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An IoT-Enabled Condition Monitoring Device: Integrating Vibration Analysis and Environmental Sensing 5

An IoT-Enabled Condition Monitoring Device: Integrating Vibr...

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5th International conference on Advanced Research in Computing, ICARC 2025

作者： Liyanage, Randeera Annasiwaththa, Buddhika Faculty of Engineering and Technology CINEC Campus Sri Lanka University of Ruhuna Faculty of Engineering Sri Lanka

ISBN: (纸本)9798331530983

Condition monitoring (CM) devices are crucial for predictive maintenance (PdM) across industries. Despite their commercial availability, the use of CM devices in small-to medium-scale industries remains low because of the cost of equipment and proprietary software systems. This research aims to overcome the challenge by developing an Internet of Things (IoT) enabled, portable CM device capable of monitoring machine vibrations and ambient conditions. The proposed battery-powered device integrates a commonly available microcontroller unit (MCU) with rich communication interfaces in combination with affordable and accessible sensors. The acquired data are wirelessly transmitted to a host computer via the Message Queuing Telemetry Transport (MQTT) protocol. During testing, offline frequency domain analysis of the vibration data for a faulty bearing, namely Hilbert and Envelope Spectrum Analysis revealed fault frequencies corresponding to an outer race defect. Ambient data collected over 24 hours provided supplementary insights into environmental fluctuations critical to industrial operations. In an industrial setting, the CM device was successful in performing its two main tasks of monitoring ambient conditions and bearing vibrations. The device positions itself as a proof-of-concept for a scalable, networked CM system, where multiple interconnected nodes could simultaneously monitor a range of machinery or individual components within a facility. This demonstrates its potential for industries to delve into the world of PdM, replacing traditional preventive maintenance mechanisms. © 2025 IEEE.

关键词： Machine vibrations

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Enhanced software Defect Prediction using Quantum Hamiltonian Generative Adversarial Network for Improved software Performance Reliability

Enhanced Software Defect Prediction using Quantum Hamiltonia...

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International conference on Inventive Computation Technologies (ICICT)

作者： Jyoti Chaudhary Vijaya Kumar A V Manikannan K Amol Sapatnekar Amit Barve Ramya Maranan Department of Computer Engineering The Technological Institute of Textile and Sciences Bhiwani Haryana India Department of Computer Science & Engineering Presidency University Bangalore Karnataka India Department of Computer Science and Engineering R.M.K Engineering College Thiruvalluvar Tamil Nadu India Faculty of Law Symbiosis Centre for Advanced Legal Studies and Research Symbiosis Law School Symbiosis International (Deemed University) Pune India Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University Gujarat India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The development of software systems with high quality and decreased maintenance costs depends on software Defect Prediction (SDF). The current dataset qualities combined with feature redundancies make confident fault detection difficult through conventional methods. Current defect prediction techniques display multiple deficits through inaccurate results along with many false alarms and dataset bias. A high-performance quantum-inspired method must be researched in working to create an accurate precision model. The project will create the Enhanced software Defect Prediction using Quantum Hamiltonian Generative Adversarial Network for Improved software Performance Reliability (DBO-QH-GAN) model. Several software metrics need to be retrieved from the PROMISE datasets before this process can be initiated. The second step includes Fuzzy K- Top Matcher (FK- TM) functionality that removes undesirable noise factors such as redundant features and missing values as well as outliers. The Botox Optimization (BotO) algorithm selects five essential metrics that encompass cyclomatic complexity as well as Halstead measures and coupling and code churn and historical defect density. These preprocessed set of characteristics are fed to a Quantum Hamiltonian Generative Adversarial Network (QH-GAN) which identifies the defects from probabilistic thresholding and thereafter optimized using Duck Bevy Optimization (DBO) to achieve the optimal possible performance. Accuracy of the model was 99.2%, precision was 99.4%, and recall was 99.1 %. The DBO-QH-GAN model sets new standards for defect prediction by providing an improved enhancement of reliability along with decreased maintenance cost.

关键词： Quantum computing Costs Accuracy software performance Predictive models Generative adversarial networks software reliability Maintenance Optimization Standards

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Enhancing SaaS Product Reliability and Release Velocity through Optimized Testing Approach

Enhancing SaaS Product Reliability and Release Velocity thro...

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IEEE International conference on software Testing Verification and Validation Workshop, ICSTW

作者： Ren Karita Tsuyoshi Yumoto Engineering Infrastructure Division Freee K.K. Tokyo Japan

ISBN: (数字)9798331534677

ISBN: (纸本)9798331534684

In mission-critical SaaS products such as payment systems, maintaining rapid release velocity while ensuring high reliability is essential. However, as these products grow more complex, the areas requiring regression testing expand, increasing testing costs and often compromising release speed. This paper proposes a methodology to achieve both rapid release velocity and high reliability by optimizing the regression test suite through defect severity analysis. Furthermore, a test architecture is proposed to balance unit, integration, and system testing. This approach reduces regression testing costs, shortens testing duration, and accelerates release cycles.

