Two Stages Segmentation Algorithm of Breast Tumor in DCE-MRI Based on Multi-Scale Feature and Boundary Attention Mechanism

Nuclearmagnetic resonance imaging of breasts often presents complex backgrounds.Breast tumors exhibit varying sizes,uneven intensity,and indistinct boundaries.These characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor segmentation.Thus,we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention mechanisms.Initially,the breast region of interest is extracted to isolate the breast area from surrounding tissues and organs.Subsequently,we devise a fusion network incorporatingmulti-scale features and boundary attentionmechanisms for breast tumor segmentation.We incorporate multi-scale parallel dilated convolution modules into the network,enhancing its capability to segment tumors of various sizes through multi-scale convolution and novel fusion techniques.Additionally,attention and boundary detection modules are included to augment the network’s capacity to locate tumors by capturing nonlocal dependencies in both spatial and channel domains.Furthermore,a hybrid loss function with boundary weight is employed to address sample class imbalance issues and enhance the network’s boundary maintenance capability through additional loss.Themethod was evaluated using breast data from 207 patients at RuijinHospital,resulting in a 6.64%increase in Dice similarity coefficient compared to the benchmarkU-Net.Experimental results demonstrate the superiority of the method over other segmentation techniques,with fewer model parameters.

Unknown DDoS Attack Detection with Fuzzy C-Means Clustering and Spatial Location Constraint Prototype Loss

Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks.

MSCNN-LSTM Model for Predicting Return Loss of the UHF Antenna in HF-UHF RFID Tag Antenna

High-frequency(HF)and ultrahigh-frequency(UHF)dual-band radio frequency identification(RFID)tags with both near-field and farfield communication can meet different application scenarios.However,it is time-consuming to calculate the return loss of a UHF antenna in a dualband tag antenna using electromagnetic(EM)simulators.To overcome this,the present work proposes a model of a multi-scale convolutional neural network stacked with long and short-term memory(MSCNN-LSTM)for predicting the return loss of UHF antennas instead of EM simulators.In the proposed MSCNN-LSTM,the MSCNN has three branches,which include three convolution layers with different kernel sizes and numbers.Therefore,MSCNN can extract fine-grain localized information of the antenna and overall features.The LSTM can effectively learn the EM characteristics of different structures of the antenna to improve the prediction accuracy of the model.Experimental results show that the mean absolute error(0.0073),mean square error(0.00032),and root mean square error(0.01814)of theMSCNNLSTM are better than those of other prediction methods.In predicting the return loss of 100UHFantennas,compared with the simulation time of 4800 s for High Frequency Structure Simulator(HFSS),MSCNN-LSTM takes only 0.927519 s under the premise of ensuring prediction accuracy,significantly reducing the calculation time,which provides a basis for the rapid design of HF-UHF RFID tag antenna.ThenMSCNN-LSTM is used to determine the dimensions of the UHF antenna quickly.The return loss of the designed dualband RFID tag antenna is−58.76 and−22.63 dB at 13.56 and 915 MHz,respectively,achieving the desired goal.

投保人高损失区间存在净损失约束的最优保险设计

在不完全保险情形下,投保人通常期望出险后能够获得保险公司足够的赔偿而将自己的实际损失控制在一定的范围内。为了满足这类投保人的需求,本文引入了投保人的净损失约束,研究在该约束下投保人的最优保险问题。研究表明:如果Arrow模型的解满足该约束,本模型的解与Arrow模型解一致,最优保单是有且仅有一个免赔额的部分保险契约,否则最优保单将存在两个免赔额。投保人效用最优时,本模型在应对高损时所提供的赔付水平始终不低于Arrow模型,而本模型在应对低损时对于IARA(DARA/CARA)型投保人所提供的赔付水平依次要低于(高于/等于)Arrow模型。此外,投保人的期望效用将随着其净损失上限的提高而逐渐增大,直到Arrow模型的解满足该约束时其效用达到最大。

