To accomplish the reliability analyses of the correlation of multi-analytical objectives,an innovative framework of Dimensional Synchronous Modeling(DSM)and correlation analysis is developed based on the stepwise mode...To accomplish the reliability analyses of the correlation of multi-analytical objectives,an innovative framework of Dimensional Synchronous Modeling(DSM)and correlation analysis is developed based on the stepwise modeling strategy,cell array operation principle,and Copula theory.Under this framework,we propose a DSM-based Enhanced Kriging(DSMEK)algorithm to synchronously derive the modeling of multi-objective,and explore an adaptive Copula function approach to analyze the correlation among multiple objectives and to assess the synthetical reliability level.In the proposed DSMEK and adaptive Copula methods,the Kriging model is treated as the basis function of DSMEK model,the Multi-Objective Snake Optimizer(MOSO)algorithm is used to search the optimal values of hyperparameters of basis functions,the cell array operation principle is adopted to establish a whole model of multiple objectives,the goodness of fit is utilized to determine the forms of Copula functions,and the determined Copula functions are employed to perform the reliability analyses of the correlation of multi-analytical objectives.Furthermore,three examples,including multi-objective complex function approximation,aeroengine turbine bladeddisc multi-failure mode reliability analyses and aircraft landing gear system brake temperature reliability analyses,are performed to verify the effectiveness of the proposed methods,from the viewpoints of mathematics and engineering.The results show that the DSMEK and adaptive Copula approaches hold obvious advantages in terms of modeling features and simulation performance.The efforts of this work provide a useful way for the modeling of multi-analytical objectives and synthetical reliability analyses of complex structure/system with multi-output responses.展开更多
The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and co...The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.展开更多
Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of i...Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of inertia and mechanical size,the dynamic model of exoskeletons is difficult to construct.Hence,an enhanced whale optimization algorithm(EWOA)is proposed to identify the exoskeleton model parameters.Meanwhile,the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints.Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion.Finally,the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform.The knee joint motion achieves a steady-state response after 0.5 s.Meanwhile,the position error of hip joint response is less than 0.03 rad after 0.9 s.In addition,the steady-state human-robot interaction torque of the two joints is constrained within 15 N·m.This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters.Furthermore,an enhanced mutation strategy is adopted to avoid whale evolution’s unsatisfactory local optimal value.展开更多
Image enhancement utilizes intensity transformation functions to maximize the information content of enhanced images.This paper approaches the topic as an optimization problem and uses the bald eagle search(BES)algori...Image enhancement utilizes intensity transformation functions to maximize the information content of enhanced images.This paper approaches the topic as an optimization problem and uses the bald eagle search(BES)algorithm to achieve optimal results.In our proposed model,gamma correction and Retinex address color cast issues and enhance image edges and details.The final enhanced image is obtained through color balancing.The BES algorithm seeks the optimal solution through the selection,search,and swooping stages.However,it is prone to getting stuck in local optima and converges slowly.To overcome these limitations,we propose an improved BES algorithm(ABES)with enhanced population learning,position updates,and control parameters.ABES is employed to optimize the core parameters of gamma correction and Retinex to improve image quality,and the maximization of information entropy is utilized as the objective function.Real benchmark images are collected to validate its performance.Experimental results demonstrate that ABES outperforms the existing image enhancement methods,including the flower pollination algorithm,the chimp optimization algorithm,particle swarm optimization,and BES,in terms of information entropy,peak signal-to-noise ratio(PSNR),structural similarity index(SSIM),and patch-based contrast quality index(PCQI).ABES demonstrates superior performance both qualitatively and quantitatively,and it helps enhance prominent features and contrast in the images while maintaining the natural appearance of the original images.展开更多
Background:Quantum-enhanced medical imaging algorithms–quantum entanglement reconstruction,quantum noise suppression,and quantum beamforming–propose possible remedies for significant constraints in traditional diagn...Background:Quantum-enhanced medical imaging algorithms–quantum entanglement reconstruction,quantum noise suppression,and quantum beamforming–propose possible remedies for significant constraints in traditional diagnostic imaging,such as resolution,radiation efficiency,and real-time processing.Methods:This work used a mixed-methods strategy,including controlled phantom experiments,retrospective multi-center clinical data analysis,and quantum-classical hybrid processing to assess enhancements in resolution,dosage efficiency,and diagnostic confidence.Statistical validation included analysis of variance(ANOVA)and receiver-operating characteristic curve analysis,juxtaposing quantum-enhanced methodologies with conventional and deep learning approaches.Results:Quantum entanglement reconstruction enhanced magnetic resonance imaging spatial resolution by 33.2%(P<0.01),quantum noise suppression facilitated computed tomography scans with a 60%reduction in radiation,and quantum beamforming improved ultrasound contrast by 27%while preserving real-time processing(<2 ms delay).Inter-reader variability(12%in Diagnostic Confidence Scores)showed that systematic training is needed,even if the performance was better.The research presented(1)a reusable clinical quantum imaging framework,(2)enhanced hardware processes(field-programmable gate array/graphics processing unit acceleration),and(3)cost-benefit analyses demonstrating a 22-month return on investment breakeven point.Conclusion:Quantum-enhanced imaging has a lot of promise for use in medicine,especially in neurology and cancer.Future research should focus on multi-modal integration(e.g.,positron emission tomography–magnetic resonance imaging),cloud-based quantum simulations for enhanced accessibility,and extensive trials to confirm long-term diagnostic accuracy.This breakthrough gives healthcare systems a technology roadmap and a reason to spend money on quantum-enhanced diagnostics.展开更多
Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly women.Thus identifying BAC could provide an expense,and be inaccurate.Recently Deep Learning(DL)method...Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly women.Thus identifying BAC could provide an expense,and be inaccurate.Recently Deep Learning(DL)methods have been introduced for automatic BAC detection and quantification with increased accuracy.Previously,classification with deep learning had reached higher efficiency,but designing the structure of DL proved to be an extremely challenging task due to overfitting models.It also is not able to capture the patterns and irregularities presented in the images.To solve the overfitting problem,an optimal feature set has been formed by Enhanced Wolf Pack Algorithm(EWPA),and their irregularities are identified by Dense-kUNet segmentation.In this paper,Dense-kUNet for segmentation and optimal feature has been introduced for classification(severe,mild,light)that integrates DenseUNet and kU-Net.Longer bound links exist among adjacent modules,allowing relatively rough data to be sent to the following component and assisting the system in finding higher qualities.The major contribution of the work is to design the best features selected by Enhanced Wolf Pack Algorithm(EWPA),and Modified Support Vector Machine(MSVM)based learning for classification.k-Dense-UNet is introduced which combines the procedure of Dense-UNet and kU-Net for image segmentation.Longer bound associations occur among nearby sections,allowing relatively granular data to be sent to the next subsystem and benefiting the system in recognizing smaller characteristics.The proposed techniques and the performance are tested using several types of analysis techniques 826 filled digitized mammography.The proposed method achieved the highest precision,recall,F-measure,and accuracy of 84.4333%,84.5333%,84.4833%,and 86.8667%when compared to other methods on the Digital Database for Screening Mammography(DDSM).展开更多
As the tableau algorithm would produce a lot of description overlaps when judging the satisfiabilities of concepts（thus wasting much space）,a clause-based enhancing mode designed for the language ALCN is proposed.Th...As the tableau algorithm would produce a lot of description overlaps when judging the satisfiabilities of concepts（thus wasting much space）,a clause-based enhancing mode designed for the language ALCN is proposed.This enhancing mode constructs a disjunctive normal form on concept expressions and keeps only one conjunctive clause,and then substitutes the obtained succinctest conjunctive clause for sub-concepts set in the labeling of nodes of a completion tree constructed by the tableau algorithm （such a process may be repeated as many times as needed）.Due to the avoidance of tremendous descriptions redundancies caused by applying ∩- and ∪-rules of the ordinary tableau algorithm,this mode greatly improves the spatial performance as a result.An example is given to demonstrate the application of this enhancing mode and its reduction in the cost of space. Results show that the improvement is very outstanding.展开更多
To solve the problems of noise,detail loss and poor contrast in the successive mean quantization transform（SMQT）,a new SMQT algorithm based on Otsu algorithm is proposed.In this algorithm,we integrate the optimal th...To solve the problems of noise,detail loss and poor contrast in the successive mean quantization transform（SMQT）,a new SMQT algorithm based on Otsu algorithm is proposed.In this algorithm,we integrate the optimal threshold selected by the Otsu algorithm into the SMQT algorithm,then obtain the successive mean quantization of the binary tree.By this algorithm,an enhanced image is output with a higher quality.From both subjective visual effect and objective quality evaluation,the experimental results show that the improved algorithm reduces noise,improves contrast and makes the image details more clear.展开更多
Abstract： Based on digital signal processor（DSP） and field programmable gate array（FPGA） techniques, the architecture of super large view field（SLVF） panoramic night vision image processing hardware platform wa...Abstract： Based on digital signal processor（DSP） and field programmable gate array（FPGA） techniques, the architecture of super large view field（SLVF） panoramic night vision image processing hardware platform was established. The panoramic unwrapping and correcting algorithm, up to a full 360°, based on coordinate rotation digital computer （CORDIC） and night vision image enhancement algorithm, based on histogram equalization theory and edge detection theory, was presented in this paper, with the purpose of processing night vision dynamic panoramic annular image. The annular image can be unwrapped and corrected to conventional rectangular panorama by the panoramic image processing algorithm, which uses the pipelined CORDIC configuration to realize a trigonometric function generator with high speed and high precision. Histogram equalization algorithm can perfectly enhance the contrast of the night vision image. Edge detection algorithm can be propitious to find and detect small dim dynamic targets in night vision circumstances. After abundant experiment, the al- gorithm for panoramic image processing and night vision image enhancement is successfully implemented in FPGA and DSP. The panoramic night vision image system is a compact device, with no external rotating parts. And the system can reliably and dynamically detect 360＊ SLVF panoramic night vision image.展开更多
Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kin...Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kinds of researches on forensic detection have been presented,and it provides less accuracy.This paper proposed a novel forgery detection technique in image frames of the videos using enhanced Convolutional Neural Network(CNN).In the initial stage,the input video is taken as of the dataset and then converts the videos into image frames.Next,perform pre-sampling using the Adaptive Rood Pattern Search(ARPS)algorithm intended for reducing the useless frames.In the next stage,perform preprocessing for enhancing the image frames.Then,face detection is done as of the image utilizing the Viola-Jones algorithm.Finally,the improved Crow Search Algorithm(ICSA)has been used to select the extorted features and inputted to the Enhanced Convolutional Neural Network(ECNN)classifier for detecting the forged image frames.The experimental outcome of the proposed system has achieved 97.21%accuracy compared to other existing methods.展开更多
Pattern matching is a very important topic in computer science. It has been used in various applications such as information retrieval, virus scanning, DNA sequence analysis, data mining, machine learning, network sec...Pattern matching is a very important topic in computer science. It has been used in various applications such as information retrieval, virus scanning, DNA sequence analysis, data mining, machine learning, network security and pattern recognition. This paper has presented a new pattern matching algorithm—Enhanced ERS-A, which is an improvement over ERS-S algorithm. In ERS-A, two sliding windows are used to scan the text from the left and the right simultaneously. The proposed algorithm also scans the text from the left and the right simultaneously as well as making comparisons with the pattern from both sides simultaneously. The comparisons done between the text and the pattern are done from both sides in parallel. The shift technique used in the Enhanced ERS-A is the four consecutive characters in the text immediately following the pattern window. The experimental results show that the Enhanced ERS-A has enhanced the process of pattern matching by reducing the number of comparisons performed.展开更多
Liver transplantation is an effective treatment for patients with end-stage liver disease. Accurate imaging evaluation of the transplanted patient is critical for ensuring that the limited donor liver is functioning a...Liver transplantation is an effective treatment for patients with end-stage liver disease. Accurate imaging evaluation of the transplanted patient is critical for ensuring that the limited donor liver is functioning appropriately. Ultrasound contrast agents(UCAs), in combination with contrastspecific imaging techniques, are increasingly accepted in clinical use for the assessment of the hepatic vasculature, bile ducts and liver parenchyma in pre-, intra- and posttransplant patients. We describe UCAs, their technical requirements, the recommended clinical indications, image interpretation and the limitations for contrastenhanced ultrasound applications in liver transplantation.展开更多
To realize the fast and accurate quantitative analysis of the mixture of polycyclic aromatic hydrocarbons(PAHs),surface-enhanced Raman spectroscopy(SERS)coupled with multivariate calibrations were employed.In this stu...To realize the fast and accurate quantitative analysis of the mixture of polycyclic aromatic hydrocarbons(PAHs),surface-enhanced Raman spectroscopy(SERS)coupled with multivariate calibrations were employed.In this study,three kinds of calibration algorithms were used to quantitative analysis of the mixture of naphthalene(Nap),phenanthrene(Phe),and pyrene(Pyr).Firstly,partial least squares(PLS)algorithm was used to select characteristic variables,then the global search capability of genetic algorithm(GA)was used for the determining of the initial weights and thresholds of back propagation(BP)neural network so that local minima was avoided.The PLS-GA-BP model exhibited superiority to quantify PAHs mixture,which achieved R2=0.9975,0.9710,0.9643,ARE=10.07%,19.28%,16.72%and RMSE=13.10,5.40,5.10 nmol L−1 for Nap,Phe,Pyr(in the PAHs mixture)concentration prediction respectively.The forecast error,ARE and RMSE have been reduced more than 50%and 60%respectively compared with the whole spectral BP model.The study indicates that accurate quantitative spectroscopic analysis of the mixture of PAHs samples can be achieved through the combination of SERS technique and PLS-GA-BP algorithm.展开更多
Coverage control for each sensor is based on a 2D directional sensing model in directional sensor networks conventionally. But the 2D model cannot accurately characterize the real environment. In order to solve this p...Coverage control for each sensor is based on a 2D directional sensing model in directional sensor networks conventionally. But the 2D model cannot accurately characterize the real environment. In order to solve this problem,a new 3D directional sensor model and coverage enhancement algorithm is proposed. We can adjust the pitch angle and deviation angle to enhance the coverage rate. And the coverage enhancement algorithm is based on an improved gravitational search algorithm. In this paper the two improved strategies of GSA are directional mutation strategy and individual evolution strategy. A set of simulations show that our coverage enhancement algorithm has a good performance to improve the coverage rate of the wireless directional sensor network on different number of nodes,different virtual angles and different sensing radius.展开更多
In mausoleum murals, existing bubbles are one kind of the most harmful defects for the repair and protection of relics. For this reason, it is necessary to detect bubbles, especially the ones with small size. A method...In mausoleum murals, existing bubbles are one kind of the most harmful defects for the repair and protection of relics. For this reason, it is necessary to detect bubbles, especially the ones with small size. A method to detect the small bubbles with enhanced terahertz （THz） images is proposed. To simulate the bubbles in the mausoleum murals, circular grooves have been hidden in the plaster and then measured by the THz reflected time domain spectroscopy imaging system. To observe the small bubbles in murals, a comprehensive enhancement algorithm is adopted to process the obtained THz images. With the enhanced method, the circular grooves in the murals can be observed clearly, even for the circular groove with a diameter of 1.5 mm. The results indicate that the proposed comprehensive method can be used to detect the tiny defects of murals.展开更多
