Sampled-data modeling and dynamical effect of output-capacitor time-constant for valley voltage-mode controlled buck-boost converter

By analyzing the output voltage ripple of a buck-boost converter with large equivalent series resistance（ESR） of output capacitor, one valley voltage-mode controller for buck-boost converter is proposed. Considering the fact that the increasing and decreasing slopes of the inductor current are assumed to be constant during each switching cycle, an especial sampleddata model of valley voltage-mode controlled buck-boost converter is established. Based on this model, the dynamical effect of an output-capacitor time-constant on the valley voltage-mode controlled buck-boost converter is revealed and analyzed via the bifurcation diagrams, the movements of eigenvalues, the Lyapunov exponent spectra, the boundary equations,and the operating-state regions. It is found that with gradual reduction of output-capacitor time-constant, the buck-boost converter in continuous conduction mode（CCM） shows the evolutive dynamic behavior from period-1 to period-2, period-4, period-8, chaos, and invalid state. The stability boundary and the invalidated boundary are derived theoretically by stability analysis, where the stable state of valley voltage-mode controlled buck-boost converter can enter into an unstable state, and the converter can shift from the operation region to a forbidden region. These results verified by time-domain waveforms and phase portraits of both simulation and experiment indicate that the sampled-data model is correct and the time constant of the output capacitor is a critical factor for valley voltage-mode controlled buck-boost converter, which has a significant effect on the dynamics as well as control stability.

Design of Data Model for Urban Transport GIS

Constructing the data model for public transportation by integrating the spatial and the non-spatial information, is the basis of reasonable plan and effective management of urban public transport. This paper presents a transit data model based on geographic information systems (GIS) technology, which utilizes arc-node networks, and manages the foundational bus data with point, link, polygon and record features. In this model, a transport network is generated and maintained in a dynamic manner, and hence supports planning, construction, management, operation and optimization functions for transit facilities and routes, as well as day-to-day transactions. Public transportation GIS established on this model foundation will remarkably upgrade the construction level and the urban service ability.

数据空间建设的实践进展与运营模式分析——基于Data Spaces Radar的案例

数据空间建设为数据要素的价值实现提供了可资借鉴的实践经验,全面解析其实践进展与运营模式,有助于破解数据流通的现实堵点、拓展数据利用的发展思路。文章首先将结构主义分析方法中的案例研究法作为主要研究方法,综合运用文献调研、比较分析等方法,以Data Spaces Radar为案例来源,从实践进展、运营模式、核心要素等维度提炼数据空间建设特征。其次,在制度与技术的双轮驱动下,数据空间建设秉持制度规范统一性、技术设计整体性和治理模式协同性的架构原则,并按照产业思维、底线思维、信任思维、链式思维、集约思维的底层逻辑,形成较为完整的运营体系。未来应着力探索和解决数据空间视域下的数据资源价值化开发路径,针对数据流通堵点、利用痛点及信任难点问题,从体制机制、政策框架与技术应用等角度为推进我国的数据要素流通利用提供有益参考。

Method of Multi-Mode Sensor Data Fusion with an Adaptive Deep Coupling Convolutional Auto-Encoder

To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion.

K-Modes聚类数据收集和发布过程中的混洗差分隐私保护方法

针对目前聚类数据收集与发布安全性不足的问题,为保护聚类数据中的用户隐私并提高数据质量,基于混洗差分隐私模型,提出一种去可信第三方的K-Modes聚类数据收集和发布的隐私保护方法。首先,使用K-Modes聚类数据收集算法对用户数据进行采样并加噪,再通过填补取值域随机排列发布算法打乱采样数据的初始顺序,使恶意攻击者不能根据用户与数据之间的关系识别出目标用户。然后,尽可能减小噪声的干扰,利用循环迭代的方式计算出新的质心完成聚类。最后,从理论层面上分析了以上3种方法的隐私性、可行性和复杂度,并利用3个真实数据集和近年来具有权威性的同类算法KM、DPLM、LDPKM等进行准确率、熵值的对比,验证所提方法的有效性。实验结果表明,所提方法的隐私保护和发布数据质量均优于当前同类算法。

