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A CFD based Optimization of Natural Ventilation Strategy for Yungang Grottoes 8th

A CFD Based Optimization of Natural Ventilation Strategy for...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Xu, Xinrui Yan, Hongbin Liu, Tingzhang Zhang, Shaoyou Gao, Lunrui Department of Automation College of Mechatronics Engineering and Automation Shanghai University Shanghai China Cultural Heritage Protection and Monitoring Center Yungang Research Institute Shanxi Datong China

ISBN: (纸本)9789819602933

Natural ventilation is not only an energy-saving ventilation method, but also the most common way to protect cultural relics. this study aims to explore the reasonable natural ventilation modes by switching doors and windows on the eave of cave 9 and cave 10, which are located in Yungang Grottoes. Using the data at 10:00 a.m. on August 21, 2023, 324 different natural ventilation strategies are carried out to simulate the humidity field and the velocity field through the CFD software FLUENT. Finally, the random forest algorithm is used to analyze the importance of doors and windows to the environment changes inside the cave. In conclusion, transom window 2, transom window 6 and door 1 are the main factors that influence the humidity field while transom window 2 and door 1 are also the decisive factors that affect velocity field. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Caves

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Insulator Image Segmentation Method based on Edge Information SNIC and Convolutional Neural Network 8th

Insulator Image Segmentation Method Based on Edge Informatio...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Junyou, Chen Runyuan, Li Yujie, Li Yanpei, Quan Chenkai, Liu Shujia, Yan Shanghai Investigation Design and Research Institute Co. Ltd. China Three Gorges Corporation Shanghai200335 China Shanghai University of Engineering Science Shanghai201620 China

ISBN: (纸本)9789819602933

Aiming at the problem of small image area occupied by insulators and difficulty in segmentation and positioning, a simple non-iterative clustering (SNIC) combining edge information and convolutional neural network insulator image segmentation method is proposed. On one hand, the edge information extracted by the anisotropic diffusion filter is introduced into the SNIC algorithm to avoid the appearance of small and semantically difficult superpixels;On the other hand, based on the multi-receptive field convolution kernel, a convolutional neural network model is created to extract richer superpixel feature information to achieve accurate classification of superpixels. the experimental results based on the CPLID dataset show that the SNIC algorithm with fusion edge information improves the image edge recall rate by 6% compared with the traditional SNIC algorithm, and the classification accuracy of the multi-receptive field convolutional neural network is improved by 3.8% compared with AlexNet. Compared with other image segmentation methods, the proposed algorithm has better segmentation and positioning effect on insulators. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Predicting Multiple Sclerosis Worsening Using Stratification-based and Time-Dependent Variables Extracted from Routine Visits Data 10th

Predicting Multiple Sclerosis Worsening Using Stratification...

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10th international conference on Machine Learning, Optimization, and Data Science, LOD 2024

作者： Guazzo, Alessandro Longato, Enrico Trescato, Isotta Tavazzi, Erica Chiò, Adriano Manera, Umberto Zocco, Grazia Cavalla, Paola Vercellino, Marco Ahmad, Lara Tavazzi, Eleonora Bergamaschi, Roberto Padman, Rema Di Camillo, Barbara Department of Information Engineering University of Padova Padova Italy Department of Neuroscience ‘Rita Levi Montalcini’ University of Turin Turin Italy Multiple Sclerosis Center Neurology Unit 1U AOU Città della Salute e della Scienza di Torino Torino Italy IRCCS Fondazione Istituto Neurologico C.Mondino Pavia Italy Heinz College of Information Systems and Public Policy of Carnegie Mellon University PittsburghPA United States Department of Comparative Biomedicine and Food Science University of Padova Legnaro Italy

ISBN: (纸本)9783031824838

Multiple Sclerosis (MS) impairs the transmission of signals in the nervous system resulting in progressive disability. MS progression is heterogeneous, patients show different evolution trajectories and managing their disease is challenging. Clinicians may benefit from the use of data-driven tools aiming at predicting disease worsening. We develop models to predict MS worsening using data collected in two MS centers. To effectively capture information from the sequences of expanded disability status scale (EDSS) measurements, we explore possible strategies to extract time-dependent variables which well describe trends of this score over time. First, we consider the whole-time span and extract EDSS patterns using a stratification method. then, we independently consider separate time periods and rely on EDSS first order descriptors. We train a Cox model and a survival support vector machine in three different settings characterized by different subsets of EDSS-derived variables. In the first setting we consider EDSS information by identifying typical EDSS evolution trajectories, in the second one we include first order descriptors of EDSS evolution, and in the third one we include all available information. We trained and tested all models within a framework able to perform optimal feature selection and unbiased estimates of model performance. For the Cox model, the best-performing setting was the first one (C-index = 0.833). However, as the C-index of the second and third settings was comparable according to both considered methodological approaches, one should consider the additional qualitative information that stratification brings to patients and clinicians. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Support vector machines

