基于分级加权模型的车身色彩感性评价方法

以某自主品牌经济型汽车车身色彩为例,进行感知实验。运用分级加权法,通过主成分分析先后确定样本权重和感性指标限定权重,继而建立感性变量与车身色彩样本的分级加权模型并获得相应的感性评价的指标系数,由此获知客户的感性诉求程度。研究结果表明:采用分级加权模型对车身色彩样本进行感性评价可避免人为主观倾向对车身色彩设计与评审工作的干扰,可为企业进行车身色彩设计并根据实际产能合理安排喷涂生产等工作提供优先选择的可靠依据,提高产品设计部门和管理层在车身选色决策过程中的科学性,从而提高市场接受率和消费者满意度。

Debris Flow Susceptibility Assessment Using a Probabilistic Approach:A Case Study in the Longchi Area, Sichuan Province, China

The Longchi area with the city of Dujiangyan, in the Sichuan province of China, is composed of Permian stone and diorites and Triassic sandstones and mudstones intercalated with slates. An abundance of loose co-seismic materials were present on the slopes after the May 12, 2008 Wenchuan earthquake, which in later years served as source material for rainfall-induced debris flows or shallow landslides. A total of 48 debris flows, all triggered by heavy rainfall on 13th August 20l0, are described in this paper. Field investigation, supported by remote sensing image interpretation, was conducted to interpret the co-seismic landslides in the debris flow gullies. Specific characteristics of the study area such as slope, aspect, elevation, channel gradient, lithology, and gully density were selected for the evaluation of debris flow susceptibility. A score was given to all the debris flow gullies based on the probability of debris flow occurrence for the selected factors. In order to get the contribution of the different factors, principal component analyses were applied. A comprehensive score was obtained for the 48 debris flow gullies which enabled us to make a susceptibility map for debris flows with three classes. Twenty-two gullies have a high susceptibility, twenty gullies show a moderate susceptibility and six gullies have a low susceptibility for debris flows.

基于可拓符号学的非物质文化遗产元素再设计研究

针对苗侗木制民居的部件创新设计,提出基于可拓符号学的非物质文化遗产遗元素(以下简称非遗)再设计研究。首先,从符号学传播理论方面分析非遗艺术符号与非遗设计符号的区别;其次利用可拓变换方法将非遗艺术符号转换为非遗设计符号;最后,提出基于可拓感性优度评价方法对非遗设计符号进行评价。文章以苗侗木制民居的吊柱设计为实例,验证了该方法的可行性。

Comparative evaluation of geological disaster susceptibility using multi-regression methods and spatial accuracy validation

Geological disasters not only cause economic losses and ecological destruction, but also seriously threaten human survival. Selecting an appropriate method to evaluate susceptibility to geological disasters is an important part of geological disaster research. The aims of this study are to explore the accuracy and reliability of multi-regression methods for geological disaster susceptibility evaluation, including Logistic Regression(LR), Spatial Autoregression(SAR), Geographical Weighted Regression(GWR), and Support Vector Regression(SVR), all of which have been widely discussed in the literature. In this study, we selected Yunnan Province of China as the research site and collected data on typical geological disaster events and the associated hazards that occurred within the study area to construct a corresponding index system for geological disaster assessment. Four methods were used to model and evaluate geological disaster susceptibility. The predictive capabilities of the methods were verified using the receiver operating characteristic(ROC) curve and the success rate curve. Lastly, spatial accuracy validation was introduced to improve the results of the evaluation, which was demonstrated by the spatial receiver operating characteristic(SROC) curve and the spatial success rate(SSR) curve. The results suggest that: 1) these methods are all valid with respect to the SROC and SSR curves, and the spatial accuracy validation method improved their modelling results and accuracy, such that the area under the curve(AUC) values of the ROC curves increased by about 3%–13% and the AUC of the success rate curve values increased by 15%–20%; 2) the evaluation accuracies of LR, SAR, GWR, and SVR were 0.8325, 0.8393, 0.8370 and 0.8539, which proved the four statistical regression methods all have good evaluation capability for geological disaster susceptibility evaluation and the evaluation results of SVR are more reasonable than others; 3) according to the evaluation results of SVR, the central-southern Yunnan Province are the highest sus-ceptibility areas and the lowest susceptibility is mainly located in the central and northern parts of the study area.

Description and Evaluation Approach for Uncertainty of RS Images Classi- fication

With the development of researches on the classification quality of remote sensing images, researchers thought that uncertainty is the main factor that influences classification quality. This study puts forward an approach to uncertainty repre- sentation, which is developed from two aspects： formalized description and comprehensive evaluation. First, we complete the classification using fuzzy surveillance approach, taking it as a formalized description of classification uncertainty. Then we in- troduce a hybrid entropy model for classification uncertainty evaluation, which can meet the requirement of comprehensive reflection of several uncertainties, while constructing the evaluation index from pixel scale with the full consideration of the different contribution to the error rate of each pixel. Finally, an application example will be studied to examine the new method. The result shows that the evaluation results fully reflect the classification quality, when compared with the conventional evaluation method which constructs models from unitary uncertainty and category scale.