15 research outputs found

    Effective Transfer of Pretrained Large Visual Model for Fabric Defect Segmentation via Specifc Knowledge Injection

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    Fabric defect segmentation is integral to textile quality control. Despite this, the scarcity of high-quality annotated data and the diversity of fabric defects present significant challenges to the application of deep learning in this field. These factors limit the generalization and segmentation performance of existing models, impeding their ability to handle the complexity of diverse fabric types and defects. To overcome these obstacles, this study introduces an innovative method to infuse specialized knowledge of fabric defects into the Segment Anything Model (SAM), a large-scale visual model. By introducing and training a unique set of fabric defect-related parameters, this approach seamlessly integrates domain-specific knowledge into SAM without the need for extensive modifications to the pre-existing model parameters. The revamped SAM model leverages generalized image understanding learned from large-scale natural image datasets while incorporating fabric defect-specific knowledge, ensuring its proficiency in fabric defect segmentation tasks. The experimental results reveal a significant improvement in the model's segmentation performance, attributable to this novel amalgamation of generic and fabric-specific knowledge. When benchmarking against popular existing segmentation models across three datasets, our proposed model demonstrates a substantial leap in performance. Its impressive results in cross-dataset comparisons and few-shot learning experiments further demonstrate its potential for practical applications in textile quality control.Comment: 13 pages,4 figures, 3 table

    Energy-Efficiency of Conveyor Belts in Raw Materials Industry

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    This book focuses on research related to the energy efficiency of conveyor transportation. The solutions presented in the Special Issue have an impact on optimizing, and thus reducing, the costs of energy consumption by belt conveyors. This is due, inter alia, to the use of better materials for conveyor belts, which reduce its rolling resistance and noise, and improve its ability to adsorb the impact energy from the material falling on the belt. The use of mobile robots designed to detect defects in the conveyor's components makes the conveyor operation safer, and means that the conveyor works for longer and there are no unplanned stops due to damage

    Development of a practical electrical tomography system for flexible contact sensing applications

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    Tactile sensing is seeing an increase in potential applications, such as in humanoid and industrial robots; health care systems and medical instrumentation; prosthetic devices; and in the context of human-machine interaction. However, these applications require the integration of tactile sensors over various objects with different surface shapes. This emphasises the need of developing sensors which are flexible in contrast with the common rigid type. Moreover, flexible sensing research is considered to be in its infancy. Many technological and system issues are still open, mainly: conformability; scalability; system integration; high system cost; sensor size; and power consumption. In light of the above, this thesis is concerned with the development of a flexible fabric-based contact sensor system. This is done through an interdisciplinary approach whereby electronics, system engineering, electrical tomography, and machine learning have been considered. This results in a practical flexible sensor that is capable of accurately detecting contact locations with high temporal resolution; and requires low power consumption.The sensor is based on the principle of electrical tomography. This is essential since this technique allows us to eliminate electrodes and wiring from within the sensing area, confining them to the periphery of the sensor. This improves flexibility all while eliminating electrode fatigue and deterioration due to repeated loading.We start by developing an electrical tomography sensor system. This comprises of a piezoresistive flexible fabric material, a data acquisition card, and a custom printed circuit board for managing both current injection and data collection. We show that current injection and voltage measurement protocols respond differently to different positions of the input contact region of interest, consequently affecting the overall performance of the tomography sensor system. Then, an approach for classifying contact location over the sensor is presented. This is done using supervised machine learning, namely discriminant analysis. Accurate touch location identification is achieved, along with an increase in the detection speed and sensor versatility. Finally, the sensor is placed over different surfaces in order to show and validate its efficiency. The main finding of this work is that electrical tomography flexible sensor systems present a very promising technology, and can be practically and effectively used for developing inexpensive and durable flexible sensors for tactile applications. The main advantage of this approach is the complete absence of wires in the internal area of the sensor. This allows the sensor to be placed over surfaces with different shapes without losing its functionality. The sensor's applicability can be further improved by using machine learning strategies due to their ability of empirical learning and extracting meaningful tactile information. The research work in this thesis was motivated by the problems faced by industrial partners which were part of the sustainable manufacturing and advanced robotics training network in Europe (SMART-e)

    The benefits of an additional practice in descriptive geomerty course: non obligatory workshop at the Faculty of Civil Engineering in Belgrade

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    At the Faculty of Civil Engineering in Belgrade, in the Descriptive geometry (DG) course, non-obligatory workshops named “facultative task” are held for the three generations of freshman students with the aim to give students the opportunity to get higher final grade on the exam. The content of this workshop was a creative task, performed by a group of three students, offering free choice of a topic, i.e. the geometric structure associated with some real or imagery architectural/art-work object. After the workshops a questionnaire (composed by the professors at the course) is given to the students, in order to get their response on teaching/learning materials for the DG course and the workshop. During the workshop students performed one of the common tests for testing spatial abilities, named “paper folding". Based on the results of the questionnairethe investigation of the linkages between:students’ final achievements and spatial abilities, as well as students’ expectations of their performance on the exam, and how the students’ capacity to correctly estimate their grades were associated with expected and final grades, is provided. The goal was to give an evidence that a creative work, performed by a small group of students and self-assessment of their performances are a good way of helping students to maintain motivation and to accomplish their achievement. The final conclusion is addressed to the benefits of additional workshops employment in the course, which confirmhigherfinal scores-grades, achievement of creative results (facultative tasks) and confirmation of DG knowledge adaption

    The contemporary visualization and modelling technologies and the techniques for the design of the green roofs

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    The contemporary design solutions are merging the boundaries between real and virtual world. The Landscape architecture like the other interdisciplinary field stepped in a contemporary technologies area focused on that, beside the good execution of works, designer solutions has to be more realistic and “touchable”. The opportunities provided by Virtual Reality are certainly not negligible, it is common knowledge that the designs in the world are already presented in this way so the Virtual Reality increasingly used. Following the example of the application of virtual reality in landscape architecture, this paper deals with proposals for the use of virtual reality in landscape architecture so that designers, clients and users would have a virtual sense of scope e.g. rooftop garden, urban areas, parks, roads, etc. It is a programming language that creates a series of images creating a whole, so certain parts can be controlled or even modified in VR. Virtual reality today requires a specific gadget, such as Occulus, HTC Vive, Samsung Gear VR and similar. The aim of this paper is to acquire new theoretical and practical knowledge in the interdisciplinary field of virtual reality, the ability to display using virtual reality methods, and to present through a brief overview the plant species used in the design and construction of an intensive roof garden in a Mediterranean climate, the basic characteristics of roofing gardens as well as the benefits they carry. Virtual and augmented reality as technology is a very powerful tool for landscape architects, when modeling roof gardens, parks, and urban areas. One of the most popular technologies used by landscape architects is Google Tilt Brush, which enables fast modeling. The Google Tilt Brush VR app allows modeling in three-dimensional virtual space using a palette to work with the use of a three dimensional brush. The terms of two "programmed" realities - virtual reality and augmented reality - are often confused. One thing they have in common, though, is VRML - Virtual Reality Modeling Language. In this paper are shown the ways on which this issue can be solved and by the way, get closer the term of Virtual Reality (VR), also all the opportunities which the Virtual reality offered us. As well, in this paper are shown the conditions of Mediterranean climate, the conceptual solution and the plant species which will be used by execution of intensive green roof on the motel “Marković”
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