Automated and adaptive geometry preparation for ar/vr-applications

Product visualization in AR/VR applications requires a largely manual process of data preparation. Previous publications focus on error-free triangulation or transformation of product structure data and display attributes for AR/VR applications. This paper focuses on the preparation of the required geometry data. In this context, a significant reduction in effort can be achieved through automation. The steps of geometry preparation are identified and examined concerning their automation potential. In addition, possible couplings of sub-steps are discussed. Based on these explanations, a structure for the geometry preparation process is proposed. With this structured preparation process, it becomes possible to consider the available computing power of the target platform during the geometry preparation. The number of objects to be rendered, the tessellation quality, and the level of detail can be controlled by the automated choice of transformation parameters. Through this approach, tedious preparation tasks and iterative performance optimization can be avoided in the future, which also simplifies the integration of AR/VR applications into product development and use. A software tool is presented in which partial steps of the automatic preparation are already implemented. After an analysis of the product structure of a CAD file, the transformation is executed for each component. Functions implemented so far allow, for example, the selection of assemblies and parts based on filter options, the transformation of geometries in batch mode, the removal of certain details, and the creation of UV maps. Flexibility, transformation quality, and timesavings are described and discussed.

Automated and Adaptive Geometry Preparation for AR/VR-Applications

Product visualization in AR/VR applications requires a largely manual process of data preparation. Previous publications focus on error-free triangulation or transformation of product structure data and display attributes for AR/VR applications. This paper focuses on the preparation of the required geometry data. In this context, a significant reduction in effort can be achieved through automation. The steps of geometry preparation are identified and examined with respect to their automation potential. In addition, possible couplings of sub-steps are discussed. Based on these explanations, a structure for the geometry preparation process is proposed. With this structured preparation process it becomes possible to consider the available computing power of the target platform during the geometry preparation. The number of objects to be rendered, the tessellation quality and the level of detail can be controlled by the automated choice of transformation parameters. We present a software tool in which partial steps of the automatic preparation are already implemented. After an analysis of the product structure of a CAD file, the transformation is executed for each component. Functions implemented so far allow, for example, the selection of assemblies and parts based on filter options, the transformation of geometries in batch mode, the removal of certain details and the creation of UV maps. Flexibility, transformation quality and time savings are described and discussed.

Implementation of Artificial Intelligence in Restaurants

The topicality. In recent years, there has been a need to study the artificial intelligence use for the operation of restaurants, as in Ukraine (and in most countries) there is no such experience. The use of artificial intelligence systems customer-to-customer and item-to-item will ensure the quality of food delivery sites, which will allow you to analyze the order of the guest and identify the patterns of his preferences thus, automatically ask him to choose a certain set, dish and successful additions to the order, which will increase the average check, or choose new establishments that will help them enter the market of restaurant services. Purpose and methods. The purpose of the study is to analyze the current state, determine the prospects for the application of existing robotic technologies in the technological process of restaurants and develop a robotization scheme of the technological process of restaurants such as salad bar. Methods are in the course of research the methods of logical generalization concerning development of the robotization scheme of technological process which were carried out by means of the computer ArchiCaD program were applied. Results. The problem of introduction and the artificial intelligence use are studied by scientists and researchers in various fields of science. Considering their scientific works, it can be noted that artificial intelligence is already actively used for the manufacture of culinary products in foreign restaurants. There are known examples of the use of barista robots, pizza robots, salad maker robots, burger maker robots, etc. The study developed the robotization scheme of the technological process of salad bar, consisting of three stages. The first stage is the service of visitors in the shopping area, where the selection of the order, payment through the terminal and the subsequent automatic receipt of culinary products and beverages. The second stage is the preparation of semi-finished products in the procurement area. This process is controlled by a chef-operator, who controls the required number of semi-finished products and cleans and cuts vegetables, fruits, meat and fish products using machines for cleaning and slicing culinary products. The program provides for the analysis of the balance and the required number of semi-finished products and the choice of components for the preparation of salads with artificial intelligence. The third stage is the automatic preparation of salad in the pre-cooking production area. The artificial intelligence placed in the system analyzes the guest’s order and activates the containers with the necessary ingredients, mixes them and unloads them into a container covered with a plastic lid, and the robot stamping element leaves the order number on the lid. The proposed scheme provides for compliance with sanitary and hygienic standards for institutions of this type. With the developed system of production activities, the required number of employees will be 5 people: cleaner in the trade area, dishwasher, tray packer, cook-operator of the pre-cooking area and system administrator of artificial intelligence. Conclusions and discussions. The authors analyze the current state, identify prospects for the application of existing robotic technologies in the technological process of restaurants and developed a robotization scheme of the technological process on the example of a salad bar. The developed scheme consists of three stages: service of visitors, preparation of semi-finished products and automatic preparation of finished goods. It is assumed that the implementation of the developed system will speed up the process of customer service, reduce the area of production facilities and, accordingly, increase the restaurant turnover.

A mechanism for automatic preparation of media objects in Ginga-NCL

This contribution proposes a mechanism for automatic preparation of media objects incorporated in Ginga-NCL. In the automatic preparation, the middleware Ginga-NCL builds a preparation plan based on the network conditions and the presentation behaviour gathered from the multimedia document that defines the application. The automatic preparation of media objects aims to reduce or avoid synchronization faults during the presentation of distributed multimedia applications.

The Mental Affordance Hypothesis

Our successful engagement with the world is plausibly underwritten by our sensitivity to affordances in our immediate environment. The considerable literature on affordances focuses almost exclusively on affordances for bodily actions such as gripping, walking or eating. I propose that we are also sensitive to affordances for mental actions such as attending, imagining and counting. My case for this ‘Mental Affordance Hypothesis’ is motivated by a series of examples in which our sensitivity to mental affordances mirrors our sensitivity to bodily affordances. Specifically, subjects perceive opportunities to perform a mental action and their doing so leads, under the right conditions, to the automatic preparation of that action. I conclude by sketching a mental affordance research program that would reinforce my case for the Mental Affordance Hypothesis and establish its ramifications for a number of debates across philosophy and psychology.