Enhancing the accuracy of real-time ship roll prediction is crucial for maritime safety and operational efficiency.To address the challenge of accurately predicting the ship roll status with nonlinear time-varying dyn...Enhancing the accuracy of real-time ship roll prediction is crucial for maritime safety and operational efficiency.To address the challenge of accurately predicting the ship roll status with nonlinear time-varying dynamic characteristics,a real-time ship roll prediction scheme is proposed on the basis of a data preprocessing strategy and a novel stochastic trainer-based feedforward neural network.The sliding data window serves as a ship time-varying dynamic observer to enhance model prediction stability.The variational mode decomposition method extracts effective information on ship roll motion and reduces the non-stationary characteristics of the series.The energy entropy method reconstructs the mode components into high-frequency,medium-frequency,and low-frequency series to reduce model complexity.An improved black widow optimization algorithm trainer-based feedforward neural network with enhanced local optimal avoidance predicts the high-frequency component,enabling accurate tracking of abrupt signals.Additionally,the deterministic algorithm trainer-based neural network,characterized by rapid processing speed,predicts the remaining two mode components.Thus,real-time ship roll forecasting can be achieved through the reconstruction of mode component prediction results.The feasibility and effectiveness of the proposed hybrid prediction scheme for ship roll motion are demonstrated through the measured data of a full-scale ship trial.The proposed prediction scheme achieves real-time ship roll prediction with superior prediction accuracy.展开更多
The denoising of microseismic signals is a prerequisite for subsequent analysis and research.In this research,a new microseismic signal denoising algorithm called the Black Widow Optimization Algorithm(BWOA)optimized ...The denoising of microseismic signals is a prerequisite for subsequent analysis and research.In this research,a new microseismic signal denoising algorithm called the Black Widow Optimization Algorithm(BWOA)optimized VariationalMode Decomposition(VMD)jointWavelet Threshold Denoising(WTD)algorithm(BVW)is proposed.The BVW algorithm integrates VMD and WTD,both of which are optimized by BWOA.Specifically,this algorithm utilizes VMD to decompose the microseismic signal to be denoised into several Band-Limited IntrinsicMode Functions(BLIMFs).Subsequently,these BLIMFs whose correlation coefficients with the microseismic signal to be denoised are higher than a threshold are selected as the effective mode functions,and the effective mode functions are denoised using WTD to filter out the residual low-and intermediate-frequency noise.Finally,the denoised microseismic signal is obtained through reconstruction.The ideal values of VMD parameters and WTD parameters are acquired by searching with BWOA to achieve the best VMD decomposition performance and solve the problem of relying on experience and requiring a large workload in the application of the WTD algorithm.The outcomes of simulated experiments indicate that this algorithm is capable of achieving good denoising performance under noise of different intensities,and the denoising performance is significantly better than the commonly used VMD and Empirical Mode Decomposition(EMD)algorithms.The BVW algorithm is more efficient in filtering noise,the waveform after denoising is smoother,the amplitude of the waveform is the closest to the original signal,and the signal-to-noise ratio(SNR)and the root mean square error after denoising are more satisfying.The case based on Fankou Lead-Zinc Mine shows that for microseismic signals with different intensities of noise monitored on-site,compared with VMD and EMD,the BVW algorithm ismore efficient in filtering noise,and the SNR after denoising is higher.展开更多
This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio network.The threshold identification method is implemented in the received signal at the second...This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio network.The threshold identification method is implemented in the received signal at the secondary user based on the square law.The proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division multiplexing.Additionally,the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio systems.In the dynamic threshold,the signal ratio-based threshold is fixed.The threshold is computed by considering the Modified Black Widow Optimization Algorithm(MBWO).So,the proposed methodology is a combination of dynamic threshold detection and MBWO.The general threshold-based detection technique has different limitations such as the inability optimal signal threshold for determining the presence of the primary user signal.These limitations undermine the sensing accuracy of the energy identification technique.Hence,the ETBED technique is developed to enhance the energy efficiency of cognitive radio networks.The projected approach is executed and analyzed with performance and comparison analysis.The proposed method is contrasted with the conventional techniques of theWhale Optimization Algorithm(WOA)and GreyWolf Optimization(GWO).It indicated superior results,achieving a high average throughput of 2.2 Mbps and an energy efficiency of 3.8,outperforming conventional techniques.展开更多
