To address the issue of low measurement accuracy caused by noise interference in the acquisition of low fluid flow rate signals with ultrasonic Doppler flow meters,a novel signal processing algorithm that combines ens...To address the issue of low measurement accuracy caused by noise interference in the acquisition of low fluid flow rate signals with ultrasonic Doppler flow meters,a novel signal processing algorithm that combines ensemble empirical mode decomposition(EEMD)and cross-correlation algorithm was proposed.Firstly,a fast Fourier transform(FFT)spectrum analysis was utilized to ascertain the frequency range of the signal.Secondly,data acquisition was conducted at an appropriate sampling frequency,and the acquired Doppler flow rate signal was then decomposed into a series of intrinsic mode functions(IMFs)by EEMD.Subsequently,these decomposed IMFs were recombined based on their energy entropy,and then the noise of the recombined Doppler flow rate signal was removed by cross-correlation filtering.Finally,an ideal ultrasonic Doppler flow rate signal was extracted.Simulation and experimental verification show that the proposed Doppler flow signal processing method can effectively enhance the signal-to-noise ratio(SNR)and extend the lower limit of measurement of the ultrasonic Doppler flow meter.展开更多
The improved cross-correlation algorithm for the strain demodulation of Vernier-effect-based optical fiber sensor(VE-OFS)is proposed in this article.The algorithm identifies the most similar spectrum to the measured o...The improved cross-correlation algorithm for the strain demodulation of Vernier-effect-based optical fiber sensor(VE-OFS)is proposed in this article.The algorithm identifies the most similar spectrum to the measured one from the database of the collected spectra by employing the cross-correlation operation,subsequently deriving the predicted value via weighted calculation.As the algorithm uses the complete information in the measured raw spectrum,more accurate results and larger measurement range can be obtained.Additionally,the improved cross-correlation algorithm also has the potential to improve the measurement speed compared to current standards due to the possibility for the collection using low sampling rate.This work presents an important algorithm towards a simpler,faster way to improve the demodulation performance of VE-OFS.展开更多
Detecting coupling pattern between elements in a complex system is a basic task in data-driven analysis. The trajectory for each specific element is a cooperative result of its intrinsic dynamic, its couplings with ot...Detecting coupling pattern between elements in a complex system is a basic task in data-driven analysis. The trajectory for each specific element is a cooperative result of its intrinsic dynamic, its couplings with other elements, and the environment. It is subsequently composed of many components, only some of which take part in the couplings. In this paper we present a framework to detect the component correlation pattern. Firstly, the interested trajectories are decomposed into components by using decomposing methods such as the Fourier expansion and the Wavelet transformation. Secondly, the cross-correlations between the components are calculated, resulting into a component cross-correlation matrix(network).Finally, the dominant structure in the network is identified to characterize the coupling pattern in the system. Several deterministic dynamical models turn out to be characterized with rich structures such as the clustering of the components. The pattern of correlation between respiratory(RESP) and ECG signals is composed of five sub-clusters that are mainly formed by the components in ECG signal. Interestingly, only 7 components from RESP(scattered in four sub-clusters) take part in the realization of coupling between the two signals.展开更多
The algorithm of Binary Image Cross-Correlation (BICC) was developed to measure the unsteady flow field. A vortex flow field was used to test the algorithm by numerical simulation. The results show that BICC is an e...The algorithm of Binary Image Cross-Correlation (BICC) was developed to measure the unsteady flow field. A vortex flow field was used to test the algorithm by numerical simulation. The results show that BICC is an effective algorithm for particle identification from consecutive images, the accurate velocity vector field can be obtained. The real velocity field in a valve chamber was measured by BICC in this study. From the full-field velocity information, the pressure and vorticity fields were also extracted by post-processing. (Edited author abstract) 6 Refs.展开更多
The elastic thickness parameter was estimated using the mobile correlation technique between the observed isostatic disturbance and the gravity disturbance calculated through direct gravimetric modeling. We computed t...The elastic thickness parameter was estimated using the mobile correlation technique between the observed isostatic disturbance and the gravity disturbance calculated through direct gravimetric modeling. We computed the vertical flexure value of the crust for a specific elastic thickness using a given topographic dataset. The gravity disturbance due to the topography was determined after the calculation. A grid of values for the elastic thickness parameter was generated. Then, a moving correlation was performed between the observed gravity data(representing actual surface data) and the calculated data from the forward modeling. The optimum elastic thickness of the particular point corresponded to the highest correlation coefficient. The methodology was tested on synthetic data and showed that the synthetic depth closely matched the original depth, including the elastic thickness value. To validate the results, the described procedure was applied to a real dataset from the Barreirinhas Basin, situated in the northeastern region of Brazil. The results show that the obtained crustal depth is highly correlated with the depth from known models. Additionally, we noted that the elastic thickness behaves as expected, decreasing from the continent towards the ocean. Based on the results, this method has the potential to be employed as a direct estimate of crustal depth and elastic thickness for any region.展开更多
