CIELAB与CIECAM02色空间均匀性比较研究

采用均匀色空间Munsell系统检验和验证了CIELAB和色貌模型CIECAM02色空间的均匀性。利用Mun- sell色卡所对应的三刺激值X,Y,Z作为模型的输入值,预测色空间模型的明度、彩度和色调,并通过预测结果比较和检验了CIELAB色空间和CIECAM02模型色空间的均匀性。研究表明:在预测明度方面,两个色空间的结果相似;在预测彩度时,CIECAM02模型较好于CIELAB色空间;在预测色调时,当色调逐渐从BG变化到B时,CIELAB所预测的色调角与由Munsell色空间计算出来的色调角之差逐渐增大,最大时超过6%,而CIECAM02所预测的色调角与Munsell色空间的色调角之差在2.5%以内,CIECAM02优于CIELAB色空间。     

Testing performance of CIECAM02 in predicting perceptual contrast

A psychophysical experiment is performed on two large-size liquid crystal displays under three viewing conditions to assess perceptual contrast.Based on the visual data,the performance of CIECAM02 in predicting perceptual contrast under different viewing conditions is tested and compared with other models by F-test.Results show that the perceptual contrast models in the form of Weber contrast using CIECAM02 brightness Q agreed better with the contrast perception of human visual system compared to the models using luminance,CIELAB lightness L?,and CIECAM02 lightness J.     

Wooden windows: Sound insulation evaluation by means of artificial neural networks

Windows are the weakest part of a façade in terms of acoustic performance: the weighted sound insulation index (R_w), measured according to ISO 140-3, is the fundamental parameter to evaluate the façade acoustic insulation.The paper aims at developing an artificial neural network (ANN) model to estimate the R_w value of wooden windows based on a limited number of windows parameters; this is a new approach because acoustic phenomena are non-linear and affected by a plurality of factors and, therefore, usually investigated through experimentation.Data set is taken from experimental campaigns carried out at the Laboratory of Acoustics, University of Perugia. A multilayer feed-forward approach was chosen and the model was implemented in MATLAB. On the basis of the results obtained by means of a preliminary training and test campaign of several ANN architectures, five main parameters were selected as network inputs: window typology, frame and shutters thickness, number of gaskets, R_w of glazing; R_w value of the window is the network output. Different ANN configurations were trained and a root mean-square error less than 3% was obtained, comparable to measurement uncertainty.This approach allows to develop a model which, with input parameters varying within appropriate ranges, can easily estimate the acoustic performance of wooden windows without experimental campaign on prototypes, saving both money and time. If the training data set is large enough, the presented approach could be very useful for design and optimization of acoustic performance of new products.     

粉末涂料电脑配色的人工神经网络模型

提出一种基于多层BP人工神经网络的粉末涂料配方预测模型;用BP算法人工神经网络建立粉末涂料反射样品的标准色度参数与配方浓度参数之间的映射关系。把人工神经网络的配方预测模型应用到典型的粉末涂料样品的测配色实验过程中。实验结果表明,基于多隐层BP网的模型可以实现粉末涂料样品的配方浓度空间与标准三刺激值颜色空间的相互映射,对64个节点的平均训练精度达到了1个CIELAB色差单位。     

Model of the appearance of avalanche bioelectric discharges in neural networks of the brain

According to neurobiological experiments, the effect of the appearance of spontaneous bioelectric discharges in neural networks of the brain satisfies the statistics of self-organized criticality. Theoretical investigations indicate that the critical behavior is an optimal regime for the storage and processing of information in the brain. Many model works focused on the approximation of the experimental data and on the investigation of information characteristics of signals, whereas the problem of dynamical mechanisms of their avalanche generation remains almost unstudied. The conditions of the appearance of high-frequency discharges at the critical dynamical threshold have been analyzed in the framework of the biophysical model of the neuronal network. A probabilistic model of the layer-by-layer activation of cells, which makes it possible to estimate the key relations between the parameters for avalanche generation of the discharge, has been proposed.     

