Nineteen seventy nine handbook of agricultural charts
1979
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In line with 4th industrial revolution (Industry 4.0), the mechatronics and related areas
are fundamental to boost the developments of industry digitalization. However, it should not be
forgotten that artificial intelligence (AI) has a great preponderance on the development of
autonomous and intelligent systems incorporating the advances in mechatronics systems. It is
common in different industries the need to identify and recognize products or objects for different
purposes such as counts, quality control, selection of objects, among others. For these reasons,
pattern recognition is increasingly being used in systems on the shop floor, usually implemented in
computer vision systems with image processing in real time. This work focuses on automatic
detection and text recognition in unstructured images for use on shop floor mechatronic systems
with vision systems, to identify and recognize patterns in products. Unstructured images are
images that does not have a pre-defined image model or is not organized in a predefined manner.
Which means that there is no predefined calibration model, the system must identify and learn by
itself to recognize the text patterns. The techniques of character recognition, also known as OCR
(Optical Character Reader), are not new in the industry, however the use of machine learning
algorithms together with the existing techniques of OCR, allow endow the systems of greater
intelligence in the patterns recognition. The results achieved throughout the paper, demonstrates
that it is possible to identify and recognize text in objects based on unstructured images with a high
level of accuracy and that these algorithms can be used in real time applications.
Tese de Doutoramento em Engenharia Mecânica, Instituto Superior Técnico, 2015
Thesis approved in public session to obtain the PhD Degree in Mechanical Engineering. Universidade de Lisboa. Instituto Superior Técnico
O surgimento de várias inovações tecnológicas permitiu a digitalização e a
transformação da indústria, acompanhando o paradigma da Indústria 4.0.
Tecnologias ligadas à robótica, aos sensores inteligentes, ao fabrico assistido por
computador, à Internet das Coisas ou à Inteligência Artificial permitem a interligação
entre os mundos físicos e virtuais. A interoperação entre todos estes domínios do
saber é o princípio para transformar o processo de produção e criar uma fábrica
inteligente.
O foco deste artigo centra-se no desenvolvimento e teste de uma smartBox, capaz
de comunicar eficientemente com os equipamentos no chão de fábrica e integrá-los
em plataformas de IIOT (Industrial Internet of Things). À smartBox estão acopladas
cartas e módulos de sensores e atuadores adequados às especificidades de cada
máquina. esta tem de ser capaz de operar em tempo-real, ter uma boa performance
e escalabilidade e bons canais de comunicação. O hardware da smartBox tem, por
isso, de ser robusto, flexível, escalável, funcional, suportar diferentes tecnologias e
permitir atualizações futuras, características essenciais a uma disseminação do
conceito de Indústria 4.0 no chão de fábrica.
Atualmente, existe no mercado uma diversidade de plataformas de hardware capazes
de suportar o desenvolvimento da smartBox e servir de Gateway com qualquer
equipamento no chão de fábrica. Por sua vez, os controladores lógicos programáveis
e as suas plataformas de programação atuais são cada vez mais versáteis e intuitivos
de programar, facilitando o processo de desenvolvimento, no sentido em que cada
equipamento do chão de fábrica tem a sua especificidade, processo e tecnologia.
Dado que muitas instalações industriais têm alguns anos de existência, por vezes as
linhas de comunicação andam a par com as linhas de potência e é necessário prestar
atenção às questões das interferências e formas de as minimizar. Nesse caso, os
protocolos de comunicação a utilizar devem corresponder aos requisitos necessários
em comunicações industriais, tal como a redundância, tolerância a interferências,
reduzida latência, e minimizar as retransmissões e a perda de pacotes de dados.
Nesse âmbito, apresentamos um caso de estudo em que é analisada a vibração de
um motor elétrico; testamos duas soluções distintas para a smartBox: uma é baseada
num sistema de aquisição de dados da National Instruments, onde o software
LabVIEW é usado para adquirir, processar e armazenar os dados num servidor OPC
(Object Linking and Embedding for Process Control). A outra solução incorpora as
plataformas Arduino e EtherCAT. As características de ambas as propostas são
analisadas e comparadas.
This paper presents two methodologies for the design and development of new products called Design-with-
IoT (DwIoT) that aims to integrate IoT technology into products, focusing on a set of guidelines for its
implementation, and the concept of Design-for-Automation (DFA), important in the development of new automation oriented products in an industry 4.0 context.