The temperature change and rate of CO2 change are correlated with a time lag, as reported in a previous paper. The correlation was investigated by calculating a correlation coefficient r of these changes for selected ...The temperature change and rate of CO2 change are correlated with a time lag, as reported in a previous paper. The correlation was investigated by calculating a correlation coefficient r of these changes for selected ENSO events in this study. Annual periodical increases and decreases in the CO2 concentration were considered, with a regular pattern of minimum values in August and maximum values in May each year. An increased deviation in CO2 and temperature was found in response to the occurrence of El Niño, but the increase in CO2 lagged behind the change in temperature by 5 months. This pattern was not observed for La Niña events. An increase in global CO2 emissions and a subsequent increase in global temperature proposed by IPCC were not observed, but an increase in global temperature, an increase in soil respiration, and a subsequent increase in global CO2 emissions were noticed. This natural process can be clearly detected during periods of increasing temperature specifically during El Niño events. The results cast strong doubts that anthropogenic CO2 is the cause of global warming.展开更多
In response to the downlink synchronization requirements of the user equipment(UE)or third-party radio equipment in fifth-generation(5G)mobile communication systems,a synchronization algorithm of primary synchroni-zat...In response to the downlink synchronization requirements of the user equipment(UE)or third-party radio equipment in fifth-generation(5G)mobile communication systems,a synchronization algorithm of primary synchroni-zation signal(PSS)was designed and developed in the 5G system based on block cross-correlation.According to the new characteristics of the 5G synchronization channel and broadcast channel,starting from the traditional downlink synchronization algorithm of long-term evolution(LTE),the detection performance of the algorithm under a low signal-to-noise ratio(SNR)is improved by introducing an incoherent accumulation,and the new scheme of joint coarse frequency offset estimation is used to improve the frequency offset estimation performance.Finally,the performance of the proposed synchronization algorithm is verified by conducting a simulation on a 5G downlink simulation platform based on MATLAB software.Simulation results show that the improved downlink synchronization algorithm has stable performance in the tapped delay line-C(TDL-C)and additive white Gaussian noise(AWGN)channels with large frequency deviation and low SNR.展开更多
Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,curr...Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.展开更多
Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,th...Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction.To improve the accuracy of short-term wind power forecast,this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer,which is based on seasonal and trend decomposition using LOESS(STL)and iTransformer model optimized by improved arithmetic optimization algorithm(IAOA).First,to fully extract the power data features,STL is used to decompose the original data into components with less redundant information.The extracted components as well as the weather data are then input into iTransformer for short-term wind power forecast.The final predicted short-term wind power curve is obtained by combining the predicted components.To improve the model accuracy,IAOA is employed to optimize the hyperparameters of iTransformer.The proposed approach is validated using real-generation data from different seasons and different power stations inNorthwest China,and ablation experiments have been conducted.Furthermore,to validate the superiority of the proposed approach under different wind characteristics,real power generation data fromsouthwestChina are utilized for experiments.Thecomparative results with the other six state-of-the-art prediction models in experiments show that the proposed model well fits the true value of generation series and achieves high prediction accuracy.展开更多
This document presents a framework for recognizing people by palm vein distribution analysis using cross-correlation based signatures to obtain descriptors. Haar wavelets are useful in reducing the number of features ...This document presents a framework for recognizing people by palm vein distribution analysis using cross-correlation based signatures to obtain descriptors. Haar wavelets are useful in reducing the number of features while maintaining high recognition rates. This experiment achieved 97.5% of individuals classified correctly with two levels of Haar wavelets. This study used twelve-version of RGB and NIR (near infrared) wavelength images per individual. One hundred people were studied;therefore 4,800 instances compose the complete database. A Multilayer Perceptron (MLP) was trained to improve the recognition rate in a k-fold cross-validation test with k = 10. Classification results using MLP neural network were obtained using Weka (open source machine learning software).展开更多
