A novel algorithm for source location by utilizing the time difference of arrival (TDOA) measurements of a signal received at spatially separated sensors is proposed. The algorithm is based on quadratic constraint tot...A novel algorithm for source location by utilizing the time difference of arrival (TDOA) measurements of a signal received at spatially separated sensors is proposed. The algorithm is based on quadratic constraint total least-squares (QC-TLS) method and gives an explicit solution. The total least-squares method is a generalized data fitting method that is appropriate for cases when the system model contains error or is not known exactly, and quadratic constraint, which could be realized via Lagrange multipliers technique, could constrain the solution to the location equations to improve location accuracy. Comparisons of performance with ordinary least-squares are made, and Monte Carlo simulations are performed. Simulation results indicate that the proposed algorithm has high location accuracy and achieves accuracy close to the Cramer-Rao lower bound (CRLB) near the small TDOA measurement error region.展开更多
Order-recursive least-squares(ORLS)algorithms are applied to the prob-lems of estimation and identification of FIR or ARMA system parameters where a fixedset of input signal samples is available and the desired order ...Order-recursive least-squares(ORLS)algorithms are applied to the prob-lems of estimation and identification of FIR or ARMA system parameters where a fixedset of input signal samples is available and the desired order of the underlying model isunknown.On the basis of several universal formulae for updating nonsymmetric projec-tion operators,this paper presents three kinds of LS algorithms,called nonsymmetric,symmetric and square root normalized fast ORLS algorithms,respectively.As to the au-thors’ knowledge,the first and the third have not been so far provided,and the second isone of those which have the lowest computational requirement.Several simplified versionsof the algorithms are also considered.展开更多
This paper focuses on the problem of adaptive blind source separation (BSS). First, a recursive least-squares (RLS) whitening algorithm is proposed. By combining it with a natural gradient-based RLS algorithm for nonl...This paper focuses on the problem of adaptive blind source separation (BSS). First, a recursive least-squares (RLS) whitening algorithm is proposed. By combining it with a natural gradient-based RLS algorithm for nonlinear principle component analysis (PCA), and using reasonable approximations, a novel RLS algorithm which can achieve BSS without additional pre-whitening of the observed mixtures is obtained. Analyses of the equilibrium points show that both of the RLS whitening algorithm and the natural gradient-based RLS algorithm for BSS have the desired convergence properties. It is also proved that the combined new RLS algorithm for BSS is equivariant and has the property of keeping the separating matrix from becoming singular. Finally, the effectiveness of the proposed algorithm is verified by extensive simulation results.展开更多
Mixed-weight least-squares (MWLS) predictive control algorithm, compared with quadratic programming (QP) method, has the advantages of reducing the computer burden, quick calculation speed and dealing with the case in...Mixed-weight least-squares (MWLS) predictive control algorithm, compared with quadratic programming (QP) method, has the advantages of reducing the computer burden, quick calculation speed and dealing with the case in which the optimization is infeasible. But it can only deal with soft constraints. In order to deal with hard constraints and guarantee feasibility, an improved algorithm is proposed by recalculating the setpoint according to the hard constraints before calculating the manipulated variable and MWLS algorithm is used to satisfy the requirement of soft constraints for the system with the input constraints and output constraints. The algorithm can not only guarantee stability of the system and zero steady state error, but also satisfy the hard constraints of input and output variables. The simulation results show the improved algorithm is feasible and effective.展开更多
The numerical dispersion phenomenon in the finite-difference forward modeling simulations of the wave equation significantly affects the imaging accuracy in acoustic reflection logging.This issue is particularly prono...The numerical dispersion phenomenon in the finite-difference forward modeling simulations of the wave equation significantly affects the imaging accuracy in acoustic reflection logging.This issue is particularly pronounced in the reverse time migration(RTM)method used for shear-wave(S-wave)logging imaging.This not only affects imaging accuracy but also introduces ambiguities in the interpretation of logging results.To address this challenge,this study proposes the use of a least-squares difference coefficient optimization algorithm aiming to suppress the numerical dispersion phenomenon in the RTM of S-wave reflection imaging logging.By optimizing the difference coefficients,the high-precision finite-difference algorithm serves as an effective operator for both forward and backward RTM processes.This approach is instrumental in eliminating migration illusions,which are often caused by numerical dispersion.The effectiveness of this optimized algorithm is demonstrated through numerical results,which indicate that it can achieve more accurate forward imaging results across various conditions,including high-and low-velocity strata,and is effective in both large and small spatial grids.The results of processing real data demonstrate that numerical dispersion optimization effectively reduces migration artifacts and diminishes ambiguities in logging interpretations.This optimization offers crucial technical support to the RTM method,enhancing its capability for accurately modeling and imaging S-wave reflections.展开更多