A novel nonlinear gray transform method is proposed to enhance the contrast of a typhoon cloud image.Generally,the typhoon cloud image obtained by a satellite cannot be directly used to make an accurate prediction of ...A novel nonlinear gray transform method is proposed to enhance the contrast of a typhoon cloud image.Generally,the typhoon cloud image obtained by a satellite cannot be directly used to make an accurate prediction of the typhoon＇s center or intensity because the contrast of the received typhoon cloud image may be bad.Our aim is to extrude the typhoon＇s eye in the typhoon cloud image.A normalized arc-tangent transformation operation is designed to enhance global contrast of the typhoon cloud image.Differential evolution algorithm is used to choose the optimal nonlinear transform parameter.Finally,geodesic activity contour model is used to extract the typhoon＇s eye to verify the performance of the proposed method.Experimental results show that the proposed method can efficiently enhance the global contrast of the typhoon cloud image while greatly extruding the typhoon＇s eye.展开更多
The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study intro...The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.展开更多
Radio resource assignment schemes and routing strategies in relay enhanced cellular networks are proposed in this paper. Under the reuse partitioning-based frequency planning framework, the intra-cell resource partiti...Radio resource assignment schemes and routing strategies in relay enhanced cellular networks are proposed in this paper. Under the reuse partitioning-based frequency planning framework, the intra-cell resource partitioning between the base station and relay nodes was addressed firstly by introducing a metric of effective reuse factor. Then, coverage-oriented and capacity-oriented rantings, as well as two link bandwidth assignment schemes＂ equal-bandwidth per link＂ and ＂equal-bandwidth per mobile station＂ were developed. These key issues and their impacts on the system performance were analyzed comprehensively and supported by simulations. Results show that the cell capacity and edge user throughput of the proposed network are superior to the traditional non-relay network when an appropriate effective reuse factor is adopted.展开更多
Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the node...Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).展开更多
In low-light environments,captured images often exhibit issues such as insufficient clarity and detail loss,which significantly degrade the accuracy of subsequent target recognition tasks.To tackle these challenges,th...In low-light environments,captured images often exhibit issues such as insufficient clarity and detail loss,which significantly degrade the accuracy of subsequent target recognition tasks.To tackle these challenges,this study presents a novel low-light image enhancement algorithm that leverages virtual hazy image generation through dehazing models based on statistical analysis.The proposed algorithm initiates the enhancement process by transforming the low-light image into a virtual hazy image,followed by image segmentation using a quadtree method.To improve the accuracy and robustness of atmospheric light estimation,the algorithm incorporates a genetic algorithm to optimize the quadtree-based estimation of atmospheric light regions.Additionally,this method employs an adaptive window adjustment mechanism to derive the dark channel prior image,which is subsequently refined using morphological operations and guided filtering.The final enhanced image is reconstructed through the hazy image degradation model.Extensive experimental evaluations across multiple datasets verify the superiority of the designed framework,achieving a peak signal-to-noise ratio(PSNR)of 17.09 and a structural similarity index(SSIM)of 0.74.These results indicate that the proposed algorithm not only effectively enhances image contrast and brightness but also outperforms traditional methods in terms of subjective and objective evaluation metrics.展开更多
基金co-supported by the National Natural Science Foundation of China(Nos.52405293,52375237)China Postdoctoral Science Foundation(No.2024M754219)Shaanxi Province Postdoctoral Research Project Funding,China。
文摘To accomplish the reliability analyses of the correlation of multi-analytical objectives,an innovative framework of Dimensional Synchronous Modeling(DSM)and correlation analysis is developed based on the stepwise modeling strategy,cell array operation principle,and Copula theory.Under this framework,we propose a DSM-based Enhanced Kriging(DSMEK)algorithm to synchronously derive the modeling of multi-objective,and explore an adaptive Copula function approach to analyze the correlation among multiple objectives and to assess the synthetical reliability level.In the proposed DSMEK and adaptive Copula methods,the Kriging model is treated as the basis function of DSMEK model,the Multi-Objective Snake Optimizer(MOSO)algorithm is used to search the optimal values of hyperparameters of basis functions,the cell array operation principle is adopted to establish a whole model of multiple objectives,the goodness of fit is utilized to determine the forms of Copula functions,and the determined Copula functions are employed to perform the reliability analyses of the correlation of multi-analytical objectives.Furthermore,three examples,including multi-objective complex function approximation,aeroengine turbine bladeddisc multi-failure mode reliability analyses and aircraft landing gear system brake temperature reliability analyses,are performed to verify the effectiveness of the proposed methods,from the viewpoints of mathematics and engineering.The results show that the DSMEK and adaptive Copula approaches hold obvious advantages in terms of modeling features and simulation performance.The efforts of this work provide a useful way for the modeling of multi-analytical objectives and synthetical reliability analyses of complex structure/system with multi-output responses.
