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Fiducial Approach for the Storage Reliability Assessment of Complex Repairable systems

Journal of systems Science & Complexity 2024年第4期37卷 1653-1671页

作者： YANG Yang ZHAO Letian CHEN Siyi YU Dan University of Chinese Academy of Sciences Beijing 100190China Computer Application Institute of Nuclear Industry Beijing 100142China Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China

In recent years,there has been a growing focus on high-reliability products in industrial and military *** plays a crucial role in these products,and as a result,research on storage reliability has gained more *** paper specifically targets a class of products with complex structures that require long-term storage,regular testing,and ***,an expression method for system availability is provided based on the reliability structure of the system and the maintenance situation of the constituent equipment in *** the expression of system availability is typically complex and difficult to compute,and the storage life of the system cannot be represented as an explicit function of the reliability indicators of the constituent equipment,it is challenging to evaluate the availability and storage life of repairable *** address this,the paper proposes a comprehensive evaluation methodology for assessing the storage reliability of complex repairable systems based on fiducial *** approach is employed to derive point estimates and determine the lower fiducial limits for both system availability and storage ***,simulation results demonstrate that this comprehensive evaluation method for storage reliability of complex repairable systems is not only convenient but also highly effective.

关键词： Availability fiducial method repairable system storage life

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Deep Learning Techniques for Weather Prediction

Deep Learning Techniques for Weather Prediction

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Dhanalakshmi, R. Paul, Tushar School of Computer Science and Engineering Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India Masters of Computer Application Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331510022

Human activities are directly impacted by weather occurrences. Forest fires, high air temperatures that lead to drought, and extreme weather events caused by global warming all make life more difficult for humans. Precautions against such climatic calamities require weather forecast models that are both accurate and effective. As a result, creating models that accurately forecast the weather is crucial. The creation of effective models of deep learning-based weather forecasting was greatly aided by technological advancements. This study suggested a hybrid weather prediction model based on convolutional and recurrent neural networks, which had a decent success rate. The dataset named Jena, which comprises 14 parameters, extensive meteorological information that has not been used for predicting the weather, was subjected to the suggested hybrid model. The experimental findings were compared using well-known machine learning, deep learning, and statistical techniques such CNN, Integrated Moving Average with Auto-Regression, LSTM, RNN, Random Forest, Multilayer Perceptrons and SVM. These comparisons showed that the suggested hybrid model had the best prediction outcome across the board. For instance, the suggested hybrid model produced the following findings based on the weather forecast data for Jena. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on central arterial pressure estimation algorithm based on clustering 2022

Research on central arterial pressure estimation algorithm b...

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2nd International Conference on Bioinformatics and Intelligent Computing, BIC 2022

作者： Zhang, Jiahao Ge, Hui Computer Application China Aerospace Academy of Systems Science and Engineering Beijing China

ISBN: (纸本)9781450395755

Central arterial pressure (CAP) plays an important role in the detection and diagnosis of cardiovascular diseases. Although the traditional universal transfer function method has good accuracy in measuring central arterial pressure, it does not take into account the characteristic information contained in pulse wave shape, and the accuracy needs to be further ***, a clustering based estimation algorithm for central arterial pressure is proposed. First of all, 5 layer Symlets wavelet and hard-soft compromised threshold was used to remove high-frequency noise of the data set. Secondly, in order to extract the characteristics of pulse waves themselves, k-means++ clustering was carried out for pulse waves in the training set. In order to reduce the over-fitting phenomenon, the initial number of cluster centers is selected as 1000. Finally, in each cluster, discrete Fourier transform is performed on pulse wave and central artery wave to obtain amplitude and phase data and train the transfer function. The test set was used for verification. Firstly, the corresponding clustering category of pulse wave was calculated, and then CAP was calculated by transfer function. The results showed that the absolute errors of systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP) were 2.50±2.22mmHg, 4.47±2.72mmHg and 4.60±3.73mmHg respectively. Compared with other algorithms, our method has better accuracy. © 2022 ACM.

