Accuracy Issues Research Articles

Fault diagnosis method of rolling bearing under variable operating conditions based on MFCCNN

A rolling bearing fault diagnosis method based on multi-scale feature fusion and cross-level connection convolutional neural network (MFCCNN) is proposed to address the issues of low accuracy and low generalization performance caused by the complex and variable actual working conditions of rolling bearings. Firstly, a Markov transition field (MTF) is used to convert a one-dimensional signal into a two-dimensional image with temporal correlation. Next, an improved channel attention mechanism is integrated into the multi-scale network to assign different weights to each channel information and extract initial features. Then, a position attention mechanism is created and incorporated into the cross-level connection to enhance the feature expression ability for deep feature extraction. The MFCCNN faults diagnosis model is developed based on the above module. Finally, the MTF images is input into the proposed model for training, and the Softmax classifier is utilized to achieve fault classification. To verify the effectiveness and superiority of the MFCCNN method, the CWRU and MFS rolling bearing dataset are selected for experimental verification. The results demonstrates that the MFCCNN model has higher diagnostic accuracy, stronger noise immunity, and generalization performance in varying operating conditions compared to other fault diagnosis methods.

A Method for Detecting Edge Continuity in Art and Design Images Based on Visual Attention Mechanism

To address the issues of low accuracy and significant noise impact in edge continuity detection of art and design images, this paper proposes a visual attention-based edge continuity detection method for art and design images. Determine the edge position points of art and design images using the Laplace operator, and obtain the horizontal and vertical gradients of the image edges by calculating the first-order difference method to determine the amplitude of the image edge gradient; By using the maximum and minimum operations in binary morphology instead of intersection and union operations, the image grayscale is determined, and the HSI color space features are determined to complete the edge feature extraction of art and design images. Using the SUSAN operator to clarify the kernel similarity zone of the edges of art and design images, removing similar edge image pixels, using spatial domain denoising and frequency domain denoising to reduce the edge noise of art and design images, and achieving edge preprocessing of art and design images. Introducing visual attention mechanism to transform the pixel space of edge features in art and design images, performing threshold segmentation on the edges of art and design images, and continuously annotating the segmented edge pixels. Introducing loss function to improve the convergence speed of detection, constructing an art and design image edge continuity detection model based on visual attention mechanism, outputting detection results, and implementing research. The experimental results show that the proposed method has better continuity due to the successful avoidance of noise interference by introducing visual attention mechanisms and other operations. As the number of detected pixels increases, the detection deviation rate of the proposed method remains between 0.02% and 0.03%. The proposed method has a lower detection bias rate and can effectively improve the performance of edge continuity detection in art and design images.

LR-SLAM: Visual Inertial SLAM System with Redundant Line Feature Elimination

The present study focuses on the simultaneous localization and mapping (SLAM) system based on point and line features. Aiming to address the prevalent issue of repeated detection during line feature extraction in low-texture environments, a novel method for merging redundant line features is proposed. This method effectively mitigates the problem of increased initial pose estimation error that arises when the same line is erroneously detected as multiple lines in adjacent frames. Furthermore, recognizing the potential for the introduction of line features to prolong the marginalization process of the information matrix, optimization strategies are employed to accelerate this process. Additionally, to tackle the issue of insufficient point feature accuracy, subpixel technology is introduced to enhance the precision of point features, thereby further reducing errors. Experimental results on the European Robotics Challenge (EUROC) public dataset demonstrate that the proposed LR-SLAM system exhibits significant advantages over mainstream SLAM systems such as ORB-SLAM3, VINS-Mono, and PL-VIO in terms of accuracy, efficiency, and robustness.

