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European Conference on Power Electronics and Applications

作者： Svenja Zöller Claudia Cabre-Piqueras Boris Schilder Alber Filba-Martinez Lukas Acker Faculty of Computer Science and Engineering Frankfurt University of Applied Science Frankfurt Germany Power Systems Dept. Catalonia Institute for Energy Research (IREC) Barcelona Spain

ISBN: (数字)9798350364446

ISBN: (纸本)9798350364453

Thermal stress is a critical failure factor for power electronics components. Hence, when developing a precise digital twin for a power system, it is imperative that thermal models provide a reliable portrayal of the component physics. In that regard, manufacturers offer simplified thermal models that enable designers and users to estimate its behaviour. Nevertheless, the accuracy of these models remains unknown, thus compromising the overall certainty of the digital twin. Therefore, it is of interest to discern whether these simplified models are sufficient for the precision required by the digital twin, and to assess the need of developing a more accurate model. Hence, this paper focuses on validating the theoretical thermal model of a power electronics switch module setup by using the deviation of the module mean time to failure (MTTF) as the acceptance parameter. To achieve this, a novel experimental validation methodology is proposed to retrieve an experimental thermal model and compare it to the theoretical one. This validation is performed considering the discrete thermal impedances and introducing the study of the coupling effects within the module.

关键词： Couplings Accuracy Europe Switches Power electronics Digital twins Power system reliability Reliability Thermal stresses Stress

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Reinforcement Learning-enabled Auctions for Self-Healing in Service Function Chaining

Reinforcement Learning-enabled Auctions for Self-Healing in ...

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IEEE International Conference on Communications Workshops, ICC

作者： Marios Avgeris Aris Leivadeas Ioannis Lambadaris Dept. of Systems and Computer Eng. Carleton University Ottawa Canada Dept. of Software Engineering and IT École de Technologie Supérieure (ÉTS) Montréal Canada

Service Function Chaining (SFC), defines the capability of interconnecting a number of ordered Service Functions (SFs) to create composite network services. A critical issue in SFC is the autonomic fault recovery, i.e., bringing the system back to its normal operation after a hardware or software failure. To address this challenge, in this paper, we propose a novel distributed methodology that treats the SFC Self-Healing problem in an Edge-Cloud infrastructure, while accounting for the various stakeholders. In particular, the individual SFC healing decisions are iteratively optimized and determined, while a Reinforcement Learning (RL)-based SFC-to-datacenter association procedure is realized. This process is complemented by a combinatorial auction-based resource allocation mechanism that resolves the potential SFC collocations at the end of each iteration. The proper operation, effectiveness and efficiency of our proposed healing mechanism is assessed under various evaluation scenarios.

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Arabic Sentiment Analysis of Mobile Banking Services Reviews

Arabic Sentiment Analysis of Mobile Banking Services Reviews

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International Conference on Social Networks Analysis, Management and Security (SNAMS)

作者： Safaa M. Khabour Dheya Mustafa Qutaibah AlThebyan Dept. of Information Systems Faculty of Information Technology and Computer Sciences Yarmouk University Irbid Jordan Dept. of Computer Engineering Faculty of Engineering The Hashemite University Zarqa Jordan Dept. of Cybersecurity Al Ain University Al Ain UAE Jordan University of Science and Technology Irbid Jordan

Online customer reviews have developed into a significant source of information about a business's performance. Due to shifting consumer expectations and growing internet penetration, the Middle East, especially Jordan, is seeing an increase in the usage of online banking. This propensity toward digital transition has been accelerated by the COVID-19 pandemic. Mobile banking service companies aim to effectively use customers' feedback to identify areas for improvement in customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on mobile banking reviews in Arabic, exploiting machine learning, natural language processing, and resampling methods to obtain subjective feedback, determine polarity, and identify customers' feelings in the banking domain. This work walks the reader through detailed steps of cleaning and preprocessing a manually annotated dataset of online banking reviews in Arabic, followed by features extraction and modeling, dataset splitting, and balancing training data. Finally, we examined classification algorithms including Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Random Forest (RF). Ensemble hard and soft voting ML methods were also evaluated. We achieved a maximum accuracy of about 94% using the LR classifier and a maximum precision of 94% using the LR and hard voting methods. Our methodology will enable banks to acquire insights on how to develop their online presence and meet the demands of customers and stakeholders.

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Utilization of Online Collaborative Tools in Software engineering: An Empirical Study on Review Meetings 6

Utilization of Online Collaborative Tools in Software Engine...

