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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Daniel Voipan Iulian Vasiliev Larisa Diaconu Andreea-Elena Voipan Marian Barbu Department of Computer Science and Information Technology "Dunărea de Jos" University of Galați Galați Romania Department of Automatic Control and Electrical Engineering "Dunărea de Jos" University of Galați Galați Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

This research paper delves into the application of Long Short-Term Memory (LSTM) neural networks within the Benchmark Simulation Model No. 2 (BSM2) to enhance the predictability and efficiency of wastewater treatment processes. The study aims to develop advanced predictive models that can simulate the dynamics of wastewater treatment more accurately and adjust operational strategies dynamically. By integrating LSTM networks, the research enables continuous prediction of Effluent Quality Index (EQI) variables under stochastic and deterministic scenarios, thereby improving the accuracy and efficiency of predicting pollutant levels. The research uses an LSTM model to learn from a comprehensive dataset derived from historical simulations of BSM2, where key parameters such as the oxygen transfer coefficient (K L a) are systematically varied to measure their impact on effluent quality. The LSTM's capability to handle complex, nonlinear data and its adaptability to time series forecasting significantly enhances model performance, offering a robust tool for real-time decision-making and process optimization in wastewater treatment facilities. This approach not only improves the accuracy and efficiency of predicting pollutant levels but also supports environmental compliance and operational sustainability, making it a valuable tool for environmental engineers and professionals in the field of wastewater treatment.

关键词： Adaptation models Accuracy Time series analysis Stochastic processes Effluents Predictive models Real-time systems Wastewater Wastewater treatment Long short term memory

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UNav-Sim: A Visually Realistic Underwater Robotics Simulator and Synthetic Data-Generation Framework

UNav-Sim: A Visually Realistic Underwater Robotics Simulator...

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International Conference on Advanced Robotics (ICAR)

作者： Abdelhakim Amer Olaya Álvarez-Tuñón Halil İbrahim Uğurlu Jonas Le Fevre Sejersen Yury Brodskiy Erdal Kayacan Department of Electrical Engineering and Computer Engineering Aarhus University Aarhus Denmark EIVA a/s Skanderborg Denmark Department of Electrical Engineering and Information Technology Automatic Control Group Paderborn University Paderborn Germany

Underwater robotic surveys can be costly due to the complex working environment and the need for various sensor modalities. While underwater simulators are essential, many existing simulators lack sufficient rendering quality, restricting their ability to transfer algorithms from simulation to real-world applications. To address this limitation, we introduce UNav-Sim, which, to the best of our knowledge, is the first simulator to incorporate the efficient, high-detail rendering of Unreal Engine 5 (UE5). UNav-Sim is open-source 1 1 https://***/open-airlab/UNav-Sim and includes an autonomous vision-based navigation stack. By supporting standard robotics tools like ROS, UNav-Sim enables researchers to develop and test algorithms for underwater environments efficiently.

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Software Implementation of a Smart Bracelet Prototype to Monitor Vital Signs, Locate, and Track COVID-19 Patients in Quarantine Zone

Software Implementation of a Smart Bracelet Prototype to Mon...

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2022 IEEE International Conference on Blockchain, Smart Healthcare and Emerging Technologies, SmartBlock4Health 2022

作者： Naji, Hasan K. Goga, Nicolae Karkar, Ammar J. M. Ali, Haider Abdullah Falahi, Mandana Marin, Iuliana Ramona-Cristina, Popa Doctoral School of Automatic Control and Computers University POLITEHNICA of Bucharest Bucharest Romania University POLITEHNICA of Bucharest Faculty of Engineering in Foreign Languages Bucharest Romania University of Groningen Molecular Dynamics Group Groningen Netherlands University of Kufa IT Research and Development Centre Department of Electronic and Communication Engineering Najaf Iraq University POLITEHNICA of Bucharest Faculty of Automatic Control and Computers Bucharest Romania Madenat Alelem University Collage Department of Computer Engineering Techniques Baghdad Iraq University POLITEHNICA of Bucharest Bucharest Romania

ISBN: (纸本)9781665460149

This paper describes the design and software implementation of a wearable prototype that allows users to monitor the vital signs of COVID-19 patients in quarantine areas. This prototype consists of two parts, the bracelet, andthe Base control unit (BCU). The bracelet is built with ESP8266 and sensors as main components, as well as the battery and other parts needed to fulfill the system's purpose (monitoring the vital signs of COVID-19 patients). At the same time, the Raspberry Pi (SCB) single board computer and GSM/GPRS/HAT are the main components of the Basic control Unit (BCU). The current work describes the main parts of the pseudocode, as well as the activity diagram for the microcontroller and Raspberry Pi. This paper describes the mechanism of sending alert messages, whereby the system's ability to configure two types of alert messages;(1) physician Messages (these Messages will be sent to the physicianassociated with the patient if one or more vital signs reach a critical value;these messages contain all measurements of a patient's vital signs);(2) Authorize messages (these messages will be sent if the quarantine rules are violated;the patient's location will be sent to the authorized person as a Google Mapslink). Also, this paper describes the graphical user interface for communication, management,. and interaction between the users of the system. © 2022 IEEE.

