Accurate and science-based prediction of bearing performance degradation has been a principal concern and a critical challenge issue in the sector of Prognostics and Health Management in the industry ...as the solution to promote the engineering system's reliability, availability, and maintainability. Deep Learning (DL) methods are currently a hotspot in Prognostics and Health Management. Nonetheless, with complex operating conditions, existing forecast models still inevitably suffer from three fatal flaws. Firstly, dynamic modern industrial systems and harsh operational environments make the degradation data of the bearing-rotor system highly stochastic and nonlinear. Secondly, in practice, the bearing-rotor system failure process will be governed by complex failure dynamics and degradation mechanisms, resulting in degradation data characterized by intense temporal order. Thirdly, deep learning models use a multi-layer or cellular design containing numerous weights and biases, generating more computational overhead. To fill this research gap, a new deep interval health monitoring and prediction framework named Lightweight Probabilistic Spatiotemporal (LPST-Net) is proposed, which is integrated with the concepts of lightweight and interval prediction and is capable of state monitoring and Remaining Useful Life (RUL) prediction for bearing-rotor systems under complex operating conditions. Mainly inspired by the improvement of the Gate Recurrent Unit (GRU), this paper designs a time series variable prediction algorithm and derives a new formulation named Weight Diminish Recurrent Unit (WDRU). It dramatically reduces the training parameters of the proposed LPST-Net framework and improves the convergence speed while ensuring prediction accuracy. The degradation data are obtained under 2 actual and complex operating conditions of bearing-rotor system unbalance and high temperature. The three metrics show that the proposed LPST-Net framework can achieve high-precision point prediction, suitable prediction intervals, and reliable probabilistic prediction results. It is also verified that the proposed LPST-Net framework has superior performance and more practical application value compared with seven mainstream methods, such as (b)Squeeze-WDRU-GPR-Net.
The rapid advancement of intelligent theories and models, exemplified by deep learning, has achieved remarkable success across numerous fields. However, given that the complexity and variability of ...offshore wind turbine systems, particularly in the context of high-power variable-frequency control of insulated bearings in offshore wind turbines, the problem of fault identification has become a recognized technical challenge. Additionally, fully exploiting the temporal characteristics of insulated bearing fault data poses a key problem that demands urgent resolution. To address these gaps, this paper pioneers a novel Lightweight Temporal Feature-focused framework, named LTFM-Net, aimed at solving the difficult problem of identifying insulated bearing faults in offshore wind turbines in practical engineering applications. Specifically, this framework enables the intelligent identification of insulated bearing faults under harsh operating conditions such as alternating voltage and variable loads, marking a first in this field. Furthermore, an innovative strategy named Weighted Diminish Recurrent Unit (WDRU) was developed, along with the derivation of its backpropagation formula, which is innovatively applied to the feature extraction module of the LTFM-Net framework for the first time. Thus, an efficient method for acquiring fault data from insulated bearings in offshore wind turbines was proposed. Based on a unified dataset, the diagnostic performance of the LTFM-Net framework was evaluated and compared with seven advanced methods. The results demonstrate that the LTFM-Net achieves precise identification of insulated bearing faults, confirming its excellent generalization, robustness, and superiority. The introduction of t-SNE for visualizing the fault characteristics of insulated bearings uncovered by the LTFM-Net framework further enhances its reliability, accuracy, and credibility.
•This study pioneered innovation by developing a lightweight temporal feature focusing framework, aimed at addressing the challenge of identifying faults.•This research facilitates the first intelligent identification of such faults, offering fresh insights into fault detection for insulated bearings and failure modes of insulated bearings.•It innovatively develops the Weight Reducing Recursive Unit (WDRU) strategy combined with an updated back propagation formulation.
There are many computer applications in the world that use databases to store, process, and use data. That translates into many different ways of handling these databases. It is therefore difficult ...to choose a solution that meets the needs of the user. This article compares three C# solutions in terms of time efficiency: the Entity Framework Core application framework, pure SQL queries, and parameterized Prepared Statement queries. The results obtained in the course of the research has shown that the fastest solution is the use of non-parameterised SQL queries. The use of Entity Framework Core is the slowest of the three tested solutions.
