Interactive Image Enhancement by Fuzzy Relaxation

In this paper, an interactive image enhancement （HE） technique based on fuzzy relaxation is presented, which allows the user to select different intensity levels for enhancement and intermit the enhancement process according to his/her preference in applications. First, based on an analysis of the convergence of a fuzzy relaxation algorithm for image contrast enhancement, an improved version of this algorithm, which is called FuzzIIE Method 1, is suggested by deriving a relationship between the convergence regions and the parameters in the transformations defined in the algorithm. Then a method called FuzzIIE Method 2 is introduced by using a different fuzzy relaxation function, in which there is no need to re-select the parameter values for interactive image enhancement. Experimental results are presented demonstrating the enhancement capabilities of the proposed methods under different conditions.

Adaptive image enhancement algorithm based on fuzzy entropy and human visual characteristics

To overcome the shortcomings of the Lee image enhancement algorithm and its improvement based on the logarithmic image processing(LIP) model, this paper proposes what we believe to be an effective image enhancement algorithm. This algorithm introduces fuzzy entropy, makes full use of neighborhood information, fuzzy information and human visual characteristics.To enhance an image, this paper first carries out the reasonable fuzzy-3 partition of its histogram into the dark region, intermediate region and bright region. It then extracts the statistical characteristics of the three regions and adaptively selects the parameter αaccording to the statistical characteristics of the image’s gray-scale values. It also adds a useful nonlinear transform, thus increasing the ubiquity of the algorithm. Finally, the causes for the gray-scale value overcorrection that occurs in the traditional image enhancement algorithms are analyzed and their solutions are proposed.The simulation results show that our image enhancement algorithm can effectively suppress the noise of an image, enhance its contrast and visual effect, sharpen its edge and adjust its dynamic range.

Traffic Scene Captioning with Multi-Stage Feature Enhancement

Traffic scene captioning technology automatically generates one or more sentences to describe the content of traffic scenes by analyzing the content of the input traffic scene images,ensuring road safety while providing an important decision-making function for sustainable transportation.In order to provide a comprehensive and reasonable description of complex traffic scenes,a traffic scene semantic captioningmodel withmulti-stage feature enhancement is proposed in this paper.In general,the model follows an encoder-decoder structure.First,multilevel granularity visual features are used for feature enhancement during the encoding process,which enables the model to learn more detailed content in the traffic scene image.Second,the scene knowledge graph is applied to the decoding process,and the semantic features provided by the scene knowledge graph are used to enhance the features learned by the decoder again,so that themodel can learn the attributes of objects in the traffic scene and the relationships between objects to generate more reasonable captions.This paper reports extensive experiments on the challenging MS-COCO dataset,evaluated by five standard automatic evaluation metrics,and the results show that the proposed model has improved significantly in all metrics compared with the state-of-the-art methods,especially achieving a score of 129.0 on the CIDEr-D evaluation metric,which also indicates that the proposed model can effectively provide a more reasonable and comprehensive description of the traffic scene.

Multi-level fuzzy comprehensive appraisal on social effects of projects

The application of multi-level fuzzy comprehensive appraisal on social effects of projects has been studied. The principles for setting up an index system have been analyzed and the index system has been set up according to projects of construction. Models for multi-level fuzzy comprehensive appraisal have been offered and relative calculation steps have been given according to project instances.

Neural Network Based Multi-level Fuzzy Evaluation Model for Mechanical Kinematic Scheme

To implement a quantificational evaluation for mechanical kinematic scheme more effectively,a multi-level and multi-objective evaluation model is presented using neural network and fuzzy theory. Firstly,the structure of evaluation model is constructed according to evaluation indicator system. Then evaluation samples are generated and provided to train this model. Thus it can reflect the relation between attributive value and evaluation result,as well as the weight of evaluation indicator. Once evaluation indicators of each candidate are fuzzily quantified and fed into the trained network model,the corresponding evaluation result is outputted and the best alternative can be selected. Under this model,expert knowledge can be effectively acquired and expressed,and the quantificational evaluation can be implemented for kinematic scheme with multi-level evaluation indicator system. Several key problems on this model are discussed and an illustration has demonstrated that this model is feasible and can be regarded as a new idea for solving kinematic scheme evaluation.

