Load Allocation Research in a Manufacturing System

Based on queuing theory, a nonlinear optimization model is proposed in this paper, which has the service load as its objective function and includes three inequality constraints of Work In Progress （WIP）. A novel transformation of optimization variables is also devised and the constraints are properly combined so as to make this model into a convex one from which the Lagrangian function and the Karurh Kuhn Tucker （KKT） conditions are derived. The interior-point method for convex optimization is presented here as a computationaUy efficient tool. Finally, this model is evaluated on a real example, from which such conclusions are reached that the optimum result can ensure the full utilization of machines and the least amount of WIP in manufacturing systems; the interior-point method needs fewer iterations with significant computational savings and it is possible to make nonlinear and complicated optimization problems convexified so as to obtain the optimum.     

Parameter estimation for load‐sharing system subject to Wiener degradation process using the expectation‐maximization algorithm

In practice, many systems exhibit load‐sharing behavior, where the surviving components share the total load imposed on the system. Different from general systems, the components of load‐sharing systems are interdependent in nature, in such a way that when one component fails, the system load has to be shared by the remaining components, which increases the failure rate or degradation rate of the remaining components. Because of the load‐sharing mechanism among components, parameter estimation and reliability assessment are usually complicated for load‐sharing systems. Although load‐sharing systems with components subject to sudden failures have been intensely studied in literatures with detailed estimation and analysis approaches, those with components subject to degradation are rarely investigated. In this paper, we propose the parameter estimation method for load‐sharing systems subject to continuous degradation with a constant load. Likelihood function based on the degradation data of components is established as a first step. The maximum likelihood estimators for unknown parameters are deduced and obtained via expectation‐maximization (EM) algorithm considering the nonclosed form of the likelihood function. Numerical examples are used to illustrate the effectiveness of the proposed method.     

Optimal design for resilient load‐sharing systems with nonidentical components

For system maintenance, strategic component restoration planning is an important conceptual framework for load‐sharing k‐out‐of‐n: G system. A cost‐effective treatment of failure events is imperative with the purpose of reinstating the system ability. This paper presents a new optimal design method for load‐sharing repairable k‐out‐of‐n: G system, in which a flowgraph is used in conjunction with multiresponse optimization. By introducing the concept of modular design, the system is partitioned into scalable and repairable maintenance modules. The determination of the optimal design depends on the type of system components, the module‐based system structure, and the repair rule setting. An extended flowgraph model, which links covariates into transition branches, is used for modeling the system failure evolution. With consideration of various system performance measurements as responses, multiresponse optimization with weighted principal component analysis is used to achieve an optimal design of maintenance modules as well as repair policy. The methodology presented in this paper provides an efficient way to design a system having nonidentical components and arbitrary repair time distributions with consideration of the variety of maintenance policies as well as the diversity of system operating conditions.     

Reliability analysis of multi‐state series systems with performance sharing mechanism under epistemic uncertainty

For real engineering systems, it is sometimes difficult to obtain sufficient data to estimate the precise values of some parameters in reliability analysis. This kind of uncertainty is called epistemic uncertainty. Because of the epistemic uncertainty, traditional universal generating function (UGF) technique is not appropriate to analyze the reliability of systems with performance sharing mechanism under epistemic uncertainty. This paper proposes a belief UGF (BUGF)‐based method to evaluate the reliability of multi‐state series systems with performance sharing mechanism under epistemic uncertainty. The proposed BUGF‐based reliability analysis method is validated by an illustrative example and compared with the interval UGF (IUGF)‐based methods with interval arithmetic or affine arithmetic. The illustrative example shows that the proposed BUGF‐based method is more efficient than the IUGF‐based methods in the reliability analysis of multi‐state systems (MSSs) with performance sharing mechanism under epistemic uncertainty.     

Artificial Intelligence Based Reliable Load Balancing Framework in Software-Defined Networks

Software-defined networking (SDN) plays a critical role in transforming networking from traditional to intelligent networking. The increasing demand for services from cloud users has increased the load on the network. An efficient system must handle various loads and increasing needs representing the relationships and dependence of businesses on automated measurement systems and guarantee the quality of service (QoS). The multiple paths from source to destination give a scope to select an optimal path by maintaining an equilibrium of load using some best algorithms. Moreover, the requests need to be transferred to reliable network elements. To address SDN’s current and future challenges, there is a need to know how artificial intelligence (AI) optimization techniques can efficiently balance the load. This study aims to explore two artificial intelligence optimization techniques, namely Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), used for load balancing in SDN. Further, we identified that a modification to the existing optimization technique could improve the performance by using a reliable link and node to form the path to reach the target node and improve load balancing. Finally, we propose a conceptual framework for SDN futurology by evaluating node and link reliability, which can balance the load efficiently and improve QoS in SDN.     