Marriage is one of the valued institutions in the traditional African society. Incidentally, it has its own challenges. When a young man or woman gets to a certain age and he/she is not married, society frowns at it. ...Marriage is one of the valued institutions in the traditional African society. Incidentally, it has its own challenges. When a young man or woman gets to a certain age and he/she is not married, society frowns at it. Thus, many couples enter into marriage relationships with a lot of excitements and expectations. However, losing one＇s spouse to death robs one of all these excitements and expectations and exposes the bereaved spouse to some dehumanizing challenges. These challenges are mostly experienced by women in the Eastern Region of Nigeria. These women are subjected to dehumanizing practices which are socially accepted in the region. This paper attempts a comparative study of some of the practices imposed on widows in Eastern Nigeria. A sample of 200 participants was purposively selected from two states in the Eastern Region of Nigeria. The study adopted the qualitative research method. Thus, the analysis was made using descriptive method. The study showed that these inhuman traditional practices in Eastern Nigeria are initiated and perpetrated by the kinsmen （Umunna and Umuada）. It also showed that the widowhood treatment is meted out on all widows, but the severity or less severity of these treatments are dependent on the socio-economic and other attributes of the widow. Finally, recommendations were made, which include the need to protect the fundamental rights of widows and their children.展开更多
The structure and processes of nickel induced lateral crystallization are studied.The structure of metal induced lateral crystallization(MILC) is improved by opening a seed window on the buried oxide,which is helpfu t...The structure and processes of nickel induced lateral crystallization are studied.The structure of metal induced lateral crystallization(MILC) is improved by opening a seed window on the buried oxide,which is helpfu to get superior quality of large grain poly Si at low temperature.By optimizing the temperature and time of annealing based on others' pervious work,the large grain poly Si with few defects are obtained,and the typical grain size is 70～80μm.The methods of etching NiSi 2 which is created after the long time annealing are also studied for the first time.Finally,a method is successfully chosen to reduce the possible contamination of Ni and to guarantee the MILC for the submicron VLSI application.展开更多
Wireless Sensor Network(WSN)comprises a massive number of arbitrarily placed sensor nodes that are linked wirelessly to monitor the physical parameters from the target region.As the nodes in WSN operate on inbuilt bat...Wireless Sensor Network(WSN)comprises a massive number of arbitrarily placed sensor nodes that are linked wirelessly to monitor the physical parameters from the target region.As the nodes in WSN operate on inbuilt batteries,the energy depletion occurs after certain rounds of operation and thereby results in reduced network lifetime.To enhance energy efficiency and network longevity,clustering and routing techniques are commonly employed in WSN.This paper presents a novel black widow optimization(BWO)with improved ant colony optimization(IACO)algorithm(BWO-IACO)for cluster based routing in WSN.The proposed BWO-IACO algorithm involves BWO based clustering process to elect an optimal set of cluster heads(CHs).The BWO algorithm derives a fitness function(FF)using five input parameters like residual energy(RE),inter-cluster distance,intra-cluster distance,node degree(ND),and node centrality.In addition,IACO based routing process is involved for route selection in inter-cluster communication.The IACO algorithm incorporates the concepts of traditional ACO algorithm with krill herd algorithm(KHA).The IACO algorithm utilizes the energy factor to elect an optimal set of routes to BS in the network.The integration of BWO based clustering and IACO based routing techniques considerably helps to improve energy efficiency and network lifetime.The presented BWO-IACO algorithm has been simulated using MATLAB and the results are examined under varying aspects.A wide range of comparative analysis makes sure the betterment of the BWO-IACO algorithm over all the other compared techniques.展开更多
