Investigating natural-inspired applications is a perennially appealing subject for scientists. The current increase in the speed of natural-origin structure growth may be linked to their superior mechanical properties...Investigating natural-inspired applications is a perennially appealing subject for scientists. The current increase in the speed of natural-origin structure growth may be linked to their superior mechanical properties and environmental resilience. Biological composite structures with helicoidal schemes and designs have remarkable capacities to absorb impact energy and withstand damage. However, there is a dearth of extensive study on the influence of fiber redirection and reorientation inside the matrix of a helicoid structure on its mechanical performance and reactivity. The present study aimed to explore the static and transient responses of a bio-inspired helicoid laminated composite(B-iHLC) shell under the influence of an explosive load using an isomorphic method. The structural integrity of the shell is maintained by a viscoelastic basis known as the Pasternak foundation, which encompasses two coefficients of stiffness and one coefficient of damping. The equilibrium equations governing shell dynamics are obtained by using Hamilton's principle and including the modified first-order shear theory,therefore obviating the need to employ a shear correction factor. The paper's model and approach are validated by doing numerical comparisons with respected publications. The findings of this study may be used in the construction of military and civilian infrastructure in situations when the structure is subjected to severe stresses that might potentially result in catastrophic collapse. The findings of this paper serve as the foundation for several other issues, including geometric optimization and the dynamic response of similar mechanical structures.展开更多
Neutron computed tomography(NCT)is widely used as a noninvasive measurement technique in nuclear engineering,thermal hydraulics,and cultural heritage.The neutron source intensity of NCT is usually low and the scan tim...Neutron computed tomography(NCT)is widely used as a noninvasive measurement technique in nuclear engineering,thermal hydraulics,and cultural heritage.The neutron source intensity of NCT is usually low and the scan time is long,resulting in a projection image containing severe noise.To reduce the scanning time and increase the image reconstruction quality,an effective reconstruction algorithm must be selected.In CT image reconstruction,the reconstruction algorithms can be divided into three categories:analytical algorithms,iterative algorithms,and deep learning.Because the analytical algorithm requires complete projection data,it is not suitable for reconstruction in harsh environments,such as strong radia-tion,high temperature,and high pressure.Deep learning requires large amounts of data and complex models,which cannot be easily deployed,as well as has a high computational complexity and poor interpretability.Therefore,this paper proposes the OS-SART-PDTV iterative algorithm,which uses the ordered subset simultaneous algebraic reconstruction technique(OS-SART)algorithm to reconstruct the image and the first-order primal–dual algorithm to solve the total variation(PDTV),for sparse-view NCT three-dimensional reconstruction.The novel algorithm was compared with other algorithms(FBP,OS-SART-TV,OS-SART-AwTV,and OS-SART-FGPTV)by simulating the experimental data and actual neutron projection experiments.The reconstruction results demonstrate that the proposed algorithm outperforms the FBP,OS-SART-TV,OS-SART-AwTV,and OS-SART-FGPTV algorithms in terms of preserving edge structure,denoising,and suppressing artifacts.展开更多
Using Euler’s first-order explicit(EE)method and the peridynamic differential operator(PDDO)to discretize the time and internal crystal-size derivatives,respectively,the Euler’s first-order explicit method–peridyna...Using Euler’s first-order explicit(EE)method and the peridynamic differential operator(PDDO)to discretize the time and internal crystal-size derivatives,respectively,the Euler’s first-order explicit method–peridynamic differential operator(EE–PDDO)was obtained for solving the one-dimensional population balance equation in crystallization.Four different conditions during crystallization were studied:size-independent growth,sizedependent growth in a batch process,nucleation and size-independent growth,and nucleation and size-dependent growth in a continuous process.The high accuracy of the EE–PDDO method was confirmed by comparing it with the numerical results obtained using the second-order upwind and HR-van methods.The method is characterized by non-oscillation and high accuracy,especially in the discontinuous and sharp crystal size distribution.The stability of the EE–PDDO method,choice of weight function in the PDDO method,and optimal time step are also discussed.展开更多
