Accurate Data Processing Research Articles

Digital signal detection and recognition in the communication field combining DAEN and CNN

In communication, the detection and recognition of digital signals have always faced problems such as low adaptability and high misclassification rate. To address these issues, this study innovatively fused the deep auto-encoder network with convolutional neural networks to construct a novel digital signal detection and recognition model. Firstly, this study utilized the powerful feature extraction capabilities of the deep auto-encoder network to extract key feature information from massive amounts of data. Then these features were combined with convolutional neural networks to construct a detection and recognition model. These results confirmed that the constructed digital signal detection and recognition model had values of 93.75% and 94.18% in data detection recognition rate and average classification accuracy, respectively. Meanwhile, this model also performed well in terms of data processing accuracy and recall. In the comparison of data processing, the accuracy and recall rates were 93.59% and 94.67%, respectively, and the performance of data detection and recognition was better than that of the comparison methods. This indicates that the constructed digital signal detection and recognition model can significantly improve the reliability and robustness of signal detection and recognition. This paper brings new breakthroughs to the development of digital signal detection and recognition technology in communication.

Seismometer Orientation Measurements of Broadband Seismic Stations in the China Digital Seismograph Network

ABSTRACT The China Digital Seismograph Network, one of the largest national seismic networks, has been operating for over four decades which provides valuable seismic data for various scientific studies. Our investigation gathered a comprehensive dataset comprising 5,456,816 three-component waveforms from 3187 seismic events that took place over nine years (2014–2022). We assessed sensor orientations at 1056 broadband stations using the P-wave polarization method. Together with our calculation results, operation and maintenance log of regional networks, on-site checking, and manual inspection, we identified and addressed issues related to temporal changes of orientation, polarity reversal, and channel mislabeling. We found that ∼65.8% of seismometers (694) were well aligned with the absolute misorientation angle ≤3°, 20.8% of seismometers (220) were fairly well aligned with the absolute misorientation angle lying between 3° and 10°, 3.6% (38) of seismometers were misaligned exceeding 10°, and 9.8% of seismometers (104) showed a temporal variation in alignment. The fairly high consistency between our numerical results and gyrocompass measurements confirms the reliability of our investigation. We further compared the results of P-wave receiver functions analysis before and after sensor orientation correction. The findings indicate that sensor misorientation angles may lead to inaccurate and unstable seismological results. Therefore, conducting a systematic assessment, diagnosis, and correction for sensor orientation would be beneficial for advancing seismological analysis by promoting consistency, efficiency, accuracy, collaboration, reproducibility, and adaptability in data processing and interpretation.

Enhanced Input-Doubling Method Leveraging Response Surface Linearization to Improve Classification Accuracy in Small Medical Data Processing

Currently, the tasks of intelligent data analysis in medicine are becoming increasingly common. Existing artificial intelligence tools provide high effectiveness in solving these tasks when analyzing sufficiently large datasets. However, when there is very little training data available, current machine learning methods do not ensure adequate classification accuracy or may even produce inadequate results. This paper presents an enhanced input-doubling method for classification tasks in the case of limited data analysis, achieved via expanding the number of independent attributes in the augmented dataset with probabilities of belonging to each class of the task. The authors have developed an algorithmic implementation of the improved method using two Naïve Bayes classifiers. The method was modeled on a small dataset for cardiovascular risk assessment. The authors explored two options for the combined use of Naïve Bayes classifiers at both stages of the method. It was found that using different methods at both stages potentially enhances the accuracy of the classification task. The results of the improved method were compared with a range of existing methods used for solving the task. It was demonstrated that the improved input-doubling method achieved the highest classification accuracy based on various performance indicators.

Organic Polymer-Based Photodiodes for Optoelectronic Reservoir Computing with Time-Based Coding.