基于特征约束和自适应损失平衡的机器遗忘方法

随着数字化进程的加速推进,数据要素已成为现代社会运转的核心驱动力.由于深度学习模型训练需要大量数据作为输入,其数据隐私保护问题也愈发重要.机器遗忘技术使模型能够删除特定数据的影响,同时保持对剩余数据的泛化性能,为深度学习模型中的数据要素安全保护提供了有效的解决方案.现有的机器遗忘方法主要分为精确遗忘和近似遗忘2类,但前者需要干预模型原始训练流程,后者则在遗忘效果和模型泛化能力之间难以找到平衡点.为此,提出了一种基于特征约束和自适应损失平衡的近似遗忘框架.首先,对于“遗忘”过程,使用同样未经过遗忘样本训练的随机模型作为教师来引导遗忘模型的特征输出,实现模型对数据要素在特征层面的遗忘.然后,采用少量剩余数据进行微调训练,来“恢复”模型对于其他数据的泛化性能.将上述机器遗忘框架视为一个多任务优化问题,在“遗忘”和“恢复”2个任务中引入自适应损失平衡,实现任务的稳步训练.以卷积神经网络模型为例,在3个公开数据集上对比了UNSIR等多种基线方法,实验结果表明,该方法构建的遗忘模型不仅保障了机器遗忘效果,在剩余数据的准确率、时间开销、预测结果分布等指标上优于同类方法,更加有效地保护了模型的泛化性能.

面向能效的多约束集成维护策略研究

针对制造系统在加工过程中,由于机床退化水平提高导致的系统质量损失增加、维护成本升高和能效降低等问题,提出一种面向能效的多约束集成维护策略。基于伽马过程建立机床退化状态与质量损失、维护成本的关系,并设立质量损失和维护成本约束,通过离散化能耗递增过程得到系统的有效能效模型,提出以能效为优化目标的多约束集成维护策略,采用模拟退火算法找到最优解和约束组合。实例分析和策略对比验证了所提维护策略的优越性和有效性。

收缩、分离和聚合:面向长尾视觉识别的特征平衡方法

数据在现实世界中通常呈现长尾分布,即,少数类别拥有大量样本,而多数类别仅有少量样本.这种数据不均衡的情况会导致在该数据集上训练的模型对于样本数量较少的尾部类别产生过拟合.面对长尾视觉识别这一任务,提出一种面向长尾视觉识别的特征平衡方法,通过对样本在特征空间中的收缩、分离和聚合操作,增强模型对于难样本的识别能力.该方法主要由特征平衡因子和难样本特征约束两个模块组成.特征平衡因子利用类样本数量来调整模型的输出概率分布,使得不同类别之间的特征距离更加均衡,从而提高模型的分类准确率.难样本特征约束通过对样本特征进行聚类分析,增加不同类别之间的边界距离,使得模型能够找到更合理的决策边界.该方法在多个常用的长尾基准数据集上进行实验验证,结果表明不但提高了模型在长尾数据上的整体分类精度,而且显著提升了尾部类别的识别性能.与基准方法BS相比较,该方法在CIFAR100-LT、ImageNet-LT和iNaturalist 2018数据集上的性能分别提升了7.40%、6.60%和2.89%.

改进YOLOv7的输电线路融冰刀闸状态识别方法

隔离刀闸状态的自动识别是冰期输电线路智能融冰倒闸操作中的关键环节。针对恶劣天气条件下,传统图像识别方法在识别融冰刀闸时精度较低的问题,提出了一种基于改进YOLOv7的融冰刀闸状态识别方法。在YOLOv7网络中引入自注意力机制(self-attention,S-A)模块,以增强网络在低对比度图像中的全局特征提取能力。同时对网络中的SPPCSPC模块进行改进,引入空洞空间金字塔池化技术(atrous spatial pyramid pooling,ASPP),提高对搭接刀闸等此类大目标的识别能力。根据搭接刀闸的特殊结构、大小和位置,在损失函数中添加约束项,增强对刀闸识别的针对性。最后,设计了一个M-MBO加速网络,利用多分支架构在推理时简化模型,提高模型识别速度。实验结果表明,在保证识别速度的同时,改进的YOLOv7模型mAP值可达97.9%,相比改进前的方法平均精度均值提高了2.5个百分点,验证了该方法的有效性。

Multi-scale UDCT dictionary learning based highly undersampled MR image reconstruction using patch-based constraint splitting augmented Lagrangian shrinkage algorithm

