Digital Proof Collective Model Using Integrated Fusion Data Modelling

2021 ◽  
Author(s):  
P. Senthil ◽  
S. Selvakumar
Keyword(s):  
Collective Model ◽  
Data Modelling ◽  
Fusion Data

On a generalization of the Rényi–Srivastava characterization of the Poisson law

2021 ◽  
Vol 58 (1) ◽  
pp. 68-82
Author(s):  
Jean-Renaud Pycke
Keyword(s):  
Life Insurance ◽  
Collective Model ◽  
New Method ◽  
Bell Polynomials ◽  
Explicit Formulas ◽  
Compound Distributions ◽  
Poisson Law ◽  
Pierre Loti

AbstractWe give a new method of proof for a result of D. Pierre-Loti-Viaud and P. Boulongne which can be seen as a generalization of a characterization of Poisson law due to Rényi and Srivastava. We also provide explicit formulas, in terms of Bell polynomials, for the moments of the compound distributions occurring in the extended collective model in non-life insurance.

scholarly journals Animal, Body, Data: Starling Murmurations and the Dynamic of Becoming In-formation

2021 ◽  
pp. 1357034X2199284
Author(s):  
Mickey Vallee
Keyword(s):  
Data Modelling ◽  
Animal Body ◽  
Topological Relationships ◽  
Animal Populations ◽  
Animal Data ◽  
Natural Scientists ◽  
The Relationship ◽  
Affective Relations

The aim of this article is to demonstrate that data modelling is becoming a crucial, if not dominant, vector for our understanding of animal populations and is consequential for how we study the affective relations between individual bodies and the communities to which they belong. It takes up the relationship between animal, body and data, following the datafication of starling murmurations, to explore the topological relationships between nature, culture and science. The case study thus embodies a data journey, invoking the tactics claimed by social or natural scientists, who generated recent discoveries in starling murmurations, including their topological expansions and contractions. The article concludes with thoughts and suggestions for further research on animal/data entanglement, and threads the concept of databodiment throughout, as a necessary dynamic for the formation and maintenance of communities.

Object-Oriented Application on Data Modelling and Knowledge Representation: A System Integration Approach

1989 ◽  
Vol 4 (4) ◽  
pp. 205-215
Author(s):  
Daniel T. Lee
Keyword(s):  
Knowledge Representation ◽  
System Integration ◽  
Object Oriented ◽  
Integrated Systems ◽  
Data Modelling ◽  
Integration Approach ◽  
Two Systems ◽  
Modelling Techniques ◽  
Modern Knowledge

Traditional data modelling techniques of DSS and modern knowledge representation methodologies of ES are inconsistent. A new unifying model is needed for integrating the two systems into a unified whole. After a brief review of data modelling techniques and knowledge representation methodologies, the unifying model will be described and integrated systems will be used to exemplify the usefulness of the unifying model.

scholarly journals Research of Target Detection and Classification Techniques Using Millimeter-Wave Radar and Vision Sensors

2021 ◽  
Vol 13 (6) ◽  
pp. 1064
Author(s):  
Zhangjing Wang ◽  
Xianhan Miao ◽  
Zhen Huang ◽  
Haoran Luo
Keyword(s):  
Millimeter Wave ◽  
Autonomous Vehicles ◽  
Texture Features ◽  
Heterogeneous Data ◽  
Environmental Perception ◽  
Radar Signal ◽  
Time Frequency ◽  
Fusion Data ◽  
Wave Radar ◽  
Automation Equipment

The development of autonomous vehicles and unmanned aerial vehicles has led to a current research focus on improving the environmental perception of automation equipment. The unmanned platform detects its surroundings and then makes a decision based on environmental information. The major challenge of environmental perception is to detect and classify objects precisely; thus, it is necessary to perform fusion of different heterogeneous data to achieve complementary advantages. In this paper, a robust object detection and classification algorithm based on millimeter-wave (MMW) radar and camera fusion is proposed. The corresponding regions of interest (ROIs) are accurately calculated from the approximate position of the target detected by radar and cameras. A joint classification network is used to extract micro-Doppler features from the time-frequency spectrum and texture features from images in the ROIs. A fusion dataset between radar and camera is established using a fusion data acquisition platform and includes intersections, highways, roads, and playgrounds in schools during the day and at night. The traditional radar signal algorithm, the Faster R-CNN model and our proposed fusion network model, called RCF-Faster R-CNN, are evaluated in this dataset. The experimental results indicate that the mAP(mean Average Precision) of our network is up to 89.42% more accurate than the traditional radar signal algorithm and up to 32.76% higher than Faster R-CNN, especially in the environment of low light and strong electromagnetic clutter.

A challenge for historical research: Making data FAIR using a collaborative ontology management environment (OntoME)

Semantic Web ◽  
2020 ◽  
pp. 1-16
Author(s):  
Francesco Beretta
Keyword(s):  
Semantic Analysis ◽  
Historical Research ◽  
Data Modelling ◽  
Fine Grained ◽  
Fair Principles ◽  
Active Research ◽  
New Research ◽  
High Level ◽  
Conceptual Data ◽  
Ontology Management

This paper addresses the issue of interoperability of data generated by historical research and heritage institutions in order to make them re-usable for new research agendas according to the FAIR principles. After introducing the symogih.org project’s ontology, it proposes a description of the essential aspects of the process of historical knowledge production. It then develops an epistemological and semantic analysis of conceptual data modelling applied to factual historical information, based on the foundational ontologies Constructive Descriptions and Situations and DOLCE, and discusses the reasons for adopting the CIDOC CRM as a core ontology for the field of historical research, but extending it with some relevant, missing high-level classes. Finally, it shows how collaborative data modelling carried out in the ontology management environment OntoME makes it possible to elaborate a communal fine-grained and adaptive ontology of the domain, provided an active research community engages in this process. With this in mind, the Data for history consortium was founded in 2017 and promotes the adoption of a shared conceptualization in the field of historical research.

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