Questa è la nostra ultima selezione di prodotti in tutto il mondo pubblicazioni e brevetti in inglese on Signal Processing, tra le tante riviste scientifiche online, classificate e focalizzate sull'elaborazione dei segnali, la quantizzazione, il denoising, i segnali digitali, i segnali analogici e la convoluzione.
Predictive Modelling of Alkali-Slag Cemented Tailings Backfill Using a Novel Machine Learning Approach
Published on 2025-03-11 by Haotian Pang, Wenyue Qi, Hongqi Song, Haowei Pang, Xiaotian Liu, Junzhi Chen, Zhiwei Chen @MDPI
Abstract: This study utilizes machine learning (ML) techniques to predict the performance of slag-based cemented tailings backfill (CTB) activated by soda residue (SR) and calcium carbide slag (CS). An experimental database consisting of 240 test results is utilized to thoroughly evaluate the accuracy of seven ML techniques in predicting the properties of filling materials. These techniques include support vector machine (SVM), random forest (RF), backpropagation (BP), genetic algorithm optimization of BP[...]
Our summary: Utilizing ML techniques to predict CTB performance, evaluating accuracy of 7 ML techniques, developing dynamic growth model
Predictive Modelling, Machine Learning, Alkali-Slag Cemented Tailings, Backfill
Publication
Adaptive Disconnector States Diagnosis Method Based on Adjusted Relative Position Matrix and Convolutional Neural Networks
Published on 2025-03-10 by Peifeng Yan, Chenzhang Chang, Dong Hua, Haomin Huang, Suisheng Liu, Peiyi Cui @MDPI
Abstract: Due to long-term outdoor working, High-Voltage Disconnectors (HVDs) are prone to potential faults. Currently, most studies on HVD state diagnosis methods have tested only one type of HVD, and the generalization capability of these methods for other HVDs has not been verified. In this paper, we propose an HVD state diagnosis method featuring adaptive recognition capabilities based on Fault Difference Signals, Adjusted Relative Position Matrix and Convolutional Neural Networks (FDS-ARPM-CNN). Firs[...]
Our summary: Adaptive Disconnector States Diagnosis Method utilizes FDS, ARPM, and CNN for accurate HVD fault diagnosis and classification, showcasing strong generalization capabilities.
Convolutional Neural Networks, Fault Difference Signals, Relative Position Matrix, Adaptive Recognition
Publication
A Lightweight Defect Detection Network Aimed at Elevator Guide Rail Pressure Plates
Published on 2025-03-10 by Ruizhen Gao, Meng Chen, Yue Pan, Jiaxin Zhang, Haipeng Zhang, Ziyue Zhao @MDPI
Abstract: In elevator systems, pressure plates secure guide rails and limit displacement, but defects compromise their performance under stress. Current detection algorithms face challenges in achieving high localization accuracy and computational efficiency when detecting small defects in guide rail pressure plates. To overcome these limitations, this paper proposes a lightweight defect detection network (LGR-Net) for guide rail pressure plates based on the YOLOv8n algorithm. To solve the problem of exce[...]
Our summary: Lightweight defect detection network for elevator guide rail pressure plates based on YOLOv8n algorithm, achieves high localization accuracy and computational efficiency, outperforms other YOLO-series models.
Defect Detection, Lightweight Network, Elevator Guide Rail, YOLOv8n
Publication
Improved Variational Mode Decomposition in Pipeline Leakage Detection at the Oil Gas Chemical Terminals Based on Distributed Optical Fiber Acoustic Sensing System
Published on 2025-03-10 by Hongxuan Xu, Jiancun Zuo, Teng Wang @MDPI
Abstract: Leakage in oil and gas transportation pipelines is a critical issue that often leads to severe hazardous accidents at oil and gas chemical terminals, resulting in devastating consequences such as ocean environmental pollution, significant property damage, and personal injuries. To mitigate these risks, timely detection and precise localization of pipeline leaks are of paramount importance. This paper employs a distributed fiber optic sensing system to collect pipeline leakage signals and process[...]
Our summary: Improved VMD algorithm with automatic parameter optimization and fuzzy dispersion entropy for enhanced denoising performance. Novel threshold setting technique reduces false alarm rate in gas pipeline leakage detection, improving accuracy and reliability. Valuable tool for enhancing safety and efficiency of oil and gas transportation systems.
