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Research on SOH Prediction of Lithium Battery Based on Improved SSA-BP Neural Network

Research on SOH Prediction of Lithium Battery Based on Impro...

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computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), IEEE international conference on

作者： Yannan Sun Jiaman Gu Ziliang An Kun Xie School of Rail Transportation Shanghai Institute of Technology Shanghai Art and Design College Shanghai Normal University Tianhua College Shanghai Faculty of Urban Operations and Management Shanghai Urban Construction Vocational College Shanghai

ISBN: (数字)9798350378542

ISBN: (纸本)9798350378559

The rapid growth of electric vehicles has led to widespread adoption of lithium batteries in energy systems, valued for their high energy density and extended cycle life. Nonetheless, frequent charge-discharge cycles and environmental conditions contribute to capacity loss, which ultimately reduces vehicle longevity. As a vital metric, the state of health (SOH) of lithium batteries is challenging to measure directly, and traditional methods often lack precision in SOH prediction. This study gathers experimental data on battery aging under various charge-discharge conditions and incorporates NASA's B05 battery data to identify health metrics. Correlation analysis is used to select optimized input variables. A BP neural network model, enhanced by the sparrow search algorithm (SSA), is developed. Furthermore, combining Levy flight and the sine cosine algorithm (SCA) improves prediction accuracy for SOH.

关键词： measurement Accuracy Input variables Computational modeling Neural networks Predictive models Prediction algorithms Lithium batteries Optimization Convergence

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Malware Detection for Portable Executables Using a Multi-input Transformer-Based Approach

Malware Detection for Portable Executables Using a Multi-inp...

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international conference on Computing, Networking and Communications (ICNC)

作者： Ting-Li Huoh Timothy Miskell Onur Barut Yan Luo Peilong Li Tong Zhang Department of Electrical and Computer Engineering University of Massachusetts Lowell Lowell MA USA Intel Corporation Santa Clara CA USA Department of Computer Science Elizabethtown College Elizabethtown PA USA

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

Malware is one of the leading cybersecurity challenges, as it disrupts the normal use of devices, causes financial losses, and steals user information. Deep learning-based methods have been increasingly used in the malware analysis field recently. In this work, we propose a novel multi-input Transformer-based approach for detecting malicious Portable Executable (PE) files. The PE raw bytes were partitioned into different byte sequences as multiple inputs in our proposed multi-input framework. This framework is comprised of convolutional neural networks (CNNs) and Transformer networks and is capable of independent learning of each input, thereby enabling a more expressive representation of the data. As a result, it is possible to capture both local spatial and time-series features, resulting in greater data comprehension. Our proposed approach outperforms the two reference methods, a LightGBM and a CNN-based model, as indicated by four metrics: accuracy, recall, precision, and F1 score.

关键词： measurement Learning systems Deep learning Accuracy Computational modeling Transformers Feature extraction

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Research on Semantic Segmentation of Remote Sensing Images Based on Improved U-net

Research on Semantic Segmentation of Remote Sensing Images B...

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Electronic Information Engineering and computer Communication (EIECC), international conference on

作者： Hong Ding Fu Lei Wenqing Zhang Lipeng Liu School of Physical Science and Technology Nantong University Nantong China Independent Researcher Tech Lead San Jose CA Electrical & Systems Engineering Washington University Missouri USA College of Engineering Peking University Beijing China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

This study explores the important role of attention mechanism in enhancing the performance of U-Net model for remote sensing image segmentation. By introducing ECA (Efficient Channel Attention) and GACM (Global Attention Context Module), the model performance is significantly improved, as evidenced by the significant increase in MIoU, accuracy and F1 *** ECA module improves the MIoU by 6.51 percentage points, the accuracy by 11.84 percentage points and the F1 scores by 14.03 percentage points, respectively, while the use of the GACM module further improved MIoU by 9.11 percentage points, accuracy by 16.03 percentage points and F1 score by 17.73 percentage points. The strategy of combining ECA with GACM results in the improvement of MIoU, accuracy and F1 value by 22.21, 22.84 and 25.71 percentage points, respectively, which proves the complementary nature of the two mechanisms and the potential of the present method in improving the accuracy and robustness of the model, which is of significant practical application value and provides an effective performance enhancement scheme for remote sensing image segmentation.

