A new method of soft computing to estimate the contribution rate of S&T progress on economic growth

In this paper, soft computing is applied to estimate the contribution rate of science and technology (S&T) progress on economic growth. First, the main influence factors of economic growth are defined, consisting of capital assets, labor force, human capital and research and development (R&D), and the human capital is calculated by improved labor-payment method. Second, target system is categorized by genetic iterative self-organizing data analysis technique algorithm (GA-ISODATA). Then, we set up the I/O model by fuzzy artificial neural network (FANN), with the capital assets, labor force, human capital and R&D as input variables, and the corresponding gross domestic product (GDP) as the output, to extract several fuzzy rules. Last, from the obtained fuzzy rules, we can get the effect of influence factors on economic growth, and calculate the economic contribution rate of S&T progress (ECRST). Take Guangdong province of China as an example, the result indicates that: during the year 2000–2008, Guangdong province (contains 21 cities) could be classified into three clusters according to the S&T progress. The first cluster (High S&T) has an ECRST of 47.52%, and contains 4 cities; the second cluster (Medium S&T) has an ECRST of 42.74%, and contains 4 cities; the third cluster (Low S&T) has an ECRST of 39.96%, and contains 13 cities; the average ECRST of Guangdong province is 44.02%. The result is accordance with the economic reality of Guangdong province, and demonstrates the validity of the proposed method.     

A study of a soft computing based method for 3D scenario reconstruction

Several recent works deal with 3D data in mobile robotic problems, e.g., mapping. Data comes from any kind of sensor (time of flight, Kinect or 3D lasers) that provide a huge amount of unorganized 3D data. In this paper we detail an efficient approach to build complete 3D models using a soft computing method, the Growing Neural Gas (GNG). As neural models deal easily with noise, imprecision, uncertainty or partial data, GNG provides better results than other approaches. The GNG obtained is then applied to a sequence. We present a comprehensive study on GNG parameters to ensure the best result at the lowest time cost. From this GNG structure, we propose to calculate planar patches and thus obtaining a fast method to compute the movement performed by a mobile robot by means of a 3D models registration algorithm. Final results of 3D mapping are also shown.     

A soft computing method of performance evaluation with MCDM based on interval-valued fuzzy numbers

This study presented a new performance evaluation method for tackling fuzzy multicriteria decision-making (MCDM) problems based on combining VIKOR and interval-valued fuzzy sets. The performance evaluation problem often exists in complex administrative processes in which multiple evaluation criteria, subjective/objective assessments and fuzzy conditions have to be taken into consideration simultaneously in management. Here, the subjective, imprecise, inexact and uncertain evaluation processes are modeled as fuzzy numbers by means of linguistic terms, as fuzzy theory can provide an appropriate tool to deal with such uncertainties. However, the presentation of linguistic expressions in the form of ordinary fuzzy sets is not clear enough [15] and [21]. Interval-valued fuzzy sets can provide more flexibility [4] and [14] to represent the imprecise/vague information that results, and it can also provide a more accurate modeling. This paper presents the interval-valued fuzzy VIKOR, which aims to solve MCDM problems in which the weights and performances of criteria are unequal by using the concepts of interval-valued fuzzy sets. A case study for evaluating the performances of three major intercity bus companies from an intercity public transport system is conducted to illustrate the effectiveness of the method.     

基于软计算的Web挖掘研究进展与前景

概述了Web挖掘的组成部分、分类和现状,指出了现有一些Web挖掘方法的局限.介绍了目前比较新的技术--软计算技术,总结了软计算技术在Web挖掘中的应用.Web数据的固有无标记、不精确、异构性和动态性,处理人与机器的交互,上下文敏感性和近似查询,个性化学习,软计算都是很合适的解决方法.     