Wind Speed Prediction Based on Improved VMD-BP-CNN-LSTM Model

Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for short-term wind speed prediction was proposed to assist in power system planning and operation in this paper. Firstly, the wind speed time series data was processed using Variational Mode Decomposition (VMD) to obtain multiple frequency components. Then, each individual frequency component was channeled into a combined prediction framework consisting of BP neural network (BPNN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) after the execution of differential and normalization operations. Thereafter, the predictive outputs for each component underwent integration through a fully-connected neural architecture for data fusion processing, resulting in the final prediction. The VMD decomposition technique was introduced in a generalized CNN-LSTM prediction model;a BPNN model was utilized to predict high-frequency components obtained from VMD, and incorporated a fully connected neural network for data fusion of individual component predictions. Experimental results demonstrated that the proposed improved VMD-BP-CNN-LSTM model outperformed other combined prediction models in terms of prediction accuracy, providing a solid foundation for optimizing the safe operation of wind farms.

The Mode of Data Exchange between PHRDC and HIS

参照国家有关个人健康档案的标准。分析HIS与PHRDC之间门诊、住院等不同医疗信息的数据交换时机及不同类型医院HIS与PHRDC的数据交换方式。以研究医疗机构与个人健康档案数据中心之间接口。并设计不同的交换方式和数据交换时机。针对不同医疗信息、医疗机构规模，提出不同的数据交换时机和交换方式，实现个人健康档案数据中心与HIS数据交换根据固化医疗信息的特点择时上传。并能挑选符合自身实际的数据交换方式。

Data Flow&Transaction Mode Classification and An Explorative Estimation on Data Storage&Transaction Volume

The public has shown great interest in the data factor and data transactions,but the current attention is overly focused on personal behavioral data and transactions happening at Data Exchanges.To deliver a complete picture of data flaw and transaction,this paper presents a systematic overview of the flow and transaction of personal,corporate and public data on the basis of data factor classification from various perspectives.By utilizing various sources of information,this paper estimates the volume of data generation&storage and the volume&trend of data market transactions for major economies in the world with the following findings:(i)Data classification is diverse due to a broad variety of applying scenarios,and data transaction and profit distribution are complex due to heterogenous entities,ownerships,information density and other attributes of different data types.(ii)Global data transaction has presented with the characteristics of productization,servitization and platform-based mode.(iii)For major economies,there is a commonly observed disequilibrium between data generation scale and storage scale,which is particularly striking for China.(i^v)The global data market is in a nascent stage of rapid development with a transaction volume of about 100 billion US dollars,and China s data market is even more underdeveloped and only accounts for some 10%of the world total.All sectors of the society should be flly aware of the diversity and complexity of data factor classification and data transactions,as well as the arduous and long-term nature of developing and improving relevant institutional systems.Adapting to such features,efforts should be made to improve data classification,enhance computing infrastructure development,foster professional data transaction and development institutions,and perfect the data governance system.

Multi-mode process monitoring based on a novel weighted local standardization strategy and support vector data description

There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.

Fuzzy BC-k-modes:一种分类矩阵对象数据的聚类算法

传统的聚类算法主要对具有单值属性的数据进行聚类研究,针对矩阵对象数据的研究较少,提出一种新的fuzzy between-cluster k-modes(简称Fuzzy BC-k-modes)聚类算法。在Fuzzy BC-k-modes算法中,采用增加簇间信息(不同类中的对象到其他类中心的距离)去修正目标函数,在对修正的目标函数寻求局部最优解时,提出隶属度矩阵的更新公式。最后,在四个真实数据集上验证了Fuzzy BC-k-modes算法的有效性,并且分析了模糊因子与隶属度间的关系。

Signal classification method based on data mining formulti-mode radar

For the multi-mode radar working in the modern electronicbattlefield, different working states of one single radar areprone to being classified as multiple emitters when adoptingtraditional classification methods to process intercepted signals,which has a negative effect on signal classification. A classificationmethod based on spatial data mining is presented to address theabove challenge. Inspired by the idea of spatial data mining, theclassification method applies nuclear field to depicting the distributioninformation of pulse samples in feature space, and digs out thehidden cluster information by analyzing distribution characteristics.In addition, a membership-degree criterion to quantify the correlationamong all classes is established, which ensures classificationaccuracy of signal samples. Numerical experiments show that thepresented method can effectively prevent different working statesof multi-mode emitter from being classified as several emitters,and achieve higher classification accuracy.