关键词： measurement Accuracy Attention mechanisms Semantic segmentation Focusing Network architecture Robustness Graph neural networks Remote sensing Context modeling

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Research on Radio Propagation Model Based on Deep Learning and Feature Images

Research on Radio Propagation Model Based on Deep Learning a...

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Electronic Information Engineering and computer Technology (EIECT), international conference on

作者： Ran Wang Jidong Cui Fang Lai Liang Yao Weiran Li Junjie Ai Jiayin Lv Henan Provincial Forestry and Ecological Construction and Development Center Zhengzhou China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331528850

ISBN: (纸本)9798331528867

The radio propagation model based on deep learning can extract features from a large amount of relevant data and learn from them to adapt to the real-time computing requirements of complex environmental scenes. In this study, we construct a multi-dimensional feature dataset and develop a U-Net-based propagation model. To enhance the performance of the model, we introduce a feature classification preprocessing module before the model input to distinguish features with different physical meanings. Additionally, Atrous Spatial Pyramid Pooling is incorporated in the encoder to improve the model's ability to learn multi-scale features. Experiments are conducted on a dataset consisting of 275 cells in an urban area, using MAE, RMSE, and MAPE as evaluation metrics. The results demonstrate that our proposed model outperforms the original U-Net architecture in terms of all evaluation metrics for overall, indoor, and outdoor areas.

关键词： Deep learning measurement Adaptation models Computational modeling Urban areas Radio propagation Predictive models Feature extraction Data models Real-time systems

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17th international conference on Information and Communication Technologies in Education, Research, and Industrial Applications, ICTERI 2021

17th International Conference on Information and Communicati...

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17th international conference on Information and Communication Technologies in Education, Research, and Industrial Applications, ICTERI 2021

ISBN: (纸本)9783031148408

The proceedings contain 37 papers. The special focus in this conference is on Information and Communication Technologies in Education, Research, and Industrial Applications. The topics include: An Intelligent Method for Forming the Advertising Content of Higher Education Institutions Based on Semantic Analysis;correlational and Non-extensive Nature of Carbon Dioxide Pricing Market;developing an Algorithm for the Management of Local Government Expenditures;development of Robo-Advisor System for Personalized Investment and Insurance Portfolio Generation;using Virtual Reality Technologies for Teaching computer Science at Secondary School;the Benefits of Using Immersive Technologies at General School;Use of VR Technologies in the System of Quality Assessment of Seafarer’s Professional Competence Formation;the Experience of Using Cloud Labs in Teaching Linux Operating System;predicting Students’ Academic Performance Based on the Cluster Analysis Method;an Approach to Business Process Model Structuredness Analysis: Errors Detection and Cost-Saving Estimation;telegram Messenger for Supporting Educational Process Under the Conditions of Quarantine Restrictions;designing a Tool for Economics Students Digital Competence measurement;about Electronic Textbook "Mathematical Tasks Programming. First Steps";algebraic Virtual Machine Project;quantization in Neural Networks;algebraic modeling of Molecular Interactions;collaborative-Factor Models of Decision-Making by Operators of the Air Navigation System in Conflict or Emergency Situations;DNS-Based Fast-Flux Botnet Detection Approach;Checkable FPGA-Based Components of Safety-Related systems;UAV Fleet Routing with Battery Recharging for Nuclear Power Plant Monitoring Considering UAV Failures;From Deutschland AG to World, Inc.? Network Analysis of the Capital Linkages of German Listed Companies;mobile Application for Healthy Maternal Behavior to Reduce Fetal Mortality.

