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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Vasanthan, Niroshini Shimran, Mohamed Ahkam, Ahmadu Ishak, Ishma Silva, Chamari Kuruppu, Thilmi Anuththara Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Information Technology Malabe Sri Lanka

ISBN: (纸本)9781665408622

Agricultural productivity plays a vital role in contributing to a nation's economy. Farmers nowadays are concerned due to disease persistence in crops and plants, and it also affects the economy indirectly, so it is important to come up with a solution to detect plant diseases and educate the farmers about the solutions to retaliate against the diseases. Proper care is mandatory to safeguard the quality of plants. The existing traditional methods consume a massive amount of time and resources hence, it's costly. Due to the importance of continuous monitoring, it seems impractical for a farmer to implement the traditional methods on large scale. The Traditional systems which are used lack the ability to identify diseases out of their predefined scope. As a solution, we came up with an auto-learning system that identifies new plant diseases and provides remedies. This paper showcases the image processing techniques to detect plant diseases, Auto ML techniques to create new models for plants and corresponding diseases, Diseases are identified using image processing, Remedies are extracted for the given plant diseases using unstructured data from web data crawling. The business intelligence model uses NLP to provide ideas about the trending plants and plant-related diseases are also discussed in this paper. © 2021 IEEE.

关键词： Image classification

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Deep-Learning-Based VCSEL Transmitter Emulator

Deep-Learning-Based VCSEL Transmitter Emulator

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Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Stavros Deligiannidis Nikos Argyris Stefanos Dris Dimitrios Kalavrouziotis Paraskevas Bakopoulos Charis Mesaritakis Adonis Bogris Department of Informatics and Computer Engineering University of West Attica Athens Greece NVIDIA Athens Greece Dept. Information and Communication Systems Engineering University of the Aegean Karlovasi Samos Greece

ISBN: (纸本)9798350345995

The design of photonic/opto-electronic structures is a crucial field of research due to the wide range of applications in which these devices are utilized, such as communications, sensing and imaging. The traditional approach to design, which involves numerical simulation and iterative adjustments, is both time-consuming and computationally intensive. The ability to design-emulate photonic structures quickly and efficiently could bring new technologies to market at a faster pace and can also facilitate the process of extracting critical performance metrics from complex fabricated devices. The Vertical Cavity Surface Emitting Laser (VCSEL) and its use as a transmitter broadly used in data center interconnects is an example of a rather complex nonlinear system also exhibiting bandwidth limitation mechanisms attributed to its frequency response and parasitic effects, and thus its numerical simulation based on conventional rate equations can only partially approximate its behavior. When it comes to modelling fabricated devices, such physical models are notoriously difficult to parameterize in order to fit to observed laboratory data; in contrast, the emulation of the experimentally recorded behavior with the use of nonlinear models based on neural networks provides a much more efficient path to modelling, and with potentially higher prediction accuracy [1]. In this work, two types of bidirectional recurrent neural networks (RNN), namely Long Short-Term Memory (bi-LSTM) and Vanilla (bi-VRNN) were used to simulate the dynamic behavior of an experimentally characterized VCSEL-based, PAM-4 transmitter, exhibiting a high prediction accuracy approaching 100%.

关键词： Interpolation Multimode fibers Neural networks Numerical simulation Optical systems Vertical cavity surface emitting lasers

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A Review on Improvement in Detection of Cyberattacks Using Artificial Intelligence for the Grid Applications

A Review on Improvement in Detection of Cyberattacks Using A...

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Electrical, Electronic and Information Sciences (eStream), Open Conference of

作者： Zivile Vilkelyte Jerzy Wojciechowski Piotr Bojarczak Saad El Fallah Jaouad Kharbach Mohammed Ouazzani Jamil Jonas Vanagas Dept. Electrical Engineering Vilnius TECH Vilnius Lithuania Dept. Control Systems and Electronics Casimir Pulaski University of Radom Radom Poland Systems and Sustainable Environment Laboratory Faculty of Engineering Sciences (FSI) Private University of Fez (UPF) Fez Morocc Université Sidi Mohamed Ben Abdellah Faculté des Sciences Dhar El Mahraz Laboratoire de Physique de Solide Fez Morocco Systems and Sustainable Environment Laboratory Faculty of Engineering Sciences (FSI) Private University of Fez (UPF) Fez Morocco Dept. Electrical Engineering Vilnius TECH Vilnius Lithuania

