An improved estimation of distribution algorithm(IEDA)is proposed in this paper for efficient design of metamaterial absorbers.This algorithm establishes a probability model through the selected dominant groups and sa...An improved estimation of distribution algorithm(IEDA)is proposed in this paper for efficient design of metamaterial absorbers.This algorithm establishes a probability model through the selected dominant groups and samples from the model to obtain the next generation,avoiding the problem of building-blocks destruction caused by crossover and mutation.Neighboring search from artificial bee colony algorithm(ABCA)is introduced to enhance the local optimization ability and improved to raise the speed of convergence.The probability model is modified by boundary correction and loss correction to enhance the robustness of the algorithm.The proposed IEDA is compared with other intelligent algorithms in relevant references.The results show that the proposed IEDA has faster convergence speed and stronger optimization ability,proving the feasibility and effectiveness of the algorithm.展开更多
Establishing the remote sensing algorithm of retrieving the absorption coefficient of seawater petroleum substances is an efficient way to improve the accuracy of retrieving a seawater petroleum concentration using a ...Establishing the remote sensing algorithm of retrieving the absorption coefficient of seawater petroleum substances is an efficient way to improve the accuracy of retrieving a seawater petroleum concentration using a remote sensing technology. A remote sensing reflectance is a basic physical parameter in water color remote sensing. Apply it to directly retrieve the absorption coefficient of seawater petroleum substances is of potential advantage. The absorption coefficient of waters containing petroleum [ACWCP, a_o（λ）], consists of the absorption coefficient of pure water [ACPW, a_w（λ）], plankton [ACP, a_（ph）（λ）], colored scraps [ACCS, a_（d,g）（λ）], and petroleum substance [ACPS, a_（oil）（λ）]. Among those, ACCS consists of the absorption coefficient of nonalgal particle [ACNP, a_d（λ）] and colored dissolved organic matter [ACCDOM, a_g（λ）]. For waters containing petroleum, the retrieved ACCS using the existing method is a combination absorption coefficient of ACNP,ACCDOM and ACPA [CAC, a_（d,g,oil）（λ）]. Therefore, the principle question is how to extract ACPS from CAC.Through the analysis of the three proportion tests conducted between the year of 2013 and 2015 and the corresponding remote sensing data, an algorithm of retrieving the absorption coefficient of petroleum substances is proposed based on remote sensing reflectance. First of all, ACPS and CAC are retrieved from the reflectance using the quasi-analytical algorithm（QAA）, with some parameter modified. Secondly, given the fact that the backscatter coefficient [BC, b_（bp）（555）] of total particles at 555 nm can be obtained completely from the reflectance, the relation between BC and ACNP in petroleum contaminated water can be established. As a result, ACNP can be calculated. Then, combining the remote sensing retrieving algorithm of a_g（440）, the method of achieving the spectral slope of the absorption coefficient can be established, from which ACCDOM,can be calculated. Finally, ACPS can be computed as the residual. The accuracy of ACPS based on this algorithm is 86% compared with the in situ measurements.展开更多
In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of GA-B...In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of GA-BP hybrid algorithm was presented by uniting respective applicability of back-propagation artificial neural network （BP-ANN） and genetic algorithm （GA）. The detailed process was as follows. Firstly, the GA trained the best weights and thresholds as the initial values of BP-ANN to initialize the neural network. Then, the BP-ANN after initialization was trained until the errors converged to the required precision. Finally, the network model, which met the requirements after being examined by the test samples, was applied to energy-absorption forecast of thin-walled cylindrical structure impacting. After example analysis, the GA-BP network model was trained until getting the desired network error only by 46 steps, while the single BP-ANN model achieved the same network error by 992 steps, which obviously shows that the GA-BP hybrid algorithm has faster convergence rate. The average relative forecast error （ARE） of the SEA predictive results obtained by GA-BP hybrid algorithm is 1.543%, while the ARE of the SEA predictive results obtained by BP-ANN is 2.950%, which clearly indicates that the forecast precision of the GA-BP hybrid algorithm is higher than that of the BP-ANN.展开更多
Considering the situation that the least-squares (LS) method for system identification has poor robustness and the least absolute deviation (LAD) algorithm is hard to construct, an approximate least absolute deviation...Considering the situation that the least-squares (LS) method for system identification has poor robustness and the least absolute deviation (LAD) algorithm is hard to construct, an approximate least absolute deviation (ALAD) algorithm is proposed in this paper. The objective function of ALAD is constructed by introducing a deterministic function to approximate the absolute value function. Based on the function, the recursive equations for parameter identification are derived using Gauss-Newton iterative algorithm without any simplification. This algorithm has advantages of simple calculation and easy implementation, and it has second order convergence speed. Compared with the LS method, the new algorithm has better robustness when disorder and peak noises exist in the measured data. Simulation results show the efficiency of the proposed method.展开更多
