浅析服装卖场陈列之色彩因素

色彩是服装卖场陈列中的灵魂,对卖场的舒适度、环境气氛以及对人的心理和生理都有巨大影响。因此,充分研究并恰当运用色彩,在卖场规划中是必不可少的,常常可以出奇制胜。本文将从色彩的心理和色彩的机能这两方面出发对服装陈列中的色彩因素进行考虑。     

基于Agent技术的人性化E-learning系统研究

设计了以Agent技术为核心的人性化的E-learning系统。以Agent技术和人工心理理论为基础,构建了ISM多层级结构化Learning-Map,在此基础上实现个性化的Learning-Map。在人性化研究方面,运用基于图像处理的人脸检测、表情识别技术实现情感的认知,并构建情感认知模型,通过智能Agent助手对认知的情感进行智能处理。     

基于GA算法的智能导购系统的研究与实现

论文以服装导购系统为例,以模拟顾客思维方式、喜好特点为目标,搭建了一个智能的Web导购系统。文中首次提出将交互式遗传算法和数量化I类理论这两种算法结合,进行系统在线学习并构建顾客的心理模型,使系统兼顾数量化I类理论的运算速度和遗传算法的非线性仿真模拟效果;同时为解决进化时间过长导致的用户疲劳问题,提出采用自主式GA来对用户历次所选的个体进行离线学习。通过对系统预测能力、运行速度的测试,以及用户对实验结果满意度的调查,证明这两种算法的结合弥补了原系统在非线性预测上的不足,使系统能够更快速、更准确、更人性化地追踪、模仿顾客的心理。     

面向个性化服装推荐的判断优化模型

针对已有服装推荐算法中服装色调与用户颜色特征不协调的问题,将服装搭配的四季色彩理论与计算机视觉领域的推荐方法相结合,提出一种面向个性化服装推荐的判断优化模型。提出四季色彩判断模型,对根据输入的人脸图像提取得到的用户颜色特征集进行分类;建立优化处理模型,根据四季色彩判断模型结果和用户所需风格进行优化处理并获得服装预推荐结果;通过用户评分及反馈机制,提高优化结果,得到最终推荐结果。实验表明,该方法能实现与用户颜色特征相协调的个性化的服装推荐,并且在实际应用中具有较高的准确率。     

“工业自动化仪表造型设计”讲座：第十讲 工业自动化仪表色彩设计（四）

一、色刺激与心理反应色刺激的心理反应是建立在色刺激的生理反应基础之上的,是人接受色刺激生理反应的经验积累。然而,色刺激虽与生理并无关系,但在心理上却作出相同的反应。这个问题,在上一讲中已经有所涉及,即色彩的冷暖反应。除了色刺激有冷暖的感受外,还有色彩的进退和伸缩感;色彩的轻重感;色彩的软硬感;注目的色彩和不注目的色彩等,下面分别介绍。 1.色彩的进退和伸缩感色彩的"进"与"退","伸"与"缩"也是色与色之间对比给人的感觉,对比中的差异表现出两色的距离,面积上的不同之感。例如当我们看到两块大小相同而色彩不同的颜色排置在相同色彩的背景中,就可发现一块色彩比另一块色离我们近些,膨胀些。或者一色与背景色比较,如它比背景色突出,可称为"前进色",相反,好象被     

人工心理模型在个性化商品推荐系统中的应用

互联网的飞速发展和广泛应用,刺激了人们对推荐信息的需求。推荐系统的应运而生,减轻了信息过量对人们的威胁。目前,个性化已经成为一种发展趋势,而能使网站更具个性化的推荐系统也将逐渐成为一种必需的网上服务。本文提出了基于数量化I类理论的人工心理模型的建模方法,并介绍了该模型在个性化商品推荐系统中的应用。     

浅谈色彩在产品设计中的作用

不仅色彩的基本知识重要,更深的内涵也需要被探讨。色彩设计在功能、人性化、设计心理、消费层次和创新等方面的作用,以及同产品设计的关系,都是一个优秀设计师必须掌握的。完美的色彩让作品更加具有诱惑力。     

