Some notes on Zadeh’s extensions

This paper shows that the Zadeh’s extensions of sendograph-metric-continuous fuzzy-valued functions are sendograph-metric-continuous fuzzy functions.     

设备远程监控及故障诊断系统

基于GPRS技术和规则引擎,设计开发了一个高校仪器设备远程监控及故障诊断系统。该系统可扩展性强,能实现事件记录、远程监控、故障自诊断等功能。能够真正意义上实现对设备从采购到报废整个生命周期的管理,提高教育投资效益,减少仪器设备重复购置,降低故障率。本文从系统的组成框架、实现流程、关键功能模块以及应用实例等几个方面来介绍该系统设计思想及实现技术。该系统具有人机界面良好,实用性强,成本投资少等特点。     

石油化工产业区配套服务业发展模式分析

随着以上海石化和上海化学工业区为主体的上海石油化工生产基地及相关产业的发展,与其配套的综合服务业已成为产业区结构与功能提升的重要内容。结合国内外相关案例及区域整体发展状况,探讨并分析了上海石油化工生产基地综合配套服务业的发展模式。对服务性产业发展状况、产业发展战略、规划选址、服务功能等进行了相关研究。     

纳米Au修饰电化学传感器检测沙林机理的研究

采用Raman光谱和循环伏安法研究了纳米A以u电极检测沙林的电化学反应机理。研究发现,以Fe“作为检测沙林分子中F的探针离子、纳米Au修饰的Au电极电化学传感器检测液相中的沙林,可同时检测到沙林分子中F和P=0基团,实验结果具备特征性。     

Cortex—M3的生态物联网数据采集分站设计

以Cortex—M3处理器LM3S811为核心,设计了面向森林生态监测的物联网数据采集终端分站系统。通过单总线数字式温湿度传感器、烟雾传感器、CO2传感器和环境光亮度传感器分别采集温度、湿度、可燃性颗粒物、CO2含量和光照等与林木生态相关的环境信息和林场防火的相关信息。由LM3S81l处理器处理并添加分站ID和校验码后...     

Lapped transform‐based codec for frame‐memory reduction in super‐quality LCD overdrive

Abstract— Overdrive is commonly used to reduce the liquid‐crystal response time and motion blur in liquid‐crystal displays (LCDs). However, overdrive requires a large frame memory in order to store the previous frame for reference. In this paper, a lapped transform‐based codec (LTC) is proposed to reduce the frame memory needed for LCD overdrive. In the latest literature, a directional prediction‐based codec (DPC) employs eight directional predictions, which takes up a large percentage of the computational complexity of the codec and does not consider the de‐correlation of the inter‐blocks. Therefore, the LTC first uses the lapped transform to decompose the correlation of the inter‐blocks in the YUV color space. A hadamard transform is then used for energy compaction. The reordered coefficients are pre‐quantized and encoded using the proposed adaptive bit‐plane coding (ABPC) method for a simple hardware implementation. The simulation results show that the proposed LTC significantly improves the DPC in both subjective and objective performance and outperforms the block truncation coding (BTC) and adaptive multi‐level BTC (AM‐BTC), which have been described in the literature.     

Manifold elastic net: a unified framework for sparse dimension reduction

It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square problem and thus the least angle regression (LARS) (Efron et al., Ann Stat 32(2):407–499, 2004), one of the most popular algorithms in sparse learning, cannot be applied. Therefore, most current approaches take indirect ways or have strict settings, which can be inconvenient for applications. In this paper, we proposed the manifold elastic net or MEN for short. MEN incorporates the merits of both the manifold learning based dimensionality reduction and the sparse learning based dimensionality reduction. By using a series of equivalent transformations, we show MEN is equivalent to the lasso penalized least square problem and thus LARS is adopted to obtain the optimal sparse solution of MEN. In particular, MEN has the following advantages for subsequent classification: (1) the local geometry of samples is well preserved for low dimensional data representation, (2) both the margin maximization and the classification error minimization are considered for sparse projection calculation, (3) the projection matrix of MEN improves the parsimony in computation, (4) the elastic net penalty reduces the over-fitting problem, and (5) the projection matrix of MEN can be interpreted psychologically and physiologically. Experimental evidence on face recognition over various popular datasets suggests that MEN is superior to top level dimensionality reduction algorithms.     

Community discovery using nonnegative matrix factorization

Complex networks exist in a wide range of real world systems, such as social networks, technological networks, and biological networks. During the last decades, many researchers have concentrated on exploring some common things contained in those large networks include the small-world property, power-law degree distributions, and network connectivity. In this paper, we will investigate another important issue, community discovery, in network analysis. We choose Nonnegative Matrix Factorization (NMF) as our tool to find the communities because of its powerful interpretability and close relationship between clustering methods. Targeting different types of networks (undirected, directed and compound), we propose three NMF techniques (Symmetric NMF, Asymmetric NMF and Joint NMF). The correctness and convergence properties of those algorithms are also studied. Finally the experiments on real world networks are presented to show the effectiveness of the proposed methods.     

Application of enhanced cluster validity index function to automatic stock portfolio selection system

This paper presents an automatic stock portfolio selection system. In the proposed approach, 53 financial indices are collected for each stock item and are consolidated into six financial ratios [Grey relational grades (GRGs)] using a Grey relational analysis model. The GRGs are processed using a modified form of the PBMF index method (designated as the Huang index function) to determine the optimal number of clusters per GRG. The resulting cluster indices are then processed using rough set theory to identify the stocks within the lower approximate sets. Finally, the GRGs of each stock item in the lower approximate sets are consolidated into a single GRG, indicating the ability of the stock item to maximize the rate of return. It is demonstrated that the proposed stock selection mechanism yields a higher rate of return than several existing portfolio selection systems.