Organizational Factors Affecting Capacity Planning Practices

Because of the high cost of most major equipment acquisitions, even a small improvement in the computer capacity planning (CCP— process can result in significant cost savings. The survey results presented here assess the effects of computing organization characteristics on various aspects of CCP practices. The results also should help MIS managers and analysts broaden their understanding of how this function can be conducted and what factors significantly affect its practice.     

Interactive decision procedure for watershed nutrient load reduction: An integrated chance-constrained programming model with risk–cost tradeoff

Nutrient load reduction is a well-recognized requirement for aquatic ecosystem restoration. However, decision making is difficult due to challenges related to uncertainty and the interaction between decision makers and modelers, including (a) the quantitative relationship between risks arising from different aspects and the fact that cost is not usually revealed and (b) the fact that decision makers are not significantly involved in the modeling process. In this study, an interactive optimal-decision procedure with risk–cost tradeoff is proposed to overcome these limitations. It consists of chance-constrained programming (CCP) models, risk scenario analysis using the Taguchi method, risk–cost tradeoff and feedback for model adaption. A hybrid intelligent algorithm (HIA) integrating Monte Carlo simulation, artificial neural networks, and an augmented Lagrangian genetic algorithm was developed and applied to solve the CCP model. The proposed decision procedure and HIA are illustrated through a case study of uncertainty-based optimal nutrient load reduction in the Lake Qionghai Watershed, China. The CCP model has four constraints associated with risk levels indicating the possibility of constraint violation. Sixteen risk scenarios were designed with the Taguchi method to recognize the interactions between multiple constraint risks and total cost. The results were analyzed using the signal-to-noise ratio, analysis of variance, and multivariate regression. The model results demonstrate how cost is affected by risk for the four constraints and show that the proposed approach can provide effective support for decision making on risk–cost tradeoffs.     

A hybrid learning-based model for on-line detection and analysis of control chart patterns

Unnatural control chart patterns (CCPs) are associated with a particular set of assignable causes for process variation. Therefore, effectively recognizing CCPs can substantially narrow down the set of possible causes to be examined, and accelerate the diagnostic search. In recent years, neural networks (NNs) have been successfully used to the CCP recognition task. The emphasis has been on the CCP detection rather than more detailed quantification of information of the CCP. Additionally, a common problem in existing NN-based CCP recognition methods is that of discriminating between various types of CCP that share similar features in a real-time recognition scheme. This work presents a hybrid learning-based model, which integrates NN and DT learning techniques, to detect and discriminate typical unnatural CCPs, while identifying the major parameter (such as the shift displacement or trend slope) and starting point of the CCP detected. The performance of the model was evaluated by simulation, and numerical and graphical results that demonstrate that the proposed model performs effectively and efficiently in on-line CCP recognition task are provided. Although this work considers the specific application of a real-time CCP recognition model for the individuals (X) chart, the proposed learning-based methodology can be applied to other control charts (such as the X-bar chart).     

PIC16F877A的开关磁阻电动机控制系统设计

设计一款结构简单、性能稳定的开关磁阻电机的控制系统。以PIC16F877A为控制核心,利用其CCP模块进行脉宽调制和周期信号捕捉,驱动SR电动机专用集成芯片FCAS20DN60BB。控制系统结构简单,性能稳定,并极大地降低成本。对一台370W、四相8/6极开关磁阻电动机进行实验,取得较好的实验结果。     

Cooperation Protocols in Multi-Agent Robotic Systems

Multi-agent robotic systems are useful in many practical applications. For some tasks, such as holding a conference, cooperation among agents are necessary. For other tasks, such as cleaning a room, multiple agents can work in parallel for better performance. This paper provides help-based (HCP) and coordination-based (CCP) protocols for controlling agents to accomplish multi-agent tasks. The HCP utilizes efficient negotiation to coordinate agents into groups. The CCP improves overall performance by exchanging local knowledge among agents and making decisions in parallel. A reactive and modularized agent architecture was employed to implement the protocols. Since each protocol is embedded into the architecture, it is efficient and effective. In addition, the protocols are deadlock-free. The protocols were utilized to solve the Object-Sorting Task, which abstracts two characteristics of tasks: parallelism and cooperation. The experimental results showed that 1) both HCP and CCP are stable under different workload; 2) the protocols can effectively utilize the agent-power to achieve super-linear improvement; 3) The CCP is better than the HCP in both performance and speedup.     