The optimizationfield has grown tremendously,and new optimization techniques are developed based on statistics and evolutionary procedures.There-fore,it is necessary to identify a suitable optimization technique for a...The optimizationfield has grown tremendously,and new optimization techniques are developed based on statistics and evolutionary procedures.There-fore,it is necessary to identify a suitable optimization technique for a particular application.In this work,Black Widow Optimization(BWO)algorithm is intro-duced to minimize the cost functions in order to optimize the Multi-Area Economic Dispatch(MAED).The BWO is implemented for two different-scale test systems,comprising 16 and 40 units with three and four areas.The performance of BWO is compared with the available optimization techniques in the literature to demonstrate the strategy’s efficacy.Results show that the optimized cost for four areas with 16 units is found to be 7336.76$/h,whereas it is 121,589$/h for four areas with 40 units using BWO.It is also noted that optimization algo-rithms other than BWO require higher cost value.The best-optimized solution for emission is achieved at 9.2784e+06 tones/h,and it is observed that there is a considerable difference between the worst and the best values.Also,the suggested technique is implemented for large-scale test systems successfully with high precision,and rapid convergence occurs in MAED.展开更多
The outbreak of COVID-19 affected global nations and is posing serious challenges to healthcare systems across the globe.Radiologists use X-Rays or Computed Tomography(CT)images to confirm the presence of COVID-19.So,...The outbreak of COVID-19 affected global nations and is posing serious challenges to healthcare systems across the globe.Radiologists use X-Rays or Computed Tomography(CT)images to confirm the presence of COVID-19.So,image processing techniques play an important role in diagnostic procedures and it helps the healthcare professionals during critical times.The current research work introduces Multi-objective Black Widow Optimization(MBWO)-based Convolutional Neural Network i.e.,MBWOCNN technique for diagnosis and classification of COVID-19.MBWOCNN model involves four steps such as preprocessing,feature extraction,parameter tuning,and classification.In the beginning,the input images undergo preprocessing followed by CNN-based feature extraction.Then,Multi-objective Black Widow Optimization(MBWO)technique is applied to fine tune the hyperparameters of CNN.Finally,Extreme Learning Machine with autoencoder(ELM-AE)is applied as a classifier to confirm the presence of COVID-19 and classify the disease under different class labels.The proposed MBWO-CNN model was validated experimentally and the results obtained were compared with the results achieved by existing techniques.The experimental results ensured the superior results of the ELM-AE model by attaining maximum classification performance with the accuracy of 96.43%.The effectiveness of the technique is proved through promising results and the model can be applied in diagnosis and classification of COVID-19.展开更多
The advancements made in Internet of Things(IoT)is projected to alter the functioning of healthcare industry in addition to increased penetration of different applications.However,data security and private are challen...The advancements made in Internet of Things(IoT)is projected to alter the functioning of healthcare industry in addition to increased penetration of different applications.However,data security and private are challenging tasks to accomplish in IoT and necessary measures to be taken to ensure secure operation.With this background,the current paper proposes a novel lightweight cryptography method for enhance the security in IoT.The proposed encryption algorithm is a blend of Cross Correlation Coefficient(CCC)and Black Widow Optimization(BWO)algorithm.In the presented encryption technique,CCC operation is utilized to optimize the encryption process of cryptography method.The projected encryption algorithm works in line with encryption and decryption processes.Optimal key selection is performed with the help of Artificial Intelligence(AI)tool named BWO algorithm.With the combination of AI technique and CCC operation,optimal security operation is improved in IoT.Using different sets of images collected from databases,the projected technique was validated in MATLAB on the basis of few performance metrics such as encryption time,decryption time,Peak Signal to Noise Ratio(PSNR),CC,Error,encryption time and decryption time.The results were compared with existing methods such as Elliptical Curve cryptography(ECC)and Rivest-Shamir-Adleman(RSA)and the supremacy of the projected method is established.展开更多
Chinese poetry has exerted a great influence on American modernist poets,especially on William Carlos Williams(1883-1963).William Carlos Williams’The Widow’s Lament in Springtime shares some connections with Chinese...Chinese poetry has exerted a great influence on American modernist poets,especially on William Carlos Williams(1883-1963).William Carlos Williams’The Widow’s Lament in Springtime shares some connections with Chinese Boudoir poems in theme,image,and technique.This paper tends to discuss the influence of Chinese Boudoir poems on William Carlos Williams based on his creating of The Widow’s Lament in Springtime,which provides a new perspective for studying the communication of Chinese and American poetry.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52231014 and 52271361)the Natural Science Foundation of Guangdong Province of China(Grant No.2023A1515010684).