This paper presents a new highly parallel algorithm for computing the minimum-norm least-squares solution of inconsistent linear equations Ax = b(A∈Rm×n,b∈R (A)). By this algorithm the solution x = A + b is obt...This paper presents a new highly parallel algorithm for computing the minimum-norm least-squares solution of inconsistent linear equations Ax = b(A∈Rm×n,b∈R (A)). By this algorithm the solution x = A + b is obtained in T = n(log2m + log2(n - r + 1) + 5) + log2m + 1 steps with P=mn processors when m × 2(n - 1) and with P = 2n(n - 1) processors otherwise.展开更多
This paper presents an Eulerian-Lagrangian algorithm for direct numerical simulation(DNS)of particle-laden flows.The algorithm is applicable to perform simulations of dilute suspensions of small inertial particles in ...This paper presents an Eulerian-Lagrangian algorithm for direct numerical simulation(DNS)of particle-laden flows.The algorithm is applicable to perform simulations of dilute suspensions of small inertial particles in turbulent carrier flow.The Eulerian framework numerically resolves turbulent carrier flow using a parallelized,finite-volume DNS solver on a staggered Cartesian grid.Particles are tracked using a point-particle method utilizing a Lagrangian particle tracking(LPT)algorithm.The proposed Eulerian-Lagrangian algorithm is validated using an inertial particle-laden turbulent channel flow for different Stokes number cases.The particle concentration profiles and higher-order statistics of the carrier and dispersed phases agree well with the benchmark results.We investigated the effect of fluid velocity interpolation and numerical integration schemes of particle tracking algorithms on particle dispersion statistics.The suitability of fluid velocity interpolation schemes for predicting the particle dispersion statistics is discussed in the framework of the particle tracking algorithm coupled to the finite-volume solver.In addition,we present parallelization strategies implemented in the algorithm and evaluate their parallel performance.展开更多
This study explored the application value of iterative decomposition of water and fatwith echo asymmetry and least-squares estimation(IDEAL-IQ)technology in the early diagnosis of ageing osteoporosis(OP).172 participa...This study explored the application value of iterative decomposition of water and fatwith echo asymmetry and least-squares estimation(IDEAL-IQ)technology in the early diagnosis of ageing osteoporosis(OP).172 participants were enrolled and underwentmagnetic resonance imaging(MRI)examinations on a 3.0T scanner.100 cases were included in the normal group(50 males and 50 females;mean age:45 years;age range:20e84 years).33 cases were included in the osteopenia group(17 males and 16 females;mean age:55 years;age range:43e83 years).39 caseswere includedintheOP group(19males and20females;meanage:58years;age range:48 e82 years).Conventional T1WI and T2WI were first obtained,followed by 3D-IDEAL-IQ-acqui-sition.Fat fraction(FF)and apparent transverse relaxation rate(R2*)resultswere automatically calculated from IDEAL-IQ-images on the console.Based on T1Wand T2W-images,300 ROIs for each participantweremanually delineated in L1-L5 vertebral bodies of five middle slices.In each age group of all normal subjects,each parameter was significantly correlated with gender.In male participants from the normal,osteopenia,and OP groups,statistical analysis revealed F values of 11319.292 and 180.130 for comparisons involving FF and R2*values,respectively(all p<0.0001).The sensitivity and specificity of FF values were 0.906 and 0.950,0.994 and 0.997,0.865 and 0.820,respectively.For R2*,they were 0.665 and 0.616,0.563 and 0.519,0.571 and 0.368,respectively.In female participants from the normal,osteopenia,and OP-groups,statis-tical analysis revealed F values of 12461.658 and 548.274 for comparisons involving FF and R2*values,respectively(all p<0.0001).The sensitivity and specificity of FF values were 0.985 and 0.991,0.996 and 0.996,0.581 and 0.678,respectively.For R2*,they were 0.698 and 0.730,0.603 and 0.665,0.622 and 0.525,respectively.Significant differences were indicated in the quanti-tative values among the three groups.FF value had good performance,while R2*value had poor performance indiscriminatingosteopenia andOP-groups.Overall,the IDEAL-IQ techniqueoffers specific reference indices that enable noninvasive and quantitative assessment of lumbar vertebrae bone metabolism,thereby providing diagnostic information for OP.展开更多
Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion...Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.展开更多
Tensor canonical decomposition （shorted as CANDECOMP/PARAFAC or CP） decomposes a tensor as a sum of rank-one tensors, which finds numerous applications in signal processing, hypergraph analysis, data analysis, etc. ...Tensor canonical decomposition （shorted as CANDECOMP/PARAFAC or CP） decomposes a tensor as a sum of rank-one tensors, which finds numerous applications in signal processing, hypergraph analysis, data analysis, etc. Alternating least-squares （ALS） is one of the most popular numerical algorithms for solving it. While there have been lots of efforts for enhancing its efficiency, in general its convergence can not been guaranteed. In this paper, we cooperate the ALS and the trust-region technique from optimization field to generate a trust-region-based alternating least-squares （TRALS） method for CP. Under mild assumptions, we prove that the whole iterative sequence generated by TRALS converges to a stationary point of CP. This thus provides a reasonable way to alleviate the swamps, the notorious phenomena of ALS that slow down the speed of the algorithm. Moreover, the trust region itself, in contrast to the regularization alternating least-squares （RALS） method, provides a self-adaptive way in choosing the parameter, which is essential for the efficiency of the algorithm. Our theoretical result is thus stronger than that of RALS in [26], which only proved the cluster point of the iterative sequence generated by RALS is a stationary point. In order to accelerate the new algorithm, we adopt an extrapolation scheme. We apply our algorithm to the amino acid fluorescence data decomposition from chemometrics, BCM decomposition and rank-（Lr, Lr, 1） decomposition arising from signal processing, and compare it with ALS and RALS. The numerical results show that TRALS is superior to ALS and RALS, both from the number of iterations and CPU time perspectives.展开更多
Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises stru...Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints.Two new algorithms,the Red Kite Optimization Algorithm(ROA)and Secretary Bird Optimization Algorithm(SBOA),are utilized on five benchmark trusses with 10,18,37,72,and 200-bar trusses.Both algorithms are evaluated against benchmarks in the literature.The results indicate that SBOA always reaches a lighter optimal.Designs with reducing structural weight ranging from 0.02%to 0.15%compared to ROA,and up to 6%–8%as compared to conventional algorithms.In addition,SBOA can achieve 15%–20%faster convergence speed and 10%–18%reduction in computational time with a smaller standard deviation over independent runs,which demonstrates its robustness and reliability.It is indicated that the adaptive exploration mechanism of SBOA,especially its Levy flight–based search strategy,can obviously improve optimization performance for low-and high-dimensional trusses.The research has implications in the context of promoting bio-inspired optimization techniques by demonstrating the viability of SBOA,a reliable model for large-scale structural design that provides significant enhancements in performance and convergence behavior.展开更多
Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narr...Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT^(*)-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT^(*),the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT^(*).Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications.展开更多
We study the split common solution problem with multiple output sets for monotone operator equations in Hilbert spaces.To solve this problem,we propose two new parallel algorithms.We establish a weak convergence theor...We study the split common solution problem with multiple output sets for monotone operator equations in Hilbert spaces.To solve this problem,we propose two new parallel algorithms.We establish a weak convergence theorem for the first and a strong convergence theorem for the second.展开更多
This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm.The algo-rithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japo...This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm.The algo-rithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japonicus.The math-ematical model is developed by simulating various biological behaviors of the Gekko japonicus,such as hybrid loco-motion patterns,directional olfactory guidance,implicit group advantage tendencies,and the tail autotomy mechanism.By integrating multi-stage mutual constraints and dynamically adjusting parameters,GJA maintains an optimal balance between global exploration and local exploitation,thereby effectively solving complex optimization problems.To assess the performance of GJA,comparative analyses were performed against fourteen state-of-the-art metaheuristic algorithms using the CEC2017 and CEC2022 benchmark test sets.Additionally,a Friedman test was performed on the experimen-tal results to assess the statistical significance of differences between various algorithms.And GJA was evaluated using multiple qualitative indicators,further confirming its superiority in exploration and exploitation.Finally,GJA was utilized to solve four engineering optimization problems and further implemented in robotic path planning to verify its practical applicability.Experimental results indicate that,compared to other high-performance algorithms,GJA demonstrates excep-tional performance as a powerful optimization algorithm in complex optimization problems.We make the code publicly available at:https://github.com/zhy1109/Gekko-japonicusalgorithm.展开更多
The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,convention...The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,conventional clustering-based methods face notable drawbacks,including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions.To overcome the performance limitations of existing methods,this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm(SC-QGA)and an improved quantum artificial bee colony algorithm hybrid K-means(IQABC-K).First,the SC-QGA algorithmis constructed based on quantum computing and integrates similarity coefficient theory to strengthen genetic diversity and feature extraction capabilities.For the subsequent clustering phase,the process based on the IQABC-K algorithm is enhanced with the core improvement of adaptive rotation gate and movement exploitation strategies to balance the exploration capabilities of global search and the exploitation capabilities of local search.Simultaneously,the acceleration of convergence toward the global optimum and a reduction in computational complexity are facilitated by means of the global optimum bootstrap strategy and a linear population reduction strategy.Through experimental evaluation with multiple algorithms and diverse performance metrics,the proposed algorithm confirms reliable accuracy on three datasets:KDD CUP99,NSL_KDD,and UNSW_NB15,achieving accuracy of 98.57%,98.81%,and 98.32%,respectively.These results affirm its potential as an effective solution for practical clustering applications.展开更多
In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic h...In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.展开更多