Methane generation in landfills and its inadequate management represent the major avoidable source of anthropogenic methane today. This paper models methane production and the potential resources expected (electrical ...Methane generation in landfills and its inadequate management represent the major avoidable source of anthropogenic methane today. This paper models methane production and the potential resources expected (electrical energy production and potential carbon credits from avoided CH4 emissions) from its proper management in a municipal solid waste landfill located in Ouagadougou, Burkina Faso. The modeling was carried out using two first-order decay (FOD) models (LandGEM V3.02 and SWANA) using parameters evaluated on the basis of the characteristics of the waste admitted to the landfill and weather data for the site. At the same time, production data have been collected since 2016 in order to compare them with the model results. The results obtained from these models were compared to experimental one. For the simulation of methane production, the SWANA model showed better consistency with experimental data, with a coefficient of determination (R²) of 0.59 compared with the LandGEM model, which obtained a coefficient of 0.006. Thus, despite the low correlation values linked to the poor consistency of experimental data, the SWANA model models methane production much better than the LandGEM model. Thus, despite the low correlation values linked to the poor consistency of the experimental data, the SWANA model models methane production much better than the LandGEM V3.02 model. It was noted that the poor consistency of the experimental data justifies these low coefficients, and that they can be improved in the future thanks to ongoing in situ measurements. According to the SWANA model prediction, in 27 years of operation a biogas plant with 33% electrical efficiency using biogas from the Polesgo landfill would avoid 1,340 GgCO2e. Also, the evaluation of revenues due to electricity and carbon credit gave a total revenue derived from methane production of US$27.38 million at a cost of US$10.5/tonne CO2e.展开更多
In this paper,we consider a block-structured convex optimization model,where in the objective the block variables are nonseparable and they are further linearly coupled in the constraint.For the 2-block case,we propos...In this paper,we consider a block-structured convex optimization model,where in the objective the block variables are nonseparable and they are further linearly coupled in the constraint.For the 2-block case,we propose a number of first-order algorithms to solve this model.First,the alternating direction method of multipliers(ADMM)is extended,assuming that it is easy to optimize the augmented Lagrangian function with one block of variables at each time while fixing the other block.We prove that O(1/t)iteration complexity bound holds under suitable conditions,where t is the number of iterations.If the subroutines of the ADMM cannot be implemented,then we propose new alternative algorithms to be called alternating proximal gradient method of multipliers,alternating gradient projection method of multipliers,and the hybrids thereof.Under suitable conditions,the O(1/t)iteration complexity bound is shown to hold for all the newly proposed algorithms.Finally,we extend the analysis for the ADMM to the general multi-block case.展开更多
A quantum steering ellipsoid(QSE)is a visual characterization for bipartite qubit systems,and it is also a novel avenue for describing and detecting quantum correlations.Herein,by using a QSE,we visualize and witness ...A quantum steering ellipsoid(QSE)is a visual characterization for bipartite qubit systems,and it is also a novel avenue for describing and detecting quantum correlations.Herein,by using a QSE,we visualize and witness the first-order coherence(FOC),Bell nonlocality(BN)and purity under non-inertial frames.Also,the collective influences of the depolarizing channel and the non-coherence-generating channel(NCGC)on the FOC,BN and purity are investigated in the QSE formalism.The results reveal that the distance from the center of the QSE to the center of the Bloch sphere visualizes the FOC of a bipartite system,the lengths of the QSE semiaxis visualize the BN,and the QSE's shape and position dominate the purity of the system.One can capture the FOC,BN and purity via the shape and position of the QSE in the non-inertial frame.The depolarizing channel(the NCGC)gives rise to the shrinking and degradation(the periodical oscillation)of the QSE.One can use these traits to visually characterize and detect the FOC,BN and purity under the influence of external noise.Of particular note is that the condition for the QSE to achieve the center of the Bloch sphere cannot be influenced by the depolarizing channel and the NCGC.The characterization shows that the conditions for the disappearance of the FOC are invariant under the additional influences of the depolarizing channel and NCGC.展开更多
Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay ...Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).展开更多
Perpendicular synthetic-antiferromagnet(p-SAF) has broad applications in spin-transfer-torque magnetic random access memory and magnetic sensors. In this study, the p-SAF films consisting of (Co/Ni)3]/Ir(tIr)/[(Ni/Co)...Perpendicular synthetic-antiferromagnet(p-SAF) has broad applications in spin-transfer-torque magnetic random access memory and magnetic sensors. In this study, the p-SAF films consisting of (Co/Ni)3]/Ir(tIr)/[(Ni/Co)3are fabricated by magnetron sputtering technology. We study the domain structure and switching field distribution in p-SAF by changing the thickness of the infrared space layer. The strongest exchange coupling field(Hex) is observed when the thickness of Ir layer(tIr) is 0.7 nm and becoming weak according to the Ruderman–Kittel–Kasuya–Yosida-type coupling at 1.05 nm,2.1 nm, 4.55 nm, and 4.9 nm in sequence. Furthermore, the domain switching process between the upper Co/Ni stack and the bottom Co/Ni stack is different because of the antiferromagnet coupling. Compared with ferromagnet coupling films, the antiferromagnet samples possess three irreversible reversal regions in the first-order reversal curve distribution.With tIrincreasing, these irreversible reversal regions become denser and smaller. The results from this study will help us understand the details of the magnetization reversal process in the p-SAF.展开更多