关键词： COVID-19

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Sample Complexity for Evaluating the Robust Linear Observers Performance under Coprime Factors Uncertainty

arXiv

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arXiv 2022年

作者： Zhang, Yifei Ukil, Sourav Kumar Sperilă, Andrei Sabău, Şerban Department of Electrical and Computer Engineering Stevens Institute of Technology NJ07030 United States Department of Automatic Control and Computer Science "Politehnica" University of Bucharest Bucharest Romania

This paper addresses the end-to-end sample complexity bound for learning in closed loop the state estimator-based robust H2 controller for an unknown (possibly unstable) Linear Time Invariant (LTI) system, when given a fixed state-feedback gain. We build on the results from Ding et al. (1994) to bridge the gap between the parameterization of all state-estimators and the celebrated Youla parameterization. Refitting the expression of the relevant closed loop allows for the optimal linear observer problem given a fixed state feedback gain to be recast as a convex problem in the Youla parameter. The robust synthesis procedure is performed by considering bounded additive model uncertainty on the coprime factors of the plant, such that a min-max optimization problem is formulated for the robust H2 controller via an observer approach. The closed-loop identification scheme follows Zhang et al. (2021), where the nominal model of the true plant is identified by constructing a Hankel-like matrix from a single time-series of noisy, finite length input-output data by using the ordinary least squares algorithm from Sarkar et al. (2020). Finally, a H∞ bound on the estimated model error is provided, as the robust synthesis procedure requires bounded additive uncertainty on the coprime factors of the model. Copyright © 2022, The Authors. All rights reserved.

关键词： Additives

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Active Exploration in Iterative Gaussian Process Regression for Uncertainty Modeling in Autonomous Racing

arXiv

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arXiv 2023年

作者： Benciolini, Tommaso Tang, Chen Leibold, Marion Weaver, Catherine Tomizuka, Masayoshi Zhan, Wei The Automatic Control Engineering The Technical University of Munich Germany The Department of Computer Science The University of Texas AustinTX United States The Department of Mechanical Engineering The University of California BerkeleyCA United States

Autonomous racing creates challenging control problems, but Model Predictive control (MPC) has made promising steps toward solving both the minimum lap-time problem and head-to-head racing. Yet, accurate models of the system are necessary for model-based control, including models of vehicle dynamics and opponent behavior. Both dynamics model error and opponent behavior can be modeled with Gaussian Process (GP) regression. GP models can be updated iteratively from data collected using the controller, but the strength of the GP model depends on the diversity of the training data. We propose a novel active exploration mechanism for iterative GP regression that purposefully collects additional data at regions of higher uncertainty in the GP model. In the exploration, a MPC collects diverse data by balancing the racing objectives and the exploration criterion;then the GP is re-trained. The process is repeated iteratively;in later iterations, the exploration is deactivated, and only the racing objectives are optimized. Thus, the MPC can achieve better performance by leveraging the improved GP model. We validate our approach in the highly realistic racing simulation platform Gran Turismo Sport of Sony Interactive Entertainment Inc for a minimum lap time challenge, and in numerical simulation of head-to-head. Our active exploration mechanism yields a significant improvement in the GP prediction accuracy compared to previous approaches and, thus, an improved racing performance. © 2023, CC BY-NC-ND.

关键词： Model predictive control

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FPGA Implementation of SIMON-128 Cryptographic Algorithm Using Artix-7 14

FPGA Implementation of SIMON-128 Cryptographic Algorithm Usi...

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14th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2022

作者： Ghayoula, Ridha Fattahi, Jaouhar Smida, Amor El Gmati, Issam Pricop, Emil Ziadia, Marwa Laval University Department of Electrical and Computer Engineering Quebec Canada Laval University Department of Computer Science and Software Engineering Quebec Canada Majmaah University Department of Medical Equipment Technology Majmaah Saudi Arabia College of Engineering Al Gunfudha Umm Al Qura University Umm Al Qura Saudi Arabia Petroleum-Gas University of Ploiesti Automatic Control Computers and Electronics Department Ploiesti Romania

ISBN: (数字)9781665495356

ISBN: (纸本)9781665495356

FPGA is a hardware architecture based on a matrix of programmable and configurable logic circuits thanks to which a large number of functionalities inside the device can be modified using a hardware description language. These functionalities must often be secured especially when the context is sensitive (military, banking, medical, legal, etc.). In this paper, we put forward an efficient implementation of SIMON's block cipher algorithm using Xilinx Vivado 2018.2. The proposed design is analyzed through simulation on Xilinx Artix-7. A prototype of our design is implemented using the xc7a35tcsg324-1 FPGA chip. Performance and results are discussed. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Privacy-Preservation for Distributed Quantized k-means Clustering

Privacy-Preservation for Distributed Quantized k-means Clust...