文摘Enhancing the accuracy of real-time ship roll prediction is crucial for maritime safety and operational efficiency.To address the challenge of accurately predicting the ship roll status with nonlinear time-varying dynamic characteristics,a real-time ship roll prediction scheme is proposed on the basis of a data preprocessing strategy and a novel stochastic trainer-based feedforward neural network.The sliding data window serves as a ship time-varying dynamic observer to enhance model prediction stability.The variational mode decomposition method extracts effective information on ship roll motion and reduces the non-stationary characteristics of the series.The energy entropy method reconstructs the mode components into high-frequency,medium-frequency,and low-frequency series to reduce model complexity.An improved black widow optimization algorithm trainer-based feedforward neural network with enhanced local optimal avoidance predicts the high-frequency component,enabling accurate tracking of abrupt signals.Additionally,the deterministic algorithm trainer-based neural network,characterized by rapid processing speed,predicts the remaining two mode components.Thus,real-time ship roll forecasting can be achieved through the reconstruction of mode component prediction results.The feasibility and effectiveness of the proposed hybrid prediction scheme for ship roll motion are demonstrated through the measured data of a full-scale ship trial.The proposed prediction scheme achieves real-time ship roll prediction with superior prediction accuracy.
基金funded by the National Natural Science Foundation of China(Grant No.51874350)the National Natural Science Foundation of China(Grant No.52304127)+2 种基金the Fundamental Research Funds for the Central Universities of Central South University(Grant No.2020zzts200)the Science Foundation of the Fuzhou University(Grant No.511229)Fuzhou University Testing Fund of Precious Apparatus(Grant No.2024T040).
文摘The denoising of microseismic signals is a prerequisite for subsequent analysis and research.In this research,a new microseismic signal denoising algorithm called the Black Widow Optimization Algorithm(BWOA)optimized VariationalMode Decomposition(VMD)jointWavelet Threshold Denoising(WTD)algorithm(BVW)is proposed.The BVW algorithm integrates VMD and WTD,both of which are optimized by BWOA.Specifically,this algorithm utilizes VMD to decompose the microseismic signal to be denoised into several Band-Limited IntrinsicMode Functions(BLIMFs).Subsequently,these BLIMFs whose correlation coefficients with the microseismic signal to be denoised are higher than a threshold are selected as the effective mode functions,and the effective mode functions are denoised using WTD to filter out the residual low-and intermediate-frequency noise.Finally,the denoised microseismic signal is obtained through reconstruction.The ideal values of VMD parameters and WTD parameters are acquired by searching with BWOA to achieve the best VMD decomposition performance and solve the problem of relying on experience and requiring a large workload in the application of the WTD algorithm.The outcomes of simulated experiments indicate that this algorithm is capable of achieving good denoising performance under noise of different intensities,and the denoising performance is significantly better than the commonly used VMD and Empirical Mode Decomposition(EMD)algorithms.The BVW algorithm is more efficient in filtering noise,the waveform after denoising is smoother,the amplitude of the waveform is the closest to the original signal,and the signal-to-noise ratio(SNR)and the root mean square error after denoising are more satisfying.The case based on Fankou Lead-Zinc Mine shows that for microseismic signals with different intensities of noise monitored on-site,compared with VMD and EMD,the BVW algorithm ismore efficient in filtering noise,and the SNR after denoising is higher.