A method of detecting chemical oxygen demand(COD) of water based on ultraviolet(UV) absorption spectra is proposed. The modeling and analysis of the standard samples and the actual water samples are carried out respec...A method of detecting chemical oxygen demand(COD) of water based on ultraviolet(UV) absorption spectra is proposed. The modeling and analysis of the standard samples and the actual water samples are carried out respectively. For the standard solution samples, the univariate linear models based on single wavelengths and the partial least square(PLS) model based on synergy interval partial least square(Si PLS) and moving window partial least square(MWPLS) are established. For the actual water samples, different pre-processing methods are used. Si PLS and MWPLS are used to select the characteristic bands. The least squares support vector machine algorithm optimized by particle swarm optimization(PSO-LSSVM) algorithm is used to establish the prediction model, and the prediction results of various models are compared. The results show that the optimal model is PSO-LSSVM which uses Si PLS to select the characteristic bands of the first derivative spectra(preprocessing method). The determination coefficient of the prediction set is 0.963 1, and the root mean square error of prediction(RMSEP) is 2.225 4 mg/L. PSO-LSSVM algorithm has good prediction performance for the analysis of COD in actual water samples by UV spectra. This paper provides a new design idea for the research and development of water quality detection optical sensor.展开更多
This paper suggests a group of statistical algorithms for calculating the total absorption coefficients based on in situ data of apparent optical property and inherent optical property collected with strict quality as...This paper suggests a group of statistical algorithms for calculating the total absorption coefficients based on in situ data of apparent optical property and inherent optical property collected with strict quality assurance according to NASA ocean bio-optic protocols in the Yellow Sea and the East China Sea in spring 2003. The band-ratios ofRrs412/Rrs555, Rrs49o/Rrs555 are used in the algorithms to derive the total absorption coefficients （at） at 412, 440, 488, 510, 532 and 555nm bands, respectively. The average relative errors between inversed and measured values are less than 25.8%, with the correlative coefficients （R2） being 0.75-0.85. Error sensitivity analysis shows that the maximum retrieval error is less than 24.0% at ＋5% error in Rrs＇s. So the statistical algorithms of this paper are practicable. In this paper, the relations between the total absorption coefficients at 412, 488, 510, 532, 555 nm and that of 440nm are also studied. The results show that the relations between the total absorption coefficients of 400-600 nm and that of 440 nm are correlated well and all of their correlative coefficients R2 are greater than 0.99. Furthermore, a regression analysis is also done for the slope of the linear relations and wavelengths, and the R2 is also 0.99. Thus it is possible to retrieve other bands＇ total absorption coefficients with only one band absorption value, which significantly reduce the number of unknown parameters in studying other ocean color related problems.展开更多
We propose a bio-optical inversion model that retrieves the absorption contributions of phytoplankton and colored detrital matter(CDM),as well as the phytoplankton size classes(PSCs),from total minus water absorption ...We propose a bio-optical inversion model that retrieves the absorption contributions of phytoplankton and colored detrital matter(CDM),as well as the phytoplankton size classes(PSCs),from total minus water absorption spectra.The model is based on three-component separation of phytoplankton size structure and a genetic algorithm.The model performance was tested on two independent datasets(the NASA bio-Optical Marine Algorithm Dataset(NOMAD) and the northern South China Sea(NSCS) dataset).The relationships between the estimated and measured values were strongly linear,especially for aCDM(412),and the Root Mean Square Error(RMSE) of the CDM exponential slope(SCDM) was relatively low.Next,the inversion model was directly applied to in-situ total minus water absorption spectra determined by an underwater meter during a cruise in September 2008,to retrieve the phytoplankton size structure in the seawater.By comparing the measured and retrieved chlorophyll a concentrations,we demonstrated that total and size-specific chlorophyll a concentrations could be retrieved by the model with relatively high accuracy.Finally,we applied the bio-optical inversion model to investigate changes in phytoplankton size structure induced by an anti-cyclonic eddy in the NSCS.展开更多
As a “global” numerical optimization method, genetic algorithm is briefly introduced. It is applied to optimize the absorbing coating to reduce EM scattering, leading to satisfactory results.