基于DataV的气象可视化数据平台设计与实现

综合分析了传统气象数据展示的缺陷,针对气象数据量大、展示直观度低、分析研判少、智能演示弱等问题,提出将DataV技术引入可视化展示平台的解决方案,结合气象业务现状,将气象预警、预报、站点、服务产品等海量信息分析后集中展示,给决策机构提供可靠的数据展示;平台总体框架设计过程中,引入数据中台技术,使用了多线程B/S架构录入数据,后台采用了MySQL数据库存储服务,数据接口使用了Mybatis框架,前端使用DataV平台进行演示;阐述了系统各项技术设计方案,并给出具体设计方法。实际运行效果表明,该技术方案可以直观反映气象大数据分析的结果,极大地提高了气象数据的交互性、实时性、灵活性,更清晰地辅助于决策。

Analysis of Incomplete Data of Accelerated Life Testing with Competing Failure Modes

Data obtained from accelerated life testing （ALT） when there are two or more failure modes, which is commonly referred to as competing failure modes, are often incomplete. The incompleteness is mainly due to censoring, as well as masking which might be the case that the failure time is observed, but its corresponding failure mode is not identified. Because the identification of the failure mode may be expensive, or very difficult to investigate due to lack of appropriate diagnostics. A method is proposed for analyzing incomplete data of constant stress ALT with competing failure modes. It is assumed that failure modes have s-independent latent lifetimes and the log lifetime of each failure mode can be written as a linear function of stress. The parameters of the model are estimated by using the expectation maximum （EM） algorithm with incomplete data. Simulation studies are performed to check＇model validity and investigate the properties of estimates. For further validation, the method is also illustrated by an example, which shows the process of analyze incomplete data from ALT of some insulation system. Because of considering the incompleteness of data in modeling and making use of the EM algorithm in estimating, the method becomes more flexible in ALT analysis.

公共数据进入数据要素市场模式研究

公共数据是数据要素市场的重要组成部分,其如何进入数据要素市场以及其参与数据要素市场的模式分为几类,学界对此少有研究。通过梳理和比较,深入挖掘国内外公共数据参与数据要素市场的模式类型,归纳得出公共数据进入数据要素市场有两大类、五种模式,包括一级市场(授权运营)+二级市场(场内交易)模式,公共数据开发利用+场外交易模式,依托数据平台构建公共数据流通市场模式,借助数据经纪商、数据中介的交易模式,以及通过(公共)数据信托参与数据市场模式等。在此归纳基础上,演绎出公共数据进入数据要素市场的理想模式,这一理想模式分为数据获取、数据产品生产、数据产品交易三个各有其独特特征的环节,可为我国公共数据进入数据要素市场的策略选择提供参考。

大数据背景下神经系统变性病教学探索与实践

随着信息技术与教育的深度融合发展,新的教育理念和教学方法不断涌现。在大数据基础上,以神经系统变性病这类疑难病为例,对传统神经病学教学模式进行创新优化,建立以“学生为中心,能力为导向”的新型教学模式,根据教学内容,结合疾病特点,运用“慕课”“雨课堂”等多种教学平台,借助视频号、病例分享公众号等多种媒体平台,结合查找各大医学网站、资源库、数据库等方式,实现了一种数字化、信息化的教学模式,有效提升学科教学中知识传播的表达力和知识创新的穿透力,充分调动学生的主观能动性,促进学生对于知识的了解、掌握、思考和创新,从而由浅入深地逐渐认清疾病的本质,全方位地认识和掌握疾病,进而取得良好的教学效果。

权益区分理论下数据法益保护模式

大数据时代各种数据权益保护学说值得进一步反思与检讨。数据权益保护问题在我国既有规则体系内完全能够得到妥善解决。因数据的非排他性与权利的排他效力间的根本冲突,数据权益难以被纳入绝对权或相对权中进行保护,而各类新型数据权利混淆了权利在民法体系中的定位,忽视了体系功能,甚至阻遏数据的共享与流通。从体系逻辑、制度调适成本与数据治理全球发展方向来看,数据权益应当被纳入法益保护模式中。数据法益保护应以事实控制状态为基础,将数据权益直接归属于事实控制人,保护范围以各个主体享有的事实利益范围为限。数据产权分置政策应经法律解释还原为数据法益归属确认的基础前提与数据主体行为规范的设计方向。