Experimental Investigation of Load Sharing in Multiple Gear Tooth Contact Using the Stress‐Optical Method of Caustics

Abstract: In this paper, a comprehensive methodology for calculating load sharing in multiple tooth contact is presented based on the experimental stress‐optical method of caustics. The technique is applied to a set of poly‐methyl‐methacrylate gears at various meshing positions covering a complete meshing cycle, including single and multiple gear tooth contact. The load sharing factor (LSF) is calculated using well‐established mathematical formulae from the photographs of the transmitted caustics and the obtained results are compared with the pertinent International Organisation for Standardisation and American Gear Manufacturers Association standards with which good agreement is verified. The proposed method is a reliable alternative for measuring load distribution in gear teeth compared with photoelasticity and other experimental techniques.     

Impact of reputation information sharing in an auction-based job allocation model for small and medium-sized enterprises

This research investigates the impact of reputation information sharing in an auction-based job allocation system using a multi-agent based simulation model based on three performance measures: economic efficiency, throughput index, and penalty index. Two forms of reputation information sharing systems, dyadic and public, were implemented and studied. Dyadic reputation systems deal with one-to-one information sharing, while public reputation systems deal with many-to-many information sharing. The paper proposes a hybrid auction mechanism, which is a combination of the sealed bid and reverse auction mechanisms. Analysis of the simulation results shows that reputation information sharing benefits the system tremendously by increasing the overall economic efficiency and throughput index, while decreasing the penalty index of the system. The results also show that as the significance of reputation in the market increases, the benefits realised in terms of economic efficiency and throughput index also increases.     

Knowledge sharing behaviors in social media

PurposeUsers on Social Media (SM) platforms make many decisions related to content sharing, such as whether to create or reuse content, whether to label for easy access by an interest group or not, and whether to disseminate to targeted individuals or broadcast to general audiences. In this study, we investigated if these content-related strategies on SM, called social media knowledge sharing behaviors, are determined by user characteristics.MethodologyUsing concepts from Self-Motivation Theory and the Affordance Theory, we examined if the knowledge sharing behaviors are influenced or correlated with user characteristics, such as the intensity of engagement on SM, a strong preference attitude for a SM platform, and multiple functional intentions for using SM. Based on this survey study of one hundred and twenty-three subjects, we developed hierarchical regression analyses to test if the SM user's knowledge decisions (Creation, Framing and Targeting) are corelated with the user's online usage intensity, their SM online platform preferences, and their functional intentions (Intensity, Preferences and Functionality). We complemented the regression models with a more comprehensive path analysis for an integrative hypothesis testing.FindingsThe main findings show that knowledge creation and knowledge targeting behaviors were correlated with multiple functional intentions (or needs) of users, meaning that users who utilized SM in order to fulfill many needs create and broadcast knowledge more than users that utilized SM in order to fulfill fewer needs.OriginalityThe study investigates the relationship between detailed knowledge sharing behaviors afforded by the social media tools and different user self-determination factors, such as intensity, preference and needs. This study further describes the attributes of social media sharing as a bundle of content sharing strategies of creation, sharing and targeting, which are used differently based on different user characteristics and motivations.     

人机交互中认知负荷的成因分析与设计策略研究

目的研究人机交互过程中认知负荷的生成机制和成因类型,并探索优化调节认知负荷的交互设计策略。方法首先研究了交互设计中认知负荷的生成机制,剖析了认知负荷、可用性和用户体验三者的内在联系;其次探讨了用户在操作过程中认知负荷的三种成因类型,并针对不同成因类型的认知负荷提出了优化调节目标及相应的设计策略;最后对一款APP进行了分析与改进设计。结论用户在操作过程中,会产生三种不同成因的认知负荷,其中,内、外在认知负荷对认知有阻碍作用,相关认知负荷对认知有促进作用。在进行交互设计时,通过降低交互信息的内在复杂性和交互性,提高用户认知图式的匹配程度,可以降低内在认知负荷,优化交互信息的组织和呈现方式可以降低外在认知负荷,明确的反馈机制可以提升相关认知负荷。对交互过程中的认知负荷进行分析和改进,有助于提升产品的可用性和用户体验。     

三联供系统中逐时冷热电负荷的模拟计算

在进行冷热电三联供系统设计时,冷热负荷与电负荷的逐时对应关系直接影响到三联供系统的配置与运行,逐时负荷的准确计算是三联供系统优化配置与运行分析的基础。本文基于对建筑冷热电负荷调查研究的基础上,采用日本三联供设计手册中的相关数据,利用“逐时能源负荷分摊比例”的方法,以宾馆建筑为例,模拟计算了该宾馆的全年逐时冷热电负荷,为三联供系统的优化设计与系统配置奠定了一定的基础。     

行星轮不均载的行星齿轮箱振动信号模型EI北大核心CSCD

行星齿轮箱有多个行星轮构成的平行传动路径可以分担输入的扭矩负载。行星轮之间的载荷分布不均会降低效率和加速疲劳。为了充分揭示行星轮载荷分布不均的故障特征,建立了行星轮不同载荷分布情况下的振动信号模型;推导出傅里叶频谱,说明行星轮载荷的不均匀分布会引起附加的信号频率成分,并可能导致系统固有频率的偏移;总结了输入扭矩和行星轮位置误差的严重程度对信号频谱结构的影响。仿真和实验结果与理论模型较好吻合。     