DMX-Light Control efectua o controlo de sistemas de luzes que funcionam segundo o protocolo DMX512, através de rádio frequência. Este sistema foi concebido para funcionar em salas de espectáculo, concertos ao vivo, ou qualquer outro tipo de evento relacionado.
O sistema permite o controlo por meio de uma mesa, bem como via PC através de um software desenvolvido em LabVIEW. A interface entre o computador e o módulo emissor é feito através da porta USB.
A Robótica é uma área multidisciplinar que tem contribuído significativamente no processo educativo dos jovens nos últimos anos. O modelo pedagógico de aprender fazendo é uma alavanca fundamental para cativar e manter os alunos interessados nas áreas tecnológicas. O projeto ROBOT@ESCOLA – Escola de Robótica, foi desenvolvido com base em componentes básicos para a construção de robôs, plataformas Arduino para programação, bem como dispositivos móveis com sistemas operativos Android, tornando a experimentação de ciência e tecnologia acessível a todos. O projeto foi desenvolvido em parceria com várias instituições de ensino da região da Beira Interior. Desta cooperação resultou uma plataforma robótica (kit) direcionada para a vertente educativa. Este kit de robótica móvel, pode ser utilizado pelos alunos para aprender a lógica dos sistemas programados, interagir com sensores e atuadores, e perceber os fenómenos físicos associados aos diversos tipos de sensores. Além dos aspetos introdutórios, o projeto possibilita explorar vários temas científicos de nível avançado. No artigo é apresentada a prática pedagógica associada à plataforma robótica, que inclui a definição do público-alvo, a metodologia pedagógica, e a avaliação para o caso de estudo apresentado, a unidade curricular de Programação de Computadores. Conclui-se através da experiência adquirida com o projeto que o uso da robótica como recurso de ensino favorece o raciocínio lógico, criatividade e relacionamento interpessoal.
Constant search for efficiency and productivity
has led to innovation on the factory shop floor, representing an
evolution of the current production systems combined with
new technologies of industrial automation and information
technology. This work presents an experimental demo of a
smartbox for Industry 4.0 scenarios, allowing sensing,
monitoring and data acquisition. We have tested two different
approaches, depending on the communication protocol used
for real time applications: OPC UA or MQTT. Raspberry Pi’s
platform act as an OPC UA server or MQTT broker,
respectively. From the measurements, data stored in a cloud
server can be accessed remotely with improved security and
visualized from a computer dashboard. One of the conclusions
that can be drawn is that both protocols allow data from the
smartbox to be stored and easily monitored from a smartphone
application or a computer web interface. MQTT is a good
option in communications requiring very low bandwidth.
However, there is a lack of suitable libraries to program alarm
features for OPC UA Servers.
Constant search for efficiency and productivity has led to innovation on the factory shop floor, representing an evolution of the current production
systems combined with new technologies of industrial automation and information technology. This work presents a versatile gateway for experimental
demonstration of Industrial IoT technologies in a loom machine, allowing sensing, monitoring and data acquisition that was not originally available. We have
implemented an approach, based on the OPC UA communication protocol for
real time applications, and OPC UA to MQTT conversion mechanism. Raspberry
Pi’s platform act as an OPC UA server. From the measurements, data stored in a
cloud server can be accessed remotely with improved security and visualized
from a computer dashboard. One of the conclusions that can be drawn is that the
proposed gateway allows data to be stored and easily monitored from a
smartphone application or a computer web interface.
“© © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
This paper presents an approach for automatic anomaly detection through vibration analysis based on machine learning algorithms.The study focuses on induction motors in a predictive maintenance context, but can be applied to other domains. Vibration analysis is an important diagnostic tool in industrial data analysis to predict anomaliescaused by equipment defects or in its use, allowing to increase its lifetime.It is not a new technique and is widely used in the industry, however withthe Industry 4.0 paradigm and the need to digitize any process, it gainsrelevance to automatic fault detection. The Isolation Forest algorithm isimplemented to detect anomalies in vibration datasets measured in anexperimental apparatus composed of an induction motor and a coupling
system with shaft alignment/misalignment capabilities. The results showthat it is possible to detect anomalies automatically with a high level ofprecision and accuracy.