In order to classify packet, we propose a novel IP classification based the non-collision hash and jumping table trie-tree (NHJTTT) algorithm, which is based on noncollision hash Trie-tree and Lakshman and Stiliadis p...In order to classify packet, we propose a novel IP classification based the non-collision hash and jumping table trie-tree (NHJTTT) algorithm, which is based on noncollision hash Trie-tree and Lakshman and Stiliadis proposing a 2-dimensional classification algorithm (LS algorithm). The core of algorithm consists of two parts: structure the non-collision hash function, which is constructed mainly based on destination/source port and protocol type field so that the hash function can avoid space explosion problem; introduce jumping table Trie-tree based LS algorithm in order to reduce time complexity. The test results show that the classification rate of NHJTTT algorithm is up to 1 million packets per second and the maximum memory consumed is 9 MB for 10 000 rules. Key words IP classification - lookup algorithm - trie-tree - non-collision hash - jumping table CLC number TN 393.06 Foundation item: Supported by the Chongqing of Posts and Telecommunications Younger Teacher Fundation (A2003-03).Biography: SHANG Feng-jun (1972-), male, Ph.D. candidate, lecture, research direction: the smart instrument and network.展开更多
Optimization of the open absorption desiccant cooling system has been carried out in the present work. A finite difference method is used to simulate the combined heat and mass transfer processes that occur in the liq...Optimization of the open absorption desiccant cooling system has been carried out in the present work. A finite difference method is used to simulate the combined heat and mass transfer processes that occur in the liquid desiccant regenerator which uses calcium chloride (CaCl2) solution as the working desiccant. The source of input heat is assumed to be the total radiation incident on a tilted surface. The system of equations is solved using the Matlab-Simulink platform. The effect of the important parameters, namely the regenerator length, desiccant solution flow rate and concentration, and air flow rates, on the performance of the system is investigated. In order to optimize the system performance, a genetic algorithm technique has been applied. The system coefficient of performance COP has been maximized for different design parameters. It has been found that the maximum values of COP could be obtained for different combinations of regenerator length solution flow rate and air flow rate. Therefore, it is essential to select the design parameters for each ambient condition to maximize the performance of the system.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
Using an improved particle swarm optimization algorithm(IPSO)to drive a transfer matrix method,a nonreciprocal absorber with an ultrawide absorption bandwidth and angular insensitivity is realized in plasma-embedded p...Using an improved particle swarm optimization algorithm(IPSO)to drive a transfer matrix method,a nonreciprocal absorber with an ultrawide absorption bandwidth and angular insensitivity is realized in plasma-embedded photonic crystals arranged in a structure composed of periodic and quasi-periodic sequences on a normalized scale.The effective dielectric function,which determines the absorption of the plasma,is subject to the basic parameters of the plasma,causing the absorption of the proposed absorber to be easily modulated by these parameters.Compared with