针对无监督环境下传统网络异常诊断算法存在异常点定位和异常数据分类准确率低等不足,通过设计一种基于改进Q-learning算法的无线网络异常诊断方法:首先基于ADU(Asynchronous Data Unit异步数据单元)单元采集无线网络的数据流,并提取数...针对无监督环境下传统网络异常诊断算法存在异常点定位和异常数据分类准确率低等不足,通过设计一种基于改进Q-learning算法的无线网络异常诊断方法:首先基于ADU(Asynchronous Data Unit异步数据单元)单元采集无线网络的数据流,并提取数据包特征;然后构建Q-learning算法模型探索状态值和奖励值的平衡点,利用SA(Simulated Annealing模拟退火)算法从全局视角对下一时刻状态进行精确识别;最后确定训练样本的联合分布概率,提升输出值的逼近性能以达到平衡探索与代价之间的均衡。测试结果显示:改进Q-learning算法的网络异常定位准确率均值达99.4%,在不同类型网络异常的分类精度和分类效率等方面,也优于三种传统网络异常诊断方法。展开更多
Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in th...Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.展开更多
Soil improvement is one of the most important issues in geotechnical engineering practice.The wide application of traditional improvement techniques(cement/chemical materials)are limited due to damage ecological en-vi...Soil improvement is one of the most important issues in geotechnical engineering practice.The wide application of traditional improvement techniques(cement/chemical materials)are limited due to damage ecological en-vironment and intensify carbon emissions.However,the use of microbially induced calcium carbonate pre-cipitation(MICP)to obtain bio-cement is a novel technique with the potential to induce soil stability,providing a low-carbon,environment-friendly,and sustainable integrated solution for some geotechnical engineering pro-blems in the environment.This paper presents a comprehensive review of the latest progress in soil improvement based on the MICP strategy.It systematically summarizes and overviews the mineralization mechanism,influ-encing factors,improved methods,engineering characteristics,and current field application status of the MICP.Additionally,it also explores the limitations and correspondingly proposes prospective applications via the MICP approach for soil improvement.This review indicates that the utilization of different environmental calcium-based wastes in MICP and combination of materials and MICP are conducive to meeting engineering and market demand.Furthermore,we recommend and encourage global collaborative study and practice with a view to commercializing MICP technique in the future.The current review purports to provide insights for engineers and interdisciplinary researchers,and guidance for future engineering applications.展开更多
随着智能体在复杂动态环境中的路径规划需求日益增长,传统Q-Learning算法在收敛速度、避障效率及全局优化能力上的局限性逐渐凸显。针对Q-Learning算法在路径规划中的不足,本文提出一种结合动态学习率、自适应探索率与蒙特卡洛树搜索(Mo...随着智能体在复杂动态环境中的路径规划需求日益增长,传统Q-Learning算法在收敛速度、避障效率及全局优化能力上的局限性逐渐凸显。针对Q-Learning算法在路径规划中的不足,本文提出一种结合动态学习率、自适应探索率与蒙特卡洛树搜索(Monte Carlo Tree Search,MCTS)的改进方法。首先,通过引入指数衰减的动态学习率与探索率,以平衡算法在训练初期的探索能力与后期的策略稳定性;其次,将MCTS与Q-Learning结合,利用MCTS的全局搜索特性优化Q值更新过程;此外,融合启发式函数以改进奖励机制,引导智能体更高效地逼近目标。实验结果表明,改进算法的平均步数、收敛速度、稳定性等相较于传统算法提升显著,本研究为复杂环境下的智能体路径规划提供了一种高效、鲁棒的解决方案。展开更多
