The Experts below are selected from a list of 43902 Experts worldwide ranked by ideXlab platform
Sun Li-juan - One of the best experts on this subject based on the ideXlab platform.
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Research on Collaborative Semantic Feature Modeling System
Computer Science, 2012Co-Authors: Sun Li-juanAbstract:To improve the performance of the collaborative Semantic Feature modeling system,a new method was presented.It represents and manages all the information and data in Features with the Feature Semantic representation and the cellular model,determines the logic and assembled relations between sub-models by matching Semantic,detects and solves operation conflicts between client systems by using Semantic area dividing and max-value space,and reduces the complexity of constraint solving of whole system by creating the temporarily model.This method can not only achieve all functions but also increase the design efficiency of the collaborative Semantic Feature modeling system greatly.Experiments on computer show that this new method is more adaptable and practicable.
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Research on manipulation localizing in Semantic Feature modeling system
Computer Engineering and Applications, 2011Co-Authors: Sun Li-juanAbstract:To improve the efficiency of constraint solving and the performance of Semantic Feature modeling system,a new method is presented.It represents all of information in Features by the Feature Semantic representation,enhances the manage- ment mechanism of Feature’s elements,separates the local Features from product models effectively by Semantic faces,and achieves the local constraint solving by creating the provisional cells.This method can not only solve the problem of direct manipulations in a complex model,but also enhance the efficiency of Semantic Feature CAD system.Experiments on comput- er show that this new method is more adaptable and practicable.
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Research on parameter range computation in Semantic Feature modeling system
Computer Engineering and Applications, 2011Co-Authors: Sun Li-juanAbstract:In Semantic Feature CAD system,determining the parameter value of Features is often a time-consuming operation, and is a trial-and-error process.To compute the parameter range rapidly when the Feature is created,this paper presents an improved method.It describes the Features’various information with Feature Semantic representation,simplifies the management strategy of Feature’faces in the cellular model,extracts and computes the face Semantics of all Features.This method can not only increase the speed and accuracy of computing parameter range,but also enhance the efficiency of Semantic Feature CAD system.Experiments on computer show that this improved method is more adaptable and practicable.
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Research on Feature conversion in Semantic Feature modeling systems
Computer Engineering and Applications, 2010Co-Authors: Sun Li-juanAbstract:This paper analyzes the Semantic Feature modeling of new generation,consults research results of Bidarra etc,uses validity criterion,and establishes methods of Feature conversion to Semantic Feature modeling.Based on the mathematical methods,this paper addresses the problem how to convert the design Feature representation into machining Feature representation in a mathe-matical model.Design Features in the design domain are represented by a set of faces of each Feature geometry and a set of attributes such as dimensions and material Feature.Machining Features in the manufacturing domain are represented by a number of faces and relationships between these faces that are meaningful for the process operations.Using a mathematical description of the Feature mapping process,machining Features can be deduced and formed by the set operation,and the difficult problem of Feature interaction can be described mathematically and converted in theory,thus to make product model editing easier.
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Coding mechanism for topological entities in Semantic Feature modeling
Application Research of Computers, 2010Co-Authors: Sun Li-juanAbstract:The naming and coding for topological entities were all for history-based modeling systems,which could not entirely support history-independent Semantic Feature modeling.This paper proposed a Feature-based method for naming topological entities,and proposed a uniform coding method of topological entities.Gave differentiate methods and coding format for the split faces and edges.And proposed virtual topological entity and sub-edge according to topological entities variation after model modifying.Remained relations between topological entities to realize history-independent model modification.The proposed method was realized in HUST-CAID.
Wei Jiahui - One of the best experts on this subject based on the ideXlab platform.
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PAKDD (Workshops) - Effectively Representing Short Text via the Improved Semantic Feature Space Mapping
Lecture Notes in Computer Science, 2019Co-Authors: Ting Tuo, Liu Haijiao, Wei JiahuiAbstract:Short text representation (STR) has attracted increasing interests recently with the rapid growth of Web and social media data existing in short text form. In this paper, we present a new method using an improved Semantic Feature space mapping to effectively represent short texts. Firstly, Semantic clustering of terms is performed based on statistical analysis and word2vec, and the Semantic Feature space can then be represented via the cluster center. Then, the context information of terms is integrated with the Semantic Feature space, based on which three improved similarity calculation methods are established. Thereafter the text mapping matrix is constructed for short text representation learning. Experiments on both Chinese and English test collections show that the proposed method can well reflect the Semantic information of short texts and represent the short texts reasonably and effectively.
