Relationship between rock uniaxial compressive strength and digital core drilling parameters and its forecast method

The rock uniaxial compressive strength(UCS)is the basic parameter for support designs in underground engineering.In particular,the rock UCS should be obtained rapidly for underground engineering with complex geological conditions,such as soft rock,fracture areas,and high stress,to adjust the excavation and support plan and ensure construction safety.To solve the problem of obtaining real-time rock UCS at engineering sites,a rock UCS forecast idea is proposed using digital core drilling.The digital core drilling tests and uniaxial compression tests are performed based on the developed rock mass digital drilling system.The results indicate that the drilling parameters are highly responsive to the rock UCS.Based on the cutting and fracture characteristics of the rock digital core drilling,the mechanical analysis of rock cutting provides the digital core drilling strength,and a quantitative relationship model(CDP-UCS model)for the digital core drilling parameters and rock UCS is established.Thus,the digital core drilling-based rock UCS forecast method is proposed to provide a theoretical basis for continuous and quick testing of the surrounding rock UCS.

APPLICATION OF NONLINEAR WATERMARK TECHNIQUES IN DIGITAL LIBRARIES

A digital watermark is an invisible mark embedded in a digital image that may be used for a number of different purposes including copyright protection. Due to the urgent need for protecting the copyright of digital products in digital library, digital watermarking has been proposed as a solution to this problem. This letter describes potential situations that nonlinear theory can be used to enhance robustness and security of the watermark in digital library. Some nonlinear watermark techniques have been enumerated. Experimental results show that the proposed scheme is superior to the general watermark scheme both in security and robustness in digital library.

Ensemble Forecasts of Tropical Cyclone Track with Orthogonal Conditional Nonlinear Optimal Perturbations

This paper preliminarily investigates the application of the orthogonal conditional nonlinear optimal perturbations(CNOPs)–based ensemble forecast technique in MM5(Fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model). The results show that the ensemble forecast members generated by the orthogonal CNOPs present large spreads but tend to be located on the two sides of real tropical cyclone(TC) tracks and have good agreements between ensemble spreads and ensemble-mean forecast errors for TC tracks. Subsequently, these members reflect more reasonable forecast uncertainties and enhance the orthogonal CNOPs–based ensemble-mean forecasts to obtain higher skill for TC tracks than the orthogonal SVs(singular vectors)–, BVs(bred vectors)– and RPs(random perturbations)–based ones. The results indicate that orthogonal CNOPs of smaller magnitudes should be adopted to construct the initial ensemble perturbations for short lead–time forecasts, but those of larger magnitudes should be used for longer lead–time forecasts due to the effects of nonlinearities. The performance of the orthogonal CNOPs–based ensemble-mean forecasts is case-dependent,which encourages evaluating statistically the forecast skill with more TC cases. Finally, the results show that the ensemble forecasts with only initial perturbations in this work do not increase the forecast skill of TC intensity, which may be related with both the coarse model horizontal resolution and the model error.

Extended Range(10–30 Days) Heavy Rain Forecasting Study Based on a Nonlinear Cross-Prediction Error Model

Extended range （10-30 d） heavy rain forecasting is difficult but performs an important function in disaster prevention and mitigation. In this paper, a nonlinear cross prediction error （NCPE） algorithm that combines nonlinear dynamics and statistical methods is proposed. The method is based on phase space reconstruction of chaotic single-variable time series of precipitable water and is tested in 100 global cases of heavy rain. First, nonlinear relative dynamic error for local attractor pairs is calculated at different stages of the heavy rain process, after which the local change characteristics of the attractors are analyzed. Second, the eigen-peak is defined as a prediction indicator based on an error threshold of about 1.5, and is then used to analyze the forecasting validity period. The results reveal that the prediction indicator features regarded as eigenpeaks for heavy rain extreme weather are all reflected consistently, without failure, based on the NCPE model; the prediction validity periods for 1-2 d, 3-9 d and 10-30 d are 4, 22 and 74 cases, respectively, without false alarm or omission. The NCPE model developed allows accurate forecasting of heavy rain over an extended range of 10-30 d and has the potential to be used to explore the mechanisms involved in the development of heavy rain according to a segmentation scale. This novel method provides new insights into extended range forecasting and atmospheric predictability, and also allows the creation of multi-variable chaotic extreme weather prediction models based on high spatiotemporal resolution data.

