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  • Open Access

    PROCEEDINGS

    High-Resolution Flow Field Reconstruction Based on Graph-Embedding Neural Network

    Weixin Jiang1,*, Zongze Li2, Qing Yuan3,*, Junhua Gong2, Bo Yu4

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.1, pp. 1-3, 2024, DOI:10.32604/icces.2024.011266

    Abstract High resolution flow field results are of great significance for exploring physical laws and guiding practical engineering practice. However, traditional activities based on experiments or direct numerical solutions to obtain high-resolution flow fields typically require a significant amount of computational time or resources. In response to this challenge, this study proposes an efficient and robust high-resolution flow field reconstruction method by embedding graph theory into neural networks, to adapt to low data volume situations. In the high resolution flow field reconstruction problem of an NS equation, the proposed model has a lower mean squared error More >

  • Open Access

    ARTICLE

    Enhancing Septic Shock Detection through Interpretable Machine Learning

    Md Mahfuzur Rahman1,*, Md Solaiman Chowdhury2, Mohammad Shorfuzzaman3, Lutful Karim4, Md Shafiullah5, Farag Azzedin1

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2501-2525, 2024, DOI:10.32604/cmes.2024.055065 - 31 October 2024

    Abstract This article presents an innovative approach that leverages interpretable machine learning models and cloud computing to accelerate the detection of septic shock by analyzing electronic health data. Unlike traditional methods, which often lack transparency in decision-making, our approach focuses on early detection, offering a proactive strategy to mitigate the risks of sepsis. By integrating advanced machine learning algorithms with interpretability techniques, our method not only provides accurate predictions but also offers clear insights into the factors influencing the model’s decisions. Moreover, we introduce a preference-based matching algorithm to evaluate disease severity, enabling timely interventions guided… More >

  • Open Access

    ARTICLE

    Reliability Prediction of Wrought Carbon Steel Castings under Fatigue Loading Using Coupled Mold Optimization and Finite Element Simulation

    Muhammad Azhar Ali Khan1, Syed Sohail Akhtar2,3,*, Abba A. Abubakar2,4, Muhammad Asad1, Khaled S. Al-Athel2,5

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2325-2350, 2024, DOI:10.32604/cmes.2024.054741 - 31 October 2024

    Abstract The fatigue life and reliability of wrought carbon steel castings produced with an optimized mold design are predicted using a finite element method integrated with reliability calculations. The optimization of the mold is carried out using MAGMASoft mainly based on porosity reduction as a response. After validating the initial mold design with experimental data, a spring flap, a common component of an automotive suspension system is designed and optimized followed by fatigue life prediction based on simulation using Fe-safe. By taking into consideration the variation in both stress and strength, the stress-strength model is used… More >

  • Open Access

    ARTICLE

    Short-Term Wind Power Prediction Based on WVMD and Spatio-Temporal Dual-Stream Network

    Yingnan Zhao*, Yuyuan Ruan, Zhen Peng

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 549-566, 2024, DOI:10.32604/cmc.2024.056240 - 15 October 2024

    Abstract As the penetration ratio of wind power in active distribution networks continues to increase, the system exhibits some characteristics such as randomness and volatility. Fast and accurate short-term wind power prediction is essential for algorithms like scheduling and optimization control. Based on the spatio-temporal features of Numerical Weather Prediction (NWP) data, it proposes the WVMD_DSN (Whale Optimization Algorithm, Variational Mode Decomposition, Dual Stream Network) model. The model first applies Pearson correlation coefficient (PCC) to choose some NWP features with strong correlation to wind power to form the feature set. Then, it decomposes the feature set More >

  • Open Access

    ARTICLE

    An Efficient Long Short-Term Memory and Gated Recurrent Unit Based Smart Vessel Trajectory Prediction Using Automatic Identification System Data

    Umar Zaman1, Junaid Khan2, Eunkyu Lee1,3, Sajjad Hussain4, Awatef Salim Balobaid5, Rua Yahya Aburasain5, Kyungsup Kim1,2,*

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1789-1808, 2024, DOI:10.32604/cmc.2024.056222 - 15 October 2024

    Abstract Maritime transportation, a cornerstone of global trade, faces increasing safety challenges due to growing sea traffic volumes. This study proposes a novel approach to vessel trajectory prediction utilizing Automatic Identification System (AIS) data and advanced deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (DBLSTM), Simple Recurrent Neural Network (SimpleRNN), and Kalman Filtering. The research implemented rigorous AIS data preprocessing, encompassing record deduplication, noise elimination, stationary simplification, and removal of insignificant trajectories. Models were trained using key navigational parameters: latitude, longitude, speed, and heading. Spatiotemporal aware processing through trajectory segmentation… More >

