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Analyzing Parameter Patterns in YOLOv5-based Elderly Person Detection Across Variations of data

Analyzing Parameter Patterns in YOLOv5-based Elderly Person ...

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IEEE Internatonal conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Ye Htet Thi Thi Zin Pyke Tin Hiroki Tamura Kazuhiro Kondo Shinji Watanabe Etsuo Chosa Interdisciplinary Graduate School of Agriculture and Engineering University of Miyazaki Miyazaki Japan Graduate School of Engineering University of Miyazaki Miyazaki Japan Faculty of Medicine University of Miyazaki Miyazaki Japan

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

This study investigates the impact of data variations on parameter patterns within a YOLOv5-based elderly person detection model. We explore how changes in camera settings and environmental factors influence the model's parameters. Our research questions focus on how these variations affect parameter patterns, model robustness, and generalizability. We aim to identify the parameters and layers of the model most susceptible to variations and develop strategies to improve the model's performance across various datasets. The experiment involves collecting data from eight elderly participants in real-life elder care facility settings. During data acquisition, only depth images are recorded using stereo-depth cameras to protect privacy. After that, we train individual YOLOv5 models for each dataset through hyperparameter tuning and transfer learning. Optimal hyperparameters and the sensitive convolutional layer of each model are then compared. Class Activation Maps (CAMs) are utilized to visualize the network's focus, followed by analysis of weight distributions and correlation to identify parameter patterns. The findings will provide valuable insights for improving elderly person detection models for smart health care and their robustness to real-world variations.

关键词： YOLO Training Transfer learning Cameras Smart systems Robustness Environmental factors data models Older adults Tuning

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A Survey of Speech Recognition Based on Deep learning

A Survey of Speech Recognition Based on Deep Learning

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Natural Language Processing (ICNLP), international conference on

作者： Youyao Liu Jiale Chen Jialei Gao Shihao Gai School of Electronic Engineering Xi'an University of Posts and Telecommunication Xi’an China

Artificial intelligence is the vane leading the world’s scientific and technological development and future lifestyle change in the 21st century, and speech recognition, as one of the indispensable technical means, is inevitably the focus of human attention. There are two problems in traditional speech recognition: first, speech recognition technology cannot be significantly improved, and second, speech recognition systems cannot accurately extract data and features. In order to solve these problems, this paper first compares the traditional speech recognition GMM-HMM model and establishes a DNN-HMM model, which proposes a method to improve the speed of speech recognition and greatly improves the recognition rate. However, DNN-HMM lacks the ability to use historical information to assist in the current task, and a second model is proposed on the basis of this problem, that is, the LstM model is used to solve the problem of insufficient contextual information, which further improves the speech recognition ability. Then, in order to solve the problem of long memory loss and speed up training, the Transformer model is cited, and in order to solve the problem that the traditional language model can only predict the next word in one direction, the BERT model, which has a bidirectional language model, is invoked.

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Real-Time Vision-Based Fault Detection System for FDM 3D Printing Using Convolutional Neural Networks

Real-Time Vision-Based Fault Detection System for FDM 3D Pri...

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MECHATRONIKA international Symposium

作者： Suliman Badour Martin Novák Department of Instrumentation and Control Engineering Czech Technical University in Prague Prague Czech Republic

ISBN: (数字)9798350394900

ISBN: (纸本)9798350394917

We present a real-time vision-based fault detection system for Fused Deposition Modeling (FDM) 3D printing, utilizing multiple You Only Look Once (YOLO) Convolutional Neural Network (CNN) models-YOLOv4 Tiny, YOLOv5, YOLOv8, and YOLOv10-to identify defects such as blobs, cracks, spaghetti, stringing, under-extrusion, and warping. Unlike previous studies that use single models, our system integrates these YOLO variants to enhance detection accuracy and incorporates an adaptive learning module for continuous improvement based on real-time data. The system logs and displays faults on a live camera feed, significantly improving quality control in additive manufacturing. Comparative analysis shows that YOLOv8 achieves a 7% increase in detection accuracy and a 30% reduction in print errors. This novel integration of multiple YOLO models with adaptive learning advances automation and reliability in 3D printing.