Digital Servo Control of a Robotic Excavator

An electro-hydraulic control system is designed and implemented for a robotic excavator known as the Lancaster University Computerised and Intelligent Excavator （LUCIE）. The excavator is being developed to autonomously dig trenches without human intervention. Since the behavior of the excavator arm is dominated by the nonlinear dynamics of the hydraulic actuators and by the large and unpredictable external disturbances when digging, it is difficult to provide adequate accurate, quick and smooth movement under traditional control methodology, e.g., PI/PID, which is comparable with that of an average human operator. The data-based dynamic models are developed utilizing the simplified refined instrumental variable （SRIV） identification algorithm to precisely describe the nonlinear dynamical behaviour of the electro-hydraulic actuation system. Based on data-based model and proportional-integral-plus （PIP） methodology, which is a non-minimal state space method of control system design based on the true digital control （TDC） system design philosophy, a novel control system is introduced to drive the excavator arm accurately, quickly and smoothly along the desired path. The performance of simulation and field tests which drive the bucket along straight lines both demonstrate the feasibility and validity of the proposed control scheme.

AN ADAPTIVELY TRAINED KERNEL-BASED NONLINEAR REPRESENTOR FOR HANDWRITTEN DIGIT CLASSIFICATION

In practice, retraining a trained classifier is necessary when novel data become available. This paper adopts an incremental learning procedure to adaptively train a Kernel-based Nonlinear Representor (KNR), a recently presented nonlinear classifier for optimal pattern representation, so that its generalization ability may be evaluated in time-variant situation and a sparser representation is obtained for computationally intensive tasks. The addressed techniques are applied to handwritten digit classification to illustrate the feasibility for pattern recognition.

Nonlinear combined forecasting model based on fuzzy adaptive variable weight and its application

In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system.

Study of Polluted Insulator Flashover Forecasting Based on Nonlinear Time Series Analysis

To solve the problem of the flashover forecasting of contaminated or polluted insulator,a flashover forecasting model of contaminated insulators based on nonlinear time series analysis is proposed in the paper.The ESDD is the key of flashover on polluted insulator.The ESDD value of insulator can be forecasted by the method of nonlinear time series analysis of the ESDD time series and a forecasting model of polluted insulator flashover is proposed in the paper.The forecasting model consists of two artificial neural networks that reflect relationship of environment,ESDD and flashover probability.The first is used to estimate the ESDD time series of insulator and the second is employed to calculate the probability of the flashover.A series of artificial pollution tests show that the results of the forecasting model is acceptable.

Application of Recurrent Wavelet Neural Networks to the Digital Communications Channel Blind Equalization

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A nonlinear combination forecasting method based on the fuzzy inference system

It has been shown in recent economic and statistical studies that combining forecasts may produce more accurate forecasts than individual ones. However, the literature on combining forecasts has almost exclusively focused on linear combining forecasts. In this paper, a new nonlinear combination forecasting method based on fuzzy inference system is present to overcome the difficulties and drawbacks in linear combination modeling of non-stationary time series. Furthermore, the optimization algorithm based on a hierarchical structure of learning automata is used to identify the parameters of the fuzzy system. Experiment results related to numerical examples demonstrate that the new technique has excellent identification performances and forecasting accuracy superior to other existing linear combining forecasts.

Nonlinear Differential Equation of Macroeconomic Dynamics for Long-Term Forecasting of Economic Development

In this article we derive a general differential equation that describes long-term economic growth in terms of cyclical and trend components. Equation is based on the model of non-linear accelerator of induced investment. A scheme is proposed for obtaining approximate solutions of nonlinear differential equation by splitting solution into the rapidly oscillating business cycles and slowly varying trend using Krylov-Bogoliubov-Mitropolsky averaging. Simplest modes of the economic system are described. Characteristics of the bifurcation point are found and bifurcation phenomenon is interpreted as loss of stability making the economic system available to structural change and accepting innovations. System being in a nonequilibrium state has a dynamics with self-sustained undamped oscillations. The model is verified with economic development of the US during the fifth Kondratieff cycle (1982-2010). Model adequately describes real process of economic growth in both quantitative and qualitative aspects. It is one of major results that the model gives a rough estimation of critical points of system stability loss and falling into a crisis recession. The model is used to forecast the macroeconomic dynamics of the US during the sixth Kondratieff cycle (2018-2050). For this forecast we use fixed production capital functional dependence on a long-term Kondratieff cycle and medium-term Juglar and Kuznets cycles. More accurate estimations of the time of crisis and recession are based on the model of accelerating log-periodic oscillations. The explosive growth of the prices of highly liquid commodities such as gold and oil is taken as real predictors of the global financial crisis. The second wave of crisis is expected to come in June 2011.

