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5th International Conference on computer science and Application Engineering, CSAE 2021

作者： Liu, Zhenqi Liu, Changhua Chen, Jiaxi University of Sheffield Automatic Control and Systems Engineering South Yorkshire Sheffield United Kingdom School of Mathematics and Computer Science Wuhan Polytechnic University Hubei Wuhan China

ISBN: (纸本)9781450389853

Leaf is an important part of plants. A rapid and accurate detection of plant leaf area is an important guidance to reasonable fertilizer application and accurate sprinkler irrigation. In order to solve the problem of large error and incomplete target region in segmenting leaves when people using Otus's threshold method, a method based on watershed algorithm was proposed to segment the leaves of Brassica napus. Firstly, the images of relevant crop leaves are collected by the digital camera on site. Then these images are preprocessed by Sobel operator and morphological operation. Thirdly, labeled images are segmented by watershed algorithm to get target images. Finally the leaf area is calculated by counting the number of pixels in target area. The experimental results show that this method can solve the problem of incomplete segmentation of the Otus's threshold method and effectively reduce the measurement error to less than 2.2%.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Mathematical morphology

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Towards a Holistic controller: Reinforcement Learning for Data Center control 21

Towards a Holistic Controller: Reinforcement Learning for Da...

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12th ACM International Conference on Future Energy Systems, e-Energy 2021

作者： Heimerson, Albin Brännvall, Rickard Sjölund, Johannes Eker, Johan Gustafsson, Jonas Lund University Dept. of Automatic Control Sweden Rise Research Institutes of Sweden Dept. of Computer Science Sweden Ericsson Research Lund University Sweden

ISBN: (纸本)9781450383332

The increased use of cloud and other large scale datacenter IT services and the associated power usage has put the spotlight on more energy-efficient datacenter management. In this paper, a simple model was developed to represent the heat rejection system and energy usage in a small DC setup. The model was then controlled by a reinforcement learning agent that handles both the load balancing of the IT workload, as well as cooling system setpoints. The main contribution is the holistic approach to datacenter control where both facility metrics, IT hardware metric and cloud service logs are used as inputs. The application of reinforcement learning in the proposed holistic setup is feasible and achieves results that outperform standard algorithms. The paper presents both the simplified DC model and the reinforcement learning agent in detail and discusses how this work can be extended towards a richer datacenter model. © 2021 ACM.

关键词： Reinforcement learning

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Biological Influence of Low-Frequency Electromagnetic Field Exposition Based on Selected Cell Death Markers

Biological Influence of Low-Frequency Electromagnetic Field ...

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ELEKTRO

作者： Robert Cichorowski Olga Kocikowska Marek Bajtoš Roman Radil Ladislav Janoušek Daria Gendosz De Carrillo Sebastian Student Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology liwice Poland Department of Engineering and Systems Biology Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Department of Physiology Faculty of Medical Sciences Silesian University of Medicine Katowice Poland Department of Electromagnetic and Biomedical Engineering Faculty of Electrical Engineering and Informational Technology Žilina Slovakia Department of Histology Faculty of Medical Sciences in Zabrze Silesian University of Medicine Zabrze Poland Biotechnology Center Silesian University of Technology Gliwice Poland

ISBN: (数字)9798350372359

ISBN: (纸本)9798350372366

This work presents the results of the examination of the HeLa cell line exposure on the ELF-EMF (extremely low-frequency electromagnetic field). In particular, the relationship between ELF-EMF exposition time and cell death. The examination of cell death hallmarks were examined by estimation levels of selected proteins - FACL4 (a protein that is a part of the ferroptosis pathway) and CK18 (Cytokeratin related to necrosis and apoptosis pathways) in the proposed model of workers exposed to ELF-EMF week.

关键词： Proteins Biological system modeling Estimation Electromagnetic fields

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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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Machine Learning Detects Anomalies in OPS-SAT Telemetry 23rd

Machine Learning Detects Anomalies in OPS-SAT Telemetry

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23rd International Conference on Computational science, ICCS 2023

作者： Ruszczak, Bogdan Kotowski, Krzysztof Andrzejewski, Jacek Musial, Alicja Evans, David Zelenevskiy, Vladimir Bammens, Sam Laurinovics, Rodrigo Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Akademicka 16 Gliwice44-100 Poland KP Labs Konarskiego 18C Gliwice44-100 Poland European Space Agency/ESOC Robert-Bosch-Str. 5 Darmstadt64293 Germany Telespazio Germany GmbH Europaplatz 5 Darmstadt64293 Germany IrbGS ltd "Viraki" Ance Parish IrbeneLV-3601 Latvia

ISBN: (纸本)9783031359941

Detecting anomalies in satellite telemetry data is pivotal in ensuring its safe operations. Although there exist various data-driven techniques for the task of determining abnormal parts of the signal, they are virtually never validated over real telemetries. Analyzing such data is challenging due to its intrinsic characteristics, as telemetry may be noisy and affected by incorrect acquisition, resulting in missing parts of the signal. In this paper, we tackle this issue and propose a machine learning approach for detecting anomalies in single-channel satellite telemetry. To validate its capabilities in a practical scenario, we build a dataset capturing the nominal and anomalous telemetry data captured on board OPS-SAT—a nanosatellite launched and operated by the European Space Agency. Our extensive experimental study showed that the proposed algorithm offers high-quality anomaly detection in real-life satellite telemetry, reaching 98.4% accuracy over the unseen test set. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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End-to-End Lip Reading in Romanian with Cross-Lingual Domain Adaptation and Lateral Inhibition