The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant ...The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.展开更多
In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selec...In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.展开更多
The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s lifetime.Many studies have been conducted for homogeneous networks,but few hav...The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s lifetime.Many studies have been conducted for homogeneous networks,but few have been performed for heterogeneouswireless sensor networks.This paper utilizes Rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial energies.The proposed algorithms lack algorithm-specific parameters and metaphorical connotations.The proposed algorithms examine the search space based on the relations of the population with the best,worst,and randomly assigned solutions.The proposed algorithms can be evaluated using any routing protocol,however,we have chosen the well-known routing protocols in the literature:Low Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Gathering in Sensor Information Systems(PEAGSIS),Partitioned-based Energy-efficient LEACH(PE-LEACH),and the Power-Efficient Gathering in Sensor Information Systems Neural Network(PEAGSIS-NN)recent routing protocol.We compare our optimized method with the Jaya,the Particle Swarm Optimization-based Energy Efficient Clustering(PSO-EEC)protocol,and the hybrid Harmony Search Algorithm and PSO(HSA-PSO)algorithms.The efficiencies of our proposed algorithms are evaluated by conducting experiments in terms of the network lifetime(first dead node,half dead nodes,and last dead node),energy consumption,packets to cluster head,and packets to the base station.The experimental results were compared with those obtained using the Jaya optimization algorithm.The proposed algorithms exhibited the best performance.The proposed approach successfully prolongs the network lifetime by 71% for the PEAGSIS protocol,51% for the LEACH protocol,10% for the PE-LEACH protocol,and 73% for the PEGSIS-NN protocol;Moreover,it enhances other criteria such as energy conservation,fitness convergence,packets to cluster head,and packets to the base station.展开更多
In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature sel...In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.展开更多
Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effect...Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.展开更多
Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,maki...Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.展开更多
Metasurfaces,composed of planar arrays of intricately designed meta-atom structures,possess remarkable capabilities in controlling electromagnetic waves in various ways.A critical aspect of metasurface design involves...Metasurfaces,composed of planar arrays of intricately designed meta-atom structures,possess remarkable capabilities in controlling electromagnetic waves in various ways.A critical aspect of metasurface design involves selecting suitable meta-atoms to achieve target functionalities such as phase retardation,amplitude modulation,and polarization conversion.Conventional design processes often involve extensive parameter sweeping,a laborious and computationally intensive task heavily reliant on designer expertise and judgement.Here,we present an efficient genetic algorithm assisted meta-atom optimization method for high-performance metasurface optics,which is compatible to both single-and multiobjective device design tasks.We first employ the method for a single-objective design task and implement a high-efficiency Pancharatnam-Berry phase based metalens with an average focusing efficiency exceeding 80%in the visible spectrum.We then employ the method for a dual-objective metasurface design task and construct an efficient spin-multiplexed structural beam generator.The device is capable of generating zeroth-order and first-order Bessel beams respectively under right-handed and left-handed circular polarized illumination,with associated generation efficiencies surpassing 88%.Finally,we implement a wavelength and spin co-multiplexed four-channel metahologram capable of projecting two spin-multiplexed holographic images under each operational wavelength,with efficiencies over 50%.Our work offers a streamlined and easy-to-implement approach to meta-atom design and optimization,empowering designers to create diverse high-performance and multifunctional metasurface optics.展开更多
With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality pred...With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.展开更多
