1-way multihead quantum finite state automata (1QFA(k)) can be thought of modified version of 1-way quantum finite state automata (1QFA) and k-letter quantum finite state automata (k-letter QFA) respectively. It has b...1-way multihead quantum finite state automata (1QFA(k)) can be thought of modified version of 1-way quantum finite state automata (1QFA) and k-letter quantum finite state automata (k-letter QFA) respectively. It has been shown by Moore and Crutchfield as well as Konadacs and Watrous that 1QFA can’t accept all regular language. In this paper, we show different language recognizing capabilities of our model 1-way multihead QFAs. New results presented in this paper are the following ones: 1) We show that newly introduced 1-way 2-head quantum finite state automaton (1QFA(2)) structure can accept all unary regular languages. 2) A language which can’t be accepted by 1-way deterministic 2-head finite state automaton (1DFA((2)) can be accepted by 1QFA(2) with bounded error. 3) 1QFA(2) is more powerful than 1-way reversible 2-head finite state automaton (1RMFA(2)) with respect to recognition of language.展开更多
近年来,入侵检测技术在网络安全中扮演着越来越重要的角色。目前的入侵检测模型所用的方法大部分是基于传统机器学习的浅层方法。浅层机器学习方法不能有效发掘数据特征,在入侵检测中存在一定的局限性。为此,论文提出了一种深度学习模型...近年来,入侵检测技术在网络安全中扮演着越来越重要的角色。目前的入侵检测模型所用的方法大部分是基于传统机器学习的浅层方法。浅层机器学习方法不能有效发掘数据特征,在入侵检测中存在一定的局限性。为此,论文提出了一种深度学习模型,该模型结合了多头注意力(multiHead attention)和双向门循环单元(BiGRU)。模型使用多头注意力和双向门循环单元从空间和时间上处理网络攻击流量,有效缓解模型复杂性,同时增加模型表现力。此外,使用最大池化方法(maxpooling)来平衡训练速度和性能,不但可以提取序列的边缘特征,还能帮助扩大感受野,由于数据不平衡会影响模型性能表现,因此使用随机过采样(Random Over Sampling)方法来处理数据不平衡的问题。实验基于UNSW-NB15数据集和CIC-IDS2017数据集,并使用准确率(Accuracy)、精确率(Precision)、召回率(Recall)和f1分数作为评估指标。实验结果表明,模型性能优秀。展开更多
With the increasing demand for power in society,there is much live equipment in substations,and the safety and standardization of live working of workers are facing challenges.Aiming at these problems of scene complex...With the increasing demand for power in society,there is much live equipment in substations,and the safety and standardization of live working of workers are facing challenges.Aiming at these problems of scene complexity and object diversity in the real-time detection of the live working safety of substation workers,an adaptive multihead structure and lightweight feature pyramid-based network(AHLNet)is proposed in this study,which is based on YOLOV3.First,we take AH-Darknet53 as the backbone network of YOLOV3,which can introduce an adaptive multihead(AMH)structure,reduce the number of network parameters,and improve the feature extraction ability of the backbone network.Second,to reduce the number of convolution layers of the deeper feature map,a lightweight feature pyramid network(LFPN)is proposed,which can perform feature fusion in advance to alleviate the problem of feature imbalance and gradient disappearance.Finally,the proposed AHLNet is evaluated on the datasets of 16 categories of substation safety operation scenarios,and the average prediction accuracy MAP_(50)reaches 82.10%.Compared with YOLOV3,MAP_(50)is increased by 2.43%,and the number of parameters is 90 M,which is only 38%of the number of parameters of YOLOV3.In addition,the detection speed is basically the same as that of YOLOV3,which can meet the real-time and accurate detection requirements for the safe operation of substation staff.展开更多
Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)scans.These factors present significant challenges for MRI-based segmentation,a...Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)scans.These factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma progression.This study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation accuracy.ResMHA-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention mechanisms.This powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range dependencies.By doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor boundaries.We rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 datasets.Notably,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse datasets.Furthermore,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset size.Radiomic features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival prediction.This model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing methods.This ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient survival.Importantly,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.展开更多
文摘1-way multihead quantum finite state automata (1QFA(k)) can be thought of modified version of 1-way quantum finite state automata (1QFA) and k-letter quantum finite state automata (k-letter QFA) respectively. It has been shown by Moore and Crutchfield as well as Konadacs and Watrous that 1QFA can’t accept all regular language. In this paper, we show different language recognizing capabilities of our model 1-way multihead QFAs. New results presented in this paper are the following ones: 1) We show that newly introduced 1-way 2-head quantum finite state automaton (1QFA(2)) structure can accept all unary regular languages. 2) A language which can’t be accepted by 1-way deterministic 2-head finite state automaton (1DFA((2)) can be accepted by 1QFA(2) with bounded error. 3) 1QFA(2) is more powerful than 1-way reversible 2-head finite state automaton (1RMFA(2)) with respect to recognition of language.
