wireless applica-tions have high data rates and mobility requirements,which have posed a chal-lenge to mobile communication technology researchers and designers.5G systems could benefit from the Universal Filtered Multicarrier(UFMC).UFMC is an alternate waveform to orthogonal frequency-division multiplexing(OFDM),infiltering process is performed for a sub-band of subcarriers rather than the entire band of subcarriers Inter Carrier Interference(ICI)between neighbouring users is reduced via the sub-bandfiltering process,which reduces out-of-band emissions.However,the UFMC system has a high Peak-to-Average Power Ratio(PAPR),which limits its capabilities.Metaheuristic optimization based Selective mapping(SLM)is used in this paper to optimise the UFMC-PAPR.Based on the cognitive behaviour of crows,the research study suggests an innovative metaheuristic opti-mization known as Crow Search Algorithm(CSA)for SLM optimization.Com-pared to the standard UFMC,SLM-UFMC system,and SLM-UFMC with conventional metaheuristic optimization techniques,the suggested technique sig-nificantly reduces PAPR.For the UFMC system,the suggested approach has a very low Bit Error Rate(BER).
文摘The generation of electricity based on renewable energy sources,parti-cularly Photovoltaic(PV)system has been greatly increased and it is simply insti-gated for both domestic and commercial uses.The power generated from the PV system is erratic and hence there is a need for an efficient converter to perform the extraction of maximum power.An improved interleaved Single-ended Primary Inductor-Converter(SEPIC)converter is employed in proposed work to extricate most of power from renewable source.This proposed converter minimizes ripples,reduces electromagnetic interference due tofilter elements and the contin-uous input current improves the power output of PV panel.A Crow Search Algo-rithm(CSA)based Proportional Integral(PI)controller is utilized for controlling the converter switches effectively by optimizing the parameters of PI controller.The optimized PI controller reduces ripples present in Direct Current(DC)vol-tage,maintains constant voltage at proposed converter output and reduces over-shoots with minimum settling and rise time.This voltage is given to single phase grid via 1�Voltage Source Inverter(VSI).The command pulses of 1�VSI are produced by simple PI controller.The response of the proposed converter is thus improved with less input current.After implementing CSA based PI the efficiency of proposed converter obtained is 96%and the Total Harmonic Distor-tion(THD)is found to be 2:4%.The dynamics and closed loop operation is designed and modeled using MATLAB Simulink tool and its behavior is performed.
文摘Frequency control of an interconnected power system in the presence of wind integration is complex since wind speed/power variations also affect system frequency in addition to load perturbations.Therefore,improving existing control schemes is necessary to maintain a stable frequency in such complex power system scenarios.In this paper,a new 2-degree of freedom combined proportional-integral and derivative control scheme is applied to a wind integrated interconnected power system.In designing the controller,several inputs used for a secondary frequency control loop are considered along with the merits of the existing controllers.The combined controller provides better control action than existing controllers in the presence of wind as is evidenced by the wide variety of results pre-sented.For tuning of the controller gains,a crow search optimization algorithm(CRSOA)is used.Results are obtained via the MATLAB/Simulink software.
文摘Purpose-Breast cancer is one of the most common malignant tumors in women,which badly have an effect on women’s physical and psychological health and even danger to life.Nowadays,mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer.Though,due to the intricate formation of mammogram images,it is reasonably hard for practitioners to spot breast cancer features.Design/methodology/approach-Breast cancer is one of the most common malignant tumors in women,which badly have an effect on women’s physical and psychological health and even danger to life.Nowadays,mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer.Though,due to the intricate formation of mammogram images,it is reasonably hard for practitioners to spot breast cancer features.Findings-The performance analysis was done for both segmentation and classification.From the analysis,the accuracy of the proposed IAP-CSA-based fuzzy was 41.9%improved than the fuzzy classifier,2.80%improved than PSO,WOA,and CSA,and 2.32%improved than GWO-based fuzzy classifiers.Additionally,the accuracy of the developed IAP-CSA-fuzzy was 9.54%better than NN,35.8%better than SVM,and 41.9%better than the existing fuzzy classifier.Hence,it is concluded that the implemented breast cancer detection model was efficient in determining the normal,benign and malignant images.Originality/value-This paper adopts the latest Improved Awareness Probability-based Crow Search Algorithm(IAP-CSA)-based Region growing and fuzzy classifier for enhancing the breast cancer detection of mammogram images,and this is the first work that utilizes this method.
