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Add-On Type Internal Resistance and Current Sensor Fault Estimation Algorithm Based on the Correlation of the Current-Voltage Data

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Transactions of the Korean Institute of Electrical Engineers 2025年第4期74卷 717-726页

作者： Kim, Young-Seoung Na, Kun-Woo Seo, U-Jeong Kim, Wooyong Dept. of Biomedical and Robotics Engineering Incheon National University Korea Republic of

Lithium-ion batteries have established themselves as a critical energy source for electric vehicles and energy storage systems. As their usage continues to grow, the issues of battery currents due to current sensor failures can lead to overcharging, over-discharging, and potentially hazardous incidents such as battery fires. To address this issue, this study proposes a current sensor fault detection algorithm based on the analysis of the data covariance matrix. The proposed algorithm is designed as an add-on type solution, enabling calculations using only voltage data extracted from the Battery Management System (BMS). It detects current sensor faults in real-time, thereby mitigating potential risks. Furthermore, the study demonstrates the algorithm’s applicability by cross-validating its performance using data from both new and aged battery models. Through this approach, the algorithm addresses current sensor fault issues across various battery states, contributing to the prevention of critical incidents such as battery fires. © The Korean Institute of Electrical Engineers.

关键词： Covariance matrix

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Add-on-type Current Sensor Freezing Fault Diagnosis Algorithm Based on Current–Voltage Data Correlation for Battery Disconnect Units

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Journal of Electrical engineering and Technology 2025年第4期20卷 2543-2552页

作者： Kim, Youngseoung Na, Kunwoo Seo, Ujeong Kim, Wooyong Dept. of Biomedical & Robotics Engineering Incheon National University Incheon Korea Republic of

Lithium-ion batteries have become a crucial energy source in electric vehicles and energy storage systems. Battery safety and reliability have emerged as significant issues with their increased usage. Failure in non-contact current sensors can lead to inaccuracies in internal current measurement, posing serious risks like overcharging and over-discharging, which may cause fires within battery circuits. To address these challenges, this study proposes an add-on-type current sensor fault diagnosis algorithm based on data analysis using a covariance matrix. The current sensor freezing fault can be captured by determining the internal resistance using the current–voltage relationship. The proposed algorithm enables fault detection without imposing additional load or modifications on the existing battery system. The performance of the proposed current sensor fault diagnosis algorithm was experimentally verified by using two cylindrical battery cells having different life conditions. © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2025.

关键词： Covariance matrix

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State of Charge Algorithm Considering Open Circuit Voltage of LiFePO4 Battery Cells for Battery Energy Storage System

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Transactions of the Korean Institute of Electrical Engineers 2025年第5期74卷 969-976页

作者： Na, Kunwoo Choi, Keun Ha Kim, Wooyong Dept. of Biomedical & Robotics Engineering Incheon National University Korea Republic of Dept. of Mechanical Engineering Korea Advanced Institute of Science and Technology Korea Republic of

Lithium iron phosphate (LFP) batteries are increasingly adopted in electric vehicles (xEVs) and battery energy storage systems (BESS) due to their thermal stability, safety, and cost-effectiveness. Despite these advantages, the inherently low electrical conductivity and flat charge-discharge voltage profile of LFP batteries present significant challenges for accurate state estimation. To address these limitations, this study employs a Sigma-Point Kalman Filter (SPKF), which demonstrates superior performance compared to the conventional Extended Kalman Filter (EKF) in handling nonlinear system dynamics. Furthermore, to account for the hysteresis behavior characteristic of LFP cells, quasi-open circuit voltage (qOCV) measurements are utilized. A Dual Sigma-Point Kalman Filter (DSPKF) is proposed to enhance the estimation of both state of charge (SoC) and hysteresis states. The experimental results validate the effectiveness of the proposed method, offering a high-precision state estimation framework that improves the reliability and performance of LFP batteries in xEV and BESS applications. Copyright © The Korean Institute of Electrical Engineers.

关键词： State of charge

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Computer-aided Detection of Tuberculosis from Microbiological and Radiographic Images

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Data Intelligence 2023年第4期5卷 1008-1032页

作者： Abdullahi Umar Ibrahim Ayse Gunnay Kibarer Fadi Al-Turjman Department of Biomedical Engineering Near East UniversityNicosiaMersin 10Turkey Research Center for Al and loT Faculty of EngineeringUniversity of KyreniaMersin 10Turkey Artificial Intelligence Engineering Dept. Al and Robotics InstituteNear East UniversityMersin 1Turkey

Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis *** can be detected from microscopic slides and chest X-ray but as a result of the high cases of tuberculosis,this method can be tedious for both Microbiologists and Radiologists and can lead to *** challenges can be solved by employing Computer-Aided Detection(CAD)via Al-driven models which learn features based on convolution and result in an output with high *** this paper,we described automated discrimination of X-ray and microscope slide images into tuberculosis and non-tuberculosis cases using pretrained AlexNet *** study employed Chest X-ray dataset made available on Kaggle repository and microscopic slide images from both Near East University Hospital and Kaggle *** classification of tuberculosis using microscopic slide images,the model achieved 90.56%accuracy,97.78%sensitivity and 83.33%specificity for 70:30 *** classification of tuberculosis using X-ray images,the model achieved 93.89%accuracy,96.67%sensitivity and 91.11%specificity for 70:30 *** result is in line with the notion that CNN models can be used for classifying medical images with higher accuracy and precision.

