An Incremental Model Transfer Method for Complex Process Fault Diagnosis

Fault diagnosis is an important measure to ensure the safety of production, and all kinds of fault diagnosis methods are of importance in actual production process. However, the complexity and uncertainty of production process often lead to the changes of data distribution and the emergence of new fault classes, and the number of the new fault classes is unpredictable. The reconstruction of the fault diagnosis model and the identification of new fault classes have become core issues under the circumstances. This paper presents a fault diagnosis method based on model transfer learning and the main contributions of the paper are as follows: 1) An incremental model transfer fault diagnosis method is proposed to reconstruct the new process diagnosis model. 2) Breaking the limit of existing method that the new process can only have one more class of faults than the old process, this method can identify M faults more in the new process with the thought of incremental learning. 3) The method offers a solution to a series of problems caused by the increase of fault classes. Experiments based on Tennessee-Eastman process and ore grinding classification process demonstrate the effectiveness and the feasibility of the method.

Incremental 4D-VAR assimilation scheme based on Lorenz model

Four-dimensional variational(4D-VAR) data assimilation method is a perfect data assimilation solution in theory, but the computational issue is quite difficult in operational implementation.The incremental 4D-VAR assimilation scheme is set up in order to reduce the computational cost. It is shown that the accuracy of the observations, the length of the assimilation window and the choice of the first guess have an important influence on the assimilation outcome through the contrast experiment. Compared with the standard 4D-VAR assimilation scheme, the incremental 4D-VAR assimilation scheme shows its advantage in the computation speed through an assimilation experiment.

A Spatio-temporal Data Model for Road Network in Data Center Based on Incremental Updating in Vehicle Navigation System

The technique of incremental updating,which can better guarantee the real-time situation of navigational map,is the developing orientation of navigational road network updating.The data center of vehicle navigation system is in charge of storing incremental data,and the spatio-temporal data model for storing incremental data does affect the efficiency of the response of the data center to the requirements of incremental data from the vehicle terminal.According to the analysis on the shortcomings of several typical spatio-temporal data models used in the data center and based on the base map with overlay model,the reverse map with overlay model (RMOM) was put forward for the data center to make rapid response to incremental data request.RMOM supports the data center to store not only the current complete road network data,but also the overlays of incremental data from the time when each road network changed to the current moment.Moreover,the storage mechanism and index structure of the incremental data were designed,and the implementation algorithm of RMOM was developed.Taking navigational road network in Guangzhou City as an example,the simulation test was conducted to validate the efficiency of RMOM.Results show that the navigation database in the data center can response to the requirements of incremental data by only one query with RMOM,and costs less time.Compared with the base map with overlay model,the data center does not need to temporarily overlay incremental data with RMOM,so time-consuming of response is significantly reduced.RMOM greatly improves the efficiency of response and provides strong support for the real-time situation of navigational road network.

Grouping tree species to estimate basal area increment in temperate multispecies forests in Durango,Mexico

Multispecies forests have received increased scientific attention,driven by the hypothesis that biodiversity improves ecological resilience.However,a greater species diversity presents challenges for forest management and research.Our study aims to develop basal area growth models for tree species cohorts.The analysis is based on a dataset of 423 permanent plots(2,500 m^(2))located in temperate forests in Durango,Mexico.First,we define tree species cohorts based on individual and neighborhood-based variables using a combination of principal component and cluster analyses.Then,we estimate the basal area increment of each cohort through the generalized additive model to describe the effect of tree size,competition,stand density and site quality.The principal component and cluster analyses assign a total of 37 tree species to eight cohorts that differed primarily with regard to the distribution of tree size and vertical position within the community.The generalized additive models provide satisfactory estimates of tree growth for the species cohorts,explaining between 19 and 53 percent of the total variation of basal area increment,and highlight the following results:i)most cohorts show a"rise-and-fall"effect of tree size on tree growth;ii)surprisingly,the competition index"basal area of larger trees"had showed a positive effect in four of the eight cohorts;iii)stand density had a negative effect on basal area increment,though the effect was minor in medium-and high-density stands,and iv)basal area growth was positively correlated with site quality except for an oak cohort.The developed species cohorts and growth models provide insight into their particular ecological features and growth patterns that may support the development of sustainable management strategies for temperate multispecies forests.