Flexural capacity prediction of reinforced UHPC beams using an interpretable machine learning model

Addressing the issues of inconsistency and limited accuracy in the current prediction models for the flexural capacity of reinforced ultrahigh-performance concrete (UHPC) beams, this paper proposes a machine-learning prediction model for the flexural capacity of reinforced UHPC beams based on interpretable Extreme Gradient Boosting (XGBoost) model. Firstly, a database containing 221 sets of experimental UHPC beam data is established, and the database quality is evaluated by Pearson correlation coefficient and Mahalanobis distance. Then, ten-fold cross-validation and the whale optimization algorithm (WOA) are employed to find the optimal hyperparameter combination for XGBoost. Different indices are used to evaluate the prediction accuracy of the XGBoost model with four typical machine learning models and other computational methods. The Shapley additive explanations (SHAP) method is used to provide interpretations of the prediction results. The results indicate that the XGBoost model outperforms the four other machine learning models and related standards. Compared with the existing formulas, the proposed prediction model demonstrates higher accuracy in predicting the flexural capacity of UHPC beams. The SHAP method effectively explains the prediction results of the XGBoost model, providing insights into accurately assessing the factors influencing the flexural capacity of UHPC beams. SHAP analysis reveals that the most significant factors affecting the flexural capacity of UHPC beams are the cross-sectional height and reinforcement ratio, while the least significant factors are the aspect ratio of steel fibers and volume fraction of steel fibers.

Regulation of the physicochemical properties of nutrient solution in hydroponic system based on the CatBoost model

Too high or low nutrient solution concentrations adversely affect hydroponic vegetables’ growth and yield performance. In addition, the temperature of the solution and the dissolved oxygen (DO) content of the water are critical factors. A real-time regulation model for nutrient solutions based on the CatBoost model was developed to address the issues of low automation and accuracy of nutrient solution configuration and management. The pH, electric conductivity (EC) and DO of the nutrient solution, and the corresponding water temperature of 96 groups of hydroponic lettuce nutrient solutions (KNO3, MgSO4, Ca(NO3)2, KH2PO4, NH4H2PO4, and microfertilizer) were measured using a gradient combination test designed according to the formula of hydroponic lettuce nutrient solution. A prediction model of the nutrient solution index was constructed using a gradient-enhanced decision tree algorithm and the accuracies of the random forest, XGBoost, and CatBoost algorithms were compared. The results show that the root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) of the simulated and measured values of the EC prediction model established by the CatBoost model were 248.28 µS/cm, 203.19 µS/cm and 0.946, respectively. At the same time, it was found that the hydroponic variable fertilization control system, composed of four modules–data acquisition, data decision, field control, and manual control–could predict the EC and pH value changes of the nutrient solution and timely add compounds for control. The application of nutrients based on the developed system was good and the weight of the cream lettuce (Flandria RZ) grown using the system was more than 200 g, which meets the requirement for high-quality cream lettuce production. Therefore, the CatBoost model system can be used to improve the hydroponic production system of vegetables by managing nutrients effectively.

A quantitative model of technological pedagogical content knowledge (TPACK) based on graphical calculation

Technological Pedagogical Content Knowledge (TPACK) is a theoretical framework used to assess information teaching. However, its application and popularity in practice is limited due to usability and accuracy issues. This study presents a quantitative model called TPACK-Venn, which is based on the graphical calculation of a Venn diagram determined by survey results. The model was tested through a teaching experiment in which students are invited to create micro-courses. Self-evaluations and objective evaluations were conducted independently. A total of 112 micro-courses and their corresponding subjective self-evaluations, as well as 224 objective evaluations, were collected as the control group. After screening, the remaining 96 scores and TPACK modeling values of micro-courses were standardized. The standardized values have an inter-rater reliability of over 80%. It is important to note that the TPACK-Venn model adjusts the significance of the normal distribution of the survey results. As the normal distribution has widespread applicability in teaching guidance. This study presents a highly efficient and practical model for applying the TPACK theoretical framework.