To solve the problem of low detection accuracy for complex weld defects,the paper proposes a weld defects detection method based on improved YOLOv5s.To enhance the ability to focus on key information in feature maps,t...To solve the problem of low detection accuracy for complex weld defects,the paper proposes a weld defects detection method based on improved YOLOv5s.To enhance the ability to focus on key information in feature maps,the scSE attention mechanism is intro-duced into the backbone network of YOLOv5s.A Fusion-Block module and additional layers are added to the neck network of YOLOv5s to improve the effect of feature fusion,which is to meet the needs of complex object detection.To reduce the computation-al complexity of the model,the C3Ghost module is used to replace the CSP2_1 module in the neck network of YOLOv5s.The scSE-ASFF module is constructed and inserted between the neck network and the prediction end,which is to realize the fusion of features between the different layers.To address the issue of imbalanced sample quality in the dataset and improve the regression speed and accuracy of the loss function,the CIoU loss function in the YOLOv5s model is replaced with the Focal-EIoU loss function.Finally,ex-periments are conducted based on the collected weld defect dataset to verify the feasibility of the improved YOLOv5s for weld defects detection.The experimental results show that the precision and mAP of the improved YOLOv5s in detecting complex weld defects are as high as 83.4%and 76.1%,respectively,which are 2.5%and 7.6%higher than the traditional YOLOv5s model.The proposed weld defects detection method based on the improved YOLOv5s in this paper can effectively solve the problem of low weld defects detection accuracy.展开更多
目的:针对生物安全实验室空间密闭、障碍物形态多(球形、立方体、圆柱体、椭球体等)及精确操作要求极高的复杂环境特性,提出一种融合改进Q-learning和粒子群优化(particle swarm optimization,PSO)算法的机械臂轨迹规划与避障算法QPSO...目的:针对生物安全实验室空间密闭、障碍物形态多(球形、立方体、圆柱体、椭球体等)及精确操作要求极高的复杂环境特性,提出一种融合改进Q-learning和粒子群优化(particle swarm optimization,PSO)算法的机械臂轨迹规划与避障算法QPSO。方法:QPSO算法采用双层优化架构,上层利用改进的Q-learning算法实现路径决策,通过非线性动态温度玻尔兹曼探索策略平衡探索与利用;下层采用含动态权重和学习因子的PSO算法优化轨迹,并结合余弦定理碰撞检测策略保障避障安全性。为验证提出算法的可行性,进行算法性能分析和避障性能测试,并与标准PSO算法、遗传算法、萤火虫算法、改进快速扩展随机树(rapidly-exploring random tree star,RRT*)算法进行对比。结果:相比标准PSO算法、遗传算法、萤火虫算法和RRT*算法,提出的QPSO算法在收敛性能、轨迹长度和避障成功率方面均有显著优势,且在确保最短路径的同时可实现最大安全距离。结论:提出的QPSO算法能有效提升复杂环境下机械臂的轨迹规划和避障效果,可为生物安全实验室等类似环境的自动化实验操作提供可靠的技术支撑。展开更多
文摘针对无监督环境下传统网络异常诊断算法存在异常点定位和异常数据分类准确率低等不足,通过设计一种基于改进Q-learning算法的无线网络异常诊断方法:首先基于ADU(Asynchronous Data Unit异步数据单元)单元采集无线网络的数据流,并提取数据包特征;然后构建Q-learning算法模型探索状态值和奖励值的平衡点,利用SA(Simulated Annealing模拟退火)算法从全局视角对下一时刻状态进行精确识别;最后确定训练样本的联合分布概率,提升输出值的逼近性能以达到平衡探索与代价之间的均衡。测试结果显示:改进Q-learning算法的网络异常定位准确率均值达99.4%,在不同类型网络异常的分类精度和分类效率等方面,也优于三种传统网络异常诊断方法。
基金supported by the National Natural Science Foundation of China [grant numbers 42088101 and 42375048]。
文摘Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.
基金funded by the National Natural Science Foundation of China(No.41962016)the Natural Science Foundation of NingXia(Nos.2023AAC02023,2023A1218,and 2021AAC02006).
