Using traditional five-interferogram algorithm to unwrap phase for length measurement, the phase steps must be equal to π/2 exactly, but it is almost impossible to achieve in nanometer positioning technique. Aiming t...Using traditional five-interferogram algorithm to unwrap phase for length measurement, the phase steps must be equal to π/2 exactly, but it is almost impossible to achieve in nanometer positioning technique. Aiming to overcome this defect of traditional five-interferogram algorithm, an improved five-interferogram algorithm is presented. This improved algorithm not only keeps the high accuracy of traditional fiveinterferogram algorithm, but also does not need absolute equal step to unwrap phase. Instead, this algorithm only needs measuring phase-shifting. With the numerical simulation, the improved five-interferogram algorithm shows high accuracy, high reliability, and feasibility in practice. It is very valuable for accurate length measurement with Fizeau interferometer and Fabry-Perot interferometer.展开更多
We propose a general method of designing phase-shifting algorithms for grating lateral shearing interferometry. The algorithms compensate for the zeroth-order effect error and phase-shifting error in varying degrees. ...We propose a general method of designing phase-shifting algorithms for grating lateral shearing interferometry. The algorithms compensate for the zeroth-order effect error and phase-shifting error in varying degrees. We derive a general expression of the phase-shifting algorithm in grating lateral shearing interferometer and introduce the corresponding design method. Based on the expression and method, four phase-shifting algorithms are designed with different phase-shifting errors to obtain high measurement accuracy. A new 13-frame phase-shifting algorithm is designed and simulated with a large zeroth-order effect. Simulation results verify the general expression and the corresponding design method.展开更多
A new error compensating five sample phase shifting algorithm which is insensitive to phase shift error is proposed to retrieve the phase distribution of a fringe pattern. It includes two steps. First, the linear ...A new error compensating five sample phase shifting algorithm which is insensitive to phase shift error is proposed to retrieve the phase distribution of a fringe pattern. It includes two steps. First, the linear phase shift error is estimated using four sample images. Then, the phase distribution is calculated with error corrected by using the phase shift error estimated in the first step. As the equations of error estimation and phase calculation are simple, this new algorithm is practical as well as effective. Computer simulations were carried out to verify the effectiveness of the algorithm. Results of two other well known error compensating algorithms are also presented, which show the new algorithm is the least sensitive to phase shift error.展开更多
Digital structured light （SL） profilometry is increasingly used in three-dimensional （3D） measurement technology. However, the nonlinearity of the off-the-shelf projectors and cameras seriously reduces the measure...Digital structured light （SL） profilometry is increasingly used in three-dimensional （3D） measurement technology. However, the nonlinearity of the off-the-shelf projectors and cameras seriously reduces the measurement accuracy. In this paper, first, we review the nonlinear effects of the projector-camera system in the phase-shifting structured light depth measurement method. We show that high order harmonic wave components lead to phase error in the phase-shifting method. Then a practical method based on frequency domain filtering is proposed for nonlinear error reduction. By using this method, the nonlinear calibration of the SL system is not required. Moreover, both the nonlinear effects of the projector and the camera can be effectively reduced. The simulations and experiments have verified our nonlinear correction method.展开更多
A phase-shifting digital holography scheme developed to investigate internal defects in artworks is described. Phase-shifting is utilized to obtain a clear reconstructed object wave from a rough surface texture. A rev...A phase-shifting digital holography scheme developed to investigate internal defects in artworks is described. Phase-shifting is utilized to obtain a clear reconstructed object wave from a rough surface texture. A reverse-transform algorithm is employed to reconstruct the object wave on its original position of unknown distance or the imaging position from the object wave information on the holographic plane. To get the clearest reconstruction the exact registration of the unknown distance is determined by applying the intensity sum as the auto-focusing function, The spatial resolution of the reconstruction image is also investigated for a variety of affecting factors. Laboratory results of reconstruction images under deformation are presented.展开更多
Although the phase-shift seismic processing method has characteristics of high accuracy, good stability, high efficiency, and high-dip imaging, it is not able to adapt to strong lateral velocity variation. To overcome...Although the phase-shift seismic processing method has characteristics of high accuracy, good stability, high efficiency, and high-dip imaging, it is not able to adapt to strong lateral velocity variation. To overcome this defect, a finite-difference method in the frequency-space domain is introduced in the migration process, because it can adapt to strong lateral velocity variation and the coefficient is optimized by a hybrid genetic and simulated annealing algorithm. The two measures improve the precision of the approximation dispersion equation. Thus, the imaging effect is improved for areas of high-dip structure and strong lateral velocity variation. The migration imaging of a 2-D SEG/EAGE salt dome model proves that a better imaging effect in these areas is achieved by optimized phase-shift migration operator plus a finite-difference method based on a hybrid genetic and simulated annealing algorithm. The method proposed in this paper is better than conventional methods in imaging of areas of high-dip angle and strong lateral velocity variation.展开更多
