Information Fusion Technology Research Articles

A study of the role of new feature fusion based on multimedia analysis on accounting investment decision methods in economic models

Accounting investing analysis has been expanding at a steady clip, and some of the findings suggest that investors' restricted reasoning and self-psychological sentiments may not always lead them to make their negative emotions influence their financial decisions, leading to a loss. Digital multimedia fusion and display are made possible, and numerous terminals may now communicate with one another in a seamless, real-time manner thanks to the growth of e-commerce and multimedia. This paper proposes a Multimedia Analysis (MA) feature fusion method for understanding the psychological emotions associated with accounting investments in the online retail environment, which can then be used to guide the development of an investment strategy that is both appropriate and successful for the target demographic. The primary goal of this study is to show how multimedia information retrieval tasks may benefit from combining text pre-filtering with image sorting. For this investigation; they used information from the reliable China Stock Market and Accounting Research (CSMAR) Database. The information fusion technology that supports this paper's investigation is used to dissect experiment outcomes, examine issues with the emotional effect of financial investment clients, and assess the paper's intended study topic. In experiments, we found a 97% accuracy rate in terms of accuracy.

Intelligent fault diagnosis methods for hydraulic components based on information fusion: review and prospects

Abstract Hydraulic component faults have the characteristics of nonlinear time-varying signal, strong concealment, and difficult feature extraction, etc. Timely and accurately fault diagnosis of hydraulic components is helpful to curb economic losses and accidents, so researches have carried out a lot of research on hydraulic components. Information fusion technology can combine multi-source data from multiple dimensions to mine fault data characteristics, which effectively improves the accuracy and reliability of fault diagnosis results. However, there is currently a lack of a comprehensive and systematic review in this domain. Therefore, in this paper, the hydraulic components information fusion fault diagnosis technologies are summarized and analyzed, including the main process information fusion fault diagnosis and the research status of information fusion fault diagnosis of hydraulic system. The methods and techniques involved in the fusion process, data source and fusion method of fault diagnosis of hydraulic components information fusion are described and summarized. The problems of information fusion in fault diagnosis of hydraulic components are analyzed, the solutions are discussed, and the research ideas of improving information fusion fault diagnosis are put forward. Finally, digital twin technology is introduced, and the advantages and research status of intelligent fault diagnosis based on digital twin are summarized. On this basis, the intelligent fault diagnosis of hydraulic components based on information fusion is summarized, and the challenges and future research ideas of applying information fusion and digital twin to intelligent fault diagnosis of hydraulic components are put forward and analyzed comprehensively.

Non-destructive discrimination of honey origin based on multispectral information fusion technology

Accurate discrimination of honey origin is of significant importance for safeguarding consumer rights and promoting the sustainable development of apiculture. Spectroscopic techniques, as rapid, efficient, and non-destructive detection methods, have been widely applied in honey research. However, studies often focus on a single analytical technique, limiting the acquisition of comprehensive chemical information and hindering improvements in discrimination accuracy. Data fusion, an information processing technique that enhances test results' accuracy by integrating data from multiple devices, holds vast potential for various applications. Therefore, this study employed data fusion technology to investigate 142 honey samples from five different origins by combining Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) and Raman spectroscopy. Discrimination models based on single-spectral and multispectral Probabilistic Neural Networks (PNN) and Support Vector Machines (SVM) were established using data-level and feature-level fusion strategies. Compared to single-spectral methods, multispectral data fusion significantly improved the geospatial traceability discrimination performance of the models. Particularly, the feature-level fusion strategy demonstrated significant discrimination effectiveness for honey origin. By optimizing the optimal SVM model with the Particle Swarm Optimization (PSO) algorithm, the final test accuracy reached 95.28 %, representing an 11.4 % increase over the best single-spectrum model's test accuracy. Therefore, the establishment of a PSO-SVM discrimination model using feature-level fusion technology enables rapid discrimination of honey origin, providing a reliable and stable method for non-destructive honey origin discrimination.

Development and application of a substation work ticket inspection tool based on big data cloud platform

In the context of the digital transformation of the power grid, based on information data mining and information fusion technology, fully utilizing big data cloud platforms to develop and apply substation work ticket inspection tools, innovating application data scenarios in the deep implementation of safety production, solving the problems of traditional work ticket inspection content and process timeliness. Through the application of substation work ticket inspection models, it is fully verified that substation work ticket inspection tools improve inspection efficiency and quality, and play a positive role in reducing personal and equipment risks during on-site operations.

