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A MATHEMATICAL MODEL ILLUSTRATING THE INHIBITORY EFFECT OF THE MICRO RNA ON THE PROTEIN PRODUCTION

UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2023年第4期85卷 101-106页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

The present paper introduces a mathematical model for the cross-talking between microRNA and Protein. Studying the qualitative properties of the proposed model, we infer that the microRNA is an inhibitor for the Prote... 详细信息

关键词： Proteins

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On heterogeneity or sub-classes aspect in construction of stylometric input datasets 26

On heterogeneity or sub-classes aspect in construction of st...

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26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Stanczyk, Urszula Baron, Grzegorz Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

Stylometric analysis of texts relies on learning characteristic traits of writing styles for authors. Once these patterns are discovered, they can be compared to the ones present in other text samples, to recognise their authorship. This recognition can be compromised if input datasets are prepared without taking into consideration possible stratification of the input space, leading to specific grouping of datapoints, or sub-classes within distinguished classes. The paper shows research dedicated to construction of various structures of input datasets, and combinations of such structures between train and test sets. In the research the influence of different stratification forms on the performance of selected popular classification systems was observed. To minimise the number of influencing factors, a task of authorship attribution was performed as binary classification with balanced classes. Stylometric descriptors exploited belonged to lexical and syntactic group, giving frequencies of occurrence for chosen style-markers. It resulted in real-valued attributes and these values were explored without applying discretisation, in order to avoid the possible bias of this procedure on observations. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2022)

关键词： Knowledge based systems

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Path Planning Algorithm for Multiple UAVs Based on Artificial Potential Field 11

Path Planning Algorithm for Multiple UAVs Based on Artificia...

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11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Ren, Chongde Chen, Jinchao Du, Chenglie Yu, Wenquan School of Software Northwestern Polytechnical University Xi'an China Northwestern Polytechnical University Department of Computer Science Xi'an China North Automatic Control Technology Research Institute Department of Land Unmanned System Research and Development Taiyuan China

ISBN: (纸本)9798350333664

Due to the lower cost and higher maneuverability, unmanned aerial vehicles (UAVs) have found extensive use in both the civilian and military worlds. Path planning, as a crucial problem in the process of UAVs flight, aims to determine the optimal routes for multiple UAVs from various starting points to a single destination. However, because of the involvement of complex conditional constraints, path planning becomes a highly challenging problem. The path planning problem involving numerous UAVs is examined in this research, and a SAAPF-MADDPG algorithm based on Artificial Potential Field (APF) is suggested as a solution. First, a SA-greedy algorithm that can change the probability of random exploration by agents based on the number of steps and successful rounds to prevent UAVs from getting trapped in a local optimum. Then, we design complex reward functions based on APF to guide UAVs to destination faster. Finally, SAAPF-MADDPG is evaluated against the MADDPG, DDPG, and MATD3 methods in simulation scenarios to confirm its efficacy. © 2023 IEEE.

关键词： autonomous navigation deep reinforcement learning multi-agent systems path planning

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Follow me droney! Will UWB technology do it right? 12

Follow me droney! Will UWB technology do it right?

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12th Asia Conference on Mechanical and Aerospace Engineering, ACMAE 2021

作者： Hanzel, Krzysztof Department of Electronics Electrical Engineering and Microelectronics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

Object tracking for consumer drones is currently a rapidly developing technology. Its implementation is currently carried out with the use of object-in-image detection, which often limits the latest possibilities of this technology. The disadvantage of such a solution is also the increase in energy demand and the need to provide additional computing power, or delays resulting from the transfer of this operation to the control device. The proposed solution to this problem in this article is the use of UWB (Ultra-wideband) technology, which is increasingly appearing in consumer devices. The possible yield related to the accuracy of determining the position of the object as well as the experimentally determined frequency of receiving information about the position was presented. Additional advantages, such as resistance to NLOS (non-line of sight) conditions, were also presented. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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Trusted Execution of Periodic Tasks for Embedded Systems*

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IFAC-PapersOnLine 2023年第2期56卷 8845-8850页

作者： Martin Gunnarsson Nils Vreman Martina Maggio RISE Cybersecurity RISE Research Institutes of Sweden Sweden Department of Automatic Control Lund University Sweden Department of Computer Science Saarland University Germany

Systems that interact with the environment around them generally run some periodic tasks. This class of systems include, among others, embedded control systems. Embedded controllers have been proven vulnerable to various security attacks, including attacks that alter sensor and actuator data and attacks that disrupt the calculation of the control signals. In this paper, we propose, and implement, a mechanism to execute a periodic task and its communication interfaces in a trusted execution environment. This allows us to execute an isolated controller, thus offering higher security guarantees. We analyse the overhead of switching between the regular (possibly compromised) execution environment and the trusted execution environment and quantify the effect of this defence mechanism on the control performance.

关键词： Security Embedded computer architectures Embedded computer control systems and applications

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Super Resolution of Unpaired MR Images Based on Domain Migration 18th

Super Resolution of Unpaired MR Images Based on Domain Migra...

