分体西装领的构成和装领工艺

介绍了西装领2种结构分体方法,探讨了西装领的制作与装领工艺,并通过比较,突出了本文所介绍的西装领制作与装领工艺的长处,旨在促进服装教学。

Retrieving reuse component based on semantic

According to the current research status of component retrieval, the component description model based on facet classification is improved by adding semantic features. Furthermore, the component retrieval process model is put forward by combining the domain ontology with the relative concept match algorithm. A detailed illustration of a component reasoning engine and a component classification engine is given and the component classification algorithm is provided by using the Naive Bayes algorithm based on domain ontology. The experimental results show that the recall ratio and the precision ratio are obviously improved by using the method based on semantics, and demonstrate the feasibility and effectiveness of the proposed method.

探讨分体西装领结构配置快速法

本文介绍两种分体西装领结构制图的方法,通过配置方法的比较,分析其优缺点,在传统分体西装领结构制图的基础上,免去剪纸样的工序,利用纸样结构制图原理与经验制图的方法相结合,得出分体领配制的第二种方法,提高了西装领配图速度,同样达到西装分体领贴服的效果。并探讨了西装领的制作与装领工艺,旨在促进板师制图速度和教学。

Flocking behavior with multiple leaders and global trajectory

Aiming at the group of autonomous agents consisting of multiple leader agents and multiple follower ones,a flocking behavior method with multiple leaders and a global trajectory was proposed.In this flocking method,the group leaders can attain the information of the global trajectory,while each follower can communicate with its neighbors and corresponding leader but does not have global knowledge.Being to a distributed control method,the proposed method firstly sets a movable imaginary point on the global trajectory to ensure that the center and average velocity of the leader agents satisfy the constraints of the global trajectory.Secondly,a two-stage strategy was proposed to make the whole group satisfy the constraints of the global trajectory.Moreover,the distance between the center of the group and the desired trajectory was analyzed in detail according to the number ratio of the followers to the leaders.In this way,on one hand,the agents of the group emerge a basic flocking behavior; on the other hand,the center of the group satisfies the constraints of global trajectory.Simulation results demonstrate the effectiveness of the proposed method.

Automatically Constructing an Effective Domain Ontology for Document Classification

An effective domain ontology automatically constructed is proposed in this paper. The main concept is using the Formal Concept Analysis to automatically establish domain ontology. Finally, the ontology is acted as the base for the Naive Bayes classifier to approve the effectiveness of the domain ontology for document classification. The 1752 documents divided into 10 categories are used to assess the effectiveness of the ontology, where 1252 and 500 documents are the training and testing documents, respectively. The Fl-measure is as the assessment criteria and the following three results are obtained. The average recall of Naive Bayes classifier is 0.94. Therefore, in recall, the performance of Naive Bayes classifier is excellent based on the automatically constructed ontology. The average precision of Naive Bayes classifier is 0.81. Therefore, in precision, the performance of Naive Bayes classifier is gored based on the automatically constructed ontology. The average Fl-measure for 10 categories by Naive Bayes classifier is 0.86. Therefore, the performance of Naive Bayes classifier is effective based on the automatically constructed ontology in the point of F 1-measure. Thus, the domain ontology automatically constructed could indeed be acted as the document categories to reach the effectiveness for document classification.

Search for gamma-ray spectral lines with the DArk Matter Particle Explorer

The DArk Matter Particle Explorer(DAMPE)is well suitable for searching for monochromatic and sharpγ-ray structures in the GeV-TeV range thanks to its unprecedented high energy resolution.In this work,we search forγ-ray line structures using five years of DAMPE data.To improve the sensitivity,we develop two types of dedicated data sets(including the BgoOnly data which is the first time to be used in the data analysis for the calorimeter-based gamma-ray observatories)and adopt the signal-to-noise ratio optimized regions of interest(ROIs)for different DM density profiles.No line signals or candidates are found between 10 and 300 GeV in the Galaxy.The constraints on the velocity-averaged cross section for xx→γγand the decay lifetime for x→γv,both at 95%confidence level,have been calculated and the systematic uncertainties have been taken into account.Comparing to the previous Fermi-LAT results,though DAMPE has an acceptance smaller by a factor of~10,similar constraints on the DM parameters are achieved and below 100 GeV the lower limits on the decay lifetime are even stronger by a factor of a few.Our results demonstrate the potential of high-energy-resolution observations on dark matter detection.

Automatically building large-scale named entity recognition corpora from Chinese Wikipedia

Named entity recognition （NER） is a core component in many natural language processing applications. Most NER systems rely on supervised machine learning methods, which depend on time-consuming and expensive annotations in different languages and domains. This paper presents a method for automatically building silver-standard NER corpora from Chinese Wikipedia. We refine novel and language-dependent features by exploiting the text and structure of Chinese Wikipedia. To reduce tagging errors caused by entity classification, we design four types of heuristic rules based on the characteristics of Chinese Wikipedia and train a supervised NE classifier, and a combined method is used to improve the precision and coverage. Then, we realize type identification of implicit mention by using boundary information of outgoing links. By selecting the sentences related with the domains of test data, we can train better NER models. In the experiments, large-scale NER corpora containing 2.3 million sentences are built from Chinese Wikipedia. The results show the effectiveness of automatically annotated corpora, and the trained NER models achieve the best performance when combining our silver-standard corpora with gold-standard corpora.

Quantum-confined superfluid: From nature to artificial

Biological ion channels show that ultrafast ions and molecules transmission are in a quantum way of single molecular or ionic chain with a certain number of molecules or ions, and we define it as ＂quantum-confined superfluid＂ （QSF）. This ordered ultrafast flow in the confined channel can be considered as ＂quantum tunneling fluid effect＂ with a ＂tunneling distance＂, which is corresponding to the period of QSF. Recent research demonstrated that artificial biomimetic nanochannels also showed the phenomenon of QSF, such as ion and water channels. The introduction of QSF concept in the fields of chemistry and biology may create significant impact. As for chemistry, the QSF effect provides new ideas for accurate synthesis in organic, inorganic, polymer, etc. We believe the implementation of the idea of QSF will promote the development of QSF biochemistry, biophysics, bioinformatics and biomedical science.

Visible light-curable polymers for biomedical applications

Photocurable systems, which offer advantages such as microfabrication and in situ fabrication, have been widely used as dental restorative materials. Because the visible light-curable(VLC) system causes no biological damage, it is popular as a dental material and is being investigated by many researchers for other medical applications. Here, the principle of the VLC system is explained and recent progress in key components including photoinitiators, monomers, macromers, and prepolymers is discussed. Finally, biomedical applications for drug delivery and soft tissue engineering are reviewed. Considering the recent development of VLC systems, its importance in the field of medical applications is expected to continue to increase in the future.