Data Processing Technology Research Articles

BIM and IoT data fusion: The data process model perspective

Digitalization in the architecture, engineering, and construction (AEC) industry has highlighted the process of gathering and combining data from numerous sources. This paper uses data fusion from information science to investigate how data from information systems like Building Information Modeling (BIM) and the Internet of Things (IoT) could be coupled to enable a data-driven AEC. However, given the large amount of data, the technological diversity, the heterogeneous data schema, and hierarchical data abstraction, BIM and IoT data fusion are not addressed systematically. This study aims to develop a BIM and IoT data fusion framework to facilitate the appropriate data process flow for various applications. To investigate the research topic, an integrative review was conducted. Specifically, a general and tailored four-step data fusion process model was proposed as the review’s guiding framework. Critical evaluations were incorporated into a road map to provide a comprehensive perspective of BIM and IoT data fusion. In addition, based on the findings, a two-level and interconnected data fusion framework was proposed, which illustrates the data process flow and differentiates the data abstraction level, data processing technologies and purpose of the fusion. Furthermore, the fusion challenges and future trends are also highlighted.

Structural fault diagnosis of UAV based on convolutional neural network and data processing technology

ABSTRACT This study presents a novel method for damage detection and identification in unmanned aerial vehicles (UAVs) using vibration data gathering and processing technologies based on deep learning. To conduct the study, a quad-rotor UAV was manufactured, and a vibration data acquisition system was developed to collect vibration data along three axes under normal and three damage scenarios. Empirical mode decomposition (EMD) was employed to reduce high-frequency noise in the signals, and the root mean square error (RMSE) feature was utilised to select the Y-axis acceleration data, which exhibits significant changes across different damage cases. Finally, a convolutional neural network was used to identify the damage based on the vibration data. Experimental results demonstrate that the proposed method achieved 97.5% accuracy using selected and noise-reduced Y-axis acceleration data, thereby indicating its usefulness in diagnosing damage types in multi-rotor UAVs.

SCP model based on the new energy vehicle collaborative detection configuration method research

Abstract To address the problem of inaccurate positioning of new energy vehicles during cooperative detection, this paper investigates the positioning accuracy of different combinations of radar and infrared sensors. In order to meet the control requirements of the vehicle co-detection formation, a distributed control method considering the vehicle dynamics characteristics is adopted for this purpose. And considering the cost of sensors, the optimal parameters are calculated, and an optimal aircraft co-detection configuration design scheme based on the SCP model (structure-behavior-performance model) is proposed. At the working level of multi-craft cooperative detection guidance, a two-layer cooperative guidance structure is designed based on the basic framework. Research on vehicle configuration design, vehicle data transmission, processing technology, and cooperative detection and guidance is carried out. The basic principles of the particle swarm optimization algorithm and the algorithmic flow of the discrete particle swarm algorithm are applied to calculate the assignment of targets by the particle swarm optimization algorithm. The GDOP values are analyzed according to the simulation results, and it can be concluded that: the change rate of 2~5 sensors accuracy can reach 71.1%, 71.6%, and 71.5%, respectively, when the infrared sensor spacing is 1km~3km. 5~10 sensors accuracy change rate is only 53.3%, 52.2%, and 49.7%, respectively, which shows that the value is improved by 20%, indicating that the collaborative sensing accuracy of the aircraft has been effectively improved, which is of great help to the collaborative detection of new energy vehicles based on SCP model.

Carbon Nanodots Memristor: An Emerging Candidate toward Artificial Biosynapse and Human Sensory Perception System.

In the era of big data and artificial intelligence (AI), advanced data storage and processing technologies are in urgent demand. The innovative neuromorphic algorithm and hardware based on memristor devices hold a promise to break the von Neumann bottleneck. In recent years, carbon nanodots (CDs) have emerged as a new class of nano-carbon materials, which have attracted widespread attention in the applications of chemical sensors, bioimaging, and memristors. The focus of this review is to summarize the main advances of CDs-based memristors, and their state-of-the-art applications in artificial synapses, neuromorphic computing, and human sensory perception systems. The first step is to systematically introduce the synthetic methods of CDs and their derivatives, providing instructive guidance to prepare high-quality CDs with desired properties. Then, the structure-property relationship and resistive switching mechanism of CDs-based memristors are discussed in depth. The current challenges and prospects of memristor-based artificial synapses and neuromorphic computing are also presented. Moreover, this review outlines some promising application scenarios of CDs-based memristors, including neuromorphic sensors and vision, low-energy quantum computation, and human-machine collaboration.