Integrating multisensory information fusion and interaction technologies in smart healthcare systems

<p>The advent of intelligent medical systems has heralded a new era in healthcare, promising enhanced diagnostic accuracy, treatment efficacy, and personalized patient care. Central to these advancements is the application of multisensory information fusion and interaction technology, which integrates diverse data types—from imaging to auditory signals and electronic health records—to facilitate comprehensive patient assessments. This study examines the efficacy of such multisensory integration within an intelligent medical system framework, focusing on its impact on diagnostic accuracy and treatment effectiveness. A hypothetical dataset encompassing various sensory inputs for a cohort of patients was analyzed, revealing a significant improvement in diagnostic precision (average accuracy of 92.3%) and treatment outcomes, with a majority of interventions rated as highly effective. These findings underscore the potential of multisensory data fusion in revolutionizing medical diagnostics and treatment planning. Despite the promising results, limitations such as sample size and data quality were acknowledged, pointing towards the necessity for further research. This study not only corroborates the value of multisensory information fusion in enhancing healthcare delivery but also highlights the pathway for future advancements in intelligent medical systems. The article’s novelty lies in its approach to integrating multisensory data with AI technologies, leading to a more nuanced understanding of patient health. This method transcends traditional diagnostic techniques, allowing for a multifaceted analysis of medical conditions. It emphasizes the potential of this technology to detect diseases earlier and more accurately, tailor treatments to individual patient needs, and improve overall healthcare efficiency.</p>

Key technologies of earthquake early warning system for China’s high-speed railway

PurposeThe purpose of this study is to introduce the top-level design ideas and the overall architecture of earthquake early-warning system for high speed railways in China, which is based on P-wave earthquake early-warning and multiple ways of rapid treatment.Design/methodology/approachThe paper describes the key technologies that are involved in the development of the system, such as P-wave identification and earthquake early-warning, multi-source seismic information fusion and earthquake emergency treatment technologies. The paper also presents the test results of the system, which show that it has complete functions and its major performance indicators meet the design requirements.FindingsThe study demonstrates that the high speed railways earthquake early-warning system serves as an important technical tool for high speed railways to cope with the threat of earthquake to the operation safety. The key technical indicators of the system have excellent performance: The first report time of the P-wave is less than three seconds. From the first arrival of P-wave to the beginning of train braking, the total delay of onboard emergency treatment is 3.63 seconds under 95% probability. The average total delay for power failures triggered by substations is 3.3 seconds.Originality/valueThe paper provides a valuable reference for the research and development of earthquake early-warning system for high speed railways in other countries and regions. It also contributes to the earthquake prevention and disaster reduction efforts.

Incremental information fusion in the presence of object variations for incomplete interval-valued data based on information entropy

Information fusion technology plays a crucial role in integrating data from multiple sources or sensors to generate comprehensive representation, which can eliminate uncertainty in multi-source information systems (Ms-IS). Incomplete interval-valued data, a generalized form of single-valued data, is commonly encountered in real-world scenarios and effectively represents uncertain information. This paper introduces a novel information entropy specifically designed to quantify the uncertainty in incomplete interval-valued data. Based on the proposed entropy, a new unsupervised fusion approach is developed. Additionally, two dynamic update mechanisms are established to obtain fusion results efficiently when collecting new objects and removing obsolete ones. The relevant static and dynamic fusion algorithms are provided, and a detailed analysis and comparison of their time complexities are conducted. Finally, the effectiveness analysis reveals that the proposed method achieves higher average classification accuracy (5% to 8.7% improvement) compared to three common fusion methods (MAXF, MEANF, and MINF), as well as the state-of-the-art entropy-based supervised fusion method (ESF). The efficiency analysis demonstrates that the average running time of dynamic fusion algorithms is significantly lower (66.9% to 85.6% reduction) compared to the static fusion algorithm, and this difference is statistically significant.

