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2023 International Conference on computer, Electronics and Electrical engineering and their Applications, IC2E3 2023

作者： Basit, Mohammad Sarosh Khan, Adeeba Farooq, Omar Izharuddin Ahmad, Ibne Aligarh Muslim University Dept. of Computer Engineering Zhcet Aligarh India Aligarh Muslim University Dept. of Electronics Engineering Zhcet Aligarh India Aligarh Muslim University Dept. of Radio-Diagnosis Jnmc Aligarh India

ISBN: (纸本)9798350338003

As the population is growing and aging, the ubiquity of neurological disorders is increasing. If these disorders are not treated at an early stage, they may pose a significant threat. To avoid further complications, the presence of these diseases must be detected as quickly as possible. With the existing pattern recognition-based techniques, an individual brain illness can be identified at a time using MRI images. It is noteworthy that the existing approaches are not capable of assisting a skilled physician or deducing a result about multiple diseases from the MRI images like a professional clinical technician can do. Through this research, the objective is to predict multiple brain illnesses, or the probability of their existence based on MRI data. To achieve this goal, dataset for Normal, Alzheimer and Brain Tumor has been used from Kaggle and combined to train a 12-layer lightweight sequential model with 2 million trainable parameters. which gave close to 98.9% accuracy for the three classes on testing data. © 2023 IEEE.

关键词： Diseases

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A study on photovoltaic output prediction uncertainty and intermittency compensation method

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Transactions of the Korean Institute of Electrical Engineers 2021年第7期70卷 961-968页

作者： Park, Woo-Geun Kim, Ji-Soo Lim, Seung-Min Kim, Chul-Hwan Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Variable Renewable Energy(VRE) is impossible to maintain stable output due to uncertainty and intermittency. In this paper, Photovoltaic(PV) output is predicted through deep learning prediction technology and compared with the actual PV output. The data used as the input of the deep learning prediction model was partially selected through correlation analysis to reduce overfitting and to have high prediction performance. A Hyperparameter optimization model was used in several deep learning prediction models and the Recurrent Neural Network(RNN) prediction model was selected after comparing the performances. The difference between Predicted PV output and actual PV is compensated using Battery Energy Storage System(BESS). Moreover, the BESS capacity is calculated and the BESS State of Charge (SoC) range profile is observed. Copyright © The Korean Institute of Electrical Engineers This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://***/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

关键词： Forecasting

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A Solid State Transformer Based on a Single-Stage Three-Phase AC-DC T-Type Converters 8

A Solid State Transformer Based on a Single-Stage Three-Phas...

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8th Southern Power Electronics Conference and the 17th Brazilian Power Electronics Conference, SPEC / COBEP 2023

作者： Oliveira Rodrigues, Robério Dos Santos Mazza, Luan Carlos Da S. Lima, Welton De Souza Oliveira, Demercil Barreto, Luiz H. Silva Colado De Araújo Honório, Dalton Federal University of Ceara Dept. Electrical Engineering Fortaleza Brazil Federal Institute of Ceara Limoeiro Do Norte Dept. Electrical Mechanical and Computer Engineering Limoeiro do Norte Brazil

ISBN: (纸本)9798350321128

Conventional power transformers are equipment that are used to raise or lower voltages and currents and transfer energy in transmission and distribution systems. They are established and widely accepted devices, however, these transformers have limitations, as they do not have an onboard control system capable of controlling voltages and currents, an increase in reactive power and propagation of harmonics to the electrical network can occur. Therefore, the development of new technologies that can perform voltage and current transformation without presenting such deficiencies is necessary, and one of these technologies is the solid-state transformer (SST). Thus, this paper proposes a three-phase and bidirectional AC-DC converter, with an active power of 50 kW, voltages of 7.2 kVac and 800 Vdc, for applications in SSTs. The proposed topology is based on Dual Active Bridge (DAB) converters applied to the secondary and primary sides, respectively. In the course of this paper, it is also proposed to use the 15 kV/20 A silicon carbide (Sic) insulated gate bipolar transistor (IGBT) for the primary side with a diode in antiparallel to the IGBT of the 10 kV/10 A junction barrier schottky (JBS) type. © 2023 IEEE.

