EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 2024-04-08T09:00:02+00:00 EAI Publications Department Open Journal Systems <p>EAI Endorsed Transactions on Industrial Networks and Intelligent Systems is open access, a peer-reviewed scholarly journal focused on ubiquitous computing, cloud computing, and cyber-physical system, all kinds of networks in large-scale factories, including a lot of traditional and new industries. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a quarterly frequency (four issues per year). Authors are not charged for article submission and processing. This journal is co-organized, and managed by Duy Tan University, Vietnam.</p> <p><strong>INDEXING</strong>: Scopus (CiteScore: 3.1), Compendex, DOAJ, ProQuest, EBSCO, DBLP</p> Early State Prediction Model for Offshore Jacket Platform Structural Using EfficientNet-B0 Neural Network 2024-03-05T11:35:09+00:00 Le Anh-Hoang Ho Viet-Dung Do Xuan-Kien Dang Thi Duyen-Anh Pham <p>Offshore Jacket Platforms (OJPs) are often affected by environmental components that lead to damage, and the early detection system can help prevent serious failures, ensuring safe operations and mining conditions, and reducing maintenance costs. In this study, we proposed a prediction model based on Convolutional Neural Networks (CNNs) aimed at determining the early stage of the OJP structure’s abnormal status. Additionally, the EfficientNet-B0 Deep Neural Network classifies normal and abnormal states, which may cause problems, by using displacement signal analysis at specific areas taken into account throughout the test. Displacement data is transferred to a 2D scalogram image by applying a continuous Wavelet converter that shows the state of the work. Finally, the scalogram image data set is used as the input of the neural network, and feasibility experimental results compared with other typical neural networks such as GoogLeNet and ResNet-50 have verified the effectiveness of the approach.</p> 2024-03-05T00:00:00+00:00 Copyright (c) 2024 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Vehicle Type Classification with Small Dataset and Transfer Learning Techniques 2024-03-07T09:39:38+00:00 Quang-Tu Pham Dinh-Dat Pham Khanh-Ly Can Hieu Dao To Hoang-Dieu Vu <p><span dir="ltr" role="presentation">This study delves into the application of deep learning training techniques using a restricted dataset, </span><span dir="ltr" role="presentation">encompassing around 400 vehicle images sourced from Kaggle. Faced with the challenges of limited </span><span dir="ltr" role="presentation">data, the impracticality of training models from scratch becomes apparent, advocating instead for the </span><span dir="ltr" role="presentation">utilization of pre-trained models with pre-trained weights. The investigation considers three prominent </span><span dir="ltr" role="presentation">models—E</span><span dir="ltr" role="presentation">ffi</span><span dir="ltr" role="presentation">cientNetB0, ResNetB0, and MobileNetV2—with E</span><span dir="ltr" role="presentation">ffi</span><span dir="ltr" role="presentation">cientNetB0 emerging as the most proficient </span><span dir="ltr" role="presentation">choice. Employing the gradually unfreeze layer technique over a specified number of epochs, E</span><span dir="ltr" role="presentation">ffi</span><span dir="ltr" role="presentation">cientNetB0 </span><span dir="ltr" role="presentation">exhibits remarkable accuracy, reaching 99.5% on the training dataset and 97% on the validation dataset. In </span><span dir="ltr" role="presentation">contrast, training models from scratch results in notably lower accuracy. In this context, knowledge distillation </span><span dir="ltr" role="presentation">proves pivotal, overcoming this limitation and significantly improving accuracy from 29.5% in training and </span><span dir="ltr" role="presentation">20.5% in validation to 54% and 45%, respectively. This study uniquely contributes by exploring transfer </span><span dir="ltr" role="presentation">learning with gradually unfreeze layers and elucidates the potential of knowledge distillation. It highlights </span><span dir="ltr" role="presentation">their e</span><span dir="ltr" role="presentation">ff</span><span dir="ltr" role="presentation">ectiveness in robustly enhancing model performance under data scarcity, thus addressing challenges </span><span dir="ltr" role="presentation">associated with training deep learning models on limited datasets. The findings underscore the practical </span><span dir="ltr" role="presentation">significance of these techniques in achieving superior results when confronted with data constraints in real-</span><span dir="ltr" role="presentation">world scenarios</span></p> 2024-03-07T00:00:00+00:00 Copyright (c) 2024 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Facial mask-wearing prediction and adaptive gender classification using convolutional neural networks 2024-03-13T09:58:23+00:00 Mohamed Oulad-Kaddour Hamid Haddadou Daniel Palacios-Alonso Cristina Conde Enrique Cabello <p>The