地铁车站设备区空调负荷动态特性分析及系统优化

通过对广州地铁某车站设备区的研究,分析全年逐时空调负荷计算和空调机组运行负荷的动态变化状况,得出地铁车站设备区空调实际负荷特性及机组冷冻水进、出水温差、设备区室内温度、室内焓值等各项性能参数的变化规律,并以此对地铁车站设备区通风空调负荷的常用工程设计及运行模式进行评估,对地铁设备区通风空调系统提出优化建议、改进措施以及可行和节能的系统运行方案。     

下一代网络核心业务平台负载共享结构及硬件配置方法的研究

分析了下一代网络核心业务平台提供的功能, 提出了核心业务平台负载共享模型,描述了负载共享的实现方法及相关技术;基于该负载共享模型,提出了核心业务平台硬件配置的优化计算方法.该方法根据业务需求及运行业务软件的计算机的特征来求解核心业务平台的最小代价硬件配置方案.仿真实验验证了该计算方法的有效性和正确性.     

Reliability Analysis and Evaluation of Differential System Based on low Load Strengthening Model

The differential is an important part of a driveline, and differential performance is related to the handling and stability performance of a vehicle. Thus, a differential with sound design structure and reasonable form and size parameters could lead to satisfactory driving performance. In this work, we analyze and evaluate the reliability of the key parts of a differential system. Firstly, each of key parts is regarded as a subsystem of a differential system, so the subsystem reliability models are obtained. A system reliability model is built based on the paths of the forces from the differential system, and system reliability is calculated. Secondly, according to the result of the analysis of system reliability and the use of the six sigma method, 45 steel or 1Cr18Ni9Ti utilized as the material for the worm shaft, system reliability is analyzed and discussed separately. Then, the reliability of the key parts and the overall system reliability increase with the low load strengthening characteristic of the material. Finally, according to the analysis and discussion, the level of system reliability matches that required for differential systems, and the cost is also considerably reduced, as demonstrated using the stress–strength interference and low‐load strengthening models. These results provide a theoretical basis for the improvement of the design of Torsen differentials. Similar methods can be used to develop automobile subsystems in the future. Copyright © 2015 John Wiley & Sons, Ltd.     

PCA-GRNN在综合气象短期负荷预测中的应用

为克服由气象因子较多且信息互嵌造成输入量多、预测时间长、预测精度低的缺点,引入主成分分析（PCA）提取气象因子特征量,与历史负荷数据共同作为建模对象;同时,针对BP神经网络动态性能的不足,建立基于广义回归神经网络（GRNN）的短期负荷预测模型。通过对实际电力负荷数据的预测,证明该方法与传统神经网络预测模型相比,明显提高预测精度和速度,具有实用性和有效性。     

Stable profit sharing in cooperative investments

This paper examines profit sharing in cooperative investments where investors bundle their capital endowments to meet the capital requirements of long term investment projects. Furthermore, investors may reinvest intertemporal gains from existing projects into new projects. Focus is on stable allocation schemes as stability is necessary to sustain the long term cooperation of investors. The paper presents sufficient conditions for the existence of stable profit sharing schemes using linear programming techniques.This complete issue was revised and published online in November 2004. The previous version contained a false date. Correspondence to: Anja De WaegenaereThe authors wish to thank two anonymous referees for comments that helped to improve the paper significantly. The research of J. Suijs is made possible by a fellowship of the Royal Netherlands Academy of Arts and Sciences (KNAW).     

《一种频域载荷识别的优化方法》

从优化的角度出发，利用遗传优化算法得到频域载荷谱，使得其产生的响应谱与实际的测量结果之间的误差足够小，这样可以认为所得到的载荷谱也就反映了实际的激励，即达到了载荷识别的目的，仿真结果说明了这种优化方法的有效性。     

User‐friendly image sharing using polynomials with different primes

Recently, Thien and Lin proposed a user‐friendly (k, n)‐threshold scheme which employs Lagrange interpolation to produce shadow images representing a shrunken version of the original image. In this paper, we introduce a new framework which uses more (k ? 1)‐degree polynomials with different primes to enhance the functionality of the user‐friendly (k, n)‐threshold framework and obtain more effective performance for large k. Since the proposed framework significantly reduces reconstruction errors compared to the previously published user‐friendly schemes, it is suitable for modern visual communication applications where features such as distributed trust, secure transmission and storage, fault tolerance, and high‐quality image reconstruction are required. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 17, 40–47, 2007     

Trust in supply forecast information sharing

In this paper, we investigate the role of trust in supply forecast signalling in a supply chain with a supplier and a manufacturer in a one-shot game. It is assumed that the supplier faces a random yield uncertainty that is multiplied by the manufacturer’s order quantity. The supplier has a private forecast of yield risk. Based on the information, the supplier decides whether to share its forecast truthfully, or not to share. On the other hand, the manufacturer is faced with two ordering strategies. If it trusts the supplier’s report, then it updates its belief on the yield risk providing a forecast signal by the supplier. Otherwise, it orders based on its prior belief. We analytically obtain the optimal order quantity where the random yield uncertainty follows uniform distribution. The intuitive result indicates that the supplier has a tendency to deviate from reporting true forecast information. The numerical results support the intuitive conclusion.