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IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Aziza I. Hussein Mady Mohammad Passent Elkafrawy Rabab Hamed M. Aly College of Engineering Energy and Technology Research Center Effat University Jeddah Saudi Arabia College of Architecture Effat University Jeddah Saudi Arabia The Higher Institute for Management and Information Technology Minya Egypt Computer Science Department Faculty of Computer Science Nahda University Beni Suef Egypt

ISBN: (数字)9798350377460

ISBN: (纸本)9798350377477

Renewable energy systems, particularly photovoltaic (PV) systems, have been played important role in the reducing carbon emissions. A primary concern in the field of photovoltaics (PV) based on the design of the maximum power point tracking (MPPT) is the capacity to accurately monitor power across many parameters in addition to ascertain the power production of solar cells or wind turbines and adjust the load to maximise power efficiency under varying weather conditions. On another hands, the hybrid smart system uses a wind catcher to reduce the amount of energy consumed by buildings from the grid, which is a historically significant architectural component for cross ventilation and passive cooling. This paper presents an updating of model that proposes improvements to the regulation of Spline MPPT with tuning PID controller by using Levy Invasive Weed optimization (LIWO)technique. A rapid, accurate, and straightforward approach for determining the (MPPT) of PV systems under consistent irradiation and partial shade, as well as wind turbines, is presented using the Spline- MPPT technique. The model of this paper employs LIWO in conjunction with a PID controller to improve the selection of PID gains. Moreover, is applied to generate the optimal values of duty cycle of the model. The comprehensive system design depicted has been simulated using MATLAB Simulink to verify the functionality of the system. The model has attained an accuracy of 93%. The outcomes of this model contribute greatly to our understanding of the suitability and efficacy of both AI and traditional MPPT controllers. This modeling will be beneficial to the renewable energy industry.

关键词： Maximum power point trackers Photovoltaic systems Renewable energy sources Accuracy Software packages Mathematical models Wind turbines Splines (mathematics) Optimization Tuning

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Detecting and Resolving Coupling-Related Infrastructure as Code Based architecture Smells in Microservice Deployments

Detecting and Resolving Coupling-Related Infrastructure as C...

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IEEE International Conference on Cloud Computing, CLOUD

作者： Evangelos Ntentos Uwe Zdun Ghareeb Falazi Uwe Breitenbücher Frank Leymann Software Architecture Group University of Vienna Vienna Austria University of Vienna Doctoral School Computer Science Vienna Austria Institute of Architecture of Application Systems University of Stuttgart Stuttgart Germany

The Infrastructure as Code (IaC) concept enables IT infrastructure to be managed as software: resources can be managed, monitored, and provisioned automatically instead of manually by developers or operations teams. Many industries have already embraced this concept widely. However, research on IaC-based deployments, particularly research focusing on loose coupling, often does not offer methods for evaluating architectural conformance, spotting architecture smells, and support for correcting the found smells. Our work strives to provide an automatic method for continuously developing microservice-based systems and the associated infrastructure. We aim to offer an automated architectural refactoring method that checks if IaCbased deployments adhere to patterns and best practices and do not contain potential architectural smells. We provide architects with viable options for enhancing architectural conformance during microservice development. In short, by continuously detecting architectural smells and suggesting possible fixes, we aim to support architecture evolution within the framework of continuous delivery practices. We evaluate our approach using three case studies and variants based on open-source microservice architectures.

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TopoMortar: A dataset to evaluate image segmentation methods focused on topology accuracy

arXiv

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arXiv 2025年

作者： Valverde, Juan Miguel Koga, Motoya Otsuka, Nijihiko Dahl, Anders Bjorholm Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark A.I. Virtanen Institute University of Eastern Finland Finland Department of Architecture Faculty of Engineering Sojo University Japan