The integration of optoelectronic devices with reservoir computing offers a novel and effective approach to in-sensor computing. This work presents a hybrid digital-physical solution that leverages the high-performance poly[(bithiophene)-alternate-(2,5-di(2-octyldodecyl)-3,6-di(thienyl)-pyrrolyl pyrrolidone)] (DPPT-TT) organic polymer-based photodiodes for the hardware implementation of reservoir computing system. The photodiodes demonstrate nonlinear photoelectric responses, fading memory, and cyclical stability, in relation to the temporal information on light stimuli. These attributes enable effective mapping, historical context sensitivity, and consistent performance, with time-encoded inputs, which are particularly essential for accurate and continuous processing of time series data. The optoelectronic reservoir computing system with pulse width modulation (PWM) coding demonstrates impressive performance in the prediction of chaotic sequences, achieving a normalized root-mean-square error as low as 0.095 with optimized parameters. The DPPT-TT-based photodiodes and time-based coding offer a hardware-efficient solution for reservoir computing, significantly advancing Internet of Things applications.

Seamless Weather Data Integration in Trajectory-Based Operations Utilizing Geospatial Information

In this study, a 4D trajectory weather (4DT-Wx) prototype system was developed and evaluated for effective weather information integration in trajectory-based operation (TBO) environments. The system has two key distinguishing features: multi-model-based trajectory services and buffer zone information provision. We constructed a distributed processing system using Apache Spark, enabling the efficient processing of large-scale weather data. The performance evaluation demonstrated excellent scalability and efficiency in processing large-scale data. An analysis of the buffer configurations highlighted that buffer zone information is valuable in decision-making processes and has the potential to enhance the system performance. The system’s practical applicability is presented through visualizations of the extracted weather information. This system is expected to enhance aviation safety and operational efficiency, providing a foundation for addressing increasingly complex weather conditions and flight scenarios in the future. The approach presented in this study marks a significant step toward effective TBO implementation and the advancement of future air traffic management. The evaluation of the 4DT-Wx system analyzed the accuracy of weather data processing and the performance of distributed processing, finding that the temperature (T) estimation had the highest accuracy, and that the parallel processing using Apache Spark was most effectively modeled by Ahmed et al.’s model. The findings suggest the potential for further optimization in integrating various weather models and developing algorithms to enhance their utilization.

Pipeline impact force observation-based intelligent measurement method for liquid flow

This paper proposes an innovative measurement method that uses the impact force generated when the liquid flows through the pipe as an observation indicator, and successfully establishes a non-linear mapping relationship between the impact force sequence and the weight of the flowing liquid by training and learning the collected impact force sequence through the CLCD (CNN-LSTM-CNN-Double) network architecture. In response to the challenges such as the prevalent interference factors and inconsistent flow time lengths in the collected data, this paper introduces a new weight ratio algorithm, WRP (Weight-Ratio-Process), which effectively improves the robustness and accuracy of data processing. The experimental results show that the effective detection rate of the method reaches 90 % when the weighing error is set to ±5g on the constructed fluid impact force test platform. When the error range is relaxed to ±15g, the effective detection rate is increased to 98 %. This achievement demonstrates the broad application potential and practical value of the method in the field of fluid transport measurement.

Multi-combination fault data augmentation method of aero-engine gas path based on Extraction TimeGAN

Aero-engine is the core power component of aircraft, and the accurate processing of engine monitoring data is the key to ensure its reliable operation and prevent faults. In the use and maintenance of aero-engines, the multi-combination fault data acquisition of gas path system is expensive and unavailable, thus intelligent multi-combination fault diagnosis under small samples scenarios poses a significant challenge. Aero-engine gas path combination fault data exhibit strong coupling, making it difficult for data augmentation methods to produce high-quality data. To solve the above problem, a novel aero-engine gas path combination fault data augmentation method based on Extraction TimeGAN is proposed. First, original data of multi-combination faults were obtained from Gasturb. To increase the complexity of generating data, Gaussian white noise of Signal-to-Noise Ratio (SNR) 20 was injected into the original data. Then, new samples with similar characteristics were generated from the original data according to different fault categories by TimeGAN, and the original data and generated data were both mapped to the latent space by Principal Component Analysis (PCA). Finally, the optimized extraction mechanism of generated data was established, and the high-density original data was identified and divided in the latent space through Kernel Density Estimation (KDE), the high-density regional hypersphere was constructed by Support Vector Data Description (SVDD), and the generated data located inside the hypersphere is extracted as the optimized generated data. In the multi-combination fault diagnosis experiment of aero-engine gas path, Area Under the Curve (AUC) of Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) reaches 99.74%, 99.82% and 99.85%. Under high noise conditions, the AUC of Extraction TimeGAN only decreases 0.94%. Furthermore, Under the condition of small sample, Extraction TimeGAN maintains the AUC of over 99%.

