In underwater target search path planning,the accuracy of sonar models directly dictates the accurate assessment of search coverage.In contrast to physics-informed sonar models,traditional geometric sonar models fail ...In underwater target search path planning,the accuracy of sonar models directly dictates the accurate assessment of search coverage.In contrast to physics-informed sonar models,traditional geometric sonar models fail to accurately characterize the complex influence of marine environments.To overcome these challenges,we propose an acoustic physics-informed intelligent path planning framework for underwater target search,integrating three core modules:The acoustic-physical modeling module adopts 3D ray-tracing theory and the active sonar equation to construct a physics-driven sonar detection model,explicitly accounting for environmental factors that influence sonar performance across heterogeneous spaces.The hybrid parallel computing module adopts a message passing interface(MPI)/open multi-processing(Open MP)hybrid strategy for large-scale acoustic simulations,combining computational domain decomposition and physics-intensive task acceleration.The search path optimization module adopts the covariance matrix adaptation evolution algorithm to solve continuous optimization problems of heading angles,which ensures maximum search coverage for targets.Largescale experiments conducted in the Pacific and Atlantic Oceans demonstrate the framework's effectiveness:(1)Precise capture of sonar detection range variations from 5.45 km to 50 km in heterogeneous marine environments.(2)Significant speedup of 453.43×for acoustic physics modeling through hybrid parallelization.(3)Notable improvements of 7.23%in detection coverage and 15.86%reduction in optimization time compared to the optimal baseline method.The framework provides a robust solution for underwater search missions in complex marine environments.展开更多
针对变压器故障诊断存在诊断精度不高和麻雀搜索算法(sparrow search algorithm,SSA)存在易陷入局部最优的问题,提出了一种基于融合正余弦和柯西变异的麻雀搜索算法(sine-cosine and Cauchy mutation sparrow search algorithm,SCSSA)...针对变压器故障诊断存在诊断精度不高和麻雀搜索算法(sparrow search algorithm,SSA)存在易陷入局部最优的问题,提出了一种基于融合正余弦和柯西变异的麻雀搜索算法(sine-cosine and Cauchy mutation sparrow search algorithm,SCSSA)优化双向长短期记忆网络(bi-directional long-short term memory,BiLSTM)的变压器故障诊断模型。首先,基于油中溶解气体分析(dissolved gas analysis,DGA)法,以5种特征量作为输入,其次利用正余弦策略和柯西变异策略对麻雀算法进行改进,然后将SCSSA算法、SSA算法和灰狼优化算法(grey wolf optimizer,GWO)在4种测试函数上进行性能对比,验证了SCSSA算法的优越性。最后利用SCSSA算法对BiLSTM网络中的参数进行优化,从而提高BiLSTM网络在变压器故障诊断中的性能。实验结果表明,所提SCSSA-BiLSTM故障诊断模型的综合诊断精度为95.1%,相比于SSA-BiLSTM、GWO-BiLSTM、BiLSTM和LSTM模型分别提高了7.3%、12.2%、14.6%、19.5%,并且SCSSA-BiLSTM模型有着更好的鲁棒性。展开更多
Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide,exacerbated by the COVID-19 pandemic.Age,cholesterol,and blood pressure datasets are becoming inadequate becau...Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide,exacerbated by the COVID-19 pandemic.Age,cholesterol,and blood pressure datasets are becoming inadequate because they cannot capture the complexity of emerging health indicators.These high-dimensional and heterogeneous datasets make traditional machine learning methods difficult,and Skewness and other new biomarkers and psychosocial factors bias the model’s heart health prediction across diverse patient profiles.Modern medical datasets’complexity and high dimensionality challenge traditional predictionmodels like SupportVectorMachines and Decision Trees.Quantum approaches include QSVM,QkNN,QDT,and others.These Constraints drove research.The“QHF-CS:Quantum-Enhanced Heart Failure Prediction using Quantum CNN with Optimized Feature Qubit Selection with Cuckoo Search in Skewed Clinical Data”system was developed in this research.This novel system leverages a Quantum Convolutional Neural Network(QCNN)-based quantum circuit,enhanced by meta-heuristic algorithms—Cuckoo SearchOptimization(CSO),Artificial BeeColony(ABC),and Particle SwarmOptimization(PSO)—for feature qubit selection.Among these,CSO demonstrated superior performance by consistently identifying the most optimal and least skewed feature subsets,which were then encoded into quantum states for circuit construction.By integrating advanced quantum circuit feature maps like ZZFeatureMap,RealAmplitudes,and EfficientSU2,the QHF-CS model efficiently processes complex,high-dimensional data,capturing intricate patterns that classical models overlook.The QHF-CS model improves precision,recall,F1-score,and accuracy to 0.94,0.95,0.94,and 0.94.Quantum computing could revolutionize heart failure diagnostics by improving model accuracy and computational efficiency,enabling complex healthcare diagnostic breakthroughs.展开更多