关键词： dual active bridge solid state transformer three-phase t-type converter

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3D modelling and virtual reality for museum heritage presentation: Contextualisation of sculpture from the tang era 10

3D modelling and virtual reality for museum heritage present...

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10th International Workshop 3D-ARCH "3D Virtual Reconstruction and Visualization of Complex Architectures"

作者： Spallone, R. Lamberti, F. Vigo, L. Ronco, F. Calandra, D. Ferraro, M. Dept. of Architecture and Design Politecnico di Torino Italy Dept. of Control and Computer Engineering Politecnico di Torino Italy Curator of Asian Arts Montreal Museum of Fine Arts MontrealQC Canada

The present research concerns the contextualisation and presentation of eleven small statues from the Tang era dated from the XI and XII centuries CE today housed at the Asian Art Museum in Turin (MAO). A multidisciplinary group with expertise in Asian art, digital representation, and information processing systems, with the support of VR@POLITO and MOD Lab Arch of the Politecnico di Torino is involved in the work. The goal is to present together several Tang-era pottery models not belonging to the same funerary outfit within the spaces of a coeval, philologically compatible hypogeal tomb. With a pronounced storytelling intent, the reconstructive 3D model represents a virtual exhibition project summarizing Tang art's architectural, pictorial, and sculptural features. The pipeline was developed through photogrammetric acquisition with Structure from Motion (SfM) technology and 3D modeling of the artworks, reconstructive modeling and texturing of a Tang tomb as the ideal space of the statues, and communication through a virtual reality (VR) experience augmented with a set of information. © Author(s) 2024.

关键词： 3D modeling

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Multimodal Deep Learning for Precise Lung Pathology Discrimination

Multimodal Deep Learning for Precise Lung Pathology Discrimi...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Sathishkumar, R. Karunamurthy, A. Jesudoss, S. Saint Anusri, E. Ugamaalya, C. Valarmathy, S. Manakula Vinayagar Institute of Technology Dept. of Cse Puducherry India Master of Computer Applications Sri Manakula Vinayagar Engineering College Puducherry India Christ Institute of Technology Dept. of Cse Puducherry India

ISBN: (纸本)9798331510022

Tuberculosis (TB) is a serious airborne infectious disease with a high fatality rate, primarily affecting the lungs. Early detection is vital for effective treatment, yet the current healthcare landscape suffers from a significant shortage of medical professionals and screening facilities, which hinders timely diagnosis. The lung cancer pandemic has further exacerbated these challenges, straining healthcare systems and complicating diagnostic procedures for respiratory diseases. Existing diagnostic systems predominantly rely on the ResNet algorithm to predict TB. However, these systems often fall short when it comes to accurately diagnosing COVID- 19, leading to reduced prediction accuracy and missed opportunities for early intervention. In light of these limitations, this study proposes a novel hybrid approach that combines Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) to facilitate the simultaneous prediction of both TB and LUNG CANCER. The integration of RNNs allows for effective temporal analysis of sequential data, while CNNs excel in extracting spatial features from medical imaging, such as X-rays or CT scans. This synergistic model is designed to improve the predictive accuracy for both diseases by leveraging the strengths of each architecture. By enhancing feature extraction and analysis capabilities, this innovative approach aims to address the pressing need for reliable diagnostic tools in the face of inadequate healthcare resources. Ultimately, the future system has the efficiency to enable earlier diagnoses and the treatment outcomes, and contribute to better public health management during dual outbreaks of TB and lung cancer. © 2024 IEEE.

关键词： Lung cancer

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Mask Generation with Meta-Learning Classifier Weight Transformer Network for Few-Shot Image Segmentation

Mask Generation with Meta-Learning Classifier Weight Transfo...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Jhou, Fong-Ci Liang, Kai-Wen Lo, Chung-Hsun Wang, Chien-Yao Chen, Yung-Fang Wang, Jia-Ching Chang, Pao-Chi National Central University Dept. Communication Engineering Taiwan National Central University Dept. Computer Science and Information Engineering Taiwan Academia Sinica Institute of Information Science Taipei Taiwan

ISBN: (纸本)9798350324174

This paper proposes a meta-learning classification weight transfer network to generate masks as a few-shot image segmentation architecture. It generates good prior masks via a pretrained classification weight transfer architecture, and uses a pretrained feature extraction architecture on query images and support images. The network architecture exploits a top-down path in a feature augmentation module to adaptively transfer information from fine to coarse features for extracting features from query images. Finally, the classification module predicts the segmentation of the query image. The experimental results show that using the mean intersection of joints (mIOU) as the evaluation mechanism, the accuracy of the 1-shot experimental results is 1.7% higher than that of the baseline. In the 5-shot experimental results, the accuracy is also improved by 2.6%. Therefore, compared with the baseline, it clearly shows that the mask generated by the meta-learning classification weight transfer network can effectively help improve the performance of few-shot image segmentation system. © 2023 IEEE.

关键词： Semantic Segmentation

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Development of Skin Cancer Detection Using Deep Spiking Neural Networks 10

Development of Skin Cancer Detection Using Deep Spiking Neur...

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10th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Habaebi, Mohamed Hadi Zulkarnain, Muhammad Amin Gunawan, Teddy Surya Fitriawan, Helmy Kartiwi, Mira Abd.rahman, Faridah International Islamic University Malaysia Electrical and Computer Engineering Dept. Kuala Lumpur53100 Malaysia Universitas Lampung Department of Electrical Engineering Bandar Lampung35145 Indonesia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia

ISBN: (纸本)9798350365283

The limitations of conventional techniques necessitate the development of new diagnostic strategies for early detection of skin cancer, the most prevalent form of cancer. This investigation investigates the application of Spiking Neural Networks (SNNs) in creating a dependable and automated skin cancer detection system. The primary goal is to evaluate the feasibility and efficacy of SNNs for this purpose. The methodology entails using the SpikingJelly framework to create and assess an SNN-based model, specifically a modified Spiking VGG-11. Focusing on images classified as melanoma or non-melanoma, the investigation employs the ISIC 2019 dataset. The Spiking VGG-11 model, which features approximately 15 million parameters, achieved a test accuracy of 81.3%, specificity of 81.7%, and precision of 81.5%. This performance is noteworthy in light of its reduced complexity compared to models such as the Spiking VGG-13, which has approximately 134 million parameters. The model's high specificity is especially advantageous in clinical environments, as it minimizes false positives and unnecessary testing. These results emphasize the potential of SNNs to offer diagnostic tools that are both efficient and effective in the detection of skin cancer. These findings underscore the potential of SNNs to enhance the early detection and diagnosis of skin cancer, offering a promising alternative to conventional diagnostic methods. © 2024 IEEE.

关键词： Lung cancer

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An intelligent photovoltaic power forecasting model based on bagged-boosted stack support vector regression with kernel linear

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Transactions of the Korean Institute of Electrical Engineers 2021年第11期70卷 1633-1639页

作者： Micha, Gerardo Ondo Kim, Chul-Hwan Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Due to the intermittent nature of Renewable Energy Sources (RES) and complexed pattern of time series data, increasing the forecasting accuracy by conventional methods and single-learner based Machine Learning (ML) algorithms is becoming increasingly challenging. Therefore, appropriate combination of ML schemes and data processing techniques is very important to increase the prediction accuracy of RES. This paper presents an intelligent Photovoltaic (PV) forecasting model using an ensemble learner approach based on a combination of bagging, boosting, and stacking algorithms and a Support Vector Regression with Kernel Linear (SVRL) meta-learner. The proposed approach pre-processes time series data using Correlation Matrix Analysis (Corr) and Principal Component Analysis (PCA). In level 0 of the STACK model, seven hybrid models are used as base learners and their individual predictions are used as input for the SVRL meta-learner in level 1. To evaluate the performance of our model, data collected from a 350KW 3rd PV power plant in Gyeongnam, South Korea, were used for simulations, and results were compared with bagging, boosting and bagging-boosting algorithms used separately, and results show higher forecasting accuracy of the proposed algorithm. © 2021 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Renewable energy resources