An optimized heterogeneous multi-access edge computing framework based on transfer learning and artificial internet of things

In most practical applications, the feature space of the training datasets and the target domain datasets are inconsistent, or the data distribution between them is inconsistent, which leads to the problem of data starvation and makes it difficult for terminal devices to obtain high accurate results. Aiming at the problems of limited terminal device resources, low accuracy of data processing results, and unsatisfactory processing speed, a Heterogeneous Multi-access Edge Computing (MEC) Framework based on Transfer Learning (TL) is proposed, abbreviated as HMECF-TL. This framework adopts a cloud-edge-end three-layer architecture. It uses model transfer to optimize the Convolutional Neural Networks (CNN) model at each layer to achieve the goal of improving data processing speed and accuracy. Furthermore, a multi-agent Deep Reinforcement Learning Algorithm having Attention Mechanism (DRLAAM) is designed to further increase the timeliness performance of computation-intensive applications. The performance of HMECSF-TL framework is verified by simulation experiments, which not only reduces the delay by more than 24.66 %, but also improves the accuracy by more than 8.34 %. The framework not only increase the computing capacity to solve the shortage of terminal device resources, but also improve the quality of data processing to solve the problem of data starvation.

Розвиток систем обліково-аналітичного забезпечення в умовах цифрової трансформації інформаційної індустрії

The article analyzes the current state and development of accounting and analytical systems in the context of the digital transformation of the information industry, a critical issue for enterprises in this sector. The study examines the main trends and challenges companies face due to the active implementation of digital technologies in accounting and analytical activities. It characterizes the critical aspects of how digital transformation impacts the processes of data collection, processing, and analysis within the modern information industry. The article proposes new approaches to organizing accounting and analytical systems that meet the digital economy’s demands. It explores the potential of modern information technologies to enhance the effectiveness of management decisions based on the analysis of large data sets, which contributes to increasing enterprises’ competitiveness in the digital environment. Additionally, the article highlights the necessity of integrating advanced technologies into financial control and management processes. The research identifies prospects for developing accounting and analytical systems, considering global digital trends. Recommendations are developed to improve the adaptability of information industry enterprises to the rapidly changing digital environment. Through a comprehensive analysis, the article underscores the importance of adapting to and leveraging digital transformation to enhance operational efficiency and maintain a competitive edge. The findings suggest that the ongoing digital revolution in accounting and analytics presents opportunities and challenges, requiring businesses to innovate and continuously adapt their systems and processes. This will ensure they remain resilient and competitive in an increasingly digitized global market. In the future, research in this field should focus on integrating the latest digital technologies, such as artificial intelligence and blockchain, which can significantly improve the accuracy and speed of data processing. Also, an important direction is the development of new analytical forecasting models based on big data, which will help to make management decisions more effectively. Keywords: Digital transformation, accounting systems, analytical systems, information industry, big data, competitiveness, financial control, management efficiency, modern information technologies, digital economy.

Improvement of the Estimation of the Vertical Crustal Motion Rate at GNSS Campaign Stations Based on the Information of GNSS Reference Stations

