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SOH Estimation of Batteries using Lithium-Ion Internal Parameters with Convolution Neural Network and Gated Recurrent

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Transactions of the Korean Institute of Electrical Engineers 2023年第3期72卷 387-394页

作者： Park, Hyun-Yong Lim, Hee-Sung Lee, Kyo-Beum Dept. of Electrical and Computer Engineering Ajou University Korea Republic of Dept. of IT Convergence Engineering Ajou University Korea Republic of SK Signet Inc Korea Republic of

This paper proposes an estimation method for Lithium-Ion Batteries SOH by learning the batteries’ internal parameters using the Convolution Neural Network and the Gated Recurrent Unit. Various equivalent circuit models exist to represent the batteries’ internal parameters. Among these equivalent circuit models, the most representative model is the Randles model, and the data measured based on the Randles model is used as learning input data. The internal parameters of batteries change non-linearly depending on the operation condition and use time. So, nonlinear features are extracted using the CNN input as the batteries' parameters. The extracted features are used as an input of the GRU to learn the characteristics of change over time, and SOH is predicted through this. The learning dataset utilizes 17IND10 LibForSecUse of EMPIR, which validates the performance of the proposed model. Copyright © The Korean Institute of Electrical Engineers.

关键词： Lithium-ion batteries

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Forecast-based Optimal Operation of EV Charging Station with PV Considering Charging Demand and Distributed System

Forecast-based Optimal Operation of EV Charging Station with...

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2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024

作者： Rho, Seoeun Chae, Myeongseok Won, Dongjun Inha University Dept. of Electrical and Computer Engineering Incheon Korea Republic of

ISBN: (纸本)9798350313604

In this paper, based on forecasting short-Term PV generation, demand, and EV charging, we propose a forecast-based optimal V2G scheduling strategy of Energy management system for minimizing the operating cost of an EV charging station and increasing the use of renewable energy. The significance of the developed method is constructing the detailed forecast model consisting of PV generation, regional load demand, and EV charging demand and analyzing the effectiveness of the scheduling participation rate in the case study. Demand types of office and residential areas were considered to establish a realistic EV charging station operation scheme, and short-Term electricity demand forecasts were made based on EV charging data. By integrating these various data, efficient power management is possible. The forecast-based scheduling results show that PV self-consumption has increased, and the main grid dependence and operating cost have decreased. © 2024 IEEE.

关键词： Forecasting

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Enhancing Mortality Prediction in ICU: A Deep Learning Framework for Multivariate Time Series Classification

Enhancing Mortality Prediction in ICU: A Deep Learning Frame...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Sathia Bhama, Ponsy R K Sarah R, Roshina Shobia, T.R. Vinothini, R. Anna University Dept. of Computer Technology MIT Campus Chennai India Sri Sairam Institute of Technology Dept. of Computer and Communication Engineering Chennai India

ISBN: (纸本)9798331541170

Relying on human diagnosis is potentially prone to errors, time-consuming and leading to treatment mistakes. Early Intensive Care Unit (ICU) mortality prediction is important for determining which patients are most likely to die and for offering appropriate interventions that will preserve their lives. However, conventional diagnostic approaches often face challenges in fully harnessing the extensive information contained within Electronic Health Record (EHR) data, particularly in capturing the intricate temporal dynamics of patient health. In this research paper, a hybrid model leverages the power of Temporal Convolutional Networks (TCN) in conjunction with Bidirectional Gated Recurrent Units (BiGRU) to effectively capture the complex temporal dependencies present in ICU time series data, before constructing the model, the data is pre-processed by cutting-edge Generative Adversarial Network (GAN)-based imputation techniques to handle missing values within the EHR data. Through this comprehensive framework, the goal is to significantly enhance mortality prediction accuracy, empower clinicians with actionable insights into patient prognosis, and ultimately improve patient outcomes in critical care settings. © 2024 IEEE.