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10th international conference on Health Informatics and Medical systems, HIMS 2024 and 10th international conference on Biomedical engineering, BIOENG 2024, held as part of the World Congress in computer Science, computer engineering and Applied Computing, CSCE 2024

10th International Conference on Health Informatics and Medi...

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10th international conference on Health Informatics and Medical systems, HIMS 2024 and 10th international conference on Biomedical engineering, BIOENG 2024, held as part of the World Congress in computer Science, computer engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031859076

the proceedings contain 44 papers. the special focus in this conference is on Health Informatics and Medical systems. the topics include: White Blood Cell Nuclei Detection and Segmentation on Ratio Channels G/B, B/G, and B/R for FPGA Implementation;mobiVitalsConnect: A Comprehensive Mobile Healthcare System for Real-Time Patient Monitoring and Data Visualization;internet-of-things Chair and the 30 s Chair Stand Test;an Overview of Smart Wheelchair Technologies;Using EEG Signal for Smart Health: An Overview;Classification of Heart Rate Variability (HRV) based on Attention Mechanism of Transformer Model;advancing Fall Detection and Prevention: Integrating Wearable Devices and a Camera-based Analysis;A Performance Comparison Between Two Speech-To-ASL-Gesture-Projection Translation Implementations;facial Tilt Detection and Correction in Panoramic X-Rays Using Depth Maps from 2D Stereo Images: A Novel Approach to Minimize Positioning Errors in Dental Imaging;EEGNet-Vision Transformer: Ensemble Model with Pseudo Labeling for Epilepsy Patients Seizure Prediction from EEG;Off-line CNN based Finger Number Gesture Recognition System Using Raspberry Pi 4;comparing Models for Sentiment Analysis of Tweets in Response to Public Health Announcements During the Pandemic;modeling the Dynamics of Infectious Diseases in a College Campus: A Case Study of the 2016 Harvard Mumps Outbreak;a Respite Work Exchange Service Mechanism based on Blockchain Technology;explainable Machine Learning-based Alzheimer’s Disease Prediction;Transformer-based Encoder Method for Classification of Heart Rate Variability (HRV);Electronic Health Record Summarization via LLM-Constructed Knowledge Graphs;perspective of Family and Staff About Using Robots;ethical Perspectives Into the Utilization of Health Informatics for Cancer Care;gender Differences in Working Memory – A Holistic Review;Automatic Wound Detection System for Multi-ethnic Populations Using YOLO;Prediction of the Effectiveness of rTMS for Depress

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Electrostatic Polarity Switching Packaging for Degradation Mitigation in Seawater Usage

Electrostatic Polarity Switching Packaging for Degradation M...

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ieee international conference on Micro Electro Mechanical systems

作者： Steven Tran Seungbeom Noh Hanseup Kim Electrical and Computer Engineering University of Utah Salt Lake City UT USA

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

this paper introduces a novel ion-deposition-resistant packaging designed to mitigate electrode degradation, thereby significantly enhancing the performance and lifespan of electrolytic-based sensors within seawater. this approach employs a polarity-switching mechanism between the packaging and sensor electrodes to repel charged oceanic ions (e.g. sodium, chlorine, magnesium, and potassium) on the electrode surface beyond the environmental Debye length (13 nm), using a 1 V input that was optimized for sufficient repulsion, lowered power consumption, and prevention of unwanted electrolysis. By pushing the ions beyond the Debye length towards an "ion repulsion zone", this mechanism minimizes the ions re-accessing the electrode surface in seawater as the electrode surface is occupied by non-ionic seawater molecules (primarily H 2 O). this enables the sensor to operate its primary electrolytic function with non-ionic seawater molecules for up to 10 seconds without producing degenerative chlorine gas (Cl 2 ), which was previously a major contributor in electrode degradation and reduced sensor lifespan. Initial experimental results validate the effectiveness of this mechanism, showing a reduction in chlorine gas generation and electrode degradation by 89% and 87%, respectively, and extending sensor lifespan by a factor of 7.9, all without compromising the primary electrolytic functionality in high-saline solutions.