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THE IMPACT OF SCANNER DOMAIN SHIFT ON DEEP LEARNING PERFORMANCE IN MEDICAL IMAGING: AN EXPERIMENTAL STUDY

arXiv

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arXiv 2024年

作者： Guo, Brian Lu, Darui Szumel, Gregory Gui, Rongze Wang, Tingyu Konz, Nicholas Mazurowski, Maciej A. Dept. of Computer Science Dept. of Electrical and Computer Engineering Dept. of Radiology Dept. of Biostatistics & Bioinformatics Duke University NC United States

Purpose: Medical images acquired using different scanners and protocols can differ substantially in their appearance. This phenomenon, scanner domain shift, can result in a drop in the performance of deep neural networks which are trained on data acquired by one scanner and tested on another. This significant practical issue is well-acknowledged, however, no systematic study of the issue is available across different modalities and diagnostic tasks. Materials and Methods: In this paper, we present a broad experimental study evaluating the impact of scanner domain shift on convolutional neural network performance for different automated diagnostic tasks. We evaluate this phenomenon in common radiological modalities, including X-ray, CT, and MRI. Results: We find that network performance on data from a different scanner is almost always worse than on same-scanner data, and we quantify the degree of performance drop across different datasets. Notably, we find that this drop is most severe for MRI, moderate for X-ray, and quite small for CT, on average, which we attribute to the standardized nature of CT acquisition systems which is not present in MRI or X-ray. We also study how injecting varying amounts of target domain data into the training set, as well as adding noise to the training data, helps with generalization. Conclusion: Our results provide extensive experimental evidence and quantification of the extent of performance drop caused by scanner domain shift in deep learning across different modalities, with the goal of guiding the future development of robust deep learning models for medical image analysis. © 2024, CC BY.

关键词： computerized tomography

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127 Level Reduced Switch Asymmetric Cascaded H-Bridge Multilevel Inverter

127 Level Reduced Switch Asymmetric Cascaded H-Bridge Multil...

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2023 IEEE International Conference on Emerging Trends in engineering, Sciences and Technology, ICES and T 2023

作者： Zulfiqar, Ismaiel Syed, Shahzada Sufyan Syed, S. Subhan Malik, Yousaf Jadoon, Wasid Ali COMSATS University Islamabad Dept. of Electrical and Computer Engineering Abbottabad Campus Abbottabad Pakistan Allied Engineering and Services Limited Regional Manager dept. of Alternative Energy Solutions Lahore Pakistan

ISBN: (纸本)9781665455602

Depending upon the uses of multilevel inverter in different applications such as Uninterruptible power supply (UPS), high voltage DC transmission (HVDC) and Variable frequency drives (VFD), there is huge research which is carried on them. This paper describes the working of 127 level Reduce Switch Asymmetrical Cascaded H Bridge multilevel inverter (RS-ACHBMLI), the main objective is to produce a pure sinusoidal waveform without the use of any filter element and to reduce total harmonic distortion (THD) up to 0.80Asymmetric topology is used in which DC sources are of different magnitudesfuels cells are the best to use as a DC source in our proposed technique. Sine wave is compared with 63DCoffsets to trigger the Insulated gate bipolar junction transistors (IGBTs) in a specific manner, six dc sources and six IGBT's are used to produce required output waveform. Low frequency PWM technique is used in level generating circuitry to produce required output. © 2023 IEEE.

关键词： Pulse width modulation

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