In this study,a completely different approach to optimization is introduced through the development of a novel metaheuristic algorithm called the Barber Optimization Algorithm(BaOA).Inspired by the human interactions ...In this study,a completely different approach to optimization is introduced through the development of a novel metaheuristic algorithm called the Barber Optimization Algorithm(BaOA).Inspired by the human interactions between barbers and customers,BaOA captures two key processes:the customer’s selection of a hairstyle and the detailed refinement during the haircut.These processes are translated into a mathematical framework that forms the foundation of BaOA,consisting of two critical phases:exploration,representing the creative selection process,and exploitation,which focuses on refining details for optimization.The performance of BaOA is evaluated using 52 standard benchmark functions,including unimodal,high-dimensional multimodal,fixed-dimensional multimodal,and the Congress on Evolutionary Computation(CEC)2017 test suite.This comprehensive assessment highlights BaOA’s ability to balance exploration and exploitation effectively,resulting in high-quality solutions.A comparative analysis against twelve widely known metaheuristic algorithms further demonstrates BaOA’s superior performance,as it consistently delivers better results across most benchmark functions.To validate its real-world applicability,BaOA is tested on four engineering design problems,illustrating its capability to address practical challenges with remarkable efficiency.The results confirm BaOA’s versatility and reliability as an optimization tool.This study not only introduces an innovative algorithm but also establishes its effectiveness in solving complex problems,providing a foundation for future research and applications in diverse scientific and engineering domains.展开更多
This paper investigates the optimization of data sampling and target labeling techniques to enhance algorithmic trading strategies in cryptocurrency markets,focusing on Bitcoin(BTC)and Ethereum(ETH).Traditional data s...This paper investigates the optimization of data sampling and target labeling techniques to enhance algorithmic trading strategies in cryptocurrency markets,focusing on Bitcoin(BTC)and Ethereum(ETH).Traditional data sampling methods,such as time bars,often fail to capture the nuances of the continuously active and highly volatile cryptocurrency market and force traders to wait for arbitrary points in time.To address this,we propose an alternative approach using information-driven sampling methods,including the CUSUM filter,range bars,volume bars,and dollar bars,and evaluate their performance using tick-level data from January 2018 to June 2023.Additionally,we introduce the Triple Barrier method for target labeling,which offers a solution tailored for algorithmic trading as opposed to the widely used next-bar prediction.We empirically assess the effectiveness of these data sampling and labeling methods to craft profitable trading strategies.The results demonstrate that the innovative combination of CUSUM-filtered data with Triple Barrier labeling outperforms traditional time bars and next-bar prediction,achieving consistently positive trading performance even after accounting for transaction costs.Moreover,our system enables making trading decisions at any point in time on the basis of market conditions,providing an advantage over traditional methods that rely on fixed time intervals.Furthermore,the paper contributes to the ongoing debate on the applicability of Transformer models to time series classification in the context of algorithmic trading by evaluating various Transformer architectures—including the vanilla Transformer encoder,FEDformer,and Autoformer—alongside other deep learning architectures and classical machine learning models,revealing insights into their relative performance.展开更多
A bandwidth-exchange cooperation algorithm based on the Nash bargaining solution （NBS） is proposed to encourage the selfish users to participate with more cooperation so as to improve the users＇ energy efficiency. ...A bandwidth-exchange cooperation algorithm based on the Nash bargaining solution （NBS） is proposed to encourage the selfish users to participate with more cooperation so as to improve the users＇ energy efficiency. As a result, two key problems, i.e. , when to cooperate and how to cooperate, are solved. For the first problem, a proposed cooperation condition that can decide when to cooperate and guarantee users＇ energy efficiency achieved through cooperation is not lower than that achieved without cooperation. For the second problem, the cooperation bandwidth allocations （CBAs） based on the NBS solve the problem how to cooperate when cooperation takes place. Simulation results show that, as the modulation order of quadrature amplitude modulation （QAM） increases, the cooperation between both users only occurs with a large signal-to-noise ratio （SNR）. Meanwhile, the energy efficiency decreases as the modulation order increases. Despite all this, the proposed algorithm can obviously improve the energy efficiency measured in bits-per-Joule compared with non-cooperation.展开更多
In order to overcome the limitations of a unitary reference station in mobile communication positioning network differential barometric altimetry(DBA)and broaden the action scope of the reference station and improve p...In order to overcome the limitations of a unitary reference station in mobile communication positioning network differential barometric altimetry(DBA)and broaden the action scope of the reference station and improve positioning accuracy of elevation,an integrated interpolation algorithm model based on generalized extended approximation(GEA)algorithm and Kriging interpolation in time-space domain of reference station is proposed.In the time domain,barometric measured data is considered the maximum value estimated by bilateral extension to avoid wrong direction of estimation,which is approaching true value.In the spatial domain,barometric relevance among multiple reference stations is utilized,the weighted coefficients of multiple reference stations is calculated by the integrated algorithm model based on the GEA algorithm and Kriging interpolation.The impact of each reference station to the measured station is quantified,so that a virtual reference station is constructed,which can overcome the limitation of barometric correction by a unitary reference station.In addition,the measurement error due to irregular change in atmospheric pressure will be eliminated.展开更多