ISBN: (数字)9798350352412

ISBN: (纸本)9798350352429

The integration of artificial intelligence and machine learning technologies into critical infrastructures, such as smart grids, has raised significant concerns regarding cybersecurity. This paper explores the dual role of artificial intelligence and machine learning in both enhancing and challenging cybersecurity within smart grid systems. By analysing the current stateof-the-art research and technology, the utilisation of artificial intelligence and machine learning to fortify cybersecurity defences while addressing potential vulnerabilities. The the emergence of cyber threats targeting Internet-of-Things-based smart grids is highlighted and solutions to mitigate these risks are proposed. Through a comprehensive review of literature, the efficacy of artificial intelligence driven cybersecurity measures in detecting and preventing cyberattacks are evaluated. Additionally, challenges associated with implementing these solutions in smart grid environments, such as data complexity and computational requirements are taken into account. The findings underscore the critical importance of ongoing research and innovation to ensure the resilience of smart grid cybersecurity.

关键词： Technological innovation Reviews Machine learning Smart grids Critical infrastructure Complexity theory computer crime

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Visual Field Prediction for Fundus Image with Generative AI

Visual Field Prediction for Fundus Image with Generative AI

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Honggu Kang Seonghyeon Ko Juchan Kim Duc-Tai Le Junghyun Bum Jongchul Han Hyunseung Choo Dept. of AI Systems Engineering Sungkyunkwan University Suwon South Korea College of Computing and Informatics Sungkyunkwan University Suwon South Korea Sungkyun AI Research Institute Sungkyunkwan University Suwon South Korea Dept. of Ophthalmology Samsung Medical Center Seoul South Korea Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

Accurate diagnosis of glaucoma is crucial due to its high risk of blindness, but the long examination time often undermines the reliability of results by examinee's subjective factors. We propose a method to reduce examination time by generating static perimetry results with Conventional Fundus Images (CFIs) utilizing the CFI2GM technique, which leverages multimodal data. Based on data from 3,306 glaucoma patients at Samsung Medical Center in Seoul, we conducted ophthalmic image translation utilizing the Pix2Pix model. Our method, combining cGAN, L1, and SSIM loss, achieved MSE 57.9886 and PSNR 30.6057 dB. Furthermore, we received positive feedback from ophthalmologist regarding the high practical applicability of the images generated by our method. This indicates that CFI2GM can enhance the reliability of glaucoma examination results as well as reduce testing time.

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LPV Modeling of the Atmospheric Flight Dynamics of a Generic Parafoil Return Vehicle

arXiv

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arXiv 2022年

作者： de Lange, Matthis H. Verhoek, Chris Preda, Valentin Tóth, Roland Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven5600MB Netherlands ESTEC European Space Agency Noordwijk2200AG Netherlands Systems and Control Lab Institute for Computer Science and Control Budapest1111 Hungary

Obtaining models that can be used for control is of utmost importance to ensure the guidance and navigation of spacecraft, like a Generic Parafoil Return Vehicle (GPRV). In this paper, we convert a nonlinear model of the atmospheric flight dynamics of an GPRV to a Linear Parameter-Varying (LPV) description, such that the LPV model is suitable for navigation control design. Automated conversion methods for nonlinear models can result in complex LPV representation, which are not suitable for controller synthesis. We apply several state-of-the-art techniques, including learning based approaches, to optimize the complexity and conservatism of the LPV embedding for an GPRV. The results show that we can obtain an LPV embedding that approximates the complex nonlinear dynamics sufficiently well, where the balance between complexity, conservatism and model performance is optimal. © 2022, CC BY-NC-SA.

关键词： Principal component analysis

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Performance Evaluation of State and Output Feedback-based Repetitive controllers for Rotational control systems

Performance Evaluation of State and Output Feedback-based Re...

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International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET)

作者： Akif Rahmatillah Edi Kurniawan Aulia Siti Aisjah Katherin Indriawati Dept. of Engineering Physics Faculty of Industrial Technology & Systems Engineering Sepuluh Nopember Institut of Technology Surabaya Indonesia Research Group of Precision Control & Measurement Research Centre for Photonics National Research and Innovation Agency Tangerang Selatan Indonesia

ISBN: (数字)9798350389920

ISBN: (纸本)9798350389937

This study examines the tracking performance of state and output feedback-based repetitive controllers (RCs) for rotational control systems. First, a state feedback RC is designed in the time domain using a state-space model, which requires full state information for implementation. The state feedback RC is modeled as a 2D hybrid system, where the control and learning actions can be designed independently using Linear Matrix Inequality (LMI) solutions. This approach is evaluated on a rotational control system expressed as a strictly proper linear system with a relative degree of 1. Second, for comparison, an output feedback RC, also known as a general RC, is designed for the same system. Numerical experiment results show that while the state feedback RC achieves lower peak and root-means-quare errors, it exhibits slower transient response compared to the output feedback RC.