With the increase in ocean exploration activities and underwater development,the autonomous underwater vehicle(AUV)has been widely used as a type of underwater automation equipment in the detection of underwater envir...With the increase in ocean exploration activities and underwater development,the autonomous underwater vehicle(AUV)has been widely used as a type of underwater automation equipment in the detection of underwater environments.However,nowadays AUVs generally have drawbacks such as weak endurance,low intelligence,and poor detection ability.The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks.To improve the underwater operation ability of the AUV,this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm.In response to the limitations of a single algorithm,an optimization scheme is proposed to improve the artificial potential field ant colony(APF-AC)algorithm.Compared with traditional ant colony and comparative algorithms,the APF-AC reduced the path length by 1.57%and 0.63%(in the simple environment),8.92%and 3.46%(in the complex environment).The iteration time has been reduced by approximately 28.48%and 18.05%(in the simple environment),18.53%and 9.24%(in the complex environment).Finally,the improved APF-AC algorithm has been validated on the AUV platform,and the experiment is consistent with the simulation.Improved APF-AC algorithm can effectively reduce the underwater operation time and overall power consumption of the AUV,and shows a higher safety.展开更多
In this study, we propose an algorithm selection method based on coupling strength for the partitioned analysis ofstructure-piezoelectric-circuit coupling, which includes two types of coupling or inverse and direct pi...In this study, we propose an algorithm selection method based on coupling strength for the partitioned analysis ofstructure-piezoelectric-circuit coupling, which includes two types of coupling or inverse and direct piezoelectriccoupling and direct piezoelectric and circuit coupling. In the proposed method, implicit and explicit formulationsare used for strong and weak coupling, respectively. Three feasible partitioned algorithms are generated, namely(1) a strongly coupled algorithm that uses a fully implicit formulation for both types of coupling, (2) a weaklycoupled algorithm that uses a fully explicit formulation for both types of coupling, and (3) a partially stronglycoupled and partially weakly coupled algorithm that uses an implicit formulation and an explicit formulation forthe two types of coupling, respectively.Numerical examples using a piezoelectric energy harvester,which is a typicalstructure-piezoelectric-circuit coupling problem, demonstrate that the proposed method selects the most costeffectivealgorithm.展开更多
Steganography is a technique for hiding secret messages while sending and receiving communications through a cover item.From ancient times to the present,the security of secret or vital information has always been a s...Steganography is a technique for hiding secret messages while sending and receiving communications through a cover item.From ancient times to the present,the security of secret or vital information has always been a significant problem.The development of secure communication methods that keep recipient-only data transmissions secret has always been an area of interest.Therefore,several approaches,including steganography,have been developed by researchers over time to enable safe data transit.In this review,we have discussed image steganography based on Discrete Cosine Transform(DCT)algorithm,etc.We have also discussed image steganography based on multiple hashing algorithms like the Rivest–Shamir–Adleman(RSA)method,the Blowfish technique,and the hash-least significant bit(LSB)approach.In this review,a novel method of hiding information in images has been developed with minimal variance in image bits,making our method secure and effective.A cryptography mechanism was also used in this strategy.Before encoding the data and embedding it into a carry image,this review verifies that it has been encrypted.Usually,embedded text in photos conveys crucial signals about the content.This review employs hash table encryption on the message before hiding it within the picture to provide a more secure method of data transport.If the message is ever intercepted by a third party,there are several ways to stop this operation.A second level of security process implementation involves encrypting and decrypting steganography images using different hashing algorithms.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
文摘Investigating natural-inspired applications is a perennially appealing subject for scientists. The current increase in the speed of natural-origin structure growth may be linked to their superior mechanical properties and environmental resilience. Biological composite structures with helicoidal schemes and designs have remarkable capacities to absorb impact energy and withstand damage. However, there is a dearth of extensive study on the influence of fiber redirection and reorientation inside the matrix of a helicoid structure on its mechanical performance and reactivity. The present study aimed to explore the static and transient responses of a bio-inspired helicoid laminated composite(B-iHLC) shell under the influence of an explosive load using an isomorphic method. The structural integrity of the shell is maintained by a viscoelastic basis known as the Pasternak foundation, which encompasses two coefficients of stiffness and one coefficient of damping. The equilibrium equations governing shell dynamics are obtained by using Hamilton's principle and including the modified first-order shear theory,therefore obviating the need to employ a shear correction factor. The paper's model and approach are validated by doing numerical comparisons with respected publications. The findings of this study may be used in the construction of military and civilian infrastructure in situations when the structure is subjected to severe stresses that might potentially result in catastrophic collapse. The findings of this paper serve as the foundation for several other issues, including geometric optimization and the dynamic response of similar mechanical structures.