In this paper, we propose an encryption scheme based on phase-shifting digital interferometry. According to the original system framework, we add a random amplitude mask and replace the Fourier transform by the Fresne...In this paper, we propose an encryption scheme based on phase-shifting digital interferometry. According to the original system framework, we add a random amplitude mask and replace the Fourier transform by the Fresnel transform. We develop a mathematical model and give a discrete formula based on the scheme, which makes it easy to implement the scheme in computer programming. The experimental results show that the improved system has a better performance in security than the original encryption method. Moreover, it demonstrates a good capability of anti-noise and anti-shear robustness.展开更多
A multiple-image encryption method based on two-step phase-shifting interferometry （PSI） and spatial multiplexing of a smooth compressed signal is proposed. In the encoding and encryption process, with the help of f...A multiple-image encryption method based on two-step phase-shifting interferometry （PSI） and spatial multiplexing of a smooth compressed signal is proposed. In the encoding and encryption process, with the help of four index matrices to store original pixel positions, all the pixels of four secret images are firstly reordered in an ascending order; then, the four reordered images are transformed by five-order Haar wavelet transform and performed sparseness operation. After Arnold transform and pixels sampling operation, one combined image can be grouped with the aid of compressive sensing （CS） and spatial multiplexing techniques. Finally, putting the combined image at the input plane of the PSI encryption scheme, only two interferograms ciphertexts can be obtained. During the decoding and decryption, utilizing all the secret key groups and index matrices keys, all the original secret images can be successfully decrypted by a wave-front retrieval algorithm of two-step PSI, spatial de-multiplexing, inverse Arnold transform, inverse discrete wavelet transform, and pixels reordering operation.展开更多
Fresnel incoherent correlation holography(FINCH)has the ability to generate three-dimensional images with a superresolution by using incoherent sources.However,there are unwanted direct current term and twin image in ...Fresnel incoherent correlation holography(FINCH)has the ability to generate three-dimensional images with a superresolution by using incoherent sources.However,there are unwanted direct current term and twin image in interferograms,so it is of great significance to find a method to eliminate them.Phase-shifting technology is a most widely used technique for this task,but its three-step phase-shifting is not suitable for the instantaneous measurement of dynamic objects,and the quality of reconstructed image with the traditional two-step phase-shifting is lower.In this paper,we present a method of enhancing the resolution through using a two-step phase-shifting technology based on the discrete wavelet transform.After two-step phase-shifting,the resulting hologram is a superposition of multiple forms.The frequency of the resulting hologram is decomposed into different levels through using discrete wavelet transform,then the image is reconstructed after retrieving the low frequency band.Various experiments have verified the effectiveness of this method.展开更多
An accurate and fast three-step self-calibrating generalized phase-shifting interferomertry(SGPSI) is proposed. In this approach, two new phase-shifting signals are constructed by the difference interferograms normali...An accurate and fast three-step self-calibrating generalized phase-shifting interferomertry(SGPSI) is proposed. In this approach, two new phase-shifting signals are constructed by the difference interferograms normalization and noise suppressing, then the unknown phase shift between the two difference phase-shifting signals is estimated quickly through searching the minimum coefficient of variation of the modulation amplitude, a limited number of pixels are selected to participate in the search process to further save time, and finally the phase is reconstructed through the searched phase shift. Through the reconstruction of phase map by the simulation and experiment, and the comparison with several mature algorithms, the good performance of the proposed algorithm is proved, and it eliminates the limitation of requiring more than three phase-shifting interferograms for high-precision SGPSI. We expect this method to be widely used in the future.展开更多
focus of all countries.As an effective new energy,the fuel cell has attracted the attention of scholars.However,due to the particularity of proton exchange membrane fuel cell(PEMFC),the performance of traditional PI c...focus of all countries.As an effective new energy,the fuel cell has attracted the attention of scholars.However,due to the particularity of proton exchange membrane fuel cell(PEMFC),the performance of traditional PI controlled phase-shifted full-bridge power electronics DC-DC converter cannot meet the needs of practical application.In order to further improve the dynamic performance of the converter,this paper first introduces several main topologies of the current mainstream front-end DC-DC converter,and analyzes their performance in the fuel cell system.Then,the operation process of the phase-shifted fullbridge power electronics DC-DC converter is introduced,and the shortcomings of the traditional PI control are analyzed.Finally,a double closed-loop adaptive fuzzy PI controller is proposed,which is characterized by dynamically adjusting PI parameters according to different working states to complete the intelligent control of phase-shifted full-bridge DC-DC converter.The simulation results in MATLAB/Simulink show that the proposed algorithm has good a control effect.Compared with the traditional algorithm,the overshoot and stabilization time of the system are shorter.The algorithm can effectively suppress the fluctuation of the output current of the fuel cell converter,and is a very practical control method.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