This paper proposes an optimal deployment method of heterogeneous multistatic radars to construct arc barrier coverage with location restrictions.This method analyzes and proves the properties of different deployment ...This paper proposes an optimal deployment method of heterogeneous multistatic radars to construct arc barrier coverage with location restrictions.This method analyzes and proves the properties of different deployment patterns in the optimal deployment sequence.Based on these properties and considering location restrictions,it introduces an optimization model of arc barrier coverage and aims to minimize the total deployment cost of heterogeneous multistatic radars.To overcome the non-convexity of the model and the non-analytical nature of the objective function,an algorithm combining integer line programming and the cuckoo search algorithm(CSA)is proposed.The proposed algorithm can determine the number of receivers and transmitters in each optimal deployment squence to minimize the total placement cost.Simulations are conducted in different conditions to verify the effectiveness of the proposed method.展开更多
The Salp Swarm Algorithm(SSA)may have trouble in dropping into stagnation as a kind of swarm intelligence method.This paper developed an adaptive barebones salp swarm algorithm with quasi-oppositional-based learning t...The Salp Swarm Algorithm(SSA)may have trouble in dropping into stagnation as a kind of swarm intelligence method.This paper developed an adaptive barebones salp swarm algorithm with quasi-oppositional-based learning to compensate for the above weakness called QBSSA.In the proposed QBSSA,an adaptive barebones strategy can help to reach both accurate convergence speed and high solution quality;quasi-oppositional-based learning can make the population away from traping into local optimal and expand the search space.To estimate the performance of the presented method,a series of tests are performed.Firstly,CEC 2017 benchmark test suit is used to test the ability to solve the high dimensional and multimodal problems;then,based on QBSSA,an improved Kernel Extreme Learning Machine(KELM)model,named QBSSA–KELM,is built to handle medical disease diagnosis problems.All the test results and discussions state clearly that the QBSSA is superior to and very competitive to all the compared algorithms on both convergence speed and solutions accuracy.展开更多
The exponentially-distributed random timestepping algorithm with boundary test is implemented to evaluate the prices of some variety of single one-sided barrier option contracts within the framework of Black-Scholes m...The exponentially-distributed random timestepping algorithm with boundary test is implemented to evaluate the prices of some variety of single one-sided barrier option contracts within the framework of Black-Scholes model, giving efficient estimation of their hitting times. It is numerically shown that this algorithm, as for the Brownian bridge technique, can improve the rate of weak convergence from order one-half for the standard Monte Carlo to order 1. The exponential timestepping algorithm, however, displays better results, for a given amount of CPU time, than the Brownian bridge technique as the step size becomes larger or the volatility grows up. This is due to the features of the exponential distribution which is more strongly peaked near the origin and has a higher kurtosis compared to the normal distribution, giving more stability of the exponential timestepping algorithm at large time steps and high levels of volatility.展开更多
Unstructured and irregular graph data causes strong randomness and poor locality of data accesses in graph processing.This paper optimizes the depth-branch-resorting algorithm(DBR),and proposes a branch-alternation-re...Unstructured and irregular graph data causes strong randomness and poor locality of data accesses in graph processing.This paper optimizes the depth-branch-resorting algorithm(DBR),and proposes a branch-alternation-resorting algorithm(BAR).In order to make the algorithm run in parallel and improve the efficiency of algorithm operation,the BAR algorithm is mapped onto the reconfigurable array processor(APR-16)to achieve vertex reordering,effectively improving the locality of graph data.This paper validates the BAR algorithm on the GraphBIG framework,by utilizing the reordered dataset with BAR on breadth-first search(BFS),single source shortest paht(SSSP)and betweenness centrality(BC)algorithms for traversal.The results show that compared with DBR and Corder algorithms,BAR can reduce execution time by up to 33.00%,and 51.00%seperatively.In terms of data movement,the BAR algorithm has a maximum reduction of 39.00%compared with the DBR algorithm and 29.66%compared with Corder algorithm.In terms of computational complexity,the BAR algorithm has a maximum reduction of 32.56%compared with DBR algorithm and53.05%compared with Corder algorithm.展开更多
Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,curr...Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.展开更多
Forming nulls in the radiation pattern of an antenna is to suppress the interference from certain directions or to cancel desired signals. This letter presents an effective method based on the Gram-Schmidt orthogonal ...Forming nulls in the radiation pattern of an antenna is to suppress the interference from certain directions or to cancel desired signals. This letter presents an effective method based on the Gram-Schmidt orthogonal algorithm for fonning nulls in the subarray of a large array. The theory and realizing methods are discussed, and the corresponding fonnulas are derived in details. The simulation results are given to demonstrate the feasibility, the availability and the quick-convergence of the presented method.展开更多
Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is pro...Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is proposed that takes advantage of a few parameters of bare-bones algorithm. To avoid premature convergence,Gaussian mutation is introduced; and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover, a circular crowded sorting approach is adopted to improve the uniformity of the population distribution.Finally, by combining the algorithm with control vector parameterization,an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multiobjective optimization algorithms through the results of solving three dynamic optimization problems.展开更多
Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,th...Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction.To improve the accuracy of short-term wind power forecast,this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer,which is based on seasonal and trend decomposition using LOESS(STL)and iTransformer model optimized by improved arithmetic optimization algorithm(IAOA).First,to fully extract the power data features,STL is used to decompose the original data into components with less redundant information.The extracted components as well as the weather data are then input into iTransformer for short-term wind power forecast.The final predicted short-term wind power curve is obtained by combining the predicted components.To improve the model accuracy,IAOA is employed to optimize the hyperparameters of iTransformer.The proposed approach is validated using real-generation data from different seasons and different power stations inNorthwest China,and ablation experiments have been conducted.Furthermore,to validate the superiority of the proposed approach under different wind characteristics,real power generation data fromsouthwestChina are utilized for experiments.Thecomparative results with the other six state-of-the-art prediction models in experiments show that the proposed model well fits the true value of generation series and achieves high prediction accuracy.展开更多
In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms...In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms to solve the problem of multi-UAV path planning.The Dung Beetle Optimization(DBO)algorithm has been widely applied due to its diverse search patterns in the above algorithms.However,the update strategies for the rolling and thieving dung beetles of the DBO algorithm are overly simplistic,potentially leading to an inability to fully explore the search space and a tendency to converge to local optima,thereby not guaranteeing the discovery of the optimal path.To address these issues,we propose an improved DBO algorithm guided by the Landmark Operator(LODBO).Specifically,we first use tent mapping to update the population strategy,which enables the algorithm to generate initial solutions with enhanced diversity within the search space.Second,we expand the search range of the rolling ball dung beetle by using the landmark factor.Finally,by using the adaptive factor that changes with the number of iterations.,we improve the global search ability of the stealing dung beetle,making it more likely to escape from local optima.To verify the effectiveness of the proposed method,extensive simulation experiments are conducted,and the result shows that the LODBO algorithm can obtain the optimal path using the shortest time compared with the Genetic Algorithm(GA),the Gray Wolf Optimizer(GWO),the Whale Optimization Algorithm(WOA)and the original DBO algorithm in the disaster search and rescue task set.展开更多
Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered so...Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.展开更多
BACKGROUND Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer.Many molecular genetic changes are associated with its occurrence.Raman spectroscopy has become a new method for the e...BACKGROUND Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer.Many molecular genetic changes are associated with its occurrence.Raman spectroscopy has become a new method for the early diagnosis of tumors because it can reflect the structures of substances and their changes at the molecular level.AIM To detect alterations in Raman spectral information across different stages of esophageal neoplasia.METHODS Different grades of esophageal lesions were collected,and a total of 360 groups of Raman spectrum data were collected.A 1D-transformer network model was proposed to handle the task of classifying the spectral data of esophageal squamous cell carcinoma.In addition,a deep learning model was applied to visualize the Raman spectral data and interpret their molecular characteristics.RESULTS A comparison among Raman spectral data with different pathological grades and a visual analysis revealed that the Raman peaks with significant differences were concentrated mainly at 1095 cm^(-1)(DNA,symmetric PO,and stretching vibration),1132 cm^(-1)(cytochrome c),1171 cm^(-1)(acetoacetate),1216 cm^(-1)(amide III),and 1315 cm^(-1)(glycerol).A comparison among the training results of different models revealed that the 1Dtransformer network performed best.A 93.30%accuracy value,a 96.65%specificity value,a 93.30%sensitivity value,and a 93.17%F1 score were achieved.CONCLUSION Raman spectroscopy revealed significantly different waveforms for the different stages of esophageal neoplasia.The combination of Raman spectroscopy and deep learning methods could significantly improve the accuracy of classification.展开更多
