基于soft形态学的椒盐噪声滤波器

在软数学形态学理论基础上,根据软形态学单调性、扩展性、和反扩展性,提出了一种SM滤波器.实验结果表明,与其他滤波器(如均值滤波器等)相比,SM滤波器在滤除椒盐噪声、保持图像原有结构方面效果更好.     

基于soft形态学的椒盐噪声滤波器

在软数学形态学理论基础上,根据软形态学单调性、扩展性、和反扩展性,提出了一种SM滤波器。实验结果表明,与其他滤波器(如均值滤波器等)相比,SM滤波器在滤除椒盐噪声、保持图像原有结构方面效果更好。     

基才soft形态学的椒盐噪声滤波器

在软数学形态学理论基础上，根据软形态学单调性、扩展性、和反扩展性，提出了一种SM滤波器。实验结果表明，与其他滤波器（如均值滤波器等）相比，SM滤波器在滤除椒盐噪声、保持图像原有结构方面效果更好。     

柔性(soft)形态学在图象边缘检测中的应用

根据柔性形态学单调性、扩展性、和反扩展性等基本理论,讨论了柔性形态学在边缘检测中的应用,柔性形态变换是在经典形态变换基础提出了提出的一些非线性算子,它放宽了经典形态变换的定义,以获得一定程度的鲁棒性,但是,还保留了经典形态算子的优良特性,进而从理论上和几何意义上讨论了其进行边缘检测的原理,及算子的选择,另外,还与标准形态算子及Ｒｏｂｅｒｔ算子的处理结果进行了比较。通过计算机模拟实验表明：基于柔性形     

Partner selection model and soft computing approach for dynamic alliance of enterprises

Partner selection is an active research topic in agile manufacturing and supply chain management. In this paper, the problem is described by a 0-1 integer programming with non-analytical objective function. Then, the solution space is reduced by defining the inefficient candidate. By using the fuzzy rule quantification method, a fuzzy logic based decision making approach for the project scheduling is proposed. We then develop a fuzzy decision embedded genetic algorithm. We compare the algorithm with tranditional methods. The results show that the suggested approach can quickly achieve optimal solution for large size problems with high probability. The approach was applied to the partner selection problem of a coal fire power station construction project. The satisfactory results have been achieved.     

A hybrid approach to interactive global illumination and soft shadows

We present a hybrid approach to simulate global illumination and soft shadows at interactive frame rates. The strengths of hardware-accelerated GPU techniques are combined with CPU methods to achieve physically consistent results while maintaining reasonable performance. The process of image synthesis is subdivided into multiple passes accounting for the different illumination effects. While direct lighting is rendered efficiently by rasterization, soft shadows are simulated using a novel approach combining the speed of shadow mapping and the accuracy of visibility ray tracing. A shadow refinement mask is derived from the result of the direct lighting pass and from a small number of shadow maps to identify the penumbral region of an area light source. This region is accurately rendered by ray tracing. For diffuse indirect illumination, we introduce radiosity photons to profit from the flexibility of a point-based sampling while maintaining the benefits of interpolation over scattered data approximation or density estimation. A sparse sampling of the scene is generated by particle tracing. An area is approximated for each point sample to compute the radiosity solution using a relaxation approach. The indirect illumination is interpolated between neighboring radiosity photons, stored in a multidimensional search tree. We compare different neighborhood search algorithms in terms of image quality and performance. Our method yields interactive frame rates and results consistent with path tracing reference solutions.     

发送邮件即时反馈——3R soft推出@MESSAG企业商务信息系统

大家都有过这样的经历,发送一封邮件后如果对方没有及时的回信,就只能耐心等待,因为无法知道发出的邮件对方是否收到?是否被打开查阅?3R soft推出的@MESSAG企业商务信息系统解决了上述问     

Time sequence data mining using time–frequency analysis and soft computing techniques

This paper presents a new approach for time series data mining and knowledge discovery. The relevant features of non-stationary time series data from power network disturbances are extracted using a multiresolution S-transform which can be treated either as a phase corrected wavelet transform or a variable window short-time Fourier transform. After extracting the relevant features from the time series data, an integrated LVQ neural network and various feed-forward neural network architectures are used for pattern recognition of disturbance waveform data. The fuzzy MLP outperforms all the other different connectionist models and is used in the final stage for encoding knowledge in the connection weights that are used to generate rules for fuzzy inferencing of the disturbance patterns. Overall pattern classification accuracy of 99% is achieved for power signal time series data. The knowledge discovery from the data has then been presented for selected patterns using the new quantification procedures. The approach presented in this paper is a general one and can be applied to any time series data sequence for mining for similarities in the data.     

