The subject of the article is a simulation of heat affected components of a device for deep drawing by extreme conditions in vacuum by high temperatures required for forming of crystallization containers made from thi...
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With the current shift in the mass media landscape from journalistic rigor to social media, personalized social media is becoming the new norm. Although the digitalization progress of the media brings many advantages,...
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Enterprise control applications are becoming more and more complex, exploiting distributed object technology in multi-tier architectures. For educational purposes, the development of a manufacturing control laboratory...
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Enterprise control applications are becoming more and more complex, exploiting distributed object technology in multi-tier architectures. For educational purposes, the development of a manufacturing control laboratory that may emphasize the new control techniques and the overall integration of the system activities with business processes is a complex and challenging task. In the paper, an educational enterprise application that integrates the ERP concept together with a flexible manufacturing system in a computer integrated laboratory is presented. It contains modularized, distributed subsystems in configurable and maintainable software, covering the business processes, from the customer order, to planned order dispatch. The software architecture contains the ERP components that are implemented using Enterprise Java Beans, deployed in a J2EE application server. It enables transactions with the business environment, shop-floor system and the database. The application is conceptually designed and analyzed using a visual model developed in unified modeling language.
Simplified mathematical models that can describe the real behavior of a building with relative small errors can provide useful information about the best operational methods of the HVAC (Heating, Ventilating and Air C...
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Simplified mathematical models that can describe the real behavior of a building with relative small errors can provide useful information about the best operational methods of the HVAC (Heating, Ventilating and Air Conditioning) systems, energy demand for heating and cooling, etc. We developed a simplified mathematical model for computing the energy demand for heating and optimized it with a Genetic Algorithm, in order to minimize the error between the model and real data. The tested period (a month) showed that the optimization method is suitable for this type of applications and the relative error was 0.14 kWh.
This paper proposes a classification framework aimed at identifying correlations between job ad requirements and transversal skill sets, with a focus on predicting the necessary skills for individual job descriptions ...
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automatic Term Recognition is used to extract domain-specific terms that belong to a given domain. In order to be accurate, these corpus and language-dependent methods require large volumes of textual data that need t...
automatic Term Recognition is used to extract domain-specific terms that belong to a given domain. In order to be accurate, these corpus and language-dependent methods require large volumes of textual data that need to be processed to extract candidate terms that are afterward scored according to a given metric. To improve text preprocessing and candidate terms extraction and scoring, we propose a distributed Spark-based architecture to automatically extract domain-specific terms. The main contributions are as follows: (1) propose a novel distributed automatic domain-specific multi-word term recognition architecture built on top of the Spark ecosystem; (2) perform an in-depth analysis of our architecture in terms of accuracy and scalability; (3) design an easy-to-integrate Python implementation that enables the use of Big Data processing in fields such as Computational Linguistics and Natural Language Processing. We prove empirically the feasibility of our architecture by performing experiments on two real-world datasets.
Embedded Feature Selection (FS) ensures the selection of few, relevant features, by directly re-designing the classifier for subsets of features. Naturally, this problem is formulated as a multi-objective optimization...
Embedded Feature Selection (FS) ensures the selection of few, relevant features, by directly re-designing the classifier for subsets of features. Naturally, this problem is formulated as a multi-objective optimization (MOO) addressing to the accuracy of the classifier and the parsimony of the feature vector. In MOOs, common ranking techniques use dominance analysis for providing a partial sorting of the solutions. Unfortunately, dominance analysis can also promote solutions less useful for the application. In order to gradually guide the search towards a user-preferred area set around the middle of the best fronts, this paper proposes an adaptive ranking algorithm with insertion of elites (ARE), which could be integrated in any MOO genetic algorithm. ARE incorporates two new procedures proposed for labeling the preferred solutions and for inserting elites in the less populated areas, whenever a biased exploration is detected. The experimental investigations illustrate that GA with ARE offers better results than NSGAII, both for electroencephalogram (EEG) feature selection problem (which likely involves weakly conflicting objectives) and MOOs with strongly conflicting objectives.
In this paper a visual self-localization method for a humanoid robot is presented. This one is based on monocular information. The goal of this method is to obtain the position (x; y) and orientation θ of the humanoi...
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In this paper a visual self-localization method for a humanoid robot is presented. This one is based on monocular information. The goal of this method is to obtain the position (x; y) and orientation θ of the humanoid robot inside the field of play. The methods proposed include some digital image processing algorithms and geometric interpretation to perform a 3D monocular reconstruction, that allows to measure the relative position between the robot and some known objects (goals and beacons), and to use them to obtain the robot's absolute position, by means of a triangulation method. Finally, the results obtained in Webots and in the real platform are presented.
The aim of the project was to build a prototype of a turbine drive with a controllable thrust vector for a long-range vertical take-off and landing (VTOL) aircraft. The construction of the prototype required the devel...
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Semantic segmentation (SS) provides the meaning of visual scenes, thus being a key stage for navigation and environment's perception. This paper presents a solution for SS compatible with assistive wearable system...
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Semantic segmentation (SS) provides the meaning of visual scenes, thus being a key stage for navigation and environment's perception. This paper presents a solution for SS compatible with assistive wearable systems equipped with color and depth cameras. In order to ensure a compact and robust description of input color-based images, both 2D and 3D features are extracted at superpixel level, after correcting the displacements of the camera by means of adequate rectifications. Random Forests (RF) are called for solving the classification problem. In this context, this paper introduces a multilayer RFbased classifier, including a separate layer for label correction. Additional two other correcting methods are proposed for the first layers of the classifier, i.e. a fast method investigating the majority label around each object, and several customizations of the graph cut algorithm using convenient cue weights. The performance of the suggested approach is experimentally verified on diverse urban street scenes.
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