In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms...In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms to solve the problem of multi-UAV path planning.The Dung Beetle Optimization(DBO)algorithm has been widely applied due to its diverse search patterns in the above algorithms.However,the update strategies for the rolling and thieving dung beetles of the DBO algorithm are overly simplistic,potentially leading to an inability to fully explore the search space and a tendency to converge to local optima,thereby not guaranteeing the discovery of the optimal path.To address these issues,we propose an improved DBO algorithm guided by the Landmark Operator(LODBO).Specifically,we first use tent mapping to update the population strategy,which enables the algorithm to generate initial solutions with enhanced diversity within the search space.Second,we expand the search range of the rolling ball dung beetle by using the landmark factor.Finally,by using the adaptive factor that changes with the number of iterations.,we improve the global search ability of the stealing dung beetle,making it more likely to escape from local optima.To verify the effectiveness of the proposed method,extensive simulation experiments are conducted,and the result shows that the LODBO algorithm can obtain the optimal path using the shortest time compared with the Genetic Algorithm(GA),the Gray Wolf Optimizer(GWO),the Whale Optimization Algorithm(WOA)and the original DBO algorithm in the disaster search and rescue task set.展开更多
In this paper,we prove that Euclid's algorithm,Bezout's equation and Divi-sion algorithm are equivalent to each other.Our result shows that Euclid has preliminarily established the theory of divisibility and t...In this paper,we prove that Euclid's algorithm,Bezout's equation and Divi-sion algorithm are equivalent to each other.Our result shows that Euclid has preliminarily established the theory of divisibility and the greatest common divisor.We further provided several suggestions for teaching.展开更多
Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convol...Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low accuracy.Therefore,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 dataset.The hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 dataset.The evaluation accuracy of the new dataset GINKS2023 created in this paper is 0.9386.Compared to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.展开更多
Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered so...Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.展开更多
To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The...To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The inspection robot utilizes multiple sensors to monitor key parameters of the fans,such as vibration,noise,and bearing temperature,and upload the data to the monitoring center.The robot’s inspection path employs the improved A^(*)algorithm,incorporating obstacle penalty terms,path reconstruction,and smoothing optimization techniques,thereby achieving optimal path planning for the inspection robot in complex environments.Simulation results demonstrate that the improved A^(*)algorithm significantly outperforms the traditional A^(*)algorithm in terms of total path distance,smoothness,and detour rate,effectively improving the execution efficiency of inspection tasks.展开更多
In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-base...In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.展开更多
In order to calculate the cross-correlation of two color images treated as vector in a holistic manner,a rapid vertical/parallel decomposition algorithm for quaternion is presented.The calculation for decomposition is...In order to calculate the cross-correlation of two color images treated as vector in a holistic manner,a rapid vertical/parallel decomposition algorithm for quaternion is presented.The calculation for decomposition is reduced from 21 times to 4 times real number multiplications with the same results.An algorithm for cross-correlation of color images based on decomposition in time domain is put forward,in which some properties pointed out in this paper can be utilized to reduce the computational complexity.Simulation results show the effectiveness and superiority of the proposed method.展开更多
A new method based on the iterative adaptive algorithm(IAA)and blocking matrix preprocessing(BMP)is proposed to study the suppression of multi-mainlobe interference.The algorithm is applied to precisely estimate the s...A new method based on the iterative adaptive algorithm(IAA)and blocking matrix preprocessing(BMP)is proposed to study the suppression of multi-mainlobe interference.The algorithm is applied to precisely estimate the spatial spectrum and the directions of arrival(DOA)of interferences to overcome the drawbacks associated with conventional adaptive beamforming(ABF)methods.The mainlobe interferences are identified by calculating the correlation coefficients between direction steering vectors(SVs)and rejected by the BMP pretreatment.Then,IAA is subsequently employed to reconstruct a sidelobe interference-plus-noise covariance matrix for the preferable ABF and residual interference suppression.Simulation results demonstrate the excellence of the proposed method over normal methods based on BMP and eigen-projection matrix perprocessing(EMP)under both uncorrelated and coherent circumstances.展开更多