Optimization of extrusion blow molding processes using soft computing and Taguchi’s method

The objective of this study is to present a new numerical strategy using soft-computing techniques to determine the optimal die gap programming of extrusion blow molding processes. In this study, the design objective is to target a uniform part thickness after parison inflation by manipulating the parison die gap openings over time. To model the whole process, that is, the parison extrusion, the mould clamping and the parison inflation, commercial finite element software (BlowSim) from the National Research Council (NRC) of Canada is used. However, the use of such software is time-consuming and one important issue in a design environment is to minimize the number of simulations to get the optimal operating conditions. To do so, we proposed a new strategy called fuzzy neural–Taguchi network with genetic algorithm (FUNTGA) that establishes a back propagation network using a Taguchis experimental array to predict the relationship between design variables and responses. Genetic algorithm (GA) is then applied to search for the optimum design of die gap parison programming. As the number of training samples is greatly reduced due to the use of orthogonal arrays, the prediction accuracy of the neural network model is closely related to the distance between sampling points and the evolved designs. The extrapolation distance concept is proposed and introduced to GA using fuzzy rules to modify the fitness function and thus improving search efficiency. The comparison of the results with commercial optimization software from NRC demonstrates the effectiveness of the proposed approach.     

Differential evolution based soft optimization to attenuate vane–rotor shock interaction in high-pressure turbines

This article presents a soft computing methodology to design turbomachinery components experiencing strong shock interactions. The study targets a reduction of unsteady phenomena using evolutionary optimization with robust, high fidelity, and low computational cost evaluations. A differential evolution (DE) algorithm is applied to optimize the transonic vane of a high-pressure turbine. The vane design candidates are examined by a cost-effective Reynolds-averaged Navier–Stokes (RANS) solver, computing the downstream pressure distortion and aerodynamic efficiency. A reduction up to 55% of the strength of the shock waves propagating downstream of the stand-alone vane was obtained. Subsequently to the vane optimization, unsteady computations of the vane–rotor interaction were performed using a non-linear harmonic (NLH) method. Attenuation above 60% of the unsteady forcing on the rotor (downstream of the optimal vane) was observed, with no stage-efficiency abatement. These results show the effectiveness of the proposed soft optimization to improve unsteady performance in modern turbomachinery exposed to strong shock interactions.     

Modeling of input–output relationships for a plasma spray coating process using soft computing tools

To automate any manufacturing process, its input–output relationships are to be known in both forward and reverse directions. The present work aims to correlate input process parameters with various responses of a plasma spray coating process. Statistical regression analysis had been carried out previously for this process based on the data collected through central composite design of experiments to establish input–output relationships in forward direction. However, the said relationships could not be accurately determined in reverse direction using the obtained regression equations due to the presence of a non-square transformation matrix. Soft computing-based approaches had been developed to model the process in both forward as well as reverse directions. The performances of the developed approaches had been tested on different cases obtained through real experiments. A comparative study had been made of these developed approaches in terms of accuracy in predictions.     

A comparison of tools for strategic simulation and scenario generation with special emphasis on ‘soft factors’

This paper describes a comparison of different tools for strategic simulation and scenario generation. Techniques and tools, namely GAMMA, a causal network editor, a connectance modeller allowing linear analysis and CRIMP, a tool based on dynamic cross-impact modelling and simulation, are presented and applied to a sample company specialised in technological breakthroughs. The results of tools are compared to identify strengths and weaknesses of these approaches.     

Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam

Evaluation of the parameters affecting the shear strength and ductility of steel–concrete composite beam is the goal of this study. This study focuses on predicting the future output of beam’s strength and ductility based on relative inputs using a soft computing scheme, extreme learning machine (ELM). Estimation and prediction results of the ELM models were compared with genetic programming (GP) and artificial neural networks (ANNs) models. Referring to the experimental results, as opposed to the GP and ANN methods, the ELM approach enhanced generalization ability and predictive accuracy. Moreover, achieved results indicated that the developed ELM models can be used with confidence for further work on formulating novel model predictive strategy in shear strength and ductility of steel concrete composite. Furthermore, the experimental results indicate that on the whole, the newflanged algorithm creates good generalization presentation. In comparison to the other widely used conventional learning algorithms, the ELM has a much faster learning ability.     

