Information Fusion Techniques Research Articles

Damage effect evaluation of CCD irradiated by laser based on multi-source information fusion

This study introduces an advanced approach for assessing the damage state of charge-coupled devices (CCDs) caused by laser interactions, leveraging a multi-source and multi-feature information fusion technique. We established an experimental system that simulates laser damage on CCDs and collects diverse data types including echo information from active laser detection based on the ‘cat's eye’ effect, plasma flash data, and surface image characteristics of the CCD. A probabilistic neural network (PNN) was utilized to integrate these data sources effectively. Our analysis demonstrated that using multiple features from single sources significantly improves the accuracy of the damage assessment compared to single-feature evaluations. The error rates using dual features from each information type were 10.65% for cat's eye echo, 7.3% for plasma flash, and 7.17% for surface image analysis. By combining all three information sources and six features, we successfully reduced the error rate to 0.85%, with the evaluation time under 60 milliseconds. These findings confirm that our multi-source, multi-feature fusion method is highly effective for the online and real-time evaluation of CCD damage, offering significant improvements in the operational reliability and safety of devices in high-energy environments.

Linear diophantine fuzzy Aczel-Alsina aggregation operators by using fuzzy-C means clustering algorithm

This paper investigates the detailed analysis of linear diophantine fuzzy Aczel-Alsina aggregation operators, enhancing their efficacy and computational efficiency while aggregating fuzzy data by using the fuzzy C-means (FCM) method. The primary goal is to look at the practical uses and theoretical foundations of these operators in the context of fuzzy systems. The aggregation process is optimised using the FCM algorithm, which divides data into clusters iteratively. This reduces computer complexity and enables more dependable aggregation. The mathematical underpinnings of Linear Diophantine Fuzzy Aczel-Alsina aggregation operators are thoroughly examined in this study, along with an explanation of their purpose in handling imprecise and uncertain data. It also investigates the integration of the FCM method, assessing its impact on simplifying the aggregation procedure, reducing algorithmic complexity, and improving the accuracy of aggregating fuzzy data sets. This work illuminates these operators performance and future directions through extensive computational experiments and empirical analysis. It provides an extensive framework that shows the recommended strategy’s effectiveness and use in a variety of real-world scenarios. We obtain our ultimate outcomes through experimental investigation, which we use to inform future work and research. The purpose of the study is to offer academics and practitioners insights on how to improve information fusion techniques and decision-making processes.

Study on Multisensory Information Fusion and Interaction Techniques in Experience Design and Optimization of Intelligent Game Systems

Study on Multisensory Information Fusion and Interaction Techniques in Experience Design and Optimization of Intelligent Game Systems

Comprehensive systematic review of information fusion methods in smart cities and urban environments

Smart cities result from integrating advanced technologies and intelligent sensors into modern urban infrastructure. The Internet of Things (IoT) and data integration are pivotal in creating interconnected and intelligent urban spaces. In this literature review, we explore the different methods of information fusion used in smart cities, along with their advantages and challenges. However, there are notable challenges in managing diverse data sources, handling large data volumes, and meeting the near-real-time demands of various smart city applications.The review aims to examine smart city applications in detail, incorporating quality evaluation and information fusion techniques and identifying critical issues while outlining promising research directions. In order to accomplish our goal, we conducted a comprehensive search of literature and applied selective criteria. We identified 59 recent studies addressing machine learning (ML) and deep learning (DL) techniques in smart city applications. These studies were obtained from various databases such as ScienceDirect (SD), Scopus, Web of Science (WoS), and IEEE Xplore. The main objective of this study is to provide more detailed insights into smart cities by supplementing existing research. The word cloud visualisation of machine learning/deep learning and information fusion in smart cities papers shows a diverse landscape, covering both technical aspects of artificial intelligence and practical applications in urban settings. Apart from technical exploration, the study also delves into the ethical and privacy implications arising in smart cities. Moreover, it thoroughly examines the challenges that must be addressed to realise this urban revolution's potential fully.