INCREMENTAL MICRO-MECHANICAL MODEL OF PLAIN WOVEN FABRIC

Warp yarns and weft yarns of plain woven fabric are the principal axes of mate- rial of fabric. They are orthogonal in their original con?guration, but are obliquely crisscross in deformed con?guration in general. In this paper the expressions of incremental components of strain tensor are derived, the non-linear model of woven fabric is linearized physically and its geometric non-linearity survives. The convenience of determining the total deformation is shown by the choice of the coordinate system of the principal axes of the material, with the convergence of the incremental methods illustrated by examples. This incremental model furnishes a basis for numerical simulations of fabric draping and wrinkling based on the micro-mechanical model of fabric.

A Modular Incremental Model for English Full Parsing

In this paper, we present a modular incremental statistical model for English full parsing. Unlike other full parsing approaches in which the analysis of the sentence is a uniform process, our model separates the full parsing into shallow parsing and sentence skeleton parsing. In shallow parsing, we finish POS tagging, Base NP identification, prepositional phrase attachment and subordinate clause identification. In skeleton parsing, we use a layered feature-oriented statistical method. Modularity possesses the advantage of solving different problems in parsing with corresponding mechanisms. Feature-oriented rule is able to express the complex lingual phenomena at the key point if needed. Evaluated on Penn Treebank corpus, we obtained 89.2% precision and 89.8% recall.

Pore water pressure increment model for saturated Nanjing fine sand subject to cyclic loading

Three groups of dynamic triaxial tests were performed for saturated Nanjing fine sand subjected to uniform cyclic loading. The tested curves of the excess pore water pressure （EPWP） ratio variation with the ratio of the number of cycles are provided. The concept of the EPWP increment ratio is introduced and two new concepts of the effective dynamic shear stress ratio and the log decrement of effective stress are defined. It is found that the development of the EPWP increment ratio can be divided into three stages： descending, stable and ascending. Furthermore, at the stable and ascending stages, a satisfactory linear relationship is obtained between the accumulative EPWP increment ratio and natural logarithm of the effective dynamic shear stress ratio. Accordingly, the EPWP increment ratio at the number of cycles N has been deduced that is proportional to the log decrement of effective stress at the cycle number N-l, but is independent of the cyclic stress amplitude. Based on the analysis, a new EPWP increment model for saturated Nanjing fine sand is developed from tested data fitting, which provides a better prediction of the curves of EPWP generation, the number of cycles required for initial liquefaction and the liquefaction resistance.

A STATISTICAL PREDICTION MODEL FOR SPRING RAINFALL OVER NORTHERN CHINA BASED ON THE INTERANNUAL INCREMENT APPROACH

The spring (March-April-May) rainfall over northern China (SPRNC) is predicted by using the interannual increment approach. DY denotes the difference between the current year and previous years. The seasonal forecast model for the DY of SPRNC is constructed based on the data that are taken from the 1965-2002 period (38 years), in which six predictors are available no later than the current month of February. This is favorable so that the seasonal forecasts can be made one month ahead. Then, SPRNC and the percentage anomaly of SPRNC are obtained by the predicted DY of SPRNC. The model performs well in the prediction of the inter-annual variation of the DY of SPRNC during 1965-2002, with a correlation coefficient between the predicted and observed DY of SPRNC of 0.87. This accounts for 76% of the total variance, with a low value for the average root mean square error (RMSE) of 20%. Both the results of the hindcast for the period of 2003-2010 (eight years) and the cross-validation test for the period of 1965-2009 (45 years) illustrate the good prediction capability of the model, with a small mean relative error of 10%, an RMSE of 17% and a high rate of coherence of 87.5% for the hindcasts of the percentage anomaly of SPRNC.

