Available functions in the phase-shifting package. by Raul Castañeda (13932971)

Available functions in the numerical propagation package. by Raul Castañeda (13932971)

Available functions in the phase reconstruction package. by Raul Castañeda (13932971)

Parameters of the Grippers Synthesis with Extremum Force Capabilities on the Basis of Deformation-Force Analysis by Krasimir Ganchev (12341770)

“…The input data for the synthesis are the first two or three transfer function (TF), as well as the angles of motion transfer. …”

The SSA based SVM objective function minimization against iterations. by Ali Ahmed (2567584)

Subjects:

Example of landmark registration to align ‘peaks’ of high density in the probability density function (PDF) of CD4 expression on blood T cells for a target distribution and chosen... by Ross J. Burton (10941910)

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

Python cv coding screenshots of all developed fingerprints on distinct surfaces. by Nisha Yadav (366131)

Computer Model Emulation with High-Dimensional Functional Output in Large-Scale Observing System Uncertainty Experiments by Pulong Ma (6105767)

“…Within each distinct spectral band, the emulator represents radiance output at irregular wavelengths as a linear combination of basis functions and random coefficients. These random coefficients are then modeled with nearest-neighbor Gaussian processes with built-in input dimension reduction via active subspace. …”

Data_Sheet_1_Parallel Implementation of Large-Scale Linear Scaling Density Functional Theory Calculations With Numerical Atomic Orbitals in HONPAS.PDF by Zhaolong Luo (9692087)

“…Therefore, sparse matrix multiplication is the most time-consuming step in the density matrix purification algorithm for linear-scaling DFT calculations. We propose to use the MPI_Allgather function for parallel programming to deal with the sparse matrix multiplication within the compressed sparse row (CSR) format, which can scale up to hundreds of processing cores on modern heterogeneous supercomputers. …”

The structure of genetic algorithm (GA). by Ali Akbar Moosavi (17769033)

“…First, physico-chemical inputs as bulk density (BD), initial water content (W_i), saturated water content (W_s), mean weight diameter (MWD), and geometric mean diameter (GMD) of aggregates, pH, electrical conductivity (EC), and calcium carbonate equivalent (CCE) were measured. Then, radial basis functions (RBFNNs), multilayer perceptron (MLPNNs), hybrid genetic algorithm (GA-NNs), and particle swarm optimization (PSO-NNs) neural networks were utilized to develop PTFs and compared their accuracy with the traditional regression model (MLR) using statistical indices. …”

The schematic view of the fruit fly optimization algorithm (FOA). by Mohsen Hesami (6917390)

Subjects:

Immune infiltration analysis by ssGSEA algorithm. by Yubin Xue (1537042)

Reward function related parameters. by Honglei Pang (22693724)

Subjects:

Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection by Samin Aref (4683934)

“…Each network is provided in .gml format or .pkl format which can be read into a networkX graph object using standard functions from the networkX library in Python. …”

Optimal scheduling model of microgrid based on improved dung beetle optimization algorithm by Yu Gao (69071)

“…First, a multiobjective optimal scheduling model of the microgrid is constructed and a typical daily output scenario generation method for wind power generation and photovoltaic power generation is constructed based on the Gaussian kernel density estimation, Frank-copula and K-means clustering algorithms. Second, to address the shortcomings of the DBO algorithm, the spiral position update strategy, adaptive weight factor, levy flight strategy and <i>t</i>-distribution variation strategy are introduced on the basis of the DBO algorithm, which effectively solves the problem of premature convergence of particles owing to falling into a local optimum. …”

Recursive attention function and loss function change. by Wang Linfeng (17102068)

Flowchart of the particle swarm optimization algorithm. by Jian Ma (170138)

Flowchart of the particle swarm optimization algorithm. by Jian Ma (170138)

Details the DLKA structure, which includes the DLKA-Attention module and the FFN module. The DLKA-Attention module mainly consists of the DLKA module, which comprises deformable de... by Xunqian Xu (21261899)