文摘This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio network.The threshold identification method is implemented in the received signal at the secondary user based on the square law.The proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division multiplexing.Additionally,the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio systems.In the dynamic threshold,the signal ratio-based threshold is fixed.The threshold is computed by considering the Modified Black Widow Optimization Algorithm(MBWO).So,the proposed methodology is a combination of dynamic threshold detection and MBWO.The general threshold-based detection technique has different limitations such as the inability optimal signal threshold for determining the presence of the primary user signal.These limitations undermine the sensing accuracy of the energy identification technique.Hence,the ETBED technique is developed to enhance the energy efficiency of cognitive radio networks.The projected approach is executed and analyzed with performance and comparison analysis.The proposed method is contrasted with the conventional techniques of theWhale Optimization Algorithm(WOA)and GreyWolf Optimization(GWO).It indicated superior results,achieving a high average throughput of 2.2 Mbps and an energy efficiency of 3.8,outperforming conventional techniques.
文摘Marriage is one of the valued institutions in the traditional African society. Incidentally, it has its own challenges. When a young man or woman gets to a certain age and he/she is not married, society frowns at it. Thus, many couples enter into marriage relationships with a lot of excitements and expectations. However, losing one＇s spouse to death robs one of all these excitements and expectations and exposes the bereaved spouse to some dehumanizing challenges. These challenges are mostly experienced by women in the Eastern Region of Nigeria. These women are subjected to dehumanizing practices which are socially accepted in the region. This paper attempts a comparative study of some of the practices imposed on widows in Eastern Nigeria. A sample of 200 participants was purposively selected from two states in the Eastern Region of Nigeria. The study adopted the qualitative research method. Thus, the analysis was made using descriptive method. The study showed that these inhuman traditional practices in Eastern Nigeria are initiated and perpetrated by the kinsmen （Umunna and Umuada）. It also showed that the widowhood treatment is meted out on all widows, but the severity or less severity of these treatments are dependent on the socio-economic and other attributes of the widow. Finally, recommendations were made, which include the need to protect the fundamental rights of widows and their children.
文摘The structure and processes of nickel induced lateral crystallization are studied.The structure of metal induced lateral crystallization(MILC) is improved by opening a seed window on the buried oxide,which is helpfu to get superior quality of large grain poly Si at low temperature.By optimizing the temperature and time of annealing based on others' pervious work,the large grain poly Si with few defects are obtained,and the typical grain size is 70～80μm.The methods of etching NiSi 2 which is created after the long time annealing are also studied for the first time.Finally,a method is successfully chosen to reduce the possible contamination of Ni and to guarantee the MILC for the submicron VLSI application.
文摘Wireless Sensor Network(WSN)comprises a massive number of arbitrarily placed sensor nodes that are linked wirelessly to monitor the physical parameters from the target region.As the nodes in WSN operate on inbuilt batteries,the energy depletion occurs after certain rounds of operation and thereby results in reduced network lifetime.To enhance energy efficiency and network longevity,clustering and routing techniques are commonly employed in WSN.This paper presents a novel black widow optimization(BWO)with improved ant colony optimization(IACO)algorithm(BWO-IACO)for cluster based routing in WSN.The proposed BWO-IACO algorithm involves BWO based clustering process to elect an optimal set of cluster heads(CHs).The BWO algorithm derives a fitness function(FF)using five input parameters like residual energy(RE),inter-cluster distance,intra-cluster distance,node degree(ND),and node centrality.In addition,IACO based routing process is involved for route selection in inter-cluster communication.The IACO algorithm incorporates the concepts of traditional ACO algorithm with krill herd algorithm(KHA).The IACO algorithm utilizes the energy factor to elect an optimal set of routes to BS in the network.The integration of BWO based clustering and IACO based routing techniques considerably helps to improve energy efficiency and network lifetime.The presented BWO-IACO algorithm has been simulated using MATLAB and the results are examined under varying aspects.A wide range of comparative analysis makes sure the betterment of the BWO-IACO algorithm over all the other compared techniques.
