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检索条件"任意字段=2023 3rd International Conference on Electrical Engineering and Computer Technology, ICEECT 2023"

共 11233 条记录，以下是221-230 订阅

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A Comparative Study on the Various Aging Laws Governing Lithium-Ion Capacitor Cycling Aging 3rd

A Comparative Study on the Various Aging Laws Governing Lith...

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3rd international conference on Emerging Electronics and Automation, E2A 2023

作者： Roy, Arunabh Saha, Pankaj Stroe, Daniel-Ioan Khanra, Munmun Department of Electronics and Instrumentation Engineering National Institute of Technology Assam Silchar788010 India Department of Energy Aalborg University Aalborg Denmark

ISBN: (纸本)9789819768011

Lithium-ion Capacitors (LICs) are energy storage devices (ESDs) that combine the benefits of Lithium-Ion Batteries (LIBs) and Supercapacitors (SCs) by filling the gap between the low power densities of LIBs and the low energy densities of SCs. As an electrochemical device, LICs experience aging processes over their life, which is indicated by electrical performance losses. To predict performance degradation, aging models are developed. This study attempts to present a comparative analysis of the four capacitive evolution predictive mechanisms (aging laws) of an LIC over its cycle life under various operating conditions. To examine and recognize the aging behavior of LIC cells, the experimental data obtained from cycling aging of eight commercial 2100F LIC cells under different operating conditions, including temperature, C-rate, and cycling depth of discharge (DOD) are used. The four aging models namely the Langmuir model, SEI Layer formation model, decay model, and a heuristic model considered for this study, are identified using the least square error minimization algorithm and the experimentally obtained capacitance versus charge cycle data. The goodness of fit of these models is analyzed by calculating the R2 and RMSE values. On comparison, it is found that the Langmuir model is best suited for the cycle aging simulation of LIC and the main cause of LIC cycling aging is a reversible gas adsorption phenomenon. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Capacitor bank

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Study of New Generation Optical Networks with Regards to Type of Optical Amplifiers Connected in the Network 23

Study of New Generation Optical Networks with Regards to Typ...

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23rd Slovak-Czech-Polish Optical conference on Wave and Quantum Aspects of Contemporary Optics

作者： Látal, Jan Šiška, Petr Hejduk, Stanislav Seyringer, Dana Dohnal, Fadi Chovan, Jozef Uherek, František Štípal, Jiří Trubák, Kamil Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. listopadu 2172/15 Ostrava-Poruba708 00 Czech Republic Research Centre for Microtechnology Vorarlberg University of Applied Sciences Hochschulstraße 1 Dornbirn6850 Austria Slovak Centre of Scientific and Technical Information International Laser Centre Ilkovičova 3 Bratislava841 04 Slovakia Institute of Electronics and Photonics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovičova 3 Bratislava812 19 Slovakia

ISBN: (数字)9781510688094

ISBN: (纸本)9781510688087

Nowadays, there is strong demand for high-bitrate systems that can provide and distribute large bandwidth while assuring minimal error rate and high-quality communication with regards to the implementation of cloud centres, new 6G networks and smart applications. This all leads to continuous dynamic development in the field of xPON networks bringing new solutions for future optical networks. The presented work focuses on NG-PON2 networks, which are represented by Time and Wavelength Division Multiplexing Passive Optical Network (TWDM-PON) (G.989.1) or Super-PON (IEEE P802.3cs). The basis of the introduced topology is in combination of wave and time access through the TWDM-PON network. In our simulation, we were focusing on the utilization of eight wavelengths operating in the C and L band with amplifiers implementation, namely Erbium Doped Fiber Amplifier (EDFA) or Semiconductor Optical Amplifier (SOA). Regarding to used amplifier the ODN (Optical Distribution Network) reach of TWDM-PON were studied as well as the bitrate of 10 Gbps per wavelength in both upstream and downstream directions with the help of parameters such as bit error rate (BER), Q-factor. From the simulation results, it is shown that by implementing an EDFA amplifier, a longer range of up to 60 km and a lower BER than that of SOA can be achieved. The proposed TWDM-PON system provides a communication network for urban as well as rural areas with guaranteed high bit rates and range. © 2025 SPIE.