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
BACKGROUND Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer.Many molecular genetic changes are associated with its occurrence.Raman spectroscopy has become a new method for the e...BACKGROUND Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer.Many molecular genetic changes are associated with its occurrence.Raman spectroscopy has become a new method for the early diagnosis of tumors because it can reflect the structures of substances and their changes at the molecular level.AIM To detect alterations in Raman spectral information across different stages of esophageal neoplasia.METHODS Different grades of esophageal lesions were collected,and a total of 360 groups of Raman spectrum data were collected.A 1D-transformer network model was proposed to handle the task of classifying the spectral data of esophageal squamous cell carcinoma.In addition,a deep learning model was applied to visualize the Raman spectral data and interpret their molecular characteristics.RESULTS A comparison among Raman spectral data with different pathological grades and a visual analysis revealed that the Raman peaks with significant differences were concentrated mainly at 1095 cm^(-1)(DNA,symmetric PO,and stretching vibration),1132 cm^(-1)(cytochrome c),1171 cm^(-1)(acetoacetate),1216 cm^(-1)(amide III),and 1315 cm^(-1)(glycerol).A comparison among the training results of different models revealed that the 1Dtransformer network performed best.A 93.30%accuracy value,a 96.65%specificity value,a 93.30%sensitivity value,and a 93.17%F1 score were achieved.CONCLUSION Raman spectroscopy revealed significantly different waveforms for the different stages of esophageal neoplasia.The combination of Raman spectroscopy and deep learning methods could significantly improve the accuracy of classification.展开更多
基金the Ministry of Science and Technology of China under Grant No.2002DEA20014.
文摘Using traditional five-interferogram algorithm to unwrap phase for length measurement, the phase steps must be equal to π/2 exactly, but it is almost impossible to achieve in nanometer positioning technique. Aiming to overcome this defect of traditional five-interferogram algorithm, an improved five-interferogram algorithm is presented. This improved algorithm not only keeps the high accuracy of traditional fiveinterferogram algorithm, but also does not need absolute equal step to unwrap phase. Instead, this algorithm only needs measuring phase-shifting. With the numerical simulation, the improved five-interferogram algorithm shows high accuracy, high reliability, and feasibility in practice. It is very valuable for accurate length measurement with Fizeau interferometer and Fabry-Perot interferometer.
基金supported by the National Science and Technology Major Project,China(No.2009ZX02202005)
文摘We propose a general method of designing phase-shifting algorithms for grating lateral shearing interferometry. The algorithms compensate for the zeroth-order effect error and phase-shifting error in varying degrees. We derive a general expression of the phase-shifting algorithm in grating lateral shearing interferometer and introduce the corresponding design method. Based on the expression and method, four phase-shifting algorithms are designed with different phase-shifting errors to obtain high measurement accuracy. A new 13-frame phase-shifting algorithm is designed and simulated with a large zeroth-order effect. Simulation results verify the general expression and the corresponding design method.
文摘A new error compensating five sample phase shifting algorithm which is insensitive to phase shift error is proposed to retrieve the phase distribution of a fringe pattern. It includes two steps. First, the linear phase shift error is estimated using four sample images. Then, the phase distribution is calculated with error corrected by using the phase shift error estimated in the first step. As the equations of error estimation and phase calculation are simple, this new algorithm is practical as well as effective. Computer simulations were carried out to verify the effectiveness of the algorithm. Results of two other well known error compensating algorithms are also presented, which show the new algorithm is the least sensitive to phase shift error.
基金Project supported by the Science and Technology Major Projects of Zhejiang Province,China(Grant No.2013C03043-5)
文摘Digital structured light （SL） profilometry is increasingly used in three-dimensional （3D） measurement technology. However, the nonlinearity of the off-the-shelf projectors and cameras seriously reduces the measurement accuracy. In this paper, first, we review the nonlinear effects of the projector-camera system in the phase-shifting structured light depth measurement method. We show that high order harmonic wave components lead to phase error in the phase-shifting method. Then a practical method based on frequency domain filtering is proposed for nonlinear error reduction. By using this method, the nonlinear calibration of the SL system is not required. Moreover, both the nonlinear effects of the projector and the camera can be effectively reduced. The simulations and experiments have verified our nonlinear correction method.