“城中村”变装：印度尼西亚的色彩立方体

正位于印度尼西亚爪哇省三宝垄市南部地区的"彩虹村"曾是严重贫困区。经过一次全新的粉刷改造,这里成为色彩绚丽的"彩虹村",增添了不少活力,也带动了旅游业的发展。艺术元素的注入画家瓦西里·康定斯基曾在《论艺术的精神》中说道:"色彩是触及灵魂的力量"。看到一个五颜六色的调色板时,人们大概会有两种感受:一种是颜色给人带来的满足和愉悦感,但持续时间短;另一种则是色彩的心理效果,能够引起人精神上的振荡,灵魂的共鸣,即使转移视线忘记色彩,这种心理效果依然存在。这种色彩心理效应也被印度尼西亚恰当地应用在城市建设与社区改造中。     

卖场环境下的服装商品陈列色彩设计

服装商品陈列色彩设计是服装色彩在卖场环境下的二次设计,它以明确、规划的色彩配置产生不同的色彩印象,表现设计师对服装色彩的选择和应用。同时,它也能突出品牌形象,强化品牌认知度,体现品牌服装的特点,唤起消费者购买欲望,最终推动销售额的增长。服装商品陈列色彩设计具有强大的功能性,既便于增强服装卖场秩序,方便选购,方便货品整理,也便以因换季或不同陈列主题而进行设计调整。     

在线试衣应用的虚实相生

个性化、差异化是人需,也是商机。服装是高度个性化的商品,满足消费者差别化的体验需求才能赢得财富。在线试衣解决方案既能满足消费者的个性化需求,也能为服装电商带来利润。服装是个性化的商品,各人的喜好款式与适合尺码不尽相同,同样款穿在别人、模特身上与穿在自身,其效果可能有云泥之别,故消费者在购买前一定要先试后买。试衣难正是服装电商遇到的瓶颈,不仅会降低购买率,而且调换等售后成本也较高,消费者也不满     

A critical review of the most popular types of neuro control

In this review article, the most popular types of neural network control systems are briefly introduced and their main features are reviewed. Neuro control systems are defined as control systems in which at least one artificial neural network (ANN) is directly involved in generating the control command. Initially, neural networks were mostly used to model system dynamics inversely to produce a control command which pushes the system towards a desired or reference value of the output (1989). At the next stage, neural networks were trained to track a reference model, and ANN model reference control appeared (1990). In that method, ANNs were used to extend the application of adaptive reference model control, which was a well‐known control technique. This attitude towards the extension of the application of well‐known control methods using ANNs was followed by the development of ANN model‐predictive (1991), ANN sliding mode (1994) and ANN feedback linearization (1995) techniques. As the first category of neuro controllers, inverse dynamics ANN controllers were frequently used to form a control system together with other controllers, but this attitude faded as other types of ANN control systems were developed. However, recently, this approach has been revived. In the last decade, control system designers started to use ANNs to compensate/cancel undesired or uncertain parts of systems' dynamics to facilitate the use of well‐known conventional control systems. The resultant control system usually includes two or three controllers. In this paper, applications of different ANN control systems are also addressed. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Fault diagnosis on bottle filling plant using genetic-based neural network

Timely detection of the pneumatic system problems is important in industry. Many techniques have been employed to solve this problem. In this paper, Genetic Algorithm (GA) based optimal configuration of neural networks is proposed for fault diagnostic of bottle filling systems. Back-propagation is used for neural networks algorithm. The back-propagation algorithm had six inputs and one output. A fitness function was designed to the minimize execution time of ANN model by keeping the number of hidden layer(s) and nodes as low as possible while the mean square error of estimated output error is minimized. The designed GA–ANN combination and the graphical user interface (GUI) eliminate the trial and error process for selection of the fastest and most accurate configuration. The performance of the proposed system was evaluated by using experimental data collected at a pneumatic work cell which attach caps to the bottles. The sensory data was collected at normal operating conditions and a series of faults were imposed to the system such as missing bottle, attaching nonworking bottle caps at two different cylinders, two air pressure problems (insufficient and low air), and not filling water. The study demonstrated the convenience, accuracy and speed of the proposed GA–NN environment. It may also be used for training for selection of ANN configurations at various applications.     