关键词： Electrodes Degradation Sea surface Prevention and mitigation Chlorine Switches Packaging Ions Sensor systems Gas detectors

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Towards an UAV Visual Navigation System based on Map Processing and Spatial Matching Techniques: A Literature Review

Towards an UAV Visual Navigation System Based on Map Proce...

引用

17th Asian conference on Intelligent Information and Database systems, ACIIDS 2025

作者： Ramanauskas, Edvardas Jodlowiec, Marcin Krótkiewicz, Marek PIXPRO UAB Šiauliai Lithuania Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw Poland School of Computer Science and Engineering International University Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819660049

this paper reviews state-of-the-art methods for UAV navigation in GNSS-denied environments, focusing on map-based spatial matching and vision-based techniques. It identifies trends and challenges in current approaches, emphasizing the integration of cartographic data, visual odometry, and data fusion. A novel methodology for identifying research gaps is presented, combining systematic literature review, qualitative and quantitative analysis with multi-criteria evaluation. the findings highlight limitations in existing methods and propose a navigation system that leverages spatial pattern matching and object recognition, integrating vision-based and GIS data. this approach addresses real-time processing challenges and offers a foundation for improving UAV navigation accuracy in GNSS-denied settings. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

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Deep Learning for Facial Emotion Recognition: Insights from EmoNet

Deep Learning for Facial Emotion Recognition: Insights from ...

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international conference on Signal Processing and Communication (ICSC)

作者： G. Vallathan Senthilkumar Meyyappan M. Raman Kumar M. Gowthami Department of ECE Geethanjali College of Engineering & Technology Hyderabad India Department of ECE Nalla Malla Reddy Engineering College Hyderabad India Department of Computer Science Vinayaka Missions Kirupananda Variyar Arts and Science College Vinayaka Missions Research Foundation (Deemed to be University) Salem India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Facial emotion classification is critical in improving human-computer interaction and applications like affective computing and mental health monitoring. Accurate recognition of emotions from images enables systems to better understand and respond to human emotional states. However, existing algorithms face challenges in capturing subtle emotional expressions, handling variations in facial features due to lighting, angles, or occlusion, and generalizing effectively across diverse datasets. EmoNet addresses these challenges through its CNN-based architecture, designed specifically for emotion classification from facial images. It utilizes convolutional layers to extract spatial features, followed by fully connected layers for classifying emotions. EmoNet excels in processing complex facial expressions, adapting to different lighting and occlusion conditions, and generalizing across varied datasets. this makes it highly suitable for real-time applications requiring accurate emotion recognition. the performance of EmoN et is evaluated using metrics such as accuracy, precision, recall, and F1-score, which highlight its effectiveness in handling diverse real-world scenarios. Notably, EmoN et achieves an impressive accuracy of 99.6% and a Kappa score of 94.1, establishing it as the top performer across all emotional categories.

关键词： Emotion recognition Accuracy Lighting Signal processing algorithms Signal processing Feature extraction Robustness Classification algorithms Convolutional neural networks Security

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Intelligent Human Behaviour Detection Leveraging ConvLSTM and LRCN Deep Learning Approaches

Intelligent Human Behaviour Detection Leveraging ConvLSTM an...

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international conference on Signal Processing and Communication (ICSC)

作者： S Chakradhar Amingad Harika Janmanchi Mohammed Sultan K Shalini S Hariharan Bhanu Prakash Maddala Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad Telangana India Artificial Intelligence and Data Science Vardhaman college of engineering Hyderabad India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

this research delves into intelligent human behavior detection from video sequences by employing advanced deep learning methodologies. the study harnesses the capabilities of Convolutional Long Short-Term Memory (ConvLSTM) and Long-term Recurrent Convolutional Networks (LRCN) architectures, which are adept at capturing both temporal and spatial dynamics in video data. Using the extensive Kinetics dataset, which includes a wide range of human activities and contexts, the video data was converted into sequential image frames for model training. the ConvLSTM and LRCN models were implemented and trained with the TensorFlow/Keras framework on GPU-accelerated systems. During training, both models exhibited effective learning, with improved accuracy and reduced loss. However, validation metrics revealed fluctuations, indicating possible overfitting and challenges in generalizing to new data. To address these issues, regularization techniques such as dropout and early stopping were applied, alongside comprehensive hyperparameter tuning. Additionally, the study investigated the use of transfer learning to enhance model performance. this research lays the groundwork for sophisticated video-based human behavior detection systems, with applications in surveillance, healthcare, and human-computer interaction. Future work will aim to bolster model robustness and broaden the scope of action recognition.