Simultaneous process mean and variance monitoring using artificial neural networks

Control chart patterns (CCPs) can be employed to determine the behavior of a process. Hence, CCP recognition is an important issue for an effective process-monitoring system. Artificial neural networks (ANNs) have been applied to CCP recognition tasks and promising results have been obtained. It is well known that mean and variance control charts are usually implemented together and that these two charts are not independent of each other, especially for the individual measurements and moving range (X–R_m) charts. CCPs on the mean and variance charts can be associated independently with different assignable causes when corresponding process knowledge is available. However, ANN-based CCP recognition models for process mean and variance have mostly been developed separately in the literature with the other parameter assumed to be under control. Little attention has been given to the use of ANNs for monitoring the process mean and variance simultaneously. This study presents a real-time ANN-based model for the simultaneous recognition of both mean and variance CCPs. Three most common CCP types, namely shift, trend, and cycle, for both mean and variance are addressed in this work. Both direct data and selected statistical features extracted from the process are employed as the inputs of ANNs. The numerical results obtained using extensive simulation indicate that the proposed model can effectively recognize not only single mean or variance CCPs but also mixed CCPs in which mean and variance CCPs exist concurrently. Empirical comparisons show that the proposed model performs better than existing approaches in detecting mean and variance shifts, while also providing the capability of CCP recognition that is very useful for bringing the process back to the in-control condition. A demonstrative example is provided.     

Effective recognition of control chart patterns in autocorrelated data using a support vector machine based approach

The effective recognition of unnatural control chart patterns (CCPs) is a critical issue in statistical process control, as unnatural CCPs can be associated with specific assignable causes adversely affecting the process. Machine learning techniques, such as artificial neural networks (ANNs), have been widely used in the research field of CCP recognition. However, ANN approaches can easily overfit the training data, producing models that can suffer from the difficulty of generalization. This causes a pattern misclassification problem when the training examples contain a high level of background noise (common cause variation). Support vector machines (SVMs) embody the structural risk minimization, which has been shown to be superior to the traditional empirical risk minimization principle employed by ANNs. This research presents a SVM-based CCP recognition model for the on-line real-time recognition of seven typical types of unnatural CCP, assuming that the process observations are AR(1) correlated over time. Empirical comparisons indicate that the proposed SVM-based model achieves better performance in both recognition accuracy and recognition speed than the model based on a learning vector quantization network. Furthermore, the proposed model is more robust toward background noise in the process data than the model based on a back propagation network. These results show the great potential of SVM methods for on-line CCP recognition.     

基于事件的社交网络上的CCP事件规划

在基于事件的社交网络(EBSNs)上,事件规划一直是一个热点研究问题.事件规划问题的核心是基于事件和用户的约束条件,对于一组事件,为每个事件选择一组用户,以最大化预先定义的目标函数.在实际应用中,事件冲突、事件容量、用户容量、社交偏好、事件偏好,简称为CCP,即冲突conflict、容量capacity、偏好preference,是规划方案需要考虑的重要因素.然而,现有的所有工作均未在研究事件规划问题时考虑CCP.为了获得更加合理有效的规划方案,首次提出一种CCP事件规划问题.相比只考虑部分因素的规划, CCP事件规划面临着问题更复杂、约束条件更多的困难.为了有效求解该问题,提出事件导向的贪心用户选择算法、事件导向的动态规划算法及基于收益预测的快速版本和事件导向的近似最优用户选择算法.大量的实验结果验证所提算法的有效性和高效性.     

Conceptual process planning – an improvement approach using QFD,FMEA, and ABC methods

Conceptual process planning (CPP) is an important technique for assessing the manufacturability and estimating the cost of conceptual design in the early product design stage. This paper presents an approach to develop a quality/cost-based conceptual process planning (QCCPP). This approach aims to determine key process resources with estimation of manufacturing cost, taking into account the risk cost associated to the process plan. It can serve as a useful methodology to support the decision making during the initial planning stage of the product development cycle. Quality function deployment (QFD) method is used to select the process alternatives by incorporating a capability function for process elements called a composite process capability index (CCP). The quality characteristics and the process elements in QFD method have been taken as input to complete process failure mode and effects analysis (FMEA) table. To estimate manufacturing cost, the proposed approach deploys activity-based costing (ABC) method. Then, an extended technique of classical FMEA method is employed to estimate the cost of risks associated to the studied process plan, this technique is called cost-based FMEA. For each resource combination, the output data is gathered in a selection table that helps for detailed process planning in order to improve product quality/cost ratio. A case study is presented to illustrate this approach.     