Preface: 2nd International Conference on Software, Data Processing and Information Technology (SDPIT 2023)

The 2023 2nd International Conference on Software, Data Processing and Information Technology (SDPIT 2023) is a leading conference held at Shenyang, China from February 18-19, 2023. It encourages communication and collaboration, hoping to provide an international platform for its participants to present, share, and learn the latest advance and development of software engineering, data processing, cloud computation, mathematical modeling and information technology.
 Academics, researchers, and students from across the world are all welcomed to exchange findings and ideas, to draw further inspirations in these research fields. It's a great opportunity to deepen your understanding in the related topics. Everyone interested in these fields were welcomed to join the online conference on our channel and to give comments and raise questions to the speeches and presentations. We are hoping we could see each other again next year.
 Each submission to SDPIT 2023 was reviewed by at least two program committee members. We would like to thank the program committee members and external reviewers for their hard work in reviewing and selecting papers. Hopefully, all participants and other interested readers can benefit scientifically from the proceedings and find it rewarding in the process.
 Chairman of Conference Committee
 Shenyang, China

Application and research on trajectory big data

In the era of big data, the formation of massive trajectory data of moving objects with rich Spatio-temporal feature information can mine data such as human activity patterns and behavioral characteristics through trajectory data processing technology. This paper and analyzes the concept of trajectory data, the application process and the research results. Firstly, this paper briefly describes the trajectory data. Secondly, it outlines the supporting techniques of trajectory data, including data cleaning, integration, and transformation. Next, it introduces the classification steps of trajectory data mining, including using the segmentation method to segment the data, extract features from the segments; besides, it models and studies the predicted questions in this topic. Then it briefly introduces the application method of trajectory data mining in data privacy and the application of trajectory data in the transportation field. Finally, the paper summarizes the challenges and future research directions of trajectory data processing.

Multimedia Data Processing Technology and Application Based on Deep Learning

With deep learning being widely used in various research fields, it is introduced into the research and analysis of multimedia data processing technology and application. First, the flow of multimedia data processing, the development of multimedia data, and the realization of multimedia data processing technology are explained and analyzed. Then, the related network results of deep learning (convolution network structure and countermeasure neural network structure) are put forward, and the image comparison of the activation function and the loss function of deep learning is analyzed, which provides functional algorithm support for the experimental analysis of deep learning in multimedia data processing technology. Finally, through the analysis of experimental data, it is concluded that deep learning has stronger advantages in the application research of multimedia data processing technology compared with other learning methods. In the multimedia data processing, the multimedia data processing technology is obviously superior to the data mining technology and data compression technology. Finally, under the support of deep learning data, we conclude that multimedia data processing technology is widely used and quoted in various fields. Therefore, with the development of multimedia, the amount of multimedia data is increasing; so, we should vigorously develop multimedia data processing technology in an all-round way.

The development dilemma and countermeasures of Chinese cross-border e-commerce enterprises under the background of big data

Cross-border e-commerce has gradually emerged with the popularity of the Internet in the world. It represents a new business model of e-commerce. The public is still the same as the daily shopping model on e-commerce platforms. However, cross-border e-commerce can be divided into two parts at home and abroad. With the continuous emergence of new social models such as the Metaverse, the data generated by online social networking is becoming larger and larger, and data science has developed accordingly, and the effective use of data has become the key to the rapid development of various industries. Big data technology is the key to data science. A data processing and analysis technology generated in the development process, its use in the development of cross-border e-commerce enterprises will help enterprises to create products that are more in line with the actual requirements of the masses, increase the profits of enterprise operations, and allow enterprises to complete the era of data science smooth transition. This paper first describes the big data technology and related theories of cross-border e-commerce, and then takes Alibaba as an example to describe the SWOT analysis of my country’s cross-border e-commerce enterprises, and then analyzes the development dilemma of China’s cross-border e-commerce enterprises, and finally designs The development strategy of my country’s cross-border e-commerce enterprises based on big data is presented. Through a questionnaire survey of cross-border e-commerce enterprises that use big data technology in my country, it is found that cross-border e-commerce enterprises that combine big data technology have a greater understanding of overseas customers’ consumption psychology, integration of warehousing and logistics resources, and talent training. promote.

