The induced generalized aggregation operators for intuitionistic fuzzy sets and their application in group decision making

In this paper, we present the induced generalized intuitionistic fuzzy ordered weighted averaging (I-GIFOWA) operator. It is a new aggregation operator that generalized the IFOWA operator, including all the characteristics of both the generalized IFOWA and the induced IFOWA operators. It provides a very general formulation that includes as special cases a wide range of aggregation operators for intuitionistic fuzzy information, including all the particular cases of the I-IFOWA operator, GIFOWA operator and the induced intuitionistic fuzzy ordered geometric (I-IFOWG) operator. We also present the induced generalized interval-valued intuitionistic fuzzy ordered weighted averaging (I-GIIFOWA) operator to accommodate the environment in which the given arguments are interval-valued intuitionistic fuzzy sets. Further, we develop procedures to apply them to solve group multiple attribute decision making problems with intuitionistic fuzzy or interval-valued intuitionistic fuzzy information. Finally, we present their application to show the effectiveness of the developed methods.     

Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm

This paper presents a short term load forecasting model based on Bayesian neural network (shorted as BNN) learned by the Hybrid Monte Carlo (shorted as HMC) algorithm. The weight vector parameter of the Bayesian neural network is a multi-dimensional random variable. In learning process, the Bayesian neural network is considered as a special Hamiltonian dynamical system, and the weights vector as the system position variable. The HMC algorithm is used to learn the weight vector parameter with respect to Normal prior distribution and Cauchy prior distribution, respectively. The Bayesian neural networks learned by Laplace algorithm and HMC algorithm and the artificial neural network (ANN) learned by the BP algorithm were used to forecast the hourly load of 25 days of April (Spring), August (Summer), October (Autumn) and January (Winter), respectively. The roots mean squared error (RMSE) and the mean absolute percent errors (MAPE) were used to measured the forecasting performance. The experimental result shows that the BNNs learned by HMC algorithm have far better performance than the BNN learned by Laplace algorithm and the neural network learned BP algorithm and the BNN learned by HMC has powerful generalizing capability, it can welly solve the overfitting problem.     

New constructions of OSBE schemes and their applications in oblivious access control

Oblivious signature-based envelope (OSBE) schemes have demonstrated their potential applications in the protection of users privacy and rights. In an OSBE protocol, an encrypted message can only be decrypted by the receiver who holds a valid signature on a public message, while the sender (encrypter) does not know whether the receiver has the signature or not. Our major contributions in this work lie in the following aspects. We improve the notion of OSBE so that a valid credential holder cannot share his/her credential with other users (i.e., all-or-nothing non-transferability). We clarify the relationship between one-round OSBE and identity-based encryption (IBE) and show that one-round OSBE and semantically secure IBE against the adaptively chosen identity attack (IND-ID-CPA) are equivalent, if the signature in the OSBE scheme is existentially unforgeable against adaptively chosen message attacks. We propose an oblivious access control scheme to protect user privacy without the aid of any zero-knowledge proof. Finally, we also highlight some other novel applications of OSBE, such as attributed-based encryption.     

涵闸工程施工期混凝土温度监测系统与水化热参数反演

针对涵闸工程混凝土温控防裂需要,研制了全自动化数字测温系统。该系统的硬件部分由数字式温度传感器、采集单元、GPRS DTU组成,软件部分包括中心服务器软件和客户端软件。系统实现了GPRS公用无线网络覆盖范围内无人值守、多测点远程测量和数据实时共享,具有体积小、无需布线、运行稳定、精度高等特点。经过实际工程多次应用,系统运行稳定可靠,达到预期性能。基于以上系统的测量数据,结合三维非稳定温度场数值计算,对特定工程的水化热参数进行了反演。规范给出普硅42.5水泥水化热参数为m=0.69,n=0.56;而根据实测数据反演的结果为m=1.85,n=0.79。根据两组参数分别进行了三维温度应力计算,得到完全不同的计算结果,前者温度应力未超标,后者多处超标需要采用温控防裂措施才能保证结构防裂安全。因此对实际工程进行温度监测和水化热参数反演是非常必要的。     

规则波作用下潜堤透浪系数数值模拟

潜堤透浪系数是衡量潜堤削弱波浪能力的主要指标。基于Boussinesq型方程的MIKE21-BW模型建立波浪数值水槽,进行规则波作用下潜堤透浪系数的数值模拟。根据Dingemans物理模型试验结果对BW模型进行参数率定和验证,验证结果良好。在此基础上,进一步分析了影响潜堤透浪系数的无因次影响参数,包含相对淹没深度、波陡、相对堤顶宽度、斜坡堤坡度等,研究不同因素组合情况下潜堤透浪系数的变化规律。建立了规则波作用下潜堤透浪系数的计算式,应用MATLAB数学软件对式中的系数进行拟合,拟合式计算值与BW模型计算值相关系数为0.858,相关性较好。另外,将拟合式与杨正己公式进行比较,并分析数据的差异与原因。研究表明:MIKE21-BW模型可以进行规则波作用下潜堤透浪系数的数值模拟。     

基于安全性能接口的调度和管理模型研究

以云计算效能提高为目的, 以研究Job调度和资源管理两大决定云计算环境能效的最重要因素为基础, 使用调度和管理对象对作为系统统计信息的参考指标进行采集的方式展开研究。性能代理和服务接口模式(PASI)已经从云环境中采集了原始数据, 通常情况下应用于云的安全技术属于被动防御模式, 如实时动态监控或者深度包检测模式(DPI), 其主要目的是实现动态检测和防御。提出了一个有效的框架以提高云计算中心的综合性能指标, 该架构的建立是基于PASI与DPI相结合的方式, 并且提出了一个基于安全PASI的Job调度和资源管理模型(JDRMSP)。实验结果表明, 该模型有效地提升了云环境的整体运作效能。     

基于城市小区域的车联网模型

随着社会进步及车辆增多,交通问题日益突出,使得城市环境下车联网的研究受到越来越多的关注。基于真实数据的分析和验证,可以得到城市不同区域的车联网网络度分布服从广义的幂律分布,即网络是无标度网络。根据此性质利用复杂网络理论建立车联网网络模型,通过分析和仿真验证该模型的正确性和有效性。     

基于射频技术的智能清洁管理系统研究

针对当前城市清洁工出现无效劳动且管理不便的问题,提出并开发了一套基于无线射频识别技术和超声波传感器技术的垃圾桶智能化管理系统。该系统通过超声波传感器自动检测垃圾桶,实现对每个垃圾桶的监控与管理。并通过RFID技术以及3G通信技术,对存储清洁工信息的电子标签进行识别,及时通知清洁工处理。由于本系统实现了城市环境智能检测、及时处理垃圾、方便工作监督的效果,因而具有较高的应用价值。     

以用户为中心的异构数据集成方法

为了实现用户数据在不同用户应用程序间的共享,将同一用户分布在不同应用程序上的数据按照统一的数据模型组织,并存放在统一的平台上,用异构数据集成技术处理用户数据模型和统一数据模型间的异构问题,使得不同应用平台之间能实现数据共享。     

基于复合左右手传输线结构的新型微带天线

基于复合左右手传输线结构的零阶谐振理论,提出了一种新型双层结构的微带天线,仿真结果表明该天线具有比单层微带天线更小的尺寸。研究了天线各结构参数对天线特性的影响,并对各参数进行了优化仿真。仿真结果表明该天线具有全向辐射特性,与传统微带天线相比,该天线具有体积小的特点。