Neural,fuzzy and neuro-fuzzy approach for concentration estimation of volatile organic compounds by surface acoustic wave sensor array

Present study evaluates application of adaptive neuro-fuzzy inference system (ANFIS) for concentration estimation of volatile organic compounds (VOCs) by analyzing response matrix of polymer-functionalized surface acoustic wave (SAW) sensor array. The performance of ANFIS is compared with that of subtractive clustering based fuzzy inference system (SC-FIS) and backpropagation artificial neural network (BP-ANN). For analysis, the raw SAW sensor array data is preprocessed by logarithmic scaling followed by dimensional autoscaling and the feature extraction by principal component analysis (PCA). For concentration prediction, the extracted feature vectors were fed as input to the three methods (ANFIS, SC-FIS and BP-ANN) independently. The performance of the three methods were evaluated on the basis of root mean square error (RMSE) and correlation value involving actual and estimated values of concentration. Five sets of SAW sensor array responses are analyzed. The analysis includes both experimental and synthetic (sensor model generated) data sets. It is found that the ANFIS has the least value of RMSE and highest value of correlation compared to SC-FIS and BP-ANN. This signifies the relative superiority of ANFIS method.     

基于模糊随机广义霍夫变换的目标匹配

为了提高工业检测中图像匹配精度和速度,提出了一种用于二维目标匹配的新算法--模糊随机广义霍夫变换(FRGHT)。此算法结合了模糊推理系统(FIS)和随机广义霍夫变换(RGHT)。模糊推理系统引入模糊集合概念,计算待配准图像中边缘点对配准参数的投票,从而可以抑制噪声,解决扭曲问题,提高了匹配精度;随机抽取待配准图像中边缘点进行投票,实现了多对一的映射,从而减少了内存需求,提高计算速度。实验表明,该方法计算速度快,匹配精度高,不受噪声污染、扭曲、遮挡、混乱等情况的影响。     

用Matlab实现空调温度模糊控制器的设计与仿真

利用Matlab模糊工具箱以及仿真工具Simulink,实现空调温度模糊控制器的设计与仿真.设计方法简单直观,易于实现.     

基于Mamdani FIS模型及RMR法的岩体质量分级研究

为解决岩体质量分级中RMR法评价体系的评分阶梯性问题,考虑到工程应用实际价值,引入推理规则简单的Mamdani模糊推理模型,综合选择岩石单轴抗压强度、岩体质量指标、节理间距、结构面性状、地下水条件和节理方向等6个影响因子作为评价指标,构建基于Mamdani FIS模型及RMR法的岩体质量评价体系。将该评价体系应用到和睦山铁矿,并将分级结果与RMR法、Q值法和BQ法进行比较。结果表明,基于Mamdani模糊推理的RMR分析法更接近于矿岩的实际情况。应用实践表明,改进后的RMR法有效解决了和睦山铁矿在岩体质量分级时存在的指标评判区间和评分的边界问题。     

Application of PLC's as front-end pre-processors in factory information systems

Factory information systems (FISs) have grown in acceptance largely because of the successful integration of a variety of data-collection, entry and display devices. The traditional factory computer system by and large ignored the moment-by-moment consequences of production processes, and focused instead on the assistance offered by automated shift-end counting and scheduling procedures. Separate systems handled the various functions (e.g. orders, raw materials, inventory, scheduling, etc.) and the assessment of productivity was left to the findings of endless meetings, negotiations and the interpretation of aged data reports. The data under consideration, however carefully collected, was so much after the event that problems forced the factory into a crisis-management mode in the hope that some restitution could be made. Real-time data is normally available in the process itself but is difficult to condense into a form that is useful for scheduling and FIS functions. Such data contains vital information as to the actual rela-time machine performance on the shop floor, and if processed correctly can be used to indicate and forecast production problems so that calm, yet timely, adjustments to schedules, etc. can be made. This paper describes how a PLC (programmable logic controller) was used to collect, pre-process and pass summary results to a low-cost supermicro computer for production monitoring and FIS purposes. This work was undertaken in response to an industry need for low-cost systems that could be retrofitted to factories with minimal disturbance to their day-to-day operations. This paper is a sequel to “Production Monitoring—A Proven Productivity Tool”, which appeared in Volume 4 Number 4 (November 1989) of this journal, on pages 335–348.     

