Development of traceability solution for furniture components

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáIn the contemporary context, characterized by intensified global competition and the constant evolution of the globalization landscape, it becomes imperative for industries, including Small and Medium Enterprises (SMEs), to undertake efforts to enhance their operational processes, often through digital technological adaptation. The present study falls within the scope of the project named “Wood Work 4.0,” which aims to infuse innovation into the wood furniture manufacturing industry through process optimization and the adoption of digital technologies. This project received funding from the European Union Development Fund, in collaboration with the North 2020 Regional Program, and was carried out at the Carpintaria Mofreita company, located in Macedo de Cavaleiros, Portugal. In this regard, this study introduces a software architecture that supports the traceability of projects in the wood furniture industry and simultaneously employs a system to identify and manage material leftovers, aiming for more efficient waste management. For the development of this software architecture, an approach that integrates the Fiware platform, specialized in systems for the Internet of Things (IoT), with an Application Programming Interface (API) specifically created to manage information about users, projects, and associated media files, was adopted. The material leftovers identification system employs image processing techniques to extract geometric characteristics of the materials. Additionally, these data are integrated into the company’s database. In this way, it was possible to develop an architecture that allows not only the capturing of project information but also its effective management. In the case of material leftovers identification, the system was able to establish, with a satisfactory degree of accuracy, the dimensions of the materials, enabling the insertion of these data into the company’s database for resource management and optimization.No contexto contemporâneo, marcado por uma competição global intensificada e pela constante evolução do cenário de globalização, torna-se imperativo para as indústrias, incluindo as Pequenas e Médias Empresas (PMEs), empreender esforços para aprimorar seus processos operacionais, frequentemente pela via da adaptação tecnológica digital. O presente estudo insere-se dentro do escopo do projeto denominado “Wood Work 4.0”, cujo propósito é infundir inovação na indústria de fabricação de móveis de madeira por meio da otimização de processos e da adoção de tecnologias digitais. Este projeto obteve financiamento do Fundo de Desenvolvimento da União Europeia, em colaboração com o programa Regional do Norte 2020 e foi realizado na empresa Carpintaria Mofreita, localizada em Macedo de Cavaleiros, Portugal. Nesse sentido, este estudo introduz uma arquitetura de software que oferece suporte à rastreabilidade de projetos na indústria de móveis de madeira, e simultaneamente emprega um sistema para identificar e gerenciar sobras de material, objetivando uma gestão de resíduos mais eficiente. Para o desenvolvimento dessa arquitetura de software, adotou-se uma abordagem que integra a plataforma Fiware, especializada em sistemas para a Internet das Coisas (IoT), com uma Interface de Programação de Aplicações (API) criada especificamente para gerenciar informações de usuários, projetos, e arquivos de mídia associados. O sistema de identificação de sobras de material emprega técnicas de processamento de imagem para extrair características geométricas dos materiais. Adicionalmente, esses dados são integrados ao banco de dados da empresa. Desta forma, foi possível desenvolver uma arquitetura que permite não só capturar informações de projetos, mas também gerenciá-las de forma eficaz. No caso da identificação de sobras de material, o sistema foi capaz de estabelecer, com um grau de precisão satisfatório, as dimensões dos materiais, possibilitando a inserção desses dados no banco de dados da empresa para gestão e otimização do uso de recursos

SAR Image Edge Detection: Review and Benchmark Experiments

Edges are distinct geometric features crucial to higher level object detection and recognition in remote-sensing processing, which is a key for surveillance and gathering up-to-date geospatial intelligence. Synthetic aperture radar (SAR) is a powerful form of remote-sensing. However, edge detectors designed for optical images tend to have low performance on SAR images due to the presence of the strong speckle noise-causing false-positives (type I errors). Therefore, many researchers have proposed edge detectors that are tailored to deal with the SAR image characteristics specifically. Although these edge detectors might achieve effective results on their own evaluations, the comparisons tend to include a very limited number of (simulated) SAR images. As a result, the generalized performance of the proposed methods is not truly reflected, as real-world patterns are much more complex and diverse. From this emerges another problem, namely, a quantitative benchmark is missing in the field. Hence, it is not currently possible to fairly evaluate any edge detection method for SAR images. Thus, in this paper, we aim to close the aforementioned gaps by providing an extensive experimental evaluation for SAR images on edge detection. To that end, we propose the first benchmark on SAR image edge detection methods established by evaluating various freely available methods, including methods that are considered to be the state of the art

