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Electronic engineering (ICEEM), International Conference on

作者： Mahmoud Elshabrawy Marwa M. Eid Abdelaziz A. Abdelhamid El-Sayed M. El-Kenawy Abdelhameed Ibrahim Dept. of Communications & Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Dep. of Computer Science Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Computer Engineering and Control Systems Dept. Faculty of Engineering University Mansoura Egypt

This study presents a dipper-throated-based ant colony optimization (DTACO) with the Seasonal Auto-Regressive Integrated Moving Average with eXogenous factor (SARIMAX) model (DTACO+SARIMAX) to forecast monkeypox cases. The work optimizes the SARIMAX model using grid search cross-validation and fine-tunes its hyperparameters using DTACO to improve prediction accuracy. The suggested model's consistency and accuracy are considerable compared to previous studies. Comparisons with state-of-the-art models validate the proposed model's predictions. DTACO+SARIMAX can be used to control disease and monitor monkeypox. Healthcare organizations and governments can better manage and track the pandemic's course by offering accurate predictions, reducing public panic, and enabling effective pandemic planning. The Analysis of Variance (ANOVA) and Wilcoxon signed-rank tests are conducted on the proposed DTACO-SARIMAX model and compared models.

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Intelligent disease detection system for greenhouse with a robotic monitoring system 2

Intelligent disease detection system for greenhouse with a r...

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2nd International Conference on Advancements in Computing, ICAC 2020

作者： Fernando, Sandunika Nethmi, Ranusha Silva, Ashen Perera, Ayesh De Silva, Rajitha Abeygunawardhana, Pradeep K.W. Dep. of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe10115 Sri Lanka

ISBN: (纸本)9781728184128

Greenhouse farming plays a significant role in the agricultural industry because of its controlled climatic features. Recent examinations have stated that the mean creation of the yields under greenhouses is lessening due to disease events in the plants. These foods have become an imposing undertaking because these plants are being assaulted by different bacterial diseases, micro-organisms, and pests. The chemicals are applied to the plants intermittently without thinking about the necessity of each plant. Several problems have occurred in the greenhouse environment due to these causes. Therefore, there is a huge necessity for a system to detect diseases at an early stage. This research focused on designing a system to detect disease, which causes yellowish in greenhouse plants. Plant yellowing can be considered a significant problem of plants that grow under greenhouse-controlled environments. Through this research is focused on the most important and one of the most attention-grabbing crop tomato. There are specific diseases that cause yellowish the tomato plant, and they have been identified. The techniques utilized for early recognition of infection are image processing, machine learning, and deep learning. Index Terms-Greenhouses, Disease diagnosis, Image processing, Machine Learning, Deep Learning, Tomato Farming. © 2020 IEEE.

关键词： Greenhouses

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AI based greenhouse farming support system with robotic monitoring

AI based greenhouse farming support system with robotic moni...

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2020 IEEE Region 10 Conference, TENCON 2020

作者： Fernando, Sandunika Nethmi, Ranusha Silva, Ashen Perera, Ayesh Silva, Rajitha De Abeygunawardhana, Pradeep W. K. Sri Lanka Institute of Information Technology Dep. of Computer Systems Engineering Malabe10115 Sri Lanka

ISBN: (纸本)9781728184555

Greenhouses plays a major role in today's agriculture since farmers can grow plants under controlled climatic conditions and can optimize production. The greenhouses are usually built in areas where the climatic conditions for the growth of plants are not optimal so requires some artificial setups to bring about productivity. Automating process of a greenhouse requires monitoring and controlling of the climatic parameters. This paper is an attempt to minimize the cost of maintaining greenhouse environments using new technologies. The end goal of this research an automated system to optimally monitor and control the environmental factors inside greenhouse by monitoring temperature, soil moisture, humidity and pH through a cloud connected mobile robot which can detect unhealthy plants using image processing and machine learning. The mobile robot navigates through a predefined map of greenhouse. Database server has created to store gathered real-time data. And the necessary accurate data represent by using proper application for analyzing. © 2020 IEEE.

关键词： Greenhouses

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Predicting Yarn Breaks in Textile Fabrics: A Machine Learning Approach

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Procedia Computer Science 2022年 207卷 2301-2310页

作者： João Azevedo Rui Ribeiro Luís Miguel Matos Rui Sousa João Paulo Silva André Pilastri Paulo Cortez EPMQ - IT Engineering Maturity and Quality Lab CCG ZGDV Institute Guimarães Portugal ALGORITMI Center Dep. Information Systems University of Minho Guimarães Portugal Somelos Ronfe Portugal Fluxodata Ronfe Portugal