文摘Soil improvement is one of the most important issues in geotechnical engineering practice.The wide application of traditional improvement techniques(cement/chemical materials)are limited due to damage ecological en-vironment and intensify carbon emissions.However,the use of microbially induced calcium carbonate pre-cipitation(MICP)to obtain bio-cement is a novel technique with the potential to induce soil stability,providing a low-carbon,environment-friendly,and sustainable integrated solution for some geotechnical engineering pro-blems in the environment.This paper presents a comprehensive review of the latest progress in soil improvement based on the MICP strategy.It systematically summarizes and overviews the mineralization mechanism,influ-encing factors,improved methods,engineering characteristics,and current field application status of the MICP.Additionally,it also explores the limitations and correspondingly proposes prospective applications via the MICP approach for soil improvement.This review indicates that the utilization of different environmental calcium-based wastes in MICP and combination of materials and MICP are conducive to meeting engineering and market demand.Furthermore,we recommend and encourage global collaborative study and practice with a view to commercializing MICP technique in the future.The current review purports to provide insights for engineers and interdisciplinary researchers,and guidance for future engineering applications.
文摘随着智能体在复杂动态环境中的路径规划需求日益增长,传统Q-Learning算法在收敛速度、避障效率及全局优化能力上的局限性逐渐凸显。针对Q-Learning算法在路径规划中的不足,本文提出一种结合动态学习率、自适应探索率与蒙特卡洛树搜索(Monte Carlo Tree Search,MCTS)的改进方法。首先,通过引入指数衰减的动态学习率与探索率,以平衡算法在训练初期的探索能力与后期的策略稳定性;其次,将MCTS与Q-Learning结合,利用MCTS的全局搜索特性优化Q值更新过程;此外,融合启发式函数以改进奖励机制,引导智能体更高效地逼近目标。实验结果表明,改进算法的平均步数、收敛速度、稳定性等相较于传统算法提升显著,本研究为复杂环境下的智能体路径规划提供了一种高效、鲁棒的解决方案。
基金supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX24_4084).
文摘To solve the problem of low detection accuracy for complex weld defects,the paper proposes a weld defects detection method based on improved YOLOv5s.To enhance the ability to focus on key information in feature maps,the scSE attention mechanism is intro-duced into the backbone network of YOLOv5s.A Fusion-Block module and additional layers are added to the neck network of YOLOv5s to improve the effect of feature fusion,which is to meet the needs of complex object detection.To reduce the computation-al complexity of the model,the C3Ghost module is used to replace the CSP2_1 module in the neck network of YOLOv5s.The scSE-ASFF module is constructed and inserted between the neck network and the prediction end,which is to realize the fusion of features between the different layers.To address the issue of imbalanced sample quality in the dataset and improve the regression speed and accuracy of the loss function,the CIoU loss function in the YOLOv5s model is replaced with the Focal-EIoU loss function.Finally,ex-periments are conducted based on the collected weld defect dataset to verify the feasibility of the improved YOLOv5s for weld defects detection.The experimental results show that the precision and mAP of the improved YOLOv5s in detecting complex weld defects are as high as 83.4%and 76.1%,respectively,which are 2.5%and 7.6%higher than the traditional YOLOv5s model.The proposed weld defects detection method based on the improved YOLOv5s in this paper can effectively solve the problem of low weld defects detection accuracy.
文摘目的:针对生物安全实验室空间密闭、障碍物形态多(球形、立方体、圆柱体、椭球体等)及精确操作要求极高的复杂环境特性,提出一种融合改进Q-learning和粒子群优化(particle swarm optimization,PSO)算法的机械臂轨迹规划与避障算法QPSO。方法:QPSO算法采用双层优化架构,上层利用改进的Q-learning算法实现路径决策,通过非线性动态温度玻尔兹曼探索策略平衡探索与利用;下层采用含动态权重和学习因子的PSO算法优化轨迹,并结合余弦定理碰撞检测策略保障避障安全性。为验证提出算法的可行性,进行算法性能分析和避障性能测试,并与标准PSO算法、遗传算法、萤火虫算法、改进快速扩展随机树(rapidly-exploring random tree star,RRT*)算法进行对比。结果:相比标准PSO算法、遗传算法、萤火虫算法和RRT*算法,提出的QPSO算法在收敛性能、轨迹长度和避障成功率方面均有显著优势,且在确保最短路径的同时可实现最大安全距离。结论:提出的QPSO算法能有效提升复杂环境下机械臂的轨迹规划和避障效果,可为生物安全实验室等类似环境的自动化实验操作提供可靠的技术支撑。