基金supported by the National Key Research and Development Program of China(No.2022YFB1902700)the Joint Fund of Ministry of Education for Equipment Pre-research(No.8091B042203)+5 种基金the National Natural Science Foundation of China(No.11875129)the Fund of the State Key Laboratory of Intense Pulsed Radiation Simulation and Effect(No.SKLIPR1810)the Fund of Innovation Center of Radiation Application(No.KFZC2020020402)the Fund of the State Key Laboratory of Nuclear Physics and Technology,Peking University(No.NPT2023KFY06)the Joint Innovation Fund of China National Uranium Co.,Ltd.,State Key Laboratory of Nuclear Resources and Environment,East China University of Technology(No.2022NRE-LH-02)the Fundamental Research Funds for the Central Universities(No.2023JG001).
文摘Neutron computed tomography(NCT)is widely used as a noninvasive measurement technique in nuclear engineering,thermal hydraulics,and cultural heritage.The neutron source intensity of NCT is usually low and the scan time is long,resulting in a projection image containing severe noise.To reduce the scanning time and increase the image reconstruction quality,an effective reconstruction algorithm must be selected.In CT image reconstruction,the reconstruction algorithms can be divided into three categories:analytical algorithms,iterative algorithms,and deep learning.Because the analytical algorithm requires complete projection data,it is not suitable for reconstruction in harsh environments,such as strong radia-tion,high temperature,and high pressure.Deep learning requires large amounts of data and complex models,which cannot be easily deployed,as well as has a high computational complexity and poor interpretability.Therefore,this paper proposes the OS-SART-PDTV iterative algorithm,which uses the ordered subset simultaneous algebraic reconstruction technique(OS-SART)algorithm to reconstruct the image and the first-order primal–dual algorithm to solve the total variation(PDTV),for sparse-view NCT three-dimensional reconstruction.The novel algorithm was compared with other algorithms(FBP,OS-SART-TV,OS-SART-AwTV,and OS-SART-FGPTV)by simulating the experimental data and actual neutron projection experiments.The reconstruction results demonstrate that the proposed algorithm outperforms the FBP,OS-SART-TV,OS-SART-AwTV,and OS-SART-FGPTV algorithms in terms of preserving edge structure,denoising,and suppressing artifacts.
文摘Using Euler’s first-order explicit(EE)method and the peridynamic differential operator(PDDO)to discretize the time and internal crystal-size derivatives,respectively,the Euler’s first-order explicit method–peridynamic differential operator(EE–PDDO)was obtained for solving the one-dimensional population balance equation in crystallization.Four different conditions during crystallization were studied:size-independent growth,sizedependent growth in a batch process,nucleation and size-independent growth,and nucleation and size-dependent growth in a continuous process.The high accuracy of the EE–PDDO method was confirmed by comparing it with the numerical results obtained using the second-order upwind and HR-van methods.The method is characterized by non-oscillation and high accuracy,especially in the discontinuous and sharp crystal size distribution.The stability of the EE–PDDO method,choice of weight function in the PDDO method,and optimal time step are also discussed.
文摘Methane generation in landfills and its inadequate management represent the major avoidable source of anthropogenic methane today. This paper models methane production and the potential resources expected (electrical energy production and potential carbon credits from avoided CH4 emissions) from its proper management in a municipal solid waste landfill located in Ouagadougou, Burkina Faso. The modeling was carried out using two first-order decay (FOD) models (LandGEM V3.02 and SWANA) using parameters evaluated on the basis of the characteristics of the waste admitted to the landfill and weather data for the site. At the same time, production data have been collected since 2016 in order to compare them with the model results. The results obtained from these models were compared to experimental one. For the simulation of methane production, the SWANA model showed better consistency with experimental data, with a coefficient of determination (R²) of 0.59 compared with the LandGEM model, which obtained a coefficient of 0.006. Thus, despite the low correlation values linked to the poor consistency of experimental data, the SWANA model models methane production much better than the LandGEM model. Thus, despite the low correlation values linked to the poor consistency of the experimental data, the SWANA model models methane production much better than the LandGEM V3.02 model. It was noted that the poor consistency of the experimental data justifies these low coefficients, and that they can be improved in the future thanks to ongoing in situ measurements. According to the SWANA model prediction, in 27 years of operation a biogas plant with 33% electrical efficiency using biogas from the Polesgo landfill would avoid 1,340 GgCO2e. Also, the evaluation of revenues due to electricity and carbon credit gave a total revenue derived from methane production of US$27.38 million at a cost of US$10.5/tonne CO2e.
