具有数量特征的利润约束关联规则发现

关联规则发现是数据挖掘的重要研究内容之一,具有广泛的应用。在以前的研究中,关联规则发现算法一般都没有考虑项目的销售数量。而在实际中,相关联项目的销售总利润对于商家来说非常重要。因此,在关联规则发现中引入销售数量的利润约束问题显得很必要。文章针对此问题提出了项集事务利润和事务利润的概念,并对项集利润和支持期望的概念进行了重新定义,接着给出了具有数量特征的利润约束关联规则挖掘问题的性质和定理,最后根据该问题的性质给出了高效的求解算法。     

精密测量相机畸变测量及数据处理

本文介绍了一种精密测量相机畸变与主距的测量及数据处理方法,介绍了这种测量原理,仪器装置,分析了其测量精度,同时给出了一种通过用正交多项式对常用的最小二乘法拟合方法的改进来增加畸变曲线拟合精库的算法。本文所测量的对象是长春光机学院正在研制的“高空CCD立靶”测量系统。     

两级电力市场环境下考虑条件风险价值的新能源场站最优售电模型EI北大核心CSCD

为充分发挥能源资源优化配置作用,我国正逐步建成“统一市场、两级运作”的两级电力市场。在两级电力市场建设初期,新能源场站参与省间电力现货交易的计划电力缺乏指导性是目前亟须研究的问题。对省间-省内两级电力市场的现行机制进行了深入研究,探讨了其运作框架。考虑省间电力现货交易与辅助服务市场的多时间、多空间耦合特性,构建了两级电力市场下新能源场站的售电决策双层优化模型,并基于条件风险价值法对下层模型进行转化,实现对新能源场站出力不确定性的控制。依托东北某省工程实际数据进行了算例分析,结果表明该模型能够在两级电力市场环境下提高新能源场站的市场收益。     

Magnetic Large-Mesoporous Nanoreactors Enable Enzymatically Regulated Background-Free and Persistently Signalling Diseases Diagnosis

Diverse diseases and increasing prevalence pose a serious threat to public health. Point-of-care testing (POCT) techniques have imposed superior requirements over sensitivity, selectivity, robustness, affordability, and high-throughput. However, transient signal, complex sample pretreatment, and low signal-to-noise ratio make POCT severely limited in detection accuracy, efficiency, and sensitivity. Here, an enzyme-assisted magnetic large-mesoporous nanoreactor (FS) is constructed for achieving persistent-chemiluminescence signal output and eliminating matrix interference in disease diagnosis. The core-shell structured FS synthesized via an interface coassembly method exhibits uniform size, large and open mesopores (≈22 nm), and intrinsic magnetic separability. Such unique FS acts as efficient nanoreactor for confined cascade reactions enable efficient persistent-chemiluminescence (pCL) signal transduction and high-SNR chromogenic analysis of diverse biomarkers. The developed pCL assays facilitate high-sensitive determination of chronic disease biomarkers glucose and uric acid with detection limits (DL) of 5.4 mg L⁻¹ and 151.2 ng L⁻¹, respectively. The proposed chromogenic immunoassay enables an ultrasensitive and visual determination of alpha-fetoprotein with a DL of 1.2 ng L⁻¹, which is superior to previously published immunoassays. The feasibility of the developed methods for real-world applications are demonstrated in 159 clinical serum samples, and the determination results agree well with the clinical data. The proposed technique is expected to promote highly sensitive disease diagnosis in primary medical institutions and resource-limited areas since not relying on expensive automatic sampling and testing instruments. The good flexibility of the customizable nanoreactor makes it a powerful tool for developing various POCT techniques for rapid, sensitive, and accurate diseases diagnosis.     

An Apoptotic Body-based Vehicle with Navigation for Photothermal-Immunotherapy by Precise Delivery and Tumor Microenvironment Regulation

Tumor precision therapy and preventing tumor recurrence and metastasis are the main challenges to tumor eradication. Herein, an apoptotic body-based vehicle with imaging navigation is developed for precise tumor delivery and photothermal-immunotherapy by IR820-conjugated apoptotic body loaded with R848 nanoparticles. The apoptotic body serves as ammunition stores as well as vehicle drive engines, while IR820 acts as a fluorescence imaging navigation and photothermal controlling system. The apoptotic body vehicle can deliver the ammunition to tumor and achieve deep penetration by macrophage-hitchhiking. Fluorescence imaging navigation opens a control window for photothermal treatment, followed by photothermal triggering of in situ vaccine formation. Further, CD47 antibody loaded hydrogel strengthens innate and adaptive immunity, simultaneously the polarization of macrophages regulates the immunosuppressive microenvironment to further promote the combined antitumor immunotherapy. With breast tumor (4T1)-bearing mice model, the apoptotic body vehicle performs excellent therapeutic efficacy for primary tumor, distant tumor, tumor metastasis, and recurrence prevention.     