Sandbox Modeling of the Fault-increment Pattern in Extensional Basins

Three series of sandbox modeling experiments were performed to study the fault-increment pattern in extensional basins. Experimental results showed that the tectonic action mode of boundaries and the shape of major boundary faults control the formation and evolution of faults in extensional basins. In the process of extensional deformation, the increase in the number and length of faults was episodic, and every ＇episode＇ experienced three periods, strain-accumulation period, quick fault-increment period and strain-adjustment period. The more complex the shape of the boundary fault, the higher the strain increment each ＇episode＇ experienced. Different extensional modes resulted in different fault-increment patterns. The horizontal detachment extensional mode has the ＇linear＇ style of fault-increment pattern, while the extensional mode controlled by a listric fault has the ＇stepwise＇ style of fault-increment pattern, and the extensional mode controlled by a ramp-flat boundary fault has the ＇stepwise-linear＇ style of fault-increment pattern. These fault-increment patterns given above could provide a theoretical method of fault interpretation and fracture prediction in extensional basins.

Maximum Power Point Tracking Using the Incremental Conductance Algorithm for PV Systems Operating in Rapidly Changing Environmental Conditions

Maximum Power Point Tracking (MPPT) is an important process in Photovoltaic (PV) systems because of the need to extract maximum power from PV panels used in these systems. Without the ability to track and have PV panels operate at its maximum power point (MPP) entails power losses;resulting in high cost since more panels will be required to provide specified energy needs. To achieve high efficiency and low cost, MPPT has therefore become an imperative in PV systems. In this study, an MPP tracker is modeled using the IC algorithm and its behavior under rapidly changing environmental conditions of temperature and irradiation levels is investigated. This algorithm, based on knowledge of the variation of the conductance of PV cells and the operating point with respect to the voltage and current of the panel calculates the slope of the power characteristics to determine the MPP as the peak of the curve. A simple circuit model of the DC-DC boost converter connected to a PV panel is used in the simulation;and the output of the boost converter is fed through a 3-phase inverter to an electricity grid. The model was simulated and tested using MATLAB/Simulink. Simulation results show the effectiveness of the IC algorithm for tracking the MPP in PV systems operating under rapidly changing temperatures and irradiations with a settling time of 2 seconds.

Species-specific,pan-European diameter increment models based on data of 2.3 million trees

Background: Over the last decades, many forest simulators have been developed for the forests of individual European countries. The underlying growth models are usually based on national datasets of varying size, obtained from National Forest Inventories or from long-term research plots. Many of these models include country-and location-specific predictors, such as site quality indices that may aggregate climate, soil properties and topography effects. Consequently, it is not sensible to compare such models among countries, and it is often impossible to apply models outside the region or country they were developed for. However, there is a clear need for more generically applicable but still locally accurate and climate sensitive simulators at the European scale, which requires the development of models that are applicable across the European continent. The purpose of this study is to develop tree diameter increment models that are applicable at the European scale, but still locally accurate. We compiled and used a dataset of diameter increment observations of over 2.3 million trees from 10 National Forest Inventories in Europe and a set of 99 potential explanatory variables covering forest structure, weather, climate, soil and nutrient deposition.Results: Diameter increment models are presented for 20 species/species groups. Selection of explanatory variables was done using a combination of forward and backward selection methods. The explained variance ranged from10% to 53% depending on the species. Variables related to forest structure(basal area of the stand and relative size of the tree) contributed most to the explained variance, but environmental variables were important to account for spatial patterns. The type of environmental variables included differed greatly among species.Conclusions: The presented diameter increment models are the first of their kind that are applicable at the European scale. This is an important step towards the development of a new generation of forest development simulators that can be applied at the European scale, but that are sensitive to variations in growing conditions and applicable to a wider range of management systems than before. This allows European scale but detailed analyses concerning topics like CO2 sequestration, wood mobilisation, long term impact of management, etc.