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Efficient Two-Stream Network for Violence Detection Using Separable Convolutional LSTM

Efficient Two-Stream Network for Violence Detection Using Se...

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international joint conference on Neural Networks (IJCNN)

作者： Islam, Zahidul Rukonuzzaman, Mohammad Ahmed, Raiyan Kabir, Md Hasanul Farazi, Moshiur Islamic Univ Technol Dept Comp Sci & Engn Dhaka Bangladesh Data61 CSIRO Canberra ACT Australia

ISBN: (纸本)9780738133669

Automatically detecting violence from surveillance footage is a subset of activity recognition that deserves special attention because of its wide applicability in unmanned security monitoring systems, internet video filtration, etc. In this work, we propose an efficient two-stream deep learning architecture leveraging Separable Convolutional LSTM (SepConvLSTM) and pre-trained MobileNet where one stream takes in background suppressed frames as inputs and other stream processes difference of adjacent frames. We employed simple and fast input pre-processing techniques that highlight the moving objects in the frames by suppressing non-moving backgrounds and capture the motion in-between frames. As violent actions are mostly characterized by body movements these inputs help produce discriminative features. SepConvLSTM is constructed by replacing convolution operation at each gate of ConvLSTM with a depthwise separable convolution that enables producing robust long-range Spatio-temporal features while using substantially fewer parameters. We experimented with three fusion methods to combine the output feature maps of the two streams. Evaluation of the proposed methods was done on three standard public datasets. Our model outperforms the accuracy on the larger and more challenging RWF-2000 dataset by more than a 2% margin while matching state-of-the-art results on the smaller datasets. Our experiments lead us to conclude, the proposed models are superior in terms of both computational efficiency and detection accuracy.

关键词： Convolution Surveillance Computational modeling Stacking computer architecture Streaming media Feature extraction

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A Study on Technology Selection for Student Attendance in Classroom Environment Based on Face Recognition

A Study on Technology Selection for Student Attendance in Cl...

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international conference on Communications, Information System and computer Engineering (CISCE)

作者： Qiang Cao Bonifacio T. Doma School of Information Technology Mapua University Makati Metro Manila Philippines Xi'an Siyuan University Xian Shaanxi China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

In modern educational management, utilizing facial recognition technology for student attendance can significantly improve attendance efficiency and accuracy, thereby enhancing teaching effectiveness. There are many attendance technologies available in classroom environments, making the adoption of an efficient and accurate technique increasingly important. This study focuses on evaluating and comparing four versions within the YOLO series - YOLOv5nu, YOLOv6n, YOLOv7-tiny, and YOLOv8n - in terms of performance in classroom environments. The research implements these models in the same classroom environment with similar datasets, comprehensively comparing them in aspects such as recognition accuracy, processing speed, loss functions, and performance metrics. Performance on the same dataset is analyzed and compared. Results indicate that despite each model having its advantages, based on two core metrics: mean average precision (mAP) and F1-Score, YOLOv7-tiny and YOLOv8n exhibit the best overall performance. These research findings provide empirical evidence for selecting appropriate facial recognition technology, contributing to further advancements in intelligent classroom management systems.

关键词： YOLO measurement Training Accuracy Face recognition Computational modeling Data models

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Research on Urban Environmental Sound Classification Model Based on Multi-Convolutional Neural Networks

Research on Urban Environmental Sound Classification Model B...

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Intelligent Computing and Human-computer Interaction (ICHCI), international conference on

作者： Kefan Cai Bingyan Chen Hongwei Zhang Guangdong University of Technology Guangzhou China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

With the rapid acceleration of urbanization, the impact of urban environmental sounds is garnering increasing attention. This study aims to develop a classification system capable of swiftly identifying various types of urban environmental sounds, thereby facilitating audio classification and noise pollution management. The research employs a Multi-Convolutional Neural Network (MULT-CNN) algorithm, which enhances the accuracy of audio classification by averaging the results of multiple CNN models. The study utilizes the widely recognized Urbansound8K dataset, which encompasses ten distinct types of urban sounds. Through model training and testing, the system's performance was evaluated using metrics such as accuracy, precision, recall, F1-Score, and processing latency. Experimental results demonstrate that the classification model performs well across most categories, particularly in Category 5 (Engine Idling) and Category 2 (Children Playing), where it achieves high classification accuracy and low processing latency. Additionally, audio classification heatmaps and confusion matrix heatmaps were developed to visually represent the model's effectiveness in audio classification. This research provides an effective technical approach for urban sound classification and management.

