Towards Tangible Cultural Heritage Experiences—Enriching VR-based Object Inspection with Haptic Feedback

VR/AR technology is a key enabler for new ways of immersively experiencing cultural heritage artifacts based on their virtual counterparts obtained from a digitization process. In this article, we focus on enriching VR-based object inspection by additional haptic feedback, thereby creating tangible cultural heritage experiences. For this purpose, we present an approach for interactive and collaborative VR-based object inspection and annotation. Our system supports high-quality 3D models with accurate reflectance characteristics while additionally providing haptic feedback regarding shape features of the object based on a 3D printed replica. The digital object model in terms of a printable representation of the geometry as well as reflectance characteristics are stored in a compact and streamable representation on a central server, which streams the data to remotely connected users/clients. The latter can jointly perform an interactive inspection of the object in VR with additional haptic feedback through the 3D printed replica. Evaluations regarding system performance, visual quality of the considered models, as well as insights from a user study indicate an improved interaction, assessment, and experience of the considered objects.

A hybrid method for traumatic brain injury lesion segmentation

<span>Traumatic brain injuries are significant effects of disability and loss of life. Physicians employ computed tomography (CT) images to observe the trauma and measure its severity for diagnosis and treatment. Due to the overlap of hemorrhage and normal brain tissues, segmentation methods sometimes lead to false results. The study is more challenging to unitize the AI field to collect brain hemorrhage by involving patient datasets employing CT scans images. We propose a novel technique free-form object model for brain injury CT image segmentation based on superpixel image processing that uses CT to analyzing brain injuries, quite challenging to create a high outstanding simple linear iterative clustering (SLIC) method. The maintains a strategic distance of the segmentation image to reduced intensity boundaries. The segmentation image contains marked red hemorrhage to modify the free-form object model. The contour labelled by the red mark is the output from our free-form object model. We proposed a hybrid image segmentation approach based on the combined edge detection and dilation technique features. The approach diminishes computational costs, and the show accomplished 96.68% accuracy. The segmenting brain hemorrhage images are achieved in the clustered region to construct a free-form object model. The study also presents further directions on future research in this domain.</span>

On Karol Wojtyła’s Aristotelian Method Part II Induction and Reduction as Aristotelian Induction (ἐπαγωγή) and Division (διαίρεσις)

This is the second of a two-part study treating Karol Wojtyła’s Aristotelian methodology. Having presented Aristotle’s method of induction (ἐπαγωγή/epagoge) and analysis (ἀνάλῠσις/analusis) or division (διαίρεσις/diairesis) in Part I, Part II discloses the logical form and force of Wojtyła’s method of induction and reduction as Aristotelian induction and division. Looking primarily to the introduction to The Acting Person, it is shown that Wojtyła utilizes the logical forms of reductio ad impossibile and reasoning on the hypothesis of the end, or effect-cause reasoning, which is special to the life sciences and the power-object model of definition as set down by Aristotle. By use of this Aristotelian methodology, Wojtyła obtains definitive knowledge of the human person that is necessary and undeniable: he discloses the εἶδος (eidos) or species of the person in the Aristotelian, Thomistic, and Phenomenological sense of the term.

APPLICATION OF AUGMENTED REALITY TECHNOLOGY IN THE ANIMATION OF THE KANCIL CHILDREN'S STORYBOOK

There are relatively many Indonesian fairy tales that are spread in the community, have characters with good and evil temperaments. Usually take folk tales about teaching goodness, behaving smartly, and being able to distinguish between good and bad. Also teaches children not to be arrogant, insulting other people. The learning process is usually in the delivery of material using only pictures, dolls, or videos that are commonly seen by children. Conventional media used for learning reduce children's enthusiasm. On this occasion, to answer the problem of media that is less attractive to children by using Augmented Reality (AR), because it can help visualize abstract concepts so that it can be used for understanding the image object and the structure of an object model. results of making applications using Augmented Reality, assessed from the aspects of cognitive, affective, psychomotor, technological, and the benefits of getting good interpretation results.

THE METHOD OF OBJECT MODELING IN THE DEVELOPMENT OF PATENTABLE DESIGNS OF TRACTION DRIVE MECHANISMS

Work objective is to develop a patentable design of locomotive traction drive mechanisms using the example of a suspension unit. Research methods: object modeling method, analytical methods for calculating dynamics of rolling stock. Research results and novelty: the design of the railway electric motor suspension unit, which will reduce costs in the course of maintenance and repair of a locomotive with a solid gear by increasing the reliability of the suspension unit parts, eliminating their wear, has been developed; a compensation coupling device has been devised. We will synthesize the object model of a technical system based on the identification of its typical structure by classifying technical systems. Conclusion: the application of the object modeling method together with the object model allows avoiding errors in the development of new designs of technical objects based on existing comparables and eliminates defects of the selected prototype. The use of the object modeling method makes it possible to synthesize patentable designs of the locomotive underframe mechanisms.