In this paper, we propose a Machine Learning (ML) approach to predict faults that may occur during the production of fabrics and that often cause production downtime delays. We worked with a textile company that produces fabrics under the Industry 4.0 concept. In particular, we deal with a client customization requisite that impacts on production planning and scheduling, where there is a crucial need of limiting machine stoppage. Thus, the prediction of machine stops enables the manufacturer to react to such situation. If a specific loom is expected to have more breaks, several measures can be taken: slower loom speed, special attention by the operator, change in the used yarn, stronger sizing recipe, etc. The goal is to model three regression tasks related with the number of weft breaks, warp breaks, and yarn bursts. To reduce the modeling effort, we adopt several Automated Machine Learning (AutoML) tools (H2O, AutoGluon, AutoKeras), allowing us to compare distinct ML approaches: using a single (one model per task) and Multi-Target Regression (MTR); and using the direct output target or a logarithm transformed one. Several experiments were held by considering Internet of Things (IoT) historical data from a Portuguese textile company. Overall, the best results for the three tasks were obtained by the single-target approach with the H2O tool using logarithm transformed data, achieving an R 2 of 0.73 for weft breaks. Furthermore, a Sensitivity Analysis eXplainable Artificial Intelligence (SA XAI) approach was executed over the selected H2OAutoML model, showing its potential value to extract useful explanatory knowledge for the analyzed textile domain.

关键词： Machine Downtime Yarn Breaks Explainable Artificial Intelligence Regression Automated Machine Learning

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Smart Harvesting based on Image Processing

Smart Harvesting based on Image Processing

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2020 International Computer Symposium, ICS 2020

作者： Joseph, S.P Wijerathna, L.L.M.C. Epa, K.G.R.D. Egalla, E.K.W.A.P.K. Abeygunawardhana, P.W.K. De Silva, R Sri Lanka Institute of Information Technology Dep. of Computer Systems Engineering Faculty of Computing Malabe10115 Sri Lanka Sri Lanka Institute of Information Technology Dep. of Software Engineering Faculty of Computing Malabe10115 Sri Lanka

ISBN: (纸本)9781728192550

Vision device is a critical component of fruit harvesting mobile robot which is designed for recognizing and harvesting of tomato fruits. The system of vision device of the moving robot, for a dedicated path identification with the use of colour detection algorithms and contour detection algorithms could be stated as an additional usage of the image-based technology. When the robot operates in real life, varying environmental parameters would not affect its activity, since the robot will be functioning mainly indoors, specifically in a greenhouse. Robust fruit segmentation algorithms for the visual system and the fruit plucking mechanism using a readymade robot arm with a soft gripper where it operates with basic hardware components like motors, controlled by a microcontroller with the intervention of kinematic theories, is being used with the goal of collecting fruit objects efficiently in the natural world. Plucking only the ripe and healthy object fruit without damaging both the fruit and the tree is the intended task of the system. The aim is to reduce human effort by promoting automation concepts in agriculture in developing countries like Sri Lanka with great potential, where agriculture plays a vital role in the development of the economy while indoor plantation and harvesting mechanism are also being developing to cater to the economy of the country as well. The way that this system is implemented will be discussed in this paper. © 2020 IEEE.

关键词： Fruits

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Cosine Windows in Interpolated DFT-based Method for an Accurate High-Frequency Distortion Assessment in Power systems

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2023年 21卷 323-328页

作者： Bracale, A. Caramia, P. Carpinelli, G. De Falco, P. Verde, P. Department of Engineering University of Naples Parthenope Centro Direzionale Is. C4 Naples80143 Italy Electrical Power Systems Naples Italy Dep. of Electrical and Information Engineering Engineering & DIAEE University of Cassino and Lazio Meridionale & La Sapienza Università di Roma Via di Biasio n. 43 Cassino03043 Italy

The transformation of electrical networks in the context of the new smart grid paradigm unavoidably involves new challenges regarding Power Quality (PQ) disturbances not only for customers but also for all the other involved stakeholders. Among PQ disturbances, waveform distortions have recently gained growing interest due to the massive presence of new technologies in distributed energy resources, in modern loads and in advanced smart metering systems. The presence of these devices determines arduous electromagnetic compatibility problems since the current and voltage waveform distortions in smart grids are characterized by spectral components above the traditional 2 kHz frequency limit, in a range extended up to 150 kHz. In this paper, an interpolated DFT-based (IDFT) method, recently proposed in the relevant literature in the field of signal processing, is properly extended for an accurate and fast assessment of power system waveform distortions in the frequency range from 2 to 150 kHz. Since DFT-based methods can suffer well-known spectral leakage problems, in this paper the IDFT is applied using cosine windows that minimize interference conditions among spectral components and maximise the estimation accuracy of the spectral component amplitude, phase angle and frequency. An optimal number of cosine window terms is also searched to improve the spectral analysis of high-frequency power system waveforms. Numerical applications on synthetic test signals and measured waveforms are carried out to quantify the accuracy and computational efforts of the proposed approach and to select the cosine window terms that better optimize the waveform distortion assessment. © 2023, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Power quality

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Assessment and recognition in technical massive open on-line courses with and without on-line laboratories

Assessment and recognition in technical massive open on-line...