基金supported by National Natural Science Foundation of China(No.61973234)Tianjin Science and Technology Plan Project(No.22YDTPJC00090)。
文摘To address the issue of low measurement accuracy caused by noise interference in the acquisition of low fluid flow rate signals with ultrasonic Doppler flow meters,a novel signal processing algorithm that combines ensemble empirical mode decomposition(EEMD)and cross-correlation algorithm was proposed.Firstly,a fast Fourier transform(FFT)spectrum analysis was utilized to ascertain the frequency range of the signal.Secondly,data acquisition was conducted at an appropriate sampling frequency,and the acquired Doppler flow rate signal was then decomposed into a series of intrinsic mode functions(IMFs)by EEMD.Subsequently,these decomposed IMFs were recombined based on their energy entropy,and then the noise of the recombined Doppler flow rate signal was removed by cross-correlation filtering.Finally,an ideal ultrasonic Doppler flow rate signal was extracted.Simulation and experimental verification show that the proposed Doppler flow signal processing method can effectively enhance the signal-to-noise ratio(SNR)and extend the lower limit of measurement of the ultrasonic Doppler flow meter.
文摘The improved cross-correlation algorithm for the strain demodulation of Vernier-effect-based optical fiber sensor(VE-OFS)is proposed in this article.The algorithm identifies the most similar spectrum to the measured one from the database of the collected spectra by employing the cross-correlation operation,subsequently deriving the predicted value via weighted calculation.As the algorithm uses the complete information in the measured raw spectrum,more accurate results and larger measurement range can be obtained.Additionally,the improved cross-correlation algorithm also has the potential to improve the measurement speed compared to current standards due to the possibility for the collection using low sampling rate.This work presents an important algorithm towards a simpler,faster way to improve the demodulation performance of VE-OFS.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 11875042 and 11505114)the Shanghai Project for Construction of Top Disciplines (Grant No. USST-SYS-01)。
文摘Detecting coupling pattern between elements in a complex system is a basic task in data-driven analysis. The trajectory for each specific element is a cooperative result of its intrinsic dynamic, its couplings with other elements, and the environment. It is subsequently composed of many components, only some of which take part in the couplings. In this paper we present a framework to detect the component correlation pattern. Firstly, the interested trajectories are decomposed into components by using decomposing methods such as the Fourier expansion and the Wavelet transformation. Secondly, the cross-correlations between the components are calculated, resulting into a component cross-correlation matrix(network).Finally, the dominant structure in the network is identified to characterize the coupling pattern in the system. Several deterministic dynamical models turn out to be characterized with rich structures such as the clustering of the components. The pattern of correlation between respiratory(RESP) and ECG signals is composed of five sub-clusters that are mainly formed by the components in ECG signal. Interestingly, only 7 components from RESP(scattered in four sub-clusters) take part in the realization of coupling between the two signals.
基金The project supported by the National Natural Science Foundation of China
文摘The algorithm of Binary Image Cross-Correlation (BICC) was developed to measure the unsteady flow field. A vortex flow field was used to test the algorithm by numerical simulation. The results show that BICC is an effective algorithm for particle identification from consecutive images, the accurate velocity vector field can be obtained. The real velocity field in a valve chamber was measured by BICC in this study. From the full-field velocity information, the pressure and vorticity fields were also extracted by post-processing. (Edited author abstract) 6 Refs.
文摘The elastic thickness parameter was estimated using the mobile correlation technique between the observed isostatic disturbance and the gravity disturbance calculated through direct gravimetric modeling. We computed the vertical flexure value of the crust for a specific elastic thickness using a given topographic dataset. The gravity disturbance due to the topography was determined after the calculation. A grid of values for the elastic thickness parameter was generated. Then, a moving correlation was performed between the observed gravity data(representing actual surface data) and the calculated data from the forward modeling. The optimum elastic thickness of the particular point corresponded to the highest correlation coefficient. The methodology was tested on synthetic data and showed that the synthetic depth closely matched the original depth, including the elastic thickness value. To validate the results, the described procedure was applied to a real dataset from the Barreirinhas Basin, situated in the northeastern region of Brazil. The results show that the obtained crustal depth is highly correlated with the depth from known models. Additionally, we noted that the elastic thickness behaves as expected, decreasing from the continent towards the ocean. Based on the results, this method has the potential to be employed as a direct estimate of crustal depth and elastic thickness for any region.