一个基于手持移动设备的现代教育信息平台

通过对移动教育特征的分析,设计并实现了一个基于手持设备的移动教育平台。该系统以手持移动计算为载体,采用C／S结构,通过移动通信技术与现代教学手段,将移动教学与传统的网络远程教学相结合,以提高教学效率,增强教和学两个环节的互动性。同时,上下文感知的应用使得系统能够向不同用户提供个性化的服务。     

A soft computing method to predict sludge volume index based on a recurrent self-organizing neural network

In this paper, a soft computing method, based on a recurrent self-organizing neural network (RSONN) is proposed for predicting the sludge volume index (SVI) in the wastewater treatment process (WWTP). For this soft computing method, a growing and pruning method is developed to tune the structure of RSONN by the sensitivity analysis (SA) of hidden nodes. The redundant hidden nodes will be removed and the new hidden nodes will be inserted when the SA values of hidden nodes meet the criteria. Then, the structure of RSONN is able to be self-organized to maintain the prediction accuracy. Moreover, the convergence of RSONN is discussed in both the self-organizing phase and the phase following the modification of the structure for the soft computing method. Finally, the proposed soft computing method has been tested and compared to other algorithms by applying it to the problem of predicting SVI in WWTP. Experimental results demonstrate its effectiveness of achieving considerably better predicting performance for SVI values.     

Power control in CDMA networks based on soft computing methods

Power control is a fundamental procedure for CDMA mobile radio communication systems. In multiservice CDMA systems, power control should be used to minimise the transmission power of each connection, in order to limit the multiple access interference, while obtaining the desirable SIR levels. This paper starts from a transmitted-power allocation algorithm (TPAA) that considers a set of uplink transmissions, which should be supported by the system. In the sequel, the TPAA algorithm is used for training an Elman neural network, which, due to its internal characteristics, is applicable in the time critical context of power control. Simulations and numerical results are analysed for obtaining a solid basis for employing our scheme in the power control of CDMA systems.     

Evaluation of lubricant condition and engine health based on soft computing methods

Neural Computing and Applications - Evaluating the condition of the lubricant plays an influential role in the maintenance engineering of mechanical systems. This study has several objectives and...     

软计算与硬计算融合的含油气性识别

随着油气勘探领域的不断扩大,测井解释面临的研究对象也越来越复杂,传统的单一基于硬计算或软计算的方法在测井解释中面临严格挑战。提出软计算与硬计算融合的4种模式。运用软计算与硬计算融合的分离模式对某油田Oilsk81、Oilsk83、Oilsk85三口井进行含油气性模式识别,比较结果表明,在这个油区运用软计算方法对含油气性进行模式识别优于硬计算,并且可以识别出较好的测井数据集。     

Object tracking method based on data computing

The data computing process is utilized in various areas such as autonomous driving. Autonomous vehicles are intended to detect and track nearby moving objects avoiding collisions and to navigate in complex situations, such as heavy traffic and dense pedestrian areas. Therefore, object tracking is the core technology in the environment perception systems of autonomous vehicles and requires the monitoring of surrounding objects and the prediction of the moving states of objects in real time. In this paper, a multiple object tracking method based on light detection and ranging (LiDAR) data is proposed by using a Kalman filter and data computing process. We suppose that the movements of the tracking objects are captured consecutively as frames; thus, model-based detection and tracking of dynamic objects are possible. A Kalman filter is applied for predicting posterior state of tracking object based on anterior state of the tracking object. State denotes the positions, shapes, and sizes of objects. By computing the likelihood probability between predicted tracking objects and clusters which registered from tracking objects, the data association process of the tracking objects can be generated. Experimental results showed enhanced object tracking performance in a dynamic environment. The average matching probability of the tracking object was greater than 92.9%.