Recently, dictionary learning(DL) based methods have been introduced to compressed sensing magnetic resonance imaging(CS-MRI), which outperforms pre-defined analytic sparse priors. However, single-scale trained dictionary directly from image patches is incapable of representing image features from multi-scale, multi-directional perspective, which influences the reconstruction performance. In this paper, incorporating the superior multi-scale properties of uniform discrete curvelet transform(UDCT) with the data matching adaptability of trained dictionaries, we propose a flexible sparsity framework to allow sparser representation and prominent hierarchical essential features capture for magnetic resonance(MR) images. Multi-scale decomposition is implemented by using UDCT due to its prominent properties of lower redundancy ratio, hierarchical data structure, and ease of implementation. Each sub-dictionary of different sub-bands is trained independently to form the multi-scale dictionaries. Corresponding to this brand-new sparsity model, we modify the constraint splitting augmented Lagrangian shrinkage algorithm(C-SALSA) as patch-based C-SALSA(PB C-SALSA) to solve the constraint optimization problem of regularized image reconstruction. Experimental results demonstrate that the trained sub-dictionaries at different scales, enforcing sparsity at multiple scales, can then be efficiently used for MRI reconstruction to obtain satisfactory results with further reduced undersampling rate. Multi-scale UDCT dictionaries potentially outperform both single-scale trained dictionaries and multi-scale analytic transforms. Our proposed sparsity model achieves sparser representation for reconstructed data, which results in fast convergence of reconstruction exploiting PB C-SALSA. Simulation results demonstrate that the proposed method outperforms conventional CS-MRI methods in maintaining intrinsic properties, eliminating aliasing, reducing unexpected artifacts, and removing noise. It can achieve comparable performance of reconstruction with the state-of-the-art methods even under substantially high undersampling factors.

考虑供应商损失规避性和资金约束的供应链决策

随着市场竞争日益激烈,在产出不确定的情况下,供应商资金不足尤为常见。基于此,构建了一个以风险中性制造商为领导者,以损失规避供应商为跟随者的Stackelberg博弈模型。在银行融资和制造商预付款融资两种模式下,分别给出了损失规避供应商的最优生产投入量决策和制造商的最优损失分担决策。通过理论和算例分析表明:当各参数在一定范围内取不同值时,供应商和制造商可能倾向于选择不同的融资模式;在预付款融资模式下,供应商为最大化损失规避,将采取更加保守的生产策略,此时,制造商会通过承担更多的损失分担比例来激励供应商增加生产投入量。

基于DIoU损失与平滑约束的结构化SVM目标跟踪方法

基于结构化SVM的目标跟踪因其优良的性能而受到了广泛的关注,但是现有方法存在损失函数不精确和模型漂移问题。针对这两个问题,首先提出基于DIoU损失与平滑约束的结构化SVM模型。该模型采用DIoU函数作为损失函数,利用t时刻超平面法向量w_(t)与t-1时刻超平面法向量w_(t-1)差值的L_(2)范数作为平滑约束。其次基于对偶坐标下降原理设计了该模型的求解算法。最后利用提出的基于DIoU损失与平滑约束的结构化SVM实现了一种多尺度目标跟踪方法。对所提出的目标跟踪方法在OTB100和VOT-ST2021数据集上进行了实验验证,实验结果表明:所提出的Scale-DCSSVM在OTB数据集上的跟踪成功率比DeepSRDCF高1.1个百分点,在VOT-ST2021上的EAO比E.T.Track高1.2个百分点。所提方法具有较优的性能。

考虑约束违反评估的源网荷储光伏储能优化配置研究

针对时序特性的光伏不合理接入配网带来的电能质量下降和线损增加问题,建立满足多约束条件下的储能投资成本和配电网线损率的储能配置模型;在此基础上,提出一种约束违反评价策略下结合曼哈顿距离的非支配总排序的MOISC-ZOA多目标配置算法,该算法利用正余弦算法强化优势个体的引导作用,并引入一种多子群策略模拟了个体的信息传递,优化储能投资成本和配网线损。测试结果表明该配置方法在降低储能成本的条件下,能将33节点网络的年度平均线损率降低14.7%,同时改善了节点系统的电压偏差。

基于改进人工神经网络算法的配电网差异化节能降损方法

为了解决盲目选择节能降损措施导致配电网损耗偏大的问题,研究基于改进人工神经网络算法的配电网差异化节能降损方法。设置合理的运行电压为上层规划目标,建立配电网差异化节能降损规划模型,设置安全负载约束、电流越限约束以及无功补偿设备约束,作为配电网差异化节能降损规划模型的约束条件。采用自适应遗传算法改进人工神经网络算法,针对配电网不同线路,利用改进后的人工神经网络算法求解配电网差异化节能降损规划模型,获取可令配电网差异化节能降损的最佳配电网运行参数,匹配配电网差异化节能降损方案。实验结果表明,各支路的有功损耗以及无功损耗均有明显地降低,配电网各支路网损率低于1%。

