A saliency-driven robotic head with bio-inspired saccadic behaviors for social robotics

This paper presents a robotic head for social robots to attend to scene saliency with bio-inspired saccadic behaviors. Scene saliency is determined by measuring low-level static scene information, motion, and object prior knowledge. Towards the extracted saliency spots, the designed robotic head is able to turn gazes in a saccadic manner while obeying eye–head coordination laws with the proposed control scheme. The results of the simulation study and actual applications show the effectiveness of the proposed method in discovering of scene saliency and human-like head motion. The proposed techniques could possibly be applied to social robots to improve social sense and user experience in human–robot interaction.     

Toward seed-insensitive solutions to local community detection

Local community detection aims at finding a community structure starting from a seed which is a given vertex in a network without global information, such as online social networks that are too large and dynamic to ever be known fully. Nonetheless, the existing approaches to local community detection are usually sensitive to seeds, i.e., some seeds may lead to missing of some true communities. In this paper, we present a seed-insensitive method called GMAC and its variation iGMAC for local community detection. They estimate the similarity among vertices by investigating vertices’ neighborhoods, and reveal a local community by maximizing its internal similarity and minimizing its external similarity simultaneously. Extensive experimental results on both synthetic and real-world data sets verify the effectiveness of our algorithms.     

Multi-scale patch-based sparse appearance model for robust object tracking

When objects undergo large pose change, illumination variation or partial occlusion, most existing visual tracking algorithms tend to drift away from targets and even fail to track them. To address the issue, in this paper we propose a multi-scale patch-based appearance model with sparse representation and provide an efficient scheme involving the collaboration between multi-scale patches encoded by sparse coefficients. The key idea of our method is to model the appearance of an object by different scale patches, which are represented by sparse coefficients with different scale dictionaries. The model exploits both partial and spatial information of targets based on multi-scale patches. Afterwards, a similarity score of one candidate target is input into a particle filter framework to estimate the target state sequentially over time in visual tracking. Additionally, to decrease the visual drift caused by frequently updating model, we present a novel two-step object tracking method which exploits both the ground truth information of the target labeled in the first frame and the target obtained online with the multi-scale patch information. Experiments on some publicly available benchmarks of video sequences showed that the similarity involving complementary information can locate targets more accurately and the proposed tracker is more robust and effective than others.     

On compatibility of uncertain multiplicative linguistic preference relations based on the linguistic COWGA

The aim of this work is to develop a new compatibility for the uncertain multiplicative linguistic preference relations and utilize it to determine the optimal weights of experts in the group decision making (GDM). First, the compatibility degree and compatibility index for the two multiplicative linguistic preference relations are proposed. Then, based on the linguistic continuous ordered weighted geometric averaging (LCOWGA) operator, some concepts of the compatibility degree and compatibility index for the two uncertain multiplicative linguistic preference relations are presented. We prove the property that the synthetic uncertain linguistic preference relation is of acceptable compatibility under the condition that the uncertain multiplicative linguistic preference relations given by experts are all of acceptable compatibility with the ideal uncertain multiplicative linguistic preference relation, which provides a theoretic basis for the application of the uncertain multiplicative linguistic preference relations in GDM. Next, an optimal model is constructed to determine the weights of experts based on the criterion of minimizing the compatibility index in GDM. Moreover, an approach to GDM with uncertain multiplicative linguistic preference relations is developed, and finally, an application of the approach to supplier selection problem with uncertain multiplicative linguistic preference relations is pointed out.     

聚氨酯改性聚异氰脲酸酯弹性体的合成和热性能分析

本文介绍用２，４，６－三（二甲胺基甲基）苯酚（ＤＭＰ－３０）作催化剂，采用一步法工艺制备聚氨酯改性聚异氰脲酸酯弹性体的研究工作，并用红外光谱、差热分析、热失重等方法对所制弹性体进行了定性、定量热性能分析。结果表明，通过调节催化剂用量、两组分摩尔比等工艺条件，可以使整个反应过程在１分钟之内完成，适合ＲＩＭ加工工艺；由红外光谱法和差热分析可定性确知所制材料中含有聚异氰脲酸酯三聚体和氨酯，由热失重分析可     

Domain ontology graph model and its application in Chinese text classification

This paper proposes an ontology learning method which is used to generate a graphical ontology structure called ontology graph. The ontology graph defines the ontology and knowledge conceptualization model, and the ontology learning process defines the method of semiautomatic learning and generates ontology graphs from Chinese texts of different domains, the so-called domain ontology graph (DOG). Meanwhile, we also define two other ontological operations—document ontology graph generation and ontology graph-based text classification, which can be carried out with the generated DOG. This research focuses on Chinese text data, and furthermore, we conduct two experiments: the DOG generation and ontology graph-based text classification, with Chinese texts as the experimental data. The first experiment generates ten DOGs as the ontology graph instances to represent ten different domains of knowledge. The generated DOGs are then further used for the second experiment to provide performance evaluation. The ontology graph-based approach is able to achieve high text classification accuracy (with 92.3 % in f-measure) over other text classification approaches (such as 86.8 % in f-measure for tf–idf approach). The better performance in the comparative experiments reveals that the proposed ontology graph knowledge model, the ontology learning and generation process, and the ontological operations are feasible and effective.     

硫代糠酸乙酯和硫代糠酸糠酯的合成

以糠酰氯、乙硫醇、糠硫醇为原料，合成了硫代糠酸乙酯和硫代糠酸糠酯，产率分别为７７％和７４％。测定了硫代糠酸乙酯的沸点、折光率及硫代糠酸糠酯的熔点、沸点。对合成的硫代糠酸乙酯进行了评香鉴定     

新时期景德镇陶瓷产业的保护与发展

景德镇陶瓷不仅历史悠久,而且品类众多,技艺精湛,文化内涵丰富。在手工业时代,它是生活的创造者;在当代,它成为历史文化的一种记忆和象征,以其独特形态自立于以现代设计为主体的科技文明之中,成为民族传统文化艺术的载体,发挥着从经济到艺术的多种功能。但是近些年来,在经济文化大发展的背景下,景德镇陶瓷产业却呈现总体不够景气、生产销售萎缩、工艺技术没有突破、后继无人等问题。我们应该清楚地认识到对于景德镇陶瓷产业的保护和发展的迫切性,以及改善现状、进行创新与应用的重要性,其保护与发展遭遇到了发展经济、技术更新和文化变迁等一系列矛盾,需要去探讨和研究。     

陈化过程中不同部位烤烟烟叶香味成分变化的研究

采用SDE-GC/MS法对陈化过程中不同部位烤烟烟叶的香味成分进行分析检测,并按照香味物质的产生来源对分析结果进行了分类研究。结果表明,陈化过程中不同部位烟叶的香味物质的变化情况差别明显。从香味成分的变化情况看,各个部位适宜的陈化时间有所不同:上部烟叶2年以上,中部烟叶1.5~2年,下部烟叶1年左右。