We present TopoMortar, a brick wall dataset that is the first dataset specifically designed to evaluate topology-focused image segmentation methods, such as topology loss functions. TopoMortar enables to investigate in two ways whether methods incorporate prior topological knowledge. First, by eliminating challenges seen in real-world data, such as small training set, noisy labels, and out-of-distribution test-set images, that, as we show, impact the effectiveness of topology loss functions. Second, by allowing to assess in the same dataset (TopoMortar) topology accuracy across dataset challenges, isolating dataset-related effects from the effect of incorporating prior topological knowledge. In these two experiments, it is deliberately difficult to improve topology accuracy without actually leveraging topology information, thus, permitting to attribute an improvement in topology accuracy to the incorporation of prior topological knowledge. To this end, TopoMortar includes three types of labels (accurate, pseudo-labels, noisy labels), two fixed training sets (large and small), and in-distribution and out-of-distribution test-set images. We compared eight loss functions on TopoMortar, and we found that clDice generally achieved the most topologically accurate segmentations, Skeleton Recall loss performed best particularly with noisy labels, and the relative advantageousness of the other loss functions depended on the experimental setting. Additionally, we show that simple methods, such as data augmentation and self-distillation, can elevate Cross entropy Dice loss to surpass most topology loss functions, and that those simple methods can enhance topology loss functions as well. clDice and Skeleton Recall loss, both skeletonization-based loss functions, were also the fastest to train, making this type of loss function a promising research direction. TopoMortar and our code can be found at https://***/jmlipman/TopoMortar. © 2025, CC BY.

关键词： Musculoskeletal system

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Optimal Qubit Reuse for Near-Term Quantum computers

arXiv

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arXiv 2023年

作者： Brandhofer, Sebastian Polian, Ilia Krsulich, Kevin Institute of Computer Architecture and Computer Engineering Center for Integrated Quantum Science and Technology University of Stuttgart Stuttgart Germany IBM Quantum IBM TJ Watson Research Center Yorktown HeightsNY United States

Near-term quantum computations are limited by high error rates, the scarcity of qubits and low qubit connectivity. Increasing support for mid-circuit measurements and qubit reset in near-term quantum computers enables qubit reuse that may yield quantum computations with fewer qubits and lower errors. In this work, we introduce a formal model for qubit reuse optimization that delivers provably optimal solutions with respect to quantum circuit depth, number of qubits, or number of swap gates for the first time. This is in contrast to related work where qubit reuse is used heuristically or optimally but without consideration of the mapping effort. We further investigate reset errors on near-term quantum computers by performing reset error characterization experiments. Using the hereby obtained reset error characterization and calibration data of a near-term quantum computer, we then determine a qubit assignment that is optimal with respect to a given cost function. We define this cost function to include gate errors and decoherence as well as the individual reset error of each qubit. We found the reset fidelity to be state-dependent and to range, depending on the reset qubit, from 67.5% to 100% in a near-term quantum computer. We demonstrate the applicability of the developed method to a number of quantum circuits and show improvements in the number of qubits and swap gate insertions, estimated success probability, and Hellinger fidelity of the investigated quantum circuits. Copyright © 2023, The Authors. All rights reserved.

关键词： Qubits

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Deep Learning based vehicle image detection using Yolo V5 with Region-Based Convolutional Neural Network 3

Deep Learning based vehicle image detection using Yolo V5 wi...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Rani, S. Swapna Mudigonda, Aditya Hemanth, S.V. Sundararajan Reddy, G. Vinoda Amirthayogam, G. Engineering College Department of Electronics and Communication Engineering Hyderabad India JNIAS School of Planning and Architecture Telangana Hyderabad500034 India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana Hyderabad501401 India PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India Telangana Hyderabad501401 India Hindustan Institute of Technology and Science Department of Information Technology Tamilnadu Chennai603103 India