To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process,an integrated diagnostic method combining the gray wolf optimization(GWO)algorithm,support vector machine(SVM),and ...To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process,an integrated diagnostic method combining the gray wolf optimization(GWO)algorithm,support vector machine(SVM),and K-means clustering was proposed.Eight input parameters—derived from molten iron conditions and external factors—were selected as feature variables.A GWO-SVM model was developed to accurately predict the energy consumption of individual heats.Based on the prediction results,the mean absolute percentage error and maximum relative error of the test set were employed as criteria to identify heats with abnormal energy usage.For these heats,the K-means clustering algorithm was used to determine benchmark values of influencing factors from similar steel grades,enabling root-cause diagnosis of excessive energy consumption.The proposed method was applied to real production data from a converter in a steel plant.The analysis reveals that heat sample No.44 exhibits abnormal energy consumption,due to gas recovery being 1430.28 kg of standard coal below the benchmark level.A secondary contributing factor is a steam recovery shortfall of 237.99 kg of standard coal.This integrated approach offers a scientifically grounded tool for energy management in converter operations and provides valuable guidance for optimizing process parameters and enhancing energy efficiency.展开更多
ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the ...ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the algorithmhas attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation,coupled with advantages in real-time performance and robustness.Nevertheless,as applications have diversified,limitations in convergence precision and a tendency toward premature convergence have become increasingly evident,highlighting a need for improvement.This reviewsystematically outlines the developmental trajectory of the PIO algorithm,with a particular focus on its core applications in UAV navigation,multi-objective formulations,and a spectrum of variantmodels that have emerged in recent years.It offers a structured analysis of the foundational principles underlying the PIO.It conducts a comparative assessment of various performance-enhanced versions,including hybrid models that integrate mechanisms from other optimization paradigms.Additionally,the strengths andweaknesses of distinct PIOvariants are critically examined frommultiple perspectives,including intrinsic algorithmic characteristics,suitability for specific application scenarios,objective function design,and the rigor of the statistical evaluation methodologies employed in empirical studies.Finally,this paper identifies principal challenges within current PIO research and proposes several prospective research directions.Future work should focus on mitigating premature convergence by refining the two-phase search structure and adjusting the exponential decrease of individual numbers during the landmark operator.Enhancing parameter adaptation strategies,potentially using reinforcement learning for dynamic tuning,and advancing theoretical analyses on convergence and complexity are also critical.Further applications should be explored in constrained path planning,Neural Architecture Search(NAS),and other real-worldmulti-objective problems.For Multi-objective PIO(MPIO),key improvements include controlling the growth of the external archive and designing more effective selection mechanisms to maintain convergence efficiency.These efforts are expected to strengthen both the theoretical foundation and practical versatility of PIO and its variants.展开更多
In recent years,Blockchain Technology has become a paradigm shift,providing Transparent,Secure,and Decentralized platforms for diverse applications,ranging from Cryptocurrency to supply chain management.Nevertheless,t...In recent years,Blockchain Technology has become a paradigm shift,providing Transparent,Secure,and Decentralized platforms for diverse applications,ranging from Cryptocurrency to supply chain management.Nevertheless,the optimization of blockchain networks remains a critical challenge due to persistent issues such as latency,scalability,and energy consumption.This study proposes an innovative approach to Blockchain network optimization,drawing inspiration from principles of biological evolution and natural selection through evolutionary algorithms.Specifically,we explore the application of genetic algorithms,particle swarm optimization,and related evolutionary techniques to enhance the performance of blockchain networks.The proposed methodologies aim to optimize consensus mechanisms,improve transaction throughput,and reduce resource consumption.Through extensive simulations and real-world experiments,our findings demonstrate significant improvements in network efficiency,scalability,and stability.This research offers a thorough analysis of existing optimization techniques,introduces novel strategies,and assesses their efficacy based on empirical outputs.展开更多
文摘A novel algorithm for source location by utilizing the time difference of arrival (TDOA) measurements of a signal received at spatially separated sensors is proposed. The algorithm is based on quadratic constraint total least-squares (QC-TLS) method and gives an explicit solution. The total least-squares method is a generalized data fitting method that is appropriate for cases when the system model contains error or is not known exactly, and quadratic constraint, which could be realized via Lagrange multipliers technique, could constrain the solution to the location equations to improve location accuracy. Comparisons of performance with ordinary least-squares are made, and Monte Carlo simulations are performed. Simulation results indicate that the proposed algorithm has high location accuracy and achieves accuracy close to the Cramer-Rao lower bound (CRLB) near the small TDOA measurement error region.
