数据产权刑法保护模式的构建

数据产权是一种具有财产权属性的新型权利,该权利与虚拟财产权和知识产权具有明显差异。数据产权属于刑法保护的公法益范畴,其应归属于市场经济秩序法益之中。现行刑法有关财产犯罪、知识产权犯罪和数据犯罪的规定均无法实现对数据产权的全面保护。刑法对数据产权的保护应以促进数据流通为主要目标,且应当覆盖数据处理的各流程阶段。对侵犯数据产权行为进行刑法规制时,应对“未经同意”获取数据行为审慎入罪。应增设妨害数据流通管理秩序罪,对“情节严重的”妨害数据流通管理秩序行为予以刑法规制。

基于邻域优势粗糙集的区分度动态属性约简算法

为解决动态环境下数值型偏序关系数据的属性约简问题,利用优势粗糙集的区分度提出一种增量式属性约简算法。在数值型信息系统环境下,定义邻域优势区分度度量,通过邻域优势区分度设出一种非增量式属性约简算法;研究和分析对象变化场景下邻域优势区分度进行增量式更新的原理;分别提出数据对象增加和减少情形下数据集属性约简的增量式更新算法。在多个UCI数据集上进行实验验证,实验结果表明,该增量式算法能够有效完成动态数据的属性约简任务。

Second-Order MaxEnt Predictive Modelling Methodology. I: Deterministically Incorporated Computational Model (2nd-BERRU-PMD)

This work presents a comprehensive second-order predictive modeling (PM) methodology designated by the acronym 2nd-BERRU-PMD. The attribute “2nd” indicates that this methodology incorporates second-order uncertainties (means and covariances) and second-order sensitivities of computed model responses to model parameters. The acronym BERRU stands for “Best- Estimate Results with Reduced Uncertainties” and the last letter (“D”) in the acronym indicates “deterministic,” referring to the deterministic inclusion of the computational model responses. The 2nd-BERRU-PMD methodology is fundamentally based on the maximum entropy (MaxEnt) principle. This principle is in contradistinction to the fundamental principle that underlies the extant data assimilation and/or adjustment procedures which minimize in a least-square sense a subjective user-defined functional which is meant to represent the discrepancies between measured and computed model responses. It is shown that the 2nd-BERRU-PMD methodology generalizes and extends current data assimilation and/or data adjustment procedures while overcoming the fundamental limitations of these procedures. In the accompanying work (Part II), the alternative framework for developing the “second- order MaxEnt predictive modelling methodology” is presented by incorporating probabilistically (as opposed to “deterministically”) the computed model responses.

Fourth-Order Predictive Modelling: II. 4th-BERRU-PM Methodology for Combining Measurements with Computations to Obtain Best-Estimate Results with Reduced Uncertainties

This work presents a comprehensive fourth-order predictive modeling (PM) methodology that uses the MaxEnt principle to incorporate fourth-order moments (means, covariances, skewness, kurtosis) of model parameters, computed and measured model responses, as well as fourth (and higher) order sensitivities of computed model responses to model parameters. This new methodology is designated by the acronym 4th-BERRU-PM, which stands for “fourth-order best-estimate results with reduced uncertainties.” The results predicted by the 4th-BERRU-PM incorporates, as particular cases, the results previously predicted by the second-order predictive modeling methodology 2nd-BERRU-PM, and vastly generalizes the results produced by extant data assimilation and data adjustment procedures.

Second-Order MaxEnt Predictive Modelling Methodology. II: Probabilistically Incorporated Computational Model (2nd-BERRU-PMP)