文摘The temperature change and rate of CO2 change are correlated with a time lag, as reported in a previous paper. The correlation was investigated by calculating a correlation coefficient r of these changes for selected ENSO events in this study. Annual periodical increases and decreases in the CO2 concentration were considered, with a regular pattern of minimum values in August and maximum values in May each year. An increased deviation in CO2 and temperature was found in response to the occurrence of El Niño, but the increase in CO2 lagged behind the change in temperature by 5 months. This pattern was not observed for La Niña events. An increase in global CO2 emissions and a subsequent increase in global temperature proposed by IPCC were not observed, but an increase in global temperature, an increase in soil respiration, and a subsequent increase in global CO2 emissions were noticed. This natural process can be clearly detected during periods of increasing temperature specifically during El Niño events. The results cast strong doubts that anthropogenic CO2 is the cause of global warming.
基金The Social Development Projects of Jiangsu Science and Technology Department(No.BE2018704).
文摘In response to the downlink synchronization requirements of the user equipment(UE)or third-party radio equipment in fifth-generation(5G)mobile communication systems,a synchronization algorithm of primary synchroni-zation signal(PSS)was designed and developed in the 5G system based on block cross-correlation.According to the new characteristics of the 5G synchronization channel and broadcast channel,starting from the traditional downlink synchronization algorithm of long-term evolution(LTE),the detection performance of the algorithm under a low signal-to-noise ratio(SNR)is improved by introducing an incoherent accumulation,and the new scheme of joint coarse frequency offset estimation is used to improve the frequency offset estimation performance.Finally,the performance of the proposed synchronization algorithm is verified by conducting a simulation on a 5G downlink simulation platform based on MATLAB software.Simulation results show that the improved downlink synchronization algorithm has stable performance in the tapped delay line-C(TDL-C)and additive white Gaussian noise(AWGN)channels with large frequency deviation and low SNR.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant(No.51677058).
文摘Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
基金supported by Yunnan Provincial Basic Research Project(202401AT070344,202301AT070443)National Natural Science Foundation of China(62263014,52207105)+1 种基金Yunnan Lancang-Mekong International Electric Power Technology Joint Laboratory(202203AP140001)Major Science and Technology Projects in Yunnan Province(202402AG050006).
文摘Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction.To improve the accuracy of short-term wind power forecast,this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer,which is based on seasonal and trend decomposition using LOESS(STL)and iTransformer model optimized by improved arithmetic optimization algorithm(IAOA).First,to fully extract the power data features,STL is used to decompose the original data into components with less redundant information.The extracted components as well as the weather data are then input into iTransformer for short-term wind power forecast.The final predicted short-term wind power curve is obtained by combining the predicted components.To improve the model accuracy,IAOA is employed to optimize the hyperparameters of iTransformer.The proposed approach is validated using real-generation data from different seasons and different power stations inNorthwest China,and ablation experiments have been conducted.Furthermore,to validate the superiority of the proposed approach under different wind characteristics,real power generation data fromsouthwestChina are utilized for experiments.Thecomparative results with the other six state-of-the-art prediction models in experiments show that the proposed model well fits the true value of generation series and achieves high prediction accuracy.
文摘This document presents a framework for recognizing people by palm vein distribution analysis using cross-correlation based signatures to obtain descriptors. Haar wavelets are useful in reducing the number of features while maintaining high recognition rates. This experiment achieved 97.5% of individuals classified correctly with two levels of Haar wavelets. This study used twelve-version of RGB and NIR (near infrared) wavelength images per individual. One hundred people were studied;therefore 4,800 instances compose the complete database. A Multilayer Perceptron (MLP) was trained to improve the recognition rate in a k-fold cross-validation test with k = 10. Classification results using MLP neural network were obtained using Weka (open source machine learning software).
基金supported by the National Natural Science Foundation of China(No.62373027).