基于TSK模糊控制系统的网络异常检测

在网络异常检测中,为了提高对异常状态的检测率,降低对正常状态的误判率,该文提出利用TSK模糊控制系统进行网络异常检测的新方法。在对TSK模糊控制系统的训练中采取梯度下降算法,充分发挥梯度下降局部细致搜索优势。实验数据采用KDDCUP99数据集,实验结果表明,基于梯度下降的模糊控制系统提高了异常检测的准确性。     

A farm irrigation system (FIS) model

A mathematical model was developed to simulate farm irrigation systems (FIS). Open borders, closed borders, and/or furrows may constitute such systems. The model simulates the entire irrigation cycle of these systems. The volume balance method and some simple flow profile curves form the basis of the FIS model. Data from 25 vegetated and nonvegetated borders and six furrows were utilized to calibrate and verify the model. The test results showed that the model was accurate with less than 8% error in prediction of any of the phases of the irrigation cycle. These preliminary tests indicate that the FIS model is reasonably accurate for engineering applications.     

Accurate shape from focus based on focus adjustment in optical microscopy

Optical microscopy allows a magnified view of the sample while decreasing the depth of focus. Although the acquired images from limited depth of field have both blurred and focused regions, they can provide depth information. The technique to estimate the depth and 3D shape of an object from the images of the same sample obtained at different focus settings is called shape from focus (SFF). In SFF, the measure of focus–sharpness–is the crucial part for final 3D shape estimation. The conventional methods compute sharpness by applying focus measure operator on each 2D image frame of the image sequence. However, such methods do not reflect the accurate focus levels in an image because the focus levels for curved objects require information from neighboring pixels in the adjacent frames too. To address this issue, we propose a new method based on focus adjustment which takes the values of the neighboring pixels from the adjacent image frames that have approximately the same initial depth as of the center pixel and then it re-adjusts the center value accordingly. Experiments were conducted on synthetic and microscopic objects, and the results show that the proposed technique generates better shape and takes less computation time in comparison with previous SFF methods based on focused image surface (FIS) and dynamic programming. Microsc. Res. Tech., 2009. © 2008 Wiley-Liss, Inc.     

Sparse distributed fuzzy inference systems

The sparse distributed architecture described would be shown to function effectively as a fuzzy inference system giving essentially the same results as conventional techniques. However, whereas the conventional model reaches a glass ceiling as the order of target systems increases due to computer architectural limitations, this design is able to surpass this limit. It uses the same principles of max–min composition to solve inference problems, and comprises fuzzy sets that can encode a level of linguistic expression typical of such systems. It however expresses fuzzy sets differently, and performs the required computation in a manner suitable to the alternative representation. It may seem a rather complicated solution for low order problems (which it is) with the situation reversing itself for high order problems, the conventional solution being complicated if not impossible and the new architecture simple. The limitation, errors and performance of the new method when compared to the conventional method is documented and quantified by software written to model the two representations considered.     

消防信息系统中电子地图子系统的广义随机Petri网工作流模型研究

消防信息系统（FIS）是消防综合管理和控制系统，其中电子地图子系统是消防信息系统的重要组成部分，也是其主要功能之一。目前用工作流来分析此系统的的研究并不多见。本文将广义随机网和工作流相结合，首先为电子地图子系统建立符合工作流概念的广义随机Petri网模型，然后利用工作流网的分析方法，对此Petri网模型进行定性和定量的分析，获得一些有价值的性能指标信息。