This work presents a comprehensive second-order predictive modeling (PM) methodology based on the maximum entropy (MaxEnt) principle for obtaining best-estimate mean values and correlations for model responses and parameters. This methodology is designated by the acronym 2nd-BERRU-PMP, where the attribute “2nd” indicates that this methodology incorporates second- order uncertainties (means and covariances) and second (and higher) order sensitivities of computed model responses to model parameters. The acronym BERRU stands for “Best-Estimate Results with Reduced Uncertainties” and the last letter (“P”) in the acronym indicates “probabilistic,” referring to the MaxEnt probabilistic inclusion of the computational model responses. This is in contradistinction to the 2nd-BERRU-PMD methodology, which deterministically combines the computed model responses with the experimental information, as presented in the accompanying work (Part I). Although both the 2nd-BERRU-PMP and the 2nd-BERRU-PMD methodologies yield expressions that include second (and higher) order sensitivities of responses to model parameters, the respective expressions for the predicted responses, for the calibrated predicted parameters and for their predicted uncertainties (covariances), are not identical to each other. Nevertheless, the results predicted by both the 2nd-BERRU-PMP and the 2nd-BERRU-PMD methodologies encompass, as particular cases, the results produced by the extant data assimilation and data adjustment procedures, which rely on the minimization, in a least-square sense, of a user-defined functional meant to represent the discrepancies between measured and computed model responses.

Fourth-Order Predictive Modelling: I. General-Purpose Closed-Form Fourth-Order Moments-Constrained MaxEnt Distribution

This work (in two parts) will present a novel predictive modeling methodology aimed at obtaining “best-estimate results with reduced uncertainties” for the first four moments (mean values, covariance, skewness and kurtosis) of the optimally predicted distribution of model results and calibrated model parameters, by combining fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters. Underlying the construction of this fourth-order predictive modeling methodology is the “maximum entropy principle” which is initially used to obtain a novel closed-form expression of the (moments-constrained) fourth-order Maximum Entropy (MaxEnt) probability distribution constructed from the first four moments (means, covariances, skewness, kurtosis), which are assumed to be known, of an otherwise unknown distribution of a high-dimensional multivariate uncertain quantity of interest. This fourth-order MaxEnt distribution provides optimal compatibility of the available information while simultaneously ensuring minimal spurious information content, yielding an estimate of a probability density with the highest uncertainty among all densities satisfying the known moment constraints. Since this novel generic fourth-order MaxEnt distribution is of interest in its own right for applications in addition to predictive modeling, its construction is presented separately, in this first part of a two-part work. The fourth-order predictive modeling methodology that will be constructed by particularizing this generic fourth-order MaxEnt distribution will be presented in the accompanying work (Part-2).

考虑载客状态的改进孤立森林浮动车异常数据检测算法

为提高浮动车数据中异常数据检测能力及不同载客状态下的模型检测分析能力,提出基于S-DTA-IIForest(Summation&Difference Third Order Average&Improvement-Isolation Forest)的浮动车数据异常检测算法。构建由相邻两项求和(S)、三阶求和平均差分(DTA)的二维度空间SDTA特征向量;提出差额累计更新和动态区分辨识的改进孤立森林IIForest算法,通过设置停止阈值参数,避免当出现新样本异常值分数大于停止阈值时,仅更新样本不更新孤立森林模型的问题,设计每个二叉树区分辨识度参数,区分辨识度位于停止区间时停止二叉树生长,提高算法收敛性能,以ROC(Receiver Operating Characteristic)曲线下面积AUC(Area Under ROC Cure)、F1-score为指标对模型精度进行对比分析,并以重庆市中心城区学府大道开展实例验证。结果表明:本文S-DTA-IIForest组合算法AUC、F1-score分别为86.63%、0.89,AUC较传统孤立森林IForest(Isolation Forest)提高32.4%,运行效率提高1.29%,具有收敛速度更快、精度更高的优势,载客条件下模型AUC、F1-score较未载客分别提高7.7%、10.8%,组合算法对载客数据有更高的检测精度,且未载客状态数据异常率较载客状态增加71.4%,未载客数据异常率更高。

数字经济环境下数据犯罪规制和认定模式的演变

在数字经济环境下,保证数据安全和保障数据共享活力同样重要,数据犯罪的规制和认定应当同时满足这两方面的需求。现有数据犯罪规制和认定采用的是权利保护模式,即通过保护数据权利主体的利益进行数据犯罪规制和认定。权利保护模式不能满足数字经济发展的需求;以非法获取行为为规制重点的前置化保护不当可能导致数据流通受阻;现行刑法规制数据犯罪行为方式的局限可能导致数据安全保护不全面。数据犯罪规制和认定应当采用秩序维护模式,关注数据价值实现过程中各方主体的利益,看重一般数据的公共产品属性。为实现数据犯罪规制和认定的秩序维护模式,立法者应当增设维护数据管理秩序的新罪名。在数据犯罪的司法认定上,也需要及时调整思路,做好保护数据安全和保障数据共享活力之间的利益平衡。

