A Short Survey and Comparison of CNN-Based Music Genre Classification Using Multiple Spectral Features DOI Creative Commons
Wangduk Seo,

Sung-Hyun Cho,

Paweł Teisseyre

и другие.

IEEE Access, Год журнала: 2023, Номер 12, С. 245 - 257

Опубликована: Дек. 25, 2023

The goal of music genre classification is to identify the given feature vectors representing certain characteristics clips. In addition, improve accuracy classification, considerable research has been conducted on extracting spectral features, which contain critical information for from clips and feeding these features into training models. particular, recent studies argue that can be enhanced by employing multiple simultaneously. Consequently, fusing a consideration in designing Hence, this paper provides short survey compares performance most CNN-based models with newly devised model employs late fusion strategy features. Our empirical study 12 public datasets, including Ballroom, ISMIR04, GTZAN, showed CNN outperforms other compared methods. Additionally, we performed an in-depth analysis validate effectiveness classification.

Язык: Английский

YOLO_Bolt: a lightweight network model for bolt detection DOI Creative Commons
Zhenyu Liu,

Haoyuan Lv

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Янв. 5, 2024

Abstract Accurate, fast, and intelligent workpiece identification is of great significance to industrial production. To cope with the limited hardware resources factory equipment, we have made lightweight improvements based on You Only Look Once v5 (YOLOv5) proposed a YOLO named YOLO_Bolt. First, ghost bottleneck deep convolution added backbone module neck YOLOv5 detection algorithm reduce model volume. Second, asymptotic feature pyramid network enhance utilization ability, suppress interference information, improve accuracy. Finally, relationship between loss function decoupling head structure was focused on, number layers redesigned according different tasks further accuracy model. We conducted experimental verification MSCOCO 2017 dataset homemade bolt dataset. The results show that compared YOLOv5s, parameters only 6.8 M, which half original On dataset, mAP increased by 2.4%. FPS 104 frames/s. 0.5 4.2%, our method 1.2% higher than latest YOLOv8s. improved can provide effective auxiliary technical support for detection.

Язык: Английский

Процитировано

11
Design Space Exploration for Edge Machine Learning Featured by MathWorks FPGA DL Processor: A Survey DOI Creative Commons
S. Bertazzoni, Lorenzo Canese, G.C. Cardarilli

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 9418 - 9439

Опубликована: Янв. 1, 2024

This paper proposes a Design Space Exploration for Edge machine learning through the utilization of novel MathWorks FPGA Deep Learning Processor IP, featured in HDL toolbox. With ever-increasing demand real-time applications, there is critical need efficient and low-latency hardware solutions that can operate at edge network, close proximity to data source. The toolbox provides flexible customizable platform deploying deep models on FPGAs, enabling effective inference acceleration embedded IoT applications. In this study, our primary focus lies investigating impact parallel processing elements performance resource FPGA-based processor. By analyzing trade-offs between accuracy, speed, energy efficiency, utilization, we aim gain valuable insights into making optimal design choices implementations. Our evaluation conducted AMD-Xilinx ZC706 development board, which serves as target device experiments. We consider all compatible Convolutional Neural Networks available within comprehensively assess performances.

Язык: Английский

Процитировано

7
ISTFormer: lightweight transformer for enhanced super-resolution of coal rock images via iterative feature extraction DOI
Hao Liu, Ye Liu, Shuanglong Yao

и другие.

The Visual Computer, Год журнала: 2025, Номер unknown

Опубликована: Фев. 3, 2025

Язык: Английский

Процитировано

1
Atomization law and dust reduction effect of air-atomizing nozzles determined by CFD and experiments DOI
Huitian Peng, Yifei Peng, Wen Nie

и другие.

Energy, Год журнала: 2025, Номер unknown, С. 134822 - 134822

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1
Multi-source physical information driven deep learning in intelligent education: Unleashing the potential of deep neural networks in complex educational evaluation DOI Creative Commons
Zhizhong Xing, Ying Yang,

Li Tan

и другие.

AIP Advances, Год журнала: 2025, Номер 15(2)

Опубликована: Фев. 1, 2025

With the urgent global demand for sustainable development, intelligent education driven by multi-source physical information has attracted widespread attention as an innovative educational model. However, in context of dual carbon, achieving and efficient development faces many difficulties, one important challenges is how to effectively evaluate students. The application deep neural networks evaluation direction digitization. Currently, there need conduct research on value empowering with networks. We first studied principles characteristics network technology evaluation; second, three major advantages were pointed out: objectivity evaluating diversified data, accuracy perception information, mining data finally, key faced clarified from perspectives environment, theoretical knowledge, interpretability. This provides new ideas methods lays foundation breaking through traditional era carbon development.