关键词： YOLO Solid modeling Adaptation models Adaptive learning Accuracy Fault detection Three-dimensional printing Real-time systems Convolutional neural networks Frequency division multiplexing

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Parameter Selection of Generalized Morse Wavelets for Water Leakage Classification

Parameter Selection of Generalized Morse Wavelets for Water ...

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international conference on Electrical engineering, Computing Science and Automatic Control (CCE)

作者： Erick Axel Martinez-Ríos Rogelio Bustamante-Bello School of Engineering and Sciences Tecnologico de Monterrey Mexico City Mexico

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

Water leakage detection in water distribution networks is crucial to mitigating water scarcity. Machine learning algorithms have been utilized to generate models that detect water leakages by employing vibration, acoustic, flow, and pressure signals. In this sense, time-frequency methods have been widely employed to extract features or represent the signals for water leakage detection. Nevertheless, the challenge of using time-frequency analysis, such as the wavelet transform, is to select an adequate mother wavelet and its parametrization to represent the time-frequency information of the signal correctly. The selection of a mother wavelet is frequently performed arbitrarily or through trial and error, or the parameterization of the wavelet transform is not reported, hindering the studies' reproducibility. Using acceleration data, this paper proposed using the Heisen-berg area and the Average Reconstruction Mean Squared Error (ARMSE) to select the Generalized Morse Wavelets (GMWs) parameters to perform water leakage classification in looped and branched water networks. The generated scalogram was used to fine-tune the GoogLeNet to classify the water leakages in the two water networks. The results of this study showed that the use of the Heisenberg area and the ARMSE to set the parameters of the GMWs led to select a β = 1.6667 and one voice per octave for the looped water network and β =2 and two voices per octave for the branched water network. Furthermore, fine-tuning the GoogLeNet with the generated scalogram shows a testing accuracy of 78.33% for the looped water network and 76.25% for the branched water network.

关键词： Wavelet transforms Vibrations Time-frequency analysis Machine learning algorithms Distribution networks Electrical engineering computing Wavelet analysis Feature extraction Reproducibility of results Testing

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Experiential Virtual learning on the impacts of Covid-19 on Air Quality through Integration of Research in stEM Education

Experiential Virtual Learning on the impacts of Covid-19 on ...

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2023 ASEE Annual conference and Exposition - The Harbor of engineering: Education for 130 Years, ASEE 2023

作者： Mitra, Madhumi Nagchaudhuri, Abhijit Klein, Will University of Maryland Eastern Shore United States University of Maryland College Park United States

Poor air quality can be detrimental to human health, by causing or exacerbating health conditions. Sensitive groups such as children and the elderly with underlying conditions are at an increased risk for adverse health effects due to poor air quality. Due to the Covid-19 Pandemic, there is an observed change in air quality due to government restrictions and lockdown from 2020-2022 in many of the populated cities of the world. The University of Maryland Eastern Shore students (UMES) students in 3 undergrad courses (Introduction to Environmental Sciences;Biology for Honors students;and Ecology) and 1 graduate course (Teaching stEM at K-12 schools), and summer-exchange undergraduate engineering and high school interns had the opportunity to research the effects of Covid-19 pandemic on air quality for selected overpopulated cities in the world. The data collected were from March 2020 through summer of 2022. The objectives of integrating this research in stEM education are: a) to find a correlation among air quality parameters because of Covid-19;b) to analyze the effects of the pandemic on CO, NO2, O3, PM2.5, PM10, and SO2 in some of the selected populated cities (Kolkata, Milan, Los Angeles, São Paulo, Shanghai, and Sydney);c) to hone content knowledge in environmental science, chemistry, physics, and statistics;and d) to enhance data analytical skills, problem solving skills, as well as presenting research. By analyzing the effects of the Covid-19 pandemic on air quality, the scope for environmental restoration is addressed as more information is disseminated on what contributes to air quality, and how humans can manipulate it. In each of the courses mentioned above, two modules were dedicated to this topic. The first module introduced the topic and background information and the second one required relevant data analysis and presentations. Since the Environmental Science class had over 40 students, the students were broken down into groups for this activity. Various

关键词： students

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FRD: Few-shot rumor detection for the novel coronavirus pneumonia epidemic

FRD: Few-shot rumor detection for the novel coronavirus pneu...