含有输入时滞的非线性系统的输出反馈采样控制

针对含有输入时滞和低阶非线性项的非线性系统,提出一种基于采样机制的无记忆输出反馈控制方法.该方法移除了传统预测控制方法预测映射难以确定的限制,同时避免了时滞依赖方法对过去时刻状态信息的依赖性,在实际中更易实现.首先,根据系统输出在采样时刻的信息,利用加幂积分技术和齐次占优思想设计了无记忆输出反馈采样控制器.然后,利用齐次系统理论提出了闭环系统的稳定性条件.最后,仿真结果验证了所提方法的有效性和优越性.

“组态软件技术”课程教学设计与实践

在工程教育专业认证持续深入推进的背景下,结合电气工程专业特色,对“组态软件技术”课程教学进行重新设计与教学实践。引入高阶思维临场认知对教学内容进行重构,应用课堂多源数据建立教学评测一体化的教学新模式。实践表明该课程的教学设计有着良好的沉浸式体验感,学生主动建构、解决问题的高阶思维能力得到提升,新的教学模式促进了课程教学的高质量发展。

数据不正当竞争行为判定的反思与完善

准确判定数据不正当竞争行为是规制该类行为的关键。实证考察2012-2023年的数据不正当竞争案件裁判文书后发现,对数据不正当竞争行为判定,普遍适用权益侵害判定范式,这误读了反不正当竞争法的法律属性;对竞争关系的认定存在拓宽化的倾向,其在数字时代反不正当竞争法适用中的现实价值和改进进路尚待厘清;既有的以“搭便车”原则、互联网行业惯例和法官创制性细则为标准具体化商业道德的进路皆无法化解商业道德的不确定性难题。对此,为促进数据健康有序流动,维护数字市场的竞争秩序,应实现数据不正当竞争行为判定路径的“系统性”变革。首先,应实现权益侵害判定范式向行为正当判定范式的有效转变,确保法院裁判结果符合反不正当竞争法的法益保护目标和契合数字经济发展的需求;其次,纠正“竞争关系决定论”的误区,调整竞争关系在数据不正当竞争案件中的角色定位,将其作为认定原告资格的重要标准和评测损害程度的因素;最后,认定商业道德应兼顾公平竞争价值与自由竞争价值,遵循“由事实到价值”的商业道德内涵的确定步骤,肯定模仿自由并慎用“搭便车”裁判标准,并且在援引互联网行业惯例时应对其是否符合法律法规和有助于维护竞争秩序予以考察,应让创设的商业道德具体细则符合反不正当竞争法的价值目标。

结合区块链和可追踪CP-ABE的数据存储与共享研究

传统密文策略属性基加密(CP-ABE)方案为资源受限的用户在云端安全存储与共享数据时,存在计算开销大和无法追踪恶意泄露密钥的用户问题,以及中心化的云存储容易造成信任和安全问题.针对上述问题,本文提出一种结合区块链和可追踪CP-ABE的数据存储与共享方案(BTABEDSS).数据所有者将数据密文存储在IPFS上,区块链上仅存储数据的唯一标识、数据的哈希值和数据密文在IPFS检索的内容哈希值等元数据信息,既保证了数据安全可信存储与访问,又缓解了区块链的存储压力.利用智能合约和CP-ABE协同实现数据的细粒度访问控制,只有满足访问控制策略的非恶意用户才能访问共享数据.使用椭圆曲线上的标量乘运算和表达性、计算性更优的有序二元决策图(OBDD)访问结构,有效降低了系统的计算和存储开销.使用概率加密方案将用户身份信息随机化处理后嵌入用户密钥,从而实现对恶意泄露密钥的用户进行高效追踪并撤销其访问权限.安全性与实验分析表明该方案安全可行,与对比方案相比,降低了系统运行成本和开销,提升了系统操作效率.

