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Intelligent Computing and Control for engineering and Business systems (ICCEBS)

作者： A. Chinnasamy P. Selvakumari Department of Data Science and Business Systems school of computing SRMIST kattankulathur campus Chennai Department of Computer Science and Engineering Chennai Institute of Technology Chennai Anna University

The DR becomes increasingly common, there is a need to automatically extract and classify disease severity. Due to diabetes problems, about 2% of people with this disease become completely blind, and DR complications make him 10% visually impaired if he has diabetes for 15 years. Furthermore, it plays an important role in the progression of blindness in middle-aged and older adults. If the disease is not recognized as soon as possible, patients can experience a severe stage of irreversible blindness. The shortage of ophthalmologists is a serious problem for the growing number of diabetic patients. Hard exudates must be found to screen and assist in disease monitoring and diagnosis. Therefore, Lloyd's clustering technique was used in this work.

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Identifying Flakiness in Quantum programs

Identifying Flakiness in Quantum Programs

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International Symposium on Empirical Software engineering and Measurement, ESEM

作者： Lei Zhang Mahsa Radnejad Andriy Miranskyy Department of Information Systems University of Maryland Baltimore County Baltimore USA Department of Computer Engineering Isfahan Branch Islamic Azad University Isfahan Iran Department of Computer Science Toronto Metropolitan University Toronto Canada

In recent years, software engineers have explored ways to assist quantum software programmers. Our goal in this paper is to continue this exploration and see if quantum software programmers deal with some problems plaguing classical programs. Specifically, we examine whether intermittently failing tests, i.e., flaky tests, affect quantum software development. To explore flakiness, we conduct a preliminary analysis of 14 quantum software repositories. Then, we identify flaky tests and categorize their causes and methods of fixing them. We find flaky tests in 12 out of 14 quantum software repositories. In these 12 repositories, the lower boundary of the percentage of issues related to flaky tests ranges between 0.26% and 1.85% per repository. We identify 46 distinct flaky test reports with 8 groups of causes and 7 common solutions. Further, we notice that quantum programmers are not using some of the recent flaky test countermeasures developed by software engineers. This work may interest practitioners, as it provides useful insight into the resolution of flaky tests in quantum programs. Researchers may also find the paper helpful as it offers quantitative data on flaky tests in quantum software and points to new research opportunities.

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Total Harmonics Distortion Prediction at the Point of Common Coupling of industrial load with the grid using Artificial Neural Network

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Energy and AI 2023年第4期14卷 294-303页

作者： Emenike Ugwuagbo Adeola Balogun Biplob Ray Adnan Anwar Chikodili Ugwuishiwu Centre for Space Transport and Propulsion (CSTP)Epe1 LASU Epe driveLagos106101Nigeria Department of Electrical&Electronics Engineering University of LagosAkoka101017LagosNigeria Centre for Intelligent Systems(CIS) School of Engineering and TechnologyCQ UniversityNorman Gardens4701RockhamptonAustralia Strategic Centre for Cyber Security Research Institute(CSRI) School of Information TechnologyDeakin UniversityGeelong3216MelbourneAustralia Department of Computer Science University of NigeriaNsukka410001EnuguNigeria

Power quality challenges have generated a lot of disputes between utilities,customers,network operators,and equipment manufacturers around the world as regards the share of responsibility for power quality solutions,this results in different levels of financial and technical losses for both the network operators and the *** of the major consequences of the operation of heavy-duty factories globally is the corruption of power quality at the point of common coupling(PCC).In order to quantify the harmonics contribution at the PCC by industrial consumers,this paper presents three-phase total harmonics distortion of current(THDi)prediction model at the *** proposed artificial neural network(ANN)models use a multilayer perceptron neural network(MLPN)to predict three-phase total harmonic *** input parameter used in the models is easily measured with basic power *** model was trained with input parameters captured at 33 kV and 132 kV voltage levels using power quality meters at five(5)different steel manufacturing ***(8)different models were designed,trained,validated,and tested with different combinations of input parameters,number of hidden layers,and number of neurons in the hidden *** results show that the model with two hidden layers which uses four major power parameters(Current,apparent power,reactive and active power)as input parameters in the training model had the best performance with a 95.5%coefficient of correlation between the measured THDi and the predicted THDi.

关键词： Total harmonics distortion(THD) Power quality(PQ) Artificial neural network(ANN)

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Retinal Image Analysis for Early Deduction of Diabetic Retinopathy

Retinal Image Analysis for Early Deduction of Diabetic Retin...