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IEEE Education engineering (EDUCON)

作者： Sergio Martin Manuel Castro Elio Sancristobal Gabriel Diaz Felix Garcia-Loro Clara Perez Blanca Quintana Rosario Gil Pedro Plaza Africa Lopez German Carro Antonio Robles Llanos Tobarra Miguel Rodriguez-Artacho Jose A. Ruiperez-Valiente Electrical and Computer Engineering Department Universidad Nacional de Educación a Distancia Madrid Spain Dep. Sistemas de Comunicación y Control Universidad Nacional de Educación a Distancia Madrid Spain Dep. Lenguajes y Sistemas Informáticos Universidad Nacional de Educación a Distancia Madrid Spain Teaching Systems Lab Masschusetts Institute of Technology USA

This paper describes the organization and results of several MOOCs delivered about technical topics (i.e., digital electronics, VHDL design on FPGAs, open education and OERs repositories and the use of STEAM technologies to encourage diversity and inclusion), where online laboratories have been used in some of them. An analysis of the enrollment, students participating on tasks or quizzes, drop-out rate and certifications requests have been done.

关键词： VHDL Laboratories Organizations Task analysis engineering education Field programmable gate arrays Certification

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Smart Monitoring and Disease Detection for Robotic Harvesting of Tomatoes 3

Smart Monitoring and Disease Detection for Robotic Harvestin...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Pasindu, Imesh Viraj, Sumeera Dilshan, Ravindu Kalhara, Akshith Senaweera, Oshada De Silva, Rajitha Jayawardena, Chandimal Sri Lanka Institute of Information Technology Dep. of Computer Systems Engineering Malabe Sri Lanka Lincoln Institute for Agri-Food Technology University of Lincoln Lincoln Agri-Robotics Centre United Kingdom Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9781665408622

Tomato is a one of the most popular produced and extensively consumed vegetables in the world. Typical agricultural systems make extensive use of human labor which is more costly and less effective. This research explores the minimization of human labor through automation. The diseases infected by tomato plants are hard to detect. Identifying these diseases in advance would save the cultivation of the disease from spreading, thereby saving the crop. It is also a difficult task to recognize the ripe harvest and experienced labor is required. The efficiency of the harvesting method will be increased by automating the identification process of ripened fruits. Manually picking tomatoes can cause some harm to the fruits during plucking due to inconsistencies in human labor. Such damage will be reduced through a better implemented robotic scheme. This paper presents the development of autonomous system for tomato harvesting and disease detection. © 2021 IEEE.

关键词： Harvesting

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Enhancements to the 5G-NR 2-Step RACH: an Unsourced Multiple Access Perspective

Enhancements to the 5G-NR 2-Step RACH: an Unsourced Multiple...

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IEEE Conference on Standards for Communications and Networking (CSCN)

作者： Patrick Agostini Jean-Francois Chamberland Federico Clazzer Johannes Dommel Gianluigi Liva Andrea Munari Krishna Narayanan Yury Polyanskiy Slawomir Stanczak Zoran Utkovski Fraunhofer Heinrich-Hertz-Institut Berlin Germany Dep. of Electrical and Computer Engineering College Station Texas A&M University TX USA German Aerospace Center (DLR) Inst. of Communications and Navigation Wessling Germany Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9798331507428

ISBN: (纸本)9798331507435

The ever increasing demand for massive connectivity, combined with the specific characteristics of machinegenerated wireless traffic, is pushing the existing solutions for device registration and data transfer beyond their operational limits. In this paper, we target specifically the 3GPP 5G New Radio random access channel, proposing some enhancements inspired by recent research results in the field of unsourced multiple access (UMAC). Aiming to enable massive grant-free connectivity for large machine-type and Internet of things terminals, we focus on modifications of the two-step random access procedures originally introduced in 3GPP Release 16. By means of simulations, we show that remarkable gains in both energy and spectral efficiency can be attained while retaining a strong resemblance to the legacy protocol.

关键词： Wireless communication Fading channels Protocols Spectral efficiency Simulation New Radio 3GPP Internet of Things Standards Signal to noise ratio

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Decoupled feedforward-feedback periodic event-triggered control for disturbance rejection ⁎

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IFAC-PapersOnLine 2020年第2期53卷 2708-2713页

作者： Ernesto Aranda-Escolástico María Guinaldo José Luis Guzmén Sebastián Dormido Dep. of Software and Systems Engineering UNED Dep. of Computer Sciences and Automatic Control UNED Dep. of Informatics ceiA3 CIESOL University of Almería

In this paper, feedforward and feedback controllers are studied considering decoupled periodic event-triggering mechanisms for output and disturbance sensors. Stability and robustness conditions for linear systems are obtained considering transportation delays and actuator saturation following the Lyapunov-Krasovskii procedure. A numerical example shows that the proposed control strategy reduces the communication between sensors and controller significantly, while the system performance is not deteriorated.

关键词： Event-triggered control feedforward control Lyapunov stability time delay disturbance rejection saturation

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