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18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Liu, Xiaodong Guo, Haipeng Liu, Huanyu Department of Automatic Test and Control School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China Information Countermeasure Technique Institute School of Computer Science and Technology Harbin Institute of Technology Harbin150080 China

ISBN: (纸本)9789819901043

High-resolution magnetic resonance imaging (MRI) is very important for doctors to judge the condition. At present, the depth learning method has been widely studied in the super-resolution reconstruction of MR images. However, due to the distribution difference between the real low-resolution MR images and the down sampled MR images used to train the super-resolution model under experimental conditions, the super-resolution performance of the super-resolution method is often seriously degraded when super-resolution is applied to the real low-resolution MR images. Blind super-resolution methods used to solve such problems in natural image super-resolution are lack of image content constraints. It is not advisable to introduce additional information that will interfere with doctors’ diagnosis in the super-resolution process of MR images. These two problems hinder the application of super-resolution method in real MR images. In order to solve these problems, an unpaired MR image super-resolution network based on domain migration is proposed. The proposed DMN can use a real set of low to high-resolution unpaired MR images as input, migrate the real low-resolution MR images to the down sampled MR image domain, and then super-resolution reconstruction is carried out through the paired super-resolution network obtained through supervision training. Experiments on fast MRI datasets verify the effectiveness of this method in solving the super-resolution problem of real MR images. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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Post-Processing of Thresholding or Deep Learning Methods for Enhanced Tissue Segmentation of Whole-Slide Histopathological Images

Post-Processing of Thresholding or Deep Learning Methods for...

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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Marczyk, Michal Wrobel, Agata Merta, Julia Polanska, Joanna Department of Data Science and Engineering Silesian University of Technology Gliwice44-100 Poland Yale Cancer Center Yale School of Medicine New HavenCT06511 United States Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

Digital pathology allows for the efficient storage and advanced computational analysis of stained histopathological slides of various tissues. Tissue segmentation is a crucial first step of digital pathology aimed at eliminating background, pen markings, and other artifacts, reducing image size, and increasing the efficiency of further analysis. In most cases, color thresholding or deep learning models are used, but their effectiveness is reduced due to complex artifacts and huge color variations between slides. We propose a post processing method to increase the tissue segmentation performance of any initial segmentation algorithm. Using a set of 197 manually annotated histopathological images of breast cancer patients and 63 images of endometrial cancer patients, we tested our method with 3 thresholding techniques and 3 deep learning-based algorithms by calculating the Dice index, Jaccard index, precision, and recall. In both datasets, applying post processing increased precision and recall for thresholding methods and mostly precision for deep learning models. Overall, applying post-processing gave better tissue segmentation performance than initial segmentation methods, significantly increasing Dice and Jaccard indices. Our results proved that thanks to post-processing, the tissue segmentation pipeline is more robust to noises and artifacts commonly present in histopathological images. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

关键词： Image segmentation

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A DRL-Based Cluster Scheduling Algorithm Configuration Optimization Approach for Artificial Intelligence Jobs

A DRL-Based Cluster Scheduling Algorithm Configuration Optim...

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IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Shilin Wen Wei Liang Siyan Zhu Ke Qiu Xiaoning Wu Department Three of Command and Control North Automatic Control Technology Institute Taiyuan China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

With the widespread application of machine learning technology across multiple domains, artificial intelligence (AI) jobs have become significant workloads for cloud service providers. However, current cloud platforms rely on pre-set scheduling algorithms, making it difficult to adjust configurations in real-time according to characteristics of AI jobs, thereby limiting job completion efficiency. Additionally, the heterogeneity and dynamism of AI jobs increase the difficulty of resource prediction and scheduling. In order to address this issue, this paper proposes an effective approach based on Deep Reinforcement Learning (DRL), by using DRL technology to adjust scheduling algorithm configurations online, converting the optimization process into a Markov process for model training, so as to optimize configurations in real-time and reduce job completion time. Simultaneously, to shorten the data sampling time for DRL training, this paper develops a cluster simulation scheduler that can efficiently simulate cluster running states, which can accelerate DRL training. Experimental results demonstrate that the simulation scheduler exhibits fast simulation rates and high simulation accuracy, and our proposed approach effectively reduces job completion time in small-scale clusters, thereby enhancing cluster resource utilization.

关键词： Training Electrical engineering Limiting Scheduling algorithms Clustering algorithms Markov processes Real-time systems Resource management Artificial intelligence Optimization

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A Deep Learning Approach for Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants 28

A Deep Learning Approach for Faults Recognition of Dissolved...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Ghinea, Liliana Maria Miron, Mihaela Ratnaweera, Harsha Electrical Engineering and Electronics Faculty of Automation Computers Department of Automatic Control and Electrical Engineering Department Galaţi Romania Computers Electrical Engineering and Electronics Faculty of Automation Department of Computer Science and Information Technology Galaţi Romania Norwegian University of Life Sciences Faculty of Science and Technology Oslo Norway

ISBN: (纸本)9798350339918

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing. © 2023 IEEE.

关键词： Wastewater treatment

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IT'S ALL IN THE EMBEDDING! FAKE NEWS DETECTION USING DOCUMENT EMBEDDINGS

arXiv

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arXiv 2023年

作者： Truicǎ, Ciprian-Octavian Apostol, Elena-Simona Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania

With the current shift in the mass media landscape from journalistic rigor to social media, personalized social media is becoming the new norm. Although the digitalization progress of the media brings many advantages, it also increases the risk of spreading disinformation, misinformation, and malformation through the use of fake news. The emergence of this harmful phenomenon has managed to polarize society and manipulate public opinion on particular topics, e.g., elections, vaccinations, etc. Such information propagated on social media can distort public perceptions and generate social unrest while lacking the rigor of traditional journalism. Natural Language Processing and Machine Learning techniques are essential for developing efficient tools that can detect fake news. Models that use the context of textual data are essential for resolving the fake news detection problem, as they manage to encode linguistic features within the vector representation of words. In this paper, we propose a new approach that uses document embeddings to build multiple models that accurately label news articles as reliable or fake. We also present a benchmark on different architectures that detect fake news using binary or multi-labeled classification. We evaluated the models on five large news corpora using accuracy, precision, and recall. We obtained better results than more complex state-of-the-art Deep Neural Network models. We observe that the most important factor for obtaining high accuracy is the document encoding, not the classification model's complexity. © 2023, CC BY-NC-ND.

关键词： Embeddings

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