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International Conference on Advanced Computing and Communication systems (ICACCS)

作者： A. Chinnasamy P. Selvakumari Department of Data Science and Business Systems School of Computing SRMIST Chennai India Department of Computer Science and Engineering Chennai Institute of Technology Anna University Chennai India

The prevalence of DR is steadily rising, which necessitates the automatic disease severity extraction and classification. About 2% of those with this illness are entirely blind because of the diabetic mellitus problem and 10% get vision impairment after 15 years of diabetes as a result of the DR complication. It is also a significant contributor to blindness in both middle aged and older age groups. The patient may develop to severe stages of irreversible blindness if the condition is not detected early. The growing number of diabetic patients face a major issue due to a lack of ophthalmologists. It is suggested that an automated DR screening system be created to aid the ophthalmologist in making decisions. One of the primary symptoms of the DR is hard exudates. The detection of hard exudates is crucial for screening purposes and aids in disease monitoring and diagnosis. Thus, utilising Lloyd’s clustering technique, this work offered a unique techniques to segment the exudates and irregularities in DR were found.

关键词： Support vector machines Image segmentation Visual impairment Blindness Blood vessels Retina Time measurement

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SPD Miscoordination in PV systems Due to Lightning Strikes

SPD Miscoordination in PV Systems Due to Lightning Strikes

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International Conference on Applied and Theoretical Electricity (ICATE)

作者： Osmel Pérez Baluja Bernardo Yaser León Avila Carlos A. García Vázquez Daniel Tudor Cotfas Petru Adrian Cotfas Orlys E. Torres Breffe Department of Electronics and Computers Faculty of Electrical Engineering and Computer Science Transilvania University of Braşov Brasov Romania Department of Electric Power Systems Faculty of Electrical Engineering Technological University of Havana Havana Cuba

ISBN: (数字)9798350388107

ISBN: (纸本)9798350388114

This study deals with the coordination of surge protection devices (SPD) in photovoltaic systems (PV). A PV farm model was developed in MATLAB Simulink with all the main components, solar panels, converter, grounding system, power transformer, cables in the DC and AC branches with their capacitances, and surge protection device throughout the installation. It was found that when lightning strikes the grounding system or in the vicinity of the PV installation, the coordination among SPD types 1, 2, and 3 is lost. Miscoordination can cause damage to several elements of the PV system, mainly the inverters. Finally, solutions were proposed to avoid SPD miscoordination during lightning strikes.

关键词： Grounding Software packages Lightning Surge protection Stars Switches Inverters Power transformers Solar panels Fault current limiters

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Decentralised Iterative Learning Control for High Performance Collaborative Tracking Problem with Output Constraints

Decentralised Iterative Learning Control for High Performanc...

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UKACC International Conference on Control (CONTROL)

作者： Bin Chen Bing Chu Department of Automatic Control and Systems Engineering University of Sheffield Sheffield U.K. School of Electronics and Computer Science University of Southampton Southampton U.K.

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

High performance collaborative tracking problem, requiring a group of independent subsystems to generate a global output that can precisely track the desired reference in a repetitive manner, has found lots of applications in practice. However, for such an important control task, existing iterative learning control (ILC) methods have not considered the constraint on each subsystem's output, which leads to potential risk within the control process. This paper proposes a novel optimisation based ILC method to address the high performance collaborative tracking problem with output constraints. The proposed ILC framework can guarantee not only each subsystem's output constraint is always satisfied during the control process, but also the monotonic convergence of a well-defined performance index to a possibly minimum value. To avoid huge computational complexity for large scale systems, we further apply the idea of the alternative direction method of multipliers (ADMM) to implement the proposed ILC frame-work in a decentralised manner, which allows the resulting decentralised methods to be applied to large scale and changing systems. Moreover, the decentralised ILC method proposed in this paper is suitable for non-minimum phase, heterogeneous and/or homogeneous systems, which is appealing in practice. Convergence properties of the proposed ILC algorithms are analysed rigorously, and numerical examples are given to demonstrate the algorithms' effectiveness.

关键词： Collaboration Process control Performance analysis Large-scale systems Task analysis Computational complexity Optimization

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Average Performance Analysis of Multi-Class Classification Based on Error-Correcting Output Codes

Average Performance Analysis of Multi-Class Classification B...

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IEEE International Conference on systems, Man and Cybernetics

作者： Manabu Kobayashi Gendo Kumoi Hideki Yagi Shigeichi Hirasawa Center for Data Science Waseda University Tokyo Japan Department of Information and Management Systems Engineering Nagaoka University of Technology Niigata Japan Department of Computer and Network Engineering University of Electro-Communications Tokyo Japan

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In machine learning, one of the methods to solve multiclass classification problems is a framework called Error-Correcting Output Codes (ECOC), which constructs a multiclass classifier by combining a lot of binary classifiers. ECOC assigns binary codewords to each category, and the multiclass classification performance varies depending on the code. In this study, we treat each element of the codeword as a random variable and evaluate the average performance of ECOC. As a result, for $M$ class classification if the number of binary classifiers is $O(\log M)$ , then the average error probability of various codes approaches that of MAP estimation. We show that the important points are the ratio between the number of binary classifiers and $\log M$ and the difference between the maximum posterior probability and the second highest posterior probability for the categories.