关键词： software testing System testing Costs conferences software as a service Mission critical systems computer architecture Life estimation software reliability Testing

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EmergenTheta: Variations on Symbolic Transition systems (Competition Contribution) 31th

EmergenTheta: Variations on Symbolic Transition Systems (Com...

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31st International conference on Tools and Algorithms for the Construction and Analysis of systems, TACAS 2025, which was held as part of the International Joint conferences on Theory and Practice of software, ETAPS 2025

作者： Mondok, Milán Bajczi, Levente Szekeres, Dániel Molnár, Vince Department of Artificial Intelligence and Systems Engineering Budapest University of Technology and Economics Budapest Hungary

ISBN: (纸本)9783031906596

EmergenTheta is our sandbox for experimental analyses. After its successful debut in SV-COMP’24, we kept some well-performing but still under-tested configurations, and complemented them with a new saturation algorithm over decision diagrams, and two ways of extending their verification power: wrapping them in a lightweight, counterexample-guided abstraction refinement (CEGAR) loop based on implicit predicate abstraction;and backwards traversal of the state space. All such analyses now rely on a common interface to the underlying symbolic transition system, integrating seamlessly into the existing Theta framework. Using this combination of proven analyses and novel extensions, EmergenTheta outperformed our expectations in SV-COMP’25. © The Author(s) 2025.

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Adjustable Visual Appearance for Generalizable Novel View Synthesis 4th

Adjustable Visual Appearance for Generalizable Novel View S...

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4th International conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Bengtson, Josef Nilsson, David Lin, Che-Tsung Büsching, Marcel Kahl, Fredrik Computer Vision Group Chalmers University of Technology Gothenburg Sweden KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9789819787012

We present a generalizable novel view synthesis method which enables modifying the visual appearance of an observed scene so rendered views match a target weather or lighting condition, without any scene specific training or access to reference views at the target condition. Our method is based on a pretrained generalizable transformer architecture and is fine-tuned on synthetically generated scenes under different appearance conditions. This allows for rendering novel views in a consistent manner for 3D scenes that were not included in the training set, along with the ability to (i) modify their appearance to match the target condition and (ii) smoothly interpolate between different conditions. Experiments on real and synthetic scenes show that our method is able to generate 3D consistent renderings while making realistic appearance changes, including qualitative and quantitative comparisons. Please refer to our project page for video results: https://***. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D modeling

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DLIoMT: Deep Learning Approaches for IoMT-Overview, challenges and the future

DLIoMT: Deep Learning Approaches for IoMT-Overview, challeng...

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International conference on computer, Electrical & Communication engineering (ICCECE)

作者： V Harshni Satyendr Singh R Indhumathi Neelam Sanjeev Kumar Subrata Chowdhury Sudhansu Shekhar Patra Department of BME SRM Institute of Science and Technology Ramapuram campus Chennai India Computer Science & Engg Department BML Munjal University Gurugram India Department of Computer Science and Engineering (E.Tech) SRM Institute of Science and Technology Vadapalani Campus Chennai India Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies Chittoor Andra Pradesh India School of Compter Applications Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar Odisha India

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

An artificial intelligence (AI) system works by combining a computer program and algorithms to make a device more efficient and intelligent for tasks that are typically performed by humans. Deep learning, machine learning, conventional neural networks, recognition, and fuzzy logic are a few of the subsets of AI with unique capabilities and functions that can enhance modern medical science. Such innovations simplify human interference in clinical diagnostic procedures, decision making, and medical imaging. To improve human health, connected biomedical network devices and software applications are interconnected to form the Internet of Medical Things (IoMT), the next generation of bioanalytical tools. This review paper provides in-depth analysis of the IoMT encasing its definition, technologies, applications, challenges, and future prospects.

关键词： Deep learning Technological innovation Reviews Smart healthcare Internet of Medical Things software Sensor systems Sensors Security Medical diagnostic imaging

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Three Ways to Integrate Microservices Into MARL

Three Ways to Integrate Microservices Into MARL

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International Youth conference on Radio Electronics, Electrical and Power engineering (REEPE)

作者： Egor F. Morgunov Department of Informatics and Control Systems Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

Microservice architecture is a key component of modern distributed software systems, providing scalability and independent deployment of individual components. However, many different factors, such as the dynamic variability of modern Internet services and the evolution of user requests, can offset the advantages of microservices. Machine learning algorithms, in particular deep reinforcement learning, are an effective way to optimize distributed software systems, allowing adaptive management of system resources in non-stationary environments. This paper discusses the integration of multi-agent reinforcement learning into a micro service architecture. The paper demonstrates the results of training intelligent agents in the road selection service based on the current weather condition. Three methods of integrating deep Q-Iearning into the microservice architecture were developed, and three classical algorithms of deep reinforcement learning were used to evaluate the effectiveness of the methods.

