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Augmented Reality Based Machine Monitoring for Legacy Machines: A retrofitting use case 30

Augmented Reality Based Machine Monitoring for Legacy Machin...

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30th International Scientific Conference Electronics, ET 2021

作者： Kancharla, Chandrakanth R. Bekaert, Lukas Lannoo, Jonas Vankeirsbilck, Jens Vanoost, Dries Boydens, Jeroen Hallez, Hans Dept. of Computer Science Dept. of Electrical Engineering Brugge8200 Belgium

ISBN: (纸本)9781665445184

Monitoring a machine and the insight it provides for appropriate maintenance is of prime importance for the modern industry. While this is fully supported in Industry 4.0, many manufacturing units today are unable to use its technologies. One of the major reasons behind that is the lack of onboard sensors to capture and communicate appropriate data from a legacy machine. Retrofitting is considered an efficient solution to include legacy machines in an industry 4.0 ecosystem by adding external sensing and connectivity. As a proof of concept, in this paper we consider an induction motor for retrofitting, a major component of any manufacturing unit. We further show how retrofitting can help in making legacy machines compliant to the new technologies from Industry 4.0. Finally, visualization of the collected data is performed over a web based Augmented Reality application. The choice for a web-based Augmented Reality application was made to enable monitoring of the sensor data over a multitude of devices. © 2021 IEEE.

关键词： Data visualization

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Potato Leaf Disease Classification using Convolutional Neural Networks 11

Potato Leaf Disease Classification using Convolutional Neura...

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11th International Conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET - SIP 2023

作者： Wasalwar, Yash Prashant Bagga, Kishan Singh Joshi, Vikash Kailash Joshi, Anuradha G H Raisoni College of Engineering Dept. of Computer Science and Engineerng Nagpur India G H Raisoni College of Engineering Nagpur India

ISBN: (纸本)9798350348422

The vegetable Potato is quite familiar to all of us. After crops like rice and wheat, one of the most widely grown crops in India is the potato. But like other crops, Potato is also vulnerable to diseases. Two diseases namely Early blight and Late blight severely hinder the growth of potatoes. These diseases, if not discovered in its early stages, damage the crops, affects the production and apparently results in significant losses. The aim is to fasten the process of illness detection so as to increase the potato production by detecting the mentioned diseases at an early stage of Cultivation by using CNN, a Deep Learning based approach. The potato crop needs to be diagnosed by detecting and classifying leaves, whether they are healthy or infected and CNN algorithm is to be implemented for the same. By collecting the visual features of the leaves of distinct potato species, this study offers a deep learning-based approach for recognising the particular early and late blight infections in potatoes. Image processing was found the best way to detect and analyze the particular diseases as it helped us to extract the very essential information from the images of the leaves. Using a dataset obtained from subsidiary of plant village dataset, consisting of 3000 samples having 1000 samples each of healthy, early blight and late blight infected leaves, a custom model was trained with some advancements from our end to detect and categorize the leaves into healthy and diseased. The CNN based model gave an accuracy of 99% when prepared over 80% train data. The output shows that CNN outperforms all in the field of Potato disease classification. © 2023 IEEE.

关键词： Crops

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Multi-label Bengali Abusive Comments Classification using Problem Transformation Method

Multi-label Bengali Abusive Comments Classification using Pr...

引用

International Conference on electrical engineering, Computing science and Automatic Control (CCE)

作者： Tahsin Tasnia Khan Abid Hassan Md Faysal Ahamed Samiul Islam Dept. of Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Dept. of Electrical & Computer Engineering North South University Dhaka Bangladesh Dept. of Computer Science & Engineering International Standard University Dhaka Bangladesh

Sentiment analysis on the Bengali language is limited to binary and multi-class classifications. This paper focuses on the multi-label classification of the Bengali abusive social media comments dataset having 10220 rows of different social media negative comments using the problem transformation method. The texts are categorized into five labels: toxic, threat, obscene, insult, and racism. Three different multi-label approaches: Binary Relevance, Label Powerset, and Classifier Chain combined with three popular machine learning algorithms: Multinomial Naive Bayes, Random Forest, and Logistic Regression are implemented in this research. This study evaluates the performance of these models by applying them to various classification scenarios, considering five-label, four-label, three-label, and two-label classifications independently. Our research outcomes are evaluated with some performance indicators: Accuracy, Precision, Recall, F1-Scores, Confusion matrix, Macro-average, and Hamming score. The combination of Label Powerset and Logistic Regression outperformed the other two classifier-model combinations because of their quick adaptation to the dataset with an accuracy of 88.07% for five label classifications, 90.12% for four label classifications, 90.56% considering three label classifications and 92.67% for two label classification individually. With 88.07% accuracy, the Label Powerset with Logistic Regression approach can be applied to classify five types of sentiment in a single text.