Jamie Reilly - One of the best experts on this subject based on the ideXlab platform.
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Semantic Feature training in combination with transcranial direct current stimulation tdcs for progressive anomia
Frontiers in Human Neuroscience, 2017Co-Authors: Jinyi Hung, Ashley Bauer, Murray Grossman, Roy H Hamilton, H Coslett, Jamie ReillyAbstract:We examined the effectiveness of a two-week regimen of a Semantic Feature training in combination with transcranial direct current stimulation (tDCS) for progressive naming impairment associated with primary progressive aphasia (N=4) or early onset Alzheimer’s Disease (N=1). Patients received a two-week regimen (10 sessions) of anodal tDCS delivered over the left temporoparietal cortex while completing a language therapy that consisted of repeated naming and Semantic Feature generation. Therapy targets consisted of familiar people, household items, clothes, foods, places, hygiene implements, and activities. Untrained items from each Semantic category provided item level controls. We analyzed naming accuracies at multiple timepoints (i.e., pre-, post-, 6-month follow-up) via a mixed effects logistic regression and individual differences in treatment responsiveness using a series of nonparametric McNemar tests. Patients showed advantages for naming trained over untrained items. These gains were evident immediately post tDCS. Trained items also showed a shallower rate of decline over six-months relative to untrained items that showed continued progressive decline. Patients tolerated stimulation well, and sustained improvements in naming accuracy suggest that current intervention approach is viable. Future implementation of a sham control condition will be crucial toward ascertaining whether neurostimulation and behavioral treatment act synergistically or alternatively whether treatment gains are exclusively attributable to either tDCS or the behavioral intervention.
Romy Heerik - One of the best experts on this subject based on the ideXlab platform.
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Reliability in content analysis: The case of Semantic Feature norms classification
Behavior Research Methods, 2017Co-Authors: Marianna Bolognesi, Roosmaryn Pilgram, Romy HeerikAbstract:Semantic Feature norms (e.g., STIMULUS: car → RESPONSE: ) are commonly used in cognitive psychology to look into salient aspects of given concepts. Semantic Features are typically collected in experimental settings and then manually annotated by the researchers into Feature types (e.g., perceptual Features, taxonomic Features, etc.) by means of content analyses—that is, by using taxonomies of Feature types and having independent coders perform the annotation task. However, the ways in which such content analyses are typically performed and reported are not consistent across the literature. This constitutes a serious methodological problem that might undermine the theoretical claims based on such annotations. In this study, we first offer a review of some of the released datasets of annotated Semantic Feature norms and the related taxonomies used for content analysis. We then provide theoretical and methodological insights in relation to the content analysis methodology. Finally, we apply content analysis to a new dataset of Semantic Features and show how the method should be applied in order to deliver reliable annotations and replicable coding schemes. We tackle the following issues: (1) taxonomy structure, (2) the description of categories, (3) coder training, and (4) sustainability of the coding scheme—that is, comparison of the annotations provided by trained versus novice coders. The outcomes of the project are threefold: We provide methodological guidelines for Semantic Feature classification; we provide a revised and adapted taxonomy that can (arguably) be applied to both concrete and abstract concepts; and we provide a dataset of annotated Semantic Feature norms.
Sun Lijuan - One of the best experts on this subject based on the ideXlab platform.
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research on boolean operation in Semantic Feature modeling system
Computer Science, 2012Co-Authors: Sun LijuanAbstract:To improve the efficiency of boolean operations in Semantic Feature modeling system,a Semantic representation based method was proposed.It represents Feature models with Semantic representation,manages Feature elements by cellular model,improves detect efficiency of Features interaction and builds new the Feature entity by splitting intersectant cells and Semantic faces.This method can not only build the boolean entity rapidly and exactly,but also avoid errors such as holes and losing geometry faces.Experiments on computer show that this new method is more adaptable and practicable.