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
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
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
为了提高人体尺寸预测的效率和准确性,该文提出了GBWO-ENN(Grey Black Wolf Optimization-Elman Neural Network)的方法。针对传统灰狼算法易于陷入局部最优和无法平衡全局与局部搜索的平衡性问题,提出了GBWO算法。该算法融合黑寡妇优...为了提高人体尺寸预测的效率和准确性,该文提出了GBWO-ENN(Grey Black Wolf Optimization-Elman Neural Network)的方法。针对传统灰狼算法易于陷入局部最优和无法平衡全局与局部搜索的平衡性问题,提出了GBWO算法。该算法融合黑寡妇优化算法中蜘蛛的运动方式对灰狼优化算法中α狼位置更新进行了优化,通过非线性递减的方法降低了收敛系数,并且提出了按位置等级更新种群的策略。随后采用GBWO算法对Elman神经网络的权值和阈值进行优化,并将GBWO-ENN模型应用于三维人体尺寸预测。实验结果表明,GBWO-ENN模型结构简单,能够准确预测人体尺寸,具有较好的预测能力。展开更多
针对铣刀磨损状态监测中信号噪声大、监测效率低等问题,提出了一种基于能量权重法的变分模态分解(VMD)与黑寡妇(BWO)-支持向量机(SVM)的铣刀磨损状态监测方法。首先,运用VMD将铣削时产生的振动信号分解成若干固有模态函数(IMF)分量,并...针对铣刀磨损状态监测中信号噪声大、监测效率低等问题,提出了一种基于能量权重法的变分模态分解(VMD)与黑寡妇(BWO)-支持向量机(SVM)的铣刀磨损状态监测方法。首先,运用VMD将铣削时产生的振动信号分解成若干固有模态函数(IMF)分量,并通过能量加权合成峭度指标自适应提取出了包含磨损状态特征的IMF分量,并进行了信号重构,对重构信号进行了特征提取;然后,利用BWO算法优化SVM的参数,构建了BWO-SVM铣刀磨损状态监测模型;最后,为了验证上述方法的有效性,以某公司真实加工现场的PHM Society 2010铣刀全寿命周期的振动数据进行了实验,并且又通过实际的工程案例对此进行了验证。研究结果表明:通过所提方法自适应提取有效分量并进行信号重构后,降噪效果明显,并通过与遗传算法(GA)和粒子群算法(PSO)优化的SVM相比,经过BWO优化的SVM的训练时间缩短至25.142 s,同时监测精度达到97.246%;采用该方法对铣刀磨损状态进行监测,能够获得更快的识别速度与更高的准确性,提高了铣刀磨损状态监测的效率。展开更多
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
A distributed generation network could be a hybrid power system that includes wind-diesel power generation based on induction generators(IGs)and synchronous generators(SGs).The main advantage of these systems is the p...A distributed generation network could be a hybrid power system that includes wind-diesel power generation based on induction generators(IGs)and synchronous generators(SGs).The main advantage of these systems is the possibility of using renewable energy in their structures.The most important challenge is to design the voltage-control loop with the frequency-control loop to obtain optimal responses for voltage and frequency deviations.In this work,the voltage-control loop is designed by an automatic voltage regulator.A linear model of the hybrid system has also been developed with coordinated voltage and frequency control.Dynamic frequency response and voltage deviations are compared for different load disturbances and different reactive loads.The gains of the SG and the static volt-ampere reactive compensator(SVC)controllers in the IG terminal are calculated using the Black Widow Optimization(BWO)algorithm to insure low frequency and voltage deviations.The BWO optimization algorithm is one of the newest and most powerful optimization methods to have been introduced so far.The results showed that the BWO algorithm has a good speed in solving the proposed objective function.A 22%improvement in time adjustment was observed in the use of an optimal SVC.Also,an 18%improvement was observed in the transitory values.展开更多
针对铣刀磨损量预测时精度低的问题,提出一种基于黑寡妇算法(BWO)优化的长短期记忆神经网络(LSTM)与AdaBoost集成学习算法相结合的铣刀磨损量预测方法。在铣刀磨损振动信号中提取时域、频域以及时频域多域特征。通过BWO算法优化LSTM的...针对铣刀磨损量预测时精度低的问题,提出一种基于黑寡妇算法(BWO)优化的长短期记忆神经网络(LSTM)与AdaBoost集成学习算法相结合的铣刀磨损量预测方法。在铣刀磨损振动信号中提取时域、频域以及时频域多域特征。通过BWO算法优化LSTM的核心参数,并将优化后的LSTM网络与AdaBoost算法进行结合,构建铣刀磨损量预测模型。最后用PHM Society 2010铣刀全寿命周期的振动数据进行实验。研究结果表明:所提方法能够有效地预测出铣刀磨损量变化值,优化后模型的平均绝对误差百分比为3.436%、均方根误差为6.471、决定系数R^(2)为0.935。该方法能够获得准确率更高的铣刀磨损量预测值,预测效率更高。展开更多
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.