     

基于Vague软集的TOPSIS方法及其应用研究

运用传统的TOPSIS方法进行不确定型决策时存在一定的困难,而Vague软集是一种处理不确定信息的重要工具。为此,给出了一种加权的Vague软集间相似度计算方法并加以证明,在对传统的TOPSIS方法进行修正的基础上,将Vague软集理论应用于TOPSIS方法中,将修正后的TOPSIS方法应用于仓储中心选址问题中,结果表明,Vague软集能极大地提高TOPSIS方法在不确定型决策领域中的应用价值。     

云计算平台上的增量分类研究

针对已有增量分类算法只是作用于小规模数据集或者在集中式环境下进行的缺点,提出一种基于Hadoop云计算平台的增量分类模型,以解决大规模数据集的增量分类。为了使云计算平台可以自动地对增量的训练样本进行处理,基于模块化集成学习思想,设计相应Map函数对不同时刻的增量样本块进行训练,Reduce函数对不同时刻训练得到的分类器进行集成,以实现云计算平台上的增量学习。仿真实验证明了该方法的正确性和可行性。     

基于贡献率的离散Hopfield 结构优化

针对离散Hopfield 神经网络(DHNN) 结构复杂的问题, 提出一种基于贡献率的结构优化算法. 该算法利用奇异值分解方法对连接权值进行设计, 进而利用贡献率的方法对DHNN进行结构优化. 优化后的网络降低了DHNN结构的复杂程度, 使网络具有类似生物神经网络的稀疏结构, 实现了DHNN网络结构的优化. 最后, 通过水质评价和数字识别对该算法进行验证, 表明了所提出算法的有效性和可行性, 同时, 还验证了其对于大规模DHNN的有效性和适用性.

     

基于粒计算的多目标排序方法研究

提出了一种基于信息系统优势类的多目标排序算法。在信息系统优劣关系的基础上,通过求取个体的优势类形成优势粒,利用Pareto解之间的不可比较特性以及优势粒的特性,采用优势粒粒度作为衡量个体优劣性的标准,并提出了基于优势粒粒度的快速排序算法。实验结果表明该算法的效率高,而且能体现对象的优势度,能显著提高多目标优化算法的效率。     

基于并行运算的双层图像锐化方法

针对低清晰度照片或图像放大后边界模糊、画质差及人们对高清图像的实际需求,基于统一计算架构(CUDA)环境,提出了一个两层结构的图像并行锐化方法,设计并实现了一个基于GPU的并行锐化算法：第一层采用并行线性插值法,反复对图像非边界部分进行计算以及边缘区域锐化处理;第二层采用改进的梯度法对图像进一步优化。放大后的图像经该方法处理后,基本上可消除图像边缘区域的锯齿,使图像画质平滑、自然、清晰。经实验验证,设计的基于GPU的并行锐化算法在效率和画质上都优于目前流行的算法,提出的方法可应用于现有图像及照片放大后处理。     

基于网格显著度的视点质量计算方法*

从模糊贴近度的角度,结合网格显著度概念,给出了一种新的视点质量计算方法。首先将场景中的每个三角形看做论域中的一个样本,从可见度大小的角度定义隶属函数,为视点构造一个模糊集合;然后结合三角形的几何面积及面片显著度给出一个对应理想视点的模糊集;最后求取两个模糊集合的贴近度作为视点质量的评价准则。实验结果表明,该算法能够较好地反映视点质量。     

Combined soft computing model for value stock selection based on fundamental analysis

The stock selection problem is one of the major issues in the investment industry, which is mainly solved by analyzing financial ratios. However, considering the complexity and imprecise patterns of the stock market, obvious and easy-to-understand investment rules, based on fundamental analysis, are difficult to obtain. Therefore, in this paper, we propose a combined soft computing model for tackling the value stock selection problem, which includes dominance-based rough set approach, formal concept analysis, and decision-making trial and evaluation laboratory technique. The objectives of the proposed approach are to (1) obtain easy-to-understand decision rules, (2) identify the core attributes that may distinguish value stocks, (3) explore the cause–effect relationships among the attributes or criteria in the strong decision rules to gain more insights. To examine and illustrate the proposed model, this study used a group of IT stocks in Taiwan as an empirical case. The findings contribute to the in-depth understanding of the value stock selection problem in practice.