world has lived an exceptional time period caused by the Coronavirus pandemic. To limit Covid-19 propagation, governments required people to wear a facial mask outside. In facial data analysis, mask-wearing on the human face creates predominant occlusion hiding the important oral region and causing more challenges for human face recognition and categorisation. The appropriation of existing solutions by taking into consideration the masked context is indispensable for researchers. In this paper, we propose an approach for mask-wearing prediction and adaptive facial human-gender classification. The proposed approach is based on convolutional neural networks (CNNs). Both mask-wearing and gender information are crucial for various possible applications. Experimentation shows that mask-wearing is very well detectable by using CNNs and justifies its use as a prepossessing step. It also shows that retraining with masked faces is indispensable to keep up gender classification performances. In addition, experimentation proclaims that in a controlled face-pose with acceptable image quality' context, the gender attribute remains well detectable. Finally, we show empirically that the adaptive proposed approach improves global performance for gender prediction in a mixed context. </p> 2024-03-13T00:00:00+00:00 Copyright (c) 2024 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Improving Performance of the Typical User in the Indoor Cooperative NOMA Millimeter Wave Networks with Presence of Walls 2024-04-08T08:08:19+00:00 Sinh Cong Lam Xuan Nam Tran <p>INTRODUCTION: The beyond 5G millimeter wave cellular network system is expecting to provide the high quality of service in indoor areas.&nbsp;<br>OBJECTIVES: Due to the high density of obstacles, the cooperative communication technique is employed to improve the user's desired signal power by finding more than one appropriate station to serve that user.&nbsp;<br>METHODS: While the conventional system utilizes additional equipment such as Reconfigurable Intelligent Surfaces (RIS) and relays to enable the cooperative features, the paper introduces a new network paradigm that utilizes the second nearest Base Station (BS) of the typical user as the Decode and Forward (DF) relay. Thus, depends on the success of decoding the message from the user' serving BS of the second nearest BS, the typical user can work with and without assistance from the relay whose operation follows the discipline of the power-domain NOMA technique. In the case of with relay assistance, the Maximum Ratio Combining technique is utilized by the typical user to combine the desired signals.&nbsp;<br>RESULTS: To examine the performance of the proposed system, the Nakagami-m and the newly developed path loss model, which considers the density of walls and their properties, are adopted to derive the coverage probability of the user with and without relay assistance. The closed-form expressions of this performance metric are derived by Gauss quadrature and Welch-Satterthwaite approximation.</p> 2024-04-08T00:00:00+00:00 Copyright (c) 2024 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Real-time Single-Channel EOG removal based on Empirical Mode Decomposition 2024-04-08T08:57:18+00:00 Kien Nguyen Trong Nhat Nguyen Luong Hanh Tan Duy Tran Trung Huong Ha Thi Thanh Duy Pham The Binh Nguyen Thanh <p>In recent years, single-channel physiological recordings have gained popularity in portable health devices and research settings due to their convenience. However, the presence of electrooculogram (EOG) artifacts can significantly degrade the quality of the recorded data, impacting the accuracy of essential signal features. Consequently, artifact removal from physiological signals is a crucial step in signal processing pipelines. Current techniques often employ Independent Component Analysis (ICA) to efficiently separate signal and artifact sources in multichannel recordings. However, limitations arise when dealing with single or a few channel measurements in minimal instrumentation or portable devices, restricting the utility of ICA. To address this challenge, this paper introduces an innovative artifact removal algorithm utilizing enhanced empirical mode decomposition to extract the intrinsic mode functions (IMFs). Subsequently, the algorithm targets the removal of segments related to EOG by isolating them within these IMFs. The proposed method is compared with existing single-channel EEG artifact removal algorithms, demonstrating superior performance. The findings demonstrate the effectiveness of our approach in isolating artifact components, resulting in a reconstructed signal characterized by a strong correlation and a power spectrum closely resembling the ground-truth EEG signal. This outperforms the existing methods in terms of artifact removal. Additionally, the proposed algorithm exhibits significantly reduced execution time, enabling real-time online analysis.</p> 2024-04-08T00:00:00+00:00 Copyright (c) 2024 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems