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Influence of Two Types of Disturbing Effects with Different Statistical Characteristics on the Efficiency of Information Processes Implementation 3

Influence of Two Types of Disturbing Effects with Different ...

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3rd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2021

作者： Nachev, Atanas Kordov, Krasimir Zhelezov, Stanimir Gueorguiev, Nikolay Pavlova, Daniela University of Library Studies and Information Technologies Sofia Bulgaria Konstantin Preslavsky University of Shumen Dept. of Computer Informatics Shumen Bulgaria Konstantin Preslavsky University of Shumen Dept. of Computer Systems and Technologies Shumen Bulgaria Institute of Metal Science Equipment and Technology with Hydroaerodynamic Center Bulgarian Academy of Sciences Sofia Bulgaria

ISBN: (纸本)9781665439817

The theory of reliability, which appeared and developed in the second half of the 20th century, in its classical provides methods that can often be used to solve problems related to the functional reliability of network structure, such as computer and communication networks and systems based on them. Therefore, it is necessary to combine the methods of the 'classical' theory of reliability with methods describing their functioning. An approach for determining the efficiency of the implementation of information processes is proposed, which is invariant in terms of the ways of their implementation. The obtained results show that the considered types of disturbing effects can have a significant impact on the efficiency of information processes. The presented analytical apparatus, due to its invariance, allows to solve a wide range of applied problems in the design of information systems of different nature. © 2021 IEEE.

关键词： Reliability theory

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Performance Comparison of 15-Level Multilevel Inverter Topologies

Performance Comparison of 15-Level Multilevel Inverter Topol...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： G. Anusha Krishan Arora Himanshu Sharma Surya Prakash Thota Chaman Verma Maria Simona Raboaca Constantin Filote Dept. of EEE Lovely Professional University Punjab India Dept. of EEE GNITS Hyderabad India Dept. of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Suceava Romania

Multilevel inverters (MLI) have gained a significant emphasis in the area of renewable energy applications in recent years. The popularity of MLIs is due to their ability to efficiently convert the sustainable sources of energy, such as wind and solar power, into usable electrical energy. One key advantage of using reduced multilevel inverter configurations is that it can help to reduce the system overall cost by decreasing number of components required. The reduction of components in the inverter circuit results in simplification of design and lower expenditure. This paper proposes a fifteen-level modified multilevel inverter with cascaded h-bridge with a minimized switch count. The simulation results of the proposed inverter demonstrate that it is able to effectively attenuate harmonics and with reduced number of components required while still providing good performance. The suggested inverter design is also evaluated on its harmonic reduction characteristics, providing a cleaner output waveform and less distortion.

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Tarski’s theorem, supermodular games, and the complexity of equilibria 11

Tarski’s theorem, supermodular games, and the complexity of...

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11th Innovations in Theoretical computer science Conference, ITCS 2020

作者： Etessami, Kousha Papadimitriou, Christos Rubinstein, Aviad Yannakakis, Mihalis School of Informatics University of Edinburgh United Kingdom Dept. of Computer Science Columbia University NY United States Dept. of Computer Science Stanford University CA United States

ISBN: (纸本)9783959771344

The use of monotonicity and Tarski’s theorem in existence proofs of equilibria is very widespread in economics, while Tarski’s theorem is also often used for similar purposes in the context of verification. However, there has been relatively little in the way of analysis of the complexity of finding the fixed points and equilibria guaranteed by this result. We study a computational formalism based on monotone functions on the d-dimensional grid with sides of length N, and their fixed points, as well as the closely connected subject of supermodular games and their equilibria. It is known that finding some (any) fixed point of a monotone function can be done in time logd N, and we show it requires at least log2 N function evaluations already on the 2-dimensional grid, even for randomized algorithms. We show that the general Tarski problem of finding some fixed point, when the monotone function is given succinctly (by a boolean circuit), is in the class PLS of problems solvable by local search and, rather surprisingly, also in the class PPAD. Finding the greatest or least fixed point guaranteed by Tarski’s theorem, however, requires d · N steps, and is NP-hard in the white box model. For supermodular games, we show that finding an equilibrium in such games is essentially computationally equivalent to the Tarski problem, and finding the maximum or minimum equilibrium is similarly harder. Interestingly, two-player supermodular games where the strategy space of one player is one-dimensional can be solved in O(log N) steps. We also show that computing (approximating) the value of Condon’s (Shapley’s) stochastic games reduces to the Tarski problem. An important open problem highlighted by this work is proving a Ω(logd N) lower bound for small fixed dimension d ≥ 3. © Kousha Etessami, Christos Papadimitriou, Aviad Rubinstein, and Mihalis Yannakakis.

关键词： One dimensional

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At-Risk People’s Safety in Fire Incidents: Insights Learned from Applying Reliability Index Method

At-Risk People’s Safety in Fire Incidents: Insights Learned...