文摘A phase-shifting digital holography scheme developed to investigate internal defects in artworks is described. Phase-shifting is utilized to obtain a clear reconstructed object wave from a rough surface texture. A reverse-transform algorithm is employed to reconstruct the object wave on its original position of unknown distance or the imaging position from the object wave information on the holographic plane. To get the clearest reconstruction the exact registration of the unknown distance is determined by applying the intensity sum as the auto-focusing function, The spatial resolution of the reconstruction image is also investigated for a variety of affecting factors. Laboratory results of reconstruction images under deformation are presented.
基金the Open Fund(PLC201104)of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Chengdu University of Technology)the National Natural Science Foundation of China(No.61072073)the Key Project of Education Commission of Sichuan Province(No.10ZA072)
文摘Although the phase-shift seismic processing method has characteristics of high accuracy, good stability, high efficiency, and high-dip imaging, it is not able to adapt to strong lateral velocity variation. To overcome this defect, a finite-difference method in the frequency-space domain is introduced in the migration process, because it can adapt to strong lateral velocity variation and the coefficient is optimized by a hybrid genetic and simulated annealing algorithm. The two measures improve the precision of the approximation dispersion equation. Thus, the imaging effect is improved for areas of high-dip structure and strong lateral velocity variation. The migration imaging of a 2-D SEG/EAGE salt dome model proves that a better imaging effect in these areas is achieved by optimized phase-shift migration operator plus a finite-difference method based on a hybrid genetic and simulated annealing algorithm. The method proposed in this paper is better than conventional methods in imaging of areas of high-dip angle and strong lateral velocity variation.
基金supported by the National Basic Research Program of China(Grant No.2011CB302903)the National Natural Science Foundation of China(Grant Nos.61272084 and 61202004)the Key Project of Natural Science Research of Jiangsu University,China(Grant No.11KJA520002)
文摘In this paper, we propose an encryption scheme based on phase-shifting digital interferometry. According to the original system framework, we add a random amplitude mask and replace the Fourier transform by the Fresnel transform. We develop a mathematical model and give a discrete formula based on the scheme, which makes it easy to implement the scheme in computer programming. The experimental results show that the improved system has a better performance in security than the original encryption method. Moreover, it demonstrates a good capability of anti-noise and anti-shear robustness.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61775121,61307003,61405122,and 11574311)Key R&D Program of Shandong Province,China(Grant No.2018GGX101002)+1 种基金the Natural Science Foundation of Shandong Province,China(Grant No.R2016FM03)the Fundamental Research Funds of Shandong University,China(Grant No.2015GN031)
文摘A multiple-image encryption method based on two-step phase-shifting interferometry （PSI） and spatial multiplexing of a smooth compressed signal is proposed. In the encoding and encryption process, with the help of four index matrices to store original pixel positions, all the pixels of four secret images are firstly reordered in an ascending order; then, the four reordered images are transformed by five-order Haar wavelet transform and performed sparseness operation. After Arnold transform and pixels sampling operation, one combined image can be grouped with the aid of compressive sensing （CS） and spatial multiplexing techniques. Finally, putting the combined image at the input plane of the PSI encryption scheme, only two interferograms ciphertexts can be obtained. During the decoding and decryption, utilizing all the secret key groups and index matrices keys, all the original secret images can be successfully decrypted by a wave-front retrieval algorithm of two-step PSI, spatial de-multiplexing, inverse Arnold transform, inverse discrete wavelet transform, and pixels reordering operation.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.51175479 and U1704155)the Natural Science Foundation of Henan Province,China(Grant Nos.16A140035 and 18A140032)the Program for Innovative Research Team(in Science and Technique)in the University of Henan Province,China(Grant No.18IRTSTHN016)。
文摘Fresnel incoherent correlation holography(FINCH)has the ability to generate three-dimensional images with a superresolution by using incoherent sources.However,there are unwanted direct current term and twin image in interferograms,so it is of great significance to find a method to eliminate them.Phase-shifting technology is a most widely used technique for this task,but its three-step phase-shifting is not suitable for the instantaneous measurement of dynamic objects,and the quality of reconstructed image with the traditional two-step phase-shifting is lower.In this paper,we present a method of enhancing the resolution through using a two-step phase-shifting technology based on the discrete wavelet transform.After two-step phase-shifting,the resulting hologram is a superposition of multiple forms.The frequency of the resulting hologram is decomposed into different levels through using discrete wavelet transform,then the image is reconstructed after retrieving the low frequency band.Various experiments have verified the effectiveness of this method.