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.
文摘为了提高人体尺寸预测的效率和准确性,该文提出了GBWO-ENN(Grey Black Wolf Optimization-Elman Neural Network)的方法。针对传统灰狼算法易于陷入局部最优和无法平衡全局与局部搜索的平衡性问题,提出了GBWO算法。该算法融合黑寡妇优化算法中蜘蛛的运动方式对灰狼优化算法中α狼位置更新进行了优化,通过非线性递减的方法降低了收敛系数,并且提出了按位置等级更新种群的策略。随后采用GBWO算法对Elman神经网络的权值和阈值进行优化,并将GBWO-ENN模型应用于三维人体尺寸预测。实验结果表明,GBWO-ENN模型结构简单,能够准确预测人体尺寸,具有较好的预测能力。
文摘针对铣刀磨损状态监测中信号噪声大、监测效率低等问题,提出了一种基于能量权重法的变分模态分解(VMD)与黑寡妇(BWO)-支持向量机(SVM)的铣刀磨损状态监测方法。首先,运用VMD将铣削时产生的振动信号分解成若干固有模态函数(IMF)分量,并通过能量加权合成峭度指标自适应提取出了包含磨损状态特征的IMF分量,并进行了信号重构,对重构信号进行了特征提取;然后,利用BWO算法优化SVM的参数,构建了BWO-SVM铣刀磨损状态监测模型;最后,为了验证上述方法的有效性,以某公司真实加工现场的PHM Society 2010铣刀全寿命周期的振动数据进行了实验,并且又通过实际的工程案例对此进行了验证。研究结果表明:通过所提方法自适应提取有效分量并进行信号重构后,降噪效果明显,并通过与遗传算法(GA)和粒子群算法(PSO)优化的SVM相比,经过BWO优化的SVM的训练时间缩短至25.142 s,同时监测精度达到97.246%;采用该方法对铣刀磨损状态进行监测,能够获得更快的识别速度与更高的准确性,提高了铣刀磨损状态监测的效率。
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.
文摘A distributed generation network could be a hybrid power system that includes wind-diesel power generation based on induction generators(IGs)and synchronous generators(SGs).The main advantage of these systems is the possibility of using renewable energy in their structures.The most important challenge is to design the voltage-control loop with the frequency-control loop to obtain optimal responses for voltage and frequency deviations.In this work,the voltage-control loop is designed by an automatic voltage regulator.A linear model of the hybrid system has also been developed with coordinated voltage and frequency control.Dynamic frequency response and voltage deviations are compared for different load disturbances and different reactive loads.The gains of the SG and the static volt-ampere reactive compensator(SVC)controllers in the IG terminal are calculated using the Black Widow Optimization(BWO)algorithm to insure low frequency and voltage deviations.The BWO optimization algorithm is one of the newest and most powerful optimization methods to have been introduced so far.The results showed that the BWO algorithm has a good speed in solving the proposed objective function.A 22%improvement in time adjustment was observed in the use of an optimal SVC.Also,an 18%improvement was observed in the transitory values.
文摘针对铣刀磨损量预测时精度低的问题,提出一种基于黑寡妇算法(BWO)优化的长短期记忆神经网络(LSTM)与AdaBoost集成学习算法相结合的铣刀磨损量预测方法。在铣刀磨损振动信号中提取时域、频域以及时频域多域特征。通过BWO算法优化LSTM的核心参数,并将优化后的LSTM网络与AdaBoost算法进行结合,构建铣刀磨损量预测模型。最后用PHM Society 2010铣刀全寿命周期的振动数据进行实验。研究结果表明:所提方法能够有效地预测出铣刀磨损量变化值,优化后模型的平均绝对误差百分比为3.436%、均方根误差为6.471、决定系数R^(2)为0.935。该方法能够获得准确率更高的铣刀磨损量预测值,预测效率更高。
