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2023 14th International Renewable Energy Congress, IREC 2023

作者： Najeeb, Mussab Schwalbe, Ulf Herget, Mathias Technical University of Ilmenau Dep. of Electrical Engineering and Information Technology Ilmenau Germany University of Applied Sciences Fulda Dep. of Electrical Engineering and Information Technology Fulda Germany

ISBN: (纸本)9798350342840

Monitoring lithium-ion batteries in their second life is of great importance when using them in stationary storage systems for grid support or in any other energy system, due to the expected different behavior and operational conditions in their second life compared to their first life. The idea of this study based on the fact that each estimation method has its advantages and drawbacks dep.nding on the application in which the batteries will be used, and that monitoring batteries in their second life required to be integrated with the battery management system to balance cells, optimize performance, and prevent overheating. The contribution of our study can be summarized in the integration of specific methods, aiming to benefit from most of their advantages and neutralizing their drawbacks as much as possible to provide more accurate and reliable online monitoring of batteries. The combination presented in this study, consisting, for state of charge estimation, of an artificial neural network and an Unscented Kalman Filter with adjusting the filtering process using Coulomb counting, and Kalman Filter for state of health estimation, showed much better reliability and accuracy than if each method was used alone. This combination has been trained and adjusted using empirical data of second-life batteries, extracted from used electric vehicle battery bank, to obtain the best possible performance, considering the specificity of these batteries in terms of operating voltage range, lower current ratio, and other parameters. © 2023 IEEE.

关键词： Lithium-ion batteries

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Toward the Improvement of Probabilistic Classifiers Using Ontologies 2

Toward the Improvement of Probabilistic Classifiers Using On...

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2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2023

作者： Apicella, Andrea Corazza, Anna Isgrò, Francesco Prevete, Roberto University of Naples Federico Ii Dep. of Electrical Engineering and Information Technology Naples Italy

ISBN: (纸本)9798350300802

Incorporating ontological knowledge into supervised machine learning pipelines is a promising avenue of research aimed at enhancing classification performance. Specifically, when probabilistic ontologies are accessible, it is plausible to combine the corresponding probabilities with the outputs of a probabilistic classifier. This integration can enhance the classification process by incorporating the probabilistic information from the ontologies alongside the classifier outputs. This work investigates the gain given by using ontologies in an image classification task. We start from the hypothesis that context information of the elements present in an image can lead the classifier toward the correct class. An implementation of this integration is proposed and its effectiveness is demonstrated, especially in situations where the classifier accuracy is relatively low. The initial experimental results support the efficacy of this approach. To assess the impact of the ontology, performance was first measured on simulated classifiers on a subset of MIT-Indoor. Then, as a real case, we ran experiments using a Neural Network pre-trained on the ImageNet dataset. © 2023 IEEE.

关键词： classification ontological knowledge ontology probabilistic classifier

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Fuzzy Logic-Based Visualization and Interpretation of NDVI Maps 14

Fuzzy Logic-Based Visualization and Interpretation of NDVI M...

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14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2024

作者： Racoviteanu, Andrei Kazak, Artur Secrieru, Nicolae Ivanovici, Mihai Transilvania University of Brasov Electronics and Computers Dep. Romania Politehnica National University of Science and Technology Applied Electronics and Information Engineering Dep. Bucharest Romania Space Technology Center Technical University of Moldova Chisinau Moldova

ISBN: (纸本)9798331513139

Normalized Difference Vegetation Index (NDVI) is widely-used for the monitoring of agriculture crops. Its interpretation is based on thresholds and the colormaps used for the visualization of NDVI maps are discrete. We propose a fuzzy logic approach for both the visualization and the interpretation of the NDVI maps. The rationale is that the NDVI values of agricultural crops exhibit a certain spread, while the interpretation based on thresholds is prone to a certain degree of incertitude. We define several membership functions for modeling three sub intervals of the NDVI values in the [0, 1] range, useful for the interpretation of the vegetation status for agricultural crops. The three sub intervals are corresponding to three categories of vegetation status: dried-out vegetation, moderately healthy and strongly developed. For a Sentinel-2 time series, we compare the visualization using the crisp logic, the Sentinel-2 color palette, and the color palettes resulting from the proposed membership functions implementing the fuzzy logic. We show that our colormap has a smoother color gradient, which can facilitate a more visually-appealing and interpretable transition of colors. We also show an example of inference for the interpretation of the NDVI values for an operational usage of the proposed fuzzy logic approach. © 2024 IEEE.

关键词： Visualization

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CHITOSAN-POLYPROPYLENE HOLLOW FIBERS COMPOSITE MEMBRANE FOR COPPER-ZINC IONS PERTRACTION

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UPB Scientific Bulletin, Series B: Chemistry and Materials Science 2023年第1期85卷 77-88页

作者： Păncescu, Florentina Mihaela Ferencz, Andreea Grosu, Vlad-Alexandru Goran, Alexandru Nechifor, Gheorghe Faculty of Chemical Engineering and Biotechnologies Dep. of Analytical Chemistry and Environmental Engineering University POLITEHNICA of Bucharest Romania Department of Electronic Technology and Reliability Faculty of Electronics Telecommunications and Information Technology University Politehnica of Bucharest 061071 Romania

Composite membranes based on cellulosic derivatives or related natural compounds with ionizing groups represent one of the viable alternatives for separation, concentration and purification of acidic aqueous solutions containing copper and zinc ions. This work presents the preparation and characterization of a chitosan–polypropylene hollow fiber composite membrane. The prepared membrane was tested in the pertraction process of copper and zinc ions from strongly acidic solutions. The obtained results show that this type of membrane achieves both the separation and the concentration of the tested solutions. The contribution of each component of the composite membrane was comparatively evaluated under the same working conditions. © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Chitosan

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Performance Analysis of Intelligent Reflecting Surfaces for 5G/6G-Enabled Future Smart Industries with a Focus on Millimeter-Wave Band Communications 59

Performance Analysis of Intelligent Reflecting Surfaces for ...

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2024 Annual IEEE International Conference on Communications Workshops, ICC Workshops 2024

作者： Khan, Muhammad Farhan Raza, Ali Iqbal, Adeel Rashid, Adnan Jamshed, Muhammad Ali Pesch, Dirk School of Computer Science and Information Technology University College Cork Cork Ireland Comsats University Islamabad Dep. of Electrical and Computer Engineering Pakistan Yeungnam University Dep. of Info. & Com. Engineering Gyeongsan-si38541 Korea Republic of Politecnico di Bari Dep. of Electrical & Information Engineering Bari Italy James Watt School of Engineering University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9798350304053

Industry 4.0's emergence necessitates reliable communication systems. Millimeter-wave (mmWave) communication is a promising solution, but challenges like limited coverage and atmospheric absorption persist. The Intelligent Reflecting Surfaces (IRS) offers transformative potential by dynamically manipulating wave propagation to improve millimeter-wave (mmWave) communication. This research investigates the impact of the distance of Industrial User Equipment (iUE) from the IRS and Base Station (BS), and the height of the IRS from the ground, along with the impact of sum throughput by increasing the number of IRS elements. The IRS-assisted system exhibits enhanced performance within the mmWave frequency band compared to the sub-6 GHz band. The observed achievable rate gains, exceeding 200% on average, underscore the significant performance enhancement enabled by IRS dep.oyments. Moreover, increasing the number of IRS elements significantly enhances the overall throughput. The decrease in required transmit power with an increasing number of Intelligent Reflecting Surfaces IRS elements can be attributed to the enhanced ability of IRS to efficiently reflect and manipulate electromagnetic waves. As more IRS elements are dep.oyed, they collectively contribute to optimizing signal propagation, allowing for better control and redirection of the transmitted signals. This phenomenon enables the system to achieve the desired communication objectives with less overall transmit power. Additionally, the attainable capacity of the examined IRS-assisted wireless communication system exhibits growth alongside an increase in the elevation of the IRS installation. Results showcase the potential of IRSs to enhance coverage and communication reliability, providing valuable insights for designing efficient wireless systems in smart factories and Industry 4.0 applications. © 2024 IEEE.

关键词： 5G mobile communication systems

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Optimal Sizing of a Wayside PV System for DC Rail Transit Systems: The Case Study of the 3 kV Cagliari - Oristano Traction System

Optimal Sizing of a Wayside PV System for DC Rail Transit Sy...

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2024 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles and International Transportation Electrification Conference, ESARS-ITEC 2024

作者： Di Pasquale, Antonio Pagano, Mario Buffarini, Guido Guidi Carones, Nicola Laurini, Marco Rullo, Emanuele University of Naples Federico Ii Dep. of Electrical Engineering and Information Technology Naples Italy Italferr S.p.a. Rome Italy

ISBN: (纸本)9798350373905

The increasing demand for transportation, along with growing environmental concerns, highlights the importance of electric rail transit systems as a sustainable solution for moving people and goods. While these systems are highly efficient, they also require a significant amount of energy. To both reduce reliance on energy purchased by distribution system operators and mitigate effects on the AC grid, the integration of renewable energy resources (e.g., wind and PV) in the traction system emerges as a valuable solution. This paper specifically focuses on integrating a wayside PV power plant. It is proposed an optimal sizing procedure aimed at maximising the economical investment's revenue while meeting technical constraints related to the limited receptivity of the traction network. Notably, the proposed method is applied to the real case of the Cagliari-Oristano 3 kV DC traction circuit. © 2024 IEEE.

关键词： Renewable energy

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MPI Impact in C+L+S Multiband Transmission Reach

MPI Impact in C+L+S Multiband Transmission Reach

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2024 Photonic Networks and Devices, NETWORKS 2024

作者： Cancela, Luís G. Pires, João O. Instituto de Telecomunicações Dep. Information Science and Technology Iscte Instituto Universitário de Lisboa Portugal Instituto de Telecomunicações Dep. Electrical and Computer Engineering Instituto Superior Técnico Portugal

Multipath interference (MPI) impact is assessed in a C+L+S multiband transmission scenario. For a typical -34 dB/span MPI, transmission reach in the L-band suffers a 20% reach penalty considering the QPSK modulation f... 详细信息

关键词： C++ (programming language)

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Logistic Regression Classification for Assessing the Risk of Kidney Tumor 2

Logistic Regression Classification for Assessing the Risk of...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Alzubi, Dalia Al Mamlook, Rabia Emhamed Nasayreh, Ahmad Aljaidi, Mohammad Alazab, Rami Gharaibeh, Hasan Al-Naamneh, Qais Concordia University Dep. Information and Systems Engineering Montreal Canada Trine University Dep. of Business Administration IN United States University Zawia Department of Industrial Engineering Tripoli Libya Yarmouk University Dep. Computer Science Irbid Jordan Zarqa University Dep. of Computer Science Zarqa Jordan Jordan University of Science and Technology Dep. Surgery and Urology Irbid Jordan Applied Science Private University Dep. of Cyber Security Amman Jordan

ISBN: (纸本)9798350373363

The kidneys have the important function of filtering waste products and toxins from the blood in the human body. Any problems affecting the kidneys can potentially impact their efficiency and overall function. Kidney Tumor (KT) is a disease that specifically affects kidney cells and causes the abnormal growth of tissue in one or both kidneys. Assessing the risk of developing a kidney tumor and identifying the key factors that contribute to this risk are crucial for ensuring patient well-being. Additionally, this information assists doctors and specialists in making faster and more accurate diagnoses, determining appropriate treatment methods, and potentially influencing the course of the disease to minimize its severity and impact. Machine learning algorithms have recently been introduced to evaluate disease risks, and in this study, we specifically focus on examining the risk of kidney tumor development and investigating the influencing factors. We employed a logistic regression model to predict if a patient is at risk of developing a kidney tumor or not, and further categorized the samples into high-risk and low-risk groups. Our model was trained and tested using a unique dataset obtained from the (KAUH) hospital in Jordan, consisting of well-balanced metadata from 120 patients with kidney issues. Our work demonstrated accuracy results ranging from 90% to 98%. Ultimately, specialists can utilize our model as an additional tool to enhance the speed and accuracy of patient diagnoses © 2023 IEEE.

关键词： Tumors

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Prediction of Electrical Characteristics of a-IGZO TFT Based on Transfer Learning-Based Variational Autoencoder

Prediction of Electrical Characteristics of a-IGZO TFT Based...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bea, Khean-Thye Wei-Hsuan Lin, Shih-Shin Hu Lin, Da-Zheng Hu, Hsin-Hui Chen, Xiu-Zhi Lin, Ting-Ru Chen, Yen-Lin Chen, Kun-Ming Cheng, Wai Khuen National Taipei University of Technology Dep. of Electronic Engineering Taipei106 Taiwan National Taipei University of Technology Dep. of Computer Science and Information Engineering Taipei106 Taiwan Taiwan Semiconductor Research Institute Hsinchu30078 Taiwan Universiti Tunku Abdul Rahman Faculty of Information and Communication Technology Kampar31900 Malaysia

ISBN: (纸本)9798350337020

In this study, we proposed a transfer-learning based variational autoencoder model for predicting the electrical characteristics in the parameter tuning process of a-IGZO TFT structure design. The result achieve a high R2 score of 0.9704 with a low-computing-power hardware-friendly method that reduced time consumption significantly compared to prior approaches. The findings have practical implications for mitigating the time-consuming nature of TCAD simulations, and the method can expand to various types of input data while ensuring high performance and generalization. We demonstrated significant improvement in generalization and accuracy through a k-fold validation. © 2023 IEEE.

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Towards Bio-Mimetic 3D Printable Human Anatomies 3

Towards Bio-Mimetic 3D Printable Human Anatomies

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Forni, Riccardo Calderonel, Danilo Coato, Damiano Dolino, Gianmarco Cesarelli, Giuseppe Ricciardi, Carlo Cesarelli, Mario Gargiulo, Paolo Inst. of Biomed. and Neural Engineering Reykjavik University Reykjavik Iceland University Federico Ii Information Technology and Electrical Engineering Dep. Napoli Italy University of Sannio Department of Engineering Benevento Italy

ISBN: (纸本)9798350378009

3D printing, or additive manufacturing, has revolutionized various industries, particularly in healthcare where it facilitates the creation of personalized medical solutions. However, achieving biomimicry, especially in anatomical models, remains a challenge due to the mismatch in mechanical properties between synthetic polymers and biological tissues. This study aims to bridge this gap by reviewing the mechanical properties of human anatomical tissues and investigating analogous properties in 3D-printable polymers. Using a combination of literature review and experimental methods, the study focuses on anatomical structures such as bones, skin, and cartilage. Four base polymers with distinct mechanical characteristics are blended to create nine different polymer compounds, which are then subjected to compression and tensile tests. Results reveal varying mechanical properties across the polymer blends, with some exhibiting promising biomimetic potential. © 2024 IEEE.

关键词： Tensile testing

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