Lagrangian relaxation hybrid with evolutionary algorithm for short-term generation scheduling

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.     

Methodological Challenges in Union Commitment Studies.

Methodological problems in studies of union commitment were identified and illustrated with data from 4,641 members and 479 stewards in 297 local teachers' unions. Using a 20-item union commitment scale, results confirmed the existence of 3 substantive factors and 1 method factor at the individual level of analysis: loyalty to the union, responsibility to the union, willingness to work for the union, and a factor of negatively worded items. Tests of measurement invariance showed that the scale captured commitment for rank-and-file members but not for union stewards. The authors also found partial measurement invariance between long-time and newer members and full measurement invariance between men and women. Finally, the authors found that violation of the statistical assumption of independence reduced model fit when individual commitment scores were analyzed without attention to the hierarchical nature of the data. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于车联网的隐私保护数据聚合研究综述

随着智能网联汽车的普及,用户数据的隐私问题成为了车联网发展中亟待解决的问题。针对车联网聚合方案的研究现状,对当前方案中存在的问题进行分析总结。首先,系统地介绍了车联网中主流系统模型和车联网中常见的攻击模型;其次,对当前国内外应用在车联网中的聚合方案的安全性、效率和优劣进行分析总结,分别从签名阶段聚合方案、用户数据收集和传输阶段聚合方案和云平台处理数据阶段聚合方案三方面对其进行讨论;最后,阐述了车联网聚合方案中存在的问题及解决方法,展望了车联网聚合方案未来的研究方向。     

同态加密的硬件卸载及其在隐私保护计算中的应用

同态加密可以满足计算外包、数据共享、数据交易等应用对隐私保护计算的需要,但是同态加密的高计算开销限制了它在实际生产中的应用.本文从硬件卸载的角度解决同态加密的高性能计算问题,基于Intel QAT加速卡实现了一个半同态加密的高性能异步卸载框架QHCS. QHCS通过重构同态加密应用的软件栈来实现高效的异步卸载,并通过引入协程机制、批量加密技术等实现加密性能的最大化.本文同时给出了偏好不同性能指标(吞吐量、延迟)的两种卸载方案.进一步地,在由GPU及QAT组成的异构计算系统中,利用QHCS完整地实现了一个隐私保护的线性回归应用.实验结果表明,QHCS的吞吐量是目前软件实现的110倍,在百万量级的高维数据上实施隐私保护的线性回归计算只需十几分钟,可以较好地满足实际应用的需要.     

Nodal ant colony optimization for solving profit based unit commitment problem for GENCOs

This paper proposes a nodal ant colony optimization (NACO) technique to solve profit based unit commitment problem (PBUCP). Generation companies (GENCOs) in a competitive restructured power market, schedule their generators with an objective to maximize their own profit without any regard for system social benefit. Power and reserve prices become important factors in decision process. Ant colony optimization that mimics the behavior of ants foraging activities is suitably implemented to search the UCP search space. Here a search space consisting of optimal combination of binary nodes for unit ON/OFF status is represented for the movement of the ants to maintain good exploration and exploitation search capabilities. The proposed model help GENCOs to make decisions on the quantity of power and reserve that must be put up for sale in the markets and also to schedule generators in order to receive the maximum profit. The effectiveness of the proposed technique for PBUCP is validated on 10 and 36 generating unit systems available in the literature. NACO yields an increase of profit, greater than 1.5%, in comparison with the basic ACO, Muller method and hybrid LR-GA.     

Paving the way for CRM success: The mediating role of knowledge management and organizational commitment

Customer relationship management (CRM) has become one of the most influential technologies in the world, and companies are increasingly implementing it to create value. However, despite significant investment in CRM technology infrastructure, empirical research offers inconsistent support for its positive impact on performance. This study develops and tests a research model analyzing the process through which CRM technology infrastructure translates into organizational performance, drawing on the resource-based view (RBV) and the knowledge-based view (KBV) of the firm. Based on an international sample of 125 hotels, the results suggest that organizational commitment and knowledge management fully mediate this process.     

一种基于同态标签的动态云存储数据完整性验证方法

在云存储服务中,为使用户可以随时验证存储在云存储服务器上数据的完整性,提出一种基于同态标签的动态数据完整性验证方法。通过引入同态标签和用户随机选择待检测数据块,可以无限次验证数据是否完好无损,并支持数据动态更新;可信第三方的引入解决了云用户与云存储服务供应商因数据完整性问题产生的纠纷,实现数据完整性的公开验证;然后给出该方法的正确性和安全性分析,以及该方法的性能分析;最后通过实验验证了该方法是高效可行的。     

知识型员工工作态度与离职倾向关系的实证研究

采用实证法研究了知识型员工工作态度总体水平及各维度与离职倾向的关系,并建立了关系模型。研究结果表明：工作态度及其维度均与离职倾向显著负相关,且组织承诺对离职倾向的影响大于工作满意度;规范承诺、内在满意度、情感承诺、外在满意度、持续承诺依次成为影响知识型员工离职倾向的主要因素,均负相关。     

基于隐私保护的可证明安全委托计算协议

通过云计算提供的委托计算服务能够为委托方节省大量的计算时间和计算成本,但如何保证委托计算的隐私性和可证明安全性是具有挑战性的问题。结合全同态加密和多线性映射技术的优势,提出基于隐私保护的可证明安全多元多项式委托计算协议。根据委托计算的输入输出隐私安全需求设计委托计算安全模型,通过多线性映射方案和全同态加密技术构造任意第三方可公开验证的委托计算协议,并在标准模型下基于多线性Diffie-Hellman困难性问题假设证明协议的安全性与隐私性。实验与性能分析结果表明,该协议可保证安全性,同时能够减少计算成本,满足大数据环境下委托计算模式的应用需求。