An hybrid detection system of control chart patterns using cascaded SVM and neural network�Cbased detector

Early detection of unnatural control chart patterns (CCP) is desirable for any industrial process. Most of recent CCP recognition works are on statistical feature extraction and artificial neural network (ANN)-based recognizers. In this paper, a two-stage hybrid detection system has been proposed using support vector machine (SVM) with self-organized maps. Direct Cosine transform of the CCP data is taken as input. Simulation results show significant improvement over conventional recognizers, with reduced detection window length. An analogous recognition system consisting of statistical feature vector input to the SVM classifier is further developed for comparison.     

基于CCP协议的汽车ECU标定系统的设计

CCP协议是一种基于CAN总线的匹配标定协议。本文介绍了该协议的基本原理，通信方式以及用于ECU标定的各种工作模式，并讨论了一种基于CCP协议的汽车ECU标定系统，该系统实现了对汽车系统中的ECU进行在线标定，并可以针对不同用户需求提供不同的定制功能，因此，在汽车电子控制系统开发中将能得到较好的应用。     

Recognition of concurrent control chart patterns using wavelet transform decomposition and multiclass support vector machines

Statistical process control charts have been widely utilized for monitoring process variation in many applications. Nonrandom patterns exhibited by control charts imply certain potential assignable causes that may deteriorate the process performance. Though some effective approaches to recognition of control chart patterns (CCPs) have been developed, most of them only focus on recognition and analysis of single patterns. A hybrid approach by integrating wavelet transform and improved particle swarm optimization-based support vector machine (P-SVM) for on-line recognition of concurrent CCPs is developed in this paper. A statistical correlation coefficient is used to determine whether the input pattern is a single or concurrent CCP. Based on wavelet transform, a raw concurrent pattern signal is decomposed into two basic pattern signals, which can be recognized by multiclass SVMs. The performance of the hybrid approach is evaluated by simulation experiments, and numerical and graphical results are provided to demonstrate that the proposed approach can perform effectively and efficiently in on-line CCP recognition task.     

Clustered maximum weight clique problem: Algorithms and empirical analysis

We introduce the Clustered Maximum Weight Clique Problem (CCP), a generalization of the Maximum Weight Clique Problem, that models an image acquisition scheduling problem for a satellite constellation. The solution of CCP represents satellite schedules that satisfy customer requests for satellite imagery. Each request has a priority, an area of interest, and a time window. Often, the area of interest is too large to be imaged by one satellite pass and it has to be divided into several smaller images. Each image has one or more opportunities for an acquisition by a satellite.The problem is modeled by a clustered weighted graph. A graph node represents one opportunity for an image acquisition by one satellite. A graph edge indicates that either two opportunities are not in conflict – can both be in a schedule, or two opportunities are not acquiring the same image. Each graph node has a weight that represents the area size of the image. The graph nodes are partitioned into clusters each of which encompasses all the opportunities of one customer request. The priority of the request is captured by the cluster weight. The time window of the request restricts the number of opportunities.The CCP deals with finding a clique of a maximum weight where the weight combines the node weights and the cluster weights. More precisely, the cluster weight is multiplied by the contribution of the sum of the weights of the clique nodes. The contribution is either a linear function or a piece-wise linear function, where the latter is meant to favour finalizing an already partially served customer request.The paper presents several mathematical programming formulations of the CCP and proposes matheuristic solution approaches. The computational study is performed on the clustered adaptations of the DIMACS and BHOSLIB benchmark instances for the Maximum Weight Clique Problem. The achieved results are encouraging.     

不确定条件下的质量交换网络综合

基于现有的质量交换网络综合方法，同时考虑不确定因素对于综合过程的影响，提出了不确定条件下质量交换网络综合的两步综合法。该方法首先确定网络结构，然后以年度总费用为综合目标，对设备尺寸和操作参数进行随机优化，并讨论了两种不同的随机模型的优化：期望值模型与机会约束模型。采用遗传算法和蒙特卡洛随机模拟相结合对上述随机优化模型进行求解。实例计算表明了这一综合方法的合理性以及算法的可行性。     

基于CCP的AMT电控单元标定系统开发

为方便、快捷、稳定地对AMT电控系统进行标定,采用基于CCP协议的通讯标准,以某AMT系统为平台,开发了AMT电控系统标定仪。试验证明,所设计的标定仪能较好的实现预期功能且性能稳定。     

