Semantic mapping and K-means applied to hybrid SOM-based document organization system construction

2008 ◽  
Author(s):  
Renato Fernandes Corrêa ◽  
Teresa Bernarda Ludermir
Keyword(s):  
Semantic Mapping ◽  
System Construction ◽  
Document Organization

Improved Semantic Mapping and SOM Applied to Document Organization

2008 ◽  
Author(s):  
Renato Fernandes Corrêa ◽  
Teresa Bernarda Ludermir
Keyword(s):  
Semantic Mapping ◽  
Document Organization

Towards Systematization

1977 ◽  
Vol 16 (03) ◽  
pp. 125-130 ◽  
Author(s):  
P. L. Reichertz
Keyword(s):  
Clinical Medicine ◽  
Medical Science ◽  
User Involvement ◽  
Empirical Science ◽  
Target System ◽  
General Strategy ◽  
System Construction ◽  
Formal Approach ◽  
Methodology Development ◽  
Planning Development

Data processing has become an important tool in theoretical and clinical medicine. The main categories of applications are : information analysis, (bio)signal processing and the field of information logistics (information systems).The problems encountered lie in the discrepancy of the basic methods of a formal approach to an empirical science, the complexity of the target system and the system ecology, i.e. the involvement of the user and the system environment during system construction and utilization.Possible solutions to these problems are the application of system techniques, inductive planning, development of medical methodology, development of methods and techniques for user involvement and assessment of motivation and education and educational planning.The necessary general strategy in the development in medical informatics is seen in the continuing systematization of the theoretical and practical approach. It is estimated that this will eventually contribute to the systematization of medical science and practice.

Monocular Semantic Mapping Based on 3D Cuboids Tracking

2021 ◽  
Author(s):  
Xingwu Ji ◽  
Zheng Gong ◽  
Ruihang Miao ◽  
Wuyang Xue ◽  
Rendong Ying
Keyword(s):  
Semantic Mapping

scholarly journals Integrating Land-Cover Products Based on Ontologies and Local Accuracy

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 236
Author(s):  
Ling Zhu ◽  
Guangshuai Jin ◽  
Dejun Gao
Keyword(s):  
Land Cover ◽  
Semantic Similarity ◽  
Mixed Forest ◽  
Global Scale ◽  
Semantic Mapping ◽  
Global Land Cover ◽  
Resolution Observation ◽  
Local Accuracy ◽  
Global Land ◽  
Broad Leaf

Freely available satellite imagery improves the research and production of land-cover products at the global scale or over large areas. The integration of land-cover products is a process of combining the advantages or characteristics of several products to generate new products and meet the demand for special needs. This study presents an ontology-based semantic mapping approach for integration land-cover products using hybrid ontology with EAGLE (EIONET Action Group on Land monitoring in Europe) matrix elements as the shared vocabulary, linking and comparing concepts from multiple local ontologies. Ontology mapping based on term, attribute and instance is combined to obtain the semantic similarity between heterogeneous land-cover products and realise the integration on a schema level. Moreover, through the collection and interpretation of ground verification points, the local accuracy of the source product is evaluated using the index Kriging method. Two integration models are developed that combine semantic similarity and local accuracy. Taking NLCD (National Land Cover Database) and FROM-GLC-Seg (Finer Resolution Observation and Monitoring-Global Land Cover-Segmentation) as source products and the second-level class refinement of GlobeLand30 land-cover product as an example, the forest class is subdivided into broad-leaf, coniferous and mixed forest. Results show that the highest accuracies of the second class are 82.6%, 72.0% and 60.0%, respectively, for broad-leaf, coniferous and mixed forest.

scholarly journals Simulation and Analysis of Self-Replicating Robot Decision-Making Systems

Computers ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 9
Author(s):  
Andrew Jones ◽  
Jeremy Straub
Keyword(s):  
Lower Risk ◽  
System Construction ◽  
System Configuration ◽  
Operating Environment ◽  
Robot System ◽  
Robotic Exploration ◽  
Mission Success ◽  
Robot Systems ◽  
System Configurations

Self-replicating robot systems (SRRSs) are a new prospective paradigm for robotic exploration. They can potentially facilitate lower mission costs and enhance mission capabilities by allowing some materials, which are needed for robotic system construction, to be collected in situ and used for robot fabrication. The use of a self-replicating robot system can potentially lower risk aversion, due to the ability to potentially replenish lost or damaged robots, and may increase the likelihood of mission success. This paper proposes and compares system configurations of an SRRS. A simulation system was designed and is used to model how an SRRS performs based on its system configuration, attributes, and operating environment. Experiments were conducted using this simulation and the results are presented.

scholarly journals Semantic 3D Mapping from Deep Image Segmentation

2021 ◽  
Vol 11 (4) ◽  
pp. 1953
Author(s):  
Francisco Martín ◽  
Fernando González ◽  
José Miguel Guerrero ◽  
Manuel Fernández ◽  
Jonatan Ginés
Keyword(s):  
Point Cloud ◽  
Autonomous Mobile Robots ◽  
Semantic Mapping ◽  
Indoor Environments ◽  
3D Mapping ◽  
Direct Mapping ◽  
Learning Techniques ◽  
3D Camera ◽  
Deep Image ◽  
Boundary Pixel

The perception and identification of visual stimuli from the environment is a fundamental capacity of autonomous mobile robots. Current deep learning techniques make it possible to identify and segment objects of interest in an image. This paper presents a novel algorithm to segment the object’s space from a deep segmentation of an image taken by a 3D camera. The proposed approach solves the boundary pixel problem that appears when a direct mapping from segmented pixels to their correspondence in the point cloud is used. We validate our approach by comparing baseline approaches using real images taken by a 3D camera, showing that our method outperforms their results in terms of accuracy and reliability. As an application of the proposed algorithm, we present a semantic mapping approach for a mobile robot’s indoor environments.

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