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Novel Multi-Scan LiDAR System for Enhanced Shadow Removal

Novel Multi-Scan LiDAR System for Enhanced Shadow Removal

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Jung-Min Kim Dae Yu Kim Dept. of Electrical and Computer Engineering Inha university Incheon Republic of Korea

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

In autonomous driving, sensor data is critical for ensuring safety in dynamic road environments. The conventional LiDAR systems face challenges when obstacles create shadow zones leading to incomplete mapping and potential detection failures. This paper presents a novel LiDAR system employing beam-splitting technology to minimize shadow regions in 3D mapping. Through multi-angle scanning, the proposed system effectively eliminates shadow regions, ensuring continuous detection in complex driving environments. Prototype testing demonstrates a reduction in shadow-induced detection errors achieving measurement accuracy within 3%. The research highlights the viability of this technology for safer autonomous driving systems.

关键词： Laser radar Accuracy Three-dimensional displays Pedestrians Roads Measurement uncertainty Prototypes Safety Autonomous vehicles Faces

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Digitally Tunable CMOS Mixer Design for Adaptive RF Front-Ends

Digitally Tunable CMOS Mixer Design for Adaptive RF Front-En...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Diptashree Das Minghan Liu Mohammad Abdi Francesco Restuccia Marvin Onabajo Dept. of Electrical and Computer Engineering Northeastern University Boston MA USA

ISBN: (数字)9798350356830

ISBN: (纸本)9798350356847

Reconfigurability and self-optimization have become essential during radio frequency integrated circuit (RFIC) design to support the growing number of devices and fast changes in the surrounding wireless spectrum. This has created the need to develop new design approaches for RFICs based on end-to-end wireless system-level performance metrics during operation in dynamically changing communication environments. This paper introduces a CMOS mixer with a wide range of digitally tunable bias current for machine learning (ML) based adaptation. The mixer is designed to become part of a self-adaptive receiver (RX) architecture that is capable of optimizing its performance in accordance to wireless channel conditions by evaluating systemlevel parameters. The proposed mixer topology has digitally tunable bias current and a programmable helper current (PHC) circuit to maintain the voltage headroom during operation with a wide tuning range. It is capable of dynamically minimizing power consumption based on performance and wireless network requirements. Post-layout simulation results show that the power consumption can be reduced up to 8x depending on momentary performance needs, while maintaining an IIP3 greater than -2.4 dBm for the entire range of operation.

关键词： Radio frequency Power demand Adaptive systems Simulation Voltage Radiofrequency integrated circuits Noise measurement Mixers Tuning Optimization

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SCN-DRL: Scheduler for large-scale Critical Notification applications based on Deep Reinforcement Learning 9

SCN-DRL: Scheduler for large-scale Critical Notification app...

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9th International Conference on Future Internet of Things and Cloud, FiCloud 2022

作者： Vrbaski, Mira Bolic, Miodrag Majumdar, Shikharesh University of Ottawa School of Electrical Engineering and Computer Science Ottawa Canada Carleton University Dept of Systems and Computer Engineering Ottawa Canada

ISBN: (数字)9781665493505

ISBN: (纸本)9781665493505

A multi-objective Scheduling approach for large-scale microservice Critical Notification system applications (SCN-DRL) based on Deep Reinforcement Learning is presented. This paper addresses optimization for three objectives: cloud service cost, cloud resource utilization, and system notification deadline. Recent advances in the deep reinforcement learning field inspired us to research how to build a solution that learns to manage the cloud container's resources directly from experience. SCN-DRL is an example solution that translates the containerized microservice cloud cluster scheduling problem into a learning problem. To the best of our knowledge, this is the first work dealing with multi-optimization objectives for critical microservice notification applications using deep reinforcement learning, where the notification load is variable and depends on the results of the other previously prior processed microservice. Our initial results show that SCNDRL performs better than state-of-The-Art heuristics and adapts to different workload conditions without a noticeable drop in performance. © 2022 IEEE.

关键词： Containers

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A Current-Limiting PI Control Scheme for Voltage Source Converters

A Current-Limiting PI Control Scheme for Voltage Source Conv...