文摘Order-recursive least-squares(ORLS)algorithms are applied to the prob-lems of estimation and identification of FIR or ARMA system parameters where a fixedset of input signal samples is available and the desired order of the underlying model isunknown.On the basis of several universal formulae for updating nonsymmetric projec-tion operators,this paper presents three kinds of LS algorithms,called nonsymmetric,symmetric and square root normalized fast ORLS algorithms,respectively.As to the au-thors’ knowledge,the first and the third have not been so far provided,and the second isone of those which have the lowest computational requirement.Several simplified versionsof the algorithms are also considered.
文摘This paper focuses on the problem of adaptive blind source separation (BSS). First, a recursive least-squares (RLS) whitening algorithm is proposed. By combining it with a natural gradient-based RLS algorithm for nonlinear principle component analysis (PCA), and using reasonable approximations, a novel RLS algorithm which can achieve BSS without additional pre-whitening of the observed mixtures is obtained. Analyses of the equilibrium points show that both of the RLS whitening algorithm and the natural gradient-based RLS algorithm for BSS have the desired convergence properties. It is also proved that the combined new RLS algorithm for BSS is equivariant and has the property of keeping the separating matrix from becoming singular. Finally, the effectiveness of the proposed algorithm is verified by extensive simulation results.
基金National Key Basic Research and Development(No.2002CB312200)
文摘Mixed-weight least-squares (MWLS) predictive control algorithm, compared with quadratic programming (QP) method, has the advantages of reducing the computer burden, quick calculation speed and dealing with the case in which the optimization is infeasible. But it can only deal with soft constraints. In order to deal with hard constraints and guarantee feasibility, an improved algorithm is proposed by recalculating the setpoint according to the hard constraints before calculating the manipulated variable and MWLS algorithm is used to satisfy the requirement of soft constraints for the system with the input constraints and output constraints. The algorithm can not only guarantee stability of the system and zero steady state error, but also satisfy the hard constraints of input and output variables. The simulation results show the improved algorithm is feasible and effective.
基金supported by Scientific Research and Technology Development Project of CNPC(2021DJ4002,2022DJ3908).
文摘The numerical dispersion phenomenon in the finite-difference forward modeling simulations of the wave equation significantly affects the imaging accuracy in acoustic reflection logging.This issue is particularly pronounced in the reverse time migration(RTM)method used for shear-wave(S-wave)logging imaging.This not only affects imaging accuracy but also introduces ambiguities in the interpretation of logging results.To address this challenge,this study proposes the use of a least-squares difference coefficient optimization algorithm aiming to suppress the numerical dispersion phenomenon in the RTM of S-wave reflection imaging logging.By optimizing the difference coefficients,the high-precision finite-difference algorithm serves as an effective operator for both forward and backward RTM processes.This approach is instrumental in eliminating migration illusions,which are often caused by numerical dispersion.The effectiveness of this optimized algorithm is demonstrated through numerical results,which indicate that it can achieve more accurate forward imaging results across various conditions,including high-and low-velocity strata,and is effective in both large and small spatial grids.The results of processing real data demonstrate that numerical dispersion optimization effectively reduces migration artifacts and diminishes ambiguities in logging interpretations.This optimization offers crucial technical support to the RTM method,enhancing its capability for accurately modeling and imaging S-wave reflections.
基金This project is supported by the National Natural Science Foundation of China
文摘This paper presents a new highly parallel algorithm for computing the minimum-norm least-squares solution of inconsistent linear equations Ax = b(A∈Rm×n,b∈R (A)). By this algorithm the solution x = A + b is obtained in T = n(log2m + log2(n - r + 1) + 5) + log2m + 1 steps with P=mn processors when m × 2(n - 1) and with P = 2n(n - 1) processors otherwise.
