多变量预测控制软件在加氢分馏装置控制中的应用

针对加氢分馏装置分馏塔工艺特点,重点分析加氢分馏塔所面临的控制问题,采用多变量预测控制软件APC—Adcon,通过实验测试、模型辨识、控制器和切换逻辑设计等工作实现该工业分馏塔系的多变量预测控制,显著提高两塔运行参数的平稳性,有效降低操作人员的操作难度。实际应用的结果表明该多变量预测控制系统可有效地克服干扰、确保工艺参数的平稳运行。通过该系统的应用使在相同的负荷下,燃料气减少5％,显著提高分馏塔的分离能力,组分切割更为合理,航空煤油收率提高0.4％,取得了显著的经济效益。     

基于离群点挖掘的RFID冷链温控研究

用RFID技术解决了冷链物流管理中的温度实时监控难题;面对随之而来的数据爆炸问题,结合RFID数据挖掘算法和冷链温控实际需求,提出了RFID离群点快速挖掘算法QOD,并采用剪枝策略使算法进一步优化,随后通过实验证明了算法的准确性;最后展望了RFID冷链温控研究的未来发展方向。     

一种新的动态网页程序的防篡改系统

动态网页技术越来越多地应用于互联网,确保动态网页脚本的安全性的需求与日俱增,该文实现了一种新的基于脆弱性数字水印技术的动态网页应用程序防篡改系统,阐明了能够应用在动态网页防篡改系统中的脆弱性数字水印提取算法应该具有的特性,评估了系统运行的效率,证明了该系统嵌入水印和检测水印的过程的高效性,还对比证明了该系统相对于入侵检测系统有着更广泛的保护范围。     

Optimal models for single-period supply chain problems with fuzzy demand

The aim of this paper is to develop an optimal technique for dealing with the fuzziness aspect of demand uncertainties. Triangular fuzzy numbers are used to model external demand, and decision models in both non-coordination and coordination situations are constructed. It is shown that in the decision models there exists a unique solution that can be expressed analytically. Based on the closed form solutions for both models, the behaviors and relationships of both the manufacturer and the retailer are quantitatively analyzed, and a cooperative policy for the optimization of the whole supply chain is put forward.     

A Bayesian approach to estimate the marginal loss distributions in operational risk management

One of the main problems in operational risk management is the lack of loss data, which affects the parameter estimates of the marginal distributions of the losses. The principal reason is that financial institutions only started to collect operational loss data a few years ago, due to the relatively recent definition of this type of risk. Considering this drawback, the employment of Bayesian methods and simulation tools could be a natural solution to the problem. The use of Bayesian methods allows us to integrate the scarce and, sometimes, inaccurate quantitative data collected by the bank with prior information provided by experts. An original proposal is a Bayesian approach for modelling operational risk and for calculating the capital required to cover the estimated risks. Besides this methodological innovation a computational scheme, based on Markov chain Monte Carlo simulations, is required. In particular, the application of the MCMC method to estimate the parameters of the marginals shows advantages in terms of a reduction of capital charge according to different choices of the marginal loss distributions.     

Decomposition heuristic to minimize total cost in a multi-level supply chain network

In this paper, the distribution planning model for the multi-level supply chain network is studied. Products which are manufactured at factory are delivered to customers through warehouses and distribution centers for the given customer demands. The objective function of suggested model is to minimize logistic costs such as replenishment cost, inventory holding cost and transportation cost. A mixed integer programming formulation and heuristics for practical use are suggested. Heuristics are composed of two steps: decomposition and post improving process. In the decomposition heuristics, the problems are solved optimally only considering the transportation route first by the minimum cost flow problem, and the replenishment plan is generated by applying the cost-saving heuristic which was originally suggested in the manufacturing assembly line operation, and integrating with the transportation plan. Another heuristic, in which the original model is segmented due to the time periods, and run on a rolling horizon based method, is suggested. With the post-improving process using tabu search method, the performances are evaluated, and it was shown that solutions can be computed within a reasonable computation time by the gap of about 10% in average from the lower bound of the optimal solutions.     

Multi-behavior agent model for planning in supply chains: An application to the lumber industry

Recent economic and international threats to western industries have encouraged companies to increase their performance in all ways possible. Many look to deal quickly with disturbances, reduce inventory, and exchange information promptly throughout the supply chain. In other words they want to become more agile. To reach this objective it is critical for planning systems to present planning strategies adapted to the different contexts, to attain better performances. Due to consolidation, the development of integrated supply chains and the use of inter-organizational information systems have increased business interdependencies and in turn the need for increased collaboration to deal with disturbance in a synchronized way. Thus, agility and synchronization in supply chains are critical to maintain overall performance. In order to develop tools to increase the agility of the supply chain and to promote the collaborative management of such disturbances, agent-based technology takes advantage of the ability of agents to make autonomous decisions in a distributed network through the use of advanced collaboration mechanisms. Moreover, because of the highly instable and dynamic environment of today's supply chains, planning agents must handle multiple problem solving approaches. This paper proposes a Multi-behavior planning agent model using different planning strategies when decisions are supported by a distributed planning system. The implementation of this solution is realized through the FOR@C experimental agent-based platform, dedicated to supply chain planning for the lumber industry.     

On the information modeling for the electronic operation of supply chains: A maritime case study

Nowadays, the manufacturing industry is adopting modern information technologies in order to optimize their business process and to achieve integration with supply chain partners that are geographically dispersed, by expanding the physical limits of their business globally. The growth of the World Wide Web and the advantages of the software technologies arising from it render this goal possible. This paper discusses how modern information technology, particularly the ISO 10303—STEP and eXtensible Markup Language—XML, can be utilized jointly to support the communication of different partners. An approach to support enterprise operation through efficient data exchanges, by adopting modern data modeling techniques for integration among partners worldwide and using the web as a communication layer, is described. The paper deals particularly with the ship repair industry as a case study of these problems and suggests a feasible solution.     

Nonparametric Bayesian Image Segmentation

Image segmentation algorithms partition the set of pixels of an image into a specific number of different, spatially homogeneous groups. We propose a nonparametric Bayesian model for histogram clustering which automatically determines the number of segments when spatial smoothness constraints on the class assignments are enforced by a Markov Random Field. A Dirichlet process prior controls the level of resolution which corresponds to the number of clusters in data with a unique cluster structure. The resulting posterior is efficiently sampled by a variant of a conjugate-case sampling algorithm for Dirichlet process mixture models. Experimental results are provided for real-world gray value images, synthetic aperture radar images and magnetic resonance imaging data.