无数据计算应用题解题策略

无数据计算应用题是题中没有明显数据而需经过计算才能回答的特殊题型，此题型有较高的抽象性和迷惑性，也给列式求解带来了一定的困难．因此，掌握其解题思路是很有必要的．

计算社会科学与科学决策的未来

计算社会科学的发展为复杂决策带来变革的可能。尽管专家知识和判断仍将是决策的重要依据,但基于客观数据和计算的判断将有可能让决策变得更加科学。计算社会科学有两大技术路径:一是以“模仿”(imitate/mimic)为基础的机器学习与人工智能技术;二是以“模拟”(simulate)为基础的行为体建模(Agent-Based Modeling,ABM)技术。而未来的一个重要方向应该是机器学习与ABM技术的结合。根据计算社会科学的这一特点,研究者需要超越“大数据计算”思维,采取“全数据计算”思维,根据需要解决的不同问题确立不同的数据需求组合,而不是让数据量或者来源来决定研究问题。鉴于计算社会科学对决策的积极作用,中国应在“决策计算社会科学”上有所作为。

对平均增长量计算方法的新思考

对平均增长量计算方法的新思考《社会经济统计学原理》在介绍平均增长量计算方法时写到：平均增长量＝研究范围内环比增长量之和平均增长量＝研究范围内环比增长量之和＝最末期定基增长量研究范围的时间长度笔者认为上述平均增长量的计算方法略欠妥当，其原因是上述平均增...

用于真空计不确定度计算的应用程序

基于冶金、化工分析、航天等领域对真空技术的不断需求,计量工作中对于各种真空计的校准工作量不断增加,如何提高针对真空计的数据计算效率就显得尤为重要。本文给出一个简单、只需将记录中的数据逐一输入相应对话框中即可得出不确定度结果的计算程序,解决了手算繁琐易错的问题,同时也大大地提高了真空计不确定度的计算效率。

高速气流条件下污秽颗粒在复合绝缘子表面的沉积判据

研究高速气流条件下的绝缘子积污对沙尘暴、动车组高速运行等状况下的绝缘子清洗、预防污闪等工作具有重要指导意义。提出了污秽颗粒在复合绝缘子表面的沉积判据,结合该判据建立了高速气流条件下复合绝缘子积污的计算流体力学(CFD)模型。以动车组高压隔离开关绝缘子为对象进行了算例分析,通过实验以及与相关文献结果对比,从污秽分布特性的角度验证了沉积判据及计算流体力学模型的有效性。仿真分析了不同粒径下的绝缘子积污,表明粒径对绝缘子积污有较为明显的影响。当粒径较小时,漩涡碰撞为污秽颗粒与绝缘子碰撞的主要方式,此时碰撞和沉积可能发生在迎风面和背风面;当粒径较大时,惯性碰撞为污秽颗粒与绝缘子碰撞的主要方式,此时碰撞和沉积只发生在迎风面;当粒径大到一定程度时,迎风面和背风面均不会发生沉积。

基于直接判据提取方式的直轴电流补偿型IPMSM最大转矩电流比控制算法

为实现内嵌式永磁同步电机(IPMSM)高性能运行,该文提出一种最大转矩电流比(MTPA)决策与控制算法,旨在简化控制结构、加快系统动态响应、增强系统鲁棒性。该算法根据dq轴电流直接计算电磁转矩的微分项,并以此作为MTPA状态的决策判据。根据判据值对d轴电流参考值进行补偿,实现对MTPA状态的在线追踪。所提算法无需实际或虚拟信号注入和解算环节,系统动态性能得以提高。d轴电流补偿控制解决了算法收敛速度对负载条件较敏感的问题,提升了控制系统的鲁棒性。理论分析、仿真计算和实验结果验证了该方法的有效性和优越性,与虚拟信号注入法相比,所提出的算法在不同负载下对MTPA状态的收敛时间降低为原来的1/5~1/2。

INTERNET-BASED MACHINING PARAMETER OPTIMIZATION AND MANAGEMENT SYSTEM FOR HIGH-SPEED MACHINING

The theory and its method of machining parameter optimization for high-speed machining are studied. The machining data collected from workshops, labs and references are analyzed. An optimization method based on the genetic algorithm (GA) is investigated. Its calculation speed is faster than that of traditional optimization methods, and it is suitable for the machining parameter optimization in the automatic manufacturing system. Based on the theoretical studies, a system of machining parameter management and optimization is developed. The system can improve productivity of the high-speed machining centers.

SECURE DATA SYNCHRONIZATION EXCHANGE SERVICE APPLICATION PROGRAM INTERFACE BASED ON SyncML PROTOCOL

The security-related problem during the data exchange is not considered in the SyncML protocol. To solve this problem, SyncML is enhanced with a secure data synchronization exchange service application program interface （SDSXS-API） to ensure the reliability, the integrity, and the security in the data transmission and exchange. The design and the implementation of SDSXS-API are also given. The proposed APIs can be conveniently used as a uniform exchange interface for any related application programs. And their effectiveness is verified in the prototype mobile database system.

