Sample points and numerical simulation results. by Xueyong Pan (20390363)

“…The <i>T</i>_max of the battery module decreased by 6.84% from 40.94°C to 38.14°C and temperature mean square deviation decreased (<i>TSD</i>) by 62.13% from 1.69 to 0.64. …”

Three-dimensional heat transfer model parameters. by Xueyong Pan (20390363)

“…The <i>T</i>_max of the battery module decreased by 6.84% from 40.94°C to 38.14°C and temperature mean square deviation decreased (<i>TSD</i>) by 62.13% from 1.69 to 0.64. …”

Optimal Latin square sampling distribution. by Xueyong Pan (20390363)

“…The <i>T</i>_max of the battery module decreased by 6.84% from 40.94°C to 38.14°C and temperature mean square deviation decreased (<i>TSD</i>) by 62.13% from 1.69 to 0.64. …”

2C discharge rate grid independence test. by Xueyong Pan (20390363)

“…The <i>T</i>_max of the battery module decreased by 6.84% from 40.94°C to 38.14°C and temperature mean square deviation decreased (<i>TSD</i>) by 62.13% from 1.69 to 0.64. …”

Feasibility diagram of design points. by Xueyong Pan (20390363)

“…The <i>T</i>_max of the battery module decreased by 6.84% from 40.94°C to 38.14°C and temperature mean square deviation decreased (<i>TSD</i>) by 62.13% from 1.69 to 0.64. …”

Related parameters of square LIBs. by Xueyong Pan (20390363)

“…The <i>T</i>_max of the battery module decreased by 6.84% from 40.94°C to 38.14°C and temperature mean square deviation decreased (<i>TSD</i>) by 62.13% from 1.69 to 0.64. …”

Multi objective optimization design process. by Xueyong Pan (20390363)

“…The <i>T</i>_max of the battery module decreased by 6.84% from 40.94°C to 38.14°C and temperature mean square deviation decreased (<i>TSD</i>) by 62.13% from 1.69 to 0.64. …”

Battery pack model. by Xueyong Pan (20390363)

“…The <i>T</i>_max of the battery module decreased by 6.84% from 40.94°C to 38.14°C and temperature mean square deviation decreased (<i>TSD</i>) by 62.13% from 1.69 to 0.64. …”

A proposed model for citrus tristeza virus (CTV) p20-mediated RNA silencing suppression. by Yongle Zhang (11355546)

Diagnostic criteria for Alcoholic cardiomyopathy. by Fei Yan (128878)

“…</p><p><b>Results:</b> Globally, ACM burden showed significant declines from 1990 to 2021, with age-standardized rates decreasing by 22.5-37.1% across prevalence, mortality and disability measures. …”

PCA-CGAN model parameter settings. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

MIT-BIH dataset proportion analysis chart. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

Wavelet transform preprocessing results. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

PCAECG_GAN. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

MIT dataset expansion quantities and Proportions. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

Experimental hardware and software environment. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

PCA-CGAN K-fold experiment table. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

Classification model parameter settings. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

MIT-BIH expanded dataset proportion chart. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

AUROC Graphs of RF Model and ResNet. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”