The problem of collision avoidance for non-cooperative targets has received significant attention from researchers in recent years.Non-cooperative targets exhibit uncertain states and unpredictable behaviors,making co...The problem of collision avoidance for non-cooperative targets has received significant attention from researchers in recent years.Non-cooperative targets exhibit uncertain states and unpredictable behaviors,making collision avoidance significantly more challenging than that for space debris.Much existing research focuses on the continuous thrust model,whereas the impulsive maneuver model is more appropriate for long-duration and long-distance avoidance missions.Additionally,it is important to minimize the impact on the original mission while avoiding noncooperative targets.On the other hand,the existing avoidance algorithms are computationally complex and time-consuming especially with the limited computing capability of the on-board computer,posing challenges for practical engineering applications.To conquer these difficulties,this paper makes the following key contributions:(A)a turn-based(sequential decision-making)limited-area impulsive collision avoidance model considering the time delay of precision orbit determination is established for the first time;(B)a novel Selection Probability Learning Adaptive Search-depth Search Tree(SPL-ASST)algorithm is proposed for non-cooperative target avoidance,which improves the decision-making efficiency by introducing an adaptive-search-depth mechanism and a neural network into the traditional Monte Carlo Tree Search(MCTS).Numerical simulations confirm the effectiveness and efficiency of the proposed method.展开更多
A new method based on the iterative adaptive algorithm(IAA)and blocking matrix preprocessing(BMP)is proposed to study the suppression of multi-mainlobe interference.The algorithm is applied to precisely estimate the s...A new method based on the iterative adaptive algorithm(IAA)and blocking matrix preprocessing(BMP)is proposed to study the suppression of multi-mainlobe interference.The algorithm is applied to precisely estimate the spatial spectrum and the directions of arrival(DOA)of interferences to overcome the drawbacks associated with conventional adaptive beamforming(ABF)methods.The mainlobe interferences are identified by calculating the correlation coefficients between direction steering vectors(SVs)and rejected by the BMP pretreatment.Then,IAA is subsequently employed to reconstruct a sidelobe interference-plus-noise covariance matrix for the preferable ABF and residual interference suppression.Simulation results demonstrate the excellence of the proposed method over normal methods based on BMP and eigen-projection matrix perprocessing(EMP)under both uncorrelated and coherent circumstances.展开更多
To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The...To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The inspection robot utilizes multiple sensors to monitor key parameters of the fans,such as vibration,noise,and bearing temperature,and upload the data to the monitoring center.The robot’s inspection path employs the improved A^(*)algorithm,incorporating obstacle penalty terms,path reconstruction,and smoothing optimization techniques,thereby achieving optimal path planning for the inspection robot in complex environments.Simulation results demonstrate that the improved A^(*)algorithm significantly outperforms the traditional A^(*)algorithm in terms of total path distance,smoothness,and detour rate,effectively improving the execution efficiency of inspection tasks.展开更多
The word“spatial”fundamentally relates to human existence,evolution,and activity in terrestrial and even celestial spaces.After reviewing the spatial features of many areas,the paper describes basics of high level m...The word“spatial”fundamentally relates to human existence,evolution,and activity in terrestrial and even celestial spaces.After reviewing the spatial features of many areas,the paper describes basics of high level model and technology called Spatial Grasp for dealing with large distributed systems,which can provide spatial vision,awareness,management,control,and even consciousness.The technology description includes its key Spatial Grasp Language(SGL),self-evolution of recursive SGL scenarios,and implementation of SGL interpreter converting distributed networked systems into powerful spatial engines.Examples of typical spatial scenarios in SGL include finding shortest path tree and shortest path between network nodes,collecting proper information throughout the whole world,elimination of multiple targets by intelligent teams of chasers,and withstanding cyber attacks in distributed networked systems.Also this paper compares Spatial Grasp model with traditional algorithms,confirming universality of the former for any spatial systems,while the latter just tools for concrete applications.展开更多
文摘In this study,a completely different approach to optimization is introduced through the development of a novel metaheuristic algorithm called the Barber Optimization Algorithm(BaOA).Inspired by the human interactions between barbers and customers,BaOA captures two key processes:the customer’s selection of a hairstyle and the detailed refinement during the haircut.These processes are translated into a mathematical framework that forms the foundation of BaOA,consisting of two critical phases:exploration,representing the creative selection process,and exploitation,which focuses on refining details for optimization.The performance of BaOA is evaluated using 52 standard benchmark functions,including unimodal,high-dimensional multimodal,fixed-dimensional multimodal,and the Congress on Evolutionary Computation(CEC)2017 test suite.This comprehensive assessment highlights BaOA’s ability to balance exploration and exploitation effectively,resulting in high-quality solutions.A comparative analysis against twelve widely known metaheuristic algorithms further demonstrates BaOA’s superior performance,as it consistently delivers better results across most benchmark functions.To validate its real-world applicability,BaOA is tested on four engineering design problems,illustrating its capability to address practical challenges with remarkable efficiency.The results confirm BaOA’s versatility and reliability as an optimization tool.This study not only introduces an innovative algorithm but also establishes its effectiveness in solving complex problems,providing a foundation for future research and applications in diverse scientific and engineering domains.