Improving compound fault diagnostic accuracy of drilling permanent magnet synchronous motor using information fusion

The diagnostic accuracy of compound faults is low due to the similarity of fault symptoms. Accurate diagnostic results can only be obtained by fusing all available information on compound faults. However, a single information fusion method cannot fuse all available information well because the characteristics of fault information are different. A method combining three kinds of information fusion technology is proposed to improve the compound fault diagnostic accuracy of the drilling permanent magnet synchronous motor (DPMSM). The fault symptoms are fused by Bayesian inference using the Bayesian Network. The additional information is fused by fuzzy logic operation using the Fuzzy Theory. The fusion results obtained from the above two methods are fused as evidence by mathematical logic using Evidence Theory to obtain the final diagnosis results. The effectiveness of the proposed method is verified by Cases. After fusion, “RoEc” fault in Case A rises to 0.933 from 0.826, “ItSc” fault in Case B rises to 0.524 from 0.262, and “RoEc” fault in Case C rises to 0.344 from 0.046 while “ItSc” down to 0.172 from 0.458. Cases indicate it can effectively improve the fault diagnostic accuracy and correct the results of missed diagnosis and misdiagnosis in compound faults of the DPMSM in drilling engineering. The effect of this method is influenced by the selection of specific experts and the accuracy of statistical data.

Research Progress on Quality Detection of Livestock and Poultry Meat Based on Machine Vision, Hyperspectral and Multi-Source Information Fusion Technologies

Presently, the traditional methods employed for detecting livestock and poultry meat predominantly involve sensory evaluation conducted by humans, chemical index detection, and microbial detection. While these methods demonstrate commendable accuracy in detection, their application becomes more challenging when applied to large-scale production by enterprises. Compared with traditional detection methods, machine vision and hyperspectral technology can realize real-time online detection of large throughput because of their advantages of high efficiency, accuracy, and non-contact measurement, so they have been widely concerned by researchers. Based on this, in order to further enhance the accuracy of online quality detection for livestock and poultry meat, this article presents a comprehensive overview of methods based on machine vision, hyperspectral, and multi-sensor information fusion technologies. This review encompasses an examination of the current research status and the latest advancements in these methodologies while also deliberating on potential future development trends. The ultimate objective is to provide pertinent information and serve as a valuable research resource for the non-destructive online quality detection of livestock and poultry meat.

The Application of Multimedia Information Fusion Technology in the Construction of University Intelligent Libraries

Multimedia information fusion technology is currently a popular technology for data processing. The advanced productivity it brings is dozens of times that of traditional data information processing. The collected and processed information is not only efficient and accurate, but also has strong scalability. With the continuous development of college education in China, the problem of the construction of intelligent library in colleges and universities has gradually surfaced. The establishment of a university intelligent library is a process of integration and common progress between disciplines. This article starts from the current situation of university libraries, studies and analyzes intelligent libraries at home and abroad, and finally combines the existing multimedia information fusion technology to analyze the theoretical level of information flow as an important idea of the current university intelligent library construction.

Retracted: The Application of Multisensor Information Fusion Technology in Environmental Restoration

Retracted: The Application of Multisensor Information Fusion Technology in Environmental Restoration

Knowledge-integrated Multi-modal Movie Turning Point Identification

The rapid development of artificial intelligence provides rich technologies and tools for the automated understanding of literary works. As a comprehensive carrier of storylines, movies are natural multimodal data sources that provide sufficient data foundations, and how to fully leverage the benefits of data remains a sustainable research hotspot. In addition, the efficient representation of multi-source data also poses new challenges for information fusion technology. Therefore, we propose a knowledge-enhanced turning points identification (KTPi) method for multimodal scene recognition. First, the BiLSTM method is used to encode scene text and integrate contextual information into scene representations to complete text sequence modeling. Then, the graph structure is used to model all scenes, which strengthens long-range semantic dependencies between scenes and enhances scene representations using graph convolution network. After, the self-supervised method is used to obtain the optimal number of neighboring nodes in sparse graph. Next, actor and verb knowledge involved in the scene text are added to the multimodal data to enhance the diversity of scene feature expressions. Finally, the teacher-student network strategy is used to train the KTPi model. Experimental results show that KTPi outperforms baseline methods in scene role recognition tasks, and ablation experiments show that incorporating knowledge into multimodal model can improve its performance.

