VMD-CPSO-BiLSTM network structure. by Yuye Zou (22806476)

“…The model employs a sophisticated three-phase methodology: (1) decomposition through Variational Mode Decomposition (VMD) to extract multiple intrinsic mode functions (IMFs) from the original time series, effectively capturing its nonlinear and complex patterns; (2) optimization using a Chaotic Particle Swarm Optimization (CPSO) algorithm to fine-tune the Bi-directional Long Short-Term Memory (BiLSTM) network parameters, thereby improving both predictive accuracy and model stability; and (3) integration of predictions from both high-frequency and low-frequency components to generate comprehensive final forecasts. …”

Ultra-Elastic, Transparent, and Conductive Gelatin/Alginate-Based Bioadhesive Hydrogel for Enhanced Human–Machine Interactive Applications by Shiqiang Zhang (195649)

“…Growing interest has focused on next-generation flexible adhesive sensors (FAS) integrated with deep learning for intelligent electronics. …”

Key parameters of DFIG. by Yanling Lv (327106)

“…Comparative simulations using the traditional NSGA-II, a multiobjective particle swarm optimization algorithm, and a multiobjective gray wolf optimization algorithm are conducted to validate the proposed algorithm. …”

Parameter values. by Yanling Lv (327106)

“…Comparative simulations using the traditional NSGA-II, a multiobjective particle swarm optimization algorithm, and a multiobjective gray wolf optimization algorithm are conducted to validate the proposed algorithm. …”

DC bus voltage variations. by Yanling Lv (327106)

“…Comparative simulations using the traditional NSGA-II, a multiobjective particle swarm optimization algorithm, and a multiobjective gray wolf optimization algorithm are conducted to validate the proposed algorithm. …”

MPPT operating curve of the DFIG. by Yanling Lv (327106)

“…Comparative simulations using the traditional NSGA-II, a multiobjective particle swarm optimization algorithm, and a multiobjective gray wolf optimization algorithm are conducted to validate the proposed algorithm. …”

Rotor-side three-phase current variation. by Yanling Lv (327106)

“…Comparative simulations using the traditional NSGA-II, a multiobjective particle swarm optimization algorithm, and a multiobjective gray wolf optimization algorithm are conducted to validate the proposed algorithm. …”

Structure of a DFIG. by Yanling Lv (327106)

“…Comparative simulations using the traditional NSGA-II, a multiobjective particle swarm optimization algorithm, and a multiobjective gray wolf optimization algorithm are conducted to validate the proposed algorithm. …”

Fig 2 - by Natsumi Nishiyama (19928990)

Node feature vector of the Karate network. by Ailian Wang (5537663)

“…We first abstract the feature vector matrix of each node from the network structural properties, and then optimize this matrix by a new objective function gradient optimization method, we generate the preliminary community delineation results with FCM method, and finally calibrate the communities to which the nodes belong. …”

Parameter Settings for LFR Networks. by Ailian Wang (5537663)

“…We first abstract the feature vector matrix of each node from the network structural properties, and then optimize this matrix by a new objective function gradient optimization method, we generate the preliminary community delineation results with FCM method, and finally calibrate the communities to which the nodes belong. …”

Community label of the node. by Ailian Wang (5537663)

“…We first abstract the feature vector matrix of each node from the network structural properties, and then optimize this matrix by a new objective function gradient optimization method, we generate the preliminary community delineation results with FCM method, and finally calibrate the communities to which the nodes belong. …”

Analysis and comparison of q parameter results. by Ailian Wang (5537663)

“…We first abstract the feature vector matrix of each node from the network structural properties, and then optimize this matrix by a new objective function gradient optimization method, we generate the preliminary community delineation results with FCM method, and finally calibrate the communities to which the nodes belong. …”

The process of OSFCM. by Ailian Wang (5537663)

“…We first abstract the feature vector matrix of each node from the network structural properties, and then optimize this matrix by a new objective function gradient optimization method, we generate the preliminary community delineation results with FCM method, and finally calibrate the communities to which the nodes belong. …”

Details of Real-World Network Datasets. by Ailian Wang (5537663)