An efficient conflict analysis method based on splitting and merging of formal contexts

Conflict situations are widespread nearly in all corners of social life, and the efficiency of conflict analysis still has much room especially for large-scale data sets. To this end, this study presents an information fusion method of fast conflict analysis based on formal concept analysis. Firstly, a novel type of three-way concepts is defined to neatly deal with three-valued formal contexts. Then, fast computation of conflict analysis is explored by using an information fusion technique. Specifically, conflict analysis based on horizontal splitting and merging of formal contexts and the one based on vertical splitting and merging of formal contexts are respectively investigated. Finally, systematic experiments are carried out on both synthetic data sets and real cases to evaluate the performance of the proposed method. According to the experimental results, conflict analysis based on vertical splitting and merging of formal contexts can effectively improve the performance, which may not be realized by using the horizontal splitting and merging strategy in some cases. This study may shed light on formal concept analysis based multi-source big data analysis.

Information Fusion from Multimodal Clinical Sensors for Effective Supplier Decision-Making in Healthcare

Effective procurement of clinical devices in healthcare demands a sophisticated decision-making approach integrating diverse data sources from multiple devices, brands, and suppliers, particularly within the context of information fusion. This study addresses this challenge by proposing an improved best-worst method harmonized with information fusion techniques and multi-criteria decision-making methodologies. The background emphasizes the dynamic nature of healthcare procurement, necessitating systematic strategies for navigating the complexities of device selection and integration. Recognizing the intricacies inherent in this challenge, the problem statement revolves around enhancing the best-worst method to amalgamate data from clinical devices while concurrently evaluating brands and suppliers. This aims to optimize performance and minimize costs within the information fusion paradigm. Our proposed methodology introduces an augmented best-worst approach, encompassing weighted criteria assessment for clinical devices, brands, and suppliers, providing a more adaptable and nuanced decision-making framework tailored to the information fusion landscape. The results showcase a structured evaluation matrix derived from refined weighted criteria, elucidating the relative performance and strengths across various entities within the healthcare procurement ecosystem. Emphasizing reliability, compatibility, innovation, and quality assurance, this process highlights pivotal factors influencing procurement decisions within the realm of information fusion.

Using Fusion Data Analysis and Compensatory Fuzzy Logic to Analyze the Socioeconomic Impact of Venezuelan Migration

The surge of Venezuelan migration has left indelible marks on various regions, notably within Babahoyo Canton, presenting both challenges and opportunities for local communities. This study delves into the socio-economic impacts of Venezuelan migration on Babahoyo throughout 2023, employing a sophisticated blend of compensatory fuzzy logic and information fusion techniques. These methodologies offer a nuanced exploration of the migration's effects, capturing the complex interplay between local perceptions, labor market fluctuations, and broader economic dynamics. The findings underscore the critical need for comprehensive integration strategies that not only facilitate the socio-cultural adaptation of migrants but also leverage local public policies to mitigate adverse impacts while maximizing potential benefits. Ultimately, this research aims to illuminate pathways for informed decision-making and policy development, ensuring that responses to Venezuelan migration in Babahoyo are both effective and empathetic, thus fostering a more integrated and resilient community.

Integrative Multi-Information Fusion for Enhanced Risk Assessment: A Multi-Criteria Decision-Making Framework

This study addresses the burgeoning challenges in autonomous Maritime navigation by employing information fusion methodologies to assess and manage multifaceted risks. The proliferation of autonomous maritime systems has led to a complex interplay among maritime-related, shore-based remote control, environmental, and emergency management factors, necessitating a comprehensive risk evaluation framework. Leveraging a multi-criteria decision-making approach and employing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), this research presents a methodical analysis of the coupling coordination degree among these risk variables. Through a meticulous examination of historical accident data and information fusion techniques, our study reveals dynamic trends in the comprehensive risk evaluation index, showcasing the evolving nature of risks inherent in autonomous Maritime navigation. The predictive insights gleaned from these analyses forecast an initial increase followed by a peak in accidents, underscoring the urgency for proactive risk mitigation strategies. This study's conclusions emphasize the pivotal role of information fusion methodologies in comprehensively assessing, understanding, and managing risks within autonomous Maritime navigation.

An Information Fusion Technique for Prognosticating Future Air Passenger Trends

The aviation industry is constantly changing and to keep up with the trends of air passengers we need predictive models. In this paper, we explore the use of Information Fusion methodologies and classical time series techniques to forecast how many passengers will be traveling by air. Predicting passenger demands is a task, due to various factors that influence travel patterns. The existing models often struggle to capture the dynamics in this field so it's crucial to develop accurate forecasting methods. By leveraging information fusion techniques like smoothing and Autoregressive Integrated Moving Average (ARIMA) our research creates models based on historical data of air passenger volumes. These techniques combine machine learning algorithms and time series analysis to identify dependencies and patterns in the dataset. Through evaluations and comparative analyses, our proposed models demonstrate promising capabilities in forecasting future air passenger volumes. Proof-of-concept experiments based on 5-fold cross-validation demonstrate the efficacy of the proposed approach in capturing underlying trends and seasonality within the dataset.

