Vehicle parameters. by Honglei Pang (22693724)

Subjects:

HIL test results of curved road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate. by Honglei Pang (22693724)

Subjects:

Comparison of average reward values in various simulation scenarios. by Honglei Pang (22693724)

Subjects:

HIL test results of split-μ road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate. by Honglei Pang (22693724)

Subjects:

Knowledge graph model rating function. by Chunjuan Li (7890182)

Machine Learning-Aided Structure Determination for TiCl₄–Capped MgCl₂ Nanoplate of Heterogeneous Ziegler–Natta Catalyst by Gentoku Takasao (6382694)

“…Here, we report nonempirical structure determination of TiCl₄-capped MgCl₂ nanoplates that is based on the combination of a genetic algorithm and density functional calculations. …”

Acc epoch loss curve of IPCA algorithm. by Lu Lin (201289)

Procedure of the DCT-based EIT algorithm. by Rongqing Chen (249906)

“…To demonstrate the increased interpretability of the EIT image through structural prior in the DCT-based approach, the DCT-based reconstructions were compared with reconstructions from a widely applied one-step Gauss-Newton solver with background prior and from the advanced GREIT algorithm. The comparisons were conducted both on simulation data and retrospective patient data. …”

Power consumption for SHA256 software algorithm. by Maytham S. Jabor (15853046)

“…When simulating both approaches, the energy consumed when replacing functions by hardware decreases up to 63%.…”

Data_Sheet_1_Functional Outcome Prediction in Ischemic Stroke: A Comparison of Machine Learning Algorithms and Regression Models.PDF by Shakiru A. Alaka (9302864)

“…We evaluate the predictive accuracy of machine-learning algorithms for predicting functional outcomes in acute ischemic stroke patients after endovascular treatment.…”

Equilibrium membrane displacement as a function of active force. by Kai Liu (159026)

PyNoetic’s classification module supports a range of popular and widely used ML classification models with just a single line of function call. by Gursimran Singh (575288)

Subjects:

Membership function. by Tuan Anh Nguyen (121944)

Flow chart diagram of blind quantum algorithm. by Sultan H. Almotiri (14029251)

“…Our study addresses five major components of the quantum method to overcome these challenges: lattice-based cryptography, fully homomorphic algorithms, quantum key distribution, quantum hash functions, and blind quantum algorithms. …”

Loss Function Comparison. by Hongwei Yue (574068)

Loss Function Comparison. by Hongwei Yue (574068)

BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data by Jean-Christophe Lachance (6619307)

“…Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a <b>B</b>iomass <b>O</b>bjective <b>F</b>unction from experimental <b>dat</b>a. …”

Test of polymethyl methacrylate (C₅O₂H₈) through algorithm: Curves of both objective function and fractional weight in the process of identifying third chemical constituent after H was assumed, i.e., step 2 in Figure 2. by Dong Hyeok Choi (12562048)

Construction of the PRG score index using integrated machine learning algorithms. by Xiangxin Wu (22320058)

Comparison curve of objective function (). by Wanjun Zhang (496858)