基金supported by the P.G.Senapathy Center for Computing Resources at IIT Madrasfunding provided by the Ministry of Education,Government of Indiasupported by the National Natural Science Foundation of China(Grant Nos.12388101,12472224 and 92252104).
文摘This paper presents an Eulerian-Lagrangian algorithm for direct numerical simulation(DNS)of particle-laden flows.The algorithm is applicable to perform simulations of dilute suspensions of small inertial particles in turbulent carrier flow.The Eulerian framework numerically resolves turbulent carrier flow using a parallelized,finite-volume DNS solver on a staggered Cartesian grid.Particles are tracked using a point-particle method utilizing a Lagrangian particle tracking(LPT)algorithm.The proposed Eulerian-Lagrangian algorithm is validated using an inertial particle-laden turbulent channel flow for different Stokes number cases.The particle concentration profiles and higher-order statistics of the carrier and dispersed phases agree well with the benchmark results.We investigated the effect of fluid velocity interpolation and numerical integration schemes of particle tracking algorithms on particle dispersion statistics.The suitability of fluid velocity interpolation schemes for predicting the particle dispersion statistics is discussed in the framework of the particle tracking algorithm coupled to the finite-volume solver.In addition,we present parallelization strategies implemented in the algorithm and evaluate their parallel performance.
基金supported by the Planned Project Grant(Grant No.3502Z20199064)from the Science and Technology Bureau of Xiamen(CN)the training project(Grant No.2020GGB067)of the youth and middle-aged talents of Fujian Provincial Health Commission(CN).
文摘This study explored the application value of iterative decomposition of water and fatwith echo asymmetry and least-squares estimation(IDEAL-IQ)technology in the early diagnosis of ageing osteoporosis(OP).172 participants were enrolled and underwentmagnetic resonance imaging(MRI)examinations on a 3.0T scanner.100 cases were included in the normal group(50 males and 50 females;mean age:45 years;age range:20e84 years).33 cases were included in the osteopenia group(17 males and 16 females;mean age:55 years;age range:43e83 years).39 caseswere includedintheOP group(19males and20females;meanage:58years;age range:48 e82 years).Conventional T1WI and T2WI were first obtained,followed by 3D-IDEAL-IQ-acqui-sition.Fat fraction(FF)and apparent transverse relaxation rate(R2*)resultswere automatically calculated from IDEAL-IQ-images on the console.Based on T1Wand T2W-images,300 ROIs for each participantweremanually delineated in L1-L5 vertebral bodies of five middle slices.In each age group of all normal subjects,each parameter was significantly correlated with gender.In male participants from the normal,osteopenia,and OP groups,statistical analysis revealed F values of 11319.292 and 180.130 for comparisons involving FF and R2*values,respectively(all p<0.0001).The sensitivity and specificity of FF values were 0.906 and 0.950,0.994 and 0.997,0.865 and 0.820,respectively.For R2*,they were 0.665 and 0.616,0.563 and 0.519,0.571 and 0.368,respectively.In female participants from the normal,osteopenia,and OP-groups,statis-tical analysis revealed F values of 12461.658 and 548.274 for comparisons involving FF and R2*values,respectively(all p<0.0001).The sensitivity and specificity of FF values were 0.985 and 0.991,0.996 and 0.996,0.581 and 0.678,respectively.For R2*,they were 0.698 and 0.730,0.603 and 0.665,0.622 and 0.525,respectively.Significant differences were indicated in the quanti-tative values among the three groups.FF value had good performance,while R2*value had poor performance indiscriminatingosteopenia andOP-groups.Overall,the IDEAL-IQ techniqueoffers specific reference indices that enable noninvasive and quantitative assessment of lumbar vertebrae bone metabolism,thereby providing diagnostic information for OP.
文摘Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.
文摘Tensor canonical decomposition （shorted as CANDECOMP/PARAFAC or CP） decomposes a tensor as a sum of rank-one tensors, which finds numerous applications in signal processing, hypergraph analysis, data analysis, etc. Alternating least-squares （ALS） is one of the most popular numerical algorithms for solving it. While there have been lots of efforts for enhancing its efficiency, in general its convergence can not been guaranteed. In this paper, we cooperate the ALS and the trust-region technique from optimization field to generate a trust-region-based alternating least-squares （TRALS） method for CP. Under mild assumptions, we prove that the whole iterative sequence generated by TRALS converges to a stationary point of CP. This thus provides a reasonable way to alleviate the swamps, the notorious phenomena of ALS that slow down the speed of the algorithm. Moreover, the trust region itself, in contrast to the regularization alternating least-squares （RALS） method, provides a self-adaptive way in choosing the parameter, which is essential for the efficiency of the algorithm. Our theoretical result is thus stronger than that of RALS in [26], which only proved the cluster point of the iterative sequence generated by RALS is a stationary point. In order to accelerate the new algorithm, we adopt an extrapolation scheme. We apply our algorithm to the amino acid fluorescence data decomposition from chemometrics, BCM decomposition and rank-（Lr, Lr, 1） decomposition arising from signal processing, and compare it with ALS and RALS. The numerical results show that TRALS is superior to ALS and RALS, both from the number of iterations and CPU time perspectives.