基金support of the University of Warsaw under’New Ideas 3B’competition in POB Ⅲ implemented under the’Excellence Initiative-Research University’Programme.
文摘This paper investigates the optimization of data sampling and target labeling techniques to enhance algorithmic trading strategies in cryptocurrency markets,focusing on Bitcoin(BTC)and Ethereum(ETH).Traditional data sampling methods,such as time bars,often fail to capture the nuances of the continuously active and highly volatile cryptocurrency market and force traders to wait for arbitrary points in time.To address this,we propose an alternative approach using information-driven sampling methods,including the CUSUM filter,range bars,volume bars,and dollar bars,and evaluate their performance using tick-level data from January 2018 to June 2023.Additionally,we introduce the Triple Barrier method for target labeling,which offers a solution tailored for algorithmic trading as opposed to the widely used next-bar prediction.We empirically assess the effectiveness of these data sampling and labeling methods to craft profitable trading strategies.The results demonstrate that the innovative combination of CUSUM-filtered data with Triple Barrier labeling outperforms traditional time bars and next-bar prediction,achieving consistently positive trading performance even after accounting for transaction costs.Moreover,our system enables making trading decisions at any point in time on the basis of market conditions,providing an advantage over traditional methods that rely on fixed time intervals.Furthermore,the paper contributes to the ongoing debate on the applicability of Transformer models to time series classification in the context of algorithmic trading by evaluating various Transformer architectures—including the vanilla Transformer encoder,FEDformer,and Autoformer—alongside other deep learning architectures and classical machine learning models,revealing insights into their relative performance.
基金The National Natural Science Foundation of China(No.61201143)Innovation Foundations of CAST(ITS)(No.F-WYY-2013-016)the Fundamental Research Funds for the Central Universities(No.HIT.IBRSEM.201309)
文摘A bandwidth-exchange cooperation algorithm based on the Nash bargaining solution （NBS） is proposed to encourage the selfish users to participate with more cooperation so as to improve the users＇ energy efficiency. As a result, two key problems, i.e. , when to cooperate and how to cooperate, are solved. For the first problem, a proposed cooperation condition that can decide when to cooperate and guarantee users＇ energy efficiency achieved through cooperation is not lower than that achieved without cooperation. For the second problem, the cooperation bandwidth allocations （CBAs） based on the NBS solve the problem how to cooperate when cooperation takes place. Simulation results show that, as the modulation order of quadrature amplitude modulation （QAM） increases, the cooperation between both users only occurs with a large signal-to-noise ratio （SNR）. Meanwhile, the energy efficiency decreases as the modulation order increases. Despite all this, the proposed algorithm can obviously improve the energy efficiency measured in bits-per-Joule compared with non-cooperation.
基金Supported by the National Key Research Program of China"Collaborative Precision Positioning Project"(2016YFB 0501900)the National Natural Science Foundation of China(11603041)+1 种基金Key Laboratory of Cognitive Radio and Information Processing,Ministry of Education(Guilin University of Electronic Technology)Guangxi Key Laboratory of Precision Navigation Technology and Application
文摘In order to overcome the limitations of a unitary reference station in mobile communication positioning network differential barometric altimetry(DBA)and broaden the action scope of the reference station and improve positioning accuracy of elevation,an integrated interpolation algorithm model based on generalized extended approximation(GEA)algorithm and Kriging interpolation in time-space domain of reference station is proposed.In the time domain,barometric measured data is considered the maximum value estimated by bilateral extension to avoid wrong direction of estimation,which is approaching true value.In the spatial domain,barometric relevance among multiple reference stations is utilized,the weighted coefficients of multiple reference stations is calculated by the integrated algorithm model based on the GEA algorithm and Kriging interpolation.The impact of each reference station to the measured station is quantified,so that a virtual reference station is constructed,which can overcome the limitation of barometric correction by a unitary reference station.In addition,the measurement error due to irregular change in atmospheric pressure will be eliminated.