文摘In this paper,we consider a block-structured convex optimization model,where in the objective the block variables are nonseparable and they are further linearly coupled in the constraint.For the 2-block case,we propose a number of first-order algorithms to solve this model.First,the alternating direction method of multipliers(ADMM)is extended,assuming that it is easy to optimize the augmented Lagrangian function with one block of variables at each time while fixing the other block.We prove that O(1/t)iteration complexity bound holds under suitable conditions,where t is the number of iterations.If the subroutines of the ADMM cannot be implemented,then we propose new alternative algorithms to be called alternating proximal gradient method of multipliers,alternating gradient projection method of multipliers,and the hybrids thereof.Under suitable conditions,the O(1/t)iteration complexity bound is shown to hold for all the newly proposed algorithms.Finally,we extend the analysis for the ADMM to the general multi-block case.
基金Project supported by the National Natural Science Foundation of China(Grant No.12175001)the Natural Science Research Key Project of the Education Department of Anhui Province of China(Grant No.KJ2021A0943)+3 种基金the Research Start-up Funding Project of High Level Talent of West Anhui University(Grant No.WGKQ2021048)an Open Project of the Key Laboratory of Functional Materials and Devices for Informatics of Anhui Higher Education Institutes(Grant No.FMDI202106)the University Synergy Innovation Program of Anhui Province(Grant No.GXXT-2021-026)the Anhui Provincial Natural Science Foundation(Grant Nos.2108085MA18 and 2008085MA20)。
文摘A quantum steering ellipsoid(QSE)is a visual characterization for bipartite qubit systems,and it is also a novel avenue for describing and detecting quantum correlations.Herein,by using a QSE,we visualize and witness the first-order coherence(FOC),Bell nonlocality(BN)and purity under non-inertial frames.Also,the collective influences of the depolarizing channel and the non-coherence-generating channel(NCGC)on the FOC,BN and purity are investigated in the QSE formalism.The results reveal that the distance from the center of the QSE to the center of the Bloch sphere visualizes the FOC of a bipartite system,the lengths of the QSE semiaxis visualize the BN,and the QSE's shape and position dominate the purity of the system.One can capture the FOC,BN and purity via the shape and position of the QSE in the non-inertial frame.The depolarizing channel(the NCGC)gives rise to the shrinking and degradation(the periodical oscillation)of the QSE.One can use these traits to visually characterize and detect the FOC,BN and purity under the influence of external noise.Of particular note is that the condition for the QSE to achieve the center of the Bloch sphere cannot be influenced by the depolarizing channel and the NCGC.The characterization shows that the conditions for the disappearance of the FOC are invariant under the additional influences of the depolarizing channel and NCGC.
文摘Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).
基金Project supported by the Natural Science Foundation of Gansu Province, China (Grant No. 22JR5RA775)the Science and Technology Program of Lanzhou, China (Grant No. 2021-1-157)+2 种基金the Guangdong Basic and Applied Basic Research Foundation, China (Grant Nos. 2020A1515110998 and 2022A1515012123)the Outstanding Youth Foundation of Gansu Academy of Science, China (Grant No. 2021YQ01)the Innovative Team Construction Project of Gansu Academy of Sciences, China (Grant No. 2020CX005-01)。
文摘Perpendicular synthetic-antiferromagnet(p-SAF) has broad applications in spin-transfer-torque magnetic random access memory and magnetic sensors. In this study, the p-SAF films consisting of (Co/Ni)3]/Ir(tIr)/[(Ni/Co)3are fabricated by magnetron sputtering technology. We study the domain structure and switching field distribution in p-SAF by changing the thickness of the infrared space layer. The strongest exchange coupling field(Hex) is observed when the thickness of Ir layer(tIr) is 0.7 nm and becoming weak according to the Ruderman–Kittel–Kasuya–Yosida-type coupling at 1.05 nm,2.1 nm, 4.55 nm, and 4.9 nm in sequence. Furthermore, the domain switching process between the upper Co/Ni stack and the bottom Co/Ni stack is different because of the antiferromagnet coupling. Compared with ferromagnet coupling films, the antiferromagnet samples possess three irreversible reversal regions in the first-order reversal curve distribution.With tIrincreasing, these irreversible reversal regions become denser and smaller. The results from this study will help us understand the details of the magnetization reversal process in the p-SAF.
