沥青砂粘弹特性实验研究

进行了沥青砂在不同加载应力和不同实验温度下单轴压缩蠕变实验,得到其在不同实验条件下的蠕变曲线,选择Burgers模型,编制非线性拟合程序,求得模型参数值,通过相关性分析,得到了模型参数与温度和应力函数关系式,分析了模型参数对沥青砂蠕变性能的影响,最后进行模型预测值与实验结果对比,结果表明Burgers模型能够描述沥青砂蠕变过程的第一阶段、第二阶段,反映了其粘弹特性。     

员工组织社会化理论的研究进展

文章主要介绍了员工组织社会化的研究现状,介绍了组织社会化概念的内涵,阐述了组织社会化内容研究的四因素模型、五因素模型与六因素模型,最后描述了组织社会化策略的二维三因素模型,并指出了不同维度组织的社会化策略将产生的不同结果。     

带暗支撑剪力墙体系抗震研究及应用 第十三届全国结构工程学术会议特邀报告

提出了钢筋混凝土带暗支撑剪力墙。对不同高宽比、不同暗支撑型式、不同暗支撑倾角和配筋比的带暗支撑低矮、中高剪力墙模型和双肢剪力墙模型进行了低周反复荷载下的抗震性能试验研究。进行了带暗支撑剪力墙结构模型与不带暗支撑剪力墙结构模型的振动台对比试验研究。在试验研究的基础上,建立了带暗支撑剪力墙的力学模型和抗震设计方法,在实际工程应用中取得了良好的效果。     

填充床潜热储热数值模型研究进展

填充床潜热储热技术广泛应用于太阳能热利用和供热通风与空气调节等领域。由于不同应用环境下的填充床潜热储热系统具有复杂的瞬态特性和较高的实验成本,在研究不同因素对储热系统性能的影响时,研究人员开发了相关数学模型,并采用不同数值计算方法进行分析。对常见的填充床储热过程数值模型进行分类,详细分析不同模型的特点与适用性;对比了模型的计算效率和误差;最后,总结了不同数值模型在潜热型填充床储热技术中的应用特点,可为同类模型的开发与应用提供依据。     

三维塑料注射成形热残余应力及收缩模拟

充模结束后的保压、冷却过程中,塑料熔体与凝固区域并存,对处于不同温度的塑料材料,分别采用可压缩粘性模型、线形粘弹性模型与弹性模型,建立了充模后的三维两相耦合计算模型及翘曲计算模型,模拟了热残余应力、收缩变形的演变过程。数值算例表明,耦合计算模型的模拟结果与实验结果在制品厚度部分区域较好地吻合,能模拟不同工艺条件对热残余应力与收缩的影响。     

基于Bouc-Wen方程的磁流变阻尼器实验建模

在实验数据的基础上建立了磁流变阻尼器输出力与不同定常电流控制量的关系,讨论了提高预测精度的方法,对修正Bouc-Wen模型进行了进一步的简化修正.新的模型继承了Bouc-Wen模型对激励的适应能力,没有使用修正Bouc-Wen模型中参数随控制电压线性变化的函数,将控制量从Bouc-Wen方程中分离了出来.本文模型的预测结果与实验数据相一致,表明本文提出的模型能准确地预测不同谐波激励和不同定常电流控制下的磁流变阻尼器输出力.     

泡沫铝材料弹性模量有限元模拟

采用C语言建立了3种泡沫铝材料模型,即不均匀结构模型、均匀结构模型和大孔缺陷结构模型,更符合实际泡沫铝材料的结构.利用ANSYS软件对建立的模型进行了有限元分析,得到了不同模型相对密度与弹性模量的关系,指出其弹性模量与密度呈指数关系.     

基于ANSYS的温控包装圆柱体模型的建立

为简化温控包装设计计算,对实际温控包装系统建立了不同的圆柱体模型。将实际温控包装箱尝试向不同圆柱体模型转化,使其设计方法更简便易行,借助ANSYS软件对其进行了热传递分析和数值模拟,利用转换模型的温控时间与实际温控系统的温控时间之间的相对误差,对各模型进行了筛选。结果表明,采用的圆柱体模型与原立方体温控包装箱的内体积、厚度相等时,最能真实反映实际保温箱的传热过程。为降低包装成本、优化温控包装设计方案提供了依据。     

