试论“中国式管理”思想在媒介经营中的运用——以南方报业传媒集团为例

"中国式管理"是管理界近年来争论的焦点,尽管其存在不成熟的地方,但作为中国人自己提出的管理思想无疑有着重要的意义。媒介作为我国的"特殊产业",在产业化之前,是完全意义上的行政单位,目前关于媒介的一个流行说法是"事业单位,企业化经营",这种特殊的身份对媒介的经营管理来说,意味着需要特殊的管理思想来指导。我们认为,我国媒介的经营不能仅仅模仿外国传媒集团的模式,要吸取"中国式管理"的思想,努力探索自己的管理模式。本文以南方报业传媒集团经营管理的实践为案例,探讨"中国式管理"思想在媒介经营中的运用。

Circumscriptions and Phylogenetic Relationships of Primula Sects. Auganthus and Ranunculoides: Evidence from nrDNA ITS Sequences

The circumscriptions of sections Auganthus Pax ex Balf. f. and Ranunculoides Chen et C. M. Hu in the genus Primula L. have been controversial, due to the different treatments of P. filchnerae Knuth. We conducted a phylogenetic analysis using sequences from the ITS region of nuclear ribosomal DNA. All five species of these two sections and some representatives of other related sections were sampled. The ITS trees show that P. filchnerae could be convincingly placed in sect. Auganthus , together with P. sinensis Sabine ex Lindl. and P. rupestris Balf. f. et Farrer, whereas sect. Ranunculoides contains two species: P. cicutariifolia Pax and P. merrilliana Schltr. These two sections are distantly related and their association with other sections needs further studies. This study also demonstrated that ITS sequences would be a suitable marker for the reconstruction of Primula phylogeny.

Multitask Data Processing in a Wireless Alarm System

Aim To achieve multitask data procssing in a wireless alarm system by computer. Methods The alarm system was composed of hardware and software. The hardware was composed of a master master computer and slave transmitters. On urgent ugent occasion, one or more of the transmitters transmitted alarm signals and the master computer received the signals; interruption, residence, graph and word processing were utilized in software to achieve multitiask data processing . Results The main computer can conduct precise and quick multitask data procesing in any condition so long as alarm signals are received. The processing speed is higher than ordinary alarm System. Conclusion The master computer can conduct safe and quick multitask data processing by way of reliable design of software and hardware , so there is no need of special processor.

A Geometric Process Repair Model for the Repairable System Consisting of One Component

This paper considers the optimal replacement problem of a repairable system consisting of one component and a single repairman, assume that the system after repair is not 'as good as new', by using the geometric process, we consider a placement policy T based on the age of the system. The problem is to determine the optimal replacement policy T * such that the long_run expected benefit per unit time is maximized. Also, the explicit expression of the long_run expected benefit per unit time can be found. In some conditions, the existence and uniqueness of the optimal policy T * can be proved, finally, we prove that the policy T * is better than the policy T * in .

基于强化学习的水下高速航行体纵向运动控制研究

水下高速航行体由于空泡特性导致其数学模型存在强非线性和强不确定性,经典控制方法如线性二次型调节控制(linear quadratic regulator, LQR)、切换控制等很难实现有效控制。针对水下高速航行体模型难以准确解耦或线性化处理;经典控制方法难以充分考虑水下环境复杂多变性以及在应对扰动时控制器可能会出现过饱和现象的问题,采用智能控制中的强化学习算法,使用在不基于准确模型的条件下与环境不断探索与交互得到控制策略的策略,完成了深度确定性策略梯度(deep deterministic policy gradient,DDPG)智能体控制器的设计。实验结果证明,设计的控制器能够保证水下高速航行体纵向运动的稳定控制,在执行器不超过饱和范围内能够应对扰动并完成下潜控制任务,具有较强的鲁棒性和更好的适应性。