基金supported by the 2021 Open Project Fund of Science and Technology on Electromechanical Dynamic Control Laboratory,grant number 212-C-J-F-QT-2022-0020China Postdoctoral Science Foundation,grant number 2021M701713+1 种基金Postgraduate Research&Practice Innovation Program of Jiangsu Province,grant number KYCX23_0511the Jiangsu Funding Program for Excellent Postdoctoral Talent,grant number 20220ZB245。
文摘The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.
基金the Deputyship for Research and Innovation,“Ministry of Education”in Saudi Arabia for funding this research(IFKSUOR3-014-3).
文摘In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.
文摘The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s lifetime.Many studies have been conducted for homogeneous networks,but few have been performed for heterogeneouswireless sensor networks.This paper utilizes Rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial energies.The proposed algorithms lack algorithm-specific parameters and metaphorical connotations.The proposed algorithms examine the search space based on the relations of the population with the best,worst,and randomly assigned solutions.The proposed algorithms can be evaluated using any routing protocol,however,we have chosen the well-known routing protocols in the literature:Low Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Gathering in Sensor Information Systems(PEAGSIS),Partitioned-based Energy-efficient LEACH(PE-LEACH),and the Power-Efficient Gathering in Sensor Information Systems Neural Network(PEAGSIS-NN)recent routing protocol.We compare our optimized method with the Jaya,the Particle Swarm Optimization-based Energy Efficient Clustering(PSO-EEC)protocol,and the hybrid Harmony Search Algorithm and PSO(HSA-PSO)algorithms.The efficiencies of our proposed algorithms are evaluated by conducting experiments in terms of the network lifetime(first dead node,half dead nodes,and last dead node),energy consumption,packets to cluster head,and packets to the base station.The experimental results were compared with those obtained using the Jaya optimization algorithm.The proposed algorithms exhibited the best performance.The proposed approach successfully prolongs the network lifetime by 71% for the PEAGSIS protocol,51% for the LEACH protocol,10% for the PE-LEACH protocol,and 73% for the PEGSIS-NN protocol;Moreover,it enhances other criteria such as energy conservation,fitness convergence,packets to cluster head,and packets to the base station.
基金supported in part by the Natural Science Youth Foundation of Hebei Province under Grant F2019403207in part by the PhD Research Startup Foundation of Hebei GEO University under Grant BQ2019055+3 种基金in part by the Open Research Project of the Hubei Key Laboratory of Intelligent Geo-Information Processing under Grant KLIGIP-2021A06in part by the Fundamental Research Funds for the Universities in Hebei Province under Grant QN202220in part by the Science and Technology Research Project for Universities of Hebei under Grant ZD2020344in part by the Guangxi Natural Science Fund General Project under Grant 2021GXNSFAA075029.
文摘In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.
文摘Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.
基金National Key Research and Development Program of China(Nos.2022YFB4700600 and 2022YFB4700605)National Natural Science Foundation of China(Nos.61771123 and 62171116)+1 种基金Fundamental Research Funds for the Central UniversitiesGraduate Student Innovation Fund of Donghua University,China(No.CUSF-DH-D-2022044)。
文摘Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.
基金support from the National Science Foundation of China(Grant Nos.62075078 and 62135004)the Knowledge Innovation Program of Wuhan-Shuguang Project(Grant No.2022010801020095).
文摘Metasurfaces,composed of planar arrays of intricately designed meta-atom structures,possess remarkable capabilities in controlling electromagnetic waves in various ways.A critical aspect of metasurface design involves selecting suitable meta-atoms to achieve target functionalities such as phase retardation,amplitude modulation,and polarization conversion.Conventional design processes often involve extensive parameter sweeping,a laborious and computationally intensive task heavily reliant on designer expertise and judgement.Here,we present an efficient genetic algorithm assisted meta-atom optimization method for high-performance metasurface optics,which is compatible to both single-and multiobjective device design tasks.We first employ the method for a single-objective design task and implement a high-efficiency Pancharatnam-Berry phase based metalens with an average focusing efficiency exceeding 80%in the visible spectrum.We then employ the method for a dual-objective metasurface design task and construct an efficient spin-multiplexed structural beam generator.The device is capable of generating zeroth-order and first-order Bessel beams respectively under right-handed and left-handed circular polarized illumination,with associated generation efficiencies surpassing 88%.Finally,we implement a wavelength and spin co-multiplexed four-channel metahologram capable of projecting two spin-multiplexed holographic images under each operational wavelength,with efficiencies over 50%.Our work offers a streamlined and easy-to-implement approach to meta-atom design and optimization,empowering designers to create diverse high-performance and multifunctional metasurface optics.