FINE-GRAINED DISTRIBUTED MULTIMEDIA SYNCHRONIZA-TION MODEL——ENHANCED FUZZY-TIMING PETRI NET

A fine grained distributed multimedia synchronization model——Enhanced Fuzzy timing Petri Net was proposed which is good at modeling indeterminacy and fuzzy. To satisfy the need of maximum tolerable jitter, the sufficient conditions are given in intra object synchronization. Method to find a proper granularity in inter object synchronization is also given to satisfy skew. Exceptions are detected and corrected as early as possible using restricted blocking method.

Multi-Level Fuzzy Comprehensive Assessment of Tourism Resources in Laiyuan County of Hebei Province

The membership of every target and the mathematic model of multi-level fuzzy comprehensive assessment are set up by using fuzzy theories and means in this study.Tourism resources of main scenic spots areas in Laiyuan County of Hebei Province are evaluated and classified by applying the model.The results of evaluation indicate that 10 of these scenic spots such as Baoziwo and Qingyunfeng are grade A,and 6 of them such as Yunpan Valley and Xianrenqiao are grade B.The peak forest scenic area in the Baishishan Geological Park and Shipuxia Scenic Area are grade A,and Jumayuan Scenic Area is grade B.Furthermore,suggestions are put forward based on the scientific and feasible development of tourism resources.

Fuzzy pattern recognition model of geological sweetspot for coalbed methane development

From the perspective of geological zone selection for coalbed methane(CBM) development, the evaluation parameters(covering geological conditions and production conditions) of geological sweetspot for CBM development are determined, and the evaluation index system of geological sweetspot for CBM development is established. On this basis, the fuzzy pattern recognition(FPR) model of geological sweetspot for CBM development is built. The model is applied to evaluate four units of No.3 Coal Seam in the Fanzhuang Block, southern Qinshui Basin, China. The evaluation results are consistent with the actual development effect and the existing research results, which verifies the rationality and reliability of the FPR model. The research shows that the proposed FPR model of geological sweetspot for CBM development does not involve parameter weighting which leads to uncertainties in the results of the conventional models such as analytic hierarchy process and multi-level fuzzy synthesis judgment, and features a simple computation without the construction of multi-level judgment matrix. The FPR model provides reliable results to support the efficient development of CBM.

基于自适应遗传算法的红外子热像图模糊增强方法

为了提高红外子热像图的边缘强度、信息熵和清晰度等,使图像视觉效果有所提高,研究了一种基于自适应遗传算法的红外子热像图模糊增强方法。对含有噪声的红外子热像图展开小波变换,分别获取高、低频小波系数,低频部分的小波系数不进行任何处理,高频部分的小波系数引入最小二乘对其进行总体估计,同时逆变换处理估计后的小波系数,完成红外子热像图去噪处理。通过模糊增强算法增强处理红外子热像图,同时引入自适应遗传算法优化处理模糊参数,实现红外子热像图模糊增强。实验结果表明,本文所提方法的图像更加清晰,且对比度也明显得到改善,其平均对比度值为0.075,平均边缘强度值为67.14,平均信息熵值为8.59,平均清晰度值为13.73,充分说明该方法获取的红外子热像图模糊增强效果更加理想,能够有效提升视觉效果。

基于灰狼自适应阈值分割和改进模糊增强的红外图像NSCT增强算法

研究低成本和便携的红外成像技术是最近几年带电检测的发展趋势,为减少红外检测环境、红外传感器以及其他因素的影响,解决红外检测中红外图像含噪声干扰、模糊和对比度低的问题,文章设计了一种基于灰狼自适应阈值分割和改进模糊增强的红外图像NSCT增强算法。对原始红外图像进行NSCT域变换;变换后含有噪声的高频分量采用VT去噪后,接着采用改进模糊增强处理;对变换后含有电力设备主体的低频分量进行灰狼自适应阈值分割为背景和前景部分,随后分别进行增强处理;最后将处理后的各分量进行逆NSCT变换。经对比应用,验证了该算法应用在变电站电力设备红外检测上的优越性:文章算法与其他算法相比在边缘强度、信息熵、对比度、标准差、峰值信噪比五类评价指标上的涨幅至少为3.94%、2.16%、9.86%、7.45%、21.86%。文章算法处理后的红外图像符合人眼视觉效果,更易于人眼识别故障,有利于电力设备热故障的检测与故障定位。

