Performance of machine learning algorithms. by Wenxue Liu (6877754)

Example measurement and analysis units for landscape elements. by Jaewon Han (8660295)

Algorithmic experimental parameter design. by 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.…”

ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

Spatial spectrum estimation for three algorithms. by 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.…”

Flowchart of the subspace fitting algorithm for interpolated coprime arrays. by Chuanxi Xing (20141665)

Coprime array with interpolated array elements. by 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.…”

The comparison of results from the proposed architecture and state-of-the-art algorithms on the UNSW-B15, NSL-KDD and KDDCUP’99 dataset. by Abdullah Asım Yılmaz (20715311)

The method for determining the chemical constituents of unknown materials in the algorithm. by Dong Hyeok Choi (12562048)

Vibration Nondestructive Testing of Continuous Welded Rails: A Finite Element Analysis by Alireza Enshaeian (20360253)

“…The frequency content of the vibrations below 700 Hz and across a range of different longitudinal stress and support conditions is computed using the power spectral density, which constitutes the input matrix of a machine learning algorithm able to learn the complex relationship among frequencies, axial stress, and support conditions. …”

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption by Xuyang Li (11431426)

Subjects:

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption by Xuyang Li (11431426)

Subjects:

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption by Xuyang Li (11431426)

Subjects:

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption by Xuyang Li (11431426)

Subjects:

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption by Xuyang Li (11431426)

Subjects:

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption by Xuyang Li (11431426)

Subjects:

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption by Xuyang Li (11431426)

Subjects:

Calculation algorithm implementing the proposed model. by Landry Djopkop Kouanang (17131748)

Calculation algorithm implementing the proposed model. by Landry Djopkop Kouanang (17131748)

Scatter diagram of different principal elements. by Jizhong Wang (7441697)