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IEEE international Symposium on Parallel and Distributed Processing with Applications and IEEE international conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Danroujing Chen Xiaoliang Chen Xuyang Wang Weicheng Wang Xiaoyu Dong School of Computer and Software Engineering Xihua University Chengdu China

With the full arrival of the digital era, fueled by both information technology and business marketing, rumors are produced and spread endlessly on social networks. During the recent novel coronavirus pneumonia epidemic, online rumors have continued to flourish. Most existing studies on traditional rumor detection rely on a large number of features in practical applications. However, the current severe epidemic scenarios have limited rumor information features, and it remains a challenging problem to detect epidemic rumors with high accuracy using only limited information. As a result, we propose a novel Few-shot Rumor Detection model (FRD) for the novel coronavirus pneumonia, which is combined with meta-learning to be able to accurately identify rumors as soon as possible in crises. Specifically, we started by using the BERT+BiLstM combination for rumor text feature extraction and representation to generate the historical rumor sample-wise vector and epidemic rumor sample-wise vector; secondly, the prototypical network was introduced to summarize the historical rumor data, and the feature vectors of samples belonging to the same category were averaged to obtain the prototype representation of historical rumor category; finally, we utilize the modified cosine similarity measure function to calculate the distance between the class-wise vector of historical rumor text and the sample-wise vector of epidemic rumor, and complete the rumor detection according to the nearest neighbor method. Our experimental results on English datasets show that the FRD rumor detection model proposed in this paper is superior to other baseline algorithms in terms of accuracy, precision, recall and macro F1 value. From the comparison of experimental results, the FRD model can effectively improve conventional rumor detection methods, and better realize the early detection of sudden epidemic rumors.

关键词： COVID-19 Epidemics Social networking (online) Pulmonary diseases Computational modeling Prototypes Nearest neighbor methods

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Late Fusion of Text and Images for Predicting User Interests in Pinterest Social Network

Late Fusion of Text and Images for Predicting User Interests...

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international conference on Electrical engineering, Computing Science and Automatic Control (CCE)

作者： Areli Cabrera Oros Jonathán de Jesús Estrella Ramírez Juan Carlos Gómez Carranza División de Ingenierías Campus Irapuato-Salamanca Universidad de Guanajuato Salamanca Gto. México

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

Due to the large amount of information on social media, predicting user interest becomes crucial for various tasks including the improvement of the user experience in these platforms. This paper introduces a late fusion model designed to predict one of 29 predefined categories (considered as the user interests) established by Pinterest for any new pin uploaded by a user. A pin is an information unit with two components: an image and a short text comment. Given a new pin, the proposed model performs a multimodal classification by transforming and classifying each data modality (image and text) independently and then fusing the category probabilities with a weighted sum to predict the final categories for the pin. To test the validity of the fusion, a set of experiments is conducted analyzing and comparing the performance of different state-of-the-art deep learning transformations for each modality; and using a logistic regression model for classification. Additionally, the fusion is evaluated under different values for a weighting factor, in charge of assigning less or more importance to each data modality in the final decision. The experiments are carried out over a dataset composed of 29,000 pins collected directly from the Pinterest website. The results show that the late fusion of image and text representations increases the accuracy of predicting user interests when compared to the use of each single modality.

关键词： Deep learning Logistic regression Accuracy Social networking (online) Transforms Predictive models Vectors Pins Web sites Multimedia communication

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YOLO-Based Detector for Enhancing Workplace Safety: Real-Time PPE Detection and Monitoring

YOLO-Based Detector for Enhancing Workplace Safety: Real-Tim...