With the enrichment of GNSS data and the improvement in data processing accuracy, GNSS technology has been widely applied in fields such as crustal deformation. The Crustal Movement Observation Network of China (CMONOC) has provided decades of Global Navigation Satellite System (GNSS) data and related data products for crustal deformation research on the Chinese mainland. The coordinate time series of continuously observed reference stations contain abundant information on crustal movements. In contrast, the coordinate time series of periodically observed campaign stations have limited data, making it difficult to separate or remove instantaneous non-tectonic movements from the time series, as performed with reference stations, to obtain a stable and reliable crustal movement velocity field. To address this issue, this paper proposes a method to improve the estimation of crustal movement velocity at campaign stations using the information of neighboring reference stations. This method constructs a Delaunay triangulation of reference stations and fits the periodic movement of each campaign station using an inverse distance weighted interpolation algorithm based on the reference station information. The crustal movement velocity of the campaign stations is then estimated after removing the periodic movement. This method was verified by its application to the estimation of the vertical motion rate at some reference and campaign stations in Yunnan Province. The results show that the accuracy of vertical motion rate estimation for virtual and real campaign stations improved by an average of 24.4% and 9.6%, respectively, demonstrating the effectiveness of the improved method, which can be applied to estimate crustal movement velocity at campaign stations in other areas.

Development of an intelligent building management systembased on BIM technology

The construction of smart cities has led to new demands for intelligent building management. This paper proposes an innovative building management system that integrates BIM, IoT, cloud computing, big data, and artificial intelligence technologies. The system architecture is rational, and its functional modules are comprehensive, achieving breakthroughs in key areas such as BIM visualization, fault diagnosis, and energy consumption optimization. Through practical testing, the system demonstrates outstanding capabilities in data processing, real-time performance, and fault recognition accuracy, providing solid technical support for the operation of green intelligent buildings and contributing to sustainable urban development.

PERFORMANCE OF ROBUST SUPPORT VECTOR MACHINE CLASSIFICATION MODEL ON BALANCED, IMBALANCED AND OUTLIERS DATASETS

In the realm of machine learning, classification models are important for identifying patterns and grouping data. Support Vector Machine (SVM) and Robust SVM are two types of models that are often used. SVM works by finding an optimal hyperplane to separate data classes, while Robust SVM is designed to deal with uncertainty and noise in the data, making it more resistant to outliers. However, SVM has limitations in dealing with class imbalance and outliers in the dataset. Class imbalance makes the model tend to predict the majority class, and outliers can interfere with model formation. This research compares the performance of SVM and Robust SVM on normal, unbalanced and outlier datasets. The software uses Python and Scikit-learn for implementation and comparison of the two models. Key features include automatic data preprocessing, model training, and evaluation with metrics such as accuracy, precision, recall, and F1 score. The results show that Robust SVM is superior in accuracy on normal datasets and is very effective in dealing with class imbalance, achieving a maximum accuracy of 100%. On datasets with outliers, Robust SVM maintains stable accuracy, demonstrating its robustness to outliers. This research contributes to correspondence management by providing more reliable classification models, improving data processing accuracy, and supporting more informed decision making in software development

Design of an Inventory Information System at ITSK Soepraoen Using the Waterfall Method

In the current digital era, the use of information technology has become an urgent need for various institutions, including educational institutions. ITSK Soepraoen has integrated information systems in several units to increase operational efficiency. However, inventory data collection is still done manually using Microsoft Excel, which has proven to be less efficient and often causes delays in data processing. This research aims to design a website-based inventory information system at ITSK Soepraoen using the Waterfall method. This system is expected to facilitate data collection and management of inventory items as well as increase accuracy, transparency and efficiency in data processing. The research method used is the Waterfall approach which consists of four stages: requirements definition, system and software design, implementation, and testing. The result of this research is a lo-fi mockup of an inventory system that is well received by users with an acceptance rate of 92.75%. This percentage is relatively high so it can be concluded that the user accepts the design that has been created and for the next stage this inventory system can be fully implemented.

Stress wave effects and their mechanisms on stress-strain curves in the elastic phase of SHPB tests