关键词： Codes Error probability Posterior probability Estimation Machine learning Random variables Performance analysis Cybernetics

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Simulation-Based Damage Detection for Composite Structures with Machine Learning Techniques 1

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40th IMAC, A Conference and Exposition on Structural Dynamics, 2022

作者： Lang, Alexandre Tavares, André Di Lorenzo, Emilio Cornelis, Bram Peeters, Bart Desmet, Wim Gryllias, Konstantinos Siemens Industry Software NV Leuven Belgium Department of Computer Science KU Leuven Leuven Belgium Department of Mechanical Engineering KU Leuven Leuven Belgium Dynamics of Mechanical and Mechatronic Systems Flanders Make Lommel Belgium

ISBN: (数字)9783031041228

ISBN: (纸本)9783031041211

With the use of composite materials rising steadily in major industries such as the automotive and aerospace, a strong effort is put into structural health monitoring (SHM) and damage detection methods for these components. Nondestructive testing (NDT) techniques such as laser Doppler vibrometry (LDV) provide a valuable experimental setting for making measurements with dense grids of points without mass loading the structure. The use of machine learning (ML) and deep learning (DL) techniques for the subsequent classification of defects has the potential of creating a reliable and automated framework for damage detection. In this work, different ML and DL approaches were assessed for the task of detecting defects on a carbon fiber plate by using frequency response data. The approaches were enriched by considering simulated datasets (created with Finite Element Analysis) in a transfer learning framework. Simulation data is easier to generate than experimental, meaning any added value provided with simulation data is advantageous. A description of the obtained results of damage detection is presented, along with a comparative overview of the different techniques. © 2022, The Society for Experimental Mechanics, Inc.

关键词： Nondestructive examination

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YOLOv8-Powered Multi-Object Detection for Safety Helmets and Number Plates in Real-Time Surveillance systems

YOLOv8-Powered Multi-Object Detection for Safety Helmets and...

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Communication, Computing and Industry 4.0 (C2I4), International Conference on

作者： Pramila S Anil D O Sirisha New Horizon College of Engineering Bengaluru India Department of CSE UVCE Bangalore University Bengaluru India Department of Computer Science and Business Systems G.Pulla Reddy Engineering College Kurnool India

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379891

This paper presents a real-time object detection system using the latest YOLOv8 model to enhance traffic monitoring and safety enforcement. The model was trained to detect helmets, riders and number plates in diverse traffic scenarios, achieving an overall accuracy of 93.4%. Significant improvements were observed in precision, recall and mean Average Precision (mAP) scores due to the combination of an optimized architecture, high-quality data and fine-tuned hyperparameters. The system maintains real-time inference capabilities with a processing speed of 11 ms per image, making it suitable for continuous surveillance and enforcement applications. This study demonstrates the potential of using advanced deep learning techniques for effective and automated traffic rule enforcement. Future work may involve further optimizations and integration with large-scale cloud-based systems for more comprehensive deployments.

关键词： Accuracy Surveillance Transfer learning Object detection Traffic control Helmets Real-time systems Safety License plate recognition Optimization

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Investigation of the separation zone for split-domain light field visualization

Investigation of the separation zone for split-domain light ...

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International Scientific-Professional Conference on Information Technology (IT)

作者： Kamran Javidi Maria G. Martini Peter A. Kara Department of Computer Science Wireless Multimedia and Networking Research Group School of Computer Science and Mathematics Faculty of Engineering Computing and the Environment Kingston University Penrhyn Road Campus Kingston upon Thames London UK Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

Light field visualization commonly provides a single content over the entire field of view. However, the angularly-selective nature of the technology enables the simultaneous visualization of different contents at different viewing angles. Yet segmenting the valid viewing area comes with content interference as well in forms of separation zones, resulting invalid viewing domains. In this paper, we introduce a study on the separation zone of split-domain light field visualization. We created a static-content-based scenario in which we split the valid viewing area into two distinct domains, with a separation zone in between them. This was achieved by merging two light field contents in the middle with an instantaneous switch. The resulting visualization on a light field display was captured by a DSLR camera from two viewing distances, and it was compared to the crosstalk effect caused by insufficient angular resolution.

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