文摘The optimizationfield has grown tremendously,and new optimization techniques are developed based on statistics and evolutionary procedures.There-fore,it is necessary to identify a suitable optimization technique for a particular application.In this work,Black Widow Optimization(BWO)algorithm is intro-duced to minimize the cost functions in order to optimize the Multi-Area Economic Dispatch(MAED).The BWO is implemented for two different-scale test systems,comprising 16 and 40 units with three and four areas.The performance of BWO is compared with the available optimization techniques in the literature to demonstrate the strategy’s efficacy.Results show that the optimized cost for four areas with 16 units is found to be 7336.76$/h,whereas it is 121,589$/h for four areas with 40 units using BWO.It is also noted that optimization algo-rithms other than BWO require higher cost value.The best-optimized solution for emission is achieved at 9.2784e+06 tones/h,and it is observed that there is a considerable difference between the worst and the best values.Also,the suggested technique is implemented for large-scale test systems successfully with high precision,and rapid convergence occurs in MAED.
文摘The outbreak of COVID-19 affected global nations and is posing serious challenges to healthcare systems across the globe.Radiologists use X-Rays or Computed Tomography(CT)images to confirm the presence of COVID-19.So,image processing techniques play an important role in diagnostic procedures and it helps the healthcare professionals during critical times.The current research work introduces Multi-objective Black Widow Optimization(MBWO)-based Convolutional Neural Network i.e.,MBWOCNN technique for diagnosis and classification of COVID-19.MBWOCNN model involves four steps such as preprocessing,feature extraction,parameter tuning,and classification.In the beginning,the input images undergo preprocessing followed by CNN-based feature extraction.Then,Multi-objective Black Widow Optimization(MBWO)technique is applied to fine tune the hyperparameters of CNN.Finally,Extreme Learning Machine with autoencoder(ELM-AE)is applied as a classifier to confirm the presence of COVID-19 and classify the disease under different class labels.The proposed MBWO-CNN model was validated experimentally and the results obtained were compared with the results achieved by existing techniques.The experimental results ensured the superior results of the ELM-AE model by attaining maximum classification performance with the accuracy of 96.43%.The effectiveness of the technique is proved through promising results and the model can be applied in diagnosis and classification of COVID-19.
基金This work was funded by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,under Grant No.(D-387-135-1443).
文摘The advancements made in Internet of Things(IoT)is projected to alter the functioning of healthcare industry in addition to increased penetration of different applications.However,data security and private are challenging tasks to accomplish in IoT and necessary measures to be taken to ensure secure operation.With this background,the current paper proposes a novel lightweight cryptography method for enhance the security in IoT.The proposed encryption algorithm is a blend of Cross Correlation Coefficient(CCC)and Black Widow Optimization(BWO)algorithm.In the presented encryption technique,CCC operation is utilized to optimize the encryption process of cryptography method.The projected encryption algorithm works in line with encryption and decryption processes.Optimal key selection is performed with the help of Artificial Intelligence(AI)tool named BWO algorithm.With the combination of AI technique and CCC operation,optimal security operation is improved in IoT.Using different sets of images collected from databases,the projected technique was validated in MATLAB on the basis of few performance metrics such as encryption time,decryption time,Peak Signal to Noise Ratio(PSNR),CC,Error,encryption time and decryption time.The results were compared with existing methods such as Elliptical Curve cryptography(ECC)and Rivest-Shamir-Adleman(RSA)and the supremacy of the projected method is established.
文摘Chinese poetry has exerted a great influence on American modernist poets,especially on William Carlos Williams(1883-1963).William Carlos Williams’The Widow’s Lament in Springtime shares some connections with Chinese Boudoir poems in theme,image,and technique.This paper tends to discuss the influence of Chinese Boudoir poems on William Carlos Williams based on his creating of The Widow’s Lament in Springtime,which provides a new perspective for studying the communication of Chinese and American poetry.