Constant search for efficiency and productivity has led to innovation on the factory shop floor,
representing an evolution of the current production systems combined with new technologies of industrial
automation and information technology. This work presents a versatile gateway for experimental
demonstration of Industrial IoT technologies in a loom machine, allowing sensing, monitoring and data
acquisition that was not originally available. We have implemented an approach, based on the OPC UA
communication protocol for real time applications, and OPC UA to MQTT conversion mechanism. Raspberry
Pi’s platform act as an OPC UA server. From the measurements, data stored in a cloud server can be accessed
remotely with improved security and visualized from a computer dashboard. One of the conclusions that can
be drawn is that the proposed gateway allows data to be stored and monitored from a smartphone application
or a computer web interface.
In line with the context of Industry 4.0, forestry, and in particular the entire ecosystem around it, also needs digitalization solutions in order to allow better interaction between all agents that work and live from the forest. It is important for a better management of forest resources allowing productivity gains, more sustainability and resilience. One of the agents that can benefit, but also contribute to better forestry, are machine producers.With digitalization, the machinery is now equipped with new and better sensors that can
be used not only for machine operations but also for forest management, through LiDAR (Light Detection
And Ranging) or RGB (Red, Green, Blue) cameras for example. On the other hand, there are new needs
for predictive maintenance or solutions for remote assistance of machines operating in the forest, typically
in isolated areas with great limitations in access to communications. Thinking about these technological
challenges, this work seeks to provide answers with communication solutions in forest machines, enabling
the digitalization of functionalities, also allowing remote access to machine controllers in order to provide
them with connectivity in an IIoT (Industrial Internet of Things) scenarios. New hardware modules designed
in partnership and according to the prerequisites of a forest machine manufacturer are presented. These
modules are a step towards digitizing the machines and opening up the scalability of new requirements,
as well as remote access through additional gateways. The results already obtained in real scenarios show
that these modules can be a concrete solution for the current and emerging needs of industrial machine
manufacturers.
Abstract: This paper describes a real case implementation of an automatic pedestrian-detection
solution, implemented in the city of Aveiro, Portugal, using affordable LiDAR technology and open,
publicly available, pedestrian-detection frameworks based on machine-learning algorithms. The
presented solution makes it possible to anonymously identify pedestrians, and extract associated
information such as position, walking velocity and direction in certain areas of interest such as
pedestrian crossings or other points of interest in a smart-city context. All data computation (3D
point-cloud processing) is performed at edge nodes, consisting of NVIDIA Jetson Nano and Xavier
platforms, which ingest 3D point clouds from Velodyne VLP-16 LiDARs. High-performance real-time
computation is possible at these edge nodes through CUDA-enabled GPU-accelerated computations.
The MQTT protocol is used to interconnect publishers (edge nodes) with consumers (the smartcity
platform). The results show that using currently affordable LiDAR sensors in a smart-city
context, despite the advertising characteristics referring to having a range of up to 100 m, presents
great challenges for the automatic detection of objects at these distances. The authors were able to
efficiently detect pedestrians up to 15 m away, depending on the sensor height and tilt. Based on the
implementation challenges, the authors present usage recommendations to get the most out of the
used technologies.
“This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Electrical Engineering. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-91334-6_40"
“This is a pre-copyedited version of a contribution published in HUSTY, M.; HOFBAUR, M. (eds) New trends in medical and service robots published by Springer. The definitive authenticated version is available online via https://doi.org/10.1007/978-3-319-59972-4_13 "
“This is a pre-copyedited version of a contribution published in Marques P., Radwan A., Mumtaz S., Noguet D., Rodriguez J., Gundlach M. (eds) Cognitive Radio Oriented Wireless Networks published by Springer. The definitive authenticated version is available online via https://doi.org/10.1007/978-3-319-76207-4_23 "
This book chapter proposes a description of smart gateways and cyber-physical systems (CPS) for the
industrial internet of things (I-IOT). It also presents a case study where a smart gateway is developed to
be used in different types of industrial equipment for the shop floor. The case study is developed under
the specifications of different industries in the region of Castelo Branco. It is a proof that the 4th industrial
revolution will be the engine for SME innovation, independence of the regions and their financial
strength. It is also proof that the cooperation between universities, industries and startups can evolve to
break barriers and add value in the improvement of regional industries competitiveness. Topics that will
be addressed on the chapter can be used for developers, students, researchers and enthusiasts to learn
topics related to I-IOT, such as data acquisitions systems, wired and wireless communication devices
and protocols, OPC servers and LabVIEW programming.