文摘近年来,入侵检测技术在网络安全中扮演着越来越重要的角色。目前的入侵检测模型所用的方法大部分是基于传统机器学习的浅层方法。浅层机器学习方法不能有效发掘数据特征,在入侵检测中存在一定的局限性。为此,论文提出了一种深度学习模型,该模型结合了多头注意力(multiHead attention)和双向门循环单元(BiGRU)。模型使用多头注意力和双向门循环单元从空间和时间上处理网络攻击流量,有效缓解模型复杂性,同时增加模型表现力。此外,使用最大池化方法(maxpooling)来平衡训练速度和性能,不但可以提取序列的边缘特征,还能帮助扩大感受野,由于数据不平衡会影响模型性能表现,因此使用随机过采样(Random Over Sampling)方法来处理数据不平衡的问题。实验基于UNSW-NB15数据集和CIC-IDS2017数据集,并使用准确率(Accuracy)、精确率(Precision)、召回率(Recall)和f1分数作为评估指标。实验结果表明,模型性能优秀。
基金supported by the General Scientific Research Project of the Education Department of Zhejiang Province,China(No.Y202146060).
文摘With the increasing demand for power in society,there is much live equipment in substations,and the safety and standardization of live working of workers are facing challenges.Aiming at these problems of scene complexity and object diversity in the real-time detection of the live working safety of substation workers,an adaptive multihead structure and lightweight feature pyramid-based network(AHLNet)is proposed in this study,which is based on YOLOV3.First,we take AH-Darknet53 as the backbone network of YOLOV3,which can introduce an adaptive multihead(AMH)structure,reduce the number of network parameters,and improve the feature extraction ability of the backbone network.Second,to reduce the number of convolution layers of the deeper feature map,a lightweight feature pyramid network(LFPN)is proposed,which can perform feature fusion in advance to alleviate the problem of feature imbalance and gradient disappearance.Finally,the proposed AHLNet is evaluated on the datasets of 16 categories of substation safety operation scenarios,and the average prediction accuracy MAP_(50)reaches 82.10%.Compared with YOLOV3,MAP_(50)is increased by 2.43%,and the number of parameters is 90 M,which is only 38%of the number of parameters of YOLOV3.In addition,the detection speed is basically the same as that of YOLOV3,which can meet the real-time and accurate detection requirements for the safe operation of substation staff.
文摘针对盾构姿态预测模型存在易过拟合、预测精度低的问题,提出一种基于融合注意力机制的盾构姿态组合预测模型。为强化有效特征的提取,抑制冗余特征信息的表达,引入基于选择性卷积核网络(selective kernel networks,SKNet)的特征注意力机制提取网络,消除固定尺寸卷积核带来的限制,并自适应形成带有注意力的特征映射。为更好地捕捉长期信息和特征模式,通过双向长短期记忆网络(bidirectional long short-term memory,BiLSTM)、门控循环单元(gated recurrent unit, GRU)得到2组隐含输出结果,再利用多头注意力机制,捕获组合模型输出的隐含特征与模型输出的盾构姿态之间的依赖关系,进一步提高预测模型对重要隐含特征的信息抓捕能力;同时,为解决地质勘察钻孔数据连续性差、精确性不足,难以应用于机器学习模型训练的问题,将基于人工先验知识的二级特征引入模型特征输入,提升模型对地层信息的感知能力。最后,基于广州地铁12号线官洲站—大学城北站盾构实例,对模型不同参数结构下的性能进行研究,并进行对比试验验证模型性能,采用可解释性试验评估特征对预测结果的影响。试验结果表明,相比其他预测模型,所提出的预测模型优越性更好,预测精度更高,解决了长时间序列高特征维度数据在传统模型下易过拟合且预测精度较低的问题。
基金the Deanship of Research and Graduate Studies at King Khalid University for funding this work through a Large Research Project under grant number RGP2/254/45.
文摘Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)scans.These factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma progression.This study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation accuracy.ResMHA-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention mechanisms.This powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range dependencies.By doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor boundaries.We rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 datasets.Notably,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse datasets.Furthermore,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset size.Radiomic features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival prediction.This model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing methods.This ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient survival.Importantly,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.