In Split Hopkinson Pressure Bar (SHPB) tests, the stress wave effects during the elastic phase of the stress-strain curve and their influence mechanisms are crucial. Due to the extremely short duration of the elastic deformation phase, the stress wave effects on the specimen during this stage cannot be ignored. This leads to significant errors in the obtained elastic stress-strain curves. However, the dynamic compressive elastic stress-strain relationship forms the basis for studying the viscoelastic behavior of materials. Accurate determination of elastic yield stress and yield strain is also essential for deriving accurate plastic stress-strain relationships. Quantitative research on the stress wave effects during the elastic compression phase of SHPB tests is fundamental for decoupling and obtaining accurate material elastic curves. This paper conducts a quantitative theoretical analysis of the structural effects caused by stress wave evolution during the elastic compression phase, based on the assumption of plane waves. It studies the deviation characteristics and main factors of the phenomenological engineering stress-strain curves of the specimen compared to the actual material stress-strain curves under different conditions, revealing the influence rules and mechanisms of this deviation. The maximum stress deviation value and its corresponding dimensionless time, as well as the variation trend of the maximum stress deviation value within different fluctuation intervals, are calculated. Additionally, the study investigates the cases where the incident wave is arc-shaped or a combination of arc and linear waves. The findings provide theoretical references for the precise design and accurate data processing of SHPB tests.

A REVIEW OF IMPLEMENTING AI-POWERED DATA WAREHOUSE SOLUTIONS TO OPTIMIZE BIG DATA MANAGEMENT AND UTILIZATION

This review examines the implementation of AI-powered data warehouse solutions to optimize big data management and utilization, analyzing 25 peer-reviewed articles published over the last decade. As organizations increasingly rely on vast amounts of data for strategic decision-making, traditional data warehousing techniques have struggled to keep pace with the volume, variety, and velocity of modern data. The integration of artificial intelligence (AI) into data warehousing processes has emerged as a critical advancement, enhancing data processing efficiency, accuracy, and scalability. This study synthesizes findings from the literature to highlight key benefits such as automated data extraction, transformation, and loading (ETL) processes, real-time analytics, and improved data quality through advanced cleansing and anomaly detection. Additionally, it identifies significant challenges including data security risks, integration complexities, and the need for specialized skills and substantial investments. The review concludes with recommendations for future research and practical applications, emphasizing the importance of strategic planning and robust security measures to fully leverage AI's potential in revolutionizing data warehousing.

Neural network-based algorithm for door handle recognition using RGBD cameras

The ability to recognize and interact with a variety of doorknob designs is an important component on the path to true robot adaptability, allowing robotic systems to effectively interact with a variety of environments and objects The problem addressed in this paper is to develop and implement a method for recognizing the position of a door handle by a robot using data from an RGBD camera. To achieve this goal, we propose a revolutionary approach designed for autonomous robots that allows them to identify and manipulate door handles in different environments using data obtained from RGBD cameras. This was achieved by creating and annotating a complete dataset consisting of 5000 images of door handles from different angles, with the coordinates of the vertices of the bounding rectangles labeled. The architectural basis of the proposed approach is based on MobileNetV2, combined with a special decoder that optimally increases the resolution to 448 pixels. A new activation function specially designed for this neural network is implemented to ensure increased accuracy and efficiency of raw data processing. The most important achievement of this study is the model's ability to work in real-time, processing up to 16 images per second. This research paves the way for new advancements in the fields of robotics and computer vision, making a substantial contribution to the practical deployment of autonomous robots in a myriad of life's spheres.

Leveraging AI techniques for predicting spatial distribution and determinants of carbon emission in China's Yangtze River Delta

This study focuses on the prediction and management of carbon emissions (CE) under the backdrop of global warming, with a particular emphasis on developing spatial planning strategies for urban clusters. In this context, we integrate artificial intelligence technologies to devise an optimized spatial analysis method based on the attributes of multi-source, urban-level spatio-temporal big data on CE. This method enhances both the accuracy and interpretability of CE data processing. Our objectives are to accurately analyze the current status of CE, predict the future spatial distribution of urban CE in the Yangtze River Delta (YRD), and identify key driving factors. We aim to provide pragmatic recommendations for sustainable urban carbon management planning. The findings indicate that: (1) the algorithm designed by us demonstrates excellent fitting capabilities in the analysis of CE data in the YRD, achieving a fitting accuracy of 0.93; (2) it is predicted that from 2025 to 2030, areas with higher CE in the YRD will be primarily concentrated in the 'Provincial Capital Belt' and the 'Heavy Industry Belt'; (3) the economic foundation has been identified as the most significant factor influencing CE in the YRD; (4) projections suggest that CE in the YRD are likely to peak by 2030.