Variational Mode Decomposition, Pipeline Leakage Detection, Distributed Optical Fiber Acoustic Sensing System, Particle Swarm Optimization
Publication
A Multi-Scale Feature Fusion Model for Lost Circulation Monitoring Using Wavelet Transform and TimeGAN
Published on 2025-03-10 by Yuan Sun, Jiangtao Wang, Ziyue Zhang, Fei Fan, Zhaopeng Zhu @MDPI
Abstract: Lost circulation is a major challenge in the drilling process, which seriously restricts the safety and efficiency of drilling. The traditional monitoring model is hindered by the presence of noise and the complexity of temporal fluctuations in lost circulation data, resulting in a suboptimal performance with regard to accuracy and generalization ability, and it is not easy to adapt to the needs of different working conditions. To address these limitations, this study proposes a multi-scale feat[...]
Our summary: A model is proposed to monitor lost circulation in drilling process by fusing features at multiple scales using wavelet transform and TimeGAN, improving accuracy and generalization ability.
wavelet transform, TimeGAN, multi-scale feature fusion, drilling process
Publication
An Improved Object Detection Algorithm for Key Components of Aircraft and Staff in Airport Scenes Based on YOLOv5
Published on 2025-03-10 by Zhige He, Yuanqing He, Yang Lv @MDPI
Abstract: With the rapid development and increasing demands of civil aviation, the accurate detection of key aircraft components and staff on airport aprons is of great significance for ensuring the safety of flights and improving the operational efficiency of airports. However, the existing detection models for airport aprons are relatively scarce, and their accuracy is insufficient. Based on YOLOv5, we propose an improved object detection algorithm, called DT-YOLO, to address these issues. We first buil[...]
Our summary: Improved object detection algorithm for key components of aircraft and staff in airport scenes based on YOLOv5, utilizing a novel D-CTR module and deformable convolutions in CNNs to enhance feature representation and detection accuracy, with significant improvements in mean average precision and other metrics on a self-built AAD-dataset.
Object Detection, YOLOv5, Airport Aprons, DT-YOLO
Publication
Method and electronic device for generating content using a diffusion model
Patent published on the 2025-03-06 in WO under Ref WO2025048358 by SAMSUNG ELECTRONICS CO LTD [KR] (Keserwani Prateek [in], Moharana Sukumar [in], Senapati Alladi Ashok Kumar [in], Ummanath Sajith [in], Ali Azhan [in], Mala Venkappa [in])
Abstract: A method for generating content using a diffusion model of an electronic device, may include: obtaining latent vectors of an input content; inputting the latent vectors into a first lightweight adapter configured for the first application type from among a plurality of lightweight adapters configured individually for application types of the plurality of applications; transforming the latent vectors of the input content into a plurality of intermediate latent vectors using the first lightweight [...]
Our summary: Method for generating content using a diffusion model of an electronic device, including obtaining latent vectors, transforming vectors with lightweight adapter, denoising operation, and generating final content.
diffusion model, electronic device, content generation, latent vectors
Patent
isogeometric analysis with versatile adaptivity
Patent published on the 2025-03-06 in WO under Ref WO2025049273 by NORTHWESTERN UNIV [US] (Liu Wing [us], Mojumder Satyajit [us], Li Hengyang [us], Li Yangfan [us], Park Chanwook [us], Saha Sourav [us], Guo Jiachen [us])
Abstract: A method of Convolution-Hierarchical Deep-learning Neural Network isogeometric Analysis (C-IGA) of a geometric shape of a subject matter comprises performing a first mapping process between a physical domain of a subject matter and a parametric domain of the subject matter; and performing a second mapping process between the parametric domain of the subject matter and a parent domain of the subject matter; wherein the first mapping process comprises constructing an C-IGA interpolation in the par[...]
Our summary: Method of isogeometric analysis with versatile adaptivity using Convolution-Hierarchical Deep-learning Neural Network for mapping physical, parametric, and parent domains of a subject matter based on CAD data.
isogeometric analysis, adaptivity, Convolution-Hierarchical Deep-learning Neural Network, C-IGA
Patent
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