基于人工神经网络组合预测油田产量

油田原油产量的准确预测可以对油田的生产管理进行合理的指导。该文探讨了应用神经网络组合方法预测油田产量，对开井数、含水率、动用储量以及往年产量同未来产量之间的复杂关系建立模型。采用了两层预测系统：第一层包含两个神经网络，一个多层前馈网络和一个函数链接网络；第二层是把第一层的两个网络输出进行组合。研究了五种不同的组合算法：平均法、最小平方回归法、模糊逻辑法、自适应前馈神经网络法和自适应函数链接神经网络法。根据油品类型分为稀油、热采稠油、常规稠油和总产量四组数据，对上述方法进行了测试，结果表明应用人工神经网络的组合预测方法优于其他的预测方法，而且适用范围广。     

Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling

The potential surface settlement, especially in urban areas, is one of the most hazardous factors in subway and other infrastructure tunnel excavations. Therefore, accurate prediction of maximum surface settlement (MSS) is essential to minimize the possible risk of damage. This paper presents a new hybrid model of artificial neural network (ANN) optimized by particle swarm optimization (PSO) for prediction of MSS. Here, this combination is abbreviated using PSO-ANN. To indicate the performance capacity of the PSO-ANN model in predicting MSS, a pre-developed ANN model was also developed. To construct the mentioned models, horizontal to vertical stress ratio, cohesion and Young’s modulus were set as input parameters, whereas MSS was considered as system output. A database consisting of 143 data sets, obtained from the line No. 2 of Karaj subway, in Iran, was used to develop the predictive models. The performance of the predictive models was evaluated by comparing performance prediction parameters, including root mean square error (RMSE), variance account for (VAF) and coefficient correlation (R ²). The results indicate that the proposed PSO-ANN model is able to predict MSS with a higher degree of accuracy in comparison with the ANN results. In addition, the results of sensitivity analysis show that the horizontal to vertical stress ratio has slightly higher effect of MSS compared to other model inputs.     

The effect of ICA and PSO on ANN results in approximating elasticity modulus of rock material

Reliable determination/evaluation of the rock deformation can be useful prior any structural design application. Young’s modulus (E) affords great insight into the characteristics of the rock. However, its direct determination in the laboratory is costly and time-consuming. Therefore, rock deformation prediction through indirect techniques is greatly suggested. This paper describes hybrid particle swarm optimization (PSO)–artificial neural network (ANN) and imperialism competitive algorithm (ICA)–ANN to solve shortcomings of ANN itself. In fact, the influence of PSO and ICA on ANN results in predicting E was studied in this research. By investigating the related studies, the most important parameters of PSO and ICA were identified and a series of parametric studies for their determination were conducted. All models were built using three inputs (Schmidt hammer rebound number, point load index and p-wave velocity) and one output which is E. To have a fair comparison and to show the capability of the hybrid models, a pre-developed ANN model was also constructed to estimate E. Evaluation of the obtained results demonstrated that a higher ability of E prediction is received developing a hybrid ICA–ANN model. Coefficient of determination (R²) values of (0.952, 0.943 and 0.753) and (0.955, 0.949 and 0.712) were obtained for training and testing of ICA–ANN, PSO–ANN and ANN models, respectively. In addition, VAF values near to 100 (95.182 and 95.143 for train and test) were achieved for a developed ICA–ANN hybrid model. The results indicated that the proposed ICA–ANN model can be implemented better in improving performance capacity of ANN model compared to another implemented hybrid model.

     

Calibrating artificial neural networks by global optimization

Artificial neural networks (ANNs) are used extensively to model unknown or unspecified functional relationships between the input and output of a “black box” system. In order to apply the generic ANN concept to actual system model fitting problems, a key requirement is the training of the chosen (postulated) ANN structure. Such training serves to select the ANN parameters in order to minimize the discrepancy between modeled system output and the training set of observations. We consider the parameterization of ANNs as a potentially multi-modal optimization problem, and then introduce a corresponding global optimization (GO) framework. The practical viability of the GO based ANN training approach is illustrated by finding close numerical approximations of one-dimensional, yet visibly challenging functions. For this purpose, we have implemented a flexible ANN framework and an easily expandable set of test functions in the technical computing system Mathematica. The MathOptimizer Professional global-local optimization software has been used to solve the induced (multi-dimensional) ANN calibration problems.     