基金supported by the National Natural Science Foundation of China(61971470).
文摘This paper proposes an optimal deployment method of heterogeneous multistatic radars to construct arc barrier coverage with location restrictions.This method analyzes and proves the properties of different deployment patterns in the optimal deployment sequence.Based on these properties and considering location restrictions,it introduces an optimization model of arc barrier coverage and aims to minimize the total deployment cost of heterogeneous multistatic radars.To overcome the non-convexity of the model and the non-analytical nature of the objective function,an algorithm combining integer line programming and the cuckoo search algorithm(CSA)is proposed.The proposed algorithm can determine the number of receivers and transmitters in each optimal deployment squence to minimize the total placement cost.Simulations are conducted in different conditions to verify the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(62076185,U1809209)supported by Zhejiang Provincial Natural Science Foundation of China(LY21F020030)+1 种基金Wenzhou Major Scientific and Technological Innovation Project(ZY2019019)Wenzhou Science and Technology Bureau(2018ZG016)。
文摘The Salp Swarm Algorithm(SSA)may have trouble in dropping into stagnation as a kind of swarm intelligence method.This paper developed an adaptive barebones salp swarm algorithm with quasi-oppositional-based learning to compensate for the above weakness called QBSSA.In the proposed QBSSA,an adaptive barebones strategy can help to reach both accurate convergence speed and high solution quality;quasi-oppositional-based learning can make the population away from traping into local optimal and expand the search space.To estimate the performance of the presented method,a series of tests are performed.Firstly,CEC 2017 benchmark test suit is used to test the ability to solve the high dimensional and multimodal problems;then,based on QBSSA,an improved Kernel Extreme Learning Machine(KELM)model,named QBSSA–KELM,is built to handle medical disease diagnosis problems.All the test results and discussions state clearly that the QBSSA is superior to and very competitive to all the compared algorithms on both convergence speed and solutions accuracy.
文摘The exponentially-distributed random timestepping algorithm with boundary test is implemented to evaluate the prices of some variety of single one-sided barrier option contracts within the framework of Black-Scholes model, giving efficient estimation of their hitting times. It is numerically shown that this algorithm, as for the Brownian bridge technique, can improve the rate of weak convergence from order one-half for the standard Monte Carlo to order 1. The exponential timestepping algorithm, however, displays better results, for a given amount of CPU time, than the Brownian bridge technique as the step size becomes larger or the volatility grows up. This is due to the features of the exponential distribution which is more strongly peaked near the origin and has a higher kurtosis compared to the normal distribution, giving more stability of the exponential timestepping algorithm at large time steps and high levels of volatility.
基金the National Key R&D Program of China(No.2022ZD0119001)the National Natural Science Foundation of China(No.61834005)+3 种基金the Shaanxi Province Key R&D Plan(No.2022GY-027)the Key Scientific Research Project of Shaanxi Department of Education(No.22JY060)the Education Research Project of XUPT(No.JGA202108)the Graduate Student Innovation Fund of Xi'an University of Posts and Telecommunications(No.CXJJZL2022011)。
文摘Unstructured and irregular graph data causes strong randomness and poor locality of data accesses in graph processing.This paper optimizes the depth-branch-resorting algorithm(DBR),and proposes a branch-alternation-resorting algorithm(BAR).In order to make the algorithm run in parallel and improve the efficiency of algorithm operation,the BAR algorithm is mapped onto the reconfigurable array processor(APR-16)to achieve vertex reordering,effectively improving the locality of graph data.This paper validates the BAR algorithm on the GraphBIG framework,by utilizing the reordered dataset with BAR on breadth-first search(BFS),single source shortest paht(SSSP)and betweenness centrality(BC)algorithms for traversal.The results show that compared with DBR and Corder algorithms,BAR can reduce execution time by up to 33.00%,and 51.00%seperatively.In terms of data movement,the BAR algorithm has a maximum reduction of 39.00%compared with the DBR algorithm and 29.66%compared with Corder algorithm.In terms of computational complexity,the BAR algorithm has a maximum reduction of 32.56%compared with DBR algorithm and53.05%compared with Corder algorithm.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant(No.51677058).