元宇宙时代法律秩序的风险挑战与制度回应

元宇宙是运用区块链、人工智能、大数据等高新数字技术创造出的虚拟世界,其本质是对现实社会生活的映射。元宇宙的“数字技术化”“技术数字化”以及类似于“数字社会”的技术特征,这些都对现行法律秩序产生了重大冲击。对此予以回应的法治路径,存在“重构”与“调整”之争。基于元宇宙时代法律秩序的本质内涵而言,会发现元宇宙对于现行法律秩序只是“新瓶装旧酒”。人们虽然可以在“元宇宙社会”中进行各种活动,但并不意味这就是人们未来的生活方式,故尚无必要为其重构法律秩序。而是应基于元宇宙的技术特征进行制度回应,从重视数字基础设施的建设、规范数据的收集与处理、确立良性算法的价值导向、落实数字平台主体的责任层面出发,以有效应对元宇宙时代带来的风险挑战。

基于双通道生成对抗网络的城市用电负荷缺失数据补全方法

用电负荷数据的完整性与有效性在负荷预测等应用中具有重要意义。传统的缺失数据补全方法缺乏对用电负荷和多种外部时空关联信息的挖掘,难以获得高精度的补全结果。文中提出了一种双通道生成对抗网络,对缺失的负荷数据进行补全。首先,根据负荷的周期性变化特征和时空关联性构建三阶负荷张量,并将影响负荷变化的多种外部因素构建为三阶辅助信息张量。然后,为满足两种张量的双输入需求,在生成对抗网络的输入层引入双通道机制,通过卷积与反卷积运算提取张量的特征;为提升网络对张量数据的训练效果和补全精度,将张量分解损失引入原始损失函数,并采用改进的混沌映射粒子群优化算法联合优化超参数和网络。最后,在真实负荷数据集上开展数据补全实验。结果表明,所提方法能够对随机缺失率不超过50%、连续缺失不超过3天的负荷数据进行准确补全。

基于数据驱动的主燃油计量装置故障诊断

为建立主燃油计量装置的故障诊断方法,提出一种基于快速存取记录器(QAR)数据的燃油计量参数估计模型建模方法,并结合液压机械控制装置工作原理,给出了基于燃油计量参数残差设计的主燃油计量装置故障诊断方案。设计基于系统辨识的燃油计量活门位置(FMVP)估计模型,采取降阶处理策略以提高模型的估计精度和动态响应速度,在此基础上给出基于多项式拟合的燃油流量(FF)估计模型。结果表明:所建立的FMVP模型估计残差不超过±2%,FF估计模型残差不超过±5%;该方案能够有效地诊断出燃油计量活门故障、线性可变差动位移传感器故障和热式流量计故障。该方案结构简单,不需要大量的调试技术,提高了故障检测精度,具有工程应用前景。

基于Visual Studio下MES系统开发与应用

针对自动化罐装产线存在工序间实时信息无法互享互通,生产数据滞后,订单、制造、仓储模块间不协同的问题,基于Visual Studio软件,在C/S架构模式下,设计开发了自动化罐装产线的MES系统。可实现订单排产功能对生产订单全周期管理、数据监控经S7协议实时采集产线数据、SQL Server数据库对信息储存和分析、仓储管控实现自动化、精益化产品库存管理。MES系统产线运行表明,订单准时交付率100%,采集产线数据完整精确,仓储管控智能精益。为产业“智改数转”提供了典型的工程应用范例和借鉴。

基于小扰动频率量测数据的电网节点惯量分布评估方法

惯量在电力系统抵抗扰动中起到关键作用。随着新能源渗透率的提升,电力系统中低惯量与惯量时空分布不均的特征日益凸显,准确评估电网节点惯量分布具有重要意义。为此,文中提出一种基于小扰动频率量测数据的节点惯量分布评估方法。首先,分析了频率与惯量的数学耦合机理,揭示了新型电力系统惯量分布特征。然后,根据单次小扰动下的频率量测数据与系统电源参数,分别提出了考虑各节点频率相较于系统中心频率变化水平偏差的节点等效惯量定义方法,以及基于自适应阶数多项式拟合的节点频率变化率计算方法,两层方法相结合即可计算出单次小扰动下的节点等效惯量。进一步,鉴于单次小扰动下求出的节点等效惯量具有一定的随机误差,提出考虑多次扰动事件评估结果的动态聚合策略,形成适用于新型电力系统的节点等效惯量分布评估方法。最后,分别以IEEE 39节点系统、中国某省级电网为例验证了所提方法的有效性。结果表明,所提方法所需数据量少,仅需各节点同步相量测量单元采集的频率量测数据即可实现对电网惯量分布的有效评估,评估结果准确性高且时效性强。