Learning optimization for CPN-based training in robot positioning control

Artificial neural net (ANN) models have been applied to the inverse kinematic problem for controlling robot positions. The selection of ANN training parameters, however, is an important yet complicated step which has to be taken before an ANN model for robot positioning control can be implemented effectively. The objective of this research is to utilize the counterpropagation network (CPN) for inverse kinematic mapping and obtain the best performance possible by systematic adjustment of network parameters. Taguchi statistical methods, efficient methods for analyzing the capability and accuracy of a system, have been used in this study. The working envelope of the robot simulated in this research is 150×150×60 mm³. The optimal accuracy and standard deviation determined by this research are 2.62 mm and 1.2 mm, respectively.     

Student models: the genetic graph approach

In this paper we examine the student model component of an intelligent computer-assisted instruction (ICAI) system. First, we briefly discuss the desirable capabilities of the student model and then describe, in detail, one approach to student modelling which is based on Goldstein's genetic graph. We expand Goldstein's definition and test it's feasibility in new domains, since his original domain was a limited, straightforward adventure game. In addition to modelling two diverse domains, subtraction and ballet, we also discuss the role of certain ICAI components in generating and maintaining the genetic graph.     

Prediction of power output of a coal-fired power plant by artificial neural network

Accurate modeling of thermal power plant is very useful as well as difficult. Conventional simulation programs based on heat and mass balances represent plant processes with mathematical equations. These are good for understanding the processes but usually complicated and at times limited with large number of parameters needed. On the other hand, artificial neural network (ANN) models could be developed using real plant data, which are already measured and stored. These models are fast in response and easy to be updated with new plant data. Usually, in ANN modeling, energy systems can also be simulated with fewer numbers of parameters compared to mathematical ones. Step-by-step method of the ANN model development of a coal-fired power plant for its base line operation is discussed in this paper. The ultimate objective of the work was to predict power output from a coal-fired plant by using the least number of controllable parameters as inputs. The paper describes two ANN models, one for boiler and one for turbine, which are eventually integrated into a single ANN model representing the real power plant. The two models are connected through main steam properties, which are the predicted parameters from boiler ANN model. Detailed procedure of ANN model development has been discussed along with the expected prediction accuracies and validation of models with real plant data. The interpolation and extrapolation capability of ANN models for the plant has also been studied, and observed results are reported.     

Overbreak prediction and optimization in tunnel using neural network and bee colony techniques

Overbreak is an undesirable phenomenon in blasting operations. The causing factors of overbreak can be generally divided as blasting and geological parameters. Due to multiplicity of effective parameters and complexity of interactions among these parameters, empirical methods may not be fully appropriated for blasting pattern design. In this research, artificial neural network (ANN) as a powerful tool for solving such complicated problems is developed to predict overbreak induced by blasting operations in the Gardaneh Rokh tunnel, Iran. To develop an ANN model, an established database comprising of 255 datasets has been utilized. A three-layer ANN was found as an optimum model for prediction of overbreak. The coefficient of determination (R²) and root mean square error (RMSE) values of the selected model were obtained as 0.921, 0.4820, 0.923 and 0.4277 for training and testing, respectively, which demonstrate a high capability of ANN in predicting overbreak. After selecting the best model, the selected model was used for optimization purpose using artificial bee colony (ABC) algorithm as one of the most powerful optimization algorithms. Considering this point that overbreak is one of the main problems in tunneling, reducing its amount causes to have a good tunneling operation. After making several models of optimization and variations in its weights, the optimum amount for the extra drilling was 1.63 m², which is 47% lower than the lowest value (3.055 m²). It can be concluded that ABC algorithm can be introduced as a new optimizing algorithm to minimize overbreak induced by tunneling.