面向模糊C均值算法的MAME聚类有效性指标

聚类有效性指标可用来评估聚类结果的有效性,并且帮助判别聚类的类别数。现有的面向模糊C均值算法的聚类有效性指标存在对于类内紧致性的刻画不太到位、对于类间分离性的度量刻画不够准确的问题。为此,基于类内紧致性和类间分离性两个角度着手设计,提出了一种新的模糊聚类有效性指标——考虑最大值和均值的指标(maximum-mean,MAME)。首先,考虑了整个数据集的综合特征,计算分别分为K类和1类的情况的比值,提出了一种新的模糊紧致性度量表达式。其次,引入最大聚类中心距离和平均聚类中心距离,提出了一种新的分离性度量方法。最后,从模糊紧致性度量表达式、分离性度量方法出发,提出了MAME指标。面向5个UCI数据集和6个人工数据集,和9个聚类有效性指标(包括CH、DB、NPC、PE、FSI、XBI、NPE、WLI和I指标)一起进行了对比实验,验证了所提指标的准确性、稳定性,说明了MAME指标的鲁棒性较好。

The Application of Weighted Association Rules in Host-Based Intrusion Detection System

Association rules are useful for determining correlations between items. Applying association rules to intrusion detection system (IDS) can improve the detection rate, but false positive rate is also increased. Weighted association rules are used in this paper to mine intrustion models, which can increase the detection rate and decrease the false positive rate by some extent. Based on this, the structure of host-based IDS using weighted association rules is proposed.

深度残差收缩网络的多特征锅炉炉管声波信号故障识别

为了提高锅炉炉管声波信号故障识别的学习效果和识别精度,采用特征向量并行和拼接两种融合方式构成特征层,以及平均得分和最大值得分两种融合方式构建决策层等不同信息融合机制,提出基于深度残差收缩网络的多特征锅炉炉管声波信号故障识别方法。首先,考虑锅炉炉管上各声波传感器的差异性,分别计算声波信号谱特征一阶和二阶差分构建三通道特征集作为二维网络的输入特征向量;然后,在卷积神经网络和双向长短时记忆网络基础上引入注意力机制构建基线模型,并采用深度残差收缩网络对二维网络的通道权重进行优化分配,提高模型的故障识别精度。大量实验结果表明:采用特征向量并行融合方式构成特征层的信息融合机制是一种更有效的策略;本文模型的识别精度得到较大程度提高,与基线模型相比较,未加权平均召回率提高了4.32%。

A novel recurrent neural network forecasting model for power intelligence center

In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network (UMRNN) for power intelligence center (PIC) was created through three steps. First, by combining with the general project uncertain element transmission theory (GPUET), the basic definitions of stochastic, fuzzy, and grey uncertain elements were given based on the principal types of uncertain information. Second, a power dynamic alliance including four sectors: generation sector, transmission sector, distribution sector and customers was established. The key factors were amended according to the four transmission topologies of uncertain elements, thus the new factors entered the power intelligence center as the input elements. Finally, in the intelligence handing background of PIC, by performing uncertain and recursive process to the input values of network, and combining unascertained mathematics, the novel load forecasting model was built. Three different approaches were put forward to forecast an eastern regional power grid load in China. The root mean square error (ERMS) demonstrates that the forecasting accuracy of the proposed model UMRNN is 3% higher than that of BP neural network (BPNN), and 5% higher than that of autoregressive integrated moving average (ARIMA). Besides, an example also shows that the average relative error of the first quarter of 2008 forecasted by UMRNN is only 2.59%, which has high precision.

Research on Micro-vibration Generator for Powering Tire Pressure Alarm System

In order to meet the self-powered requirement by microelectronic products and micro-electrostatic vibration generator and avoid the environmental pollution caused by waste batteries,many countries have started their researches on piezoelectric power generation based on the researches on the recovery device of rotational energy of piezoelectric effect.The piezoelectric power generation has many advantages compared with other generation patterns.The piezoelectric power generation has become a new field of research.In this study,the piezoelectric vibrators were used to convert energy.According to the requirements by tire pressure alarm system,the energy conversion and storage were researched and analyzed by using the passive sensing device used in automotive inspection.The converted energy was then provided to the alarm,achieving intelligent control and utilization of piezoelectric vibrators.