Human life is invaluable,and timely efforts are crucial to rescue individuals trapped under debris following an earthquake.To identify opportunities for improving post-earthquake search and rescue(SAR)robotics,we get ...Human life is invaluable,and timely efforts are crucial to rescue individuals trapped under debris following an earthquake.To identify opportunities for improving post-earthquake search and rescue(SAR)robotics,we get insights through four different sources:(i)A literature review of disaster robotics and victim psychology,(ii)A public survey on earthquake awareness and their view of rescue robots,(iii)Semi-structured interviews with first responders,and(iv)Responses from GenAI chatbots which were prompted to assume the role of expert rescuers.The triangulated analysis show that there are challenges in mobility,autonomy,communication,situational awareness,and human-robot collaboration.The public respondents showed high acceptance of robot-assisted rescue and prioritised survivor detection,sensing,and communication as essential functionalities of robots.The experts expressed limitations in current equipment,the need for improved victim localisation,and interest in XR-based training and robot-assisted debris handling.The GenAI chatbots highlighted structural risk assessment,multi-sensor fusion,and supervised autonomy.Therefore,this study identifies critical robot features,outlines multi-modal interaction requirements,and highlights gaps in current SAR practice.These findings offer robot design directions for developing effective,trustworthy SAR robots,which can be integrated to future response disaster-workflows.展开更多
Deep learning has become integral to robotics,particularly in tasks such as robotic grasping,where objects often exhibit diverse shapes,textures,and physical properties.In robotic grasping tasks,due to the diverse cha...Deep learning has become integral to robotics,particularly in tasks such as robotic grasping,where objects often exhibit diverse shapes,textures,and physical properties.In robotic grasping tasks,due to the diverse characteristics of the targets,frequent adjustments to the network architecture and parameters are required to avoid a decrease in model accuracy,which presents a significant challenge for non-experts.Neural Architecture Search(NAS)provides a compelling method through the automated generation of network architectures,enabling the discovery of models that achieve high accuracy through efficient search algorithms.Compared to manually designed networks,NAS methods can significantly reduce design costs,time expenditure,and improve model performance.However,such methods often involve complex topological connections,and these redundant structures can severely reduce computational efficiency.To overcome this challenge,this work puts forward a robotic grasp detection framework founded on NAS.The method automatically designs a lightweight network with high accuracy and low topological complexity,effectively adapting to the target object to generate the optimal grasp pose,thereby significantly improving the success rate of robotic grasping.Additionally,we use Class Activation Mapping(CAM)as an interpretability tool,which captures sensitive information during the perception process through visualized results.The searched model achieved competitive,and in some cases superior,performance on the Cornell and Jacquard public datasets,achieving accuracies of 98.3%and 96.8%,respectively,while sustaining a detection speed of 89 frames per second with only 0.41 million parameters.To further validate its effectiveness beyond benchmark evaluations,we conducted real-world grasping experiments on a UR5 robotic arm,where the model demonstrated reliable performance across diverse objects and high grasp success rates,thereby confirming its practical applicability in robotic manipulation tasks.展开更多
Cooperative multi-UAV search requires jointly optimizing wide-area coverage,rapid target discovery,and endurance under sensing and motion constraints.Resolving this coupling enables scalable coordination with high dat...Cooperative multi-UAV search requires jointly optimizing wide-area coverage,rapid target discovery,and endurance under sensing and motion constraints.Resolving this coupling enables scalable coordination with high data efficiency and mission reliability.We formulate this problem as a discounted Markov decision process on an occupancy grid with a cellwise Bayesian belief update,yielding a Markov state that couples agent poses with a probabilistic target field.On this belief–MDP we introduce a segment-conditioned latent-intent framework,in which a discrete intent head selects a latent skill every K steps and an intra-segment GRU policy generates per-step control conditioned on the fixed intent;both components are trained end-to-end with proximal updates under a centralized critic.On the 50×50 grid,coverage and discovery convergence times are reduced by up to 48%and 40%relative to a flat actor-critic benchmark,and the aggregated convergence metric improves by about 12%compared with a stateof-the-art hierarchical method.Qualitative analyses further reveal stable spatial sectorization,low path overlap,and fuel-aware patrolling,indicating that segment-conditioned latent intents provide an effective and scalable mechanism for coordinated multi-UAV search.展开更多