Quality Monitoring of Resistance Spot Welding Based on a Digital Twin

As an important means to realize intelligent manufacturing, a digital twin is a digital expression of physical entities, which realizes virtual–real interaction and the iterative optimization of product design and manufacturing by constructing a bridge of information mapping between the physical world and the virtual world. Resistance spot welding technology is widely used in automotive manufacturing, aerospace and other fields as a spot linking process for the manufacture of thin sheet structures. The fusion nugget growth process of resistance spot welding is particularly important for its joint quality. Resistance spot welding is a highly nonlinear coupled process, and physical models make it difficult to accurately monitor its quality. Taking 2219/5A06 aluminum plates with different thicknesses as the research object, digital twin technology is applied to monitor the welding process of aluminum plate. In order to improve the key technologies such as information interaction in the digital twin system, a data acquisition system for resistance spot welding process is established and a real-time data processing technology based on wavelet threshold analysis is proposed. Based on real-time data, the processed process parameters are tested in twin space to validate the feasibility of the solution.

Telemetry Data Processing and Analysis Platform for Flight Test Based on Flink

Telemetry data processing has extremely high real-time requirements in flight tests. In response to the problems faced by the traditional telemetry data processing system, such as insufficient computing capacity and low scalability, the Flink-based telemetry data processing and analysis technology is proposed to give full play to the cluster computing capacity of Flink, improve the throughput of real-time processing and reduce the processing delay. After flight test verification, the platform meets the real-time processing of multiple telemetry data and has significantly improved the stability, processing efficiency, and scalability compared with the traditional system.

МЕТОД ФОРМИРОВАНИЯ СТРУКТУР ЦИФРОВЫХ ДВОЙНИКОВ ПРЕДМЕТНО-ОРИЕНТИРОВАННЫХ ОБЪЕКТОВ В ПРОСТРАНСТВЕ ОТКРЫТЫХ ИСТОЧНИКОВ НА ОСНОВЕ ФОРМАЛИЗМОВ ТЕОРИИ МНОЖЕСТВ, ГРАФОВ, ТЕОРИИ КАТЕГОРИЙ И ТЕОРИИ ПОРОЖДАЮЩИХ ЯЗЫКОВ ХОМСКОГО

For the material of this article, open sources can be presented as information posted on the Internet for multiple and unlimited use in the form of machine-readable systematized data, in formats that allow their separate automated processing. Any of the open sources is partially structured content, cha¬racterized by the fact that it consists of fuzzy overlaps and connections formalized by a number of sustainable rules. Aim. The purpose of the study is to produce a mathematical description of the rules for the decomposition of links of virtual images of content based on statistical data of comparisons in order to formulate a digital analogue of the verbal structure. The research is accompanied by the development of a modular cross-platform predictive analytics system for distributed open sources of the social digital environment based on multi-stream data processing technologies. The system is based on the creation of a prototype of a digital twin, which allows monitoring and subsequent analysis of open sources. A cross-platform digital twin can be created for various open sources. Currently, among them, social networks are a priority for the analysis of the studied data. Materials and methods. Turning to the same type of code content, it becomes necessary to formalize the rules for categorizing the semantic meanings of the structure of connections of the object of study, which can be described in the language of graph theory, have a suitable structure in Lie matrices, obey the laws of transitivity and have properties that allow you to recreate connectivity in a quasi-projection. A method is proposed that makes it possible to produce sequential calculus that does not contradict each other according to the basic rules of axiomatics. Results. The method allows for real-time analysis of the data flow of open sources, identification of digital traces of research objects, identification of the structure of their connections. Conclusion. With the help of the proposed group of algorithmized mathematical iterations, it becomes possible to create a combination of local systems with feedback subsystems of predictive analytics of open sources of Internet resources and local systems for various purposes.

Anthropomorphic information networks and converging technologies: challenge to humanity vs step forward?

The impact of convergening technologies on the social processes of the information society is investigated. It is grounded that at the beginning of the 21st century, the globalization of society is realized by means of convergening technologies. They are based on innovative communication technologies, in particular, technologies of artificial intelligence. The converging technologies of social structure expansion transform it into a network architect, where sustainability of social relations is the most important ergument. The recognition of the lack of administrative influence to achieve socially important goals leads to the fact that convergence of technologies is an irreversible part of the new technological way. The strengthening of the informational pressure that accompanies the formation of the network society further removes the person from realization of its potential, replacing critical, rational thinking with stereotyped, formalized information and communication exchange. As a result, the more dispersed (convergent) are social institutions and their networks, the less capable and willing to control them. Humanity was first faced with the challenge of its intellect, ability to create, control, recognize. Is society ready to give part of the management of the virtual network and artificial intelligence to the face of global problems? This question is before humanity. That is why the author underlies the idea that social modernization requires interpretation of convergence as a strategic innovation. Lack of public control over the convergence of networks leads to reverse – diverging processes. It is shown that the latter produce different forms of social alienation. And the emergence of anthropomorphic networks, based on the technologies of big data processing, machine learning and artificial intelligence, sharply pose questions of the future before every inhabitant of the planet.