other quasi-periodic sequences,the Octonacci sequence is superior both in relative bandwidth and absolute bandwidth.Under further optimization using IPSO with 14 parameters set to be optimized,the absorption characteristics of the proposed structure with different numbers of layers of the smallest structure unit N are shown and discussed.IPSO is also used to address angular insensitive nonreciprocal ultrawide bandwidth absorption,and the optimized result shows excellent unidirectional absorbability and angular insensitivity of the proposed structure.The impacts of the sequence number of quasi-periodic sequence M and collision frequency of plasma1ν1 to absorption in the angle domain and frequency domain are investigated.Additionally,the impedance match theory and the interference field theory are introduced to express the findings of the algorithm.展开更多
Fabricating of metal foams with desired morphological parameters including pore size,porosity and pore opening is possible now using sintering technology.Thus,if it is possible to determine the morphology of metal foa...Fabricating of metal foams with desired morphological parameters including pore size,porosity and pore opening is possible now using sintering technology.Thus,if it is possible to determine the morphology of metal foam to absorb sound at a given frequency,and then fabricate it through sintering,it is expected to have optimized metal foams for the best sound absorption.Theoretical sound absorption models such as Lu model describe the relationship between morphological parameters and the sound absorption coefficient.In this study,the Lu model was used to optimize the morphological parameters of aluminum metal foam for the best sound absorption coefficient.For this purpose,the Lu model was numerically solved using written codes in MATLAB software.After validating the proposed codes with benchmark data,the genetic algorithm(GA)was applied to optimize the affecting morphological parameters on the sound absorption coefficient.The optimization was carried out for the thicknesses of 5 mm to 40 mm at the sound frequency range of 250 Hz–8000 Hz.The optimized parameters ranged from 50%to 95%for porosity,0.1 mm to 4.5 mm for pore size,and 0.07 mm to 0.6 mm for pore opening size.The result of this study was applied to fabricate the desired aluminum metal foams for the best sound absorption.The novel approach applied in this study,is expected to be successfully applied in for best sound absorption in desired frequencies.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
基金supported by the National Key Research and Development Program(2021YFB3502500).
文摘An improved estimation of distribution algorithm(IEDA)is proposed in this paper for efficient design of metamaterial absorbers.This algorithm establishes a probability model through the selected dominant groups and samples from the model to obtain the next generation,avoiding the problem of building-blocks destruction caused by crossover and mutation.Neighboring search from artificial bee colony algorithm(ABCA)is introduced to enhance the local optimization ability and improved to raise the speed of convergence.The probability model is modified by boundary correction and loss correction to enhance the robustness of the algorithm.The proposed IEDA is compared with other intelligent algorithms in relevant references.The results show that the proposed IEDA has faster convergence speed and stronger optimization ability,proving the feasibility and effectiveness of the algorithm.