For 5G millimeter wave(mm-Wave)user equipments(UEs),all test cases must be evaluated in Over-The-Air(OTA)manner.Test time increases dramatically compared to Sub-6 GHz.Therefore,test time reduction is of great signific...For 5G millimeter wave(mm-Wave)user equipments(UEs),all test cases must be evaluated in Over-The-Air(OTA)manner.Test time increases dramatically compared to Sub-6 GHz.Therefore,test time reduction is of great significance for 5G mm-Wave OTA testing.Among all test cases,beam peak search is the most time-consuming,taking up the majority of the overall test time.Therefore,the objective of this work is to determine a suitable beam peak search grid for 5G mm-Wave UEs with satisfactory accuracy and efficiency.Through radiation property investigation of 5G mm-Wave commercial UEs,more reasonable reference array configuration(4×2)and reference deployment scenario(composite beam)are proposed for beam peak search grid analysis.The effect of different grid configurations on beam peak search precision are characterized quantitatively.The determination of associated measurement uncertainty(MU)term along with quantitative analysis approach are proposed based on statistical analysis.Finally,the recommended minimum number of beam peak search grid points is 182 based on the proposed 4×2 array under composite beam scenario.Compared with currently-required 1106 points in 3GPP/CTIA specifications,over 80%reduction can be achieved without increasing the MU limit.The feasibility of the proposed MU analysis as well as the recommended grids is demonstrated through measurements.展开更多
Engineering optimization problems are often characterized by high dimensionality,constraints,and complex,multimodal landscapes.Traditional deterministic methods frequently struggle under such conditions,prompting incr...Engineering optimization problems are often characterized by high dimensionality,constraints,and complex,multimodal landscapes.Traditional deterministic methods frequently struggle under such conditions,prompting increased interest in swarm intelligence algorithms.Among these,the Cuckoo Search(CS)algorithm stands out for its promising global search capabilities.However,it often suffers from premature convergence when tackling complex problems.To address this limitation,this paper proposes a Grouped Dynamic Adaptive CS(GDACS)algorithm.Theenhancements incorporated intoGDACS can be summarized into two key aspects.Firstly,a chaotic map is employed to generate initial solutions,leveraging the inherent randomness of chaotic sequences to ensure a more uniform distribution across the search space and enhance population diversity from the outset.Secondly,Cauchy and Levy strategies replace the standard CS population update.This strategy involves evaluating the fitness of candidate solutions to dynamically group the population based on performance.Different step-size adaptation strategies are then applied to distinct groups,enabling an adaptive search mechanism that balances exploration and exploitation.Experiments were conducted on six benchmark functions and four constrained engineering design problems,and the results indicate that the proposed GDACS achieves good search efficiency and produces more accurate optimization results compared with other state-of-the-art algorithms.展开更多
基金supported by Natural Science Foundation of Hu'nan Province(2024JJ5409)。
文摘In underwater target search path planning,the accuracy of sonar models directly dictates the accurate assessment of search coverage.In contrast to physics-informed sonar models,traditional geometric sonar models fail to accurately characterize the complex influence of marine environments.To overcome these challenges,we propose an acoustic physics-informed intelligent path planning framework for underwater target search,integrating three core modules:The acoustic-physical modeling module adopts 3D ray-tracing theory and the active sonar equation to construct a physics-driven sonar detection model,explicitly accounting for environmental factors that influence sonar performance across heterogeneous spaces.The hybrid parallel computing module adopts a message passing interface(MPI)/open multi-processing(Open MP)hybrid strategy for large-scale acoustic simulations,combining computational domain decomposition and physics-intensive task acceleration.The search path optimization module adopts the covariance matrix adaptation evolution algorithm to solve continuous optimization problems of heading angles,which ensures maximum search coverage for targets.Largescale experiments conducted in the Pacific and Atlantic Oceans demonstrate the framework's effectiveness:(1)Precise capture of sonar detection range variations from 5.45 km to 50 km in heterogeneous marine environments.(2)Significant speedup of 453.43×for acoustic physics modeling through hybrid parallelization.(3)Notable improvements of 7.23%in detection coverage and 15.86%reduction in optimization time compared to the optimal baseline method.The framework provides a robust solution for underwater search missions in complex marine environments.