文摘Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints.Two new algorithms,the Red Kite Optimization Algorithm(ROA)and Secretary Bird Optimization Algorithm(SBOA),are utilized on five benchmark trusses with 10,18,37,72,and 200-bar trusses.Both algorithms are evaluated against benchmarks in the literature.The results indicate that SBOA always reaches a lighter optimal.Designs with reducing structural weight ranging from 0.02%to 0.15%compared to ROA,and up to 6%–8%as compared to conventional algorithms.In addition,SBOA can achieve 15%–20%faster convergence speed and 10%–18%reduction in computational time with a smaller standard deviation over independent runs,which demonstrates its robustness and reliability.It is indicated that the adaptive exploration mechanism of SBOA,especially its Levy flight–based search strategy,can obviously improve optimization performance for low-and high-dimensional trusses.The research has implications in the context of promoting bio-inspired optimization techniques by demonstrating the viability of SBOA,a reliable model for large-scale structural design that provides significant enhancements in performance and convergence behavior.
基金National Natural Science Foundation of China(32301712)Natural Science Foundation of Jiangsu Province(BK20230548+3 种基金BK20250876)Project of Faculty of Agricultural Equipment of Jiangsu University(NGXB20240203)A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD-2023-87)Open Funding Project of the Key Laboratory of Modern Agricultural Equipment and Technology(Jiangsu University),Ministry of Education(MAET202101)。
文摘Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT^(*)-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT^(*),the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT^(*).Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications.
基金supported by the Science and Technology Fund of TNU-Thai Nguyen University of Science.
文摘We study the split common solution problem with multiple output sets for monotone operator equations in Hilbert spaces.To solve this problem,we propose two new parallel algorithms.We establish a weak convergence theorem for the first and a strong convergence theorem for the second.
基金CHINA POSTDOCTORAL SCIENCE FOUNDATION(Grant No.2025M771925)Young Scientists Fund(C Class)(Grant No.32501636)Special Fund of Fundamental Scientific Research Business Expense for Higher School of Central Government(Grant No.2572025JT04).
文摘This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm.The algo-rithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japonicus.The math-ematical model is developed by simulating various biological behaviors of the Gekko japonicus,such as hybrid loco-motion patterns,directional olfactory guidance,implicit group advantage tendencies,and the tail autotomy mechanism.By integrating multi-stage mutual constraints and dynamically adjusting parameters,GJA maintains an optimal balance between global exploration and local exploitation,thereby effectively solving complex optimization problems.To assess the performance of GJA,comparative analyses were performed against fourteen state-of-the-art metaheuristic algorithms using the CEC2017 and CEC2022 benchmark test sets.Additionally,a Friedman test was performed on the experimen-tal results to assess the statistical significance of differences between various algorithms.And GJA was evaluated using multiple qualitative indicators,further confirming its superiority in exploration and exploitation.Finally,GJA was utilized to solve four engineering optimization problems and further implemented in robotic path planning to verify its practical applicability.Experimental results indicate that,compared to other high-performance algorithms,GJA demonstrates excep-tional performance as a powerful optimization algorithm in complex optimization problems.We make the code publicly available at:https://github.com/zhy1109/Gekko-japonicusalgorithm.
基金supported by the NSFC(Grant Nos.62176273,62271070,62441212)The Open Foundation of State Key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications)under Grant SKLNST-2024-1-062025Major Project of the Natural Science Foundation of Inner Mongolia(2025ZD008).
文摘The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,conventional clustering-based methods face notable drawbacks,including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions.To overcome the performance limitations of existing methods,this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm(SC-QGA)and an improved quantum artificial bee colony algorithm hybrid K-means(IQABC-K).First,the SC-QGA algorithmis constructed based on quantum computing and integrates similarity coefficient theory to strengthen genetic diversity and feature extraction capabilities.For the subsequent clustering phase,the process based on the IQABC-K algorithm is enhanced with the core improvement of adaptive rotation gate and movement exploitation strategies to balance the exploration capabilities of global search and the exploitation capabilities of local search.Simultaneously,the acceleration of convergence toward the global optimum and a reduction in computational complexity are facilitated by means of the global optimum bootstrap strategy and a linear population reduction strategy.Through experimental evaluation with multiple algorithms and diverse performance metrics,the proposed algorithm confirms reliable accuracy on three datasets:KDD CUP99,NSL_KDD,and UNSW_NB15,achieving accuracy of 98.57%,98.81%,and 98.32%,respectively.These results affirm its potential as an effective solution for practical clustering applications.
