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2D Polynomial Regression Modeling for Predictive Analysis of Glucose Concentration in NP-Model Fractal Microwave Sensor Using Frequency and Transmission Coefficient Disparities

2D Polynomial Regression Modeling for Predictive Analysis of...

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International Electrical engineering Congress (iEECON)

作者： Nutyada Nantapanich Pisit Mahatthanajatuphat Norakamon Wongsin Wilaiporn Lee Department of Electronics and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Thanyaburi Pathum Thani Thailand Regular Program in Mathematics-Science Yothinburana School Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

The article introduces a two-dimensional polynomial regression model for the predictive analysis of glucose concentration in a fractal microwave sensor NP model, utilizing frequency and transmission coefficient differences to examine their correlation with glucose concentration levels. The sensor employed in this investigation is fabricated on an economical FR-4 substrate to minimize costs. The sensor structure is developed with the fractal structure reduction method established by the NP model to operate at a frequency of roughly 2.97 GHz. The CST Studio software is utilized to analyze the findings from the simulation of the suggested sensor structure. Furthermore, the proposed sensor is integrated into a test apparatus comprising a sensor platform and a liquid tube constructed from PPA material. The liquid under examination is a glucose and water mixture at varying concentrations. It was analyzed using a VNA instrument to evaluate its frequency response and the magnitude of the alteration in the transmission coefficient. The values are utilized to create a two-dimensional polynomial regression model. The experiments demonstrated that the proposed sensor model possesses an Adjusted R² value of 0.99499 and an average error of approximately 3.57% for glucose concentration analysis throughout the range of 5% to 30%, with sensitivities of 0.00879 dB/% for insertion loss and 0.092 MHz/% for frequency variation, facilitating accurate glucose concentration prediction analysis.

关键词： Analytical models Adaptation models Correlation Liquids Predictive models Microwave sensors Polynomials Fractals Glucose Predictive analytics

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Evaluating the Impact of Data Cleaning for Improving Road Damage Detection Through Vision Transformer

Evaluating the Impact of Data Cleaning for Improving Road Da...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Andy Desman Kahlil Muchtar Maulisa Oktiana Maya Fitria Yuwaldi Away Fardian Fardian Hammam Riza Dept. of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Dept. of Electrical and Computer Engineering Department of Informatics Universitas Syiah Kuala The Master Program in Artificial Intelligence Universitas Syiah Kuala (USK) Banda Aceh Indonesia Department of Informatics BRIN (National Research and Innovation Agency) Indonesia The Master Program in Artificial Intelligence Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

The WHO predicts that by 2030 road accidents will be the 5th leading cause of death. Globally, road accidents account for 1.25 million casualties each year, and road defects cause 34% of these casualties. The road survey process in many countries have several challenges, one of which is detection using cameras that do not have a recognition system. In this study, a model with YOLOS architecture based on Vision Transformer trained on the RDD2022 dataset successfully recognizes road damage well, as indicated by the number of objects detected, bounding box on accurate objects, and the ability to recognize objects with inconsistent shadow and light inference. This research uses assessment parameters such as Average Precision (AP) and Average Recall (AR) to determine the overall performance of the model. The model achieves the highest AP value at Intersection of Union (IoU) 0.5, 0.75, and 0.5-0.95, worth 62.1%, 37.1%, and 36.2% respectively, and the highest AR value in Large, Medium, and Small Areas, worth 42.1%, 60.3%, and 75.4% respectively. The supplementary material can be found through this link: https://***/watch?v=LzkI2e_IORE.

关键词： YOLO Surveys computer vision Road accidents Roads Manuals Transformers Cameras Cleaning Tuning

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Momentum-Based Distributed Economic Dispatch of Smart Grids in Time-Varying Directed Networks

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IEEE Transactions on Network Science and engineering 2025年

作者： Zhang, Keke Lu, Qingguo Zhou, Qixing Li, Huaqing Xia, Dawen Huang, Tingwen Chongqing University College of Computer Science China Southwest University College of Electronic and Information Engineering China Kaili University College of Microelectronics and Artificial Intelligence College of Big Data Engineering China Texas A&M University at Qatar Science Program Doha23874 Qatar