Trilateration method based node localization and energy efficient routing using rsa for under water wireless sensor network

UWSN refers to a collection of numerous underwater wireless sensor-nodes dispersed throughout the marine environment. Proposed work develops node-localization and optimal relay node selection based routing approach for UWSN. Target nodes initially listen to the beacon for a certain amount of time whenever they are within range of an anchor node before retrieving an anchor-node with RSS data. Next, the Euclidean distance between a target node and an anchor node is determined. After that, another two anchor nodes were placed within the transmission range. The suggested approach then uses modified COOT-optimization to determine the target sensor node's coordinates in an effort to reduce localization-error. After that, the process of uneven clustering and selecting the cluster heads was done. For identifying the ideal relay node, the proposed technique employs a parameter-based approach to CH discovery, particularly RSO. Finally, using an information fusion technique, the sensing information is sent through relay nodes. Proposed localization and routing algorithm is validated using some of the parameter which achieves better performance like 94% localization accuracy, 6% localization error, 2.4% throughput value and 81% packet delivery ratio. It performs better when compared to other existing approaches such as DEEC, SEP, ELEACH and LEACH whose localization accuracy are 93%, 84%, 80% and 78%. Thus the techniques utilized in this proposed approach are the best choice for node localization and routing in UWSN.

Online Dynamic Modelling for Digital Twin Enabled Sintering Systems: An Iterative Update Data-Driven Method

The sintering process is a crucial thermochemical process in the blast furnace iron-making system. Tumble strength (TS), as a vital performance to assess sinter quality, is difficult to monitor due to the lack of timely measurement. Constructing a data-driven model for TS is an alternative for monitoring TS. However, the time-varying dynamic sintering process makes the task of modelling challenging. And the data are incomplete and insufficient in practice for modelling since there are unknown time delays in the system and lack actual TS value. The digital twin (DT) technique is a powerful tool to simulate the system dynamics with the real-time interaction between physical processes and virtual agents in cyberspace. This paper introduces a DT-enabled equivalent of the sintering system and proposes online data-driven modelling for TS monitoring. The time delay in the system is estimated for variable sequence alignment based on a modified maximum information coefficient method. The data used for modelling is enriched based on a multi-source information fusion technique. An adaptive update method is proposed to deal with the time-varying dynamics. The iterative forgetting factor-based algorithm is designed for the support vector regression method and guarantees a fast computational speed. Implementation and validation of the model on a DT-enabled sintering system show the efficiency of the proposed method. The accuracy of TS monitoring reaches 99.6% by analysis of 3 months’ data.

Bispectral analysis and information fusion technique for bearing fault classification

The feasibility and effectiveness of data fusion for the fault classification of bearing faults have been very well iterated in the literature. However, all previous endeavors have been limited to time, frequency, and time-frequency domain techniques. The use of higher-order spectral analysis (HOSA), especially Bispectrum and Trispectrum, for fault detection is gaining importance in recent studies due to the many advantages of HOSA. Bispectral features provide a valuable tool for capturing higher-order statistical relationships in signals, making them particularly effective in detecting nonlinearities and distinguishing between Gaussian and non-Gaussian data. Their robustness to noise and ability to reveal hidden information make them advantageous in applications such as vibration analysis, speech recognition, and image processing, where complex signal interactions and nonlinearity play a significant role in data interpretation and pattern recognition. This paper proposes a methodology for the fusion of the data from the vibration and the acoustic sensors for the fault detection of roller element bearings using bispectral features. Higher-order spectral characteristics are derived from vibration and acoustic sensor data, and they are fused using artificial neural networks and various other machine learning algorithms like support vector machine, K nearest neighbor, Naïve Bayes algorithm, and decision tree. This work primarily aims to evaluate the performance of each classifier when applied to the fused data, in contrast to the performance when using individual sensor data alone. The outcomes revealed that, even though the accuracy of the acoustic sensor data was lower in comparison to the vibration sensor data, which exhibited the highest performance of 100% accuracy with nearly all the classifiers, the fused data achieved remarkable results of 100% accuracy with artificial neural networks and decision trees. However, the Naïve Bayes algorithm yielded the lowest accuracy when applied to the fused data. The primary objective of this paper is to demonstrate the application of bispectrum analysis for data fusion and to enhance confidence in fault detection. It achieves this by maintaining the capability to accurately and dependably detect faults, even when a single sensor encounters issues or falls short of anticipated performance standards.