关键词： Erbium doped fiber amplifiers

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Road Object Detection for Visually Impaired People in Bangladesh 3rd

Road Object Detection for Visually Impaired People in Bang...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Tui, Nazmun Nahar Hamza, Amir Rahman, Mohammad Shahidur Computer Science and Engineering Shahjalal University of Science and Technology Akhalia Sylhet Bangladesh

ISBN: (纸本)9789819739363

Road object detection is a critical aspect of developing assistive technologies to enhance the mobility and safety of visually impaired individuals. In the context of Bangladesh, a densely populated and diverse environment, the need for accurate and contextually relevant road object detection is particularly significant. This paper introduces a novel road object detection dataset tailored specifically for the Bangladeshi context, aimed at facilitating the advancement of computer vision systems for assisting visually impaired pedestrians. The proposed dataset, named "BanROD" (Bangladesh Road Object Detection Dataset), is constructed by capturing images across Sylhet, Bangladesh. We primarily constructed this dataset which is trained on a baseline machine learning model. The dataset is annotated with meticulously labeled bounding boxes around road objects of interest including persons, CNGs, cars, upstairs-downstairs, and other obstacles. To ensure diversity, images are collected under different lighting conditions, weather scenarios, and traffic densities. In addition to dataset creation, we train and evaluate a deep learning-based object detection model "MobileNetV2" on the BanROD dataset. We then evaluate the model’s performance by measuring loss and average precision to find out how well it performs on real-world images. This solution is only for mobile devices as they are more handy for blind people around us. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Deep Transfer Learning for Multiclass Dermatology Classification: A Comprehensive Study 3rd

Deep Transfer Learning for Multiclass Dermatology Classifica...

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3rd international conference on Emerging Electronics and Automation, E2A 2023

作者： Rao, S. V. Achuta Murty, P. T. Satyanarayana Maram, Balajee Garg, Ankit Daniya, T. Kumar, B. Santhosh Data Science Research Laboratories Sree Dattha Institute of Engineering and Science Sheriguda Telangana Hyderabad501 510 India Shri Vishnu Engineering College for Women Bhimavaram India AIT- Department of Computer Science and Engineering Chandigarh University Punjab Gharuan Mohali140055 India GMR Institute of Technology Rajam532127 India Telangana Ibrahimpatnam501506 India

ISBN: (纸本)9789819768011

Accurate and prompt diagnosis is crucial to the treatment of patients with dermatological diseases, which are a significant cause for worry in the medical community. By automating the diagnosing process, deep learning algorithms have recently demonstrated significant promise in the field of dermatology. In-depth research on the use of deep transfer learning for multiclass dermatological classification is presented in this publication. To train deep neural networks, the study makes use of a broad and comprehensive dermatology dataset that includes a variety of skin disorders. Modern pre-trained models are used with transfer learning techniques to make it easier to extract discriminative features. In order to improve classification performance, we delve into the complex process of model fine-tuning and investigate data augmentation techniques. Our research shows that multiclass dermatology classification accuracy has significantly improved, with models delivering reliable findings for a variety of skin disorders. We offer a thorough evaluation of the model's performance, emphasizing its ability to distinguish between disorders that appear to be visually identical and assisting dermatologists in their diagnostic procedures. The field of dermatology will be significantly impacted by the study's findings. Our work helps to the creation of precise, effective, and available dermatological diagnostic tools by bridging the gap between conventional diagnosis techniques and cutting-edge deep learning technologies. We also go over potential difficulties, ethical issues, and prospective future research routes, highlighting how crucial it is to keep this field innovative. This thorough study highlights the importance of artificial intelligence in improving patient care in the field of dermatology and the possibility of deep transfer learning to revolutionize dermatology diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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Generation of Cell-Painted Nuclei Structures from Brightfield Images Using Residual-WGAN Model 3rd

Generation of Cell-Painted Nuclei Structures from Brightfiel...