文摘In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms to solve the problem of multi-UAV path planning.The Dung Beetle Optimization(DBO)algorithm has been widely applied due to its diverse search patterns in the above algorithms.However,the update strategies for the rolling and thieving dung beetles of the DBO algorithm are overly simplistic,potentially leading to an inability to fully explore the search space and a tendency to converge to local optima,thereby not guaranteeing the discovery of the optimal path.To address these issues,we propose an improved DBO algorithm guided by the Landmark Operator(LODBO).Specifically,we first use tent mapping to update the population strategy,which enables the algorithm to generate initial solutions with enhanced diversity within the search space.Second,we expand the search range of the rolling ball dung beetle by using the landmark factor.Finally,by using the adaptive factor that changes with the number of iterations.,we improve the global search ability of the stealing dung beetle,making it more likely to escape from local optima.To verify the effectiveness of the proposed method,extensive simulation experiments are conducted,and the result shows that the LODBO algorithm can obtain the optimal path using the shortest time compared with the Genetic Algorithm(GA),the Gray Wolf Optimizer(GWO),the Whale Optimization Algorithm(WOA)and the original DBO algorithm in the disaster search and rescue task set.
基金Supported by the Natural Science Foundation of Chongqing(General Program,NO.CSTB2022NSCQ-MSX0884)Discipline Teaching Special Project of Yangtze Normal University(csxkjx14)。
文摘In this paper,we prove that Euclid's algorithm,Bezout's equation and Divi-sion algorithm are equivalent to each other.Our result shows that Euclid has preliminarily established the theory of divisibility and the greatest common divisor.We further provided several suggestions for teaching.
基金supported by Science and Technology Innovation Programfor Postgraduate Students in IDP Subsidized by Fundamental Research Funds for the Central Universities(Project No.ZY20240335)support of the Research Project of the Key Technology of Malicious Code Detection Based on Data Mining in APT Attack(Project No.2022IT173)the Research Project of the Big Data Sensitive Information Supervision Technology Based on Convolutional Neural Network(Project No.2022011033).
文摘Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low accuracy.Therefore,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 dataset.The hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 dataset.The evaluation accuracy of the new dataset GINKS2023 created in this paper is 0.9386.Compared to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.
文摘Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.
文摘To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The inspection robot utilizes multiple sensors to monitor key parameters of the fans,such as vibration,noise,and bearing temperature,and upload the data to the monitoring center.The robot’s inspection path employs the improved A^(*)algorithm,incorporating obstacle penalty terms,path reconstruction,and smoothing optimization techniques,thereby achieving optimal path planning for the inspection robot in complex environments.Simulation results demonstrate that the improved A^(*)algorithm significantly outperforms the traditional A^(*)algorithm in terms of total path distance,smoothness,and detour rate,effectively improving the execution efficiency of inspection tasks.
基金Shanxi Province Higher Education Science and Technology Innovation Fund Project(2022-676)Shanxi Soft Science Program Research Fund Project(2016041008-6)。
文摘In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.
基金supported by the National Natural Science Foundation of China (6060402160874054)
文摘In order to calculate the cross-correlation of two color images treated as vector in a holistic manner,a rapid vertical/parallel decomposition algorithm for quaternion is presented.The calculation for decomposition is reduced from 21 times to 4 times real number multiplications with the same results.An algorithm for cross-correlation of color images based on decomposition in time domain is put forward,in which some properties pointed out in this paper can be utilized to reduce the computational complexity.Simulation results show the effectiveness and superiority of the proposed method.
基金The National Natural Science Foundation of China(No.U19B2031).
文摘A new method based on the iterative adaptive algorithm(IAA)and blocking matrix preprocessing(BMP)is proposed to study the suppression of multi-mainlobe interference.The algorithm is applied to precisely estimate the spatial spectrum and the directions of arrival(DOA)of interferences to overcome the drawbacks associated with conventional adaptive beamforming(ABF)methods.The mainlobe interferences are identified by calculating the correlation coefficients between direction steering vectors(SVs)and rejected by the BMP pretreatment.Then,IAA is subsequently employed to reconstruct a sidelobe interference-plus-noise covariance matrix for the preferable ABF and residual interference suppression.Simulation results demonstrate the excellence of the proposed method over normal methods based on BMP and eigen-projection matrix perprocessing(EMP)under both uncorrelated and coherent circumstances.