ISBN: (纸本)9798350372120

Detecting and classifying vehicles is a modern technology with numerous uses. Administration and regulation of traffic is one of the primary uses. Projects utilizing image processing to prevent traffic accidents heavily rely on vehicle monitoring and detection. Monitoring and recording human movement in surveillance situations require the ability to follow moving objects. Considering its significance, it provides a valuable image processing-based vehicle detection technique - a vehicle tracking and detection system based on images. The recently released high-resolution road vehicle dataset supports deep learning-based vehicle detection and monitoring on the Python platform. It includes over 100 well-defined pictures taken from movies from various locations. This data is produced using a range of image processing methods. The most recent iteration of the YOLO model, the v5 model, was employed for detection. R-CNN is additionally utilized for model detection. The four stages in the Region-Based Convolutional Neural Network (RCNN) process are preprocessing, segmentation, feature selection, and classification. The first step is identifying moving vehicles accurately. Preprocessing the original image is necessary. This enhances the image's quality, valuable information extracted, or unwanted areas cropped out. Segmentation is the second step. The region's borders are established, and the entire image is divided into numerous smaller areas using a threshold. The vehicle image's most pertinent aspects or characteristics (features) that aid in precise detection are found in the third stage, feature selection. A region-based convolutional neural network (RCNN) is employed to determine the categorization. The Yolov5 method can be used to identify and categorize items. The Region-based Convolutional Neural Network (RCNN) approach enhances accuracy and temporal complexity over current segmentation algorithms. © 2024 IEEE.

关键词： Vehicles

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Optimal Partitioning of Quantum Circuits using Gate Cuts and Wire Cuts

arXiv

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arXiv 2023年

A limited number of qubits, high error rates, and limited qubit connectivity are major challenges for effective near-term quantum computations. Quantum circuit partitioning divides a quantum computation into a set of computations that include smaller-scale quantum (sub)circuits and classical postprocessing steps. These quantum subcircuits require fewer qubits, incur a smaller effort for satisfying qubit connectivity requirements, and typically incur less error. Thus, quantum circuit partitioning has the potential to enable quantum computations that would otherwise only be available on more matured hardware. However, partitioning quantum circuits generally incurs an exponential increase in quantum computing runtime by repeatedly executing quantum subcircuits. Previous work results in non-optimal subcircuit executions hereby limiting the scope of quantum circuit partitioning. In this work, we develop an optimal partitioning method based on recent advances in quantum circuit knitting. By considering wire cuts and gate cuts in conjunction with ancilla qubit insertions and classical communication, the developed method can determine a minimal cost quantum circuit partitioning. Compared to previous work, we demonstrate the developed method to reduce the overhead in quantum computing time by 73% on average for 56% of evaluated quantum circuits. Given a one hour runtime budget on a typical near-term quantum computer, the developed method could reduce the qubit requirement of the evaluated quantum circuits by 40% on average. These results highlight the ability of the developed method to extend the computational reach of near-term quantum computers by reducing the qubit requirement at a lower increase in quantum circuit executions. Copyright © 2023, The Authors. All rights reserved.

关键词： Qubits

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RadLink: Linking Clinical Entities from Radiology Reports

RadLink: Linking Clinical Entities from Radiology Reports

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Foundation and Large Language Models (FLLM), International Conference on

作者： Yongli Mou Hanbin Chen Gwendolyn Isabella Lode Daniel Truhn Sulayman Sowe Stefan Decker Chair of Computer Science 5 RWTH Aachen University Germany Department of Diagnostic and Interventional Radiology University Hospital RWTH Aachen Germany Fraunhofer Institute for Applied Information Technology FIT Germany

ISBN: (数字)9798350354799

ISBN: (纸本)9798350354805

Radiology reports are a critical source of information for patient diagnosis and treatment in the medical domain. However, the vast amount of data contained in these reports is often unstructured, making it challenging to extract and normalize relevant clinical entities. Named Entity Normalization (NEN) is essential for mapping these entities to a standard ontology, facilitating better data integration, retrieval, and analysis. In this paper, we introduce RadLink, a benchmark for NEN in radiology. RadLink builds upon 425 expert-annotated radiology reports from the RadGraph dataset, extending it for NEN by mapping entities to the Unified Medical Language System (UMLS) ontology. We employ a combination of morphological and semantic matching approaches to generate normalization annotations, followed by human review for validation. We aim to set a standard with our benchmark for evaluating NEN methods in the radiology domain, that facilitate interoperability across healthcare systems and accelerate medical research by providing structured, standardized data.

关键词： Accuracy Reviews Large language models Unified modeling language Semantics Radiology Ontologies Benchmark testing Medical diagnostic imaging Standards

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A Study on Data-Driven Probability Estimator Design for Video Coding

A Study on Data-Driven Probability Estimator Design for Vide...

