ARTIFICIAL NEURAL NETWORK FOR MODELS OF HUMAN OPERATOR by Martin Ruzek

“…The artificial neural networks seems to be a promising method for the modeling of a human operator because the architecture of the ANN is directly inspired by the biological neuron. …”
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Simplified models for describing multitasking modes in living recognition systems by Golovina, Alina Andreevna, Yakhno, Vladimir Grigorevich

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Helium Speech Recognition Method Based on Spectrogram with Deep Learning by Yonghong Chen, Shibing Zhang, Dongmei Li

“…This study introduces deep learning into helium speech recognition and proposes a spectrogram-based dual-model helium speech recognition method. …”
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Subsystem for recognition of emergency situations on the road by О. Ворона, О. Польшакова

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Voice recognition enhancement by genetic algorithm by Mohamed Salah Salhi, Ali Hamdan Alenezi

“…The study proposes a hybrid model between SOM and GA to take advantage of each of their properties and improve recognition rates. …”
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YOLO-SG: Seafloor Topography Unit Recognition and Segmentation Algorithm Based on Lightweight Upsampling Operator and Attention Mechanisms by Yifan Jiang, Ziyin Wu, Fanlin Yang, Dineng Zhao, Xiaoming Qin, Mingwei Wang, Qiang Wang

“…Additionally, it integrates a lightweight, general upsampling operator to create a new feature fusion network, thereby improving the model’s ability to fuse and represent features. …”
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Data-Driven Dynamic Graph Convolution Transformer Network Model for EEG Emotion Recognition Under IoMT Environment by Xing Jin, Fa Zhu, Yu Shen, Gwanggil Jeon, David Camacho

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Semi-Supervised Class-Incremental Sucker-Rod Pumping Well Operating Condition Recognition Based on Multi-Source Data Distillation by Weiwei Zhao, Bin Zhou, Yanjiang Wang, Weifeng Liu

“…However, existing deep learning-based operating condition recognition methods are constrained by several factors: the limitations of traditional operating condition recognition methods based on single-source and multi-source data, the need for large amounts of labeled data for training, and the high robustness requirement for recognizing complex and variable data. …”
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Intention Recognition of Space Noncooperative Targets Using Large Language Models by Heng Jing, Qinbo Sun, Zhaohui Dang, Hua Wang

“…The use of prompt tuning V2 (P-tuning V2) and low-rank adaptation (LoRA) fine-tuning enhances the models’ performance. A dataset of 50,688 nominal samples and 8,448 perturbed samples was created through computer simulation based on expert knowledge, focusing on intention recognition of approaching targets in space station on-orbit operation and surveillance scenarios. …”
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Modeling and Simulation of Athlete’s Error Motion Recognition Based on Computer Vision by Luo Dai

“…In this article, a multifeature fusion error action expression method based on silhouette and optical flow information is proposed to overcome the shortcomings in the effectiveness of a single error action expression method based on the fusion of features for human body error action recognition. We analyse and discuss the human error action recognition method based on the idea of template matching to analyse the key issues that affect the overall expression of the error action sequences, and then, we propose a motion energy model based on the direct motion energy decomposition of the video clips of human error actions in the 3 Deron action sequence space through the filter group. …”
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Lightweight human activity recognition method based on the MobileHARC model by Xingyu Gong, Xinyang Zhang, Na Li

“…We systematically evaluate the proposed models on four public datasets. Experimental results show that MobileHARC achieves superior recognition performance, and uses fewer Floating-Point Operations per Second (FLOPs) and parameters compared to current models.…”
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Selective state models are what you need for animal action recognition by Edoardo Fazzari, Donato Romano, Fabrizio Falchi, Cesare Stefanini

“…This study introduces Mamba-MSQNet, a novel architecture family for multilabel Animal Action Recognition using Selective Space Models. By transforming the state-of-the-art MSQNet model with Mamba blocks, we achieve significant reductions in computational requirements: up to 90% fewer Floating point OPerations and 78% fewer parameters compared to MSQNet. …”
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FM‐YOLOv8:Lightweight gesture recognition algorithm by Fanghai Li, Xitai Na, Jinshuo Shi, Qingbin Sun

“…However, the existing gesture recognition models have the problem of a large number of model parameters and high computational complexity, which makes them unable to meet the needs of end‐to‐end industrial deployment. …”
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Anomaly Detection Method for Hydropower Units Based on KSQDC-ADEAD Under Complex Operating Conditions by Tongqiang Yi, Xiaowu Zhao, Yongjie Shi, Xiangnan Jing, Wenyang Lei, Jiang Guo, Yang Meng, Zhengyu Zhang

“…Then, an ADEAD algorithm is designed, which incorporates local density information and improves anomaly detection accuracy and stability through multi-model ensemble and density-adaptive strategies. Validation experiments using 14-month actual operational data from a 550 MW unit at a hydropower station in Southwest China show that the KSQDC algorithm achieves a silhouette coefficient of 0.64 in condition recognition, significantly outperforming traditional methods, and the KSQDC-ADEAD algorithm achieves comprehensive scores of 0.30, 0.34, and 0.23 for anomaly detection at three key monitoring points, effectively improving the accuracy and reliability of anomaly detection. …”
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A lightweight power quality disturbance recognition model based on CNN and Transformer by ZHANG Bide, QIU Jie, LOU Guangxin, ZHOU Can, LUO Qingqing, LI Tianqian

“…Finally, pooling layers, fully connected layers, and Softmax are applied to complete the recognition PQDs. Simulation experiments demonstrate that the CaT model effectively recognizes PQDs with fewer parameters and floating point operations, achieving high accuracy and strong noise robustness. …”
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YOLOv8-POS: a lightweight model for coal-rock image recognition by Yanqin Zhao, Wenyu Wang

“…A novel approach, designated YOLOv8-POS, is introduced to address the issue of false detections in coal-rock image recognition tasks, frequently caused by factors such as image defocus, dim lighting, and worker occlusion, and to further enhance the model’s accuracy and reduce its complexity. …”
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A CNN-LSTM model for road surface recognition of electric balance vehicles by Feifei Yu, Yongxian Huang, Canyi Du, Feng Li, Yongkang Gong, Guoyan Chen

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Intention Recognition of AAV Swarm Based on GAT-EPool-BiGRU Model by Jiajun Yuan, Xiang Jia, Yu An, Liang Geng, Lei Shu

“…The experiments were conducted using 13 features, including altitude, velocity, and acceleration, covering 7 types of tactical intent such as attack, feint, and reconnaissance. With recognition accuracy as the primary evaluation metric, the results demonstrate that the proposed model achieves an intent recognition accuracy of 95.5%, outperforming mainstream models—5.4% higher than Transformer and 8% higher than GCN-LSTM. …”
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An air target intention data extension and recognition model based on deep learning by Bo Cao, Qinghua Xing, Longyue Li, Weijie Lin

“…Abstract As the core part of battlefield situational awareness, air target intention recognition plays an important role in modern air operations. …”
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GDFC-YOLO: An Efficient Perception Detection Model for Precise Wheat Disease Recognition by Jiawei Qian, Chenxu Dai, Zhanlin Ji, Jinyun Liu

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