Comparison of digital study model superimposition methods using implant-supported crowns and best-fit algorithms

Research regarding orthodontic changes using the superimposition of digital study models (DSMs) is commonplace. Information regarding the accuracy of data processing by superimposition software is limited. The study aimed to compare different methods of superimposing DSMs using implant-supported crowns (ISC) as a stable reference structure. DSMs containing ISCs were sourced from a database of patients treated with clear aligner therapy. The DSM representing the planned treatment outcome was superimposed on the pretreatment DSM. Three tooth points were selected for comparison on the contralateral side of each ISC. Differences in Cartesian coordinates for each tooth point for each arch superimposition method, used by the Geomagic Control X(3D systems, Rock Hill, NC) software system, were recorded. Paired t tests for the reference standard superimposition method best-fit high-resolution using the entire dental arch compared with initial, best-fit low-resolution, and best-fit high-resolution using the ISC only were calculated. The DSMs of 54 dental arches containing ISCs were evaluated. All mean differences for displacements of selected points on the contralateral side to the ISC in the 3 Cartesian planes were<0.05 mm (P<0.05) and below the threshold of clinical significance. In addition, the standard superimposition techniques (initial, best-fit low-resolution, and best-fit high-resolution) resulted in nonstatistically significant and nonclinically significant differences in the position of the ISC. Researchers can be confident that the described superimposition methodologies, with and without ISCs as a stable reference structure, are a valid method for accurately assessing most intraarch dental changes.

Research and comparison of algorithms based on multi-modal fusion

Abstract A multi-modal fusion algorithm is an important method for information fusion on multi-modal data provided by different sensors. It can make full use of the advantages of multiple sensors and improve the accuracy and robustness of data processing and decision-making. This paper aims to study the performance difference between the extended Kalman filter (EKF) algorithm and other algorithms in multi-modal fusion and explore a method to fuse multiple algorithms further to improve the accuracy of fusion results. The author uses the classic test set data set for experiments to evaluate the performance of different algorithms. By comparing and analyzing the performance of each algorithm in data fusion tasks, we can get their advantages and disadvantages in different scenarios. Among them, the extended Kalman filter algorithm is a classical algorithm based on Bayesian filtering, which can estimate the system state through recursive state estimation and covariance update. In addition, the author also uses linear regression, random forest, and other algorithms to compare. Then, the author uses several test sets to evaluate the performance of each algorithm. Through a comprehensive analysis of the MSE and MAE errors output by the algorithm, the applicability, advantages, and disadvantages of each algorithm in different scenarios are obtained.

Comprehensive analysis of the mining accident forecasting and risk assessment methodologies: Case study – Stanterg Mine

Purpose. This research aims to outline a methodology for accident forecasting and risk assessment in mining operations using the Stanterg Mine as a case study. It emphasises the crucial role of reliable mining accident reporting and accurate data processing in forecasting accidents and effectively managing risks during mining operations. Methods. The research paper analyzes various methodologies for forecasting mining accidents using Excel and Simple Linear Regression Method (SLRM) to analyse data selected from the Stanterg Mine accidents. Findings. The forecast indicates that on average there are about 3 accidents per month at the Stanterg Mine. The analysis, based on a one-month study of 42 reported accidents, and assuming a steady production rate, suggests an increased risk of accidents. This is supported by a thorough assessment using a 3×3 risk assessment matrix tailored to the Stanterg Mine. Stope mining is highlighted as the most hazardous area, associated with risks ranging from moderate to extreme levels. Originality. Mining accident analysis at the Stanterg Mine involves an examination of the incidents, including factors leading to accidents, encountered hazards and their consequences. Accident forecasting entails studying historical data, identifying patterns, and using predictive modelling to anticipate future incidents. This proactive approach enables mining companies to proactively address risks and take preventative measures, reducing the probability of accidents. Practical implications. The systematic processing and analysis of mining accidents has revealed valuable insights into the practical application of risk assessment in mining operations. The examination of accidents at the Stanterg Mine provides researchers with crucial knowledge for effective risk assessment and management in the mining sector.