Distributed Computation Models for Data Fusion System Simulation

An attempt has been made to develop a distributed software infrastructure model for onboard data fusion system simulation, which is also applied to netted radar systems, onboard distributed detection systems and advanced C3I systems. Two architectures are provided and verified: one is based on pure TCP/IP protocol and C/S model, and implemented with Winsock, the other is based on CORBA (common object request broker architecture). The performance of data fusion simulation system, i.e. reliability, flexibility and scalability, is improved and enhanced by two models. The study of them makes valuable explore on incorporating the distributed computation concepts into radar system simulation techniques.

充分发挥农业优势,把我省农副产品加工工业提到一个新的水平

为了实现我省经济、社会发展战略目标,最近省的第八次党代会明确提出:必须“全面发挥资源优势,在近期内尤其要发挥农业资源优势”,这是从我省实际情况出发作出的重大战略决策。

及时关闭电脑省电又长命

大多数用户都愿意让电脑开机候用,而不会考虑耗电量与电脑使用寿命方面的问题.据计算,一般情况下,一台PC机耗电量相当于2至3盏100瓦的家用灯泡,一台17英寸的彩电显视器的耗电量则会增加一半,而一台没有节能装置的激光打印机的耗电量相当于5盏超过100瓦的高能灯泡.目前,最有效降低电脑能耗的方法是不用电脑时就把它关了.美国电子产品市场信息公司进行的研究表明,每隔15分钟开关一次电脑实际上将延长电脑的使用寿命.

OBJECT ORIENTED DATA MODELLING WITH APPLICATIONS TO CAD

An object oriented data modelling in computer aided design (CAD) databases is focused. Starting with the discussion of data modelling requirements for CAD applications, appropriate data modelling features are introduced herewith. A feasible approach to select the “best” data model for an application is to analyze the data which has to be stored in the database. A data model is appropriate for modelling a given task if the information of the application environment can be easily mapped to the data model. Thus, the involved data are analyzed and then object oriented data model appropriate for CAD applications are derived. Based on the reviewed object oriented techniques applied in CAD, object oriented data modelling in CAD is addressed in details. At last 3D geometrical data models and implementation of their data model using the object oriented method are presented.

The Physical Significance of the Synthetic Running Correlation Coefficient and Its Applications in Oceanic and Atmospheric Studies

In order to study the temporal variations of correlations between two time series,a running correlation coefficient(RCC)could be used.An RCC is calculated for a given time window,and the window is then moved sequentially through time.The current calculation method for RCCs is based on the general definition of the Pearson product-moment correlation coefficient,calculated with the data within the time window,which we call the local running correlation coefficient(LRCC).The LRCC is calculated via the two anomalies corresponding to the two local means,meanwhile,the local means also vary.It is cleared up that the LRCC reflects only the correlation between the two anomalies within the time window but fails to exhibit the contributions of the two varying means.To address this problem,two unchanged means obtained from all available data are adopted to calculate an RCC,which is called the synthetic running correlation coefficient(SRCC).When the anomaly variations are dominant,the two RCCs are similar.However,when the variations of the means are dominant,the difference between the two RCCs becomes obvious.The SRCC reflects the correlations of both the anomaly variations and the variations of the means.Therefore,the SRCCs from different time points are intercomparable.A criterion for the superiority of the RCC algorithm is that the average value of the RCC should be close to the global correlation coefficient calculated using all data.The SRCC always meets this criterion,while the LRCC sometimes fails.Therefore,the SRCC is better than the LRCC for running correlations.We suggest using the SRCC to calculate the RCCs.

An evaluation on using soil aggregate stability as the indicator of interrill erodibility

Aggregate stability is a very important predictor of soil structure and strength, which influences soil erodibility. Several aggregate stability indices were selected erodibility of four soil properties from temperate for estimating interrill types with contrasting and subtropical regions of China. This study was conducted to investigate how closely the soil interrill erodibility factor in the Water Erosion Prediction Project （WEPP） model relates to soil aggregate stability. The mass fractal dimension （FD）, geometric mean diameter （GMD）, mean weight diameter （MWD）, and aggregate stability index （ASI） of soil aggregates were calculated. A rainfall simulator with a drainable flume （3.0 m long × 1.0 m wide × 0.5 m deep） was used at four slope gradients （5°,10 °,15° and 20°）, and four rainfall intensities （0.6, 1.1, 1.7 and 2.5 mm/min）. Results indicated that the interriU erodibility （Ki） values were significantly correlated to the indices of ASI, MWD, GMD, and FD computed from the aggregate wet-sieve data. The Kihad a strong positive correlation with FD, as well as a strong negative correlation with ASI, GMD, and MWD. Soils with a higher aggregate stability and lower fractal dimension have smaller Ki values. Stable soils were characterized by a high percentage of large aggregates and the erodible soils by a high percentage of smaller aggregates. The correlation coefficients of Ki with ASI and GMD were greater than those with FD and MWD, implying that both the ASI and GMD may be better alternative parameters for empirically predicting the soil Ki factor. ASI and GMD are more reasonable in interrill soil erodibility estimation, compared with Ki calculation in original WEPP model equation. Results demonstrate the validation of soil aggregation characterization as an appropriate indicator of soil susceptibility to erosion in contrasting soil types in China.