The construction of teaching quality evaluation system of health management majors in universities based on information fusion technology

Abstract This paper begins with the multi-level processing of instructional multi-source data using information fusion techniques, with varying degrees of abstraction of the raw data for each level of processing. The set of all possible outcomes that an instructional framework can recognize for a given instructional problem is identified through D-S evidence theory. Secondly, the m factors that affect the object of teaching evaluation are grouped into the set of factors affecting the evaluation by fuzzy evaluation method, and their factor set weight values are calculated. Finally, the teaching quality evaluation system was constructed based on the factor set weight values. The results show that the experimental obtained Kendall’s harmony coefficient reaches 0.9028, and the evaluation is reliable. The overall score rate in teaching and assessment evaluation is 0.71 to reach the expected value, which indicates that the teaching quality evaluation index system established this time has relatively good internal consistency. The evaluation index system constructed in this paper makes the classroom teaching evaluation highly operable, which can be accepted by students and teachers in the evaluation process and ensures the accuracy of the evaluation results.

Analysis of the Path to Improve the Effectiveness of Ideological and Political Education in Universities Based on Information Fusion Technology

Abstract This paper firstly constructs a reasonable education resource model according to the features of Civic Education Resources (CERs) and proposes an integration scheme of CER Library in universities based on information fusion technology. Secondly, the storage structure of Lucene’s inverted index is optimized for the management features of the CER Model, and a full-text index library of educational resources for resource retrieval is constructed. Then the advantages and features of information fusion techniques are used to provide college students with exclusive, practical, personalized and customized Civic Education measures to innovate the concept of ideological and political education (IPE) in colleges and universities. Finally, through the subject index of ideological education resources constructed based on the LDA model, the semantic processing of user queries, the design of effective experimentations to confirm the accuracy of the retrieval of ideological education resources, and its evaluation indexes are considered comprehensively from several aspects such as retrieval speed and accuracy rate. The results show that the maximum P @ N value of improved Lucene index retrieval is 1, which is 0.4 larger than that of traditional Lucene-based index retrieval, and the average performance of improved Lucene index retrieval is improved than that of traditional Lucene-based index retrieval in P @ N indexes. This study helps universities to innovate the concept of IPE to retain the ideas up to date and retain pace with the times.

Convolutional Neural Networks and Feature Fusion for Flow Pattern Identification of the Subsea Jumper

The gas–liquid two-phase flow patterns of subsea jumpers are identified in this work using a multi-sensor information fusion technique, simultaneously collecting vibration signals and electrical capacitance tomography of stratified flow, slug flow, annular flow, and bubbly flow. The samples are then processed to obtain the data set. Additionally, the samples are trained and learned using the convolutional neural network (CNN) and feature fusion model, which are built based on experimental data. Finally, the four kinds of flow pattern samples are identified. The overall identification accuracy of the model is 95.3% for four patterns of gas–liquid two-phase flow in the jumper. Through the research of flow profile identification, the disadvantages of single sensor testing angle and incomplete information are dramatically improved, which has a great significance on the subsea jumper’s operation safety.

A machine learning decision support system for determining the primary factors impacting cancer survival and their temporal effect

It is critical for healthcare providers to accurately determine lung cancer patients' prognostics and develop customized treatment plans. However, lung cancer has proven to be a complex disease, and every patient responds differently to treatment options, making survivability predictions highly challenging. This study proposes a holistic machine learning model that can assist healthcare providers in predicting the temporal effects of lung cancer-related factors on one-, five-, and ten-year survival rates. Variable selection algorithms such as genetic algorithm (GA) and Baruta are employed along with data balancing methods to achieve parsimonious models for survival prediction. Classification results are obtained through logistic regression and extreme gradient boosting algorithms followed by an information fusion technique to combine the classification results and identify the temporal effects of lung cancer variables over time. Results demonstrate that the prediction power of the classification models improved as the survival period increased. The models trained using the GA and intersection variable sets generated better average prediction scores. The study contributes to the cancer literature by analyzing the varying temporal impacts of lung cancer variables over varying time periods. Medical professionals can use these findings to understand better the longitudinal characteristics of lung cancer patients’ survival indicators.