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Data Compression Conference (DCC)

作者： Heiner Kirchhoffer Christian Rudat Michael Schäfer Jonathan Pfaff Heiko Schwarz Detlev Marpe Thomas Wiegand Fraunhofer Heinrich Hertz Institute Berlin Germany Institute for Computer Science Free University of Berlin Berlin Germany Chair of Media Technology Technical University of Berlin Berlin Germany

Data-driven optimization is employed to study alternative approaches [1] to the probability estimator of the the Enhanced Compression Model (ECM) (which includes additional coding tools on top of the Versatile Video Coding standard). In ECM, each context model uses a weighted sum of two hypotheses for probability estimation with different associated adaptation rates. Four alternative approaches are studied:

关键词： Video coding Adaptation models Estimation Data compression Encoding Standards Optimization

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Software Evolution of I4.0 Digital Twins with Semantic Patching

Software Evolution of I4.0 Digital Twins with Semantic Patch...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Nico Braunisch Sebastian Heppner Igor Garmaev Marko Ristin Torben Miny Tobias Kleinert Hans Wernher Van De Venn Martin Wollschlaeger Institute of Applied Computer Science TU Dresden Dresden Germany Chair of Information and Automation Systems RWTH Aachen University Aachen Germany Institute of Mechatronic Systems Zurich University of Applied Sciences (ZHAW) Zurich Switzerland

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

We examine semantic patching, an important technique of software evolution, that allows us to automate to a large extent the adaption of external open-source libraries for Digital Twins with a high degree of correctness. While this technique is widely known in specialized software engineering circles, we aim to popularize it among the automation community. We evaluate and compare its advantages against other adaptation methods in three practical use cases where the widely-used aas-core Software Development Kit is adapted for different lighthouse projects in Industrie 4.0, written in Python, C#, and Java, respectively. The results highlight the effectiveness of semantic patching in automating the process, minimizing errors and ensuring a seamless transition.

关键词： Industries Industrial electronics Java Semantics Software Libraries Digital twins

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In Silico Modeling of COVID-19 Pandemic Course Differentiation Using the FOD Model

引用

Coronaviruses 2022年第4期3卷 45-57页

作者： Roterman, Irena Stapor, Katarzyna Fabian, Piotr Konieczny, Leszek Department of Bioinformatics and Telemedicine Jagiellonian University Medical College Medyczna Kraków Poland Institute of Automatic Electronics Computer Science Silesian University of Technology Akademicka Gliwice Poland Chair of Medical Biochemistry Jagiellonian University Medical College Kopernika Kraków Poland

Background: The strange and still unclear scenarios of COVID-19 pandemic development have raised the question about the reason for the observed essential state and personal differences con-cerning the expansion and severity of the infection process. Some custom activities are taken into consid-eration in an attempt to explain the phenomenon. Alcohol in the diet is suggested in this paper as the possible factor which could explain the observed differentiation. It easily penetrates cells modifying their natural internal environment, and independently influences tissues as the toxic agent being the source of acetyl aldehyde. Objective: The process in which the cell seems to be the most sensitive to altered environmental conditions is the protein folding;in particular, its portion occurring in the endoplasmic reticulum where freshly synthesized polypeptides fold and then are introduced to the cell membrane influencing its property and in particular its fluidity, which is the critical parameter deciding the virus penetration into the cell. Methods: The application of a mathematical model, fuzzy oil drop model FOD, expressing the influence of the environment on the protein folding process shows the mechanism of this influence. Results: The differences between statistical assessment of epidemy in Europe and the Far East, which may be correlated with alcohol consumption, suggest the influence of diet on the status of epidemy in these regions. Conclusion: The protein folding seems to be the process most sensitive to environmental conditions in the cell. The different diet customs, including the use of alcohol, may disturb the folding process, lower-ing as the result the number of proteins needed for cell membrane stability, thus increasing its fluidity and the cell susceptibility to virus penetration. Observations presented in this paper are based on the initial period of pandemic development and have not been intentionally modified to prevent the influence of additional fac

关键词： cell membrane stability COVID-19 environment for folding micelle pandemic protein folding protein structure

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