融合聚类和小生境搜索的多模态多目标优化算法

针对多模态多目标优化中种群多样性难以维持和所得等价Pareto最优解数量不足问题,提出一种融合聚类和小生境搜索的多模态多目标优化算法(multimodal multi-objective optimization algorithm with clustering and niching searching,CSSMPIO)。首先利用基于聚类的特殊拥挤距离非支配排序方法(clustering-based special crowding distance,CSCD)初始化种群;引入自适应物种形成策略生成稳定的小生境,在不同的小生境子空间并行搜索和保持等价Pareto最优解;采用特殊拥挤距离非支配排序策略实现个体选优、精英学习策略避免过早收敛。通过在14个多模态多目标函数上进行测试,并与7种新提出的多模态多目标优化算法进行对比实验以及Wilcoxon秩和检验发现,CSSMPIO的总体性能优于对比算法。最后将算法用于基于地图的测试问题,进一步证明了算法的有效性。

Research on Geological Structure Mark of Coal and Gas Outbursts in Pingdingshan Mining Area

Based on the study of regional displaying rules of coal and gas outburst controlled by geological structure in Pingdingshan mining area, the geological structure features in outburst sites were investigated emphatically. The combination type, orientation and least seam thickness in outburst sites were put forward. This research provides a geological mark for forecasting gas outbursts in deep mining.

High-Resolution Hindcast of Record-Breaking Rainfall in Beijing and Impact of Topography

In this paper,a hindcast study of the record-breaking rainfall event occurring in Beijing on 21July 2012,is conducted by using the Weather Research and Forecasting(WRF)model forced by National Centers for Environmental Prediction(NCEP)Global Forecasting System(GFS)outputs,paired with an investigation of the impact of topography in this region.The results indicate that WRF can reasonably predict the salient features of orographic precipitation;the 24-h rainfall amount and spatial distribution compare reasonably well with the observations.The hindcast simulation also indicates that rainfall events can be predicted approximately 36 h ahead.When the topography is removed,the spatial distribution of rainfall changes remarkably,suggesting the importance of the topography in determining rainfall structure.These results also indicate that prediction of such city-scale heavy rainfall events would benefit from a high-resolution prediction system.

优化分类的弱目标孪生网络跟踪研究

针对传统孪生网络算法对模糊、低分辨率等弱目标跟踪效果不佳的问题,提出了优化分类预测的孪生网络算法。首先通过引入可变形卷积模块,提高骨干网络特征提取能力,其次在分类分支中引入位置信息,提升算法对于目标的识别能力,最后使用轻量级的卷积神经网络进行分类预测和边界预测任务,在规避多尺度测试的同时,进一步利用了图像的语义信息,使跟踪结果具有较高的可信度。在OTB2015、VOT2018公共数据集上进行的大量实验表明,本文算法综合表现优于主流同类算法,对模糊、形变、快速运动等多种复杂场景具有较好的适应性。

Evaluation of global high-resolution reanalysis products based on the Chinese global oceanography forecasting system

The global high-resolution marine reanalysis products that were independently developed by the National Marine Environmental Forecasting Center based on the Chinese Global Oceanography Forecasting System(CGOFS),are evaluated by comparing their climatologies with internationally recognized data from WOA(Word Ocean Atlas),SODA(Simple Ocean Data Assimilation),AVISO(Archiving,Validation,and Interpretation of Satellite Oceanographic Data),and C-GLORS(Global Ocean Reanalysis System).The results show that the SST RMSEs of CGOFS and SODA against WOA are 0.51℃ and 0.43℃ respectively;and in the North Pacific,the SST of CGOGS is closer to that of WOA than SODA.The SSS RMSEs of CGOFS and SODA compared with WOA are 0.48 PSU and 0.40 PSU,respectively.CGOFS can reproduce the main large-scale ocean circulation globally,and obtain a similar vertical structure of the Equatorial Undercurrent as SODA.The RMSE of the CGOFS global sea-level anomaly against AVISO is 0.018 m.The monthly averaged sea-ice extents are between those of SODA and C-GLORS in each month;the growth and ablation characteristics of the ice volume are consistent with SODA and C-GLORS;but the ice volume of CGOFS is greater than that of SODA and C-GLORS.In general,the climatology of the CGOFS global high-resolution reanalysis products are basically consistent with similar international products,and can thus provide reliable data for the improvement of marine science and technology in China.

A New Approach to Prediction of Fault of Manufacturing Quality for FMS

This paper makes a study of the cause of manufacturing fault,develops the and/or- fault-tree of manufacturing quality fault for MC,and presents a new concept of faint manufacturing quality fault(FMQF)and the decision making tree with which the fault of manufacturing system would be found out from FMQF.An approach to identification of FMQF,based on fuzzy set theory,is presented,which can be used for estimating the status of equipment with the deviation of control charts.Based on the study above,an expert system for the flexible manufacturing system's FMQF detection and prediction is built.

