Automated Evaluation of Indoor Dimensional Tolerance Compliance Using the TLS Data and BIM

DEVELOPMENT OF AUTOMATED EVALUATION OF POSITIONING ACCURACY FOR SPECIALIZED NETWORKS’ POINTS IN TRILATERATION NETWORKS

Наука. Исследования. Практика: сборник избранных статей по материалам Международной научной конференции (Санкт-Петербург, Октябрь 2020) ◽

10.37539/srp293.2020.70.81.003 ◽

2020 ◽

Author(s):

Мурат Газизович Мустафин ◽

Глеб Андреевич Фролов

Keyword(s):

Coordinate System ◽

Visual Basic ◽

Microsoft Excel ◽

Positioning Accuracy ◽

Automated Evaluation ◽

Visual Basic For Applications ◽

Multiple Configurations

В данной работе рассмотрен принцип работы созданного алгоритма, позволяющего автоматически определять среднюю квадратическую погрешность планового положения пунктов сетей трилатерации и представлены результаты автоматизации данного процесса при различных конфигурациях сети, с использованием пакетов Microsoft Excel, Visual Basic for Applications. This paper presents automatic solution for evaluating accuracy of positioning for specialized networks’ points in a plane coordinate system. The paper presents results of automation of this process through analysis of multiple configurations of trilateral networks using Microsoft Excel, Visual Basic for Applications.

Download Full-text

Language Features for Automated Evaluation of Cognitive Behavior Psychotherapy Sessions

10.21437/interspeech.2018-1518 ◽

2018 ◽

Cited By ~ 2

Author(s):

Nikolaos Flemotomos ◽

Victor Martinez ◽

James Gibson ◽

David Atkins ◽

Torrey Creed ◽

...

Keyword(s):

Cognitive Behavior ◽

Automated Evaluation

Download Full-text

How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

Journal of Personalized Medicine ◽

10.3390/jpm11010032 ◽

2021 ◽

Vol 11 (1) ◽

pp. 32

Author(s):

Oliwia Koteluk ◽

Adrian Wartecki ◽

Sylwia Mazurek ◽

Iga Kołodziejczak ◽

Andrzej Mackiewicz

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Health Care ◽

Medical Data ◽

General Process ◽

New Era ◽

Automated Evaluation ◽

Machine Learning Applications ◽

And Training ◽

Current Standards

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

Download Full-text

Automated evaluation of the surface point quality in dimensional X-ray computed tomography

tm - Technisches Messen ◽

10.1515/teme-2019-0116 ◽

2020 ◽

Vol 87 (2) ◽

pp. 111-121 ◽

Cited By ~ 2

Author(s):

Andreas Michael Müller ◽

Lorenz Butzhammer ◽

Florian Wohlgemuth ◽

Tino Hausotte

Keyword(s):

Computed Tomography ◽

Surface Point ◽

Random Measurement Error ◽

X Ray ◽

Random Measurement ◽

Automated Evaluation ◽

Dimensional Measurements ◽

X Ray Computed ◽

Model Measurement

AbstractX-ray computed tomography (CT) enables dimensional measurements of numerous measurands with a single scan, including the measurement of inner structures. However, measurement artefacts complicate the applicability of the technology in some cases. This paper presents a methodology to assess the surface point quality of computed tomography measurements without the requirement of a CAD model. Measurement artefacts lowering the surface point quality can therefore automatically be detected. The correlation of quality values with the random measurement error is demonstrated. The presented method can in principle be used to weight single fit points to reduce the measurement uncertainty of CT measurements.

Download Full-text

Sensors in the Autoclave-Modelling and Implementation of the IoT Steam Sterilization Procedure Counter

Sensors ◽

10.3390/s21020510 ◽

2021 ◽

Vol 21 (2) ◽

pp. 510

Author(s):

Lukas Boehler ◽

Mateusz Daniol ◽

Ryszard Sroka ◽

Dominik Osinski ◽

Anton Keller

Keyword(s):

The Body ◽

Surgical Instruments ◽

Sensor System ◽

Temperature Profiles ◽

Measured Temperature ◽

Steam Sterilization ◽

Automated Evaluation ◽

Normative Requirements ◽

Thermal Simulations ◽

The Relationship

Surgical procedures involve major risks, as pathogens can enter the body unhindered. To prevent this, most surgical instruments and implants are sterilized. However, ensuring that this process is carried out safely and according to the normative requirements is not a trivial task. This study aims to develop a sensor system that can automatically detect successful steam sterilization on the basis of the measured temperature profiles. This can be achieved only when the relationship between the temperature on the surface of the tool and the temperature at the measurement point inside the tool is known. To find this relationship, the thermodynamic model of the system has been developed. Simulated results of thermal simulations were compared with the acquired temperature profiles to verify the correctness of the model. Simulated temperature profiles are in accordance with the measured temperature profiles, thus the developed model can be used in the process of further development of the system as well as for the development of algorithms for automated evaluation of the sterilization process. Although the developed sensor system proved that the detection of sterilization cycles can be automated, further studies that address the possibility of optimization of the system in terms of geometrical dimensions, used materials, and processing algorithms will be of significant importance for the potential commercialization of the presented solution.

Download Full-text