Authoring of Automatic Data Preparation and Scene Enrichment for Maritime Virtual Reality Applications

2014 ◽  
pp. 426-434 ◽  
Author(s):  
Benjamin Mesing ◽  
Uwe von Lukas
Keyword(s):  
Virtual Reality ◽  
Data Preparation ◽  
Automatic Data

scholarly journals Fuzzy Cloud Evaluation of Service Quality Based on DP-FastText

2021 ◽  
Vol 20 ◽  
pp. 149-167
Author(s):  
Huanzhuo Ye ◽  
Yuan Li
Keyword(s):  
Service Quality ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Quality Evaluation ◽  
Classification Model ◽  
Data Preparation ◽  
Model Framework ◽  
Automatic Data ◽  
Fine Grained ◽  
Service Quality Evaluation

This study proposes a service quality evaluation model framework which integrates automatic data acquisition, intelligent data processing and real-time data analysis with online comment data as data sources by introducing natural language processing technology based on management methods to break the traditional idea of over-reliance on human resources for service quality evaluation. The framework is mainly divided into text data preparation, fine-grained sentiment analysis and fuzzy cloud evaluation models. Data preparation module is responsible for preparing the initial data, and the fine-grained sentiment analysis module is responsible for pre-training a fine-grained sentiment classification model. The fuzzy cloud evaluation module uses the data obtained from the first two modules to evaluate service quality. By applying the model into catering industry, the feasibility of the model is proved and individuality, efficiency, dynamicity and intelligence of the model give it more advantage in the practice of service quality evaluation

scholarly journals Pengaruh Konversi Nurbs Ke Polygonal Pada Desain Mobil 3d Terhadap Penilaian Kualitas 3d Model

2021 ◽  
Vol 6 (2) ◽  
pp. 119
Author(s):  
Awaludin Abid ◽  
Kusrini Kusrini ◽  
Amir Fatah Sofyan
Keyword(s):  
Virtual Reality ◽  
Augmented Reality ◽  
Real Time ◽  
Hausdorff Distance ◽  
Visual Quality ◽  
Quality Metrics ◽  
Data Preparation ◽  
Mesh Quality ◽  
Output Data ◽  
3D Mesh

Di Industri otomotif, biaya prototyping meningkat berbanding lurus dengan kompleksitas dan dependensi kendaraan. Sebagai alternatif untuk prototyping fisik dapat memanfaatkan teknologi baru seperti Augmented Reality (AR) dan Virtual Reality (VR) digunakan. Penggunaan VR dan AR melibatkan real-time rendering data CAD yang mengkonsumsi banyak memori dan mengurangi kinerja aplikasi. Persiapan data memiliki peran penting untuk meningkatkan kinerja sementara tetap mempertahankan topologi dan kualitas mesh. Proses optimalisasi data CAD yang digunakan yaitu Tessellation atau mengkonversi NURBS ke Polygons, berperan untuk menghasilkan output data yang memiliki efisien kinerja dengan topologi serta kualitas mesh yang baik. Hadirnya software 3D Data preparation dan optimasi pada kelas Tessellator. Autodesk Maya merupakan software pemodelan 3D yang mendukung Non-Uniform Rational Basis Spline ataupun CAD memiliki fitur mengkonversi model NURBS ke polygons, pemilihan kebutuhan atau requirement pada tessellation berpengaruh terhadap hasil output. Penilaian dilakukan menggunakan penilaian Objektif menggunakan 3D mesh visual quality metrics berbasis vertex-position Hausdorff Distance sehingga didapatkan requirement pada Tessellation yang efektif. Hasil dari konversi memiliki topologi yang serupa dengan software khusus data preparation dan optimasi, sedangkan hasil penilaian mesh visual quality metrics requirement yang mendekati yaitu menggunakan Tessellation Method Count dan General. Kata Kunci— Tessellation, Mesh Visual Quality, CAD, Polygon In automotive industry, cost of prototyping increases directly with complexity and dependencies of vehicle. As an alternative to physical prototyping can utilize new technologies such as Augmented Reality (AR) and Virtual Reality (VR) are used. And involves the real-time rendering of CAD data which consumes a lot of memory and reduces application performance. Data preparation has an important role to improve performance while maintaining topology and mesh quality. Process of optimizing CAD data used is Tessellation or converting NURBS to Polygons, whose role is to produce output data that has an efficient performance with topology and good mesh quality. Autodesk Maya is a 3D modeling software that supports Non-Uniform Rational Base Spline or CAD which has the feature of converting NURBS models to polygons, the selection of requirements or requirements on tessellation influences the output results. The assessment is done using objective assessment with 3D mesh visual quality metrics based on Hausdorff Distance vertex-position so that the requirements for effective Tessellation are obtained. The results of the conversion have a topology similar to special data preparation and optimization software, while the results of the mesh visual quality metrics requirement approach are close to using the Count and General Tessellation method. Keywords— Tessellation, Mesh Visual Quality, CAD, Polygon