Industrial Internet Data Collection and Processing Technology Research

With the rapid development of the Industrial Internet of Things, data acquisition and processing technology has become a research hotspot. This paper focuses on the industrial Internet data acquisition and processing technology. In view of the current situation and existing problems of industrial Internet data collection technology, such as slow transmission speed, low data quality, poor security, etc., from the three aspects of data transmission, data processing and data security. Specifically, the transmission mode based on the Internet of Things, distributed data processing technology and encryption algorithm and other technical means are adopted to improve the speed and quality of data transmission and ensure the data security. The research results of this paper provide reference and reference for the further development of industrial Internet data acquisition and processing technology, which has certain theoretical and practical significance.

Study on Intelligent Analysis and Processing Technology of Computer Big Data Based on Clustering Algorithm

Aim: Clustering belongs to unsupervised learning, which divides the data objects into the data set into multiple clusters or classes, so that the objects in the same cluster have high similarity Background: The clustering of spatial data objects can be solved by optimization based on the clustering objective function. Objective: Study on intelligent analysis and processing technology of computer big data based on clustering algorithm. Methods: First, a new dynamic self-organizing feature mapping model is proposed, and the training algorithm of the model is given. Then, the spectral clustering technology and related concepts are introduced. The spectral clustering algorithm is studied and analyzed, and a spectral clustering algorithm that automatically determines the number of clusters is proposed. Furthermore, an algorithm for constructing a discrete Morse function to find the optimal solution is proposed, proving that the constructed function is the optimal discrete Morse function. At the same time, two optimization models based on the discrete Morse theory are constructed. Finally, the optimization model based on discrete Morse theory is applied to cluster analysis, and a density clustering algorithm based on the discrete Morse optimization model is proposed. Results: This study is focused on designing and implementing a partitional-based clustering algorithm based on big data, that is suitable for clustering huge datasets to meet low computational requirements. The experiments are conducted in terms of time and space complexity and it is observed that the measure of clustering quality and the run time is capable of running in very less time without negotiating the quality of clustering. The results show that the experiments are carried out on the artificial data set and the UCI data set. Conclusion: The efficiency and superiority of the new model, are verified by comparing it with the clustering results of the DBSCAN algorithm.

A COMPREHENSIVE STUDY ON BIG DATA FRAMEWORKS

With the advent of cloud computing technology, the generation of data from various sources has increased during the last few years. The current data processing technology must handle the enormous volumes of newly created data. Therefore, the studies in the literature have concentrated on big data, which has enormous volumes of almost unstructured data. Dealing with such data needs well-designed frameworks that fulfil developers’ needs and fit colourful purposes. Moreover, these frameworks can use for storing, processing, structuring, and analyzing data. The main problem facing cloud computing developers is selecting the most suitable framework for their applications. The literature includes many works on these frameworks. However, there is still a severe gap in providing comprehensive studies on this crucial area of research. Hence, this article presents a novel comprehensive comparison among the most popular frameworks for big data, such as Apache Hadoop, Apache Spark, Apache Flink, Apache Storm, and MongoDB. In addition, the main characteristics of each framework in terms of advantages and drawbacks are also deeply investigated in this article. Our research provides a comprehensive analysis of various metrics related to data processing, including data flow, computational model, overall performance, fault tolerance, scalability, interval processing, language support, latency, and processing speed. To our knowledge, no previous research has conducted a detailed study of all these characteristics simultaneously. Therefore, our study contributes significantly to the understanding of the factors that impact data processing and provides valuable insights for practitioners and researchers in the field

Vulnerability detection in the network traffic flow of the RADIUS protocol based on the object-oriented model


 
 
 The RADIUS protocol was analyzed from the point of view of its functionality and security. The internal structure and the division into functions were shown. There were described the structure of the RADIUS network, the functions of the network access server or NAS, and the RADIUS server. The advantages of the centralized secure data processing technology based on the RADIUS protocol were shown. Mechanisms of secure processing of requests at the authentication and authorization stage were described. Sources and types of protocol vulnerabilities were studied and possible attack scenarios were identified. The relevance of creating models for evaluating the vulnerabilities of the RADIUS protocol was substantiated, and the methodology for building the model was chosen. An object-oriented model of the RADIUS protocol has been developed. A software application was developed and various attacks on the RADIUS protocol were tested. A number of potential vulnerabilities have been identified.
 