Industry 4.0 is the movement towards a fourth industrial
revolution that will consist in the digitization and integration
of all value chain. In Europe, this movement is led by the German
RAMI 4.0 (Reference Architecture for Industry 4.0) proposal,
which is attracting a lot of attention from industry, academia
and other practitioners. Under the RAMI 4.0 scope there is an
Administration Shell proposal to abstract physical and logical
assets in a standardized way. Once abstracted, assets become
Industry 4.0 Components and can be fully integrated in the
Cyber Physical Production System or value chain. This work
focuses on the utilization of software components within the
Administration Shell. There is a necessity to represent software
components and their relation to industrial asset. Therefore, control
and monitoring applications involving software components
and other assets can be represented in compliance with the I4.0
Component Model. To address this necessity the Smart Object
Self Description information model is proposed and applied to a
real case study scenario.
“© © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
Este trabalho consiste no desenvolvimento e validação de modelos de Machine Learning para a otimização de um sistema de rega de precisão utilizando algoritmos de classificação. A finalidade é atribuir a cada solo,
localizado a sul do concelho do Fundão, Portugal, uma classe de aptidão para o regadio, classes essas que identificam as zonas regáveis, não regáveis bem como as que precisam de intervenção para serem regadas. Os dados dos casos de estudo foram anteriormente recolhidos por uma aluna de Mestrado da Escola
Superior Agrária do IPCB (Portugal), onde incluíam vários condicionalismos (características dos solos que podem condicionar a aptidão para o regadio). A análise exploratória dos dados permitiu utilizar apenas os valores dos resultados relativamente às características dos solos que podem condicionar a aptidão para
o regadio rejeitando assim todo o cálculo efetuado para a obtenção dos mesmos. Desta forma os dados do caso de estudo foram enriquecidos com esta informação para a aplicação nos algoritmos de Machine Learning. Em geral, o facto de retirar estas características que não revelavam impacto no estudo ajudaram a melhorar os modelos de classificação bem como a sua precisão. Diferentes algoritmos de Machine Learning foram desenvolvidos, testados e validados, tais como, Support Vetor Machine, kNN, Árvore de Decisão, Naive
Bayes e Regressão Logística, para otimizar um sistema de rega de precisão de modo a atribuir uma a classe de aptidão de rega a novos solos introduzidos. A comparação dos modelos demonstrou que o método Naive Bayes é o que apresenta uma melhor precisão na altura de gerar uma classe de previsão.
This paper presents the initial developments of new hardware devices targeted for CAN (Controller Area Network) bus communications in forest machines. CAN bus is a widely used protocol for communications in the automobile area. It is also applied in industrial vehicles and machines due to its robustness, simplicity, and operating flexibility. It is ideal for forestry machinery producers who need to couple their equipment to a machine that allows the transportation industry to recognize the importance of standardizing communications between tools and machines. One of the problems that producers sometimes face is a lack of flexibility in commercialized hardware modules; for example, in interfaces for sensors and actuators that guarantee scalability depending on the new functionalities required. The hardware device presented in this work is designed to overcome these limitations and provide the flexibility to standardize communications while allowing scalability in the development of new products and features. The work is being developed within the scope of the research project “SMARTCUT—Remote Diagnosis, Maintenance and Simulators for Operation Training and Maintenance of Forest Machines”, to incorporate innovative technologies in forest machines produced by the CUTPLANT S.A. It consists of an experimental system based on the PIC18F26K83 microcontroller to form a CAN node to transmit and receive digital and analog messages via CAN bus, tested and validated by the communication between different nodes. The main contribution of the paper focuses on the presentation of the development of new CAN bus electronic control units designed to enable remote communication between sensors and actuators, and the main controller of forest machines.
This paper presents the development of a bin-picking solution based on low-cost vision systems for the manipulation of automotive electrical connectors using machine learning techniques. The automotive sector has always been in a state of constant growth and change, which also implies constant challenges in the wire harnesses sector, and the emerging growth of electric cars is proof of this and represents a challenge for the industry. Traditionally, this sector is based on strong human work manufacturing and the need arises to make the digital transition, supported in the context of Industry 4.0, allowing the automation of processes and freeing operators for other activities with more added value. Depending on the car model and its feature packs, a connector can interface with a different number of wires, but the connector holes are the same. Holes not connected with wires need to be sealed, mainly to guarantee the tightness of the cable. Seals are inserted manually or, more recently, through robotic stations. Due to the huge variety of references and connector configurations, layout errors sometimes occur during seal insertion due to changed references or problems with the seal insertion machine. Consequently, faulty connectors are dumped into boxes, piling up different types of references. These connectors are not trash and need to be reused. This article proposes a bin-picking solution for classification, selection and separation, using a two-finger gripper, of these connectors for reuse in a new operation of removal and insertion of seals. Connectors are identified through a 3D vision system, consisting of an Intel RealSense camera for object depth information and the YOLOv5 algorithm for object classification. The advantage of this approach over other solutions is the ability to accurately detect and grasp small objects through a low-cost 3D camera even when the image resolution is low, benefiting from the power of machine learning algorithms.