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6th International Conference on computer Science and engineering, UBMK 2021

作者： Akman, Ibrahim Turhan, Cigdem Hacaloglu, Tuna Dept. of Computer Engineering Atilim University Ankara Turkey Dept of Software Engineering Atilim University Ankara Turkey Dept of Information Systems Engineering Atilim University Ankara Turkey

ISBN: (纸本)9781665429085

Software development involves a significant amount of team effort where collaboration and communication of the team members are crucial. The team meetings are core activities in all stages of the software development process. Even though these meetings often are conducted face-to-face (F2F) with a lackof technology utilization, changing global conditions such as the COVID-19 pandemic require other solutions urgently without interrupting the software development schedule. For this purpose, online collaborative tools provide new opportunities for software teams to work together avoiding waste in time and resources and the relevant literature is immature. This study aims to assess the factors affecting the integration of online collaborative tools to SE practices with a special reference to review meetings. For this purpose, a sample of 73 SE sophomore and junior students who are future software professionals participated in experimental review meetings based on predefined scenarios. The findings indicate that the utilization of OCT's has positive effects on the participants' actual performance and improves the interaction between team members compared to F2F meetings. © 2021 IEEE

关键词： Software design

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Sampled-Data Output Feedback Stabilization of a Family of High-Order systems with Delays

Sampled-Data Output Feedback Stabilization of a Family of Hi...

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American Control Conference (ACC)

作者： Congran Zhao Wei Lin School of Mathematics and Statistics Jiangsu Normal University Xuzhou China Dept. of Electrical Systems and Computer Engineering Case Western Reserve University Cleveland Ohio USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Sampled-data control via output feedback is studied for a family of strongly nonlinear systems with state and input delays. Under sample and hold, a delay-free output feedback control scheme is developed based on the emulation method, adding a power integrator (AAPI) technique, and recursive design of nonlinear observers. With the aid of Lyapunov-Krasovskii functional theorem, together with the idea of homogeneous domination, we prove that the proposed sampled-data output feedback controller makes the hybrid closed-loop systems with delays and uncertainty globally asymptotically stable, if the input delay and sampling period are limited. The family of time-delay uncertain systems under consideration goes beyond the Lipschitz or linear growth condition and is genuinely nonlinear as it contains uncontrollable/unobservable linearization and is not stabilizable, even locally, by any linear or smooth feedback. Application of the sampled-data control scheme presented in this paper is illustrated by an example with simulation.

关键词： Uncertain systems Uncertainty Emulation Observers Delays Closed loop systems Nonlinear systems

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A Reinforcement-Learning Self-Healing Approach for Virtual Network Function Placement

A Reinforcement-Learning Self-Healing Approach for Virtual N...

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IEEE Symposium on Network Operations and Management

Modern networking paradigms like Service Function Chaining (SFC) allow for services to be broken down to a series of ordered and interconnected Virtualized Network Functions (VNFs) that can be hosted in generic servers in EdgeCloud datacenters. Nonetheless, a critical issue arises, when a hardware or software failure occurs and the VNFs of an SFC need to be repositioned, allowing to autonomously bring the system back to its normal operation, a process called self-healing. In this paper, a distributed methodology is proposed that aims to address this challenge, considering the requirements of all involved actors. Specifically, a Reinforcement Learning (RL) based algorithm is proposed that allows to iteratively optimize and determine an SFC healing solution upon a datacenter failure. As a second stage, a revenue-driven resource allocation mechanism is integrated, to resolve the contention for resources in an already functional datacenter that potentially occurs due to the repositioning. Various simulation scenarios prove the efficiency of our proposed resilient healing mechanism.

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Design and Experimental Verification of a Laser Engraving Machine

Design and Experimental Verification of a Laser Engraving Ma...

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Emerging Power Technologies (ICEPT), International Conference on

作者： Malik Shahaan Khalid Syed Muhammad Jaleed Arslan Zafar Sohaib Ali Khan Hafiz Zia Ur Rehman Zeashan Hameed Khan Dept. of Mechatronics Engineering Air University Islamabad Pakistan Department of Computer & Information Systems Engineering (CIS) N.E.D UET Karachi Pakistan Dept. of Avionics Engineering CAE NUST Islamabad Pakistan

This paper aims to develop a low-cost laser engraving machine. Materials like plastics, acrylic, glass, wood, cardboard, and leather have been used to engrave desired inputs. It have been found that this laser engraving process has a higher precision and accuracy as compared to traditional embellishing and embossing. The laser beam acquires thermal energy, which emerges from the machine and engraves the material. CREO 2.0 software is used for the 3D modeling and simulation of the machine. Arduino and different controller boards are tried and used in assembling the machine in less time. Different dependency and dimensional tests are conducted in order to validate the machine. The results are found to be satisfactory in terms of quality and cost. The final assembly is experimentally verified which is based on a 2D gantry that is mounted with a laser LED payload for laser engraving on various compatible materials.