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IEEE International Energy Conference and Exhibition (EnergyCon)

作者： Zaint A. Alexakis Antonio T. Alexandridis Electrical and Computer Engineering Dept. University of Patras Patras Greece

In modern power systems, the good and safe operation of voltage source converters (VSCs) is of principal importance. The aim is twofold: to regulate the active and reactive power injected to the grid and simultaneously to protect the VSC from overcurrent situations usually occurred after some abnormal grid conditions. A novel solution to this open problem is provided in this paper. Specifically, we present a novel current-limiting control scheme that enables to accurately limit the RMS current value of the VSC, even under abrupt faults. The controllers are implemented in the well-known d-q synchronously rotating reference frame for a VSC that is connected to a stiff bus through an LC filter. The synchronization with the grid-bus is carried out by a robust synchronization unit (RSU), whereas a modified PQ controller that efficiently accommodates the nature of the RSU and the inner-loop control scheme is implemented. The proposed design is analyzed through large signal analysis; it is established that the system is locally-input to state stable (I-ISS). Also, asymptotic stability and convergence to the equilibrium, is proven, in the domain of attraction as it is determined by the limiting current controller operation. Finally, the studied system is tested under various simulation scenarios where in conventional situations large current values are normally expected. In our case, the current-limiting property is fully verified and a smooth response to equilibrium is observed.

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A Physics-Based Temperature Dependent Analytical Model For 2DEG Density in AlGaN/GaN HEMT Devices

A Physics-Based Temperature Dependent Analytical Model For 2...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Kashfia Tajmim Nabila Jerry L. Hudgins Dept. of Electrical and Computer Engineering University of Nebraska-Lincoln Lincoln USA

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

Accurate modeling of the temperature dependent two-dimensional electron gas (2DEG) is very crucial in applied power electronics, given its critical role in High Electron Mobility Transistor (HEMT) characteristics. In view of this, the temperature dependency of 2 DEG has been analyzed for different gate voltages and modeled for the temperature range from 300K to 500K. Relying on the device physics, the temperature dependence of various parameters including bandgap variation, fermi level energy, barrier height reduction, threshold voltage changes and their effects on electron sheet charge density (2DEG) has been included. This model will also integrate the effects of sub-band energy variations, polarization changes, and material property adjustment due to temperature changes. The goal is to provide a robust analytical framework that can predict device behavior accurately across a spectrum of operational gate voltage and temperature conditions. This anticipated model will facilitate the development of power electronics that are more reliable and efficient, capable of operating effectively in diverse and demanding environments.

关键词： Temperature dependence Temperature distribution Analytical models HEMTs Logic gates Power electronics Wide band gap semiconductors Semiconductor process modeling MODFETs Aluminum gallium nitride

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Study of the contextual factors of a project affecting the build performance in Continuous Integration

Study of the contextual factors of a project affecting the b...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Benjamin, Jetty Mathew, Juby Jose, Rubin Thottupurathu Amal Jyothi College of Engineering Dept. of Computer Applications Kerala Kanjirappally686 518 India Apj Abdul Kalam Technological University Kerala Thiruvananthapuram695016 India Amal Jyothi College of Engineering Dept. of Computer Science and Engineering Kerala Kanjirappally686 518 India

ISBN: (纸本)9798350303452

Continuous Integration (CI) has a significant and substantial impact on software engineering. CI optimize the development processes, enhance the code quality and facilitate a collaborative environment resulting in the effective release of high quality software features to the end users. The performance and outcome of CI build is a major concern in CI environment. In this paper we address the impact of contextual factors of software projects that can influence the performance of build in Continuous Integration/Continuous Deployment (CI/CD) pipeline. We studied 588 large-scale GitHub projects that uses the Travis CI build system. An experimental study is conducted on these projects to leverage the history of prior build success and failures. Three ML models are trained on the data. The aim of the study was to identify the contextual factors of a project, influencing the performance of build. We used Logistic Regression, Decision Tree and Extreme Gradient Boost model to predict the build outcome. We identified that the contextual factors has a significant impact and the study will further helps to develop techniques to preemptively detect when a CI build environment is most likely to trigger a failure. © 2023 IEEE.

关键词： Decision trees

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Solar Irradiance Prediction with Ensemble Learning Method as Input for Battery Operation Optimization

Solar Irradiance Prediction with Ensemble Learning Method as...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Ray Colucci Imad Mahgoub Dept. of Computer Science & Electrical Engineering F.A.U. Boca Raton FL USA

The current use of fossil fuels as a major source of energy on a global scale has had a negative impact on the environment in terms of pollution and global warming. There is currently a paradigm shift to the utilization of renewable energy sources such as solar and wind power. However, the challenge is that these options are inherently intermittent and expensive to install. Data from these sources can be used as input to algorithms to optimize the operation of microgrids to provide energy to both residential and commercial buildings. A key component to enhance the performance of a microgrid is a battery energy storage system (BESS). Our goal is to design an algorithm to optimize battery operation. The objective of this paper is to predict the solar irradiance to provide input to battery operation optimization. We investigate the comparative performances of different strategies. The accuracy of five popular machine learning algorithms are estimated, namely Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Kernel ridge regression (KRR), and Linear Regression. Voting, stacking, and bagging ensemble methods are utilized to further enhance the accuracy. Our results show stacking to outperform the other methods. Our experiment produced better predictions than other work using the same dataset.

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