Instrumentation For Quantitative Microscopy

1977 ◽  
Vol 35 ◽  
pp. 206-209
Author(s):  
B. Ralph ◽  
A.R. Jones
Keyword(s):  
Volume Fraction ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Automatic Data ◽  
Phase Volume Fraction ◽  
Statistical Confidence ◽  
Resultant Data ◽  
Automatic Data Collection ◽  
Third Dimension ◽  
Evolution Of Microstructure

In all fields of microscopy there is an increasing interest in the quantification of microstructure. This interest may stem from a desire to establish quality control parameters or may have a more fundamental requirement involving the derivation of parameters which partially or completely define the three dimensional nature of the microstructure. This latter categorey of study may arise from an interest in the evolution of microstructure or from a desire to generate detailed property/microstructure relationships. In the more fundamental studies some convolution of two-dimensional data into the third dimension (stereological analysis) will be necessary.In some cases the two-dimensional data may be acquired relatively easily without recourse to automatic data collection and further, it may prove possible to perform the data reduction and analysis relatively easily. In such cases the only recourse to machines may well be in establishing the statistical confidence of the resultant data. Such relatively straightforward studies tend to result from acquiring data on the whole assemblage of features making up the microstructure. In this field data mode, when parameters such as phase volume fraction, mean size etc. are sought, the main case for resorting to automation is in order to perform repetitive analyses since each analysis is relatively easily performed.

Top-Down Processing and Visual Reorientation Illusions in a Virtual Reality Environment

2004 ◽  
Vol 63 (3) ◽  
pp. 143-149 ◽  
Author(s):  
Fred W. Mast ◽  
Charles M. Oman
Keyword(s):  
Virtual Reality ◽  
Visual Processing ◽  
Line Length ◽  
Perceptual Cues ◽  
Virtual Reality Environment ◽  
Top Down ◽  
Test Conditions ◽  
The Subject ◽  
Processing Mechanisms

The role of top-down processing on the horizontal-vertical line length illusion was examined by means of an ambiguous room with dual visual verticals. In one of the test conditions, the subjects were cued to one of the two verticals and were instructed to cognitively reassign the apparent vertical to the cued orientation. When they have mentally adjusted their perception, two lines in a plus sign configuration appeared and the subjects had to evaluate which line was longer. The results showed that the line length appeared longer when it was aligned with the direction of the vertical currently perceived by the subject. This study provides a demonstration that top-down processing influences lower level visual processing mechanisms. In another test condition, the subjects had all perceptual cues available and the influence was even stronger.

Laparoscopic Cholecystectomy

Swiss Surgery ◽  
2002 ◽  
Vol 8 (6) ◽  
pp. 250-254
Author(s):  
Vogelbach ◽  
Bogdan ◽  
Rosenthal ◽  
Pfefferkorn ◽  
Triponez
Keyword(s):  
Virtual Reality ◽  
Laparoscopic Cholecystectomy ◽  
Neue Technik

Fragestellung: Die dieser Untersuchung zugrunde liegende Frage war, ob das angewandte Ausbildungskonzept geeignet war, um am Beispiel der Einführung der laparoskopischen Cholezystektomie eine neue Operationsmethode in einer universitären Ausbildungsklinik mit einer grossen Anzahl Chirurgen zu etablieren. Patienten und Methodik: Seit Einführung der ersten laparoskopischen Cholezystektomie wurden alle Cholezystektomien während zwei Jahren (Mai 1990 bis Mai 1992) prospektiv erfasst. Ein Ausbildungskonzept wurde gewählt, bei dem jeweils ein Operateur durch einen Tutor geschult wurde und so 15 konsekutive Eingriffe durchführte, um dann die Technik einem weiteren auszubildenden Chirurgen zu instruieren. Resultate: In zwei Jahren wurden 355 Patienten cholezystektomiert. 60% der Operationen wurden laparoskopisch durchgeführt oder begonnen. 40% der Operationen wurden offen durchgeführt. In den ersten zwei Jahren konnten 13 Operateure (durchschnittlich 16 Operationen / Operateur , range 1 - 60) in die neue Technik eingeführt werden. Es traten keine schweren Komplikationen, insbesondere keine Gallenwegsverletzungen in dieser Einführungsphase auf. Diskussion: In der Literatur wird dieses Vorgehen bei der Einführungsphase seit 1992 wiederholt vorgestellt, diskutiert und empfohlen. Zwischenzeitlich gibt es Richtlinien von Fachgesellschaften und nationalen Institutionen, welche die Ausbildung zur Ausführung neuer chirurgischer Techniken reglementieren. In den letzten Jahren verlagern sich die ersten Ausbildungsschritte in Richtung Trainingskurse an skill-stations und virtual reality Trainer. Schlussfolgerung: Das beschriebene Ausbildungskonzept bewährte sich in der Einführungsphase der laparoskopischen Cholezystektomie zu Beginn der 90er-Jahre.

Virtual reality-enabled treatment of nightmares.

Dreaming ◽  
2018 ◽  
Vol 28 (3) ◽  
pp. 205-224 ◽  
Author(s):  
Patrick McNamara ◽  
Kendra Holt Moore ◽  
Yiannis Papelis ◽  
Saikou Diallo ◽  
Wesley J. Wildman
Keyword(s):  
Virtual Reality
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