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International Conference on System Reliability and science (ICSRS)

作者： Naser Kazemi Eilaki Trond Nordvik Ilona Heldal Carolyn Ahmer Bjarne Christian Hagen Dept. of Civil Engineering Western Norway University of Applied Science Bergen Norway Dept. of Computer Science Electrical Technology and Natural Sciences Western Norway University of Applied Science Bergen Norway Dept. of Safety Chemistry and Biomedical Laboratory Sciences Western Norway University of Applied Science Haugesund Norway

Escaping from a fire to a safe place can take longer and be more difficult for people belonging to at-risk groups. While there are several models addressing uncertainty during evacuation, the effect of these on the elderly and people with disabilities, especially those people who are living alone is seldom exemplified. Considering a common type of detached two-story building with a prevalent layout, this paper illustrates how safety analysis can be developed for these groups based on the fire safety index method. The computations illustrate the probability of being trapped in fire (fatality) and safety index (beta index). The timeline evacuation model for two fire scenarios considers appropriate probability distribution functions for each variable with indeterministic nature. Sensitivity analysis of the variables has been done to find the most important and effective parameters for fire safety for the chosen at-risk groups. The response time to the fire, the area of buildings, and the length of escape route are the most important variables. Also, fire growth is more critical in homes without detection systems. The type of movement disability that needs different kinds of assistive tools, has no great effect on the safety level of people that live in the same home layout and people with visual impairment who need care staff to assist for evacuation encounter the same risk of fatality as other people with movement disabilities approximately.

关键词： Uncertainty Sensitivity analysis Layout Buildings Visual impairment Probability distribution Indexes

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Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization

arXiv

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arXiv 2023年

作者： Balaskas, Konstantinos Karatzas, Andreas Sad, Christos Siozios, Kostas Anagnostopoulos, Iraklis Zervakis, Georgios Henkel, Jörg The Department of Physics Aristotle University of Thessaloniki Thessaloniki54124 Greece Karlsruhe Institute of Technology Karlsruhe76131 Germany The School of Electrical Computer and Biomedical Engineering Southern Illinois University Carbondale CarbondaleIL62901 United States The Dept. of Computer Engineering & Informatics University of Patras Patras26504 Greece

Deep Neural Networks (DNNs) have shown significant advantages in a wide variety of domains. However, DNNs are becoming computationally intensive and energy hungry at an exponential pace, while at the same time, there is a vast demand for running sophisticated DNN-based services on resource constrained embedded devices. In this paper, we target energy-efficient inference on embedded DNN accelerators. To that end, we propose an automated framework to compress DNNs in a hardware-aware manner by jointly employing pruning and quantization. We explore, for the first time, per-layer fine- and coarse-grained pruning, in the same DNN architecture, in addition to low bit-width mixed-precision quantization for weights and activations. Reinforcement Learning (RL) is used to explore the associated design space and identify the pruning-quantization configuration so that the energy consumption is minimized whilst the prediction accuracy loss is retained at acceptable levels. Using our novel composite RL agent we are able to extract energy-efficient solutions without requiring retraining and/or fine tuning. Our extensive experimental evaluation over widely used DNNs and the CIFAR-10/100 and ImageNet datasets demonstrates that our framework achieves 39% average energy reduction for 1.7% average accuracy loss and outperforms significantly the state-of-the-art approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Leveraging graph neural networks for supporting Automatic Triage of Patients

arXiv

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arXiv 2024年

作者： Defilippo, Annamaria Veltri, Pierangelo Lió, Pietro Guzzi, Pietro Hiram Dept. Medical and Surgical Sciences Data Analytics Research Center Magna Graecia University of Catanzaro Catanzaro Italy DIMES Department of Informatics Modeling Electronics and Systems UNICAL Rende Cosenza Italy Department of Computer Science and Technology Cambridge University Cambridge United Kingdom Dept. Medical and Surgical Sciences Magna Graecia University of Catanzaro Catanzaro Italy

Patient triage plays a crucial role in emergency departments, ensuring timely and appropriate care based on correctly evaluating the emergency grade of patient conditions. Triage methods are generally performed by human operator based on her own experience and information that are gathered from the patient management process. Thus, it is a process that can generate errors in emergency-level associations. Recently, Traditional triage methods heavily rely on human decisions, which can be subjective and prone to errors. Recently, a growing interest has been focused on leveraging artificial intelligence (AI) to develop algorithms able to maximize information gathering and minimize errors in patient triage processing. We define and implement an AI-based module to manage patients’ emergency code assignments in emergency departments. It uses emergency department historical data to train the medical decision process. Data containing relevant patient information, such as vital signs, symptoms, and medical history, are used to accurately classify patients into triage categories. Experimental results demonstrate that the proposed algorithm achieved high accuracy outperforming traditional triage methods. By using the proposed method we claim that healthcare professionals can predict severity index to guide patient management processing and resource allocation. Copyright © 2024, The Authors. All rights reserved.

关键词： Errors

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Reducing Incidents in Microservices by Repaying Architectural Technical Debt

Reducing Incidents in Microservices by Repaying Architectura...