  • Open Access

    ARTICLE

    ResMHA-Net: Enhancing Glioma Segmentation and Survival Prediction Using a Novel Deep Learning Framework

    Novsheena Rasool1,*, Javaid Iqbal Bhat1, Najib Ben Aoun2,3, Abdullah Alharthi4, Niyaz Ahmad Wani5, Vikram Chopra6, Muhammad Shahid Anwar7,*

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 885-909, 2024, DOI:10.32604/cmc.2024.055900 - 15 October 2024

    Abstract Gliomas are aggressive brain tumors known for their heterogeneity, unclear borders, and diverse locations on Magnetic Resonance Imaging (MRI) scans. These factors present significant challenges for MRI-based segmentation, a crucial step for effective treatment planning and monitoring of glioma progression. This study proposes a novel deep learning framework, ResNet Multi-Head Attention U-Net (ResMHA-Net), to address these challenges and enhance glioma segmentation accuracy. ResMHA-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention mechanisms. This powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture… More >

  • Open Access

    ARTICLE

    Re-Distributing Facial Features for Engagement Prediction with ModernTCN

    Xi Li1,2, Weiwei Zhu2, Qian Li3,*, Changhui Hou1,*, Yaozong Zhang1

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 369-391, 2024, DOI:10.32604/cmc.2024.054982 - 15 October 2024

    Abstract Automatically detecting learners’ engagement levels helps to develop more effective online teaching and assessment programs, allowing teachers to provide timely feedback and make personalized adjustments based on students’ needs to enhance teaching effectiveness. Traditional approaches mainly rely on single-frame multimodal facial spatial information, neglecting temporal emotional and behavioural features, with accuracy affected by significant pose variations. Additionally, convolutional padding can erode feature maps, affecting feature extraction’s representational capacity. To address these issues, we propose a hybrid neural network architecture, the redistributing facial features and temporal convolutional network (RefEIP). This network consists of three key components:… More >

  • Open Access

    ARTICLE

    Improving Multiple Sclerosis Disease Prediction Using Hybrid Deep Learning Model

    Stephen Ojo1, Moez Krichen2,3,*, Meznah A. Alamro4, Alaeddine Mihoub5, Gabriel Avelino Sampedro6, Jaroslava Kniezova7,*

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 643-661, 2024, DOI:10.32604/cmc.2024.052147 - 15 October 2024

    Abstract Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis (MS), a chronic autoimmune neurological condition. It disrupts signals between the brain and body, causing symptoms including tiredness, muscle weakness, and difficulty with memory and balance. Traditional methods for detecting MS are less precise and time-consuming, which is a major gap in addressing this problem. This gap has motivated the investigation of new methods to improve MS detection consistency and accuracy. This paper proposed a novel approach named FAD consisting of Deep Neural Network… More >

  • Open Access

    PROCEEDINGS

    Investigation of Multiaxial Creep Rupture Mechanisms and Life Prediction in High-Temperature Alloys Under Complex Environments

    Dongxu Zhang1,*, Kaitai Feng1, Menghui Lv1, Zhixun Wen2

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.4, pp. 1-3, 2024, DOI:10.32604/icces.2024.012317

    Abstract Modern advanced equipment is often in high-temperature and high-load service environment for a long time, in which multiaxial creep rupture is one of the important failure modes of key components. For example, typical structures under multiaxial stresses state, such as aero-engine turbine blades film cooling holes and turbine disk groove connection structures, are usually prioritized for creep rupture failure in high-temperature, high-pressure and high-speed loading environments. At present, the coupling mechanism between temperature and stress fields in complex environments, as well as the rupture mechanisms and life characteristics of structures with multiaxial stresses in service… More >

  • Open Access

    PROCEEDINGS

    Lifetime Prediction of Polyethylene Pipe Due to Aging Failure in Hydrogen-Blended Natural Gas Environment

    Dukui Zheng1, Jingfa Li1,*, Bo Yu1, Zhiqiang Huang1, Yindi Zhang1, Cuiwei Liu2

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.011669

    Abstract In the low and medium pressure urban gas pipe network, transporting the hydrogen-blended natural gas through polyethylene pipe is an important means to realize the large-scale delivery and utilization of hydrogen-blended natural gas. However, due to the characteristics of polymer material, polyethylene pipes will experience aging phenomenon, which will lead to the deterioration of performance and eventually result in brittle damage and failure. Therefore, it is of great significance to analyze and predict the lifetime of polyethylene pipe due to the aging in the hydrogen-blended natural gas environment to ensure the safe transportation. In this… More >

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