Theoretical vibration analysis of soft mounted electrical machines regarding rotor eccentricity based on a multibody model

The paper presents a simplified multibody model of a soft mounted electrical machine for theoretical vibration analysis, considering different kinds of rotor eccentricity. The most important dynamic eccentricities in electrical machines—eccentricity of rotor mass, magnetic eccentricity, and bent rotor deflection—are analyzed. The aim of the paper is not to replace a detailed three-dimensional finite-element calculation by a simplified plane multibody model, but to show the mathematical correlation between rotor dynamics, electromagnetic influence, oil film characteristics of the sleeve bearings, the influence of a soft foundation, and excitations by different kinds of rotor eccentricity, based on a simplified analytical model. Beside the absolute orbits of the rotor, stator, shaft journals, and bearing housings, also the relative orbits between rotor and stator and between shaft journals and bearing housings are mathematically described. In addition to these theoretical considerations, a numerical example for a soft mounted 2-pole induction motor is also shown. Finally, the paper shows the possibility of optimizing the system—electrical machine and foundation—regarding vibrations, caused by rotor eccentricities.     

Analyzing soft tissue stiffness of human upper arms during physical dynamic and quasi-static impacts in human–machine interaction

Knowledge of the changes in the behavior of human soft tissue stiffness during physical impact in human–machine interaction (HMI) plays a vital role in the development of biofidelity testing devices such as a human dummy. These testing devices are widely applied as an effective means to validate the safety of machinery during dynamic or static contact with humans in HMI. In this study, we assess changes in soft tissue stiffness in the upper arm of individuals under both dynamic (0.7 and 0.25 m/s) and quasi-static (QS) impacts under a constrained contact condition. Three impactor shapes (cylindrical, cubic, and spherical) are used in this study. Impact experiments are conducted using impactors attached to a pendulum. The soft-tissue displacement is determined using an ultrasound device. The impact force-displacement curves illustrate the nonlinear behavior of the soft tissue stiffness under both dynamic and QS impacts. By utilizing the “Linear Mixed Model” statistical analysis, we found that changes in the impact velocity significantly influenced the changes in the nonlinear behavior of soft tissue stiffness while there was no significant effect of the changes in the impactor shape on the nonlinear behavior of the soft tissue stiffness. Additionally, we revealed that the changes in the soft tissue stiffness are influenced by the size of the contact area. Moreover, we demonstrated a range of changes in soft tissue stiffness for different impact velocities, which provide valuable information for developing future validation test devices in HMI, such as the design and evaluation of dummy skin.     

Investigating the effect of training–testing data stratification on the performance of soft computing techniques: an experimental study

Abstract

Cross-validation of soft computing techniques needs to be done efficiently to avoid overfitting and underfitting. This is more important in petroleum reservoir characterisation applications where the often-limited training and testing data subsets represent Wells with known and unknown target properties, respectively. Existing data stratification strategies have been haphazardly chosen without any experimental basis. In this study, the optimal training–testing stratification proportions have been rigorously investigated using the prediction of porosity and permeability of petroleum reservoirs as an experimental case. The comparative performances of seven traditional and advanced machine learning techniques were considered. The overall results suggested a recommendable optimum training stratification that could serve as a good reference for researchers in similar applications.     

On combination rule in Dempster–Shafer theory using OWA-based soft likelihood functions and its applications in environmental impact assessment

Dempster–Shafer theory (DST) was presented as an effective mathematical tool to represent uncertainty. Its significant innovation is to allow the allocation of the belief of mass to sets or intervals, and it becomes a valuable method in the field of decision making and evaluation when accurate information is not available or when knowledge is expressed subjectively by humans. A crucial research issue in DST is the combination of multi-sources of evidence. In this paper, a novel combination rule for Dempster–Shafer structures is developed based on ordered weighted average (OWA)-based soft likelihood functions proposed by Yager. First, the belief intervals, including the belief measures and plausibility measures, of all the hypotheses in the frame of discernment (FOD) are calculated. Second, the representative value of belief interval is defined based on golden rule introduced by Yager. Third, the soft likelihood value of each hypothesis is calculated based on the proposed OWA-based soft likelihood function for belief interval, which can be considered as the combined evidence. The final evaluation results can be employed for practical applications, such as decision making and evaluation. In addition, the improved evidence combination rule is presented which takes into account the weight of evidence. Several illustrative examples are conducted to manifest the use of the developed methods. Finally, an application for environmental impact assessment is given to demonstrate the usefulness of the developed combination rule in DST.