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3rd international conference on Emerging Electronics and Automation, E2A 2023

作者： Aravindan, Abhinav Anthiyur Palanisamy, Rohini Department of Electronics and Communication Engineering Indian Institute of Information Technology Design and Manufacturing Kancheepuram Tamil Nadu Chennai India

ISBN: (纸本)9789819768011

Cell painting is a high-content morphological assay, with a wide range of applications particularly in the field of pharmacology, contributing significantly to the advancement of biomedical research. The prediction of cell painting from brightfield images facilitates rapid analysis and identification of cellular features across numerous samples. This study aims to generate nuclei cell-painted images from corresponding brightfield images using deep learning methods to extract visual information, thus eliminating the need for expensive equipment and the staining process associated with the cell painting assay. A Conditional Generative Adversarial Network (GAN) with Wasserstein loss and Gradient Penalty (GP) is utilized, comprising a U-Net generator along with a ResNet attention input, and a patch discriminator. The brightfield images are fed as input to the ResNet. The output of the ResNet, along with the brightfield images, is then fed into the U-Net model to generate the nuclei cell painting. The evaluation of the predicted images reveals promising results, with a Mean Absolute Error (MAE) value of 0.21, Mean Square Error (MSE) value of 0.34, Structural Similarity Index Measure (SSIM) value of 0.66, and a Peak Signal to Noise Ratio (PSNR) value of 53.42. These metrics collectively demonstrate a notable enhancement compared to the current industry-standard (Cross-Zamirski et al, Label-free prediction of cell painting from brightfield images, 2022). Thus, the framework comprising a Residual WGAN architecture is proposed for the generation of cell-painted nuclei images from brightfield microscopy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mean square error

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Tri Focus Net: A CNN-Based Model with Integrated Attention Modules for Pest and Insect Detection in Agriculture 3rd

Tri Focus Net: A CNN-Based Model with Integrated Attention M...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Montashir Fahim, A.S.M. Efat, Anwar Hossain Mahedy Hasan, S.M. Oishe, Mahjabin Rahman Jannat, Nahrin Mitu, Mostarina Department of Computer Science and Engineering Rajshahi University of Engineering and Technology Rajshahi6204 Bangladesh

ISBN: (纸本)9789819739363

Pests and insects pose a significant threat to global agriculture, causing crop damage and quality loss, with a massive annual costs. Early detection of these agricultural challenges is essential for sustainable farming and food security. Challenges in early pest detection include class imbalance, computational limitations, and the need for comprehensive feature learning. In response, our study introduces "Tri Focus Net (TFN)," a hybrid model that seamlessly integrates multiple attention mechanisms, including Channel, Soft, and Squeeze-and-Excitation Attentions, effectively addressing these issues. This combination empowers TFN to discern critical details both globally and locally within images, significantly enhancing feature extraction and robustness. Additionally, we have successfully integrated DenseNet 201 with TFN, further enhancing our model’s feature extraction capabilities and robustness. In our work, we achieved exceptional results, attaining a maximum accuracy of 94.20% for the insects dataset and a perfect 100% accuracy for the pest dataset. Our contribution to agriculture lies in providing a cost-effective, precise, and robust solution for pest and insect detection, underpinning the sustainability of crop health and the agricultural industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Livestock

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Feature Techniques with a Custom Convolutional Model for Breast Tumor Surveillance in Mammograms 3rd

Feature Techniques with a Custom Convolutional Model for Bre...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Mahmud, Md. Tanim Islam, Md. Shamiul Yasar, Samin Rahman, Md. Saifur Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka1216 Bangladesh

ISBN: (纸本)9789819739363

The most occurring cancer worldwide is breast cancer in which abnormal breast cells grow and form tumors. If left unchecked, it can be more fatal and can spread throughout the body. Globally, 2.3 million cases have been diagnosed and there were 685,000 fatalities in 2020, according to the World Health Organization (WHO). To overcome the fatalities, an early diagnosis is the best approach. Manual diagnosis is not an easy process for this cancer, using mammography images, and always requires an expert person. In this paper, we have used a VGG16-based model to eradicate the feature from the intermediate layers and concatenate the intermediate layers. Then, we used a Convolutional Neural Network (CNN) to train the feature and predict the accuracy and classification. We have evaluated our performance analysis with confusion matrix analysis, ROC curve analysis, and other methods. In our results, we have accomplished an accuracy of 94% among the datasets. Comparing our suggested framework to state-of-the-art (SOTA) technology demonstrates that it increased accuracy, and additional study may enable it to be enhanced to decrease false positives and false negatives in screening mammography findings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diseases

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Research on Obstacle Detection Method of Rail Transit Based on Deep Learning 3rd

Research on Obstacle Detection Method of Rail Transit Based ...