TPTVer： A Trusted Third Party Based Trusted Verifier for Multi-Layered Outsourced Big Data System in Cloud Environment

Cloud computing is very useful for big data owner who doesn't want to manage IT infrastructure and big data technique details. However, it is hard for big data owner to trust multi-layer outsourced big data system in cloud environment and to verify which outsourced service leads to the problem. Similarly, the cloud service provider cannot simply trust the data computation applications. At last,the verification data itself may also leak the sensitive information from the cloud service provider and data owner. We propose a new three-level definition of the verification, threat model, corresponding trusted policies based on different roles for outsourced big data system in cloud. We also provide two policy enforcement methods for building trusted data computation environment by measuring both the Map Reduce application and its behaviors based on trusted computing and aspect-oriented programming. To prevent sensitive information leakage from verification process,we provide a privacy-preserved verification method. Finally, we implement the TPTVer, a Trusted third Party based Trusted Verifier as a proof of concept system. Our evaluation and analysis show that TPTVer can provide trusted verification for multi-layered outsourced big data system in the cloud with low overhead.

Solubility of Emodin in Alcohols

Emodin is a kind of anthraquiones with pharmaceutical activities.The solubilities of emodin in ethanol,1-octanol,and ethanol+1-octanol at different temperatures were measured using an analytical method.The solubility of emodin in these solvents increased with an increase in temperature.With the temperature deviating from room temperature,the dependence of the solubility of emodin on temperature became a lot more remarkable.The solubility of emodin in ethanol was more sensitive to temperature than that in 1-octanol.The solubility data of emodin in the same species of solvent at different temperatures was correlated with an empirical equation.The calculated results agreed with the experimental data well.

Similarity measure design for high dimensional data

Information analysis of high dimensional data was carried out through similarity measure application. High dimensional data were considered as the a typical structure. Additionally, overlapped and non-overlapped data were introduced, and similarity measure analysis was also illustrated and compared with conventional similarity measure. As a result, overlapped data comparison was possible to present similarity with conventional similarity measure. Non-overlapped data similarity analysis provided the clue to solve the similarity of high dimensional data. Considering high dimensional data analysis was designed with consideration of neighborhoods information. Conservative and strict solutions were proposed. Proposed similarity measure was applied to express financial fraud among multi dimensional datasets. In illustrative example, financial fraud similarity with respect to age, gender, qualification and job was presented. And with the proposed similarity measure, high dimensional personal data were calculated to evaluate how similar to the financial fraud. Calculation results show that the actual fraud has rather high similarity measure compared to the average, from minimal 0.0609 to maximal 0.1667.

Local and global approaches of affinity propagation clustering for large scale data

Recently a new clustering algorithm called 'affinity propagation' (AP) has been proposed, which efficiently clustered sparsely related data by passing messages between data points. However, we want to cluster large scale data where the similarities are not sparse in many cases. This paper presents two variants of AP for grouping large scale data with a dense similarity matrix. The local approach is partition affinity propagation (PAP) and the global method is landmark affinity propagation (LAP). PAP passes messages in the subsets of data first and then merges them as the number of initial step of iterations; it can effectively reduce the number of iterations of clustering. LAP passes messages between the landmark data points first and then clusters non-landmark data points; it is a large global approximation method to speed up clustering. Experiments are conducted on many datasets, such as random data points, manifold subspaces, images of faces and Chinese calligraphy, and the results demonstrate that the two ap-proaches are feasible and practicable.

A Novel Robust Nonlinear Dynamic Data Reconciliation

Outlier in one variable will smear the estimation of other measurements in data reconciliation （DR）. In this article, a novel robust method is proposed for nonlinear dynamic data reconciliation, to reduce the influence of outliers on the result of DR. This method introduces a penalty function matrix in a conventional least-square objective function, to assign small weights for outliers and large weights for normal measurements. To avoid the loss of data information, element-wise Mahalanobis distance is proposed, as an improvement on vector-wise distance, to construct a penalty function matrix. The correlation of measurement error is also considered in this article. The method introduces the robust statistical theory into conventional least square estimator by constructing the penalty weight matrix and gets not only good robustness but also simple calculation. Simulation of a continuous stirred tank reactor, verifies the effectiveness of the proposed algorithm.