Parameters of the genetic algorithm. حسب Jian Ma (170138)

Comparison of different path optimization models across multiple datasets: Visual analysis of path length, transport cost, and delay rate, showcasing the performance of each model... حسب Jiangjiang He (9676377)

الموضوعات:

Overall Architecture of the GL-SSL Model for Smart Port Path Optimization, Integrating GCN, LSTM, and SSL for Efficient Multimodal Path Planning and Dynamic Decision-Making in Port... حسب Jiangjiang He (9676377)

الموضوعات:

Comparison of path length and search time. حسب Kuo Hu (5349881)

Path smoothing. حسب Yu Gu (162342)

"…Divide the entire path planning process into two stages: quickly finding the initial path and optimizing the path. …"

The actual training effect of four algorithms on original photo data (All images are original images taken by the author). حسب Xiaojun Wang (50332)

الموضوعات:

Architecture of the LSTM Module in the GL-SSL Model for Smart Port Path Optimization, Capturing Temporal Dependencies and Integrating Attention Mechanism for Dynamic Path Planning... حسب Jiangjiang He (9676377)

الموضوعات:

G R code algorithm. حسب R. Sakthivel (2589547)

"…<div><p>Loss-less data compression becomes the need of the hour for effective data compression and computation in VLSI test vector generation and testing in addition to hardware AI/ML computations. …"

Dispersion signal in principal and residual space in PCA-FFT and KPCA-FFT algorithm. حسب Emmanuel Ndongue Esseme (22615091)

The specific architecture of the FcaNet-cycleGan style transfer method. حسب Xiaojun Wang (50332)

الموضوعات:

Coprime array with interpolated array elements. حسب Chuanxi Xing (20141665)

"…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…"

Numbers of iterations (#Ite) and eigenvalue calculations (#Eig) in the DC algorithm on the random and chain graph datasets. حسب Tomokaze Shiratori (9635271)

الموضوعات:

A comparison of the absorption coefficients obtained from the current simulation method (FEM) with those obtained by Sharma et al. [13]. حسب Xiaogang Li (299205)

الموضوعات:

Pose errors of 5PUS-RPUR robot with different algorithms. حسب Zesheng Wang (15244172)

Planning data for different plots. حسب Manxian Yang (20521600)

"…In the context of multi-machine scheduling, the BNSGA-III algorithm outperforms the NSGA-II, NSGA-III, and MOEA/D algorithms, achieving improvements in total travel distance (12.3% to 34.4%), path balance (60.9% to 66.2%), and workload distribution (78.7% to 92.9%). …"

Common turning methods. حسب Manxian Yang (20521600)

"…In the context of multi-machine scheduling, the BNSGA-III algorithm outperforms the NSGA-II, NSGA-III, and MOEA/D algorithms, achieving improvements in total travel distance (12.3% to 34.4%), path balance (60.9% to 66.2%), and workload distribution (78.7% to 92.9%). …"

Schematic diagram of AIC operation. حسب Manxian Yang (20521600)

"…In the context of multi-machine scheduling, the BNSGA-III algorithm outperforms the NSGA-II, NSGA-III, and MOEA/D algorithms, achieving improvements in total travel distance (12.3% to 34.4%), path balance (60.9% to 66.2%), and workload distribution (78.7% to 92.9%). …"

Partial field data of Huaxing Farm. حسب Manxian Yang (20521600)

"…In the context of multi-machine scheduling, the BNSGA-III algorithm outperforms the NSGA-II, NSGA-III, and MOEA/D algorithms, achieving improvements in total travel distance (12.3% to 34.4%), path balance (60.9% to 66.2%), and workload distribution (78.7% to 92.9%). …"

Different operating angles θ = 90°, θ = 180°. حسب Manxian Yang (20521600)

"…In the context of multi-machine scheduling, the BNSGA-III algorithm outperforms the NSGA-II, NSGA-III, and MOEA/D algorithms, achieving improvements in total travel distance (12.3% to 34.4%), path balance (60.9% to 66.2%), and workload distribution (78.7% to 92.9%). …"

Farm land preparation operation scheduling plan. حسب Manxian Yang (20521600)

"…In the context of multi-machine scheduling, the BNSGA-III algorithm outperforms the NSGA-II, NSGA-III, and MOEA/D algorithms, achieving improvements in total travel distance (12.3% to 34.4%), path balance (60.9% to 66.2%), and workload distribution (78.7% to 92.9%). …"