文摘Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.
文摘Forming nulls in the radiation pattern of an antenna is to suppress the interference from certain directions or to cancel desired signals. This letter presents an effective method based on the Gram-Schmidt orthogonal algorithm for fonning nulls in the subarray of a large array. The theory and realizing methods are discussed, and the corresponding fonnulas are derived in details. The simulation results are given to demonstrate the feasibility, the availability and the quick-convergence of the presented method.
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
基金National Natural Science Foundations of China(Nos.61222303,21276078)National High-Tech Research and Development Program of China(No.2012AA040307)+1 种基金New Century Excellent Researcher Award Program from Ministry of Education of China(No.NCET10-0885)the Fundamental Research Funds for the Central Universities and Shanghai Leading Academic Discipline Project,China(No.B504)
文摘Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is proposed that takes advantage of a few parameters of bare-bones algorithm. To avoid premature convergence,Gaussian mutation is introduced; and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover, a circular crowded sorting approach is adopted to improve the uniformity of the population distribution.Finally, by combining the algorithm with control vector parameterization,an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multiobjective optimization algorithms through the results of solving three dynamic optimization problems.
基金supported by Yunnan Provincial Basic Research Project(202401AT070344,202301AT070443)National Natural Science Foundation of China(62263014,52207105)+1 种基金Yunnan Lancang-Mekong International Electric Power Technology Joint Laboratory(202203AP140001)Major Science and Technology Projects in Yunnan Province(202402AG050006).
文摘Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction.To improve the accuracy of short-term wind power forecast,this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer,which is based on seasonal and trend decomposition using LOESS(STL)and iTransformer model optimized by improved arithmetic optimization algorithm(IAOA).First,to fully extract the power data features,STL is used to decompose the original data into components with less redundant information.The extracted components as well as the weather data are then input into iTransformer for short-term wind power forecast.The final predicted short-term wind power curve is obtained by combining the predicted components.To improve the model accuracy,IAOA is employed to optimize the hyperparameters of iTransformer.The proposed approach is validated using real-generation data from different seasons and different power stations inNorthwest China,and ablation experiments have been conducted.Furthermore,to validate the superiority of the proposed approach under different wind characteristics,real power generation data fromsouthwestChina are utilized for experiments.Thecomparative results with the other six state-of-the-art prediction models in experiments show that the proposed model well fits the true value of generation series and achieves high prediction accuracy.
基金supported by the National Natural Science Foundation of China(No.62373027).
文摘In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms to solve the problem of multi-UAV path planning.The Dung Beetle Optimization(DBO)algorithm has been widely applied due to its diverse search patterns in the above algorithms.However,the update strategies for the rolling and thieving dung beetles of the DBO algorithm are overly simplistic,potentially leading to an inability to fully explore the search space and a tendency to converge to local optima,thereby not guaranteeing the discovery of the optimal path.To address these issues,we propose an improved DBO algorithm guided by the Landmark Operator(LODBO).Specifically,we first use tent mapping to update the population strategy,which enables the algorithm to generate initial solutions with enhanced diversity within the search space.Second,we expand the search range of the rolling ball dung beetle by using the landmark factor.Finally,by using the adaptive factor that changes with the number of iterations.,we improve the global search ability of the stealing dung beetle,making it more likely to escape from local optima.To verify the effectiveness of the proposed method,extensive simulation experiments are conducted,and the result shows that the LODBO algorithm can obtain the optimal path using the shortest time compared with the Genetic Algorithm(GA),the Gray Wolf Optimizer(GWO),the Whale Optimization Algorithm(WOA)and the original DBO algorithm in the disaster search and rescue task set.
文摘Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.
基金Supported by Beijing Hospitals Authority Youth Programme,No.QML20200505.