In this paper, we investigate the economic dispatch problem of smart grids in time-varying directed networks. The EDP essentially revolves around optimizing the distribution of generation power amongst multiple generators, with the aim of fulfilling load demands at the lowest possible total generation cost, while strictly conforming to the constraints imposed by the local generation capacity. For a faster resolution of the EDP, we propose an efficient distributed accelerated economic dispatch algorithm that incorporates a unified momentum acceleration strategy into the push-pull deviation tracking approach. The involved acceleration strategy under specific momentum parameters includes two well-known acceleration strategies, i.e., the heavy-ball and Nesterov acceleration strategies, which is more flexible and provides additional improvements in convergence. We present rigorously theoretical proof of linear convergence to the optimal dispatch with explicit bounds for step size and momentum parameters. Finally, to verify the effectiveness of our algorithm and the correctness of the theoretical analysis, we conduct simulations of diverse EDP studies in smart grids. © 2013 IEEE.

关键词： Smart power grids

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A Multi-UAV Charging Station Enabling Free Landing by Grid Pattern Transmitter

A Multi-UAV Charging Station Enabling Free Landing by Grid P...

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

作者： Jungho Kim Hyunkyeong Jo Seoktae Seo Bonyoung Lee Hyungki Min Franklin Bien School of Electrical and Computer Engineering Ulsan National Institute of Science and Technology Ulsan Korea Program in Information & Communication Technology Convergence Ulsan National Institute of Science and Technology Ulsan Korea

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

This paper proposed a novel landing platform with wireless power transfer (WPT) to charge multiple unmanned aerial vehicles (UAVs) without restrictions on landing locations or alignment. A transmitter is designed using wires to charge a wide area. To overcome the disadvantages of system made by wires, a grid pattern transmitter and double power line are proposed. These structures provide multiple charging with free positioning of receivers and excite the transmitter lines in wide area properly. The optimized system is composed of 0.15m square cells, providing a 0.9 m by 0.9 m charging area. By experiment, it can efficiently charge four 0.1 m square receivers located anywhere in transmitter. The overall efficiency of four receivers is 37.5%. Also, the experimental results verify that it is unnecessary to align the receiver within the cell, as it has a 2.62% error due to rotation and a 2.94 % error due to position within the cell.

关键词： Coils Magnetic flux leakage Transmitters Wires Windings Receivers Wireless power transfer Autonomous aerial vehicles Current distribution Power electronics

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GraphT5: Unified Molecular Graph-Language Modeling via Multi-Modal Cross-Token Attention

arXiv

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arXiv 2025年

作者： Kim, Sangyeup Kim, Nayeon Piao, Yinhua Kim, Sun Computer Science and Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of AIGENDRUG Co. Ltd Korea Republic of

Molecular language modeling tasks such as molecule captioning have been recognized for their potential to further understand molecular properties that can aid drug discovery or material synthesis based on chemical reactions. Unlike the common use of molecule graphs in predicting molecular properties, most methods in molecular language modeling rely heavily on SMILES sequences. This preference is because the task involves generating a sequence of multiple tokens using transformer-based models. Therefore, a main challenge is determining how to integrate graph data, which contains structural and spatial information about molecules, with text data. In addition, simply using both 1D SMILES text and 2D graph as inputs without addressing how they align and represent the molecule structure in different modalities makes it challenging to fully utilize structural knowledge about molecules. To this end, we propose GraphT5, a multi-modal framework that integrates 1D SMILES text and 2D graph representations of molecules for molecular language modeling. Specifically, we introduce a novel cross-token attention module in GraphT5 to bridge the gap arising from the fundamental differences between the two modalities of molecule representations. Cross-token attention exploits implicit information between SMILES and graphs of molecules, resulting from their interactions at a fine-grained token level that benefits molecular language modeling. Extensive experiments including molecule captioning, IUPAC name prediction tasks, and case studies show that our GraphT5 outperforms the latest baseline approaches, which validates the effectiveness of our GraphT5 in sufficiently utilizing 1D SMILES text and 2D graph representations. © 2025, CC BY-NC-ND.