The ‘Two oceans and one sea' extended range numerical prediction system with an ultra-high resolution atmosphere-ocean-land regional coupled model

The‘Two Oceans and One Sea’area(West Pacific,Indian Ocean,and South China Sea;15°S–60°N,39°–178°E)is a core strategic area for the‘21st Century Maritime Silk Road’project,as well as national defense.With the increasing demand for disaster prevention and mitigation,the importance of 10–30-day extended range prediction,between the conventional short-term(around seven days)and the climate scale(longer than one month),is apparent.However,marine extended range prediction is still a‘blank point’in China,making the early warning of marine disasters almost impossible.Here,the authors introduce a recently launched Chinese national project on a numerical forecasting system for extended range prediction in the‘Two Oceans and One Sea’area based on a regional ultra-high resolution multi-layer coupled model,including the scientific aims,technical scheme,innovation,and expected achievements.The completion of this prediction system is of considerable significance for the economic development and national security of China.

Extratropical Transition and Re-Intensification of Typhoon Toraji(2001):Large-Scale Circulations, Structural Characteristics, and Mechanism Analysis

With the use of data from the National Centers for Environmental Prediction Climate Forecast System Reanalysis,the environment and structure of typhoon Toraji(2001)are investigated during the re-intensification(RI)stage of its extratropical transition(ET),a process in which a tropical cyclone transforms into an extratropical or mid-latitude cyclone.The results provide detailed insight into the ET system and identify the specific features of the system,including wind field,a cold and dry intrusion,and a frontal structure in the RI stage.The irrotational wind provides the values of upper-and lower-level jets within the transitioning tropical cyclone and the cyclone over Shandong Peninsula,accompanied with the reduced radius of maximum surface winds around the cyclone center in the lower troposphere.Simultaneously,dry air intrusion enhances the formation of fronts and leads to strong potential instability in the southwest and northeast quadrants.The distribution of frontogenesis shows that the tilting term associated with vertical motion dominates the positive frontogenesis surrounding the cyclone center,especially in the RI stage.The diagnostics of the kinetic energy budget suggest that the divergent kinetic energy generation whose time evolution corresponds well to that of cyclone center pressure is the primary factor for the development of Toraji in the lower troposphere.The ET of Toraji is a compound pattern that contains a development similar to that of a B-type extratropical cyclone within the maintaining phase and an A-type extratropical cyclone within the strengthening period,which corresponds to the distribution of the E-P fluxes with vertically downward propagation in the maintaining stage and upwards momentum in the strengthening phase.

Advanced prediction for multiple disaster sources of laneway under complicated geological conditions

The driving safety in the laneway is often controlled by multiple disaster sources which include fault fracture zone, water-bearing body, goaf and collapse column. The advanced prediction of them has become a hotspot. Based on analysis of physical characteristics of the disaster sources and comparative evaluation of accuracy of the main advanced geophysical detection methods, we proposed a comprehen- sive judging criterion that tectonic interface can be judged by the elastic wave energy anomaly, strata water abundance can be discriminated by apparent resistivity response difference and establish a reason- able advanced prediction system. The results show that the concealed disaster sources are detected effec- tively with the accuracy rate of 80% if we use advanced prediction methods of integrated geophysics combined with correction of seismic and electromagnetic parameters, moreover, applying geological data, we may then distinguish types of the disaster sources and fulfill the qualitative forecast. Therefore, the advanced prediction system pays an important referential and instructive role in laneway driving project.

数据流形边界及其分布条件的增量式降维算法

为了解决增量流形学习中的噪声干扰,以及对不同分布状态下的新数据进行流形降维问题,本文提出一种数据流形边界及其分布条件的增量式降维算法(incremental dimensionality reduction algorithm based on data manifold boundaries and distribution state,IDR-DMBDS)。该算法首先分析噪声概率分布同时对数据降噪,确定降噪数据的流形形态为主流形,并在主流形上表征出噪声的分布形式,以此获得近似的原数据流形边界,然后基于流形边界判别新数据的分布状态,最后将分布于原流形形态之上以及之外的新数据分别映射至低维空间。实验表明,该算法能够有效实现基于流形的增量式高维含噪数据的低维特征挖掘。