基金Project supported by the National Natural Science Foundation of China (Grant No. 61905039)Jilin Scientific and Technological Development Program, China (Grant No. 20190701018GH)+1 种基金Education Department of Jilin Province, China (Grant No. JJKH20190691KJ)State Key Laboratory of Applied Optics.
文摘An accurate and fast three-step self-calibrating generalized phase-shifting interferomertry(SGPSI) is proposed. In this approach, two new phase-shifting signals are constructed by the difference interferograms normalization and noise suppressing, then the unknown phase shift between the two difference phase-shifting signals is estimated quickly through searching the minimum coefficient of variation of the modulation amplitude, a limited number of pixels are selected to participate in the search process to further save time, and finally the phase is reconstructed through the searched phase shift. Through the reconstruction of phase map by the simulation and experiment, and the comparison with several mature algorithms, the good performance of the proposed algorithm is proved, and it eliminates the limitation of requiring more than three phase-shifting interferograms for high-precision SGPSI. We expect this method to be widely used in the future.
基金This work was supported in part by the Natural Science Foundation of Jiangsu Province under Grant BK20200969(L.Z.,URL:http://std.jiangsu.gov.cn/)in part by the Natural Science Foundation for Universities of Jiangsu Province under Grant 20KJB520008(Y.Y.,URL:http://jyt.jiangsu.gov.cn/)+2 种基金in part by the Nantong Science and Technology Plan Project under Grant JC2020148(Y.Y.,URL:http://kjj.nantong.gov.cn/)JC2020151(Y.C.,URL:http://kjj.nantong.gov.cn/)JC2019095(L.R.,URL:http://kjj.nantong.gov.cn/).
文摘focus of all countries.As an effective new energy,the fuel cell has attracted the attention of scholars.However,due to the particularity of proton exchange membrane fuel cell(PEMFC),the performance of traditional PI controlled phase-shifted full-bridge power electronics DC-DC converter cannot meet the needs of practical application.In order to further improve the dynamic performance of the converter,this paper first introduces several main topologies of the current mainstream front-end DC-DC converter,and analyzes their performance in the fuel cell system.Then,the operation process of the phase-shifted fullbridge power electronics DC-DC converter is introduced,and the shortcomings of the traditional PI control are analyzed.Finally,a double closed-loop adaptive fuzzy PI controller is proposed,which is characterized by dynamically adjusting PI parameters according to different working states to complete the intelligent control of phase-shifted full-bridge DC-DC converter.The simulation results in MATLAB/Simulink show that the proposed algorithm has good a control effect.Compared with the traditional algorithm,the overshoot and stabilization time of the system are shorter.The algorithm can effectively suppress the fluctuation of the output current of the fuel cell converter,and is a very practical control method.
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.
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Supported by Beijing Hospitals Authority Youth Programme,No.QML20200505.
文摘BACKGROUND Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer.Many molecular genetic changes are associated with its occurrence.Raman spectroscopy has become a new method for the early diagnosis of tumors because it can reflect the structures of substances and their changes at the molecular level.AIM To detect alterations in Raman spectral information across different stages of esophageal neoplasia.METHODS Different grades of esophageal lesions were collected,and a total of 360 groups of Raman spectrum data were collected.A 1D-transformer network model was proposed to handle the task of classifying the spectral data of esophageal squamous cell carcinoma.In addition,a deep learning model was applied to visualize the Raman spectral data and interpret their molecular characteristics.RESULTS A comparison among Raman spectral data with different pathological grades and a visual analysis revealed that the Raman peaks with significant differences were concentrated mainly at 1095 cm^(-1)(DNA,symmetric PO,and stretching vibration),1132 cm^(-1)(cytochrome c),1171 cm^(-1)(acetoacetate),1216 cm^(-1)(amide III),and 1315 cm^(-1)(glycerol).A comparison among the training results of different models revealed that the 1Dtransformer network performed best.A 93.30%accuracy value,a 96.65%specificity value,a 93.30%sensitivity value,and a 93.17%F1 score were achieved.CONCLUSION Raman spectroscopy revealed significantly different waveforms for the different stages of esophageal neoplasia.The combination of Raman spectroscopy and deep learning methods could significantly improve the accuracy of classification.