Design and Construction of a Digital Multichannel Analyzer for HPGe Detector Using Digital Signal Processing Technique

The outstanding advantage of digital signal processing (DSP) techniques and Field Programmable Gate Array (FPGA) technology is capable of improving the quality of the experimental measurements for nuclear radiation. In this article, a compact DMCA 8 K was designed and manufactured using DSP technique based on FPGA technology. In particular, the output of the preamplifier is completely processed by digital techniques which are obtained from the analog-to-digital converter (ADC) to calculate the baseline, DC offset, energy peaks, pile up, threshold discrimination and then the form of energy spectrum. The Spartan-6 board is used as a hardware for the development of the digital multichannel analyzer (DMCA), which is equipped with the 14-bit AD6645 with 62.5 Msps sample rate. The application software for instrument control, data acquisition and data processing was written under C++ builder via the RS-232 interface. The designed DMCA system has been tested with a HPGe detector using gamma sources of 60Co and 137Cs and a reference pulser.

Nonlinear Evolution Characteristics of the NCEP Ensemble Forecast Products

By using NCEP/NCAR reanalysis products to forecast the nonlinear evolution of the spatial and temporal characteristics, the results shows that on the Spatial dimensions, NCEP ensemble forecast that the products of nonlinear evolution have obvious zonal features. The overall distribution situation is the nonlinear evolution of the southern hemisphere, which is larger than that of the northern hemisphere. In the same hemisphere, low value area is near the equator, and high value area for middle and high latitude area. On the time dimension, the nonlinear evolution of NCEP ensemble prediction products will increase with the extension of the forecast period. In addition, the nonlinear evolution of NCEP ensemble forecast products in North America is greater than the Asian region.

Digital Economy: A Vision From The Future

The article shows that the foundation of the digital economy can be a new paradigm for predicting the future from the future,i.e.from the future in which the development goal has already been achieved.This allows to minimize all costs and completely avoid incorrect system solutions of the existing trial-and-error approach.Using the achievements of the technological revolution of Industry 4.0,an effective digital economy can be formed only when it is seen as an economy of coordinated interests between the state,business,society and the interests of each individual in real time at every local level.This will make it possible to solve the problem of ensuring the high quality of life not of citizens in general,but of each individual.

数字经济、农业产业结构升级对农民增收的效应

【目的】数字经济作为我国经济发展的新动能,对农民增收具有重要作用。研究两者之间的关系及其作用机制,可以丰富数字经济与农民收入的研究内容,充实经济高质量发展的经验证据,为加快数字乡村战略的实施进程提供启示。【方法】基于农业产业结构升级视角,运用2012—2021年全国31个省份的面板数据,构建数字经济评价指标体系,采用熵值法测算数字经济发展水平,借助固定效应模型和最小二乘法(OLS)探究数字经济对农民增收的影响及其作用机制,并运用面板门槛模型分析数字经济对农民增收的非线性溢出特征,最后借助分位数回归等方法分析数字经济对农民增收的异质性。【结果】(1)数字经济能够促进农民增收,经过工具变量法、改变样本容量等一系列检验后,结论依然成立。其中数字经济对农民的家庭经营性收入、工资性收入、财产性收入产生显著促进效应,对农民转移性收入的影响不显著。(2)数字经济的发展提升了农业产业结构的合理化以及高级化水平,助推农业产业结构的升级,从而促进农民收入的增长。(3)数字经济的增收效应具有非线性特征,且该效应在农业产业结构合理化的调节作用下得到进一步强化。(4)数字经济会长期带动农民收入增长;数字经济对不同收入水平农民增收的影响效应呈“倒U型”,其中中等收入阶层是数字经济发展的最大受益者;数字经济发展对中、西部农民增收的推动力明显强于东部地区。【结论】数字经济、农业产业结构升级是提高农民收入水平的关键要素,基于此,提出加强数字经济建设力度、创新数字经济和农业产业结构融合发展机制、完善数字经济发展的利益分配机制以及促进区域数字经济与农业产业结构统筹联动等对策建议。

直面挑战:审计师数字化专长是否有助于提高审计质量?