Optimization of Innovative Paths of Physical Education Teaching in Primary and Secondary Schools under Information Integration Technology

Abstract This article utilizes information fusion technology to provide a more diversified and hierarchical teaching process for physical education, making the content of physical education classroom richer and more vivid. The article firstly discusses the multi-source heterogeneous information fusion technology. It proposes a mass function that combines the output results of the cumulative layer of probabilistic neural network to construct the D-S evidence theory. The D-S evidence theory realizes the decision-level information fusion of the primary results of probabilistic neural networks. In the study, the TPACK knowledge framework was combined with the informationization platform to establish an intelligent classroom for physical education teaching applicable to primary and secondary schools, and a teaching experiment was designed for the selected research subjects. The experiment was conducted to analyze the data mainly from three dimensions: teachers’ and students’ behaviors in the smart classroom, teaching effectiveness and teaching evaluation. The results of data analysis showed that the average time of each learning node of students at the school was more than 50 seconds. At the same time, the coding coverage of teachers’ teaching behaviors was 41.85% lower than the mean value of coding coverage of students’ learning behaviors. In the experimental group, students’ jump rope scores improved by about 9.05% to 20.44% after one month of learning. These findings suggest that an intelligent classroom for physical education teaching relying on information fusion technology can effectively integrate multi-source data, thus providing strong support for improving the effectiveness of physical education teaching in primary and secondary schools.

Optimization of Innovative Path of Physical Education Teaching in Colleges and Universities under Information Integration Technology

Abstract In the era of swift advancements in information technology, the domain of physical education (PE) at colleges and universities stands at the cusp of transformative change, necessitating innovative approaches to teaching methodologies. This paper delves into integrating information fusion technology within PE to refine instructional strategies and bolster student physical health. Through the employment of surface electromyography (EMG) signal collection, Analysis of motion imagery features, and a comprehensive approach to multi-information fusion, this investigation analyzes the EMG data, heart rates, and motion images of 60 students. The findings reveal that information fusion technology serves as an effective tool for monitoring exercise states and physiological metrics in real-time. A notable aspect of the research is using a Dual Stream CNN-ReliefF algorithm, enhancing the precision of interpreting human motion intentions. The study confirms the method’s accuracy and reliability, as the monitored average heart rate, EMG signals, blood pressure, and body temperature align with readings from specialized medical devices. Consequently, adopting information fusion technology in PE teaching significantly enhances educational methodologies and outcomes. This research underscores the potential of technology-driven solutions in redefining physical education, fostering a more effective and scientifically informed learning environment.

The Role of Information Convergence Technology in Reshaping the Multiple Directions of Ideological and Political Education in Colleges and Universities

Abstract The integration of information technology and teaching promotes the reform of teaching in colleges and universities, and this paper extracts students’ learning state in classroom learning based on information fusion technology and explores the remodeling role of Civic and Political Education from multiple dimensions. Face expression recognition is used to extract students’ expressions in classroom learning, and their learning status is evaluated by combining head posture estimation. The z-score method is used to quantify the students’ classroom state data in a dimensionless way, and the learning state data with different scales are transformed into numerical values that can be calculated directly to realize the quantification of learning interest. Four dimensions are examined to explore the remodeling effect of civic and political education: students’ cognitive attention, learning emotions, course acceptance, and information fusion. The results show that the classroom state of student 1 under the information fusion of Civic Education has been in the state of conscientiousness, with cognitive attention greater than 0.25, and the cognitive attention values of students 5 and 6 are at [0.1966,0.2822], which indicates that the cognitive attention of students in the classroom of Civic Education is high. Under the dimension of information fusion, the dimension of information fusion of all 6 students is greater than 0.5, which indicates that students can make connections with other knowledge fusion in the Civic Education classroom.

Information Integration Technology Helps the High Quality Development of Chinese International Education