基于CCP协议的混合动力车VCU标定系统开发

CCP协议(CAN Calibration Protocol)可实现可靠,准确和快速的动态标定。本文在简要介绍CCP协议的原理,标定系统硬、软件的基础上,着重讨论了CCP协议驱动接口程序和FLASH擦写程序的设计,及标定系统在HEV整车控制器标定上的应用。     

Controllability of probabilistic Boolean control networks with time-variant delays in states时变时延概率布尔控制网络的能控性

This paper investigates the controllability of probabilistic Boolean control networks (PBCNs) with time-variant delays in states. By cutting the time sequence, we split the network into at most countably infinitely many subnetworks with no delays, where any one of the longest subnetworks is called a controllability constructed path (CCP). When the CCP is of infinite length, we prove that the network is controllable iff any CCP is controllable, and give an equivalent condition for the controllability of the network. When it is of finite length, we give a necessary condition and a sufficient condition for the controllability of the network, and show that the controllability of the network is not equivalent to the controllability of a CCP.     

基于系统动力学的对虾养殖品质风险应用

针对目前南美白对虾养殖环节存在的溯源点不清晰、养殖过程监管薄弱、品质评价不健全等品质风险问题,以 HACCP 为基准,结合国内外在养殖环节关键影响要素研究现状,对养殖环节建模,利用判断树的方法初选品质风险控制的关键控制点,然后结合 Vensim 软件建立南美白对虾养殖品质风险动力学模型,从而确定关键控制点的核心影响因素。仿真结果表明：南美白对虾养殖品质风险动力学模型能动态仿真五类影响要素对于幼虾品质和成活率的变化关系,从而反映品质风险动态变化情况。由此为南美白对虾溯源管理信息系统中溯源点的选取和养殖过程的实时监控和过程品质动态评价提供理论参考。     

基于随机几何的无线中继网络上行链路精细化性能分析

无线网络用户呈现数量剧增、分布灵活多变的趋势,而传统蜂窝网络的拓扑结构已不能满足所有用户的服务需求。为改善小区边缘区域的上行覆盖率,构建了一种基于随机几何的无线中继网络模型,其中基站服从泊松点过程分布,中继节点围绕基站服从截断式聚类过程分布。在此网络中,中继节点通过放大转发策略将小区边缘用户的数据上传至基站。为了精细化分析网络模型的性能,文中基于信干比理论推导出每跳链路的条件覆盖率的矩,从而获得信干比meta分布的解析表达式,即条件覆盖率的分布。相比传统的针对覆盖率期望值的性能分析,基于信干比meta分布的分析能够揭示出条件覆盖率大于一定阈值的网络用户比例。实验仿真结果验证了所推导出的理论表达式的正确性。另外,通过调整中继节点分布的半径以及方差参数等,研究了中继节点的分布参数对信干比meta分布的影响。最后,通过比较上行功率控制的功率补偿因子对网络覆盖率的影响,为后期研究网络性能优化提供了帮助。     

An effective approach for land-cover classification from airborne lidar fused with co-registered data

Airborne lidar provides accurate height information of objects on the earth and has been recognized as a reliable and accurate surveying tool in many applications. In particular, lidar data offer vital and significant features for urban land-cover classification, which is an important task in urban land-use studies. In this article, we present an effective approach in which lidar data fused with its co-registered images (i.e. aerial colour images containing red, green and blue (RGB) bands and near-infrared (NIR) images) and other derived features are used effectively for accurate urban land-cover classification. The proposed approach begins with an initial classification performed by the Dempster–Shafer theory of evidence with a specifically designed basic probability assignment function. It outputs two results, i.e. the initial classification and pseudo-training samples, which are selected automatically according to the combined probability masses. Second, a support vector machine (SVM)-based probability estimator is adopted to compute the class conditional probability (CCP) for each pixel from the pseudo-training samples. Finally, a Markov random field (MRF) model is established to combine spatial contextual information into the classification. In this stage, the initial classification result and the CCP are exploited. An efficient belief propagation (EBP) algorithm is developed to search for the global minimum-energy solution for the maximum a posteriori (MAP)-MRF framework in which three techniques are developed to speed up the standard belief propagation (BP) algorithm. Lidar and its co-registered data acquired by Toposys Falcon II are used in performance tests. The experimental results prove that fusing the height data and optical images is particularly suited for urban land-cover classification. There is no training sample needed in the proposed approach, and the computational cost is relatively low. An average classification accuracy of 93.63% is achieved.