2219铝合金焊接接头软化模型的建立与应用

通过2219铝合金TIG焊接接头不同区域的微区拉伸试验以及母材在热循环过程中不同温度下的拉伸试验,获得了相应的屈服强度和抗拉强度,建立了基于温度、温度历史及应变强化效应的接头软化模型。将此软化模型应用到TIG焊接接头残余应力的有限元模拟中,并将应力模拟值与X射线衍射应力实测值进行对比。结果表明,与常规模型相比,软化模型中的残余应力分布水平出现了不同程度的降低,此外,残余应力在焊缝附近区域、起弧端与收弧端区域及横向分布上下降幅度较为明显。与实测值相比,应用接头软化模型计算得到的焊接残余应力分布与之更为接近,提高了残余应力的计算精度,验证了接头软化模型的有效性。     

AlN/橡胶复合材料等效热导率数值计算

利用计算机生成不同的AlN/橡胶复合材料等效结构单元,基于三维格子玻尔兹曼模型计算了复合材料的等效热导率。实验制备了AlN/橡胶复合材料,并测定了不同填充量下复合材料的热导率,用以验证模型的有效性。将LBM计算结果与实验结果及Maxwell、Bruggeman、Nielsen等模型进行了比较,发现本文数值计算结果与Maxwell模型吻合较好,相比较于Bruggeman模型与Nielsen模型更加接近实验值。研究了AlN颗粒尺寸及分布方式对复合材料导热性能的影响。结果表明,一定体积分数范围内,粒径较小的AlN颗粒填充橡胶复合材料的等效热导率较大,当体积分数增大到20%,粒径较大的复合材料内先开始形成导热网络,大大提高了热导率;随机分布比均匀分布方式下的复合材料的等效热导率大,不同的粒子空间分布结构是影响复合材料热导率的关键因素。     

An empirical study of demand forecasting of non-volatile memory for smart production of semiconductor manufacturing

As high-speed computing is crucial to empower intelligent manufacturing for Industry 4.0, non-volatile memory (NVM) is critical semiconductor component of the cloud and data centre for the infrastructures. The NVM manufacturing is capital intensive, in which capacity utilisation significantly affects the capital effectiveness and profitability of semiconductor companies. Since capacity migration and expansion involve long lead times, demand forecasting plays a critical role for smart production of NVM manufacturers for revenue management. However, the shortening product life cycles of integrated circuits (IC), the fluctuations of semiconductor supply chains, and uncertainty involved in demand forecasting make the present problem increasingly difficult in the consumer electronics era. Focusing on the realistic needs of NVM demand forecasting, this study aims to develop a decision framework that integrates an improved technology diffusion model and a proposed adjustment mechanism to incorporate domain insights. An empirical study was conducted in a leading semiconductor company for validation. A comparison of alternative approaches is also provided. The results have shown the practical viability of the proposed approach.     

Short-term forecasting of electricity prices in the colombian electricity market

The restructuring of the electricity-generating industry from protected monopoly to an open competitive market has presented producers with a problem scheduling generation: finding the optimal bidding strategy to maximise their profits. In order to solve this scheduling problem, a reliable system capable of forecasting electricity prices is needed. This work evaluates the forecasting capabilities of several modelling techniques for the next-day-prices forecasting problem in the Colombian market, measured in USD/MWh. The models include exogenous variables such as reservoir levels and load demand. Results show that a segmentation of the prices into three intervals, based on load demand behaviour, contribute to an important standard deviation reduction. Regarding the models under analysis, Takagi?Sugeno?Kang models and ARMAX models identified by means of a Kalman filter perform the best forecasting, with an error rate below 6%.     

Hybrid Deep Learning Architecture to Forecast Maximum Load Duration Using Time-of-Use Pricing Plans

Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models. Especially, we need the adequate model to forecast the maximum load duration based on time-of-use, which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power grid. However, the existing single machine learning or deep learning forecasting cannot easily avoid overfitting. Moreover, a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum load duration based on time-of-use. To overcome these limitations, we propose a hybrid deep learning architecture to forecast maximum load duration based on time-of-use. Experimental results indicate that this architecture could achieve the highest average of recall and accuracy (83.43%) compared to benchmark models. To verify the effectiveness of the architecture, another experimental result shows that energy storage system (ESS) scheme in accordance with the forecast results of the proposed model (LSTM-MATO) in the architecture could provide peak load cost savings of 17,535,700 KRW each year comparing with original peak load costs without the method. Therefore, the proposed architecture could be utilized for practical applications such as peak load reduction in the grid.     