基金The National Natural Science Foundation of China under contract No.41271364the Key Projects in the National Science and Technology Pillar Program of China under contract No.2012BAH32B01-4the Program for Scientific Research Start-up Funds of Guangdong Ocean University under contract No.E16187
文摘Establishing the remote sensing algorithm of retrieving the absorption coefficient of seawater petroleum substances is an efficient way to improve the accuracy of retrieving a seawater petroleum concentration using a remote sensing technology. A remote sensing reflectance is a basic physical parameter in water color remote sensing. Apply it to directly retrieve the absorption coefficient of seawater petroleum substances is of potential advantage. The absorption coefficient of waters containing petroleum [ACWCP, a_o（λ）], consists of the absorption coefficient of pure water [ACPW, a_w（λ）], plankton [ACP, a_（ph）（λ）], colored scraps [ACCS, a_（d,g）（λ）], and petroleum substance [ACPS, a_（oil）（λ）]. Among those, ACCS consists of the absorption coefficient of nonalgal particle [ACNP, a_d（λ）] and colored dissolved organic matter [ACCDOM, a_g（λ）]. For waters containing petroleum, the retrieved ACCS using the existing method is a combination absorption coefficient of ACNP,ACCDOM and ACPA [CAC, a_（d,g,oil）（λ）]. Therefore, the principle question is how to extract ACPS from CAC.Through the analysis of the three proportion tests conducted between the year of 2013 and 2015 and the corresponding remote sensing data, an algorithm of retrieving the absorption coefficient of petroleum substances is proposed based on remote sensing reflectance. First of all, ACPS and CAC are retrieved from the reflectance using the quasi-analytical algorithm（QAA）, with some parameter modified. Secondly, given the fact that the backscatter coefficient [BC, b_（bp）（555）] of total particles at 555 nm can be obtained completely from the reflectance, the relation between BC and ACNP in petroleum contaminated water can be established. As a result, ACNP can be calculated. Then, combining the remote sensing retrieving algorithm of a_g（440）, the method of achieving the spectral slope of the absorption coefficient can be established, from which ACCDOM,can be calculated. Finally, ACPS can be computed as the residual. The accuracy of ACPS based on this algorithm is 86% compared with the in situ measurements.
基金Project(50175110) supported by the National Natural Science Foundation of ChinaProject(2009bsxt019) supported by the Graduate Degree Thesis Innovation Foundation of Central South University, China
文摘In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of GA-BP hybrid algorithm was presented by uniting respective applicability of back-propagation artificial neural network （BP-ANN） and genetic algorithm （GA）. The detailed process was as follows. Firstly, the GA trained the best weights and thresholds as the initial values of BP-ANN to initialize the neural network. Then, the BP-ANN after initialization was trained until the errors converged to the required precision. Finally, the network model, which met the requirements after being examined by the test samples, was applied to energy-absorption forecast of thin-walled cylindrical structure impacting. After example analysis, the GA-BP network model was trained until getting the desired network error only by 46 steps, while the single BP-ANN model achieved the same network error by 992 steps, which obviously shows that the GA-BP hybrid algorithm has faster convergence rate. The average relative forecast error （ARE） of the SEA predictive results obtained by GA-BP hybrid algorithm is 1.543%, while the ARE of the SEA predictive results obtained by BP-ANN is 2.950%, which clearly indicates that the forecast precision of the GA-BP hybrid algorithm is higher than that of the BP-ANN.
基金supported by Important National Science & Technology Specific Projects (No.2011ZX05021-003)
文摘Considering the situation that the least-squares (LS) method for system identification has poor robustness and the least absolute deviation (LAD) algorithm is hard to construct, an approximate least absolute deviation (ALAD) algorithm is proposed in this paper. The objective function of ALAD is constructed by introducing a deterministic function to approximate the absolute value function. Based on the function, the recursive equations for parameter identification are derived using Gauss-Newton iterative algorithm without any simplification. This algorithm has advantages of simple calculation and easy implementation, and it has second order convergence speed. Compared with the LS method, the new algorithm has better robustness when disorder and peak noises exist in the measured data. Simulation results show the efficiency of the proposed method.
基金supported by the National Natural Science Foundation of China(No.61963031)the Inner Mongolia Autonomous Region Natural Science Foundation(No.2019MS06017)the Scientific Research Projects of Colleges and Universities in Inner Mongolia Autonomous Region of China(No.NJZY20122)。
文摘A method of detecting chemical oxygen demand(COD) of water based on ultraviolet(UV) absorption spectra is proposed. The modeling and analysis of the standard samples and the actual water samples are carried out respectively. For the standard solution samples, the univariate linear models based on single wavelengths and the partial least square(PLS) model based on synergy interval partial least square(Si PLS) and moving window partial least square(MWPLS) are established. For the actual water samples, different pre-processing methods are used. Si PLS and MWPLS are used to select the characteristic bands. The least squares support vector machine algorithm optimized by particle swarm optimization(PSO-LSSVM) algorithm is used to establish the prediction model, and the prediction results of various models are compared. The results show that the optimal model is PSO-LSSVM which uses Si PLS to select the characteristic bands of the first derivative spectra(preprocessing method). The determination coefficient of the prediction set is 0.963 1, and the root mean square error of prediction(RMSEP) is 2.225 4 mg/L. PSO-LSSVM algorithm has good prediction performance for the analysis of COD in actual water samples by UV spectra. This paper provides a new design idea for the research and development of water quality detection optical sensor.