关键词： Transfer functions

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A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

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International Journal of computers and applications 2024年第8期46卷 666-685页

作者： Benedek Szabo, Aron Katona, Jozsef Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Instrumentation and Automation Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science Kecskemét Hungary

The integration of machine learning (ML) into mobile applications presents unique challenges, particularly in resource-constrained environments such as iOS devices. Skin lesion classification is a critical task in dermatology, where accurate and efficient diagnostic tools can significantly aid in early detection of malignant lesions. This study aims to implement a machine learning-based iOS mobile application and develop a binary classification model for skin lesion images to determine whether a lesion is malignant. The research utilized Create ML to develop a convolutional neural network optimized for iOS, employing a transfer learning approach. A logistic regression model was cascaded with the convolutional neural network N to enhance classification accuracy. The model's performance was assessed through various validation metrics, ensuring its robustness and efficiency within the constraints of mobile hardware. A curated dataset from the International Skin Imaging Collaboration archive was used for training and testing. The model achieved an accuracy of 92%, a precision of 90%, a recall of 93%, and an F1-score of 91% in classifying skin lesions. These metrics validate the model's efficacy in identifying malignant lesions. Data curation involved collecting, labeling, and preparing a dataset from publicly available sources, ensuring the inclusion of diagnostically relevant features. The final model was integrated into an iOS application using Core ML and Vision frameworks. The developed application demonstrates reliable performance in classifying skin lesions with high accuracy on iOS devices. The inclusion of comprehensive performance metrics justifies the efficacy of the proposed approach. Future work will explore enhancements in model architecture and object detection capabilities to further improve diagnostic precision and application usability. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image recognition

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Identification of Age, Plumage, and Sex of Bird Species Using Pre-Trained Deep Convolutional Neural Network 2

Identification of Age, Plumage, and Sex of Bird Species Usin...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Banerjee, Sourik Gupta, Richa Pandian, J. Arun Saranya, S. Mariappan, L. Thanga Vellore Institute of Technology Department of Computer Application Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350306415

The research aims to identify the age, plumage, and sex of bird species using standard Pre-trained Deep Convolutional Neural Networks (Pre-DCNNs). The proposed work involves collecting various bird images, which are then used for training and validating the pre-trained DCNNs. The dataset contains 200 images of four bird species. The dataset was split into twelve labeled classes of images based on age, plumage, and sex. The standard image manipulation techniques such as cropping, flipping, mirroring, rotating, shearing, and skewing were used as image augmentation techniques to increase the size of the proposed dataset. The proposed data is preprocessed, including feature extraction and data cleaning, to ensure the performance of the Pre-Trained DCNNs. The Pre-Trained DCNNs were trained on 90% of the dataset to recognize patterns in bird images. The accuracy of the pre-trained DCNNs was evaluated using metrics such as classification accuracy. The research acknowledged the advantages and limitations of the standard Pre-Trained DCNNs on the age, plumage, and sex of birds using the images. Also, it compares the performance of the Pre-Trained DCNNs on the proposed bird species image dataset. © 2024 IEEE.

关键词： Bird Plumage Identification Bird Species Identification Deep Convolutional Neural Networks Image Augmentation Pre-trained networks

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A Study Of Cyclone Tracking Mechanism Using Deep Learning Techniques

A Study Of Cyclone Tracking Mechanism Using Deep Learning Te...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Paul, Tushar Dhanalakshmi, R. Masters of Computer Application School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India

ISBN: (纸本)9798350315127

With over 90 storms emerging worldwide each year, tropical cyclones (TCs) are the most damaging weather systems to emerge over the tropical oceans. For the purpose of providing advanced warning to the impacted areas, fast TC detection and tracking is essential. Remote sensing is essential for the detection of these storms since they originate over open oceans that are far from the continents. Here, we describe an innovative deep learning approach-based automated technique for TC detection from satellite photos. Our research presents a three-phase deep learning architecture for TC detection that consists of three components: 1) a classifier - convolutional neural network (CNN);2) a wind speed filter;and 3) a detector - Mask convolutional neural network (CNN). Using Bayesian optimization, the hyperparameters of the pipeline as a whole are tuned to display optimal performance. Results show that the suggested method produces test images with specificity (97.59%), high precision (97.10%) and accuracy (86.55%). © 2023 IEEE.

关键词： Tropical cyclone

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Applying Process Mining to Sensor Data in Smart Environment: A Comparative Study 7th

Applying Process Mining to Sensor Data in Smart Environment:...