色貌模型的人工神经网络方法的研究

色貌模型(CAM)主要解决不同观察条件、不同背景和不同环境下的颜色真实再现问题。采用人工神经网络(ANN)的方法来实现目前最新的色貌模型CIECAM02的预测,包括正向预测(从色度参数到色貌属性参数)和逆向预测(从色貌属性参数到色度参数),应用自然色系统(NCS)中的部分色样作为神经网络的训练和测试样本。由于正向输出色貌属性参数空间不是均匀的,对于网络预测精度用特殊方法评估,而对于逆向模型则可直接利用LAB色差公式评价。测试的结果表明:用神经网络对CIECAM02模型的预测达到了较高的精度。     

Prediction of Chinese color system appearance scales using various color appearance models

The chromaticities of the Chinese color system dataset are applied to eight color appearance models (CAMs). Models used are: CIELAB, Hunt, Nayatani, RLAB, LLAB, ZLAB, CIECAM97s, CIECAM02.     

Critical phenomena in model neural networks

Dynamical properties of a sparsely connected model network composed of N binary decision elements with randomly chosen asymmetrical couplings are studied statistically. In the thermodynamic limit exact results based on the time evolution of the Hamming distance of two different initial states predict the occurrence of two phases upon varying any of the network parameters: a frozen and a chaotic phase. Extensive computer simulations agree well with the results. The model is also shown to share behavioral similarities with the Kauffmann model pointing to the existence of formal relationships.     

基于Sigmoid-iCAM色貌模型的真实影像再现算法

为了模拟人类视觉系统成像机制,真实再现人眼对外界场景的视觉感知,为机器视觉提供符合生物视觉特性的理想图像,从色度学的角度研究了彩色图像的真实影像再现技术。针对色貌模型存在图像模糊、对比度低的缺点,在iCAM色貌模型的基础上,引入了人工神经网络的非线性Sigmoid函数,提出了基于Sigmoid-iCAM色貌模型的真实影像再现算法,并给出了该模型处理不同类型图像的控制参数统计规律。与基于CIECAM02、iCAM色貌模型的真实影像再现算法相比,该算法在对色调失真图像实现颜色复原的同时,有效地提高了图像的对比度和局部细节,很好地实现了视觉系统的颜色恒定性。     

人工神经网络作为打印机色空间转换工具的问题

通过分析大量实测的打印机输入(R,G,B)与输出(L ,a ,b )数据之间的对应关系,发现在一些颜色区域存在着RGB与L a b 之间的二对一甚至多对一的关系。另一方面,一个已知的事实是,当一个神经网络已经建立后,任一个(或一组)输入只能产生一个(或一组)输出。结合以上两个事实得出的结论是,在要求打印机的输出颜色能准确地复现指定颜色的情况下,神经网络不适合作为由指定颜色的L a b 值来寻求打印机的相应输入RGB的工具。     

A multineuron interaction model for neural networks

A multineuron interaction model (RS model) with an energy function given by the product of the squared distances in phase space between the state of the net and the stored patterns is studied in detail within a mean-field approach. Two limits are considered: when the patterns and antipatterns are stored (as in the Hopfield model), PAS case, and when only the patterns are taken into account, OPS case. TheT=0 solutions for the proper memories are exactly obtained for all finite values of, as a consequence of the energy function: whenever one of the overlaps is exactly one the corresponding equations decouple and no configuration average is required. Special interest is focused on the OPS situation, which presents a peculiar phase space topology. On the other hand, the PAS configuration recovers the Hopfield model in the appropriate limit, while keeping associative memory abilities far beyond the critical values of other models when the full Hamiltonian is considered.     

Pair-correlated patterns in Hopfield model of neural networks

We study the retrieval properties of the Hopfield model of neural networks when the memorized patterns are statistically correlated in pairs. There is a finite correlationk between the memories of each pair, but memories of different pairs are uncorrelated. The analysis is restricted to the case of an arbitrary but finite number of memories in the thermodynamic limit. We find that there are two retrieval regimes: for 0<T<(1–k) the system recognizes the stored patterns and for (1–k)<T<(1+k) the system is able to recognize pairs, but it is not able to distinguish between its two patterns.     

A neural networks based model of inverse hysteresis

A novel and simple approach based on transformation using neural networks is proposed in this paper to model the inverse behavior of hysteresis. In this approach, a continuous transformation is used to construct an elementary inverse hysteresis operator (EIHO), which can extract the change tendency of inverse hysteresis. Then based on the EIHO, an expanded input space is constructed to transform the multi-valued mapping of inverse hysteresis into a one-to-one mapping. Based on the constructed expanded input space, a neural network is employed to approximate the inverse hysteresis. Both experiment and simulation are implemented to validate the effectiveness of the proposed approach. These results indicate that the proposed approach has derived satisfactory modeling performance.     

The investigation of model selection criteria in artificial neural networks by the Taguchi method

Artificial neural networks (ANNs) have been successfully used for solving variety of problems. One major disadvantage of ANNs is that there is no formal systematic model building approach. This paper presents the application of the Taguchi method in the optimization of the design parameters of the ANNs. The performances of the ANNs were determined by the Taguchi method considering factors relevant for ANNs’ performance. The properties affecting the performance of the ANNs and their levels on the peak analytical function were determined by performing computational experiments. After training the network, the values of the statistical data criteria were determined and the optimum parameter levels were obtained in terms of the performance statistics. The performance of ANNs is shown to be better in the case of the application of the Taguchi method rather than in the case of random choice of factor values.     

涂料颜色配方预测的人工神经网络模型实验研究

人工神经网络技术近年来在颜色空间变换或色域映射的研究中显示了特有的效果。人工神经网络用于粉末涂料配方的预测,其特点是用BP算法神经网络建立粉末涂料反射样品的标准色度参数与配方浓度参数之间的映射关系。通过对典型颜色样本训练和预测,结果表明基于多隐层BP网的模型可以实现粉末涂料样品的配方浓度空间与标准三刺激值颜色空间的相互映射,即用BP网络实现粉末涂料的配方预测。     

量子过程神经网络模型算法及应用

为提高过程神经网络的逼近和泛化能力, 从研究过程神经元信息处理的量子计算实现机理入手, 提出基于量子旋转门及多位受控非门的物理意义构造量子过程神经元的新思想. 将离散化后的过程式输入信息作为受控非门的控制位, 经过量子旋转门作用后控制目标量子位的状态, 以目标量子位处于状态|1>概率幅作为量子过程神经元的输出. 以量子过程神经元为隐层, 普通神经元为输出层, 可构成量子过程神经网络. 基于量子计算机理推导了该模型的学习算法. 将该模型用于太阳黑子数年均值预测, 应用结果表明, 所提方法与普通过程神经网络相比, 预测精度有所提高, 对于复杂预测问题具有一定理论意义和实用价值.     

A canonical model for gradient frequency neural networks

We derive a canonical model for gradient frequency neural networks (GFNNs) capable of processing time-varying external stimuli. First, we employ normal form theory to derive a fully expanded model of neural oscillation. Next, we generalize from the single oscillator model to heterogeneous frequency networks with an external input. Finally, we define the GFNN and illustrate nonlinear time-frequency transformation of a time-varying external stimulus. This model facilitates the study of nonlinear time-frequency transformation, a topic of critical importance in auditory signal processing.     

Investigation of biopolymer networks by means of AFM

Natural hydrogel alginate was investigated by means of atomic force microscopy (AFM) to gain microscale information on the morphological and rheological properties of the biopolymer network cross-linked by various cations. Local rheological properties of the gels measured by force spectroscopy gave correlation between increasing ion selectivity and increasing polymer elasticity. Adhesive forces acting between the surface of the gel and the probe, and also the intrinsic rheological properties of bulk polymers affect the microscopical image formation.     

Scanner color management model based on improved back-propagation neural network

Scanner color management is one of the key techniques for color reproduction in information optics. A new scanner color management model is presented based on analyzing rendering principle of scanning objects. In this model, a standard color target is taken as experimental sample. Color blocks in color shade area are used to substitute complete color space to solve the difficulties in selecting experimental color blocks. Immune genetic algorithm is used to correct back-propagation neural network （BPNN） to speed up the convergence of the model. Experimental results show that the model can improve the accuracy of scanner color management.