基金Supported by the Subsystem of Calibration and Validation, HY-1 Ground Application System, National Satellite Ocean Application Ser-vice (NSOAS). China High-Tech "863" Project (Nos. 2001AA636010, 2002AA639160 and 2002AA639200). The Ocean Science Fund Sponsor Project for the Youth, State Oceanic Administration (No. 2005415). The Director’s Science and Technology Fund Sponsor Project for the Youth, NSOAS.
文摘This paper suggests a group of statistical algorithms for calculating the total absorption coefficients based on in situ data of apparent optical property and inherent optical property collected with strict quality assurance according to NASA ocean bio-optic protocols in the Yellow Sea and the East China Sea in spring 2003. The band-ratios ofRrs412/Rrs555, Rrs49o/Rrs555 are used in the algorithms to derive the total absorption coefficients （at） at 412, 440, 488, 510, 532 and 555nm bands, respectively. The average relative errors between inversed and measured values are less than 25.8%, with the correlative coefficients （R2） being 0.75-0.85. Error sensitivity analysis shows that the maximum retrieval error is less than 24.0% at ＋5% error in Rrs＇s. So the statistical algorithms of this paper are practicable. In this paper, the relations between the total absorption coefficients at 412, 488, 510, 532, 555 nm and that of 440nm are also studied. The results show that the relations between the total absorption coefficients of 400-600 nm and that of 440 nm are correlated well and all of their correlative coefficients R2 are greater than 0.99. Furthermore, a regression analysis is also done for the slope of the linear relations and wavelengths, and the R2 is also 0.99. Thus it is possible to retrieve other bands＇ total absorption coefficients with only one band absorption value, which significantly reduce the number of unknown parameters in studying other ocean color related problems.
基金Supported by the Key Projects of the National Natural Science Foundation of China(Nos.41076014,U0933005,41176035,40906022,41206029)
文摘We propose a bio-optical inversion model that retrieves the absorption contributions of phytoplankton and colored detrital matter(CDM),as well as the phytoplankton size classes(PSCs),from total minus water absorption spectra.The model is based on three-component separation of phytoplankton size structure and a genetic algorithm.The model performance was tested on two independent datasets(the NASA bio-Optical Marine Algorithm Dataset(NOMAD) and the northern South China Sea(NSCS) dataset).The relationships between the estimated and measured values were strongly linear,especially for aCDM(412),and the Root Mean Square Error(RMSE) of the CDM exponential slope(SCDM) was relatively low.Next,the inversion model was directly applied to in-situ total minus water absorption spectra determined by an underwater meter during a cruise in September 2008,to retrieve the phytoplankton size structure in the seawater.By comparing the measured and retrieved chlorophyll a concentrations,we demonstrated that total and size-specific chlorophyll a concentrations could be retrieved by the model with relatively high accuracy.Finally,we applied the bio-optical inversion model to investigate changes in phytoplankton size structure induced by an anti-cyclonic eddy in the NSCS.
文摘As a “global” numerical optimization method, genetic algorithm is briefly introduced. It is applied to optimize the absorbing coating to reduce EM scattering, leading to satisfactory results.