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7th International Conference on Smart City applications, SCA 2022

作者： Iman, Elkodssi Laanaoui, My Driss Sbai, Hanae Mathematics Computer Science and Application Laboratory FST Mohammedia Hassan II University of Casablanca Casablanca Morocco Computer Systems Engineering Laboratory Cadi Ayyad University of Marrakech Marrakech Morocco

ISBN: (纸本)9783031268519

Business process management is a popular approach that has caught the attention of several researchers over the years. BPM has been extended to Business Process Intelligence in order to add more intelligence to managing business processes. Process Mining is one of the emerging areas of Business Process Intelligence, which aims to automatically discover, verify and improve business processes. Nowadays, the emergence of smart environment. The data that occurs in this environment are more varied and are kind of unstructured data. In this sense, applying process mining, to analyze processes in a smart context is a challenging task, since the process mining techniques relies on a structured event log. In this paper, we present a comparative study of existing approaches on applying process mining techniques in smart BPM, as a first step towards developing a new approach to discover and analyze business processes from sensor data perspective. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data mining

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Toward a New Framework for Process-Aware IoT Discovery 7th

Toward a New Framework for Process-Aware IoT Discovery

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7th International Conference on Smart City applications, SCA 2022

作者： Elkodssi, Iman Laanaoui, My Driss Sbai, Hanae Mathematics Computer Science and Application Laboratory FST Mohammedia Hassan II University of Casablanca Casablanca Morocco Computer Systems Engineering Laboratory Cadi Ayyad University of Marrakech Marrakesh Morocco

ISBN: (纸本)9783031268519

People’s lives have become more pleasant and uncomplicated in recent years as a result of recent technological advancements. Data sharing and connectivity across systems, devices, and people have been made easier with the introduction of IoT. In reality, the Internet of Things (IoT) provides a platform for ordinary items to become smarter, everyday communication to become more in-formed, and everyday processes to become more intelligent. In this regard, to manage business processes in the new IoT environments, many heterogeneous frameworks have been proposed. Since the number of IoT devices or things is increasing day by day and the number of services is also increasing, while dis-covering these IoT sensitive processes becomes a vital challenge and the traditional strategies of discovery of services are not efficient enough to handle this problem. The problem, approaches of discovering processes from sensor logs do not highlight IOT concepts in the output process models, which make process analysis and decision made more and more difficult. In this paper, we present a new process mining based approach to discover and analyze business process aware IoT. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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Nge-BatikVR: Innovative Batik Learning and Marketing Through Virtual Reality Serious Games with Immersive Hands-On Experience 15

Nge-BatikVR: Innovative Batik Learning and Marketing Through...

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15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Ramdhan, Dimas Pratama, Galih Dea Wijaya, Michael Francis Syahputra, Muhammad Edo Wihendro Game Application & Technology Program School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350383829

Batik is a cultural heritage of Indonesia, recognized by WHO as an Intangible Cultural Heritage. Batik is dyed by skilled craftsmen who make patterns with dots and lines on the fabric from melted wax. The process is complicated, so few people can experience all the steps in crafting Batik. From these problems, immersive learning media are needed so everyone can learn and gain experience in Batik crafting from start to finish. In this study, we will present Nge-BatikVR, a serious game application that introduces Batik through Virtual Reality and offers an immersive experience of learning Batik from various regions with interactive hands-on feature aimed at people to better understand and learn Batik. Nge-BatikVR offers four main features called Sinau (Learn), Kuis (Quiz), Nge-Batik (Simulation) and Toko (Shopping), with the main purpose to present interactive and engaging media useful for introducing and learning Batik with a fully immersive experience. © 2023 IEEE.

关键词： Serious games

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Novel Robust Span-based Model for Event Detection 3

Novel Robust Span-based Model for Event Detection

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3rd International conference on computer, Big Data and Artificial Intelligence, ICCBDAI 2022

作者： Zhang, Hang Yuan, Xiaoguang Song, Yingyi Yang, Yuting Wang, Rui Liu, Bing Han, Cuihong Beijing Institute of Computer Technology and Application Yongding Road Beijing100854 China College of Systems Engineering National University of Defense Technology Sifangping Street Changsha410073 China

Event detection (ED) aims to identify events and their types from the given text, which is the vital subtask of event extraction. However, poor quality training corpus can seriously affect the validity of current dominating deep learning-based techniques. In order to improve the robustness of the model, this paper converts the ED task into the named entity recognition (NER) task, and uses the span-based NER framework to identify and classify event triggers. In addition, we ameliorate the framework through data cleaning strategy, hierarchical learning rate strategy, sentence-level loss function and model ensemble strategy. The experimental results suggest that our approach achieves state-of-the-art performance on the widely used Chinese dataset. Moreover, the deep analysis has confirmed the effectiveness of the improvement strategy in resisting data pollution. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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