C2f structure. by Xiaozhou Feng (2918222)

“…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. …”

Experimental environment configuration. by Xiaozhou Feng (2918222)

“…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. …”

Ablation experiment results table. by Xiaozhou Feng (2918222)

“…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. …”

YOLOv8 identification results. by Xiaozhou Feng (2918222)

“…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. …”

LSKA module structure diagram. by Xiaozhou Feng (2918222)

“…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. …”

Comparison of mAP curves in ablation experiments. by Xiaozhou Feng (2918222)

“…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. …”

FarsterBlock structure. by Xiaozhou Feng (2918222)

“…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. …”

Sample augmentation and annotation illustration. by Xiaozhou Feng (2918222)

“…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. …”

YOLOv8 model architecture diagram. by Xiaozhou Feng (2918222)

“…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. …”

FLMP-YOLOv8 architecture diagram. by Xiaozhou Feng (2918222)

“…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. …”

Supplementary file 1_Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery.docx by Veera Narayana Balabathina (22518524)

“…</p>Methods<p>Thirteen supervised classification algorithms were comparatively evaluated, encompassing traditional spectral/statistical classifiers—Maximum Likelihood, Mahalanobis Distance, Minimum Distance, Parallelepiped, Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and Binary Encoding—and machine learning algorithms including Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN). …”

A classification scheme of bipartite randomization algorithms, based on whether the matrix row and columns sums are preserved exactly (Fixed, <i>F</i>), preserved on average (Propo... by Zachary P. Neal (9946331)

LSTM model’s equations. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”

Parameter’s interpretation. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”

The models’ training parameters. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”

Model’s measure methods. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”

Association point and relationship. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”

Video 1_A hybrid elastic-hyperelastic approach for simulating soft tactile sensors.mp4 by Berith Atemoztli De la Cruz Sánchez (21758708)

“…A significant challenge for simulating tactile sensors is balancing the trade-off between accuracy and processing time in simulation algorithms and models. To address this, we propose a hybrid approach that combines elastic and hyperelastic finite element simulations, complemented by convolutional neural networks (CNNs), to generate synthetic tactile maps of a soft capacitive tactile sensor. …”

The model successfully identified the motion structure in experiments I and II. by Elnaz Nemati (21402730)

Supplementary file 1_Hamiltonian formulations of centroid-based clustering.pdf by Myeonghwan Seong (21159605)