The virtual biomodel as a complementary resource for the diagnosis of mandibular ankylosis

Ankylosis of the mandibular joint is one of many pathologies that affect the temporomanbibular joint. It comprises the mandible condyle, the base of the skull bone and the joint itself, inducing changes in the skull bone and face complex, affecting their growth to different degrees, which can cause morphofunctional alterations. The objective of this paper is to show the gains of using a virtual biomodel as an additional resource in the diagnosis of mandibular joint ankylosis, besides the analysis of multiplanar slices.     

Customers segmentation in eco-friendly hotels using multi-criteria and machine learning techniques

This study aims to investigate the travellers' choice behaviour towards green hotels through existing online travel reviews on TripAdvisor. Accordingly, a method combining segmentation and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) techniques was developed to segment travellers based on their provided reviews and to prioritize green hotel attributes based on their level of importance in each segment. The data were taken from travellers' online reviews of Malaysian eco-friendly hotels on TripAdvisor. The results showed that the sleep quality was one of the most imporant factors for eco-hotel selection in the majority of segments. The developed method in this study was able to analyse travellers’ reviews and ratings on eco-friendly hotels to identify the future choice behaviour and aid travellers in their decision-making process. The study provides new insights for hotel managers and green policy makers on developing environmental-friendly practices.     

Multiple structure recovery with T-linkage

This work addresses the problem of robust fitting of geometric structures to noisy data corrupted by outliers. An extension of J-linkage (called T-linkage) is presented and elaborated. T-linkage improves the preference analysis implemented by J-linkage in term of performances and robustness, considering both the representation and the segmentation steps. A strategy to reject outliers and to estimate the inlier threshold is proposed, resulting in a versatile tool, suitable for multi-model fitting “in the wild”. Experiments demonstrate that our methods perform better than J-linkage on simulated data, and compare favorably with state-of-the-art methods on public domain real datasets.     

Internal preference mapping and the issue of satiety

Internal preference mapping (IPM) and Landscape Segmentation Analysis® (LSA) are two techniques broadly used to unfold consumers’ overall product liking ratings and create spatial maps that will provide further insights on consumers’ preferences. IPM is based on a vector model while LSA involves an ideal point model. Through a simulation and the analysis of 27 market research data sets, it is shown that IPM consistently creates a hedonic dimension that prevents the identification of satiety prone attributes (intensities higher or lower than a optimal level being disliked by the consumers) on that dimension. As a result, subsequent steps taken upon generating an IPM map such as the investigation of drivers of liking, population segmentation and the estimation of optimal product profiles have also a strong likelihood of resulting in distorted results, the level of distortion being dependent on the actual configuration of the underlying structure that IPM tried to uncover. It is also shown that a technique based on ideal points such as LSA does not exhibit this systematic artifact when unfolding liking data. Consequently, sensory scientists and market researchers should use caution when interpreting and using results issued from an internal preference mapping analysis.     

一种车牌自动识别系统设计方法

通过对连续视频流分析的方法判断并捕获车牌图像帧;基于数学形态学方法和边缘特征分析来进行车牌定位,进行二值化、引入多指标联合评价函数判断反色等处理;最后基于连通体分析的方法切分字符。实验表明本系统设计方法是可行的。     

具有三级索引词库结构的中文分词方法研究

提出了一种分组并具有三级索引结构的词库组织体系,给出了合适的索引密度间隔;针对系统基本词库的扩充问题,考虑了一种基于词频统计并具有过滤功能的关键词自动抽取和小词条添加方法。大量仿真实验结果表明,采用该方法可较大提高中文文本的切词速度及信息的查全查准率。     

基于高斯拟合的自适应多阈值分割算法

提出一种新的基于邻域平均直方图的快速高斯拟合函数参数估计的算法,对形态滤波后的邻域平均直方图进行峰谷检出,对各峰区数据单独进行截断数据统计分析确定拟合参数,较好地避免了多峰交叠所带来的干扰,从而实现了复杂图像的自适应多阈值分割。实验表明,该算法能有效提高拟合精度和分割的鲁棒性。     

基于语义的词义消歧算法初探

词义消歧是自然语言处理中的难题之一。结合《知网》,从语义的角度出发,结合词性信息、搭配实例和语义相关度等信息,提出了一种多策略的词义消歧方法。     

HNC问答处理系统关键算法研究

HNC问答处理系统是一种崭新的问答处理模式,与传统的问答处理系统相比,它把对语言的处理深入到语言概念空间中进行,以求尽可能地减少语言中的同义和多义模糊。本文在介绍HNC问答处理系统整体构架的同时,设计和实现了问答系统中的3个关键算法,服务于问答系统的不同阶段处理需要。实际应用表明,在这些算法指导下的HNC问答处理系统具有良好的问题处理能力。