关键词： Training Deep learning Surveillance Video sequences Transfer learning Robustness Behavioral sciences Convolutional neural networks Long short term memory Tuning

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Real Time Machinery Health Detection Via thermal Imaging

Real Time Machinery Health Detection Via Thermal Imaging

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international conference on Signal Processing and Communication (ICSC)

作者： Jahiya Khan Manisha J Nene Dept. of Computer Science and Engineering Defence Institute of Advanced Technology (Deemed to be University) Pune India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Marine vessels are equipped with advanced and integrated complex machines that operate in challenging conditions, where the failure rate is high. One of the most vital elements of these machines is fault diagnosis and maintaining 100 percent operational states. Any machinery breakdown at sea during maritime operations can have severe consequences leading to operational impacts and reducing combat efficiency. the existing maintenance routines - PPM (Planned Preventive Maintenance) and CBPM (Condition based Predictive Maintenance) are reactive approaches that may miss early signs of material degradation, leading to unplanned machinery breakdown at sea. An efficient method to counter this issue in real time is thermal imaging. the dataset required to develop a thermal prognostic model for real-time machinery health detection is not available. this paper presents a systematic approach to develop a prognostic model for real time machinery health detection via thermal imaging. We have developed a custom thermal image dataset representing various machinery conditions-normal, defective and degraded. We have developed our real-time prognostic model ‘thermal Prognostic Model’. this model targets fuel systems to prevent major fires and accidents at sea by predicting potential failures and taking corrective actions with mAP50 0.956, precision 0.945 and recall 0.957.

关键词： YOLO Systematics Electric breakdown Signal processing Predictive models Real-time systems Machinery Prognostics and health management Preventive maintenance thermal degradation

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Enhancing Non-Orthogonal Multiple Access with Practical Successive Interference Cancellation: A MIMO Approach with Interference-Predicted MMSE Techniques

Enhancing Non-Orthogonal Multiple Access with Practical Succ...

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international conference on Signal Processing and Communication (ICSC)

作者： Neeraj Dwivedi Sachin Kumar Sudeep Tanwar Sudhanshu Tyagi Computer Science Engineering (CSE) Amity University Lucknow Uttar Pradesh Computer Science Engineering (CSE) Nirma University Ahmedabad Gujarat Electronics&Communication Engineering (ECE) Thapar University Patiyala Punjab

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

In the realm of forthcoming wireless mobile networks, non-orthogonal multiple access, commonly referred to as NOMA, has been identified as a viable radio access technique. It employs successive interference cancellation (SIC) as the primary receiver mechanism and utilizes the power domain for user multiplexing on the transmitter side, anticipating advancements in mobile devices. Nonetheless, a substantial segment of the latest research endeavors to evaluate the effectiveness of NOMA on the assumption that the receiver has full SIC capability. this study examines the potential to reconcile the disparity between the theoretical and actual efficacy of successive interference cancellation (SIC) in non-orthogonal multiple access (NOMA) systems by employing multi-input multi-output (MIMO) technology with zero-forcing (ZF) and minimum mean square error (MMSE) SIC methodologies. this document presents the interference-predicted minimum mean square error (IPMMSE) interference cancellation (IC) method, which modifies the MMSE weight factor to accommodate interference signals. Furthermore, they provide a study of the error consequences linked to practical SIC designs. We propose the remaining interference-predicted MMSE (RIPMMSE) interference cancellation as a resolution to the issue of residual interference, based on this premise. On the other hand, the findings of the simulation show that the suggested IC schemes that forecast interference signals outperform NOMA with standard ZF and MMSE IC techniques. this is despite the fact that practical IC schemes produce a higher bit error rate (BER) in comparison to traditional orthogonal multiple access (OMA).

关键词： Wireless communication NOMA Interference cancellation Spectral efficiency System performance Bit error rate Mean square error methods Receivers Standards Signal resolution

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