基于历史数据驱动的厂站接线图自动生成

在分析电网变电站主接线方式及特征的基础上,结合电网历史图形数据,研究实现无拓扑信息场景下的电网厂站接线图自动生成。利用设备双重命名规范构建拓扑关系;通过解析历史图形间隔的组成结构,自动构建厂站特征库,为绘制目标厂站间隔提供数据基础。基于特征向量及子图匹配算法进行特征模板匹配,进而研究基于图形模板和间隔模板的布局算法,完成基于历史数据驱动的图形自动生成,达到电网厂站接线图图模数一体化构建的目的,为自动化人员的日常运维工作提供支撑。经过实际项目应用,自动生成的图形满足要求。

“元宇宙+ChatGPT”赋能个人科学数据共享模式构建研究

个人科学数据是数据要素的重要组成部分之一,其高价值特征使其成为国家关键性的基础战略资源,运用新一代信息技术构建和优化个人科学数据共享模式具有理论意义和应用价值。首先界定了研究边界并分析了相关研究进展,然后讨论了元宇宙赋能个人科学数据共享的交互逻辑,探索了ChatGPT赋能个人科学数据市场的信息组织与服务,在此基础上构建了“元宇宙+ChatGPT”赋能个人科学数据市场共享模式,并对其技术实现路径和共享实践进路进行了系统论述。通过引入元宇宙技术框架与GPT大语言模型,构建个人科学数据市场共享模式,以期为科学数据共享、交易、流通的科学发展提供理论依据和实践参考。

基于人工神经网络的透射水体亚硝酸盐含量模拟估计

亚硝酸盐是水体的重要测试指标,对水体质量的评估有着重要意义。采用透射高光谱结合人工神经网络(ANN)建立水体亚硝酸盐含量估算模型。首先采用试剂配制10种浓度的亚硝酸氮标准溶液(0.02、0.04、0.06、0.08、0.10、0.12、0.14、0.16、0.18和0.20 mg/L),并使用OCEAN-HDX-XR微型光纤光谱仪扫描10次各浓度亚硝酸盐溶液在181.1~1023.1 nm的透射光谱,取平均值作为各浓度亚硝酸盐溶液原始透射光谱,分别使用最大最小均一化(MMN)、标准正态变化(SNV)、多元散射校正(MSC)、以及二阶差分(SOD)四种光谱预处理方法,并结合ANN方法建立水体亚硝酸盐含量估算模型,通过比较模型的精度来选择最优的模型进行水体亚硝酸盐含量的估计。结果显示,基于二阶差分预处理下的BP-ANN神经网络预测模型中的均方根误差RMSE为0.032367,平均绝对误差MAE为0.016895,决定系数R^(2)为0.987403,与二次有理高斯过程回归(QR-GPR)和二次支持向量机(Q-SVM)预测模型相比,该模型的拟合效果更好,精确度更高。提出了反向传播人工神经网络(BP-ANN)高光谱水质亚硝酸盐参数的反演方法,为水质亚硝酸盐参数动态检测提供了新方法。

科学数据出版在我国的实践研究——以《图书馆杂志》为例

通过对《图书馆杂志》科学数据出版实践进行分析,为我国科学数据出版发展提供参考和借鉴。文章梳理科学数据出版的发展历程,归纳科学数据出版的三种主要模式:独立数据出版、论文辅助资料数据出版、数据论文出版。文章深入分析《图书馆杂志》科学数据出版模式、科学数据出版流程和数据出版管理平台的服务功能,对《图书馆杂志》科学数据出版业务的未来进行展望,认为《图书馆杂志》数据出版尚处于探索阶段,还需对标国际知名出版机构的科学数据出版经验,完善科学数据出版的各个环节,以期更好地服务科研群体,助力我国学术期刊科学数据出版高质量发展。