关键词： Tuberculosis Deep Learning Pretrained AlexNet Chest X-ray Microscopic slide

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Reflector-Integrated Ultrasound Image-Guided Access: Towards Clinical Applications

Reflector-Integrated Ultrasound Image-Guided Access: Towards...

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Medical Imaging 2025: Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Tang, Yichuan Chheda, Dhruv Hersum, Mikkel Taubert, Jena Murakami, Ryo Zhang, Haichong Dept. of Robotics Engineering Worcester Polytechnic Institute Worcester MA United States Dept. of Mechanical Engineering Worcester Polytechnic Institute Worcester MA United States Dept. of Biomedical Engineering Worcester Polytechnic Institute Worcester MA United States Dept. of Computer Science Worcester Polytechnic Institute Worcester MA United States

ISBN: (纸本)9781510685949

Ultrasound (US) image-guided access is widely used in surgical operations such as percutaneous nephrolithotomy (PCNL), percutaneous coronary intervention (PCI) and lumbar puncture (LP). One major challenge conventional US image-guided access faces is sustaining proper alignment between the needle path and the US image plane to maximize needle visibility, since the needle and the US transducer are controlled by each individual hand. Reflector-integrated ultrasound (ref-US) image-guided access was originally proposed to provide by-default alignment of the needle path and the image plane, however issues like bulky size, unconventional handling style and encapsulation of acoustic medium pose difficulties to clinical application of ref-US image-guided access. In this work we focused on improving clinical applicability of ref-US image-guided needle access mechanism. First, the size of the ref-US image guided access mechanism is minimized, and handling style of the mechanism is modified to follow the handling style of a normal ultrasound transducer by using double acoustic reflectors. Second, we use gel-like medium to encapsulate ref-US image-guided access mechanism to reduce imaging artifacts and to prevent medium leakage, while both are common issues in encapsulation using liquid medium. We conducted imaging validation using gelatin phantom with embedded point targets to show the mechanism's sensitivity to the needle tip and small targets. We also conducted insertion validation where needle is inserted into the kidney inside a pig cadaver using proposed ref-US image-guided access mechanism. Validation results indicate that the gap between the concept of ref-US image-guided access towards clinical applications has been bridged. © 2025 SPIE

关键词： acoustic reflector gel-like medium image-guided intervention

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A Hybrid Approach for Enhancing Single-Cell Type Clustering

A Hybrid Approach for Enhancing Single-Cell Type Clustering

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International Conference on biomedical engineering and Applications (ICBEA)

作者： Nattaphon Suphaphimol Hyundoo Jeong Navadon Khunlertgit Biomedical Engineering Institute Chiang Mai University Chiang Mai Thailand Dept. of Biomedical & Robotics Engineering Incheon National University Incheon S. Korea Dept. of Computer Engineering Faculty of Engineering Chiang Mai University Chiang Mai Thailand

ISBN: (数字)9798350375299

ISBN: (纸本)9798350375305

Recent developments in single-cell RNA sequencing (scRNA-seq) technology have made it possible to conduct a thorough analysis of the ways in which cells express their transcriptomes in response to various physiological states and cues from the outside world. "Dropout," the phenomenon in which a gene is observed at low or moderate levels in one type of cell but not in another, poses a major analytical challenge when analyzing scRNA-seq data. Taking on the problem of dropout events in gene expression datasets is the main goal of this work. Our proposed approach consists of two main steps: first, we apply the consensus clustering-based algorithm, ccImpute, to impute dropout events, and then we utilize scCLUE, an ensemble feature selection and network similarity measurement-based single-cell clustering method, to cluster single-cell types. Our experimental results with four publicly available datasets show that the proposed approach outperforms the original scCLUE and other clustering algorithms. By addressing the problems caused by dropout events in scRNA-seq data, it improves clustering performance and facilitates more effective separation of similar cell populations.

关键词： Sequential analysis RNA Clustering methods Clustering algorithms biomedical measurement Feature extraction Physiology Gene expression biomedical engineering

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An improved CNN-LSTM deep model for Classification of guideRNA in CRISPR-Casl2 System 2

An improved CNN-LSTM deep model for Classification of guideR...

