Latest Publications & Patents on Neural Networks
This is our latest selection of worldwide publications and patents in english on Neural Networks, between many scientific online journals, classified and focused on neural network, artificial neuron, epoch, neural architecture, machine learning, deep learning and support vector machine.
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New Method for Improving Tracking Accuracy of Aero-Engine On-Board Model Based on Separability Index and Reverse Searching
Published on 2025-02-22 by Hui Li, Yingqing Guo, Xinyu Ren @MDPI
Abstract: Throughout its service life, an aero-engine will experience a series of health conditions due to the inevitable performance degradation of its major components, and characteristics will deviate from their initial states. For improving tracking accuracy of the self-tunning on-board engine model on the engine output variables throughout the engine service life, a new method based on the separability index and reverse search algorithm was proposed in this paper. By using this method, a qualified tr[...]
Our summary: New method proposed for improving tracking accuracy of on-board aero-engine model throughout service life, based on separability index and reverse search algorithm. Higher accuracy maintained in engine life compared to sample memory factors method. Real-time monitoring of engine gas path parameters possible with training set center generated by proposed method.
tracking accuracy, aero-engine, separability index, reverse searching
Publication
Automated Anomaly Detection and Causal Analysis for Civil Aviation Using QAR Data
Published on 2025-02-20 by Xin Dang, Congcong Hua, Chuitian Rong @MDPI
Abstract: Flight Operations Quality Assurance (FOQA) is an internationally recognized solution to ensure the safety of civil aircraft flights based on Quick Access Recorder (QAR) data. The traditional approach to anomaly detection in civil aviation is to detect the over-limit values of monitoring parameters for each monitoring event based on the standards issued by civil aviation authorities. Usually, for each anomaly detection operation routine, this only works for one monitoring event. Furthermore, the [...]
Our summary: Automated anomaly detection and causal analysis method called MAD-XFP proposed for civil aviation using QAR data. Enhanced machine learning model with feature engineering and hyper-parameter optimization. Shapley additive interpretation method used for causal analysis of detected anomalies.
anomaly detection, causal analysis, civil aviation, QAR data
Publication
A Case Study of Wind Turbines
Published on 2025-02-20 by Xingfeng Chen, Yunli Zhang, Wu Xue, Shumin Liu, Jiaguo Li, Lei Meng, Jian Yang, Xiaofei Mi, Wei Wan, Qingyan Meng @MDPI
Abstract: Small Target Detection and Identification (TDI) methods for Remote Sensing (RS) images are mostly inherited from the deep learning models of the Computer Vision (CV) field. Compared with natural images, RS images not only have common features such as shape and texture but also contain unique quantitative information such as spectral features. Therefore, RS TDI in the CV field, which does not use Quantitative Remote Sensing (QRS) information, has the potential to be explored. With the rapid devel[...]
Our summary: Small target detection and identification methods for wind turbines in remote sensing images were studied using deep learning models from the computer vision field. Integration of quantitative remote sensing information with deep learning models showed significant improvement in accuracy for wind turbine detection.
Wind Turbines, Remote Sensing, Deep Learning, Quantitative Remote Sensing
Publication
A Deep Semantic Segmentation Approach to Accurately Detect Seam Gap in Fixtured Workpiece Laser Welding
Published on 2025-02-20 by Fotios Panagiotis Basamakis, Dimosthenis Dimosthenopoulos, Angelos Christos Bavelos, George Michalos, Sotiris Makris @MDPI
Abstract: The recent technological advancements in today’s manufacturing industry have extended the quality control operations for welding processes. However, the realm of pre-welding inspection, which significantly influences the quality of the final products, remains relatively uncharted. To this end, this study proposes an innovative vision system designed to extract the seam gap width and centre between two components before welding and make informed decisions regarding the initiation of[...]