Investigation and application of big data and AI-Powered provincial power grid accurate planning support platform

ABSTRACT This study presents an accurate planning assistance platform for big data and artificial intelligence-based provincial power grids. The platform greatly increases the efficiency of data integration and management by building a planning big database containing fundamental geographic information, thematic data, remote sensing image data, and planning business data. Technically speaking, this paper addresses the issues of accuracy and speed in the processing of remote sensing image data by using deep learning algorithms like convolutional neural networks (CNN) and full convolutional neural networks (FCN) to achieve automatic classification of high-resolution optical remote sensing images and residential area extraction. In the meantime, the intelligent routing approach, which is based on cost surface modelling and multi-criteria decision-making, optimises the routing of power lines by carefully taking into account a wide range of variables, including land use, accessibility for vehicles, and environmental impact. In addition to resolving the primary issues with present power grid planning, this study’s exact planning support platform for provincial power grids greatly enhances real-time, accurate, and scientific planning.

Accurate Range-Free Localization Using Cuckoo Search Optimization in IoT and Wireless Sensor Networks

Precise positioning of sensors is critical for the performance of various applications in the Internet of Things and wireless sensor networks. The efficiency of these networks heavily depends on the precision of sensor node locations. Among various localization approaches, DV-Hop is highly recommended for its simplicity and robustness. However, despite its popularity, DV-Hop suffers from significant accuracy issues, primarily due to its reliance on average hop size for distance estimation. This limitation often results in substantial localization errors, compromising the overall network effectiveness. To address this gap, we developed an enhanced DV-Hop approach that integrates the cuckoo search algorithm (CS). Our solution improves the accuracy of node localization by introducing a normalized average hop size calculation and leveraging the optimization capabilities of CS. This hybrid approach refines the distance estimation process, significantly reducing the errors inherent in traditional DV-Hop. Findings from simulations reveal that the developed approach surpasses the accuracy of both the original DV-Hop and multiple other current localization methods, providing a more precise and reliable localization method for IoT and WSN applications.

Thermal imaging-based identification of facial features in noisy environment

<p class="Abstract">Face identification is amongst the most efficacious and extensive applications in biometrics involving extraction and locating facial features. With identification being monotonous task attributable to reliance on parameters like varied cameras, fluctuating backgrounds, and exposure to the environment in which an individual is present. Thermal imaging is endeavoring to resolve the accuracy issue of apparent imaging, such as lighting and brightness intensity, among all biometric variables. This paper presents a study of thermal imaging and effective methods involved in the feature extraction process for facial features with thermal imaging under the influence of varied noise. A novel face dataset is created TID comprising 27 thermal images and its corresponding visual band image using Fluke 480 Ti Pro camera. The study analyses detection efficiency of six feature extraction techniques in visible and thermal bands in facial features identification. Also, the influence of noise in the thermal band within the region of interest using feature points FIN, FOUT has been estimated. Throughout TID dataset, ORB extraction technique has been able to identify strongest inlier features FIN to a maximum extent with detection around the nose, eyes, and mouth. Further, results indicate feature detection in thermal images being invariant to effect of noise for detecting facial features.</p>

Remotely geolocating static events observed by citizens using data collected by mobile devices

The increasing use of smartphones has led to a surge in crowdsourcing initiatives, where citizens easily collect and upload information using advanced sensors, leveraging the collective efforts of the crowd. However, these devices face accuracy issues that must be addressed before they can be used effectively in certain applications. While most research has focused on GNSS-based positioning errors, compass-based orientation errors have received far less attention. This paper presents a novel method for determining the geolocation of static events by combining contributions from multiple volunteers located around a target event. Instead of straight lines, the method applies funnel-shaped regions to represent the azimuths toward the target location and encompasses various phases to address errors stemming from measured azimuths. These compass-based orientation errors have attracted less attention compared to GNSS-based positioning errors. The approach mitigates the uncertainty in azimuth measurements, producing a set of regions that contain the target location with different levels of confidence. Its reliability was tested in three case studies with known target locations, using metrics such as area and compactness. The results were promising, indicating that the developed approach is particularly useful in applications that do not require a high degree of accuracy (common in many crowdsourcing projects), such as the geolocation of forest fire ignitions − once in the vicinity they can be easily spotted. Implementing this approach within a system that collates citizens’ contributions can furnish invaluable information to authorities, empowering prompt action during emergency scenarios.