基金funding from the European Commission by the Ruralities project(grant agreement no.101060876).
文摘In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.
基金support from the National Key R&D Program of China(Grant No.2020YFB1711100).
文摘To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process,an integrated diagnostic method combining the gray wolf optimization(GWO)algorithm,support vector machine(SVM),and K-means clustering was proposed.Eight input parameters—derived from molten iron conditions and external factors—were selected as feature variables.A GWO-SVM model was developed to accurately predict the energy consumption of individual heats.Based on the prediction results,the mean absolute percentage error and maximum relative error of the test set were employed as criteria to identify heats with abnormal energy usage.For these heats,the K-means clustering algorithm was used to determine benchmark values of influencing factors from similar steel grades,enabling root-cause diagnosis of excessive energy consumption.The proposed method was applied to real production data from a converter in a steel plant.The analysis reveals that heat sample No.44 exhibits abnormal energy consumption,due to gas recovery being 1430.28 kg of standard coal below the benchmark level.A secondary contributing factor is a steam recovery shortfall of 237.99 kg of standard coal.This integrated approach offers a scientifically grounded tool for energy management in converter operations and provides valuable guidance for optimizing process parameters and enhancing energy efficiency.
基金supported by the National Natural Science Foundation of China under grant number 62066016the Natural Science Foundation of Hunan Province of China under grant number 2024JJ7395+2 种基金International and Regional Science and Technology Cooperation and Exchange Program of the Hunan Association for Science and Technology under grant number 025SKX-KJ-04Hunan Provincial Postgraduate Research Innovation Project under grant numberCX20251611Liye Qin Bamboo Slips Research Special Project of JishouUniversity 25LYY03.
文摘ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the algorithmhas attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation,coupled with advantages in real-time performance and robustness.Nevertheless,as applications have diversified,limitations in convergence precision and a tendency toward premature convergence have become increasingly evident,highlighting a need for improvement.This reviewsystematically outlines the developmental trajectory of the PIO algorithm,with a particular focus on its core applications in UAV navigation,multi-objective formulations,and a spectrum of variantmodels that have emerged in recent years.It offers a structured analysis of the foundational principles underlying the PIO.It conducts a comparative assessment of various performance-enhanced versions,including hybrid models that integrate mechanisms from other optimization paradigms.Additionally,the strengths andweaknesses of distinct PIOvariants are critically examined frommultiple perspectives,including intrinsic algorithmic characteristics,suitability for specific application scenarios,objective function design,and the rigor of the statistical evaluation methodologies employed in empirical studies.Finally,this paper identifies principal challenges within current PIO research and proposes several prospective research directions.Future work should focus on mitigating premature convergence by refining the two-phase search structure and adjusting the exponential decrease of individual numbers during the landmark operator.Enhancing parameter adaptation strategies,potentially using reinforcement learning for dynamic tuning,and advancing theoretical analyses on convergence and complexity are also critical.Further applications should be explored in constrained path planning,Neural Architecture Search(NAS),and other real-worldmulti-objective problems.For Multi-objective PIO(MPIO),key improvements include controlling the growth of the external archive and designing more effective selection mechanisms to maintain convergence efficiency.These efforts are expected to strengthen both the theoretical foundation and practical versatility of PIO and its variants.
文摘In recent years,Blockchain Technology has become a paradigm shift,providing Transparent,Secure,and Decentralized platforms for diverse applications,ranging from Cryptocurrency to supply chain management.Nevertheless,the optimization of blockchain networks remains a critical challenge due to persistent issues such as latency,scalability,and energy consumption.This study proposes an innovative approach to Blockchain network optimization,drawing inspiration from principles of biological evolution and natural selection through evolutionary algorithms.Specifically,we explore the application of genetic algorithms,particle swarm optimization,and related evolutionary techniques to enhance the performance of blockchain networks.The proposed methodologies aim to optimize consensus mechanisms,improve transaction throughput,and reduce resource consumption.Through extensive simulations and real-world experiments,our findings demonstrate significant improvements in network efficiency,scalability,and stability.This research offers a thorough analysis of existing optimization techniques,introduces novel strategies,and assesses their efficacy based on empirical outputs.