LED投光灯叉翅散热器热分析及优化

为探究叉翅散热器在某100W LED舞台投光灯上的散热强化作用并进行优化设计,通过数值模拟与实验验证的方法,对影响叉翅散热器散热性能的主要几何因素及其机理进行了分析。以LED芯片最高温度和散热器质量为优化目标,对短翅长度与间距进行了单因素分析,然后通过NSGA-Ⅱ算法进行双目标优化,并进行模糊C均值聚类,得到了不同应用场景下散热器配置。结果表明:叉翅散热器可有效增强散热性能,其结构可增加翅片表面平均对流换热系数,短翅长度和间距对单位质量散热性能影响均存在最优值,短翅过长或间距过小均会使翅片表面对流换热系数下降。双目标优化后的叉翅散热器可在几乎不改变散热器质量时,降低LED芯片最高温度2.33℃。

含干热岩区域综合能源系统日前优化调度

为了研究干热岩增强地热系统的发电性能和潜力,建立含干热岩综合能源系统的优化调度模型。首先,依据干热岩对流换热和有机朗肯循环发电机理,建立干热岩增强地热系统运行模型,为含干热岩区域综合能源系统的运行和规划提供模型基础;其次,针对系统调度中的风电、光伏不确定性波动,基于模糊信息间隙决策理论对不确定性半径和调度成本进行协同优化,从风险规避和机会寻取两方面量化系统不确定性波动变化范围,并评估相应的风险损失和机会收益;最后,通过算例仿真验证所提模型的有效性。

基于强化层次模糊熵的柴油机故障诊断方法

针对多尺度模糊熵(multi-scale fuzzy entropy,简称MFE)算法中多尺度化过程采用滑动均值滤波器导致原始信号高频信息丢失的问题,提出强化层次模糊熵方法(enhanced hierarchical fuzzy entropy,简称EHFE),用于表征原始信号中富含的高低频故障模式信息。结合萤火虫算法优化支持向量机(firefly algorithm optimized support vector machine,简称FAOSVM),提出一种基于EHFE和FAOSVM的柴油机故障诊断方法。柴油机试验数据对比分析表明:相比于现有方法,所提出方法能够充分表征柴油机故障信号富含的模式信息,并且能够有效识别柴油机正时齿轮故障,识别精度达到99.6%,在极小样本下也能达到较好的识别精度。

含分布式电源的新型配电网承载力模糊综合评估及提升策略研究

随着大量分布式电源广泛并入配电网,新型配电网的承载力评估成为系统安全运行的关键,因此有必要对含分布式电源的新型配电网承载力进行综合评估并在此基础上考虑适当的提升策略。首先,建立了新型配电网分布式电源承载力的评估模型,并采用二阶锥松弛技术加快模型的求解速度。其次,建立了多维度多层次的新型配电网承载力实时评估体系,根据得到的各指标动态综合权重,采用模糊综合评价法计算配电网承载力的综合得分。最后,提出了一种基于改善电压约束的配电网承载力提升策略。在IEEE 33节点系统配电网模型进行算例分析,通过比较所提方法和传统方法下配电网承载力变化情况及综合评估分数,证明了所提提升策略的有效性和评估体系的科学性。

基于聚类分割的裂缝检测方法

针对道路表面颗粒噪声干扰严重,传统聚类算法提取的裂缝轮廓不完整且连续性较差等问题。本文提出一种超像素及快速模糊C-均值聚类的路面裂缝提取方法,首先利用直方图均衡化提高正常路面和路面裂缝的对比度;然后,采用Gabor纹理特征结合简单线性迭代聚类方法生成超像素;最后,根据直方图信息计算隶属度矩阵,将裂缝超像素块聚合成完整的裂缝区域。依靠融合特征作为相似性度量的超像素分割方法,可提升超像素初始分割效果,获得更准确的超像素边缘。快速模糊C-均值聚类算法通过将直方图信息引入到目标函数,减少聚类分割的计算量,提升了计算效率。为证明所提算法的准确性,在公共数据集Crack Forest Dataset、DeepCrack上,与其他3种方法进行对比,实验结果表明本文算法能够有效地检测和提取路面裂缝,可为道路灾害检测提供有效信息。