Application of image processing technology based on hierarchical analysis in visual communication system

Abstract To address the problem of poor visual expression due to traditional methods’ poor image enhancement effect, this paper proposes an image processing model for visual communication of the human-computer interaction interface based on hierarchical analysis. The edge contour feature extraction method is used to extract the convenient information features of the HCI interface, and the cross-sectional information fusion technique is combined to realize the visual communication information enhancement processing of the HCI interface, extract the spatial multi-resolution feature amount of the visual communication image of the HCI interface, and realize the visual communication optimization design of the HCI interface. In the image processing model proposed in this paper, the traditional PCANet and computer vision methods are compared to test the effectiveness of the three image recognition methods. The recognition accuracy of the method proposed in this paper is 82.55%, 85.28%, 87.06%, and 85.87% for the four types of images, respectively; the recognition accuracy of PCANet is 75.53%, 76.48%, 81.26%, and 74.74%, respectively; and the recognition accuracy of computer vision method is 73.79%, 72.83%, 79.36%, and 73.71%. Comparing the data, it can be seen that the model in this paper can improve the fidelity of visual communication and the discriminative ability of interface output, which can greatly improve the performance of the graphical human-computer interaction interface.

A similarity measure of complex-valued evidence theory for multi-source information fusion

The study of complex-valued evidence theory has become an interesting topic, particularly in the context of information fusion techniques. These studies mainly focus on its geometric interpretation and application. Moreover, little work has been conducted with regard to conflict measure. Therefore, this paper defines a complex similarity measure which quantifies the degree of conflict among complex-valued mass functions by measuring the degree of consistency among complex-valued mass functions. It also satisfies some basic properties, such as non-negativity, symmetry and etc. Complex similarity measure is an extension of traditional similarity measure in complex space, which will collapse to a similarity measure of real space when complex-valued mass functions collapse to mass functions of real space. Furthermore, some numerical examples illustrate the rationality and effectiveness of complex similarity measure. By employing complex similarity measure, this paper proposes a method to combine information and reduce the time cost associated with complex-valued Dempster's rule of combination. Experimental results on datasets indicate that for some data sets (Take iris for example), the proposed method can not only reduce the time cost (75%), but also improve the accuracy of fusion results (96%).

Deep learning ensembles for accurate fog-related low-visibility events forecasting

In this paper we propose and discuss different Deep Learning-based ensemble algorithms for a problem of low-visibility events prediction due to fog. Specifically, seven different Deep Learning (DL) architectures have been considered, from which multiple individual learners are generated. Hyperparameters of the models, including parameters concerning data preprocessing, models architecture and training procedure, are randomly selected for each model within a pre-defined discrete range. Also, every model is trained with slightly different data sampled randomly, assuring that every models introduce variety in the ensemble. Then, three different information fusion techniques are employed to build the ensemble models. The influence of the filtering process and the elitism level (the percentage of the individual models entering the ensemble) is also assessed. The performance of the proposed methodology have been tested in two real problems of low-visibility events prediction due to orographical and radiation fog, at the north of Spain. Comparison with different Machine Learning, alternative DL algorithms and meteorological-based methods show the good performance of the proposed deep learning ensembles in this problem.

Investigation into maize seed disease identification based on deep learning and multi-source spectral information fusion techniques

Detection of diseases in maize seeds is crucial for their quality evaluation and disease control. This study uses hyperspectral imaging (HSI) and deep learning methods for analysis and identification. Successive projections algorithm (SPA) and principal component analysis (PCA) were applied to extract feature variables, and data-level fusion, feature-level fusion, and decision-level fusion were employed to process different types of feature data. Classification models with different fusion strategies were built using partial least squares discriminant analysis (PLS-DA), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN-RB). The results show that the modeling performance based on spectral features outperforms that based on color and texture features. Among them, the accuracy of CNN-RB based on feature variable modeling was 94.44 %, which was better than RF (93.89 %) and SVM (92.78 %), and only second to PLS-DA (97.78 %). Different fusion strategies had different performances, among which the decision-level fusion had the best effect, with an accuracy of 98.12 %. The study shows that the proposed CNN-RB model can effectively enhance the feature extraction ability of the network, and the multi-source information fusion technique can improve the recognition performance of the model. The method has great potential for application in seed disease detection.