文摘BACKGROUND Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer.Many molecular genetic changes are associated with its occurrence.Raman spectroscopy has become a new method for the early diagnosis of tumors because it can reflect the structures of substances and their changes at the molecular level.AIM To detect alterations in Raman spectral information across different stages of esophageal neoplasia.METHODS Different grades of esophageal lesions were collected,and a total of 360 groups of Raman spectrum data were collected.A 1D-transformer network model was proposed to handle the task of classifying the spectral data of esophageal squamous cell carcinoma.In addition,a deep learning model was applied to visualize the Raman spectral data and interpret their molecular characteristics.RESULTS A comparison among Raman spectral data with different pathological grades and a visual analysis revealed that the Raman peaks with significant differences were concentrated mainly at 1095 cm^(-1)(DNA,symmetric PO,and stretching vibration),1132 cm^(-1)(cytochrome c),1171 cm^(-1)(acetoacetate),1216 cm^(-1)(amide III),and 1315 cm^(-1)(glycerol).A comparison among the training results of different models revealed that the 1Dtransformer network performed best.A 93.30%accuracy value,a 96.65%specificity value,a 93.30%sensitivity value,and a 93.17%F1 score were achieved.CONCLUSION Raman spectroscopy revealed significantly different waveforms for the different stages of esophageal neoplasia.The combination of Raman spectroscopy and deep learning methods could significantly improve the accuracy of classification.
基金co-supported by the Foundation of Shanghai Astronautics Science and Technology Innovation,China(No.SAST2022-114)the National Natural Science Foundation of China(No.62303378),the National Natural Science Foundation of China(Nos.124B2031,12202281)the Foundation of China National Key Laboratory of Science and Technology on Test Physics&Numerical Mathematics,China(No.08-YY-2023-R11)。
文摘The problem of collision avoidance for non-cooperative targets has received significant attention from researchers in recent years.Non-cooperative targets exhibit uncertain states and unpredictable behaviors,making collision avoidance significantly more challenging than that for space debris.Much existing research focuses on the continuous thrust model,whereas the impulsive maneuver model is more appropriate for long-duration and long-distance avoidance missions.Additionally,it is important to minimize the impact on the original mission while avoiding noncooperative targets.On the other hand,the existing avoidance algorithms are computationally complex and time-consuming especially with the limited computing capability of the on-board computer,posing challenges for practical engineering applications.To conquer these difficulties,this paper makes the following key contributions:(A)a turn-based(sequential decision-making)limited-area impulsive collision avoidance model considering the time delay of precision orbit determination is established for the first time;(B)a novel Selection Probability Learning Adaptive Search-depth Search Tree(SPL-ASST)algorithm is proposed for non-cooperative target avoidance,which improves the decision-making efficiency by introducing an adaptive-search-depth mechanism and a neural network into the traditional Monte Carlo Tree Search(MCTS).Numerical simulations confirm the effectiveness and efficiency of the proposed method.
基金The National Natural Science Foundation of China(No.U19B2031).
文摘A new method based on the iterative adaptive algorithm(IAA)and blocking matrix preprocessing(BMP)is proposed to study the suppression of multi-mainlobe interference.The algorithm is applied to precisely estimate the spatial spectrum and the directions of arrival(DOA)of interferences to overcome the drawbacks associated with conventional adaptive beamforming(ABF)methods.The mainlobe interferences are identified by calculating the correlation coefficients between direction steering vectors(SVs)and rejected by the BMP pretreatment.Then,IAA is subsequently employed to reconstruct a sidelobe interference-plus-noise covariance matrix for the preferable ABF and residual interference suppression.Simulation results demonstrate the excellence of the proposed method over normal methods based on BMP and eigen-projection matrix perprocessing(EMP)under both uncorrelated and coherent circumstances.
文摘To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The inspection robot utilizes multiple sensors to monitor key parameters of the fans,such as vibration,noise,and bearing temperature,and upload the data to the monitoring center.The robot’s inspection path employs the improved A^(*)algorithm,incorporating obstacle penalty terms,path reconstruction,and smoothing optimization techniques,thereby achieving optimal path planning for the inspection robot in complex environments.Simulation results demonstrate that the improved A^(*)algorithm significantly outperforms the traditional A^(*)algorithm in terms of total path distance,smoothness,and detour rate,effectively improving the execution efficiency of inspection tasks.
文摘The word“spatial”fundamentally relates to human existence,evolution,and activity in terrestrial and even celestial spaces.After reviewing the spatial features of many areas,the paper describes basics of high level model and technology called Spatial Grasp for dealing with large distributed systems,which can provide spatial vision,awareness,management,control,and even consciousness.The technology description includes its key Spatial Grasp Language(SGL),self-evolution of recursive SGL scenarios,and implementation of SGL interpreter converting distributed networked systems into powerful spatial engines.Examples of typical spatial scenarios in SGL include finding shortest path tree and shortest path between network nodes,collecting proper information throughout the whole world,elimination of multiple targets by intelligent teams of chasers,and withstanding cyber attacks in distributed networked systems.Also this paper compares Spatial Grasp model with traditional algorithms,confirming universality of the former for any spatial systems,while the latter just tools for concrete applications.
