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In this paper, the multi-source information fusion (MIF) of human-vehicle–road-based energy management strategy (EMS) is developed for a four-wheel drive plug-in hybrid electric vehicle (4WD PHEV). First, the characteristics of 4WD PHEV configuration are analyzed. Second, a fuzzy inference method is used for driving intention recognition. Actual driving experiments are performed through the developed virtual simulated driving environment to obtain the inter-vehicle motion characteristics. Based on the MIF of driving intention, inter-vehicle motion characteristics and historical velocity, an online GA-BP velocity prediction method is developed. The online model predictive control framework is then proposed to achieve minimal fuel consumption, which combines the mathematical model of MIF based GA-BP and Dynamic Programming (DP) algorithm. The DP algorithm is selected as the solver for rolling optimization in the prediction time domain. The MIF based EMS is established with real time application capability. Finally, the simulation results show that the MIF-based strategy improves the vehicle economy by 25.11% compared with the rule based strategy. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As a pivotal research method in the field of granular computing (GrC), fuzzy rough sets (FRSs) have garnered significant attention due to their successful overcoming of the limitations of traditional rough sets in handling continuous data. This paper is dedicated to exploring the application potential of FRS models within the framework of multi-source complex information systems, which undoubtedly holds profound research significance. Firstly, a novel multi-source mixed information system (MsMIS), encompassing five distinct data types, is introduced, thereby enriching the dimensions of data processing. Subsequently, a similarity function, designed based on the unique attributes of the data, is utilized to accurately quantify the similarity relations among objects. Building on this foundation, fuzzy T-norm operators are employed to integrate the similarity matrices derived from different data types into a cohesive whole. This integration not only lays a solid foundation for subsequent model construction but also highlights the value of multi-source information fusion in the analysis of the MsMIS. The integrated results are subsequently utilized to develop FRS models. Through rigorous examination from the perspective of information granularity, the rationality of the FRS model is proven, and its mathematical properties are explored. This paper contributes to the theoretical advancement of FRS models in GrC and offers promising prospects for their practical implementation. [ABSTRACT FROM AUTHOR]

In this study, spindle precision bearings and rotary vector (RV) reducer thin-walled bearings were taken as research objects. Based on the analysis of the metamorphic layer of bearings, the multidimensional research on the surface topography of parts by multisource information fusion (grinding force, grinding temperature, grinding strain, grinding vibration, and grinding acoustic emission (AE)) was put forward. The research shows that there are "dark layers" in the outer ring and inner ring of spindle precision bearings, and "white layers" and "dark layers" in the outer ring and inner ring of RV reducer thin-walled bearings. Feed speed has the greatest influence on grinding force, wheel speed, and grinding depth have the greatest influence on grinding temperature, wheel speed has the greatest influence on grinding strain, feed speed, and wheel speed have the greatest influence on grinding vibration, and workpiece speed has the greatest influence on grinding AE. When the grinding force is minimum (x -axis: 1.3 N, y -axis: 0.9 N); the grinding temperature is highest (110.8°C), the grinding strain is maximum (46.6%), the grinding vibration is maximum (vibration range: 400 m/s2–400 m/s2) and the grinding AE is maximum (variation range: 2 V–1 V), the surface topography is most prominent (Ra: 0.97 μm). Grinding wheel speed and grinding depth have the greatest influence on surface topography, followed by feed speed, while workpiece speed has less influence. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Anhui Agricultural Sciences is the property of Journal of Anhui Agricultural Sciences Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Information fusion enables the integration and transformation of complimentary data from different sources, providing a unified representation for centralized knowledge discovery, which can contribute to effective decision-making, classification, prediction, and more. Multi-source interval-valued data, represented in the form of intervals to capture uncertainty phenomenons, is a common type of symbolic data that finds extensive applications in the real world. This paper aims to investigate the effective fusion of multi-source interval-valued data and to design dynamic updating algorithms for the situations involving multiple dimensions. The objective is to enhance the efficiency of fusion processes. Firstly, this paper use the Kullback–Leibler divergence to measure the dissimilarity between interval distributions, and construct fuzzy similarity relation. Furthermore, we define a fuzzy information granule structure of interval-valued. Secondly, the concept of fuzzy similarity relations is utilized to construct fuzzy decision-making for objects. Subsequently, based on the aforementioned fuzzy information granule structure and fuzzy decision-making, we propose a novel measure called fuzzy approximate conditional entropy and design a corresponding entropy fusion model. Finally, we discuss various scenarios where dynamic changes occur simultaneously in the attributes and information sources of dynamic multi-source interval-valued data. We design corresponding dynamic update algorithms for these situations. Numerical experiments are conducted on nine UCI datasets to validate our proposed fusion method. The experimental results indicate that our fusion approach exhibits improved classification performance compared to the common fusion methods. The designed dynamic update algorithms are also capable of reducing computation time and enhancing fusion efficiency. [ABSTRACT FROM AUTHOR]

The transformer plays a critical role in maintaining the stability and smooth operation of the entire power system, particularly in power transmission and distribution. The paper begins by providing an overview of traditional fault diagnosis methods for transformers, including dissolved gas analysis and vibration analysis techniques, elucidating their developmental trajectory. Building upon these traditional methods, numerous researchers have aimed to enhance and optimize them through intelligent technologies such as neural networks, machine learning, and support vector machines. These researchers have addressed common issues in traditional fault diagnosis methods, such as the low correlation between characteristic parameters and faults, ambiguous fault descriptions, and the complexity of feature analysis. However, due to the complexity of transformer structures and the uncertainties in operating environments, the collection and analysis of characteristic parameters becomes highly intricate. Researchers have further refined algorithms and feature values based on intelligent diagnostic algorithms for transformers. The goal is to improve diagnostic speed, mitigate the impact of measurement noise, and further advance the adaptability of artificial intelligence technology in the field of transformers. On the other hand, the excellent multi-parameter analysis capability of artificial intelligence technology is more suitable for transformer diagnostic techniques that involve the fusion of multiple information sources. Through the powerful data acquisition, processing, and decision-making capabilities provided by intelligent algorithms, it can comprehensively analyze non-electrical parameters such as oil and gas characteristics, vibration signals, temperature, along with electrical parameters like short-circuit reactance and load ratio. Moreover, it can automatically analyze the inherent relationship between faults and characteristic quantities and provide decision-making suggestions. This technique plays a pivotal role in ensuring transformer safety and power network security, emerging as a prominent direction in transformer fault diagnosis research. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