Prediction of undrained shear strength based on KMR model

The accurate prediction of undrained shear strength is of significant importance in areas such as slope stability, earthquake resistance, and pile foundation design. Therefore, enhancing the accuracy of undrained shear strength prediction is crucial. The research results indicate that employing the KMR model can improve the computational accuracy by 4.4% to 18.9%. Furthermore, compared to conventional empirical formulas, the KMR model evidently processes data more rapidly and can predict relevant parameters more accurately. This method provides a new research idea to some extent for addressing the issue of low prediction accuracy of traditional machine learning models for geotechnical parameters.

Advancements in Indoor Precision Positioning: A Comprehensive Survey of UWB and Wi-Fi RTT Positioning Technologies

High-precision indoor positioning is essential for various applications, such as the Internet of Things, robotics, and smart manufacturing, requiring accuracy better than 1 m. Conventional indoor positioning methods, like Wi-Fi or Bluetooth fingerprinting, typically provide low accuracy within a range of several meters, while techniques such as laser or visual odometry often require fusion with absolute positioning methods. Ultra-wideband (UWB) and Wi-Fi Round-Trip Time (RTT) are emerging radio positioning technologies supported by industry leaders like Apple and Google, respectively, both capable of achieving high-precision indoor positioning. This paper offers a comprehensive survey of UWB and Wi-Fi positioning, beginning with an overview of UWB and Wi-Fi RTT ranging, followed by an explanation of the fundamental principles of UWB and Wi-Fi RTT-based geometric positioning. Additionally, it compares the strengths and limitations of UWB and Wi-Fi RTT technologies and reviews advanced studies that address practical challenges in UWB and Wi-Fi RTT positioning, such as accuracy, reliability, continuity, and base station coordinate calibration issues. These challenges are primarily addressed through a multi-sensor fusion approach that integrates relative and absolute positioning. Finally, this paper highlights future directions for the development of UWB- and Wi-Fi RTT-based indoor positioning technologies.

UTILIZING BLOCKCHAIN AND SMART CONTRACTS TO ENHANCE TRANSPARENCY AND EFFICIENCY IN HUMAN RESOURCES MANAGEMENT PROCESSES: A CONCEPTUAL APPROACH

Combining the principles of the blockchain and smart contracts with the human resources management framework can be viewed as a chance for further advancement of organisational transparency and performance. This approach looks at how the characteristics of blockchain require decentralisation and immutability to help solve common HRM issues, including data accuracy, recruitment and payroll issues. For instance, using smart contracts, some of the human resource operational processes can be transacted only when specific contractual conditions are met, hence leaving out any middlemen and reducing corruption cases. Additionally, using blockchain in the HRM will make it easy to provide verifiable employee details, efficient management of human capital, and accreditation of performance management systems. Ensuring the existence of an accurate and immutable employee register strengthens the shareholders' confidence; the improvement of business decisions based on the data provided by the HR department meets the increasing trend for HR data transfers. It is, therefore, believed that if organisations continue to implement digital solutions, blockchain has the potential to grow as the premier option for enhancing engagement and performance as an element of HRM. This study, therefore, seeks to establish a literature review on how blockchain and smart contracts could be applied in HRM and the consequential recommendation for future research and practice. Extending from the comprehensive literature review of the current state of HRM technologies, this study aims to develop a framework regarding how these technologies can be utilised to foster increased transparency, efficiency and equity in strategically managing people resources.

A Lightweight Deep-Learning Visual SLAM for Indoor Dynamic Environment Using Yolov10

Vision simultaneous localization and mapping (SLAM) technology has become a key research direction in the field of mobile robotics in recent years. However, the accuracy and stability of traditional vision SLAM technology greatly affected by the dynamic environment, and the mainstream dynamic feature point rejection method combining vision and semantic segmentation techniques is not applicable to edge-end devices with limited resources and high real-time requirements. This research suggests a visual SLAM algorithm based on the YOLOv10n lightweight target detection model and the GCNv2 feature point extraction model to accomplish real-time dynamic feature point rejection to address those problems. To compensate for the detection accuracy and stability issues of the YOLOv10n model while leveraging its real-time advantages, the algorithm also employs multi-target Kalman filtering with data association through the Hungarian algorithm, history window smoothing, and potential dynamic feature point recording methods to enhance robustness. The algorithm is validated on the TUM RGB-D Dataset, and the results demonstrate that the method can effectively reject the dynamic feature points in the dynamic environment, and it has a significant improvement in the accuracy and stability of the visual SLAM system.