关键词： Heating systems Training measurement Accuracy Computational modeling Neural networks Urban areas Optimization Engines Testing

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Maximizing Energy Yield: The Optimal Tilt Angle and Orientation in Agrivoltaic systems with Light Productivity Factor

Maximizing Energy Yield: The Optimal Tilt Angle and Orientat...

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Electrical, computer and Energy Technologies (ICECET), international conference on

作者： Adnin Tazrih Natasha Foad Taghizadeh Sara Deilami School of Engineering Macquarie University Sydney Australia

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

The combined agriculture and photovoltaic system are a trade-off between the amount of light received by the solar panel and the percentage of the light reaching the ground level. The light reduction percentage (% LR) factor for a specific plant, cultivated under the agrivoltaic (AV) system has been taken into consideration by previous research. Previous research considered the Light Reduction Percentage (% LR) factor for a specific plant in the agrivoltaic (AV) system, indicating a 40.92 % light reduction under a fixed structure, meeting Photo-synthetically Active Radiation (PAR) requirements. This study introduces the Light Productivity Factor (LPF) [1] along with light reduction to maximize energy yield, determine structural requirements, and supervise harvesting conditions for the crops. The main purpose is to estimate optimal tilt angles and orientations for fixed-tilted Photovoltaic (PV) structures, optimizing irradiance collection on PV modules while meeting minimum light reduction for shade-tolerant plants. Results demonstrate satisfactory agrivoltaic system performance with a maximum total irradiance of 1929 $kW / m ^2 / year$ , light reduction of 36.73 % for monofacial and 39.06 % for bifacial module, and LPF values of 0.9 and 1.5 for monofacial and bifacial panels respectively and determining the value of tilt angle of 9° for both the panel type and orientation of 190° for monofacial and 185° for bifacial module respectively.

关键词： Productivity measurement System performance Computational modeling Crops Agriculture Solar panels

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Improving the Efficiency of Smart Grid Scheduling: A Linear Regression Model with Meta Job Information

Improving the Efficiency of Smart Grid Scheduling: A Linear ...

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Cyber Resilience (ICCR), international conference on

作者： Hazem Al-Najjar Nadia Al-Rousan Department of Computer science College of Information Technology University of Petra Amman Jordan Department of Computer Engineering Faculty of electrical Engineering and information systems German Jordianian University

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

In order to forecast the run time of the jobs that were submitted, this research provides two linear regression prediction models that include continuous and categorical factors. A continuous predictor is built using the number of CPUs, average CPU speed, and memory size, whereas a categorical predictor is built using the user ID, group ID, and executable ID. According to the findings, the prediction rates for categorical and continuous predictors are 61% and 1%, respectively. sixty times better than the earlier models, which used continuous variables as a foundational model to determine task complexity and weight. The effectiveness of the categorical predictor in enhancing a job scheduling problem is then evaluated by combining it with three suggested job scheduling strategies. The suggested algorithms incorporated metrics—predicted run time, waiting time, and resource requirement—to select the smallest jobs. According to the results, Algorithm 3 performs better than earlier models in both performance metrics. The variation in total execution time and average waiting time is 1.14 to 1.76 and 4.5, respectively compared to previous models, Additionally, Algorithms 1 and 2 demonstrating superior performance in every scenario.

关键词： measurement Processor scheduling Computational modeling Linear regression Predictive models Prediction algorithms Smart grids

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