Adaptive exponential smoothing versus conventional approaches for lumpy demand forecasting: case of production planning for a manufacturing line

Production planning in a lumpy demand environment can be tenuous, with potentially costly forecasting errors. This paper addresses the issue of selecting the smoothing factor used in lumpy demand forecasting models. We propose a simple adaptive smoothing approach to replace the conventional industrial practice of choosing a smoothing factor largely based on the analyst or engineer's experience and subjective judgment. The Kalman filter approach developed in this study processes measurements to estimate the state of a linear system and utilises knowledge from states of measurements and system dynamics. Performances of an array of forecasting models that have been shown to work well in lumpy demand environments are compared with respect to the proposed adaptive smoothing factor and the conventional smoothing constant across a spectrum of lumpy demand scenarios. All models using the adaptive smoothing factor based on Kalman filter weighting function generate smaller errors than their conventional counterparts, especially under high lumpiness demand environments. Our proposed approach is particularly useful when production management is concerned about simplicity and transferability of knowledge due to constant personnel turnaround and low retention rate of expertise.     

INAR implementation of newsvendor model for serially dependent demand counts

The classic newsvendor model was developed under the assumption that period-to-period demand is independent over time. In real-life applications, the notion of independent demand is often challenged. In this paper, we propose a dynamic implementation of the newsvendor model based on a class of integer-valued autoregressive (INAR) models when facing correlated discrete demand. Motivated by application, we consider INAR models with underlying Poisson error innovations and with underlying negative-binomial error innovations to accommodate overdispersion scenarios. We numerically compare our proposal with the standard newsvendor solution and with a standard autoregressive-based newsvendor solution. Our results show that an appropriately specified INAR-based newsvendor solution not only outperforms the standard case but also the approximating forecasting approaches. Moreover, even in the presence of autocorrelation, the use of the standard autoregressive model as an approximating approach can lead to increased costs over and above the standard implementation of the newsvendor model based on no forecasting.     

A reliable decision support system for fresh food supply chain management

The paper proposes a decision support system (DSS) for the supply chain of packaged fresh and highly perishable products. The DSS combines a unique tool for sales forecasting with order planning which includes an individual model selection system equipped with ARIMA, ARIMAX and transfer function forecasting model families, the latter two accounting for the impact of prices. Forecasting model parameters are chosen via two alternative tuning algorithms: a two-step statistical analysis, and a sequential parameter optimisation framework for automatic parameter tuning. The DSS selects the model to apply according to user-defined performance criteria. Then, it considers sales forecasting as a proxy of expected demand and uses it as input for a multi-objective optimisation algorithm that defines a set of non-dominated order proposals with respect to outdating, shortage, freshness of products and residual stock. A set of real data and a benchmark – based on the methods already in use – are employed to evaluate the performance of the proposed DSS. The analysis of different configurations shows that the DSS is suitable for the problem under investigation; in particular, the DSS ensures acceptable forecasting errors and proper computational effort, providing order plans with associated satisfactory performances.     

基于ROA的移动实时商业智能框架研究

针对商业智能随全球移动通信的普及和移动通讯设备的智能化,企业和组织对移动商务需求逐步增大,但传统商业智能对硬件和软件设施的要求在现阶级移动终端中可能无法完全实现的应用现状,提出基于R（）A的移动实时商业智能系统的体系结构．对该体系结构中的服务器端架构、移动客户端架构以及通信安全机制进行详细阐述,并运用Android通过实例对该架构中关键技术的实现进行详细的阐述．     

企业需求预测准确性存在的问题及其应对策略(二)

鉴于需求预测在企业经营活动中具有重要地位,且会受到受各种因素影响,本文在对企业实际需求预测的方法、过程、系统、管理等问题进行梳理和分析的基础上,指出了通过优化需求预测方法、完善需求预测系统、改进需求预测管理,可有效控制需求预测和未来市场情况的偏差,从而持续提高需求预测的准确性,促进企业生产、销售的良性运行。     

企业需求预测准确性存在的问题及其应对策略(一)

鉴于需求预测在企业经营活动中具有重要地位,且会受到受各种因素影响,本文在对企业实际需求预测的方法、过程、系统、管理等问题进行梳理和分析的基础上,指出了通过优化需求预测方法、完善需求预测系统、改进需求预测管理,可有效控制需求预测和未来市场情况的偏差,从而持续提高需求预测的准确性,促进企业生产、销售的良性运行。     

Role of travel demand models in appraisal and policy-making

The purpose of this paper is to examine the role of travel demand models in the appraisal and policy-making process. The travel demand modelling process is described, with particular emphasis on identifying where policy issues can be examined and possible weaknesses in the methodology. Then the historical development of the models is considered. This is a mixture of policy-making, public pressure, government response, and analytical development. It is shown how the national road-building programme is intimately linked with the process of forecasting the demand for road space. However, the forecasting procedure is pragmatic rather than theoretically sound, and not very accurate. Similar weaknesses are found at a local level. The paper is concluded by consideration of ways of improving the forecasting procedures.