End-to-End Lip Reading in Romanian with Cross-Lingual Domain...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Emilian-Claudiu Mănescu Răzvan-Alexandru Smădu Andrei-Marius Avram Dumitru-Clementin Cercel Florin Pop Faculty of Mathematics and Computer Science University of Bucharest Romania Faculty of Automatic Control and Computers University Politehnica of Bucharest Romania National Institute for Research and Development in Informatics - ICI Bucharest Romania

Lip reading or visual speech recognition has gained significant attention in recent years, particularly because of hardware development and innovations in computer vision. While considerable progress has been obtained, most models have only been tested on a few large-scale datasets. This work addresses this shortcoming by analyzing several architectures and optimizations on the underrepresented, short-scale Romanian language dataset called Wild LRRo. Most notably, we compare different backend modules, demonstrating the effectiveness of adding ample regularization methods. We obtain state-of-the-art results using our proposed method, namely cross-lingual domain adaptation and unlabeled videos from English and German datasets to help the model learn language-invariant features. Lastly, we assess the performance of adding a layer inspired by the neural inhibition mechanism.

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Improving the detection of noisy labels in image datasets using modified Confidence Learning

Improving the detection of noisy labels in image datasets us...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Adam Popowicz Krystian Radlak Slawomir Lasota Karolina Szczepankiewicz Michal Szczepankiewicz Faculty of Automatic Control Silesian University of Technology Electronics and Computer Science Gliwice Poland Warsaw University of Technology Warsaw Poland

ISBN: (数字)9781665468589

ISBN: (纸本)9781665468596

The effectiveness of machine learning algorithms, including deep neural networks (DNN) for classifying image data, depends on proper preparation of the training dataset. Erroneously labeled images in the training data will degrade algorithmic efficiency and cause unpredictable model behavior, thus reduce its safety. Verifying labels in the numerous available databases remains a complicated and laborious task. In this article, we present a MultiNET approach that allows for efficient verification of labeled image datasets. We adapt a state-of-the-art technique, namely Confidence Learning, extending its flexibility and improving the effectiveness by combining outcomes from various DNN architectures. Thanks to the proposed modification, it is possible to automatically detect incorrect labels while minimizing the number of false positives, thus making the verification process much less burdensome. The technique may be of use for researchers and software engineers dealing with externally supplied image datasets.

关键词： Deep learning Training Machine learning algorithms Databases Training data computer architecture Data models

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Towards Automated Detection of Adversarial Attacks on Tabular Data 18

Towards Automated Detection of Adversarial Attacks on Tabula...

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18th Conference on computer science and Intelligence Systems, FedCSIS 2023

作者： Biczyk, Piotr Wawrowski, Lukasz Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka Gliwice44-100 Poland Ukasiewicz Research Network Institute of Innovative Technologies Emag Ul. Leopolda 31 Katowice40-189 Poland Qed Software Sp. Z O.o. Mazowiecka 11/49 Warsaw00-052 Poland

ISBN: (纸本)9788396744784

The paper presents a novel approach to investigating adversarial attacks on machine learning classification models operating on tabular data. The employed method involves using diagnostic parameters calculated on an approximated representation of a model under attack and analyzing differences in these diagnostic parameters over time. The hypothesis researched by the authors is that adversarial attack techniques, even if attempting a low-profile modification of input data, influence those diagnostic attributes in a statistically significant way. Thus, changes in diagnostic attributes can be used for detecting attack events. Three attack approaches on real-world datasets were investigated. The experiments confirm the approach as a promising technique to be further developed for detecting adversarial attacks. © 2023 Polish Information Processing Society.

关键词： Information use

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Performance Analysis for CRUD Operations in Relational Databases from Java Programs Using Hibernate

Performance Analysis for CRUD Operations in Relational Datab...

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International Conference on control Systems and computer science (CSCS)

作者： Alexandru-Marius Bonteanu Cătălin Tudose Ana Magdalena Anghel Department of Computer Science “Politehnica” University of Bucharest Bucharest Romania Learning Management Department Luxoft Romania Bucharest Romania Department of Automatic Control “Politehnica” University of Bucharest Bucharest Romania

Data is information or a collection of information about something. Almost every application nowadays needs to persist data so that they can keep track of their state in case of any trouble that might appear. Databases are the main piece of an application for persisting data and they can be of two types: SQL or NoSQL. SQL (Structured Query Language) is a query language used to create, read, update, or delete (CRUD operations) the information in most relational databases. Data is structured in a set of tables in the relational database model, with rows as entries and columns as specific attributes of the entry. Over the years, new relational database management systems appeared. A comparison between some of the most used relational database management systems tied to a Java application using Hibernate is worth analyzing in the given context. The evaluation is done on timing for each CRUD operation by using Java Microbenchmark Harness, because these are the most used operations, for thousands and hundreds of thousands of entries. Depending on the majority type of operation, switching the database management system might help.

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End-to-End Lip Reading in Romanian with Cross-Lingual Domain Adaptation and Lateral Inhibition

arXiv

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arXiv 2023年

作者： Mănescu, Emilian-Claudiu Smădu, Razvan-Alexandru Avram, Andrei-Marius Cercel, Dumitru-Clementin Pop, Florin Faculty of Mathematics and Computer Science University of Bucharest Romania Faculty of Automatic Control and Computers University Politehnica of Bucharest Romania National Institute for Research and Development in Informatics - ICI Bucharest Romania

关键词： Speech recognition

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