Язык: Английский

Процитировано

1
Robust nonlinear control of permanent magnet synchronous motor drives: An evolutionary algorithm optimized passivity-based control approach with a high-order sliding mode observer DOI Creative Commons
Youcef Belkhier,

Siham Fredj,

Haroon Rashid

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 145, С. 110256 - 110256

Опубликована: Фев. 14, 2025

Язык: Английский

Процитировано

1
Bearing remaining useful life prediction with an improved CNN-LSTM network using an artificial gorilla troop optimization algorithm DOI
Yonghua Li, Zhe Chen,

Chaoqun Hu

и другие.

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability, Год журнала: 2024, Номер unknown

Опубликована: Янв. 17, 2024

To address the problem of reliance on a priori knowledge and difficult hyperparameter selection in feature fusion. The effect different convolutional kernel sizes filters fusion is investigated firstly, based which an artificial Gorilla Troops Optimizer (GTO) enhanced Convolutional Long-Short Term Memory Neural Network (CNN-LSTM) method for bearing lifetime prediction suggested. GTO algorithm was used to optimize hyperparameters such as size CNN-LSTM, pooling layer size, batch number hidden neurons, rate learning with goal minimizing mean squared error remaining useful life (RUL) prediction. From optimized CNN-LSTM network analyze monitored performance degradation data, construct health indicators (HI) reflecting degradation, build model. Typical cycle data has been validation proposed method. results indicate that have better trending robustness, leading smaller errors outcomes.

Язык: Английский

Процитировано

6
DEDU: Dual-Enhancing Dense-UNet for Lowlight Image Enhancement and Denoise DOI Creative Commons
Hyungjoo Park,

Hanseo Lim,

Dong-Young Jang

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 24071 - 24078

Опубликована: Янв. 1, 2024

In this paper, we propose an innovative image enhancement algorithm called "Dual-Enhancing-Dense-UNet (DEDUNet)" that simultaneously performs brightness and reduces noise. This model is based on Convolutional Neural Network (CNN) algorithms incorporates techniques such as Decoupled Fully Connection (DFC) attention, skip connections, shortcut, Cross-Stage-Partial (CSP) dense blocks to address the noise removal aspects of enhancement. The dual approach offers a new solution for restoring improving high-quality images, presenting opportunities in fields computer vision processing. Our experimental results substantiate superior performance proposed algorithm, showcasing significant improvements key indicators. Specifically, achieves Peak Signal-to-Noise Ratio (PSNR) 19.17, Structural Similarity Index (SSIM) 0.71, Learned Perceptual Image Patch (LPIPS) 0.30, Mean Absolute Error (MAE) 0.09, Multiply-Accumulate (MAC) 0.696G. These highlight algorithm's remarkable quality capabilities, demonstrating considerable advantage over existing methods. Experimental demonstrate efficiency terms improvement compared

Язык: Английский

Процитировано

4
Graph Neural Networks for building and civil infrastructure operation and maintenance enhancement DOI Creative Commons
Sajith Wettewa, Lei Hou, Guomin Zhang

и другие.

Advanced Engineering Informatics, Год журнала: 2024, Номер 62, С. 102868 - 102868

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

4
A comprehensive review on application of drone, virtual reality and augmented reality with their application in dragline excavation monitoring in surface mines DOI Creative Commons
Piyush Singh,

Vmsr Murthy,

Dheeraj Kumar

и другие.

Geomatics Natural Hazards and Risk, Год журнала: 2024, Номер 15(1)

Опубликована: Март 25, 2024

Application of drone technology combined with LiDAR and Virtual Reality/Augmented Reality in surface mining operational optimization is growing on a high trajectory. The sector has demonstrated increased interest using drones for everyday tasks such as bench face mapping, dump planning, dragline disposal per balance diagram mining. One the key requirements application 3-dimensional mapping area space management safe efficient manner. can assist judicious near keeping mind available dump/pit slope stability requirements. This research article presents review how Technology based cloud computing architecture accelerate mine planning activity simple side cast method. Furthermore, it discusses current applications AI-driven 3D computer vision techniques automating data analytics point clouds extraction terrain parameters plan strategies by proper positioning large mine.

Язык: Английский

Процитировано

3