云应用程序编程接口安全研究综述:威胁与防护

云时代,云应用程序编程接口(API)是服务交付、能力复制和数据输出的最佳载体。然而,云API在开放服务和数据的同时,增加了暴露面和攻击面,攻击者通过数据劫持和流量分析等技术获取目标云API的关键资源,能够识别用户的身份和行为,甚至直接造成背后系统的瘫痪。当前,针对云API的攻击类型繁多,威胁与防护方法各异,缺乏对现有攻击和防护方法的系统总结。该文梳理了云API安全研究中云API面临的威胁和防护方法,分析了云API的演化历程和类别划分;讨论了云API的脆弱性以及云API安全研究的重要性;提出了云API安全研究框架,涵盖身份验证、云API分布式拒绝服务(DDoS)攻击防护、重放攻击防护、中间人(MITM)攻击防护、注入攻击防护和敏感数据防护6个方面相关研究工作综述。在此基础上,探讨了增加人工智能(AI)防护的必要性。最后给出了云API防护的未来挑战和发展趋势。     

磁悬浮轴承热泵压缩机设计与应用研究

磁悬浮轴承具有无摩擦、无磨损以及无需润滑等诸多优点,在鼓风机、压缩机和暖通领域有广阔的应用前景。针对65 kW、30 000 r/min热泵压缩机,基于最大承载力要求和电机转子轴径限制,设计了一款5自由度（5-DOF）主动磁悬浮轴承。针对压缩机成柜后出现的模态共振问题,通过增加安装机座支撑强度的方法抑制和消除了共振模态。该磁悬浮轴承成功应用于实际热泵压缩机,在全工况运行范围,磁悬浮轴承系统的灵敏度函数小于12 dB,转子振动峰-峰值最大值不超过80 μm,工作转速范围轴振峰-峰值小于20 μm,满足ISO 14839规定的长期稳定运行要求。     

基于B-样条曲线暗通道的图像去雾算法

针对暗原色去雾算法在高亮背景图像中出现光晕（Halo）效应的问题,提出一种基于B-样条曲线加gamma（γ）修正透射率去除Halo效应的算法。通过分析暗原色透射率图像的统计直方图,得出Halo效应的产生是由于HE算法透射率直方图中低灰度值占比较大,因此需要对直方图低灰度包络进行修正。寻找直方图中第1个低灰度波峰,在2个波谷间进行子区间分割,并以区间边界点作为样条区间节点计算三次样条曲线,以拟合低灰度包络。基于透射率图中修正位置对修正结果的关键性作用,采用改进的大津法从横向和纵向两个维度对透射率图进行分割,以确定修正区域,实现精准补偿,提高抗噪性能。根据不同雾图选择合适的γ修正系数修正拟合的灰度包络并对图像进行还原,验证不同修正系数对结果的影响。实验结果表明,当修正系数γ>1.5时,Halo效应能被有效减缓或消除,与HE算法相比,该算法的对比度、信息熵最大可分别提高10.1%和9.9%,算法并行度可达到M或N级。     

Cross-Domain Gated Learning for Domain Generalization

Domain generalization aims to improve the generalization capacity of a model by leveraging useful information from the multi-domain data. However, learning an effective feature representation from such multi-domain data is challenging, due to the domain shift problem. In this paper, we propose an information gating strategy, termed cross-domain gating (CDG), to address this problem. Specifically, we try to distill the domain-invariant feature by adaptively muting the domain-related activations in the feature maps. This feature distillation process prevents the network from overfitting to the domain-related detailed information, and thereby improves the generalization ability of learned feature representation. Extensive experiments are conducted on three public datasets. The experimental results show that the proposed CDG training strategy can excellently enforce the network to exploit the intrinsic features of objects from the multi-domain data, and achieve a new state-of-the-art domain generalization performance on these benchmarks.