文摘针对变压器故障诊断存在诊断精度不高和麻雀搜索算法(sparrow search algorithm,SSA)存在易陷入局部最优的问题,提出了一种基于融合正余弦和柯西变异的麻雀搜索算法(sine-cosine and Cauchy mutation sparrow search algorithm,SCSSA)优化双向长短期记忆网络(bi-directional long-short term memory,BiLSTM)的变压器故障诊断模型。首先,基于油中溶解气体分析(dissolved gas analysis,DGA)法,以5种特征量作为输入,其次利用正余弦策略和柯西变异策略对麻雀算法进行改进,然后将SCSSA算法、SSA算法和灰狼优化算法(grey wolf optimizer,GWO)在4种测试函数上进行性能对比,验证了SCSSA算法的优越性。最后利用SCSSA算法对BiLSTM网络中的参数进行优化,从而提高BiLSTM网络在变压器故障诊断中的性能。实验结果表明,所提SCSSA-BiLSTM故障诊断模型的综合诊断精度为95.1%,相比于SSA-BiLSTM、GWO-BiLSTM、BiLSTM和LSTM模型分别提高了7.3%、12.2%、14.6%、19.5%,并且SCSSA-BiLSTM模型有着更好的鲁棒性。
文摘Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide,exacerbated by the COVID-19 pandemic.Age,cholesterol,and blood pressure datasets are becoming inadequate because they cannot capture the complexity of emerging health indicators.These high-dimensional and heterogeneous datasets make traditional machine learning methods difficult,and Skewness and other new biomarkers and psychosocial factors bias the model’s heart health prediction across diverse patient profiles.Modern medical datasets’complexity and high dimensionality challenge traditional predictionmodels like SupportVectorMachines and Decision Trees.Quantum approaches include QSVM,QkNN,QDT,and others.These Constraints drove research.The“QHF-CS:Quantum-Enhanced Heart Failure Prediction using Quantum CNN with Optimized Feature Qubit Selection with Cuckoo Search in Skewed Clinical Data”system was developed in this research.This novel system leverages a Quantum Convolutional Neural Network(QCNN)-based quantum circuit,enhanced by meta-heuristic algorithms—Cuckoo SearchOptimization(CSO),Artificial BeeColony(ABC),and Particle SwarmOptimization(PSO)—for feature qubit selection.Among these,CSO demonstrated superior performance by consistently identifying the most optimal and least skewed feature subsets,which were then encoded into quantum states for circuit construction.By integrating advanced quantum circuit feature maps like ZZFeatureMap,RealAmplitudes,and EfficientSU2,the QHF-CS model efficiently processes complex,high-dimensional data,capturing intricate patterns that classical models overlook.The QHF-CS model improves precision,recall,F1-score,and accuracy to 0.94,0.95,0.94,and 0.94.Quantum computing could revolutionize heart failure diagnostics by improving model accuracy and computational efficiency,enabling complex healthcare diagnostic breakthroughs.
文摘Human life is invaluable,and timely efforts are crucial to rescue individuals trapped under debris following an earthquake.To identify opportunities for improving post-earthquake search and rescue(SAR)robotics,we get insights through four different sources:(i)A literature review of disaster robotics and victim psychology,(ii)A public survey on earthquake awareness and their view of rescue robots,(iii)Semi-structured interviews with first responders,and(iv)Responses from GenAI chatbots which were prompted to assume the role of expert rescuers.The triangulated analysis show that there are challenges in mobility,autonomy,communication,situational awareness,and human-robot collaboration.The public respondents showed high acceptance of robot-assisted rescue and prioritised survivor detection,sensing,and communication as essential functionalities of robots.The experts expressed limitations in current equipment,the need for improved victim localisation,and interest in XR-based training and robot-assisted debris handling.The GenAI chatbots highlighted structural risk assessment,multi-sensor fusion,and supervised autonomy.Therefore,this study identifies critical robot features,outlines multi-modal interaction requirements,and highlights gaps in current SAR practice.These findings offer robot design directions for developing effective,trustworthy SAR robots,which can be integrated to future response disaster-workflows.
基金funded by Guangdong Basic and Applied Basic Research Foundation(2023B1515120064)National Natural Science Foundation of China(62273097).
文摘Deep learning has become integral to robotics,particularly in tasks such as robotic grasping,where objects often exhibit diverse shapes,textures,and physical properties.In robotic grasping tasks,due to the diverse characteristics of the targets,frequent adjustments to the network architecture and parameters are required to avoid a decrease in model accuracy,which presents a significant challenge for non-experts.Neural Architecture Search(NAS)provides a compelling method through the automated generation of network architectures,enabling the discovery of models that achieve high accuracy through efficient search algorithms.Compared to manually designed networks,NAS methods can significantly reduce design costs,time expenditure,and improve model performance.However,such methods often involve complex topological connections,and these redundant structures can severely reduce computational efficiency.To overcome this challenge,this work puts forward a robotic grasp detection framework founded on NAS.The method automatically designs a lightweight network with high accuracy and low topological complexity,effectively adapting to the target object to generate the optimal grasp pose,thereby significantly improving the success rate of robotic grasping.Additionally,we use Class Activation Mapping(CAM)as an interpretability tool,which captures sensitive information during the perception process through visualized results.The searched model achieved competitive,and in some cases superior,performance on the Cornell and Jacquard public datasets,achieving accuracies of 98.3%and 96.8%,respectively,while sustaining a detection speed of 89 frames per second with only 0.41 million parameters.To further validate its effectiveness beyond benchmark evaluations,we conducted real-world grasping experiments on a UR5 robotic arm,where the model demonstrated reliable performance across diverse objects and high grasp success rates,thereby confirming its practical applicability in robotic manipulation tasks.