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ACS/IEEE international conference on Computer Systems and Applications

作者： Souha Amri Laila Ouannes Anis Sahbani Najoua Essoukri Ben Amara Ecole Nationale d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Sousse Tunisie Enova Robotics S.A. CNRS Institule for intelligent Systems and Robotics (ISIR) Sorbonne Université Paris France

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

Ensuring the safety of workers in hazardous environments is crucial, given the potential severity of accidents. Indeed, Personal Protective Equipment (PPE), including safety helmets, vests, and boots, is vital in mitigating risks. However, adherence to PPE usage often falls short due to inadequate safety awareness among workers. To address this challenge, we aim to develop a robust safety monitoring system by leveraging advancements in deep learning and object detection technology. Our proposed method uses YOLOv7 and YOLOv8 algorithms for real-time detection and classification of safety equipment, including helmets, vests, and boots. For this aim, we carefully collect and annotate datasets from diverse sources like Roboflow and Kaggle. Then, we accurately evaluate performance metrics such as precision, recall, and speed through rigorous training and testing of various YOLOv7 and YOLOv8 configurations on annotated data batches. By conducting thorough comparative analyses, we aim to identify the most effective model for each PPE category, considering both detection accuracy and computational efficiency. Finally, the object detection system is deployed through a web interface to determine whether a person is protected or not.

关键词： Personal protective equipment Training Accuracy Computational modeling Employment streaming media Helmets Real-time systems Safety Monitoring

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A smart fog-to-cloud system in airport: challenges and lessons learnt

A smart fog-to-cloud system in airport: challenges and lesso...

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international conference on Mobile data Management

作者： Antonio Salis Jens Jensen Telco & Media Division Engineering Sardegna Srl Cagliari Italy STFC RAL UK Research and Innovation Didcot United Kingdom

ISBN: (数字)9781728146638

ISBN: (纸本)9781728146645

Airports are one relevant environment in the smart cities implementation. Recent progresses in IoT have made it possible to develop advanced real-time services and assistance tools for travelers, making use of fog computing as an architectural model that places itself between the Cloud and the IoT, in the Cloud-to-Things Continuum, fulfilling requirements for strict real-time responses. The main tool exploits the concept of proximity marketing and covers the essential experience through the airport onto the flight, but also allows travelers to spend their waiting time by discovering the facilities of the airport, supported by a recommendation system using machine learning. The system provides also the airport planners an aggregated and anonymized view, like heat maps, of travelers behavior that highlight bottlenecks in the infrastructure, or highlight situations that require intervention, such as emergencies. The app in based on Android and makes use of the fog-to-cloud platform developed by the Horizon2020-funded mF2C project, giving evidence that the adoption of the fog-to-cloud approach brings significant benefits in terms of performance and optimization of resource usage, giving an objective evidence of the impact of the mF2C framework.

关键词： Airports Edge computing Cloud computing Computer architecture Security Real-time systems Androids

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Detecting Malignant Tumors Using a Network of Spiking Neurons Through a Population Based stochastic Temporal Encoding

Detecting Malignant Tumors Using a Network of Spiking Neuron...

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IEEE India conference (INDICON)

作者： Agnivo Ghosh Ayan Chakraborty Aniruddha Chandra Sumit Kundu Saswat Chakrabarti Department of Electronics and Communication Engineering National Institute of Technology Durgapur Durgapur India G S Sanyal School of Telecommunications Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Spiking neural network (SNN) has gained attention of researchers for its energy efficient and brain-like cognitive abilities. SNNs are now being explored for medical diagnosis to leverage their benefit of sparse, event driven and energy-efficient information processing. In this work an SNN has been designed to classify malignant and benign tumors from cell tissue data obtained from the Wisconsin breast cancer dataset. A novel population-based stochastic temporal encoding scheme has been adopted to convert the cell tissue features into equivalent neuronal spike trains. A supervised learning algorithm has been developed in synergy with the encoding scheme. Our method has resulted in an average accuracy of 90.8% and a best accuracy of 94% for the classification task. The proposed method performs reasonably well (within 2%) in comparison to popular machine learning algorithms such as Support Vector Machine (SVM) with linear kernel and Multi Layered Perceptron (MLP). The present method has a promising potential for further exploration in medical and healthcare applications.

关键词： Support vector machines Accuracy Machine learning algorithms Supervised learning Benign tumors Spiking neural networks Encoding Breast cancer Energy efficiency Classification algorithms

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