This is our latest selection of worldwide publications and patents in english on Signal Processing, between many scientific online journals, classified and focused on signal processing, quantization, denoising, digital signal, analog signal and convolution.
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
A Deep Learning Model Based on Bidirectional Temporal Convolutional Network (Bi-TCN) for Predicting Employee Attrition
Published on 2025-03-10 by Farhad Mortezapour Shiri, Shingo Yamaguchi, Mohd Anuaruddin Bin Ahmadon @MDPI
Abstract: Employee attrition, which causes a significant loss for an organization, is the term used to describe the natural decline in the number of employees in an organization as a result of numerous unavoidable events. If a company can predict the likelihood of an employee leaving, it can take proactive steps to address the issue. In this study, we introduce a deep learning framework based on a Bidirectional Temporal Convolutional Network (Bi-TCN) to predict employee attrition. We conduct extensive exp[...]
Our summary: Deep learning model based on Bi-TCN predicts employee attrition with high accuracy rates, outperforming classical and state-of-the-art approaches. GAN-based data augmentation technique improves model accuracy on IBM dataset. SHAP method identifies key features influencing attrition.
Deep Learning, Bidirectional Temporal Convolutional Network, Employee Attrition, Predictive Modeling
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
Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
Published on 2025-03-10 by Pathomthat Chiradeja, Yijuan Liang, Chaiyan Jettanasen @MDPI
Abstract: Sign language plays a crucial role in bridging communication barriers within the Deaf community. Recognizing sign language sentences remains a significant challenge due to their complex structure, variations in signing styles, and temporal dynamics. This study introduces an innovative sign language sentence recognition (SLSR) approach using Hybrid Graph Embedding and Adaptive Convolutional Networks (HGE-ACN) specifically developed for single-handed wearable glove devices. The system relies on se[...]
Our summary: This study introduces a novel approach for recognizing sign language sentences using Hybrid Graph Embedding and Adaptive Convolutional Networks (HGE-ACN) on single-handed wearable glove devices. The system captures dynamic spatial-temporal relationships in motion and curvature data, extracting robust features to handle variations in signing speed and individual signer styles. Extensive experiments show superior accuracy and computational efficiency, with promising applications in assistive tools and educational technologies.
Sign Language, Sentence Recognition, Hybrid Graph Embedding, Adaptive Convolutional Networks
Publication
Multi-Granularity Temporal Knowledge Graph Question Answering Based on Data Augmentation and Convolutional Networks
Published on 2025-03-10 by Yizhi Lu, Lei Su, Liping Wu, Di Jiang @MDPI
Abstract: The multi-granularity temporal knowledge graph question-answering model consists of two core tasks: question information extraction and knowledge graph embedding representation. Existing studies typically compute the relevance score between the question and the associated temporal knowledge graph to identify the answer. However, current multi-granularity temporal knowledge graph datasets are relatively scarce, and most research has not fully exploited the potential of these limited datasets, res[...]
Our summary: The model addresses limitations in capturing complex temporal relationships and semantic relationships within questions by proposing a multi-granularity temporal knowledge graph question-answering model based on data augmentation and convolutional networks. This approach outperforms baseline models on publicly available datasets.
Knowledge Graph, Question Answering, Data Augmentation, Convolutional Networks
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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