关键词： Electronic health record

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Classification of Brain Tumor Images using Segmentation and Transfer Learning 5

Classification of Brain Tumor Images using Segmentation and ...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Bindu, J. Hima Devi, M. Uma Dept. of Computer Science and Engineering Andhra Pradesh Guntur522213 India

ISBN: (纸本)9798350395235

Classifying brain tumors is considered imperative for developing successful treatments. In this regard, binary classification of brain tumor was the major objective of this paper. This work uses a magnetic resonance imaging (MRI) dataset for classification purpose. Due to data paucity, the classifying frameworks in this proposed work tested the transfer learning method for extracting features by utilizing four convolutional neural networks (CNNs) of pre-trained capabilities. This research aimed at decreasing the training time, improving accuracy in classifying images, and preventing overfitting. Instead of considering the same datasets, the study considered segmented images of the original dataset for training. Four segmentation methods were used for creating a new segmented dataset of the original dataset. The authors done segmentation and trained the architecture with few pre-processes for three separate epoch numbers in place for investigating the effect on classification accuracy and time consumption. Additionally, profits were gained from obtaining suitable results within a minimal number of epochs in a constrained time amount. The experimental data proved that the transfer learning method has produced accurate outcomes with a classification accuracy of 96.98%. © 2024 IEEE.

关键词： Magnetic resonance imaging

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A Narrative Assistant for Traffic Accidents Based on Large Language Models (LLM) 37

A Narrative Assistant for Traffic Accidents Based on Large L...

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37th Annual Conference on Legal Knowledge and Information systems, JURIX 2024

作者： Kung, Jo-Chi Chang, Chia-Hui Huang, Huai-Hsuan Chien, Kuo-Chun Dept. of Computer Science & Information Engineering National Central University Taiwan

ISBN: (纸本)9781643685625

This study introduces the Collision Clarification Generator (CCG), a Large Language Model-based system designed to assist in documenting traffic accidents. The CCG comprises three modules: Questioning, Information Extraction, and Accident Sequence Generation, which collectively streamline the process of gathering and structuring accident information. The system employs predefined question templates and a standardized Traffic Accident Record Format (TARF) to ensure comprehensive data collection. Evaluation of the CCG involved both human assessment and LLM-based automatic evaluation. Results showed an F1 score of 0.909 in human evaluation, and scores exceeding 7 out of 10 for accuracy and completeness in LLM-based assessment. These findings demonstrate the CCG’s effectiveness in accurately documenting accident information, potentially facilitating subsequent legal and insurance processes. © 2024 The Authors.

关键词： Highway accidents

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Analysis on the Contribution and Economic Feasibility of Pumped Storage Hydropower in Korea Power System Considering the Penetration of Renewable Energy Generation

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Transactions of the Korean Institute of Electrical Engineers 2023年第6期72卷 709-716页

作者： Kim, Hyun Joong Oh, Jae Young Yoon, Yong Tae Yoon, Yong Beum Dept. of Electrical and Computer Engineering Seoul National University Korea Republic of Dept. KEPCO International Nuclear Graduate School Korea Republic of

The government of South Korea proclaimed Energy Transition 3020 and the details thereupon with the generation mix. According to the Basic Plan for Long-term Electricity, additional Pump Storage Hydropower (PSH) were planned to be installed as measure for the variability from the Renewables. However, despite of the astronomical hard cost required for the PSHs, there has been a lack of assessments on the economic feasibility and effectiveness of the PSHs. According to the simulation, there were total three scenarios with high load variability due to the Renewables. The monitoring factors in each scenario were changes, in the generation cost, generation by fuel type, reserve capability by the PSHs, and emission of product gas. Furthermore, the economic feasibility was assessed from the perspectives of the PSHs. Based on the simulation, it was concluded that the additional PSHs decrease the daily generation cost and the ramping of the thermal generation, the greater reserve capabilities procured. On the other hand, emission of the product gas increases due to greater pumping load on the thermal generation. Moreover, it is economically infeasible for the PSHs to salvage their initial investment cost. Thus, this paper implicates the regulatory improvements for the formulation of the just market environment. Copyright © The Korean Institute of Electrical Engineers.