“Products-Knowledge” Two-Dimensional Increment Model of Fashion Industry Value Chain Based on Industry Analysis

Fashion industry has a complex characteristic for it spans the first, second, and third industries. In addition, the characteristic of creative industry has high value-added for its knowledge outputting, which makes the traditional value-added analysis based on supply chain not easy and good enough to interpret its industry value-added features. From the perspective of "products-knowledge" two-dimensional analysis,a fashion industry value chain increment model is built,by simulating the process of "product flow" and "information flow" value-added. The fashion industry value chain increment model provides an effective way for the enterprise strategy formulation and production strategy adjustment.

热液成矿系统构造控矿理论

构造对成矿的控制是热液成矿系统的典型特征之一,系统剖析多重尺度控矿构造的几何学、运动学、动力学、流变学和热力学对认识矿床成因和预测找矿至关重要;而如何实现控矿构造格架、渗透性结构、成矿流体通道和矿化变形网络由静态到多尺度时-空四维动态的转变,查明流体通道和矿床增量生长过程与控制因素,揭示热液成矿系统的构造-流体耦合成矿机制和定位规律是亟待解决的关键科学难题。为此,我们在对已有相关成果系统梳理的基础上,提出了科学构建热液成矿系统构造控矿理论的基本要点与对应方法及应用范畴:(1)流体而非构造是构造控矿理论的中心,热液系统的流体流动与成矿作用受控于断裂带格架及其渗透性结构,其中渗透率是将流体流动与流体压力变化联系起来理解控矿构造的核心;(2)不同控矿构造组合的关键控制是构造差应力和流体压力的大小,而矿化类型的变化可能是由于构造应力场引起的容矿构造方位的不同和赋矿围岩之间的强度差异所致;(3)流体通道的生长始于超压流体储库上游围岩中孤立的微裂隙沿流体压力梯度最大的方向、随裂隙发育且相互连结而形成新的长裂隙,并最终连通形成断裂网络内的流体通道,矿床的增量生长发生在高流体通量的短爆发期,断层反复滑动驱动其内流体压力、流速和应力快速变化,当由此诱发的流体通道生长破坏了流体系统的动态平衡时,随之而来的流体快速降压就成为金属沉淀成矿的关键驱动因素;(4)以热液裂隙-脉系统野外地质观测和构造-蚀变-矿化网络三维填图为基础,通过宏观与微观各级控矿构造相结合、地质历史与构造应力分析相结合、局部与区域点-线-面相结合、浅部与深部相结合、时间与空间相结合、定性和定量相结合,对各种控矿因素开展多学科、多尺度、多层次、全方位综合研究,是应遵循的基本原则;(5)通过构造-蚀变-矿化网络填图,将蚀变-矿化体与控矿构造的类型、形态、规模、产状和间距等几何学特征联系起来,利用热液裂隙-脉系统和断裂网络拓扑学及矿体三维几何结构分析等定量方法查明控矿构造格架和渗透性结构并揭示矿化变形网络的连通性与成矿潜力;(6)合理构建地质模型,选取合适的热力学参数和动力学边界条件,利用HCh和COMSOL等方法,定量模拟成矿过程中的流体流动、热-质传递、应力变形和化学反应等的时-空变化,是揭示构造-流体耦合成矿机理和定位规律、预测矿化中心和确定找矿目标的有效途径。进而提出了构造控矿理论的研究流程:聚焦构造-流体耦合成矿机制和定位规律这一关键科学问题,选择热液裂隙-脉系统和构造-蚀变-矿化网络为重点研究对象;通过几何学描述、运动学判断、流变学分析、动力学解析和热力学综合,厘定控矿构造格架,定位矿化中心,示踪成矿流体通道和多种矿化样式的增量生长过程及其关键控制,揭示渗透性结构的时-空演变规律及构造再活化与成矿定位的成因关联,建立构造-流体耦合成矿模式,服务新一轮战略找矿突破。以胶东焦家金矿田为例,开展控矿构造理论研究和成矿预测应用实践,证实了其科学性和有效性。