Non-Contact Evaluation Methods for Infrastructure Condition Assessment

The United States infrastructure, e.g. roads and bridges, are in a critical condition. Inspection, monitoring, and maintenance of these infrastructure in the traditional manner can be expensive, dangerous, time-consuming, and tied to human judgment (the inspector). Non-contact methods can help overcoming these challenges. In this dissertation two aspects of non-contact methods are explored: inspections using unmanned aerial systems (UASs), and conditions assessment using image processing and machine learning techniques. This presents a set of investigations to determine a guideline for remote autonomous bridge inspections

Vision 21: Interdisciplinary Science and Engineering in the Era of Cyberspace

The symposium Vision-21: Interdisciplinary Science and Engineering in the Era of Cyberspace was held at the NASA Lewis Research Center on March 30-31, 1993. The purpose of the symposium was to simulate interdisciplinary thinking in the sciences and technologies which will be required for exploration and development of space over the next thousand years. The keynote speakers were Hans Moravec, Vernor Vinge, Carol Stoker, and Myron Krueger. The proceedings consist of transcripts of the invited talks and the panel discussion by the invited speakers, summaries of workshop sessions, and contributed papers by the attendees

Image analysis of circulating fluidized bed hydrodynamics

The goal of this thesis is to design methods to estimate the local concentration and velocity of particles observed in digital videos of the inner wall of a circulating fluidized bed (CFB) riser. Understanding the dynamics of these rapidly moving particles will allow researchers to design cleaner and more efficient CFB facilities. However, the seemingly random motion exhibited by the particles in three dimensions, coupled with the varying image quality, make it difficult to extract the required information from the images. Given a video sequence, a method for detecting particles and tracking their spatial location is developed. By exploiting the presence of specular reflections, individual particles are first identified along the focal plane by an image filter specifically designed for this purpose. Once the particle locations are known, a local optical flow model is used to approximate the motion field across two images in order to track particles from one frame of the sequence to another. An evaluation of the proposed method indicates its potential to estimate particle count, location, concentration and velocity in an efficient and reliable manner. The method is fully automated and is expected to be an important analysis tool for researchers with the National Energy Technology Laboratory, part of the national laboratory system of the Department of Energy

DESIGN OF A GAIT ACQUISITION AND ANALYSIS SYSTEM FOR ASSESSING THE RECOVERY OF MICE POST-SPINAL CORD INJURY

Current methods of determining spinal cord recovery in mice, post-directed injury, are qualitative measures. This is due to the small size and quickness of mice. This thesis presents a design for a gait acquisition and analysis system able to capture the footfalls of a mouse, extract position and timing data, and report quantitative gait metrics to the operator. These metrics can then be used to evaluate the recovery of the mouse. This work presents the design evolution of the system, from initial sensor design concepts through prototyping and testing to the final implementation. The system utilizes a machine vision camera, a well-designed walkway enclosure, and image processing techniques to capture and analyze paw strikes. Quantitative results gained from live animal experiments are presented, and it is shown how the measurements can be used to determine healthy, injured, and recovered gait

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Design and implementation of a vision system for microassembly workstation

Rapid development of micro/nano technologies and the evolvement of biotechnology have led to the research of assembling micro components into complex microsystems and manipulation of cells, genes or similar biological components. In order to develop advanced inspection/handling systems and methods for manipulation and assembly of micro products and micro components, robust micromanipulation and microassembly strategies can be implemented on a high-speed, repetitive, reliable, reconfigurable, robust and open-architecture microassembly workstation. Due to high accuracy requirements and specific mechanical and physical laws which govern the microscale world, micromanipulation and microassembly tasks require robust control strategies based on real-time sensory feedback. Vision as a passive sensor can yield high resolutions of micro objects and micro scenes along with a stereoscopic optical microscope. Visual data contains useful information for micromanipulation and microassembly tasks, and can be processed using various image processing and computer vision algorithms. In this thesis, the initial work on the design and implementation of a vision system for microassembly workstation is introduced. Both software and hardware issues are considered. Emphasis is put on the implementation of computer vision algorithms and vision based control techniques which help to build strong basis for the vision part of the microassembly workstation. The main goal of designing such a vision system is to perform automated micromanipulation and microassembly tasks for a variety of applications. Experiments with some teleoperated and semiautomated tasks, which aim to manipulate micro particles manually or automatically by microgripper or probe as manipulation tools, show quite promising results