基金supported by the National Natural Science Foundation of China(51875465)
文摘The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.
基金Supported by National Key Research and Development Program of China(Grant No.2022YFF0708903)Ningbo Municipal Key Technology Research and Development Program of China(Grant No.2022Z006)Youth Fund of National Natural Science Foundation of China(Grant No.52205043)。
文摘Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of inertia and mechanical size,the dynamic model of exoskeletons is difficult to construct.Hence,an enhanced whale optimization algorithm(EWOA)is proposed to identify the exoskeleton model parameters.Meanwhile,the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints.Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion.Finally,the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform.The knee joint motion achieves a steady-state response after 0.5 s.Meanwhile,the position error of hip joint response is less than 0.03 rad after 0.9 s.In addition,the steady-state human-robot interaction torque of the two joints is constrained within 15 N·m.This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters.Furthermore,an enhanced mutation strategy is adopted to avoid whale evolution’s unsatisfactory local optimal value.
基金supported by the Research on theKey Technology of Damage Identification Method of Dam Concrete Structure based on Transformer Image Processing(242102521031)the project Research on Situational Awareness and Behavior Anomaly Prediction of Social Media Based on Multimodal Time Series Graph(232102520004)Key Scientific Research Project of Higher Education Institutions in Henan Province(25B520019).
文摘Image enhancement utilizes intensity transformation functions to maximize the information content of enhanced images.This paper approaches the topic as an optimization problem and uses the bald eagle search(BES)algorithm to achieve optimal results.In our proposed model,gamma correction and Retinex address color cast issues and enhance image edges and details.The final enhanced image is obtained through color balancing.The BES algorithm seeks the optimal solution through the selection,search,and swooping stages.However,it is prone to getting stuck in local optima and converges slowly.To overcome these limitations,we propose an improved BES algorithm(ABES)with enhanced population learning,position updates,and control parameters.ABES is employed to optimize the core parameters of gamma correction and Retinex to improve image quality,and the maximization of information entropy is utilized as the objective function.Real benchmark images are collected to validate its performance.Experimental results demonstrate that ABES outperforms the existing image enhancement methods,including the flower pollination algorithm,the chimp optimization algorithm,particle swarm optimization,and BES,in terms of information entropy,peak signal-to-noise ratio(PSNR),structural similarity index(SSIM),and patch-based contrast quality index(PCQI).ABES demonstrates superior performance both qualitatively and quantitatively,and it helps enhance prominent features and contrast in the images while maintaining the natural appearance of the original images.
文摘Background:Quantum-enhanced medical imaging algorithms–quantum entanglement reconstruction,quantum noise suppression,and quantum beamforming–propose possible remedies for significant constraints in traditional diagnostic imaging,such as resolution,radiation efficiency,and real-time processing.Methods:This work used a mixed-methods strategy,including controlled phantom experiments,retrospective multi-center clinical data analysis,and quantum-classical hybrid processing to assess enhancements in resolution,dosage efficiency,and diagnostic confidence.Statistical validation included analysis of variance(ANOVA)and receiver-operating characteristic curve analysis,juxtaposing quantum-enhanced methodologies with conventional and deep learning approaches.Results:Quantum entanglement reconstruction enhanced magnetic resonance imaging spatial resolution by 33.2%(P<0.01),quantum noise suppression facilitated computed tomography scans with a 60%reduction in radiation,and quantum beamforming improved ultrasound contrast by 27%while preserving real-time processing(<2 ms delay).Inter-reader variability(12%in Diagnostic Confidence Scores)showed that systematic training is needed,even if the performance was better.The research presented(1)a reusable clinical quantum imaging framework,(2)enhanced hardware processes(field-programmable gate array/graphics processing unit acceleration),and(3)cost-benefit analyses demonstrating a 22-month return on investment breakeven point.Conclusion:Quantum-enhanced imaging has a lot of promise for use in medicine,especially in neurology and cancer.Future research should focus on multi-modal integration(e.g.,positron emission tomography–magnetic resonance imaging),cloud-based quantum simulations for enhanced accessibility,and extensive trials to confirm long-term diagnostic accuracy.This breakthrough gives healthcare systems a technology roadmap and a reason to spend money on quantum-enhanced diagnostics.