JomRonda : Mobile Application for Security Guard Patrolling

Security patrolling is the process of routinely walking or driving through an area to maintain safety and follow the rules. Few studies discovered issues in security, accuracy and dependability of using non-computerized devices such as watchman clock. One of the issues is hard to monitor the tasks where the watchman tends to avoid their duties by attempting to use the duplicity and adjustment to the watchman’s clock and the key. Yet, the watchman forgets to make daily reports in a logbook at each checkpoint resulting in inaccuracy for authorities to attend and managing any serious emergency. Focussing on the issues, this application is designed with patrolling techniques called geofencing whereby the watchman's location is tracked and data about patrolling is recorded to a database once crosses a virtual boundary. Stakeholders get benefit from the application by keeping track of and reading daily reports digitally. Furthermore, it contains SOS features or international standards for signalling distress allowing users to alert one another in the event of an emergency in the area via a form and an in-app notification. This project follows a methodology known as the Modified Waterfall Model, which entails four stages: requirement gathering and analysis, design, implementation and testing. Future improvements are daily and monthly reporting to authorities can be generated and sent through email. Yet, the application needs temporary storage and the database will be updated as soon as the internet connection is restored.

Research on Intelligent Screening of International Communication Information and National and Regional Communication Strategies Based on Deep Learning Algorithm

In the context of globalization, the importance of international communication has become increasingly prominent. However, in the face of massive international communication information, how to effectively select and formulate communication strategies has become a big challenge. The traditional manual screening method is not only time-consuming and labor-intensive but also easy to be affected by subjective factors. This study aims to address the efficiency and accuracy issues in current international communication information screening and dissemination strategy formulation through the application of deep learning algorithms. It is of great theoretical and practical value to use deep learning algorithms to conduct intelligent screening of international communication information and research on national and regional communication strategies. To explore the application of deep learning algorithms in the intelligent screening of international communication information and the research of national and regional communication strategies, first, we will introduce the basic principles and methods of deep learning, as well as its applications in natural language processing (NLP), image recognition, and other fields. Then, we will explore how to use deep learning algorithms for intelligent screening of international dissemination information, including information classification, clustering, topic modeling, etc. Combined with practical cases, the application of deep learning algorithms in national and regional communication strategies will be studied, including communication content generation, communication channel selection, and communication effect evaluation.

The pressure control of independent load port electro-hydraulic load simulation system using fixed time integral sliding mode active disturbance rejection

To address the issues of motion disturbance and low loading accuracy in electro-hydraulic load simulation systems, and to effectively mitigate the impact of uncertainty on system performance, this paper proposes a novel independent load port electro-hydraulic load simulation system. A nonlinear variable structure state equation is derived to describe the pressure working states of the left and right chambers of the servo valve and hydraulic cylinder under loading conditions. In light of the presence of substantial motion disturbances, an integrated sliding mode automatic disturbance rejection composite controller is proposed with a focus on fixed-time convergence. To mitigate system chattering and enhance system loading performance and disturbance immunity, a fixed-time integral sliding mode controller was developed within the framework of active disturbance rejection control. Stability analysis was also carried out. Simulation research demonstrates that the pressure control accuracy of the composite controller is 99.88%, 99.86%, and 99.83% respectively when the system step command is given with disturbance frequencies of 1 Hz, 3 Hz, and 5 Hz. Similarly, under the same disturbance frequencies, the pressure control accuracy is 99.88%, 99.86%, and 99.84% respectively when the system sine command is given. The simulation results demonstrate that the independent load port electro-hydraulic load simulation system exhibits a notable energy-saving effect. Specifically, energy savings of around 45% and 48% were achieved compared to the original system. The research and exploration conducted have laid a strong groundwork for future in-depth studies on the independent load port electro-hydraulic load simulation system.

