Semantic Mapping of Component Framework Interface Ontologies for Interoperability of Vehicle Applications

PurposeThe purpose of this paper is threefold. Firstly, a new concept, teacher buoyancy, is introduced. Based on the significance to study how teachers bounce back from minor and frequent setbacks (vs. major adversities emphasized in resilience) in their daily work and the research on buoyancy by Martin and Marsh, a dual-component framework to conceptualize this new concept is introduced. Secondly, the development of a new instrument, the Teacher Buoyancy Scale (TBS), to measure it is presented. Thirdly, results of a study using the TBS are reported, which provide insights into how teacher buoyancy can be fostered.Design/methodology/approachThe study employed a quantitative design. A total of 258 teachers taking a part-time initial teacher education (ITE) program completed the TBS. Their responses were analyzed by exploratory factor analysis (EFA). In addition to descriptive statistics and reliability coefficients, Pearson correlation coefficients were calculated to examine the relationship among the factors.FindingsThe data analysis indicated five factors, namely, Coping with difficulties, Bouncing back cognitively and emotionally, Working hard and appraising difficulties positively, Caring for one's well-being and Striving for professional growth. These factors can be readily interpreted by the dual-component framework. Correlations among the factors further revealed that enabling factors can be subdivided into more proximal personal strengths relating to direct coping, and more distal personal assets pertaining to personal well-being. It is the latter that correlates most highly with perceived teacher buoyancy.Originality/valueThe most original contribution of this paper is the proposal of the new concept of teacher buoyancy which is teachers' capacity to deal with the everyday challenges that most teachers face in their teaching. The delineation between buoyancy and resilience sharpens the focus of the problem domain that is most relevant to teachers. The development of the TBS provides a useful and reliable instrument to examine teacher buoyancy in future studies.

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Monocular Semantic Mapping Based on 3D Cuboids Tracking

2021 IEEE International Symposium on Circuits and Systems (ISCAS) ◽

10.1109/iscas51556.2021.9401071 ◽

2021 ◽

Author(s):

Xingwu Ji ◽

Zheng Gong ◽

Ruihang Miao ◽

Wuyang Xue ◽

Rendong Ying

Keyword(s):

Semantic Mapping

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Integrating Land-Cover Products Based on Ontologies and Local Accuracy

Information ◽

10.3390/info12060236 ◽

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.

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Semantic 3D Mapping from Deep Image Segmentation

Applied Sciences ◽

10.3390/app11041953 ◽

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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Definition and implementation of a semantic mapping for UML-RT using a timed pi-calculus

Proceedings of the Second International Workshop on Behaviour Modelling Foundation and Applications - BM-FA '10 ◽

10.1145/1811147.1811148 ◽

2010 ◽

Cited By ~ 1

Author(s):

Juergen Dingel ◽

Eyrak Paen ◽

Ernesto Posse ◽

Rezoanoor (Ruben) Rahman ◽

Karolina Zurowska

Keyword(s):

Semantic Mapping ◽

Pi Calculus

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Semantic Mapping in Video Retrieval

ACM SIGIR Forum ◽

10.1145/3190580.3190606 ◽

2018 ◽

Vol 51 (3) ◽

pp. 161-162

Author(s):

Maaike H.T. de Boer

Keyword(s):

Video Retrieval ◽

Semantic Mapping

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Semantic Mapping for Mobile Robots in Indoor Scenes: A Survey

Information ◽

10.3390/info12020092 ◽

2021 ◽

Vol 12 (2) ◽

pp. 92

Author(s):

Xiaoning Han ◽

Shuailong Li ◽

Xiaohui Wang ◽

Weijia Zhou

Keyword(s):

Mobile Robots ◽

Semantic Information ◽

Semantic Mapping ◽

Human Beings ◽

Future Directions ◽

Essential Requirement ◽

Semantic Map ◽

Semantic Maps ◽

Great Progress ◽

Indoor Scenes

Sensing and mapping its surroundings is an essential requirement for a mobile robot. Geometric maps endow robots with the capacity of basic tasks, e.g., navigation. To co-exist with human beings in indoor scenes, the need to attach semantic information to a geometric map, which is called a semantic map, has been realized in the last two decades. A semantic map can help robots to behave in human rules, plan and perform advanced tasks, and communicate with humans on the conceptual level. This survey reviews methods about semantic mapping in indoor scenes. To begin with, we answered the question, what is a semantic map for mobile robots, by its definitions. After that, we reviewed works about each of the three modules of semantic mapping, i.e., spatial mapping, acquisition of semantic information, and map representation, respectively. Finally, though great progress has been made, there is a long way to implement semantic maps in advanced tasks for robots, thus challenges and potential future directions are discussed before a conclusion at last.

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