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Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

作者： Saulo S. de Toledo Antonio Martini Dag I.K. Sjøberg Agata Przybyszewska Johannes Skov Frandsen Dept. of Informatics University of Oslo Oslo Norway Dept. of Computer Science IT University of Copenhagen Copenhagen Denmark

Architectural technical debt (ATD) may create a substantial extra effort in software development, which is called interest. There is little evidence about whether repaying ATD in microservices reduces such interest. Objectives: We wanted to conduct a first study on investigating the effect of removing ATD on the occurrence of incidents in a microservices architecture. Method: We conducted a quantitative and qualitative case study of a project with approximately 1000 microservices in a large, international financing services company. We measured and compared the number of software incidents of different categories before and after repaying ATD. Results: The total number of incidents was reduced by 84%, and the numbers of critical- and high-priority incidents were both reduced by approximately 90% after the architectural refactoring. The number of incidents in the architecture with the ATD was mainly constant over time, but we observed a slight increase of low priority incidents related to inaccessibility and the environment in the architecture without the ATD. Conclusion: This study shows evidence that refactoring ATDs, such as lack of communication standards, poor management of dead-letter queues, and the use of inadequate technologies in microservices, reduces the number of critical- and high-priority incidents and, thus, part of its interest, although some low priority incidents may increase.

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Application of Automated Quality Control in Smart Factories - A Deep Learning-based Approach

Application of Automated Quality Control in Smart Factories ...

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Annual IEEE Systems Conference

作者： Subbalakshmi Mandapaka Catalina Diaz Hasbanny Irisson Aditya Akundi Viviana Lopez Douglas Timmer Dept. of Computer Science University of Texas Rio Grande Valley Texas United States Informatics and Engineering Systems Dept. University of Texas Rio Grande Valley Texas United States Manufacturing and Industrial Eng. University of Texas Rio Grande Valley Texas United States

Industry 4.0 is the ongoing automation of conventional manufacturing and industrial applications using smart technology. Quality control (QC) is a set of procedures to ensure that a manufactured product adheres to a defined set of quality criteria or meets the requirements of the customer. Many applications within the manufacturing domain employ image-processing or machine learning systems but deep learning-based applications are rare. The goal of this project is to leverage deep learning methods for the automation of quality control. A visual QC automation application is proposed that utilizes a camera placed over a product assembly line containing 3-D printed product samples in a smart factory prototype setup for data collection. After model training, the model will perform object detection and recognition for analyzing complex free-form products and perform product dimension and surface analysis to identify the products that meet the quality control guidelines.

关键词： Training Analytical models Visualization Manufactured products Prototypes Quality control Object detection

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Information Transfer in Spintronics Networks under Worst Case Uncertain Parameter Errors

Information Transfer in Spintronics Networks under Worst Cas...

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IEEE Conference on Decision and Control

作者： A. A. Rompokos F. C. Langbein E. Jonckheere Dept. of Elec. Eng. Univ. of Southern California Los Angeles CA School of Computer Science and Informatics Cardiff University UK

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

A novel quantum landscape optimization with respect to bias field control inputs is developed with the goal of achieving optimal transfer fidelity subject to robustness against bias field, spin couplings and other uncertainties. This objective is achieved by minimization of a convex combination of fidelity error and worst-case perturbation of fidelity error under directional perturbation of uncertain parameters. The novelty is that the end-point perturbations of the parameters are points of a random uniform sampling of the sphere centered at the nominal values of the parameters. This reveals that the previously developed perfect state transfer with zero sensitivity solution keeps high fidelity and robustness under large rather than differential perturbations.

关键词： Robust control Couplings Sensitivity Uncertainty Perturbation methods Minimization Robustness

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U-Net Based Image Segmentation Techniques for Development of Non-Biocidal Fouling-Resistant Ultra-Thin Two-dimensional (2D) Coatings

U-Net Based Image Segmentation Techniques for Development of...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Bichar Dip Shrestha Gurung Ramesh Devadig Tuyen Do Venkataramana Gadhamshetty Etienne Z. Gnimpieba Dept. of Computer Science University of South Dakota Vermillion USA Dept. of Civil and Environmental Engineering South Dakota School of Mines and Technology Rapid City USA Dept. of Biomedical Engineering University of South Dakota Vermillion USA

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Bacterial adhesion to the metallic surfaces creates a complex biofilm network, resulting in many problems like corrosion and fouling. Precise quantitative analysis of the surface coverage of cells can be vital in decoding biofilm-related issues. We present a deep learning-based approach to automate the microbes’ segmentation from the Scanning Electron Microscope (SEM) images of biofilm developed on coated multilayer graphene nickel samples. We collected SEM images from multilayer graphene nickel exposed to Oleidesulfovibrioalaskensis (OA-G20) for 30 days. Then we manually annotated the microbes with the help of subject expertise and trained a deep-learning U-Net architecture. In order to deal with larger image sizes, we perform patched-based image training and techniques for predicting segmentation masks over the larger image. Intersection over Union (IOU) was calculated for the evaluation of the performance of the model. After training the image for multiple epochs and extracting the optimal model parameters from the learning curve, we were able to get 70.64% mean IOU score. The patched-based technique for image training and image inference showed promising output during the segmentation.

关键词： Training Grain boundaries Image segmentation Scanning electron microscopy Graphene Nonhomogeneous media Nickel

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