文摘Cooperative multi-UAV search requires jointly optimizing wide-area coverage,rapid target discovery,and endurance under sensing and motion constraints.Resolving this coupling enables scalable coordination with high data efficiency and mission reliability.We formulate this problem as a discounted Markov decision process on an occupancy grid with a cellwise Bayesian belief update,yielding a Markov state that couples agent poses with a probabilistic target field.On this belief–MDP we introduce a segment-conditioned latent-intent framework,in which a discrete intent head selects a latent skill every K steps and an intra-segment GRU policy generates per-step control conditioned on the fixed intent;both components are trained end-to-end with proximal updates under a centralized critic.On the 50×50 grid,coverage and discovery convergence times are reduced by up to 48%and 40%relative to a flat actor-critic benchmark,and the aggregated convergence metric improves by about 12%compared with a stateof-the-art hierarchical method.Qualitative analyses further reveal stable spatial sectorization,low path overlap,and fuel-aware patrolling,indicating that segment-conditioned latent intents provide an effective and scalable mechanism for coordinated multi-UAV search.
基金supported by the Beijing Natural Science Foundation under Grant L253002.
文摘For 5G millimeter wave(mm-Wave)user equipments(UEs),all test cases must be evaluated in Over-The-Air(OTA)manner.Test time increases dramatically compared to Sub-6 GHz.Therefore,test time reduction is of great significance for 5G mm-Wave OTA testing.Among all test cases,beam peak search is the most time-consuming,taking up the majority of the overall test time.Therefore,the objective of this work is to determine a suitable beam peak search grid for 5G mm-Wave UEs with satisfactory accuracy and efficiency.Through radiation property investigation of 5G mm-Wave commercial UEs,more reasonable reference array configuration(4×2)and reference deployment scenario(composite beam)are proposed for beam peak search grid analysis.The effect of different grid configurations on beam peak search precision are characterized quantitatively.The determination of associated measurement uncertainty(MU)term along with quantitative analysis approach are proposed based on statistical analysis.Finally,the recommended minimum number of beam peak search grid points is 182 based on the proposed 4×2 array under composite beam scenario.Compared with currently-required 1106 points in 3GPP/CTIA specifications,over 80%reduction can be achieved without increasing the MU limit.The feasibility of the proposed MU analysis as well as the recommended grids is demonstrated through measurements.
基金supported in part by the Ministry of Higher Education Malaysia(MOHE)through Fundamental Research Grant Scheme(FRGS)Ref:FRGS/1/2024/ICT02/UTM/02/10,Vot.No:R.J130000.7828.5F748the Scientific Research Project of Education Department of Hunan Province(Nos.22B1046 and 24A0771).
文摘Engineering optimization problems are often characterized by high dimensionality,constraints,and complex,multimodal landscapes.Traditional deterministic methods frequently struggle under such conditions,prompting increased interest in swarm intelligence algorithms.Among these,the Cuckoo Search(CS)algorithm stands out for its promising global search capabilities.However,it often suffers from premature convergence when tackling complex problems.To address this limitation,this paper proposes a Grouped Dynamic Adaptive CS(GDACS)algorithm.Theenhancements incorporated intoGDACS can be summarized into two key aspects.Firstly,a chaotic map is employed to generate initial solutions,leveraging the inherent randomness of chaotic sequences to ensure a more uniform distribution across the search space and enhance population diversity from the outset.Secondly,Cauchy and Levy strategies replace the standard CS population update.This strategy involves evaluating the fitness of candidate solutions to dynamically group the population based on performance.Different step-size adaptation strategies are then applied to distinct groups,enabling an adaptive search mechanism that balances exploration and exploitation.Experiments were conducted on six benchmark functions and four constrained engineering design problems,and the results indicate that the proposed GDACS achieves good search efficiency and produces more accurate optimization results compared with other state-of-the-art algorithms.