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Virtualization of Tiny Embedded systems with a robust real-time capable and extensible Stack Virtual Machine REXAVM supporting Material-integrated Intelligent systems and Tiny Machine Learning

arXiv

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arXiv 2023年

作者： Bosse, Stefan Bornemann, Sarah Lüssem, Björn University of Bremen Dept. Mathematics & Computer Science Bremen Germany University of Siegen Dept. Mech. Engineering Siegen Germany University of Bremen Dept. Electrical Engineering Bremen Germany Institute for Microsensors -actuators and -systems Bremen Germany

In the past decades, there has been a significant increase in sensor density and sensor deployment, driven by a significant miniaturization and decrease in size down to the chip level, addressing ubiquitous computing, edge computing, as well as distributed sensor networks. Material-integrated and intelligent systems (MIIS) provide the next integration and application level, but they create new challenges and introduce hard constraints (resources, energy supply, communication, resilience, and security). Commonly, low-resource systems are statically programmed processors with application-specific software or application-specific hardware (FPGA). This work demonstrates the need for and solution to virtualization in such low-resource and constrained systems towards resilient distributed sensor and cyber-physical networks using a unified low-resource, customizable, and real-time capable embedded and extensible stack virtual machine (REXAVM) that can be implemented and cooperate in both software and hardware. In a holistic architecture approach, the VM specifically addresses digital signal processing and tiny machine learning. The REXAVM is highly customizable through the use of VM program code generators at compile time and incremental code processing at run time. The VM uses an integrated, highly efficient just-in-time compiler to create Bytecode from text code. This paper shows and evaluates the suitability of the proposed VM architecture for operationally equivalent software and hardware (FPGA) implementations. Specific components supporting tiny ML and DSP using fixed-point arithmetic with respect to efficiency and accuracy are discussed. An extended use-case section demonstrates the usability of the introduced VM architecture for a broad range of applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Real time systems

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VECOM: Variation Resilient Encoding and Offset Compensation Schemes for Reliable ReRAM Based DNN Accelerator

arXiv

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arXiv 2023年

作者： Jang, Je-Woo Nguyen, Thai-Hoang Yang, Joon-Sung School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Dept. of Semiconductor Systems Engineering Yonsei University Seoul Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

Resistive Random-Access Memory (ReRAM)-based Processing In-Memory (PIM) Accelerator has emerged as a promising computing architecture for memory-intensive applications, such as Deep Neural Networks (DNNs). However, due to its immaturity, ReRAM devices often suffer from various reliability issues, which hinder the practicality of the PIM architecture and lead to a severe degradation in DNN accuracy. Among various reliability issues, device variation and offset current from High Resistance State (HRS) cell have been considered as major problems in a ReRAM-based PIM architecture. Due to these problems, the throughput of the ReRAM-based PIM is reduced as fewer wordlines are activated. In this paper, we propose VECOM, a novel approach that includes a variation-resilient encoding technique and an offset compensation scheme for a robust ReRAM-based PIM architecture. The first technique (i.e., VECOM encoding) is built based on the analysis of the weight pattern distribution of DNN models, along with the insight into the ReRAM's variation property. The second technique, VECOM offset compensation, tolerates offset current in PIM by mapping the conductance of each Multi-level Cell (MLC) level added with a specific offset conductance. Experimental results in various DNN models and datasets show that the proposed techniques can increase the throughput of the PIM architecture by up to 9.1 times while saving 50% of energy consumption without any software overhead. Additionally, VECOM is also found to endure low R-ratio ReRAM cell (up to 7) with a negligible accuracy drop. © 2023, CC BY.

关键词： Deep neural networks

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Human Activity Recognition in Free Living Using a Wearable Sensor: A Two-Stage Approach

Human Activity Recognition in Free Living Using a Wearable S...

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International Conference on Awareness Science and Technology (iCAST)

作者： Tianhui Li Xue Zhou Kota Kokubun Wenxi Chen Division of Information System The University of Aizu Aizu-Wakamatsu Japan Dept. of Computer and Information Systems The University of Aizu Aizu-Wakamatsu Japan Dept. of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Japan

A novel two-stage approach for human activity recognition (HAR) using a wearable sensor is proposed in this study. The goal is to achieve both coarse-level and fine-level recognition of activities. The study focuses on five common activities: desk-working, driving, walking, downstairs, and upstairs. Three-axis acceleration signals were collected using a wrist-worn accelerometer sensor, and the activities were annotated during the participant’s daily routines. The acceleration data were preprocessed through segmentation, feature extraction, and normalization. In the first stage, a traditional machine learning model (decision tree) was used for coarse-level classification, distinguishing between activities with exercise and without exercise. In the second stage, deep learning models were employed for fine-level classification to recognize specific activities within the coarse classes. The performance of the models was evaluated using accuracy, sensitivity, and specificity. The proposed approach showed promising results with accuracies of 93.53%, 93.52%, 85.70%, 98.11%, and 85.91% respectively for desk-working, driving, walking, downstairs, and upstairs, demonstrating the effectiveness of the two-stage strategy for HAR. The proposed two-stage approach outperforms the one-stage method (decision tree classifier and deep learning model), particularly in imbalanced data, achieving a graceful equilibrium between sensitivity and specificity. The study highlights the importance of considering the complexity and variability of daily activities for accurate classification and resource optimization.

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