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2nd IEEE International Conference on Artificial Intelligence in Everything, AIE 2022

作者： Ameen, Zubaida Said Ibrahim, Abdullahi Umar Mubarak, Auwalu Saleh Al-Turjman, Fadi Near East University Research Genter for Ai and IoT Ai and Robotics Institute Artificial Intelligence Dept. Turkey Near East University Biomedical Engineering Nicosia Turkey Ai and Robotics Institute NearEast University Artificial Intelligence Engineering Dept. Nicosia Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

Gene editing with CRISPR-Cas12 method has immense applications in diverse organisms from bacteria, and plants to humans especially in treating genetic diseases. GuideRNA and Cas enzyme are the two components required by the Cas12 gene editing method. However, the CRISPR-Cas12 system varies depending on the guideRNA sequence used. Therefore, it is important to develop improved methods for designing guideRNA sequences needed for gene editing. Here, we utilized a combination of convolutional neural networks and long short-term memory a type of recurrent neural network (bi-LSTM) model to classify guideRNA as either active or inactive, and the addition of an embedding layer for encoding guideRNA sequence improved model performance. Accuracy, AUC and F1-score were used to evaluate CNN-LSTM and achieved outstanding performance. CNN-LSTM will help in the design of guideRNA for genes with CRISPR-Cas12 system. © 2022 IEEE.

关键词： Embeddings

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ID 318: A Review on Vibration sensors and control methods for cantilever beams

ID 318: A Review on Vibration sensors and control methods fo...

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2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2021

作者： Devassy, Honey Vijay Anand, L.D. Hepsiba, D. Karunya Institute of Technology and Sciences Dept. of Robotics Engineering Coimbatore India Karunya Institute of Technology and Sciences Dept. of Biomedical Engineering Coimbatore India

ISBN: (纸本)9781665428293

Regular monitoring of vibration characteristics of industrial equipments and civil structures become necessary nowadays to ensure sufficient safety margins. This paper reviews the different types as well as technological enhancements of vibration sensors with a special emphasis on cantilever beam vibrations. The vibration analysis of cantilever beams have been widely used for crack/damage detection, modulus of elasticity determination and effective vibration control. Currently existing active and passive control schemes of cantilever beams are also discussed in the paper. © 2021 IEEE.

关键词： Crack detection

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Deep Learning Methods For Prediction of HLA-Peptide Interactions in IDB 2

Deep Learning Methods For Prediction of HLA-Peptide Interact...

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2nd IEEE International Conference on Artificial Intelligence in Everything, AIE 2022

作者： Ameen, Zubaida Said Ibrahim, Abdullahi Umar Mubarak, Auwalu Saleh Al-Turjman, Fadi Ghali, Umar Muhammad Near East University Research Center for Ai and IoT Ai and Robotics Institute Artificial Intelligence Engineering Dept. Turkey Near East University Biomedical Engineering Nicosia Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Cyprus Near East University Medical Biochemistry Dept. Turkey

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

Inflammatory bowel disease (IBD) is a complicated gastrointestinal disease linked to so many factors. Studying the interactions and functions of proteins in diseases is a challenging task. Here, we tried to understand the relationship between Human Leukocyte Antigen (HLA) genes and peptides in the development of IBD. Two types of HLA genes, HLA DRB1∗ 13:01 and HLA DRB1*15:01 were considered which are highly important for presenting peptides to T-cells as one of their immunological activities, therefore, it is necessary to study their interaction with peptides. An improved version of deep learning models based on convolutional, recurrent neural networks and a hybrid of both models were proposed in this study by utilizing an embedding matrix as part of the model architecture for better encoding of amino acid features, this technique has proven to be highly effective when compared to the state of the art models. The different deep model architectures were trained and tested to predict HLA DRB1*13:01 and HLA DRB1*15:01 peptide interactions. The hybrid method gave the best performance with 0.987 as the Spearman correlation. The models proposed in this work will give better insight into the interaction between peptides and HLA genes in the development of IBD. © 2022 IEEE.

关键词： Peptides

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Artificial neural network model for the characterization of human locomotion parameters

Artificial neural network model for the characterization of ...

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2021 International Conference on Communication, Control and Information Sciences, ICCISc 2021

作者： Ashmi, A.M. Mathew, Anila Sivanandan, K.S. Jayaraj, S. National Institute of Technology Calicut Dept. of Electrical Engineering Calicut India Karunya Institute of Technology and Sciences Dept. of Robotics Engineering Coimbatore India Manipal Academy of Higher Education Dept. of Biomedical Engineering Karnataka India

ISBN: (纸本)9781665412797

Artificial walking aids compensate the physical limitations experienced by handicapped persons in their routine activities and help them to reduce their dependence on others. The characteristics of healthy gait in terms of angular velocity, angular acceleration, linear velocity and linear acceleration needs to be examined for designing a walking aid. The human locomotion was analyzed using high resolution video camera for normal level walking. The angular displacement of knee and hip were recorded and an artificial neural network model was developed for estimating the human locomotion parameters. The average value based approach was employed in the developed model for linearization. The output response of the model can be used for activating joint angles in the walking aid and helped in estimating linear displacement. Also, the complexity involved in the dynamics of human gait can be reduced to a greater extent with the help of this modeling technique. © 2021 IEEE.

关键词： Walking aids

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