Our summary: Innovative vision system for accurately detecting seam gap width and centre in fixtured workpiece laser welding, incorporating deep learning semantic segmentation network and conventional computer vision techniques, with precision of 0.1 mm.
semantic segmentation, vision system, deep learning, laser welding
Publication
Drunk Driver Detection Using Multiple Non-Invasive Biosignals
Published on 2025-02-20 by Sang Hyuk Kim, Hyo Won Son, Tae Mu Lee, Hyun Jae Baek @MDPI
Abstract: This study aims to decrease the number of drunk drivers, a significant social problem. Traditional methods to measure alcohol intake include blood alcohol concentration (BAC) and breath alcohol concentration (BrAC) tests. While BAC testing requires blood samples and is impractical, BrAC testing is commonly used in drunk driving enforcement. In this study, the multiple biological signals of electrocardiogram (ECG), photoplethysmogram (PPG), and electrodermal activity (EDA) were collected non-inva[...]
Our summary: Decrease drunk driving incidents by detecting intoxication levels using non-invasive biosignals collected from ECG, PPG, and EDA. Data analyzed with machine learning algorithms achieved 88% accuracy in classifying intoxication levels within 30-second intervals.
Drunk Driver Detection, Non-Invasive Biosignals, Machine Learning Algorithms, Heart Rate Variability
Publication
Deep Reinforcement Learning for a Self-Driving Vehicle Operating Solely on Visual Information
Published on 2025-02-20 by Robertas Audinys, ygimantas likas, Justas Radkevi?ius, Mantas utas, Armantas Ostreika @MDPI
Abstract: This study investigates the application of Vision Transformers (ViTs) in deep reinforcement learning (DRL) for autonomous driving systems that rely solely on visual input. While convolutional neural networks (CNNs) are widely used for visual processing, they have limitations in capturing global patterns and handling complex driving scenarios. To address these challenges, we developed a ViT-based DRL model and evaluated its performance through extensive training in the MetaDrive simulator and tes[...]
Our summary: Application of Vision Transformers in deep reinforcement learning for self-driving vehicles operating solely on visual information. Developed ViT-based DRL model outperformed CNN baselines in MetaDrive simulator. Model exhibited superior adaptability in high-fidelity AirSim simulator.
Deep Reinforcement Learning, Vision Transformers, Autonomous Driving Systems, Visual Input
Publication
Fluid flow simulation
Patent published on the 2025-02-19 in EP under Ref EP4510034 by ROLLS ROYCE PLC [GB] (Loh Jessica Sher En [gb], Arafat Naheed Anjum [gb], Kong Wai Kin Adams [gb], Chan Wai Lee [gb], Conduit Bryce D [gb], Lim Wei Xan [gb], Oo Thant Zin [gb])
Abstract: A method of using a computer implemented neural network for a simulation of aerodynamic performance of a technical object having a geometry, the method comprising:Training the neural network using a plurality of sets of encodings of pre-computed computational fluid dynamics, CFD, outputs, wherein the training is generated using inputs comprising:a geometry of at least one training technical object;spatial locations of input nodes of the neural network as node attributes;a relationship between th[...]
Our summary: Method using neural network for aerodynamic simulation of technical objects, training with pre-computed CFD outputs, generating predicted aerodynamic performance.
Fluid flow simulation, neural network, aerodynamic performance, computational fluid dynamics
Patent
Method, system and computer readable medium for assisting or enhancing 5g radio planning
Patent published on the 2025-02-19 in EP under Ref EP4510672 by UNIV CATALUNYA POLITECNICA [ES] (Almasan Paul [es], SuÁrez-varela JosÉ [es], Lutu Andra [es], Cabellos-aparicio Albert [es], Barlet-ros Pere [es])
Abstract: A method, system, and computer programs for assisting or enhancing 5G radio planning are proposed. The method comprises obtaining information about a new deployment of 5G-generation radio cells in a given area; obtaining information about previous-generation radio cells already deployed in the given area, and computing a plurality of key performance indicators, KPI, of the previous-generation radio cells by processing the obtained information, providing a plurality of KPI previous-generation rec[...]
Our summary: Method, system, and computer programs for assisting or enhancing 5G radio planning by obtaining information about new and previous-generation radio cells, computing key performance indicators, generating representations using deep learning, and producing KPIs with a neural network model.
5G, radio planning, deep learning, neural network
Patent
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