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 >

Umar Zaman¹, Junaid Khan², Eunkyu Lee^1,3, Sajjad Hussain⁴, Awatef Salim Balobaid⁵, Rua Yahya Aburasain⁵, Kyungsup Kim^1,2,*

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 >

Novsheena Rasool^1,*, Javaid Iqbal Bhat¹, Najib Ben Aoun^2,3, Abdullah Alharthi⁴, Niyaz Ahmad Wani⁵, Vikram Chopra⁶, Muhammad Shahid Anwar^7,*

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 >

Xi Li^1,2, Weiwei Zhu², Qian Li^3,*, Changhui Hou^1,*, 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 >

Stephen Ojo¹, Moez Krichen^2,3,*, Meznah A. Alamro⁴, Alaeddine Mihoub⁵, Gabriel Avelino Sampedro⁶, Jaroslava Kniezova^7,*

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 >

Dongxu Zhang^1,*, 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 >

Dukui Zheng¹, Jingfa Li^1,*, 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 >

Junhua Gong¹, Yujie Chen^2,*, Bo Yu², Bin Chen¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.4, pp. 1-2, 2024, DOI:10.32604/icces.2024.012531

Abstract Traditional interface reconstruction methods often rely on numerical approaches, which can be inefficient when dealing with large bubbles, requiring extensive computational resources. To address this issue, we propose a novel model based on convolutional neural networks aimed at rapidly and accurately predicting the equations governing circular bubbles. This model takes the volume fraction of the main-phase fluid surrounding each computational grid cell as input variables and is capable of precisely forecasting the coordinates and radii of bubbles. To further enhance model performance, we employ the Optuna hyperparameter optimizer to fine-tune the model's parameters. Upon training More >

Jiaxin Wu^1,2, Yi Zhan¹, Min Luo^1,*, Boo Cheong Khoo²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.011058

Abstract Research on the mechanism of fluid flows (particularly nonlinear) on solid structures is of great scientific and engineering significance, as well as to implement effective control by using intelligent solid structures (i.e., agents). These dynamical systems involve complex interactions of fluid dynamics and solid mechanics and, thus are typically defined as fluid-structure interaction (FSI) problems. For effective analysis of FSI systems and implementing active control, numerical modeling that couples fluid and solid solvers proves to be an effective approach. However, the efficiency and accuracy of conventional numerical methods for solving such problems are limited due… More >

Jiacheng Shao^1,2, Jingxiu Ling^1,2,3, Rongchang Zhang^1,2, Xiaoyuan Cheng^1,2, Hao Zhang³

Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.40, No.1, pp. 1-8, 2024, DOI:10.23967/j.rimni.2024.01.004 - 19 January 2024

Abstract Based on the construction data of Fuzhou Metro Line 4 in Fujian Province, China, this paper proposes a soil pressure prediction model that combines Long Short-Term Memory (LSTM) and Particle Swarm Optimization (PSO). The values of Mean Absolute Error, Mean Squared Error, and Coefficient of Determination are 0.007MPa, 0.007%, and 0.93, respectively, indicating an improvement in accuracy.Wang-Mendel algorithm is used to establish fuzzy rules. The Mean Absolute Error and Mean Squared Error of the rotating speed of the screw machine are 0.065rpm and 1.528%, and the Coefficient of Determination is 0.82. The calculation accuracy of More >