关键词： Unified Modeling Language

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Comparative Analysis of Normal and ASD EEG Data Using Hjorth Parameters

Comparative Analysis of Normal and ASD EEG Data Using Hjorth...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Muliyadi Muliyadi Melinda Melinda Yuwaldi Away Syahrul Gazali Doctoral Program School of Engineering Post Graduate Program Universitas Syiah Kuala Banda Aceh Indonesia Electrical Engineering Department Politeknik Negeri Lhokseumawe Aceh Indonesia Dept. Electrical Engineering and Computer Engineering Faculty Universitas Syiah Kuala Banda Aceh Indonesia Dept. Neurology Universitas Syiah Kuala Banda Aceh Indonesia Mahar Mardjono National Brain Center Hospital Jakarta Indonesia

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

The increasing prevalence of autism spectrum disorder (ASD) in recent decades has underscored the need for more accurate and objective diagnostic methods. Electroencephalography (EEG) holds promise as a tool in ASD neurobiological research due to its ability to capture neural oscillatory activity with high temporal resolution. This study aims to analyze the neurophysiological differences between individuals with ASD and neurotypical populations by examining EEG data using Hjorth parameters. The preprocessing stage involved using a Butterworth filter to enhance EEG signal quality by eliminating noise in the frequency range of 4-40 Hz. The Hjorth parameters, including Activity, Mobility, and Complexity, were used to assess the statistical characteristics of the EEG signals. The analysis was conducted on EEG data collected from 5 typical individuals and 5 ASD ASD subjects. Our findings reveal that the Hjorth Activity parameter is notably lower in the ASD group across all regions, with the most significant differences observed in the left and right frontal areas, where average activity measures 0.236 μV 2 and 0.060 μV 2 in ASD individuals, compared to 0.512 μV 2 and 0.138 μV 2 in the neurotypical group. However, the Hjorth Complexity and Mobility values remained consistent between the two groups, implying that the fundamental structure of the EEG signals is relatively preserved. These findings offer insights into the neurobiological mechanisms of ASD and suggest the potential use of Hjorth parameters in developing more targeted interventions for individuals with ASD.

关键词： Human computer interaction Autism Frontal lobe Noise Neural activity Electroencephalography Complexity theory Signal resolution Robots Optimization

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Development of a Phase-Locked Loop Circuit in Running Frequency Mode for Capacitance-to-Voltage Conversion

Development of a Phase-Locked Loop Circuit in Running Freque...

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International Electrical engineering Congress (iEECON)

作者： Tada On-khong Noppadon Sisuk Paramote Wardkein Ratchanoo Katman Kriangsak Prompak Department of Physics Faculty of Science Naresuan University Phitsanulok Thailand Program in Computer Engineering Faculty of Industrial Technology Phibunsongkhram Rajabhat University Phitsanulok Thailand Department of Telecommunication School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Program of Physics Faculty of Science and Technology Phibunsongkhram Rajabhat University Phitsanulok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

Capacitance-to-voltage conversion is essential for capacitive sensors in various industries, including touch interfaces, medical devices, and moisture measurement. However, circuit design faces challenges like non-linearity, noise, and small capacitance variations affecting voltage signals. This article proposes a phase-locked loop in free-running oscillator mode with a frequency-to-voltage converter to enhance accuracy and stability. Test results show a 97.9% measurement accuracy, demonstrating reduced noise and improved stability. The proposed circuit is ideal for high-precision applications in diverse environments, overcoming limitations of traditional methods.

关键词： Accuracy Sensitivity Moisture measurement Noise Moisture Capacitance Frequency conversion Circuit stability Phase locked loops Standards

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Evaluation of an artificial intelligence-based model in diagnosing periodontal radiographic bone loss

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Clinical Oral Investigations 2025年第4期29卷 1-8页

作者： Cassiano, Luanny de Brito Avelino da Silva, Jordão Paulino Cassiano Martins, Agnes Andrade Barbosa, Matheus Targino Rodrigues, Katryne Targino Barbosa, Ádylla Rominne Lima da Silva Gomes, Gabriela Ellen Maia, Paulo Raphael Leite de Oliveira, Patrícia Teixeira de Sousa Lopes, Maria Luiza Diniz da Silva, Ivanovitch Medeiros Dantas de Aquino Martins, Ana Rafaela Luz Department of Dentistry Federal University of Rio Grande do Norte - UFRN Rio Grande do Norte Natal Brazil Postgraduate Program in Electrical and Computer Engineering Center of Technology Rio Grande do Norte Natal Brazil Department of Dentistry Federal University of Rio Grande do Norte Av. Senador Salgado Filho 1787 Rio Grande do Norte Natal 59056-000 Brazil