物理信息神经网络求解五阶emKdV方程的正反问题

该文利用物理信息神经网络(PINNs)对扩展的五阶mKdV(emKdV)方程的正反问题进行求解,并对孤子的动力学行为进行分析、模拟.针对正问题,选用双曲正切函数tanh作为激活函数求解方程的一、二、三孤子解,并将PINNs方法求得的数据驱动解与借助简化的Hirota方法给出的方程精确解进行比较,一孤子解的精度为O(10^(-4)),二、三孤子解的精度为O(10^(-3)).针对反问题,分别由一、二、三孤子解的数据进行驱动求解方程的两个待定系数,并在不同的噪声下探究算法的鲁棒性.当在训练数据中加入1%的初始噪声或观测噪声时,待求系数的预测精度可分别达到O(10^(-3))和O(10^(-2));当加入3%的初始噪声或观测噪声时,预测精度依然可以达到O(10^(-2));由实验数据分析可知观测噪声对PINNs模型的影响要略大于初始噪声.

处理面板数据的偏序综合评价方法

作为主流处理截面数据的多准则综合评价方法,面对“升级”版的截面即面板数据,几乎失去了“评价”能力。由于时间权重难以通过经验或理论途径获取,使得依赖多个指标进行集结的聚合函数无法运算,导致无法比较方案。应用易于获取的时间权重空间代替时间权重作为聚合函数参数,围绕权重空间极值点集构建偏序关系,进而运行聚合函数实施方案比较。结果表明:只要明确时间和指标各自的权重空间,便能通过偏序Hasse图表达评价结果,不仅能比较优劣,还能反映稳健程度。最后,通过辽宁省14个城市物流行业的面板数据,可以看出偏序综合评价方法不仅能有效处理面板数据,同时具有稳健和分层的特色功能。

Second-Order MaxEnt Predictive Modelling Methodology. III: Illustrative Application to a Reactor Physics Benchmark

This work illustrates the innovative results obtained by applying the recently developed the 2nd-order predictive modeling methodology called “2nd- BERRU-PM”, where the acronym BERRU denotes “best-estimate results with reduced uncertainties” and “PM” denotes “predictive modeling.” The physical system selected for this illustrative application is a polyethylene-reflected plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. This benchmark is modeled using the neutron transport Boltzmann equation (involving 21,976 uncertain parameters), the solution of which is representative of “large-scale computations.” The results obtained in this work confirm the fact that the 2nd-BERRU-PM methodology predicts best-estimate results that fall in between the corresponding computed and measured values, while reducing the predicted standard deviations of the predicted results to values smaller than either the experimentally measured or the computed values of the respective standard deviations. The obtained results also indicate that 2nd-order response sensitivities must always be included to quantify the need for including (or not) the 3rd- and/or 4th-order sensitivities. When the parameters are known with high precision, the contributions of the higher-order sensitivities diminish with increasing order, so that the inclusion of the 1st- and 2nd-order sensitivities may suffice for obtaining accurate predicted best- estimate response values and best-estimate standard deviations. On the other hand, when the parameters’ standard deviations are sufficiently large to approach (or be outside of) the radius of convergence of the multivariate Taylor-series which represents the response in the phase-space of model parameters, the contributions stemming from the 3rd- and even 4th-order sensitivities are necessary to ensure consistency between the computed and measured response. In such cases, the use of only the 1st-order sensitivities erroneously indicates that the computed results are inconsistent with the respective measured response. Ongoing research aims at extending the 2nd-BERRU-PM methodology to fourth-order, thus enabling the computation of third-order response correlations (skewness) and fourth-order response correlations (kurtosis).