Wavelet transform for rotary machine fault diagnosis:10 years revisited

In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning (DL), are widely used for various computer vision tasks such as image classification, object detection, and image segmentation. There are numerous types of CNNs designed to meet specific needs and requirements, including 1D, 2D, and 3D CNNs, as well as dilated, grouped, attention, depthwise convolutions, and NAS, among others. Each type of CNN has its unique structure and characteristics, making it suitable for specific tasks. It's crucial to gain a thorough understanding and perform a comparative analysis of these different CNN types to understand their strengths and weaknesses. Furthermore, studying the performance, limitations, and practical applications of each type of CNN can aid in the development of new and improved architectures in the future. We also dive into the platforms and frameworks that researchers utilize for their research or development from various perspectives. Additionally, we explore the main research fields of CNN like 6D vision, generative models, and meta-learning. This survey paper provides a comprehensive examination and comparison of various CNN architectures, highlighting their architectural differences and emphasizing their respective advantages, disadvantages, applications, challenges, and future trends.

A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends

Coupled retrieval of heavy metal nickel concentration in agricultural soil from spaceborne hyperspectral imagery.

EA-CNN: A smart indoor 3D positioning scheme based on Wi-Fi fingerprinting and deep learning

Frequency-based leak signature investigation using acoustic sensors in urban water distribution networks

As a general and rigid mathematical tool, wavelet theory has found many applications and is constantly developing. This article reviews the development history of wavelet theory, from the construction method to the discussion of wavelet properties. Then it focuses on the design and expansion of wavelet transform. The main models and algorithms of wavelet transform are discussed. The construction of rational wavelet transform (RWT) is provided by examples emphasizing the advantages of RWT over traditional wavelet transform through a review of the literature. The combination of wavelet theory and neural networks is one of the key points of the review. The review covers the evolution of Wavelet Neural Network (WNN), the system architecture and algorithm implementation. The review of the literature indicates the advantages and a clear trend of fast development in WNN that can be combined with existing neural network algorithms. This article also introduces the categories of wavelet-based applications. The advantages of wavelet analysis are summarized in terms of application scenarios with a comparison of results. Through the review, new research challenges and gaps have been clarified, which will serve as a guide for potential wavelet-based applications and new system designs.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09785993.pdf

A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities

A hierarchical auxiliary deep neural network architecture for large-scale indoor localization based on Wi-Fi fingerprinting

The purpose of this paper is to apply the acoustic features, Mel Frequency Cepstral Coefficient (MFCC) and Gammatone Frequency Cepstral Coefficient (GFCC), to underwater signal classification. Underwater acoustic signals are vibration signals, and their characteristics are similar to speech signals. The auditory feature extraction method in speech recognition can also be applied to the underwater environment. For underwater communication, we simulate two models designed for underwater target detection and localization. One is the deterministic model, which is considered as basic model; the other is to combine the deterministic model and statistic model, which is called combined model. The geometric channel model facilitates the generation of the database for different geometric settings. The database is generated by adjusting the parameters of the underwater environment. The classifier adopts a convolutional neural network (CNN). The input to the CNN is the feature maps after feature extraction. We choose continuous wavelet transform (CWT) and short-time Fourier transform (STFT) for comparison. Experiments show the effectiveness of the system architecture and superiority of the proposed algorithm in underwater signal classification and target localization.

Underwater Target Detection and Localization with Feature Map and CNN-Based Classification

The large-scale deployment of sustainable energy sources has become a mandatory goal to reduce pollution from electricity production. As photovoltaic (PV) plants replace conventional synchronous generators (SGs), their significant inherent rotational inertia characteristics are reduced. The high penetration of PV results in reduced system inertia, leading to system frequency instability. Virtual inertial control (VIC) technology has attracted increasing interest because of its ability to mimic inertia. Adoption of the energy storage system (ESS) is hindered by the high cost, although it can be used to provide virtual inertia. The determined forecast gives PVs the ability to reserve power before shading and compensate the power when a system power drop occurs, which can increase system inertia. Nevertheless, it has forecast errors and energy waste in a stable state. To improve the stability of the microgrid and improve the ESS efficiency, this study proposes an adaptive forecasting-based (AFB) VIC method using probabilistic forecasts. The adaptive power reserve and virtual inertia control are proposed to reduce energy waste and increase system inertia. The simulation results reveal that the proposed method has adaptive system inertia to reduce the reserved power, required ESS power capacity, and battery aging.

/pdf/frequency-regulation-in-adaptive-virtual-inertia-and-power-u061px8f.pdf

Frequency regulation in adaptive virtual inertia and power reserve control with high PV penetration by probabilistic forecasting

Visual-based target tracking is one of the critical methodologies for the control problem of multi-robot systems. In dynamic mobile environments, it is common to lose the tracking targets due to partial visual occlusion. Technologies based on deep learning (DL) provide a natural solution to this problem. DL-based methods require less human intervention and fine-tuning. The framework has flexibility to be retrained with customized data sets. It can handle massive amounts of available video data in the target tracking system. This paper discusses the challenges of robot tracking under partial occlusion and compares the system performance of recent DL models used for tracking, namely you-only-look-once (YOLO-v5), Faster region proposal network (R-CNN) and single shot multibox detector (SSD). A series of experiments are committed to helping solve specific industrial problems. Four data sets are that cover various occlusion statuses are generated. Performance metrics of F1 score, precision, recall, and training time are analyzed under different application scenarios and parameter settings. Based on the metrics mentioned above, a comparative metric P is devised to further compare the overall performance of the three DL models. The SSD model obtained the highest P score, which was 13.34 times that of the Faster RCNN model and was 3.39 times that of the YOLOv5 model with the designed testing data set 1. The SSD model obtained the highest P scores, which was 11.77 times that of the Faster RCNN model and was 2.43 times that of the YOLOv5 model with the designed testing data set 2. The analysis reveals different characteristics of the three DL models. Recommendations are made to help future researchers to select the most suitable DL model and apply it properly in a system design.

/pdf/mobile-robot-tracking-with-deep-learning-models-under-the-3o1rx1ih.pdf

Mobile Robot Tracking with Deep Learning Models under the Specific Environments

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