Identification of Neural Network Model of Robot to Solve the Optimal Control Problem

To calculate the optimal control, a satisfactory mathematical model of the control object is required. Further, when implementing the calculated controls on a real object, the same model can be used in robot navigation to predict its position and correct sensor data, therefore, it is important that the model adequately reflects the dynamics of the object. Model derivation is often time-consuming and sometimes even impossible using traditional methods. In view of the increasing diversity and extremely complex nature of control objects, including the variety of modern robotic systems, the identification problem is becoming increasingly important, which allows you to build a mathematical model of the control object, having input and output data about the system. The identification of a nonlinear system is of particular interest, since most real systems have nonlinear dynamics. And if earlier the identification of the system model consisted in the selection of the optimal parameters for the selected structure, then the emergence of modern machine learning methods opens up broader prospects and allows you to automate the identification process itself. In this paper, a wheeled robot with a differential drive in the Gazebo simulation environment, which is currently the most popular software package for the development and simulation of robotic systems, is considered as a control object. The mathematical model of the robot is unknown in advance. The main problem is that the existing mathematical models do not correspond to the real dynamics of the robot in the simulator. The paper considers the solution to the problem of identifying a mathematical model of a control object using machine learning technique of the neural networks. A new mixed approach is proposed. It is based on the use of well-known simple models of the object and identification of unaccounted dynamic properties of the object using a neural network based on a training sample. To generate training data, a software package was written that automates the collection process using two ROS nodes. To train the neural network, the PyTorch framework was used and an open source software package was created. Further, the identified object model is used to calculate the optimal control. The results of the computational experiment demonstrate the adequacy and performance of the resulting model. The presented approach based on a combination of a well-known mathematical model and an additional identified neural network model allows using the advantages of the accumulated physical apparatus and increasing its efficiency and accuracy through the use of modern machine learning tools.

POSSIBILITIES OF CYBER-PHYSICAL APPROACH TO STUDYING FREQUENCY PROPERTIES OF CLOSED SYSTEM WITH INCOMPLETE INFORMATION ABOUT CONTROL OBJECT MODEL

The article considers a method of reducing the time spent on the experimental study of the frequency properties of an object with an unknown mathematical model by using the cyber-physical approach to the automation of the experiment. Nonparametric estimates of unknown frequency characteristics of an object are received from experimental data on the reaction of the object's output to the input harmonic signal in the form of a mixture of sinusoidal signals of different frequencies. To divide the output signal into components corresponding to each frequency, a computer technology is used that implements an optimization procedure for finding the values of both real and imaginary frequency characteristics, according to the frequencies represented in the harmonic input signal. The method is also suitable for accelerated evaluation of the frequency characteristics of an object with an unknown delay. There are considered the aspects of frequency properties estimation in the problem of closed system stability analysis, which is supposed to control an object with incomplete information about its model using a series-connected proportional-integral controller. The results of quick estimating the frequency characteristics of the object are used to identify the parameters of its transfer function. To solve the parameterization problem, there are used automation tools for calculating the transfer function according to data on the points of frequency characteristics implemented as part of the open-access computer mathematics system Scilab. There is given an example illustrating the possibilities of developing a control system using a reduced-order object model, as one of the applications of the results of parametric identification of the transfer function

USACE Advanced Modeling Object Standard : Release 1.0

The U.S. Army Corps of Engineers (USACE) Advanced Modeling Object Standard (AMOS) has been developed by the CAD/BIM Technology Center for Facilities, Infrastructure, and Environment to establish standards for support of the Advanced Modeling process within the Department of Defense (DoD) and the Federal Government. The critical component of Advanced Modeling is the objects themselves- and either make the modeling process more difficult or more successful. This manual is part of an initiative to develop a nonproprietary Advanced Modeling standard that incorporates both vertical construction and horizontal construction objects that will address the entire life cycle of facilities within the DoD. The material addressed in this USACE Advanced Modeling Object Standard includes a classification organization that is needed to identify models for specific use cases. Compliance with this standard will allow users to know whether the object model they are getting is graphically well developed but data poor or if it does have the data needed for creating contract documents. This capability will greatly reduce the designers’ efforts to either build an object or search/find/edit an object necessary for the development of their project. Considering that an advanced model may contain hundreds of objects this would represent a huge time savings and improve the modeling process.

Coupled Delft3D-Object Model to Predict Mobility of Munition on Sandy Seafloor

The coupled Delft3D-object model has been developed to predict the mobility and burial of objects on sandy seafloors. The Delft3D model is used to predict seabed environmental factors such as currents, waves (peak wave period, significant wave height, wave direction), water level, sediment transport, and seabed change, which are taken as the forcing term to the object model consisting of three components: (a) physical parameters such as diameter, length, mass, and rolling moment; (b) dynamics of the rolling cylinder around its major axis; (c) an empirical sediment scour model with re-exposure parameterization. The model is compared with the observational data collected from a field experiment from 21 April to 13 May 2013 off the coast of Panama City, Florida. The experimental data contain both object mobility using sector scanning sonars and maintenance divers as well as simultaneous environmental time series data of the boundary layer hydrodynamics and sediment transport conditions. Comparison between modeled and observed data clearly shows the model’s capabilities and limitations.