A Novel Fuzzy Direct Torque Control System for Three-level Inverter-fed Induction Machine

Diode clamped multi-level inverter （DCMLI） has a wide application prospect in high-voltage and adjustable speed drive systems due to its low stress on switching devices, low harmonic output, and simple structure. However, the problem of complexity of selecting vectors and capacitor voltage unbalance needs to be solved when the algorithm of direct torque control （DTC） is implemented on DCMLI. In this paper, a fuzzy DTC system of an induction machine fed by a three-level neutral-point-clamped （NPC） inverter is proposed. After introducing fuzzy logic, optimal selecting switching state is realized by applying various strategies which can distinguish the grade of the errors of stator flux linkage, torque, the neutral-point potential, and the position of stator flux linkage. Consequently, the neutral-point potential unbalance, the dr/dr of output voltage and the switching loss are restrained effectively, and desirable dynamic and steady-state performances of induction machines can be obtained for the DTC scheme. A design method of the fuzzy controller is introduced in detail, and the relevant simulation and experimental results have verified the feasibility of the proposed control algorithm.

A NEW UNSUPERVISED CLASSIFICATION ALGORITHM FOR POLARIMETRIC SAR IMAGES BASED ON FUZZY SET THEORY

In this letter, a new method is proposed for unsupervised classification of terrain types and man-made objects using POLarimetric Synthetic Aperture Radar (POLSAR) data. This technique is a combi-nation of the usage of polarimetric information of SAR images and the unsupervised classification method based on fuzzy set theory. Image quantization and image enhancement are used to preprocess the POLSAR data. Then the polarimetric information and Fuzzy C-Means (FCM) clustering algorithm are used to classify the preprocessed images. The advantages of this algorithm are the automated classification, its high classifica-tion accuracy, fast convergence and high stability. The effectiveness of this algorithm is demonstrated by ex-periments using SIR-C/X-SAR (Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar) data.

基于Fuzzy-PID控制器的网络化智能温度控制系统

系统由单片机AT89C52作为主控单元,把增量型PID作为调节器,兼顾PID参数模糊自整定产生PWM波在现场总线上通过数据融合的方法实现对多站点温度的调节,可以有效地把握系统的动态与稳态特性,使控制过程达到优化。该系统包括电源、控制算法、温度检测、键盘输入、LED显示、自动报警、上位机通信以及开关量输入输出控制等。

MULTI-GRAY EDGE DETECTION BASED ON GENERALIZED FUZZY SET

In this letter, drawbacks of the classical algorithm to enhance the fuzzy contrast among adjacent regions are analyzed. Based on it, a new fuzzy enhancement algorithm and a linear fuzzy distribution that maps the gray images to corresponding generalized fuzzy set are proposed. Results of two examples illustrate that the algorithm is more effective and faster when used to detect the multi-level edges of images.

The construction of normalized enhanced water index and the extraction of supra-glacial water based on WorldView-2 imagery

As an important part of the mass balance of the Ice Sheet,Supra-glacial Water not only reflects the diversity of polar environmental changes,but also plays an important role in the study of global climate and environmental changes.In this paper,we chose northern Greenland as the research area,and constructed a Normalized Enhanced Water Index(NEWI)based on the high-precision WorldView-2 images of different phases during the ablation period in northern Greenland,followed by a statistical analysis on the spectral characteristics of the images were for the typical features in the study area.Then the fuzzy areas with similar gray values of thin sea ice and shallow ice water bodies were located,according to the distribution rules of ground objects and histogram graphic features of the images,so as to enhance the contrast of ground objects between the regions,and finally the extraction of the fine range of water bodies on the ice surface.Experimental results showed that the proposed index effectively highlighted the ice water with the water of the reflectivity difference,compared with the commonly used water index NDWI,etc.,especially in shallow water,which contributes to differentiation from other objects.The precision evaluation showed that the applied method of extraction has higher degree of refinement compared with other methods,by which the ice water can get complete ice water effectively.