关键词： Power markets

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Hybrid RNN-CNN model for predicting stock market trends 1

Hybrid RNN-CNN model for predicting stock market trends

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Bhanujyothi, H.C. Jacob, I Jeena GITAM Deemed to be University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350356816

Stock market prediction has become a challenging task in today's world of valuable and best investment. Simple models cannot do stock market prediction to predict future values with high accuracy. The stock market has becoming more complex and volatile, necessitating accurate models that understand its complex, nonlinear dynamics. This paper creates a model to forecast future price of stock market combining Convolutional Neural Network (CNN) and Long-Short Term Memory model (LSTM). Both CNN and LSTM are used for detecting spatial and temporal patterns. Tesla Datasets is used for model implementation. This paper uses Mean Squared Error (MSE) measures as metrics to demonstrate the model's enhanced performance. displays the expected value, compares it to the actual values, and illustrates the potential improvement of the epochs on this model. © 2024 IEEE.

关键词： Convolutional neural networks

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Rough Set Based Recipe Clustering Approach 10

Rough Set Based Recipe Clustering Approach

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Das, Priyanka Mukherjee, Chandrani Gupta, Samiksha Roy, Sayan Shah, Khushi Singh, Shreya Institute of Engineering & Management Dept. of Computer Application and Science Kolkata India

ISBN: (纸本)9798350384369

Online cooking instructions including ingredient lists and cooking methods are now widely available due to the proliferation of recipe sharing websites. Finding novel and complex patterns in such vast, ambiguous categorical data sets is a crucial task. Conventional clustering analysis algorithms frequently fail to address the uncertainty surrounding the exact value of features found in these categorical data sets. Thus to aid in decision-making, rough set based clustering is frequently used. This study aims to identify the optimal clustering feature from the recipe data that will result in the grouping of related food products by applying the rough purity notion. The recipe data has been regarded as a categorical valued information system in this instance, and information theoretic attribute purity is taken into account using the rough purity technique. © 2024 IEEE.

关键词： Food chemistry

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ResNet-50: The Deep Networks for Automated Breast Cancer Classification using MR Images 2

ResNet-50: The Deep Networks for Automated Breast Cancer Cla...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Das, Tejaswini Nayak, Debasish Swapnesh Kumar Kar, Anindita Jena, Lambodar Swarnkar, Tripti University Dept. of Computer Application FET-ITER Bhubaneswar India University FET-ITER Dept. of Computer Science and Engineering Bhubaneswar India Bhubaneswar India

ISBN: (纸本)9798350370188

One in every four cancer cases in women is breast cancer, which is the most prevalent malignancy in this group globally. In 2020, breast cancer will account for one in every eight new instances of cancer, according to projections of 2.3 million new cases. Breast cancer identification, both early and accurate, is critical to improve patient outcomes and survival rates. Magnetic resonance imaging (MRI) has developed as a valuable method for diagnosing breast cancer. Deep learning algorithms, notably convolutional neural networks (CNNs), have demonstrated exceptional effectiveness in a variety of medical image processing applications, including breast cancer classification. The ResNet-50 architecture has received a lot of interest in this context because of its excellent performance in image recognition tasks. ResNet-50 is a deep residual network that introduces skip connections and residual learning to solve the difficulty of training very deep neural networks. A total number of 1480 cancer MRI samples are collected from the Kaggle database, having two classes, which include healthy and malignant scans. It is observed that our proposed model with the data pre-processing techniques has achieved a classification accuracy of 92.01%. © 2024 IEEE.

关键词： Magnetic resonance imaging

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A Likelihood Ratio Test of Genetic Relationship among Languages

A Likelihood Ratio Test of Genetic Relationship among Langua...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Akavarapu, V.S.D.S. Mahesh Bhattacharya, Arnab Dept. of Computer Science and Engineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9798891761148

Lexical resemblances among a group of languages indicate that the languages could be genetically related, i.e., they could have descended from a common ancestral language. However, such resemblances can arise by chance and, hence, need not always imply an underlying genetic relationship. Many tests of significance based on permutation of wordlists and word similarity measures appeared in the past to determine the statistical significance of such relationships. We demonstrate that although existing tests may work well for bilateral comparisons, i.e., on pairs of languages, they are either infeasible by design or are prone to yield false positives when applied to groups of languages or language families. To this end, inspired by molecular phylogenetics, we propose a likelihood ratio test to determine if given languages are related based on the proportion of invariant character sites in the aligned wordlists applied during tree inference. Further, we evaluate some language families and show that the proposed test solves the problem of false positives. Finally, we demonstrate that the test supports the existence of macro language families such as Nostratic and Macro-Mayan. © 2024 Association for Computational Linguistics.

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