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Annual Allerton Conference on Communication, control, and Computing

作者： Apostolos I. Rikos Nicolas E. Manitara Gabriele Oliva Christoforos N. Hadjicostis Department of Electrical and Computer Engineering Division of Systems Engineering Boston University Boston MA USA Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Department of Engineering Unit of Automatic Control Università Campus Bio-Medico di Roma Rome Italy

In this paper, we enhance a distributed version of the well known k-means algorithm with privacy-preservation features. While ensuring that sensitive or confidential information remains undisclosed to unauthorized entities (in our case these entities are curious nodes within the network), we maintain the desirable features of the distributed algorithm: the transmitted values are quantized, which optimizes bandwidth utilization and alleviates communication bottlenecks, and nodes possess the ability to collectively determine when to terminate the algorithm, which enables the conservation of valuable resources. We introduce a novel privacy-preserving protocol that not only preserves the privacy of a node’s state, but it also ensures confidentiality about its cluster affiliation (i.e., it does not reveal to curious nodes whether a node participates in the update calculation for a specific centroid value). Moreover, we precisely characterize topological conditions that guarantee privacy preservation for individual nodes. Our distributed algorithm allows the formation of exclusive clusters within a finite time frame on any static and strongly connected directed graph.

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automatic Detection of Lumbar Disc Herniation Using YOLOv7

Automatic Detection of Lumbar Disc Herniation Using YOLOv7

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Prisilla, Ardha Ardea Pusparani, Yori Chang, Wen-Thong Liau, Ben-Yi Jan, Yih-Kuen Ardhianto, Peter Lin, Chih-Yang Lung, Chi-Wen Asia University Taichung Department of Digital Media Design Taiwan LaSalle College Jakarta Department of Fashion Design Jakarta Indonesia Budi Luhur University Department of Visual Communication Design Jakarta Indonesia Department of Computer Science and Information Engineering Taichung Taiwan Feng Chia University Department of Automatic Control Engineering Taichung Taiwan Asia University Department of Creative Product Design Taichung Taiwan Soegijapranata Catholic University Department of Visual Communication Design Semarang Indonesia National Central University Department of Mechanical Engineering Taoyuan Taiwan

ISBN: (纸本)9798350324174

The detection of lumbar disc herniation (LDH) through magnetic resonance imaging (MRI) poses a challenge due to the various shapes, sizes, angles, and regions associated with bulges, protrusions, extrusions, and sequestrations. One potential solution is using deep learning methods to identify lumbar abnormalities in MRI images automatically. The YOU ONLY LOOK ONCE (YOLO) model series has gained popularity for training deep learning algorithms for real-time biomedical image detection. This study aims to assess the performance of the latest YOLOv7 in detecting LDH across different regions of the lumbar intervertebral disc. The analysis revealed that YOLOv7 exhibits a poor performance and low detection rate of LDH across the L1-L2, L2-L3, L3-L4, L4-L5, and L5-S1 regions. © 2023 IEEE.

关键词： Magnetic resonance imaging

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LiDAR-based drone navigation with reinforcement learning

TechRxiv

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TechRxiv 2023年

作者： Miera, Pawel Szolc, Hubert Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

Reinforcement learning is of increasing importance in the field of robot control and simulation plays a key role in this process. In the unmanned aerial vehicles (UAVs, drones), there is also an increase in the number of published scientific papers involving this approach. In this work, an autonomous drone control system was prepared to fly forward (according to its coordinates system) and pass the trees encountered in the forest based on the data from a rotating LiDAR sensor. The Proximal Policy Optimization (PPO) algorithm, an example of reinforcement learning (RL), was used to prepare it. A custom simulator in the Python language was developed for this purpose. The Gazebo environment, integrated with the Robot Operating System (ROS), was also used to test the resulting control algorithm. Finally, the prepared solution was implemented in the Nvidia Jetson Nano eGPU and verified in the real tests scenarios. During them, the drone successfully completed the set task and was able to repeatably avoid trees and fly through the forest. © 2023, CC BY.

关键词： Drones

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ELSA: Euler-Lagrange Skeletal Animations - Novel and Fast Motion Model Applicable to VR/AR Devices 21st

ELSA: Euler-Lagrange Skeletal Animations - Novel and Fast Mo...

引用

21st International Conference on Computational Science, ICCS 2021

作者： Wereszczyński, Kamil Michalczuk, Agnieszka Foszner, Pawel Golba, Dominik Cogiel, Michal Staniszewski, Michal Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland KP Labs Gliwice Poland

ISBN: (纸本)9783030779764

Euler Lagrange Skeletal Animation (ELSA) is the novel and fast model for skeletal animation, based on the Euler Lagrange equations of motion and configuration and phase space notion. Single joint’s animation is an integral curve in the vector field generated by those PDEs. Considering the point in the phase space belonging to the animation at current time, by adding the vector pinned to this point and multiplied by the elapsed time, one can designate the new point in the phase space. It defines the state, especially the position (or rotation) of the joint after this time elapses. Starting at time 0 and repeating this procedure N times, there is obtained the approximation, and if the N→ ∞ the integral curve itself. Applying above, to all joint in the skeletal model constitutes ELSA. © 2021, Springer Nature Switzerland AG.

关键词： Animation

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