基金supported by the National Science and Technology Innovation 2030 Next-Generation Artifical Intelligence Major Project(2018AAA0101801)the National Natural Science Foundation of China(72271188)。
文摘With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.
基金supported by Research Program supported by the National Natural Science Foundation of China(No.62201249)the Jiangsu Agricultural Science and Technology Innovation Fund(No.CX(21)1007)+2 种基金the Open Project of the Zhejiang Provincial Key Laboratory of Crop Harvesting Equipment and Technology(Nos.2021KY03,2021KY04)University-Industry Collaborative Education Program(No.201801166003)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.SJCX22_1042).
文摘With the increase in ocean exploration activities and underwater development,the autonomous underwater vehicle(AUV)has been widely used as a type of underwater automation equipment in the detection of underwater environments.However,nowadays AUVs generally have drawbacks such as weak endurance,low intelligence,and poor detection ability.The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks.To improve the underwater operation ability of the AUV,this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm.In response to the limitations of a single algorithm,an optimization scheme is proposed to improve the artificial potential field ant colony(APF-AC)algorithm.Compared with traditional ant colony and comparative algorithms,the APF-AC reduced the path length by 1.57%and 0.63%(in the simple environment),8.92%and 3.46%(in the complex environment).The iteration time has been reduced by approximately 28.48%and 18.05%(in the simple environment),18.53%and 9.24%(in the complex environment).Finally,the improved APF-AC algorithm has been validated on the AUV platform,and the experiment is consistent with the simulation.Improved APF-AC algorithm can effectively reduce the underwater operation time and overall power consumption of the AUV,and shows a higher safety.
基金the Japan Society for the Promotion of Science,KAKENHI Grant Nos.20H04199 and 23H00475.
文摘In this study, we propose an algorithm selection method based on coupling strength for the partitioned analysis ofstructure-piezoelectric-circuit coupling, which includes two types of coupling or inverse and direct piezoelectriccoupling and direct piezoelectric and circuit coupling. In the proposed method, implicit and explicit formulationsare used for strong and weak coupling, respectively. Three feasible partitioned algorithms are generated, namely(1) a strongly coupled algorithm that uses a fully implicit formulation for both types of coupling, (2) a weaklycoupled algorithm that uses a fully explicit formulation for both types of coupling, and (3) a partially stronglycoupled and partially weakly coupled algorithm that uses an implicit formulation and an explicit formulation forthe two types of coupling, respectively.Numerical examples using a piezoelectric energy harvester,which is a typicalstructure-piezoelectric-circuit coupling problem, demonstrate that the proposed method selects the most costeffectivealgorithm.
文摘Steganography is a technique for hiding secret messages while sending and receiving communications through a cover item.From ancient times to the present,the security of secret or vital information has always been a significant problem.The development of secure communication methods that keep recipient-only data transmissions secret has always been an area of interest.Therefore,several approaches,including steganography,have been developed by researchers over time to enable safe data transit.In this review,we have discussed image steganography based on Discrete Cosine Transform(DCT)algorithm,etc.We have also discussed image steganography based on multiple hashing algorithms like the Rivest–Shamir–Adleman(RSA)method,the Blowfish technique,and the hash-least significant bit(LSB)approach.In this review,a novel method of hiding information in images has been developed with minimal variance in image bits,making our method secure and effective.A cryptography mechanism was also used in this strategy.Before encoding the data and embedding it into a carry image,this review verifies that it has been encrypted.Usually,embedded text in photos conveys crucial signals about the content.This review employs hash table encryption on the message before hiding it within the picture to provide a more secure method of data transport.If the message is ever intercepted by a third party,there are several ways to stop this operation.A second level of security process implementation involves encrypting and decrypting steganography images using different hashing algorithms.
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.