 

Data storage mechanism of industrial IoT based on LRC sharding blockchain

With the rapid development of Industry 4.0, the data security of Industrial Internet of Things in the Industry 4.0 environment has received widespread attention. Blockchain has the characteristics of decentralization and tamper-proof. Therefore, it has a natural advantage in solving the data security problem of Industrial Internet of Things. However, current blockchain technologies face challenges in providing consistency, scalability and data security at the same time in Industrial Internet of Things. To address the scalability problem and data security problem of Industrial Internet of Things, this paper constructs a highly scalable data storage mechanism for Industrial Internet of Things based on coded sharding blockchain. The mechanism uses coded sharding technology for data processing to improve the fault tolerance and storage load of the blockchain to solve the scalability problem. Then a cryptographic accumulator-based data storage scheme is designed which connects the cryptographic accumulator with the sharding nodes to save storage overhead and solve the security problem of data storage and verification. Finally, the scheme is proved to be security and the performance of the scheme is evaluated.

CONDITION BASED PREDICTIVE MAINTENANCE ON SHIP’S MAJOR EQUIPMENT USING AI

This project mainly aims at diagnostics and prognostics approach for Main Engine fitted onboard Ship. Keeping in mind the complexity of equipment’s fitted in ship and the frequency of defect, there is always a demands for prognostic maintenance approach rather than TBM (Time based Maintenance). Under these circumstances, the CbPM(Condition based predictive maintenance) helps in predicting the fault of a running machine and signals to perform maintenance on it only when required, thereby impacting operational readiness and logistic stability. Condition-based maintenance (CBM) is a program that banks upon three factor- data processing, maintenance decision making and data acquisition. The paper mainly focuses on implementing CBM on a mechanical system with emphasis on technology, algorithm and model for data processing. This project focuses on implementing CBM on machines rather than TBM by knowing the past defect history; maintenance schedule, periodic routine and then applying AI tool on the same for predicting the machines future condition. This will thereby help in improving the overall life of the equipment. An instance on the use of various classification algorithms is taken into picture and compared their accuracy based on the F1 score in this paper. Keywords -Diagnostic, Prognostics, Condition-based predictive maintenance (CbPM), Main Engine, PPM (Planned preventive Maintenance)

Manufacturing of ceramic venus figurine replica from a mold by combining traditional and digital technologies

In this paper, we propose a method for creating a ceramic Venus figurine replica from a mold in the Museum of Ceramic Techniques collection in Koło, Poland stored at the museum for many years. Moreover, none of the Venus figurines in this collection have been preserved to the present day. Our process starts with partially degraded mold fragments and ends with an actual copy of the Venus figurine in three forms: faience kilned to bisque, porcelain, and glazed faience. The entire process involves understanding old manufacturing technology, conservation supervision, and state-of-the-art 3D scanning, data processing, and 3D printing technologies. We began with a preservation state evaluation that included a technical analysis of the degradation state of the mold. Then, we applied 3D scanning technology using the structured light method. Later, we integrated all mold fragments into front and back 3D models. These 3D models were optimized for 3D printing technology and were used to manufacture the mold. Finally, the printed fragments were corrected using traditional techniques performed by art conservators. This process took comparatively less time and produced a more realistic result than would have been observed if only 3D software had been used. This process also enabled us to create educational materials for the Koło museum about old manufacturing technologies. From the point of view of museum visitors, the copies are made with sufficient detail that they are indistinguishable from handmade artifacts. Our estimates show that combining both techniques (traditional and digital) saved approximately 25% of the time that would have been used if the individual techniques had been used separately. We also believe that our use case could be transferred to a broader group of objects that use molding in their production processes.

The effect of free‐surface reflections on depth images for acquisition with buried sources and receivers: Investigation through the point spread function#

AbstractIn reverse time migration, if sources or receivers are buried at certain depths, the interference between the primary wave and the free‐surface reflection causes losing spectral contents in seismic data. If they are not properly compensated, errors will be carried to the target during the reverse time migration and cause a distorted image by so‐called ghost image. By invoking the point spread function, we investigate how distorted seismic data are mapped to the subsurface. Quantitative relations between the free‐surface parameters (i.e. source/receiver depth, free‐surface incident angle and near‐surface velocity) and wavenumber‐domain illumination can be created, which map missing frequency contents in seismic data to missing wavenumber contents in the point spread function. After transformed back to the space domain, we can investigate its effect on the image distortion. Numerical calculations expand the above approach to handle complex overburden structures and more realistic acquisition geometries. The proposed method provides us with a useful tool to investigate deghosting related problems, for example optimizing parameters of acquisition geometry, evaluating capabilities of existing acquisition systems and data processing technologies in suppressing the free‐surface reflection effect in the depth image, or developing new deghosting approaches.