Use of Dependency‐Annotated Learner Corpora in Measuring Syntactic Complexity for Granularity, Accuracy, Consistency, and Transparency: Implications for Research and Teaching

AbstractSyntactic complexity has long been used to gauge language learners' performance, proficiency, and development, offering language teachers valid recommendations for syllabus design and materials development. Progress in syntactic complexity research foreshadows the imperative of measuring syntactic features at a high level of granularity, which remains, however, inadequately represented in the current operationalization of syntactic complexity. Beyond this misalignment, researchers also caution against the issues of consistency and accuracy in its measurement. We thus advocate for a methodological synergy by combining automatized dependency annotation and self‐built learner corpora, arguing that this can effectively address the aforementioned concerns and promote the initiative for transparency and openness in applied linguistics. Next, we concisely showcase how to manage this method step by step, which can be done easily with tools, and some practical considerations, so researchers and teachers can compile their do‐it‐yourself corpora. We conclude by discussing the implications of adopting this method for research and teaching. How this method can figure into potential areas of inquiry is identified with examples, including task‐based language teaching, writing assessment, and additional language syntactic acquisition. Pedagogically, we illustrate how this method can help language teachers identify gaps in learners' language production, raise their language awareness, and inform learner‐centered instruction.

Fault diagnosis of rolling bearings under variable operating conditions based on improved graph neural networks

Abstract To address the issues of low diagnostic accuracy, insufficient generalization, and poor robustness in traditional fault diagnosis methods across different equipment and varying operating conditions, this paper proposes an improved graph neural network-based fault diagnosis method for rolling bearings to enhance model performance under complex conditions. First, the optimized wavelet transform coefficient features are used as nodes, and by exploring the correlations between features, node adjacency relationships are constructed. The associations between fault modes and feature node graphs under different conditions are studied, and a fault feature graph sample set based on subgraph structures is built, providing data for the subsequent graph neural network learning. Then, a multi-head attention mechanism (MHGAT) and multi-scale feature adaptive perception pooling (MSF-ASAP) are integrated to construct a multi-head graph attention mechanism model based on multi-scale feature adaptive perception pooling (MSM-GAT). MHGAT enhances the model's ability to perceive global information by learning different features from multiple perspectives and dimensions, thus improving the model's generalization. MSF-ASAP adaptively selects and aggregates information at multiple scales, enabling the model to effectively extract key features under various operating conditions, resist noise interference, and adapt to local information changes, thus improving the model's robustness under varying conditions and noisy environments. Experimental results under multiple and continuously varying conditions demonstrate that the proposed method outperforms traditional methods in terms of diagnostic accuracy and robustness. Notably, it exhibits excellent generalization when identifying unknown conditions, achieving over 95% accuracy in recognizing new conditions and maintaining over 92.5% accuracy in noisy environments.

Bearing life prediction method based on novel health indicators and improved similarity curve matching

Abstract To tackle the issues of low accuracy in similarity curve matching and the challenges in measuring the similarity of degraded curve segments in rolling bearing life prediction methods, we propose a novel bearing remaining useful life (RUL) prediction method utilizing new health indicators and improved similarity curve matching techniques. Initially, time and frequency domain features are extracted from the bearing vibration signals. Subsequently, the multi-dimensional features are meticulously screened using monotonicity, Spearman's correlation, and uncertainty metrics to construct the health indicator (HI) and the health indicator library for the bearing. Subsequently, the support vector data description (SVDD) combined with the PID search algorithm is employed to ascertain the initial prediction time of the bearing and to construct the corresponding health indicator library. Following this, both global and local feature capture are conducted using dual dynamic time warping (DDTW) in conjunction with a sliding window to predict the remaining service life of rolling bearings.
The proposed method is validated on the PHM2012 and XJTU-SY datasets and benchmarked against the latest research. The results demonstrate that our method significantly enhances the prediction accuracy of the remaining useful life of bearings, underscoring the effectiveness and superiority of the proposed approach.and superiority of the proposed approach.