文摘In order to classify packet, we propose a novel IP classification based the non-collision hash and jumping table trie-tree (NHJTTT) algorithm, which is based on noncollision hash Trie-tree and Lakshman and Stiliadis proposing a 2-dimensional classification algorithm (LS algorithm). The core of algorithm consists of two parts: structure the non-collision hash function, which is constructed mainly based on destination/source port and protocol type field so that the hash function can avoid space explosion problem; introduce jumping table Trie-tree based LS algorithm in order to reduce time complexity. The test results show that the classification rate of NHJTTT algorithm is up to 1 million packets per second and the maximum memory consumed is 9 MB for 10 000 rules. Key words IP classification - lookup algorithm - trie-tree - non-collision hash - jumping table CLC number TN 393.06 Foundation item: Supported by the Chongqing of Posts and Telecommunications Younger Teacher Fundation (A2003-03).Biography: SHANG Feng-jun (1972-), male, Ph.D. candidate, lecture, research direction: the smart instrument and network.
文摘Optimization of the open absorption desiccant cooling system has been carried out in the present work. A finite difference method is used to simulate the combined heat and mass transfer processes that occur in the liquid desiccant regenerator which uses calcium chloride (CaCl2) solution as the working desiccant. The source of input heat is assumed to be the total radiation incident on a tilted surface. The system of equations is solved using the Matlab-Simulink platform. The effect of the important parameters, namely the regenerator length, desiccant solution flow rate and concentration, and air flow rates, on the performance of the system is investigated. In order to optimize the system performance, a genetic algorithm technique has been applied. The system coefficient of performance COP has been maximized for different design parameters. It has been found that the maximum values of COP could be obtained for different combinations of regenerator length solution flow rate and air flow rate. Therefore, it is essential to select the design parameters for each ambient condition to maximize the performance of the system.
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Using an improved particle swarm optimization algorithm(IPSO)to drive a transfer matrix method,a nonreciprocal absorber with an ultrawide absorption bandwidth and angular insensitivity is realized in plasma-embedded photonic crystals arranged in a structure composed of periodic and quasi-periodic sequences on a normalized scale.The effective dielectric function,which determines the absorption of the plasma,is subject to the basic parameters of the plasma,causing the absorption of the proposed absorber to be easily modulated by these parameters.Compared with other quasi-periodic sequences,the Octonacci sequence is superior both in relative bandwidth and absolute bandwidth.Under further optimization using IPSO with 14 parameters set to be optimized,the absorption characteristics of the proposed structure with different numbers of layers of the smallest structure unit N are shown and discussed.IPSO is also used to address angular insensitive nonreciprocal ultrawide bandwidth absorption,and the optimized result shows excellent unidirectional absorbability and angular insensitivity of the proposed structure.The impacts of the sequence number of quasi-periodic sequence M and collision frequency of plasma1ν1 to absorption in the angle domain and frequency domain are investigated.Additionally,the impedance match theory and the interference field theory are introduced to express the findings of the algorithm.
基金paper was the output of a research project(Registration No.9597/22)which was financially supported by Shahid Beheshti University of Medical Sciences.
文摘Fabricating of metal foams with desired morphological parameters including pore size,porosity and pore opening is possible now using sintering technology.Thus,if it is possible to determine the morphology of metal foam to absorb sound at a given frequency,and then fabricate it through sintering,it is expected to have optimized metal foams for the best sound absorption.Theoretical sound absorption models such as Lu model describe the relationship between morphological parameters and the sound absorption coefficient.In this study,the Lu model was used to optimize the morphological parameters of aluminum metal foam for the best sound absorption coefficient.For this purpose,the Lu model was numerically solved using written codes in MATLAB software.After validating the proposed codes with benchmark data,the genetic algorithm(GA)was applied to optimize the affecting morphological parameters on the sound absorption coefficient.The optimization was carried out for the thicknesses of 5 mm to 40 mm at the sound frequency range of 250 Hz–8000 Hz.The optimized parameters ranged from 50%to 95%for porosity,0.1 mm to 4.5 mm for pore size,and 0.07 mm to 0.6 mm for pore opening size.The result of this study was applied to fabricate the desired aluminum metal foams for the best sound absorption.The novel approach applied in this study,is expected to be successfully applied in for best sound absorption in desired frequencies.
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.