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3rd international conference on Information Control, electrical engineering, and Rail Transit, ICEERT 2023

作者： Tao, Yun Xu, Yongneng Nanjing University of Science and Technology Jiangsu Nanjing210094 China

ISBN: (纸本)9789819788903

A lightweight obstacle detection model based on an improved YOLOv5s detection network is proposed to address the shortcomings of traditional train track obstacle detection methods, such as poor real-time performance and low detection accuracy for small targets. On the basis of introducing linear bottleneck and reverse residual structure, the backbone network, together with SE, forms a new network module MobileNetV3, which makes the model lighter without reducing the accuracy rate. Starting from the feature fusion of neck, a weighted bidirectional feature pyramid BiFPN is introduced to enhance the underlying information of feature maps, enabling information fusion of feature maps at different scales, thereby enhancing feature information. Adding CA spatial attention mechanism at the end of the model feature extraction network reduces the loss of important position information during the training process. The experimental results show that the improved model improves the detection accuracy by 1.2 percentage points compared to the original YOLOv5s algorithm on a self-made diversified rail transit dataset, while reducing the model size by 4.1 MB and increasing the detection speed by 7 FPS. It has certain feasibility and superiority in detecting track obstacles. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information fusion

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Oral Cancer Detection at an Earlier Stage 3rd

Oral Cancer Detection at an Earlier Stage

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Pal, Ankur Oshiro, Shiho verma, Prem Kumari Yadav, Mithilesh Kumar Singh Raman, Ashish Singh, Prabhat Singh, Nagendra Pratap Department of Computer Science and Engineering Dr. B. R. Ambedkar National Institute of Technology Jalandhar Jalandhar India Ryukyus University Nishihara Japan Department of Electronics and Communication Dr. B. R. Ambedkar National Institute of Technology Jalandhar Jalandhar India Department of Electronics and Communication NIT Hamirpur HimachalPradesh Hamirpur India

ISBN: (纸本)9789819719457

Worldwide, oral cancer is one of the most common cancers. Even with easy access to the oral cavity and significant improvements in treatment, oral cancer death rates remain high, primarily due to late-stage diagnoses and less successful treatments. The best way to treat cancer is to detect it early, and oral cancer is among the most expensive types in the United States. Patients’ survival rates are improved, and medical costs are reduced when early diagnosis occurs. Numerous techniques have been investigated by researchers for detecting oral malignancies in the early stages. An extensive review of the various methods analyzed by researchers to detect oral cancer at an early stage is presented in the paper. An overview of algorithms used for each step of cancer detection algorithms is provided, along with a comparison of different methods for cancer identification and classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Detection of Chronic Heart Failure Using Deep Learning and Machine Learning 3rd

Detection of Chronic Heart Failure Using Deep Learning and M...

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Sai, P. H. V. Sesha Talpa Bharathi, V. V. Pratibha Nandanan, Umeshbabu Chikode, Suraj Raju Munnolimath, Mrutyunjaya Prakash, Kini Bhanu Kumar, G. S. Naveen Bhaumik, Amiya Department of Mechanical Engineering Malla Reddy College of Engineeing and Technology Hyderabad India Department of Mechanical Engineering Mahaveer Institute of Science and Technology Hyderabad India School of Mechanical Engineeing Lincoln University College Petaling Jaya Malaysia Department of Computer Science and Engineering Malla Reddy University Hyderabad India Malla Reddy University Hyderabad India Lincoln University College Petaling Jaya Malaysia

ISBN: (纸本)9789819783540

The objective of this paper was to use machine learning (ML) and deep learning (DL) approaches to identify chronic heart failure (HF). We were able to replicate the method utilized in clinical practice since the models we built in this work are based on many combinations of feature categories, including clinical features, echocardiography data, and laboratory findings. The incremental value for each feature category was also looked at. There were 422 subjects used in all. A suggested machine learning method includes steps for feature selection, class imbalance management, and classification. When features from all categories were included, the results for the diagnosis of HF were quite satisfactory, with high accuracy (97.56%), sensitivity (100%), and specificity (96.55%). Even with the single feature type used, the results were still quite good. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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