基于大数据技术的35kV配电网线损无功补偿方法

当前的35 kV配电网线损无功补偿约束条件一般为单层级,导致无功补偿极限损耗值加大,为此,提出了基于大数据技术的35 kV配电网线损无功补偿方法。利用大数据技术的配电网线损补偿节点定位,构建35 kV配电网线损无功补偿模型,设定配电网线损多阶约束条件,求解35 kV配电网线损无功补偿模型,从而实现35 kV配电网线损无功补偿。设计对比实验,实验结果证明该方法的无功补偿极限损耗值均被较好地控制在了150 kW以下,在无功补偿的过程中控制效果更佳,具有实际的应用价值。

支持多约束的K-匿名化方法

K-匿名化(K-anonymization)是数据发布环境下保护数据隐私的一种重要方法.目前,K-匿名化方法主要针对单一约束条件进行处理,而实际应用中涉及到大量的多约束条件,使K-匿名化问题更加复杂.如果简单地将单一约束K-匿名化方法应用到多约束情况,会造成大量的信息损失及过低的处理效率.根据多约束之间的关系,通过继承Classfly算法的元组概括过滤思想,提出多约束K-匿名化方法Classfly+及相应的3种算法,包括朴素算法、完全IndepCSet算法和部分IndepCSet的Classfly+算法.实验结果显示,Classfly+能够很好地降低多约束K-匿名化的信息损失,改善匿名化处理的效率.

基于网损因子迭代的安全约束机组组合算法

针对考虑安全约束的机组组合(security constrained unit commitment,SCUC)问题,在传统SCUC模型的基础上,建立考虑有功网损及其在电网中分布的SCUC模型,提出一种基于网损因子迭代的SCUC算法。此算法每次迭代先解固定网损因子的SCUC问题,求得机组的运行状态,然后进行交流潮流计算,更新网损因子,进入下一次迭代。针对可能出现的网损因子振荡问题,提出SCUC和经济调度相结合的方法,选择对应发电成本较小的机组启停状态,进行经济调度优化和网损因子迭代计算,直至算法收敛。对IEEE 30和IEEE 118节点系统进行的仿真计算验证了所提算法的正确性和有效性。

基于潮流计算的配电系统安全域模型

现有配电系统安全域(distribution systems security region,DSSR)模型均未精确计及网损与电压约束,故与实际结果存在偏差。文章基于交流潮流方程建立精确计及网损与电压约束的DSSR新模型。基于逐点仿真法可以观测该模型下的DSSR局部边界。通过实际算例对比文中DSSR模型与传统基于直流潮流的线性DSSR模型,刻画二者误差,证实文中DSSR模型更为精确。通过分析网损和电压约束对误差的影响,给出文中模型应用场景的建议,为DSSR的实际应用提供了依据。

损失约束下多产品报童问题的求解方法研究

分析损失约束下多产品报童问题的解空间,并将其分为三个区域,给出了不同解区域的求解思路.参照Abdel-Malek的研究,提出了基于损失边际效用排序的删除法(LMU-D),来解决模型的非负约束问题,并与线性近似规划法相结合解决带积分符号的非线性约束优化问题.最后通过算例验证了方法和模型的有效性.

电压约束及网损对配电网大供电能力计算的影响

目前计算配电网大供电能力(TSC)的模型法与解析法均未计及电压约束和网损。文中利用配电网潮流计算研究了电压约束和网损对TSC计算结果的影响。首先,给出了基于潮流计算的配电网N-1安全校验方法,能精确计及网损、电压约束和调压措施。然后对TSC计算在有无考虑电压约束以及分别或同时考虑两种调压措施共5种场景下的TSC结果进行对比验证。结果表明,当前文献方法得到的TSC值偏大。网损对TSC的影响不可忽略,原因是N-1发生负荷转带后由于供电路径变长,其末端电压过低以及网损比正常运行时变大。计及调压措施后,电压约束也会使TSC值减小,除馈线过长或无载调压情况外其影响可以忽略。

不同约束下多产品报童问题解的比较研究

不同约束形式代表决策者不同的决策过程。传统多产品报童问题只考虑预算和能力等线性约束,而不涉及损失约束,显然有违真实的决策过程。为说明损失约束模型不同于预算约束模型的决策特点,通过算例对两个模型的解进行了分析和比较,总结了各自的决策特点。预算约束模型参照基于预算边际效用删除法和一般迭代方法(GIM法)求解。损失约束模型应用基于损失边际效用排序的删除法与线性近似规划法求解。另外对GIM法中衡量误差的公式作了修正。