借鉴审计师行业专长的度量方法,将数字化客户占比较高的审计师定义为数字化专长审计师,并检验在数字化变革过程中,积极参与数字化审计并取得数字化审计经验的审计师是否能够取得更好的审计结果。结果发现数字化审计专长审计师在数字化市场领域能够带来更优的审计质量,并且他们的特殊技能带来的积极作用能够被资本市场的投资者和分析师识别和认可(股价同步性更低且分析师预测更精准)。该结论为在数字化变革中,审计师如何能动应对大样本档案提供了证据,并为实务中事务所发展数字化审计提供了一些启示。

水利部数字孪生流域模型管理云平台设计及应用研究

【目的】智慧水利是推动新阶段水利高质量发展的实施路径之一,建设数字孪生流域是实现智慧水利的核心与关键。为提升水利部数字孪生平台建设中水利模型的易用性、可扩展性、复用率和可维护性【方法】按照数字孪生流域水利模型建设“标准化、模块化、云服务”的要求,采用“分治+集成”的思想,基于组件技术、工作流技术、SOA架构和容器技术等构建水利部数字孪生流域模型管理云平台。平台通过提供水利专业模型、智能识别模型的组件化封装和流程化搭建功能,以微服务的方式对外提供模型共享和模型计算服务。【结果】依托重点研发项目分别在示范区域——屯溪流域和白鹤滩水库开展“洪水预报-预警-调度”业务应用和工程管理范围形变监测业务应用,验证了平台设计的合理性和应用的可行性。【结论】研究成果可为数字孪生流域模型平台的建设与应用提供一定技术支持和经验借鉴。

数字经济与实体经济融合是否提升了企业创新能力——基于正反向融合度的非线性检验

促进数字经济与实体经济深度融合,依托数字技术与设备实现核心领域突破性创新,是高质量发展的关键。编制我国非竞争投入产出表,从供需双层面和广义狭义双范畴,可构建并测算我国数字经济与实体经济正、反向融合度,探究数字经济与实体经济融合对实体企业创新影响及内在机制。研究发现,融合度对实体创新的影响存在非线性效应,正向融合度整体提升实体企业创新质量和效率,这与技术吸收能力强化相关;反向融合度处于合理区间时才能拉动创新增长,否则将因成本效应和适应性不足降低实体工业创新质量和效率。

数字孪生驱动的长江流域干旱防御平台设计与开发

在全球气候变化的背景下,长江流域发生了多次严重的高温干旱灾害,流域抗旱管理面临着旱情监测告警效率较低、旱灾预报预警精度不高、抗旱预案推演能力不足等瓶颈,迫切需要开展数字化转型。从长江流域抗旱减灾业务管理和“四预”应用需求出发,基于智慧水利和数字孪生建设的总体要求,综合运用WebGL、GIS等技术,建立了干旱防御数字孪生平台,研发了遥感干旱监测评估、干旱专业模型动态加载、旱警水位超限预警、抗旱预案可视化等关键技术,初步实现了“预报-预警-预演-预案”全链条贯通业务应用,切实提升了长江流域抗旱管理智能化、精细化水平,为流域干旱防灾减灾提供了技术支撑。

数字物流赋能可持续发展的机制与效应——基于物流碳生产率视角

在社会经济全面数字化和可持续发展背景下,从物流碳生产率视角探究数字物流赋能可持续发展的机制与效应意义重大。基于2015—2020年我国省际面板数据,运用纵横向拉开档次法和CRITIC-G1-Bonferroni算子,分别对各地区数字物流和可持续发展水平进行测算,进而以面板固定效应模型、中介效应模型、门槛模型和空间模型,对数字物流、物流碳生产率与可持续发展之间的逻辑关系进行实证检验。研究发现,数字物流以非线性递增的态势显著促进可持续发展水平的提升,且在东部、东北地区的作用强于中西部地区;其中,物流碳生产率的提高是数字物流释放可持续发展红利的重要机制。同时,数字物流对可持续发展还具有空间溢出效应,表明其对地区间可持续发展水平相互协调、带动也有着不可忽视的积极作用;随着传统产业全面数字化对可持续发展水平的影响不断加深,该溢出效应变得愈加显著,从而带动地区可持续发展水平的均衡提升。为了巩固数字物流为可持续发展带来的红利优势,首先要促进社会资本参与物流数字化转型,建设共享的物流平台和基础设施,淘汰过剩落后产能,形成开放统一的物流市场,并通过设立管理部门制定并监督低碳环保标准。此外,应通过财政支持和税收优惠加强中西部基础设施建设,同时建立地区合作机制,共享资源和技术,优化物流网络,促进区域间协调发展。