his experimental study focuses on the comparison between two different sensors for vibration signals: a magnetoresistive sensor and an accelerometer as a calibrated reference. The vibrations are collected from a variable speed inductor motor setup, coupled to a ball bearing load with adjustable misalignments. To evaluate the performance of the magnetoresistive sensor against the accelerometer, several vibration measurements are performed in three different axes: axial, horizontal and vertical. Vibration velocity measurements from both sensors were collected and analyzed based on spectral decomposition of the signals. The high cross-correlation coefficient between spectrum vibration signatures in all experimental measurements shows good agreement between the proposed magnetoresistive sensor and the reference accelerometer performances. The results demonstrate the potential of this type of innovative and non-contact approach to vibration data collection and a prospective use of magnetoresistive sensors for predictive maintenance models for inductive motors in Industry 4.0 applications.
The rise of Industry 4.0 has highlighted simulation optimisation as a decision-making tool for scheduling complex-manufacturing systems, specifically when resources are expensive and multiple jobs compete for the same resources. In this context, simulation optimisation provides an important mean to predict, evaluate and improve the short-term performance of the manufacturing system.
An important scheduling function is controlled job release; jobs (or orders) are not released immediately to the shop floor, as they arrive to the production system, but release is controlled to stabilize work-in-process, reduce manufacturing lead times and meet customer delivery requirements. While there exists a broad literature on job release, reported release procedures typically use simple rules and greedy heuristics to determine which job to select for release. While this is justified by its simplicity, the advent of Industry 4.0 and its advanced scheduling techniques question its adequateness.
In this study, an integer linear programming model is used to select jobs to be released to the shop floor. While there are some recent studies that use a similar procedure, these studies assume the release decision for a given set of jobs is optimized in discrete time intervals. In contrast, in this study, we analyse the impact of different triggering intervals. Experimental results for a pure flow shop support our contention that simulation optimisation as a decision-making tool for job release is likely to be too important to be overlooked
“© © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
The concept of smart cities grew with the need to rethink the use of urban spaces based
on the constant technological advances and respecting sustainability. Today the urbanism and the
methodologies to think about the city are changing, as citizens want more access to digital
information on almost everything. Therefore, cities need to be planned and equipped with
infrastructures that enable connectivity between the citizens’ devices and the digital information.
This challenge raises technological problems, such as traffic management, in an attempt to
guarantee fair network access to all users. Solutions based on wireless resource management and
self-organizing networks are key when design the connectivity for these smart cities. This paper
presents a study on forecasting the daily load of Wi-Fi city hotspots, taking also in consideration the
weather conditions. This is particularly interesting to predict the network load and resource
requirements needed to ensure proper quality of service is provided to the hotspot users. The study
was performed in a Wi-Fi hotspot located in the city of Castelo Branco, Portugal. The results show
the ARIMA model is capable of identifying and forecasting seasonality events for one week in
advance including its capability to correlate the number of hotspot users with weather conditions.
© © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
An important scheduling function of manufacturing systems is controlled order release. While there exists a broad literature on order release, reported release procedures typically use simple sequencing rules and greedy heuristics to determine which jobs to select for release. While this is appealing due to its simplicity, its adequateness has recently been questioned. In response, this study uses an integer linear programming model to select orders for release to the shop floor. Using simulation, we show that optimisation has the potential to improve performance compared to ‘classical’ release based on pool sequencing rules. However, in order to also outperform more powerful pool sequencing rules, load balancing and timing must be considered at release. Existing optimisation-based release methods emphasise load balancing in periods when jobs are on time. In line with recent advances in Workload Control theory, we show that a better percentage tardy performance can be achieved by only emphasising load balancing when many jobs are urgent. However, counterintuitively, emphasising urgency in underload periods leads to higher mean tardiness. Compared to previous literature we further highlight that continuous optimisation-based release outperforms periodic optimisation-based release. This has important implications on how optimised-based release should be designed.
“This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Mechanical Engineering. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-79168-1_20".