基于振动台试验的鱼线固定梅瓶文物响应规律性研究

为了探索鱼线固定梅瓶文物在实际博物馆的地震响应以及抗震效果,首先选取了典型梅瓶文物,并在三层钢筋混凝土框架结构中开展了24种地震工况的振动台试验,然后建立了上述试验所用梅瓶文物的有限元模型,验证了有限元模型的准确性,最后采用增量动力分析法分析了该文物在两种常见直径鱼线保护措施下的运动响应。结果表明:鱼线固定梅瓶文物在地震作用下会产生滑移、摇摆、倾覆以及鱼线断裂等现象;不同楼层下的文物响应差别较大,尤其在大震作用下,高楼层的鱼线固定梅瓶文物易发生倾覆和鱼线断裂破坏,要重视高楼层文物的震前保护措施;在较强的地震作用下,仅依靠增大鱼线直径有时对控制文物的倾覆情况起不到关键性决定作用,需要进一步采取其它措施对文物进行保护。

基于PSO-BP神经网络的分拣机器人视觉反馈跟踪

针对分拣机器人视觉反馈跟踪精度差、耗时较长的问题,研究基于粒子群算法-反向传播(particle swarm optimization-back propagation,PSO-BP)神经网络的分拣机器人视觉反馈跟踪方法,以提升视觉反馈跟踪效果。依据分拣机器人的视觉反馈信息,建立分拣机器人运动学模型,并求解分拣机器人机械臂输出位置和输入位置的误差函数;利用PSO算法优化BP神经网络的权值与偏置;在权值与偏置优化后的BP神经网络内,输入误差函数,预测分拣机器人视觉反馈跟踪控制量;利用预测视觉反馈跟踪控制量,在线调整增量式比例-积分-微分(proportional-integral-derivative,PID)的参数,输出高精度的分拣机器人视觉反馈跟踪控制量,实现分拣机器人视觉反馈跟踪。实验结果表明,该方法可有效视觉反馈跟踪分拣机器人机械臂的关节角;存在干扰情况下,在运行时间为10 s左右时,阶跃响应趋于稳定;有干扰情况下,视觉反馈跟踪的平均误差为0.09 cm,耗时平均值为0.10 ms;无干扰情况下,平均误差为0.03 cm,耗时平均值为0.04 ms。

考虑氯离子侵蚀的深层隧道地震易损性分析

氯离子侵蚀对隧道的抗震性能产生不利影响,研究隧道在侵蚀环境下的地震易损性对全寿命周期的抗震评估至关重要.为了量化分析隧道长期在氯离子侵蚀作用下抗震性能的退化趋势,基于地震易损性理论,结合考虑氯离子侵蚀影响的混凝土和钢筋强度时变模型,分别选取峰值地面加速度和隧道直径变形率作为地震动强度指标和结构损伤指标,通过采用增量动力分析(incremental dynamic analysis,IDA)方法进行时程分析,深入探讨了深层隧道的地震易损性,得到了不同服役时间及不同损伤状态下的地震易损性曲线.结果表明:在相同的服役时间和地震动强度下,轻微损伤的发生概率最高,其次是中等和严重损伤;在相同的损伤状态和地震动强度下,随着服役时间的延长,氯离子侵蚀导致隧道抗震性能下降,损伤概率上升;在轻微损伤状态下,当峰值地面加速度为0.30g且服役达100 a时,损伤概率是服役初期的2.39倍.研究结果可为深层隧道的抗震设计提供科学依据.