关键词： Microwave antennas State feedback Transient response Velocity control control systems Radar antennas Telecommunications Time-domain analysis Output feedback Convergence

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Non-collocated Vibration Suppression using Delay-Based controllers with Multiple Fixed Delays*

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IFAC-PapersOnLine 2023年第2期56卷 180-185页

作者： Adrian Saldanha Wim Michiels Tomáš Vyhlídal Department of Computer Science KU Leuven Celestijnenlaan 200A 3001 Leuven Belgium Dept. Instrumentation and Control Engineering Faculty of Mechanical Engineering and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6 Czechia

The problem of non-collocated vibration absorption is targeted utilizing a multiparameter delay-based controller. The combined objective is to achieve closed-loop stability and vibration suppression at a target location, which is different from the position of the absorber. This problem can be translated into an optimization problem of shaping the poles of a closed-loop system with delays subject to zero-location constraints. The solution approach involves remodelling the closed-loop system as a system of delay-differential algebraic equations (DDAEs) which allows to systematically account for delays in the control law, thereby enabling a straightforward technique for computation of controller parameters. Finally, the proposed design method is validated by simulation on a lumped parameter model of a mass-spring-damper system.

关键词： vibration suppression time-delay systems DDAE optimization

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Molecule Generation from Input-Attributions over Graph Convolutional Networks

arXiv

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arXiv 2022年

作者： Savoia, Dylan Ragno, Alessio Capobianco, Roberto Dept. of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Italy Sony AI

It is well known that Drug Design is often a costly process both in terms of time and economic effort. While good Quantitative Structure-Activity Relationship models (QSAR) can help predicting molecular properties without the need to synthesize them, it is still required to come up with new molecules to be tested. This is mostly done in lack of tools to determine which modifications are more promising or which aspects of a molecule are more influential for the final activity/property. Here we present an automatic process which involves Graph Convolutional Network models and input-attribution methods to generate new molecules. We also explore the problems of over-optimization and applicability, recognizing them as two important aspects in the practical use of such automatic tools. © 2022, CC BY.

关键词： Molecules

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Modeling the Productivity of a Sugar Factory using Machine Learning Methods

Modeling the Productivity of a Sugar Factory using Machine L...

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International computer Sciences and Information Technologies (CSIT)

作者： Nataliia Lutska Lidiia Vlasenko Nataliia Zaiets Vitaliy Lysenko dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798350334326

Relevant today is the digitalization of industrial enterprises, which is an evolutionary continuation of the introduction of MES/MOM. At the same time, an important role is played by the construction of mathematical models for various purposes, which are the main component of the Digital Twin development. Digital Twin provides a new philosophy for conducting enterprise production processes based on these models, which is aimed at improving the efficiency of both individual sectors and the enterprise as a whole. To increase the profit of the enterprise, it is necessary to ensure the planned productivity of technological processes, the final indicator of which is the yield of sugar. Taking into account that a significant number of material flows operate at the enterprise, only production flows with which the operator-technologist works are singled out in the work. The selected flows, based on expert assessments, were assigned weighted values of the impact on the overall performance of the sugar production. This made it possible to build a mathematical model that predicts the yield of sugar from input flows. The structure and parameters of this model are determined by machine learning methods. This is a feed-forward neural network MLP 7-23-23-1, which provides forecast accuracy with an error of less than 1%. The model can be used to model, correct, and predict the process conditions of an enterprise, which will increase the overall productivity of the sugar factory.

关键词： Productivity Biological system modeling Machine learning Predictive models Mathematical models Sugar industry Production facilities

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Semi-Supervised GCN for learning Molecular Structure-Activity Relationships

arXiv

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arXiv 2022年

作者： Ragno, Alessio Savoia, Dylan Capobianco, Roberto Dept. of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Italy Sony AI

Since the introduction of artificial intelligence in medicinal chemistry, the necessity has emerged to analyse how molecular property variation is modulated by either single atoms or chemical groups. In this paper, we propose to train graph-to-graph neural network using semi-supervised learning for attributing structure-property relationships. As initial case studies we apply the method to solubility and molecular acidity while checking its consistency in comparison with known experimental chemical data. As final goal, our approach could represent a valuable tool to deal with problems such as activity cliffs, lead optimization and de-novo drug design. © 2022, CC BY.

关键词： Graph neural networks

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