This study helps athletes avoid and reduce the risk of injury in training more effectively by constructing a sports training injury risk assessment system to ensure they can train and compete safely ...and healthily. Based on the B/S model and .NET framework, this paper successfully develops a sports training injury risk assessment system and proposes a human exercise training detection program. The system integrates the measurement of physiological parameters such as blood oxygen saturation and blood pressure changes. It constructs a kinematic model to analyze the forces in training through inverse dynamics. In the system test, the response time was only 0.09ms/frame and the standby power consumption was as low as 11.43mW, demonstrating superior operational and energy efficiency. In addition, it was found that under specific conditions, such as after holding breath for 27.5s, the non-contact oximetry measurement showed a strong linear relationship with the physiological parameter detection module, which may predict the risk of falling when the peak motion acceleration SMV exceeds 3.23m
/s. Through this system, athletes can understand their body stress and physiological changes in the training process in real time, effectively avoiding potential training injuries, thus safeguarding their training safety and health.
•A parallel method to extract a high-resolution drainage network is proposed.•The parallel method improves the computational efficiency.•The extracted drainage networks are highly precise and ...high-resolution.
High-resolution Digital Elevation Models (DEMs) can be used to extract high-accuracy prerequisite drainage networks. A higher resolution represents a larger number of grids. With an increase in the number of grids, the flow direction determination will require substantial computer resources and computing time. Parallel computing is a feasible method with which to resolve this problem. In this paper, we proposed a parallel programming method within the .NET Framework with a C# Compiler in a Windows environment. The basin is divided into sub-basins, and subsequently the different sub-basins operate on multiple threads concurrently to calculate flow directions. The method was applied to calculate the flow direction of the Yellow River basin from 3 arc-second resolution SRTM DEM. Drainage networks were extracted and compared with HydroSHEDS river network to assess their accuracy. The results demonstrate that this method can calculate the flow direction from high-resolution DEMs efficiently and extract high-precision continuous drainage networks.
Upwelling phenomenon is one of the most important dynamic process in the ocean, which brings nutrients from the depths of the ocean into the surface layer, leading to an enhancement of the primary ...production and playing a considerable role in the coastal ecosystem. Deep learning (DL) based segmentation methods have been providing state-of-the-art performance in the last few years. These methods have been successfully applied to oceanic remote sensing image segmentation, classification, and detection tasks. In particular, U-Net, has become one of the most popular for these applications. This paper proposes UpwellRes-Net, a deep fully convolutional neural network architecture, for automatic upwelling detection and pixel-segmentation on sea surface temperature (SST) images. The proposed model is based on U-Net structure and residual learning, thus, combining the strengths of both approaches. The main objective of this study is to investigate the performance of deep learning in the extraction of upwelling area. Hence, UpwellRes-Net is trained and optimized on satellite-derived SST database provided by the Moderate Resolution Imaging Spectroradiometer (MODIS). Experiments on the southern Atlantic Moroccan coast show the superiority of the proposed model to a transfer learning based model developed for the same. Deep learning based upwelling detection system can be a cost effective, accurate and convenient way for objective analysis of upwelling phenomenon.
All kind of business web application vulnerability statistics are growing in recent and new forms of hacker's attacks, such as Watering Hole Attack are appearing. Well secured enterprise web ...applications are threatened by smaller vulnerable webs misused by hackers in targeted attacks. This paper discuss an effective way of creating better secured web application, applicable also in business sector by support them with semantic web platform security and certificate transparency. The main issue - improperly validated user inputs, the biggest security vulnerability often misused by hackers - could be effectively fixed by open-source HTML Purifier library which is usable in various programming languages. Moreover, we will discuss the latest internet security threats and the security of SSL/TSL practice for business web application with proactive on-line embedded prevention, detection and response system.
Interactive Approach to Learning of Sorting Algorithms Mavrevski, Radoslav; Traykov, Metodi; Trenchev, Iavn
International Journal of Online and Biomedical Engineering,
01/2019, Letnik:
15, Številka:
8
Journal Article
Recenzirano
Odprti dostop
Today we live in a society of high technologies, advanced information and com-munication systems in every field, including education. So, in modern education, teachers make full use of the ...possibilities of modern Information and Communi-cation Technologies (ICT). In this case, the attitude of the teachers towards the use of computers, to achieve the educational goals, is very important. To have the technologies sustained and significant effect, students in secondary and higher schools need to understand how to use them. The goal of this article is to help of students in secondary and higher schools to acquire enough practical program-ming skills and to learn the sorting algorithms, i.e. the article considers basic sort-ing algorithms. We developed and describe here software with name “Visual sorting” that shows visual, the execution of the basic sorting algorithms: Bubble sort; Selection sort; Insertion sort; Merge sort. Also, our software provides inter-active tracking of the performance (step by step) of different sorting algorithms.
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