关键词： Training Scalability Roads Neural networks Web and internet services Microservice architectures computer architecture software systems Deep reinforcement learning Resource management

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The Need for Smart Shop Floor Management in the Automotive Industry: Potentials, Challenges and Requirements 6

The Need for Smart Shop Floor Management in the Automotive I...

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6th International conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Kutz, Janika Viljoen, Altus Feike, Maximilian Przybilla, Leonard Guhl, Jakob Neuhüttler, Jens Krcmar, Helmut Fraunhofer IAO Fraunhofer Institute for Industrial Engineering IAO Heilbronn74076 Germany Informatik 17 - Information Systems and Business Process Management Krcmar Lab Technical University of Munich i17 Boltzmannstraße 3Garching b München85748 Germany Bauhaus-University Weimar Weimar99423 Germany

Industry 4.0 and the accompanying digital transformation of the modern-day factory have led to various advances in manufacturing. Shop floor management (SFM) - a core instrument of production management - has to align with these digital advances as well. However, due to SFM's strong human-centric roots and manufacturers predominantly conducting SFM through analog means, it raises the question of what sort of digital support should be used for SFM and what specifications should be included in digital SFM systems. This study, consisting of a case study comprising semi-structured interviews and on-site inspections at a large original equipment manufacturer (OEM), provides concrete requirements for digitally-supported SFM derived from practice. We investigated which elements from analog SFM should be retained and where possible improvements through digital support exist. Moreover, we identified definitive improvement potentials such as the aggregation of data sources, as well as the preparation, performance, and documentation of shop floor meetings without disruptions in information flows. We conclude that digital transformation in the SFM context neither implies the mere introduction of software to solve specific problems, nor the full automation of SFM processes through digital solutions (what we call "digital" SFM). Rather, we highlight the importance of "smart SFM": the adoption of sociotechnical systems in which SFM's traditional social characteristics are maintained but are simultaneously sensibly augmented with digital solutions to improve SFM processes. © 2025 Elsevier B.V.. All rights reserved.

关键词： Automotive industry

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PIL Environment to Validate Wind Turbine IEC61131-Based Real Time Controllers on Digital Twins 16th

PIL Environment to Validate Wind Turbine IEC61131-Based Real...

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16th APCA International conference on Automatic Control and Soft Computing, CONTROLO 2024

作者： Martínez-Inchusta, A. Santos-Peñas, M. Sierra-García, J.E. Peñacoba, M. Computer Engineering Department Complutense University of Madrid Madrid28040 Spain Institute of Knowledge Technology Complutense University of Madrid Madrid28040 Spain Department of Digitalization University of Burgos Burgos09006 Spain

ISBN: (纸本)9783031817236

Control systems are crucial for the proper performance of different devices. Some applications, such as wind turbines, sometimes require complex controllers to achieve maximum efficiency. This leads to a demand for verification and validation techniques that minimize potential risks during the implementation phase on the final device, with the associated high costs. One approach to this checking is the processor-in-the-loop (PIL) validation method, which runs the control software on the physical hardware and communicates it with the virtual model, all in real time and deterministically. PIL techniques can be combined with digital twins (DTs) to improve validation procedures and ensure that the control software runs within the required processing times (validating the hardware) and that the outputs are correct and take the system to its target state (validating the control software). In this work, a PIL-DT architecture is proposed to validate real-time controllers programmed in the IEC61131 standard using any commercial PC and virtual models simulated with Simulink Desktop Real-Time®. The architecture allows the control program to be deployed on the virtual or real controller in order to control the virtual or real plant, without any modification. This ensures the same behavior between the virtual controller and the real one. The proposal has been successfully tested with a wind turbine pitch controller as a use case. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Proportional control systems

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Architecting Digital Twins for Intelligent Transportation systems

Architecting Digital Twins for Intelligent Transportation Sy...

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IEEE International conference on software Architecture Workshops (ICSAW)

作者： Hiya Bhatt Sahil Karthik Vaidhyanathan Rahul Biju Deepak Gangadharan Ramona Trestian Purav Shah Software Engineering Research Centre IIIT Hyderabad India Computer Systems Group IIIT Hyderabad India Faculty of Science and Technology Middlesex University London

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

Modern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent solutions capable of real-time monitoring, predictive analytics, and adaptive control. This paper proposes an architecture for DigIT, a Digital Twin (DT) platform for Intelligent Transportation systems (ITS), designed to overcome the limitations of existing frameworks by offering a modular and scalable solution for traffic management. Built on a Domain Concept Model (DCM), the architecture systematically models key ITS components enabling seamless integration of predictive modeling and simulations. The architecture leverages machine learning models to forecast traffic patterns based on historical and real-time data. To adapt to evolving traffic patterns, the architecture incorporates adaptive Machine Learning Operations (MLOps), automating the deployment and lifecycle management of predictive models. Evaluation results highlight the effectiveness of the architecture in delivering accurate predictions and computational efficiency.

关键词： Adaptation models software architecture Transportation computer architecture Predictive models Traffic control Real-time systems Digital twins Safety Predictive analytics

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