“…We first abstract the feature vector matrix of each node from the network structural properties, and then optimize this matrix by a new objective function gradient optimization method, we generate the preliminary community delineation results with FCM method, and finally calibrate the communities to which the nodes belong. …”

Network structure dataset. by Ailian Wang (5537663)

“…We first abstract the feature vector matrix of each node from the network structural properties, and then optimize this matrix by a new objective function gradient optimization method, we generate the preliminary community delineation results with FCM method, and finally calibrate the communities to which the nodes belong. …”

The chain cable parameters. by Huiyuan Zheng (12889196)

“…Nevertheless, a 40% reduction in roll motion is achieved (3.36° in Condition 4 vs. 5.60° in Condition 1), along with a 24.5% reduction in yaw motion (39.22° in Condition 4 vs. 51.94° in Condition 1). (3) In irregular wave simulations, the ballast tanks effectively reduce the heave amplitude by up to 8.34% in sea state level 4 and 6.06% in sea state level 8, thereby enhancing its wave-following performance in the heave degree of freedom. (4) A CNN_BiLSTM_Attention algorithm is developed using hydrodynamic analysis generated datasets to predict the pitch motion time series of the platform under different ballast water conditions and sea states, while the model has a superior prediction performance (R² = 0.9658, RMSE = 0.5343, MAE = 0.3188, representing a 4.82% increase in R² and 30.31% reduction in RMSE compared to the original model). …”

Mooring system. by Huiyuan Zheng (12889196)

“…Nevertheless, a 40% reduction in roll motion is achieved (3.36° in Condition 4 vs. 5.60° in Condition 1), along with a 24.5% reduction in yaw motion (39.22° in Condition 4 vs. 51.94° in Condition 1). (3) In irregular wave simulations, the ballast tanks effectively reduce the heave amplitude by up to 8.34% in sea state level 4 and 6.06% in sea state level 8, thereby enhancing its wave-following performance in the heave degree of freedom. (4) A CNN_BiLSTM_Attention algorithm is developed using hydrodynamic analysis generated datasets to predict the pitch motion time series of the platform under different ballast water conditions and sea states, while the model has a superior prediction performance (R² = 0.9658, RMSE = 0.5343, MAE = 0.3188, representing a 4.82% increase in R² and 30.31% reduction in RMSE compared to the original model). …”

The “Guo Hai Shi 1” offshore test platform. by Huiyuan Zheng (12889196)

“…Nevertheless, a 40% reduction in roll motion is achieved (3.36° in Condition 4 vs. 5.60° in Condition 1), along with a 24.5% reduction in yaw motion (39.22° in Condition 4 vs. 51.94° in Condition 1). (3) In irregular wave simulations, the ballast tanks effectively reduce the heave amplitude by up to 8.34% in sea state level 4 and 6.06% in sea state level 8, thereby enhancing its wave-following performance in the heave degree of freedom. (4) A CNN_BiLSTM_Attention algorithm is developed using hydrodynamic analysis generated datasets to predict the pitch motion time series of the platform under different ballast water conditions and sea states, while the model has a superior prediction performance (R² = 0.9658, RMSE = 0.5343, MAE = 0.3188, representing a 4.82% increase in R² and 30.31% reduction in RMSE compared to the original model). …”

Difference comparison. by Huiyuan Zheng (12889196)

“…Nevertheless, a 40% reduction in roll motion is achieved (3.36° in Condition 4 vs. 5.60° in Condition 1), along with a 24.5% reduction in yaw motion (39.22° in Condition 4 vs. 51.94° in Condition 1). (3) In irregular wave simulations, the ballast tanks effectively reduce the heave amplitude by up to 8.34% in sea state level 4 and 6.06% in sea state level 8, thereby enhancing its wave-following performance in the heave degree of freedom. (4) A CNN_BiLSTM_Attention algorithm is developed using hydrodynamic analysis generated datasets to predict the pitch motion time series of the platform under different ballast water conditions and sea states, while the model has a superior prediction performance (R² = 0.9658, RMSE = 0.5343, MAE = 0.3188, representing a 4.82% increase in R² and 30.31% reduction in RMSE compared to the original model). …”