文摘Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly women.Thus identifying BAC could provide an expense,and be inaccurate.Recently Deep Learning(DL)methods have been introduced for automatic BAC detection and quantification with increased accuracy.Previously,classification with deep learning had reached higher efficiency,but designing the structure of DL proved to be an extremely challenging task due to overfitting models.It also is not able to capture the patterns and irregularities presented in the images.To solve the overfitting problem,an optimal feature set has been formed by Enhanced Wolf Pack Algorithm(EWPA),and their irregularities are identified by Dense-kUNet segmentation.In this paper,Dense-kUNet for segmentation and optimal feature has been introduced for classification(severe,mild,light)that integrates DenseUNet and kU-Net.Longer bound links exist among adjacent modules,allowing relatively rough data to be sent to the following component and assisting the system in finding higher qualities.The major contribution of the work is to design the best features selected by Enhanced Wolf Pack Algorithm(EWPA),and Modified Support Vector Machine(MSVM)based learning for classification.k-Dense-UNet is introduced which combines the procedure of Dense-UNet and kU-Net for image segmentation.Longer bound associations occur among nearby sections,allowing relatively granular data to be sent to the next subsystem and benefiting the system in recognizing smaller characteristics.The proposed techniques and the performance are tested using several types of analysis techniques 826 filled digitized mammography.The proposed method achieved the highest precision,recall,F-measure,and accuracy of 84.4333%,84.5333%,84.4833%,and 86.8667%when compared to other methods on the Digital Database for Screening Mammography(DDSM).
基金The National Natural Science Foundation of China(No.60775029)the Science and Technology Program of Zhejiang Province(No.2007C33072)
文摘As the tableau algorithm would produce a lot of description overlaps when judging the satisfiabilities of concepts（thus wasting much space）,a clause-based enhancing mode designed for the language ALCN is proposed.This enhancing mode constructs a disjunctive normal form on concept expressions and keeps only one conjunctive clause,and then substitutes the obtained succinctest conjunctive clause for sub-concepts set in the labeling of nodes of a completion tree constructed by the tableau algorithm （such a process may be repeated as many times as needed）.Due to the avoidance of tremendous descriptions redundancies caused by applying ∩- and ∪-rules of the ordinary tableau algorithm,this mode greatly improves the spatial performance as a result.An example is given to demonstrate the application of this enhancing mode and its reduction in the cost of space. Results show that the improvement is very outstanding.
基金Supported by the National Natural Science Foundation of China(61503289)Hubei Province Science and Technology Support Program(2015BAA120,2015BCE068)
文摘To solve the problems of noise,detail loss and poor contrast in the successive mean quantization transform（SMQT）,a new SMQT algorithm based on Otsu algorithm is proposed.In this algorithm,we integrate the optimal threshold selected by the Otsu algorithm into the SMQT algorithm,then obtain the successive mean quantization of the binary tree.By this algorithm,an enhanced image is output with a higher quality.From both subjective visual effect and objective quality evaluation,the experimental results show that the improved algorithm reduces noise,improves contrast and makes the image details more clear.
文摘Abstract： Based on digital signal processor（DSP） and field programmable gate array（FPGA） techniques, the architecture of super large view field（SLVF） panoramic night vision image processing hardware platform was established. The panoramic unwrapping and correcting algorithm, up to a full 360°, based on coordinate rotation digital computer （CORDIC） and night vision image enhancement algorithm, based on histogram equalization theory and edge detection theory, was presented in this paper, with the purpose of processing night vision dynamic panoramic annular image. The annular image can be unwrapped and corrected to conventional rectangular panorama by the panoramic image processing algorithm, which uses the pipelined CORDIC configuration to realize a trigonometric function generator with high speed and high precision. Histogram equalization algorithm can perfectly enhance the contrast of the night vision image. Edge detection algorithm can be propitious to find and detect small dim dynamic targets in night vision circumstances. After abundant experiment, the al- gorithm for panoramic image processing and night vision image enhancement is successfully implemented in FPGA and DSP. The panoramic night vision image system is a compact device, with no external rotating parts. And the system can reliably and dynamically detect 360＊ SLVF panoramic night vision image.
文摘Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kinds of researches on forensic detection have been presented,and it provides less accuracy.This paper proposed a novel forgery detection technique in image frames of the videos using enhanced Convolutional Neural Network(CNN).In the initial stage,the input video is taken as of the dataset and then converts the videos into image frames.Next,perform pre-sampling using the Adaptive Rood Pattern Search(ARPS)algorithm intended for reducing the useless frames.In the next stage,perform preprocessing for enhancing the image frames.Then,face detection is done as of the image utilizing the Viola-Jones algorithm.Finally,the improved Crow Search Algorithm(ICSA)has been used to select the extorted features and inputted to the Enhanced Convolutional Neural Network(ECNN)classifier for detecting the forged image frames.The experimental outcome of the proposed system has achieved 97.21%accuracy compared to other existing methods.
文摘Pattern matching is a very important topic in computer science. It has been used in various applications such as information retrieval, virus scanning, DNA sequence analysis, data mining, machine learning, network security and pattern recognition. This paper has presented a new pattern matching algorithm—Enhanced ERS-A, which is an improvement over ERS-S algorithm. In ERS-A, two sliding windows are used to scan the text from the left and the right simultaneously. The proposed algorithm also scans the text from the left and the right simultaneously as well as making comparisons with the pattern from both sides simultaneously. The comparisons done between the text and the pattern are done from both sides in parallel. The shift technique used in the Enhanced ERS-A is the four consecutive characters in the text immediately following the pattern window. The experimental results show that the Enhanced ERS-A has enhanced the process of pattern matching by reducing the number of comparisons performed.
基金Supported by National Natural Science Foundation of ChinaNo.81371554+1 种基金Science and Technology Planning Project of Guangdong Province of ChinaNo.2013B021800092
文摘Liver transplantation is an effective treatment for patients with end-stage liver disease. Accurate imaging evaluation of the transplanted patient is critical for ensuring that the limited donor liver is functioning appropriately. Ultrasound contrast agents(UCAs), in combination with contrastspecific imaging techniques, are increasingly accepted in clinical use for the assessment of the hepatic vasculature, bile ducts and liver parenchyma in pre-, intra- and posttransplant patients. We describe UCAs, their technical requirements, the recommended clinical indications, image interpretation and the limitations for contrastenhanced ultrasound applications in liver transplantation.
基金National Natural Sci ence Foundation of China(No.41476081)the Major Research and Development Project in Shandong Province(No.2019GHY112027)the Shandong Provincial Natural Science Foundation(No.ZR2020MF121).
文摘To realize the fast and accurate quantitative analysis of the mixture of polycyclic aromatic hydrocarbons(PAHs),surface-enhanced Raman spectroscopy(SERS)coupled with multivariate calibrations were employed.In this study,three kinds of calibration algorithms were used to quantitative analysis of the mixture of naphthalene(Nap),phenanthrene(Phe),and pyrene(Pyr).Firstly,partial least squares(PLS)algorithm was used to select characteristic variables,then the global search capability of genetic algorithm(GA)was used for the determining of the initial weights and thresholds of back propagation(BP)neural network so that local minima was avoided.The PLS-GA-BP model exhibited superiority to quantify PAHs mixture,which achieved R2=0.9975,0.9710,0.9643,ARE=10.07%,19.28%,16.72%and RMSE=13.10,5.40,5.10 nmol L−1 for Nap,Phe,Pyr(in the PAHs mixture)concentration prediction respectively.The forecast error,ARE and RMSE have been reduced more than 50%and 60%respectively compared with the whole spectral BP model.The study indicates that accurate quantitative spectroscopic analysis of the mixture of PAHs samples can be achieved through the combination of SERS technique and PLS-GA-BP algorithm.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61175126)National Research Foundation for the Doctoral Program of Higher Education of China(Grant No.20112304110009)the Fundamental Research Funds for the Central Universities of China(Grant No.HEUCFZ1209)
文摘Coverage control for each sensor is based on a 2D directional sensing model in directional sensor networks conventionally. But the 2D model cannot accurately characterize the real environment. In order to solve this problem,a new 3D directional sensor model and coverage enhancement algorithm is proposed. We can adjust the pitch angle and deviation angle to enhance the coverage rate. And the coverage enhancement algorithm is based on an improved gravitational search algorithm. In this paper the two improved strategies of GSA are directional mutation strategy and individual evolution strategy. A set of simulations show that our coverage enhancement algorithm has a good performance to improve the coverage rate of the wireless directional sensor network on different number of nodes,different virtual angles and different sensing radius.