书面双语驱动的渐进式微生物学双语教学模式探索与实践

微生物学作为生命科学学科专业领域重要的基础必修课程,是培养适应当前生物经济快速发展、生物制造迭代升级、生物新质生产力高质量发展创新人才的核心单元之一。全球化的发展和跨文化交流的增加,大大提高了微生物学课程中引入双语教学的必要性。不同于口语双语,书面双语能力基本为各行业所需的,应用机会要大得多。文章从中国的现实出发,遵循采用外文原版教材原则,建立融入教学理念、教学目标、英文原版教材、多元化协同渐进式教学方式和形成性考核评价体系的“五位一体”渐进式微生物学双语教学模式。该模式在黑龙江八一农垦大学部分本科专业和硕士点进行了初步实践,取得了较好的效果。书面双语驱动的渐进式微生物学双语教学模式,淡化口语,让双语教学更加适应中国英语水平现状。

协同性三次分配:贫困治理视域下农民农村共同富裕的实现机制

在“后2020时代”,相对贫困治理与共同富裕场域交叠、逻辑同构、成效相承,二者具有一体推进的关系,共同服务于中国式现代化的伟大实践。贫困治理视域下,要推进农民农村共同富裕,关键是要破解城乡空间资源配置失衡之“困”所致的农村之“贫”。协同性三次分配机制作为一种关系创新,通过资源要素回流、地方市场建构、农民无差受惠的“三重奏”,为城乡资源要素连接与激活、动能输入与转化提供制度性通道,改变了农村低水平的要素自循环与贫困再生产。它是渐进平衡贫困治理模式下联动、协同、演化三大功能整合互动的可操作化机制,通过三种分配形式的制度合力保障了相对贫困治理以发展为内容导向、以共享为价值导向、以均衡为目标导向,可有力有效推进农民农村共同富裕。

基于IDA方法的柱承式筒仓结构地震易损性分析

易损性分析是柱承式筒仓结构开展基于性能设计研究的重要基础。为此,选用某钢筋混凝土柱承式筒仓为研究对象,通过有限元软件ABAQUS,分别采用混凝土损伤塑性模型和理想弹塑性模型(Dracker-Prager)模拟筒仓结构及仓内粮食颗粒,建立考虑粮食-仓体相互作用的柱承式筒仓非线性有限元模型。根据增量动力分析法(IDA),按照谱相容性原则选取10条地震动记录,分别以最大层间位移角和地面峰值加速度作为筒仓结构工程需求参数和地震动强度参数,进行空仓、半仓、满仓三种储粮工况的柱承式筒仓结构地震易损性分析以及地震损伤风险评估。结果表明:(1)三种储粮工况下柱承式筒仓结构最大层间位移角均发生在柱顶位置,支承柱进入塑性状态的层间位移角分别为0.015、0.013、0.011 rad;(2)储粮质量大小是决定柱承式筒仓结构损伤刚度退化的重要因素;(3)三种储粮工况下的柱承式筒仓结构均满足“小震不坏、中震可修、大震不倒”的抗震设防目标;(4)满仓工况筒仓50年超越倒塌极限状态的概率为0.45%,小于FEMA P750中定义的50年倒塌风险的限值1%。研究成果可为筒仓结构基于性能的设计研究及抗震性能评估提供依据。

三阶段自适应采样和增量克里金辅助的昂贵高维优化算法

代理辅助进化算法已广泛应用于求解代价高昂的多目标优化问题,但大多数由于代理模型的局限性而仅限于解决决策变量低维的问题。为了解决高维的昂贵多目标优化问题,提出了一种基于三阶段自适应采样策略的改进增量克里金辅助的进化算法。该算法使用改进的增量克里金模型来近似每个目标函数,此模型的超参数根据预测的不确定性进行自适应更新,降低计算复杂度的同时保证模型在高维上的准确性;此外,在模型管理方面提出一种三阶段自适应采样的策略,将采样过程分为不同的优化阶段以更有针对性的选择个体,能够首先保证收敛性,提高算法的收敛速度。为了验证算法的有效性,在包含各种特征的两组测试问题DTLZ(deb-thiele-laumanns-zitzler)、MaF(many-objective function)和路径规划实际工程问题上与最新的同类型算法进行实验对比,结果表明该算法在解决决策变量高维的昂贵多目标优化问题上具有较强的竞争力。