Objective: To develop an artificial intelligence model based on convolutional neural network for detecting and measuring periodontal radiographic bone loss (RBL). Materials and methods: Keypoint annotations were carried out in 595 digital bitewing radiographic images using a computer Vision Annotation Tool. The dataset was splitted: 416 of these images were trained using the You Only Look Once version 8 architecture with pose estimation (YOLO-v8-pose), 119 images were destined for the validation set, and 60 images were used for the test set, resulting in a model capable of detecting keypoints related to the cementoenamel junction (CEJ) and alveolar bone crest (ABC). In order to evaluate the performance of the obtained model, the following metrics were analyzed: F1-Score, precision, sensitivity and mean average precision (mAP). Then, an algorithm was implemented to measure the RBL by calculating the Euclidean distance between CEJ and ABC. Results: The model achieved an F1-Score of 66,89%, precision of 61,1%, a sensitivity of 73,9% and an mAP of 73.8%. Conclusions: The developed model and its algorithm for identifying and measuring periodontal radiographic bone loss demonstrated promising performance, thereby presenting a potential tool for assisting in periodontal diagnosis. Further studies comparing the developed model with manual measurements performed by specialists are necessary for its validation. Clinical relevance: Applying artificial intelligence in clinical dental practice can support diagnosis, streamline clinical workflows, and inform treatment planning, representing a significant advancement in dental automation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Alveolar bone loss computer neural networks Deep learning Periodontitis Radiography

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Effective Dimension Aware Fractional-Order Stochastic Gradient Descent for Convex Optimization Problems

arXiv

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arXiv 2025年

作者： Partohaghighi, Mohammad Marcia, Roummel Chen, YangQuan Electrical Engineering and Computer Science graduate program University of California Merced United States Department of Applied Mathematics University of California Merced MercedCA United States Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

Fractional-order stochastic gradient descent (FOSGD) leverages a fractional exponent to capture long-memory effects in optimization, yet its practical impact is often constrained by the difficulty of tuning and stabilizing this exponent. In this work, we introduce 2SED Fractional-Order Stochastic Gradient Descent (2SEDFOSGD), a novel method that synergistically combines the Two-Scale Effective Dimension (2SED) algorithm with FOSGD to automatically calibrate the fractional exponent in a data-driven manner. By continuously gauging model sensitivity and effective dimensionality, 2SED dynamically adjusts the exponent to curb erratic oscillations and enhance convergence rates. Theoretically, we demonstrate how this dimension-aware adaptation retains the benefits of fractional memory while averting the sluggish or unstable behaviors frequently observed in naive fractional SGD. Empirical evaluations across multiple benchmarks confirm that our 2SED-driven fractional exponent approach not only converges faster but also achieves more robust final performance, suggesting broad applicability for fractional-order methodologies in large-scale machine learning and related domains. © 2025, CC BY.

关键词： Convex optimization

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Periodic phase diagrams in micromagnetics with an eigenvalue solver

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IEEE Transactions on Magnetics 2025年

作者： Ai, Fangzhou Lin, Zhuonan Duan, Jiawei Lomakin, Vitaliy University of California Department of Electrical and Computer Engineering San DiegoCA92093 United States University of California Center for Memory and Recording Research San DiegoCA92093 United States University of California Program in Materials Science and Engineering San Diego92093 United States

This work introduces an approach to compute periodic phase diagram of micromagnetic systems by solving a periodic linearized Landau-Lifshitz-Gilbert (LLG) equation using an eigenvalue solver with the Finite Element Method formalism. The linear operator in the eigenvalue problem is defined as a function of the periodic phase-shift wave vector. The dispersion diagrams are obtained by solving the eigenvalue problem for complex eigenfrequencies and corresponding eigenstates for a range of prescribed wave vectors. The presented approach incorporates a calculation of the periodic effective field, including the exchange and magnetostatic field components. The approach is general in that it allows 3D problems to be handled with any 1D, 2D, and 3D periodicities. The ability to calculate periodic diagrams provides insights into the spin wave propagation and localized resonances in complex micromagnetic structures. © 1965-2012 IEEE.

关键词： Spin waves

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