基金supported by the 973 Program of China under Grant No.2013CBA01702National Natural Science Foundation of China under Grant No.11474206,No.91233202,No.11374216,and No.11404224+3 种基金Program for New Century Excellent Talents in University under Grant No.NCET-12-0607Scientific Research Project of Beijing Education Commission under Grant No.KM201310028005Specialized Research Fund for the Doctoral Program of Higher Education under Grant No.20121108120009the Beijing Youth Top-Notch Talent Training Plan under Grant No.CIT&TCD201504080
文摘In mausoleum murals, existing bubbles are one kind of the most harmful defects for the repair and protection of relics. For this reason, it is necessary to detect bubbles, especially the ones with small size. A method to detect the small bubbles with enhanced terahertz （THz） images is proposed. To simulate the bubbles in the mausoleum murals, circular grooves have been hidden in the plaster and then measured by the THz reflected time domain spectroscopy imaging system. To observe the small bubbles in murals, a comprehensive enhancement algorithm is adopted to process the obtained THz images. With the enhanced method, the circular grooves in the murals can be observed clearly, even for the circular groove with a diameter of 1.5 mm. The results indicate that the proposed comprehensive method can be used to detect the tiny defects of murals.
基金supported by National Natural Science Foundation of China (No. 40805048,No. 11026226)Typhoon Research Foundation of Shanghai Typhoon Institute/China Meteorological Administration (No. 2008ST01)+1 种基金Research Foundation of State Key Laboratory of Remote Sensing Science,Jointly sponsored by the Instituteof Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University (No. 2009KFJJ013)Research Foundation of State Key Laboratory of Severe Weather/Chinese Academy of Meteorological Sciences (No. 2008LASW-B03)
文摘A novel nonlinear gray transform method is proposed to enhance the contrast of a typhoon cloud image.Generally,the typhoon cloud image obtained by a satellite cannot be directly used to make an accurate prediction of the typhoon＇s center or intensity because the contrast of the received typhoon cloud image may be bad.Our aim is to extrude the typhoon＇s eye in the typhoon cloud image.A normalized arc-tangent transformation operation is designed to enhance global contrast of the typhoon cloud image.Differential evolution algorithm is used to choose the optimal nonlinear transform parameter.Finally,geodesic activity contour model is used to extract the typhoon＇s eye to verify the performance of the proposed method.Experimental results show that the proposed method can efficiently enhance the global contrast of the typhoon cloud image while greatly extruding the typhoon＇s eye.
文摘The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.
基金Chinese National Science Found for Creative Research Groups (Grant No.60521002)Chinese National Key Technology R&D Program(Grant No.2005BA908B02)Science Foundation of Shanghai Municipal Commission of Science and Technology, Chinese(Grant No.05dz05802)
文摘Radio resource assignment schemes and routing strategies in relay enhanced cellular networks are proposed in this paper. Under the reuse partitioning-based frequency planning framework, the intra-cell resource partitioning between the base station and relay nodes was addressed firstly by introducing a metric of effective reuse factor. Then, coverage-oriented and capacity-oriented rantings, as well as two link bandwidth assignment schemes＂ equal-bandwidth per link＂ and ＂equal-bandwidth per mobile station＂ were developed. These key issues and their impacts on the system performance were analyzed comprehensively and supported by simulations. Results show that the cell capacity and edge user throughput of the proposed network are superior to the traditional non-relay network when an appropriate effective reuse factor is adopted.
文摘Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).
基金supported by the Natural Science Foundation of Shandong Province(nos.ZR2023MF047,ZR2024MA055 and ZR2023QF139)the Enterprise Commissioned Project(nos.2024HX104 and 2024HX140)+1 种基金the China University Industry-University-Research Innovation Foundation(nos.2021ZYA11003 and 2021ITA05032)the Science and Technology Plan for Youth Innovation of Shandong's Universities(no.2019KJN012).
文摘In low-light environments,captured images often exhibit issues such as insufficient clarity and detail loss,which significantly degrade the accuracy of subsequent target recognition tasks.To tackle these challenges,this study presents a novel low-light image enhancement algorithm that leverages virtual hazy image generation through dehazing models based on statistical analysis.The proposed algorithm initiates the enhancement process by transforming the low-light image into a virtual hazy image,followed by image segmentation using a quadtree method.To improve the accuracy and robustness of atmospheric light estimation,the algorithm incorporates a genetic algorithm to optimize the quadtree-based estimation of atmospheric light regions.Additionally,this method employs an adaptive window adjustment mechanism to derive the dark channel prior image,which is subsequently refined using morphological operations and guided filtering.The final enhanced image is reconstructed through the hazy image degradation model.Extensive experimental evaluations across multiple datasets verify the superiority of the designed framework,achieving a peak signal-to-noise ratio(PSNR)of 17.09 and a structural similarity index(SSIM)of 0.74.These results indicate that the proposed algorithm not only effectively enhances image contrast and brightness but also outperforms traditional methods in terms of subjective and objective evaluation metrics.