Abstract As China’s position in the international arena continues to rise, and its interaction with the rest of the world increases, Chinese language international education has received widespread attention and developed rapidly. Based on information fusion technology, this paper constructs a vocabulary frequency analysis model that combines Bayesian estimation and D-S evidential reasoning, specifically used to calculate the hotness value of Chinese language international education. In the in-depth analysis of the Chinese language international education field, high-frequency words reflect the core themes in teaching and learning. At the same time, centrality indicates the degree of interconnection among the research themes. In the in-depth analysis of the Chinese international Education field, high-frequency vocabulary reflects the core teaching and learning themes, and the centrality indicates the degree of interconnection among the research themes. The frequency of Chinese as a foreign language is as high as 170 times, with centrality reaching 0.33, and these high-frequency keywords constitute the cornerstone of the development of Chinese international Education in China. When analyzing the data of the foreign students coming to China, it is found that the Number of Asian students accounted for 59.29% of the total number of 274976 students by the end of the study period. By 2022, the number of colleges and universities enrolling international students in China will increase to 831, and the Chinese global education market will reach 7.56 billion yuan in 2022, showing a saturated market status. The information fusion technology adopted in this paper not only reveals the development trend of Chinese international Education, but also provides a powerful analytical tool to promote the high-quality development of this field. A powerful analyzing tool.

Information fusion technology helps promote the teaching practice of art design specialty in colleges and universities

Abstract This study explores the application of information fusion technology in teaching art and design majors in colleges and universities, aiming to improve the teaching effect and students’ practical ability. The study adopts the flipped classroom teaching model and combines the multiple linear regression method to analyze the effectiveness of the teaching model. The model includes three stages: teaching preparation, teaching process and teaching reflection. The practice teaching system includes online and offline integration of on-campus practice, humanistic literacy practice, on-site participation practice and other multivariate systems. The study results showed that the students who adopted this teaching model scored 4.237, 4.388, and 4.186 (out of 5) in learning self-efficacy, learning adaptability, and learning engagement, respectively, indicating that the students were in the middle to upper level in these areas. The results of regression analysis showed that learning self-efficacy and learning adaptability had a significant positive effect on learning engagement. The conclusion indicates that the application of information fusion technology can significantly improve the learning self-efficacy, learning adaptability and learning engagement of art and design majors, thus improving the quality of teaching and students’ practical ability. This provides new perspectives and methods for teaching art design majors in colleges and universities.

Micro Course Design and Online Education Strategies of Information Fusion Technology in Smart Education Environment

Micro Course Design and Online Education Strategies of Information Fusion Technology in Smart Education Environment

Application of Information Integration Technology to Help Minority Traditional Arts in Chinese Painting Art Designs

Abstract This study utilizes the application of information fusion technology in the artistic design of Chinese paintings, especially exploring how to incorporate the elements of traditional ethnic minority art to enrich and develop the spatial aesthetic expression of Chinese paintings. The study first analyzes the differences in spatial artistic expression between Chinese and Western painting. It focuses on the spatial expression methods of Chinese painting and their application in ethnic minority traditional art. The mathematical model of minority art plane is constructed through the method of coordinate space and its transformation. The PAD emotional attribute model is used to quantitatively analyze the dynamic characteristics of Chinese painting art design works. The results show that the traditional elements of ethnic minorities incorporated in Chinese painting art design can effectively enrich the emotional expression of the paintings and enhance the aesthetic value of the art works. In 2022, the content of traditional elements of ethnic minorities in Chinese paintings increased by about 20 times compared with that in 2015, showing the increasing popularity of this art form. This study reveals the potential application value of information fusion technology in traditional art design and provides a new development direction for Chinese painting art design.

Research on Evaluation Index System of Urban Landscape Design Driven by Information Integration Technology

Abstract This study focuses on the urban landscape design of city A, and uses information fusion technology to evaluate. Through the analysis of the measured data by the entropy weighting method and the ensemble optimal weighting method, and compared with the established index system, the results reveal the weights of different landscape elements. Regarding ecological landscape, the CR value was 0.003, in which landscape diversity and green space rate were the leading indicators, and the weights were 0.2976. The weights of landscape dominance and uniformity were relatively medium, both of which were 0.1579. In the evaluation of aesthetic landscape, the CR value was 0.0309, and the highway alignment and urban landscape indicators in the urban landscape were the most important, with a weight of 0.2044, while the weight of the indication information was the lowest, only 0.0266. The CR value of the cultural landscape was 0.0518, of which the weight of scenic connectivity was the highest (0.5559), followed by the number and scale of scenic spots (0.3537), and the weight of historical and cultural elements was the lowest (0.0904). In the past few years, landscape design has accounted for the most significant proportion of landscape construction investment, especially reaching a peak of about 57,000 yuan in 2022. From 2018 to 2022, the cumulative investment in urban landscape design is about 246664 yuan.