During the thermal design of CNC machine tool feed systems, it is essential to obtain the thermal characteristics of components to improve the machining precision and reduce research and development costs. However, the traditional mechanism and the data-driven models with single structural information input generally cannot meet the actual prediction requirements. In this paper, a method based on heterogeneous correlation information fusion was proposed, aiming to predict the temperature increase characteristics of Multi-Component Feed System (MCFS). Firstly, the heterogeneous correlation temperature information of components for feed system was collected and calibrated via the temperature sensor (contact type) and thermal imager (non-contact type), respectively. Then, the heterogeneous information features are extracted from various aspects, and the fusion information feature matrix is constructed based on correlation screening and principal component analysis. Finally, the ensemble learning method based on Extreme Gradient Boosting was used to establish the temperature rise prediction model, and the Aquila Optimizer (AO) and three-fold cross-test were used to improve the accuracy and stability of the obtained prediction model. To verify the effectiveness of this method, a testing platform for MCFS heterogeneous information collection and testing was built. Experiments and data collection were carried out on a specialized machine tool under different working conditions. The experimental results have shown that the proposed temperature rise model can achieve accurate prediction of different components of feed system under various working conditions. Compared to the traditional prediction model with a single data structure information input, the information acquisition is more comprehensive, and the error is significantly reduced. [ABSTRACT FROM AUTHOR]

The museum system is exposed to a high risk of seismic hazards. However, it is difficult to carry out seismic hazard prevention to protect cultural relics in collections due to the lack of real data and diverse types of seismic hazards. To address this problem, we developed a deep-learning-based multi-source feature-fusion method to assess the data on seismic damage caused by collected cultural relics. Firstly, a multi-source data-processing strategy was developed according to the needs of seismic impact analysis of the cultural relics in the collection, and a seismic event-ontology model of cultural relics was constructed. Additionally, a seismic damage data-classification acquisition method and empirical calculation model were designed. Secondly, we proposed a deep learning-based multi-source feature-fusion matching method for cultural relics. By constructing a damage state assessment model of cultural relics using superpixel map convolutional fusion and an automatic data-matching model, the quality and processing efficiency of seismic damage data of the cultural relics in the collection were improved. Finally, we formed a dataset oriented to the seismic damage risk analysis of the cultural relics in the collection. The experimental results show that the accuracy of this method reaches 93.6%, and the accuracy of cultural relics label matching is as high as 82.6% compared with many kinds of earthquake damage state assessment models. This method can provide more accurate and efficient data support, along with a scientific basis for subsequent research on the impact analysis of seismic damage to cultural relics in collections. [ABSTRACT FROM AUTHOR]

Copyright of Smart Agriculture is the property of Smart Agriculture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Addressing the issues that single-source information cannot comprehensively reflect the operational status of equipment, redundant features fail to diagnose effectively, and information fusion is challenging, this study explores a novel method of feature extraction, selection, and fusion for wind turbine rolling bearing from the perspective of multi-viewpoint and multi-source heterogeneous information fusion, aimed at diagnosing inner ring crack faults of rolling bearings. Initially, based on the failure mechanism of rolling bearings, a multi-source and multi-domain feature set is constructed from both signal processing and data-driven perspectives. By investigating the latent relationships among feature variables, a random forest model is utilized to optimize and reduce the dimensionality of the multi-source feature set. Subsequently, an improved PCR6 method is employed for decision-level fusion of the random forest classification results, thereby facilitating fault extraction, dimensionality reduction, and fault classification of wind turbine bearings from multi-source and multi-viewpoint perspectives. The results indicate that the constructed multi-source and multi-viewpoint feature set enhances the model's recognition performance (with a 5% increase in accuracy), and the fusion of features and decision layers further improves the accuracy of fault diagnosis. In cases where single-feature set classification is erroneous, the proposed decision-layer fusion model's classification probability can provide accurate fault classification. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of single-sensor sensing information characterization ability, which is easily interfered with by external environmental factors, a method of intelligent perception is proposed in this paper. This method integrates multi-source and multi-level information, including spindle temperature field, spindle thermal deformation, operating parameters, and motor current. Firstly, the internal and external thermal-error-related signals of the spindle system are collected by sensors, and the feature parameters are extracted; then, the radial basis function (RBF) neural network is utilized to realize the preliminary integration of the feature parameters because of the advantages of the RBF neural network, which offers strong multi-dimensional solid nonlinear mapping ability and generalization ability. Thermal-error decision values are then generated by a weighted fusion of different pieces of evidence by considering uncertain information from multiple sources. The spindle thermal-error sensing experiment was based on the spindle system of the VMC850 (Yunnan Machine Tool Group Co., LTD, Yunnan, China) vertical machining center of the Yunnan Machine Tool Factory. Experiments were designed for thermal-error sensing of the spindle under constant speed (2000 r/min and 4000 r/min), standard variable speed, and stepped variable speed conditions. The experiment's results show that the prediction accuracy of the intelligent-sensing model with multi-source information fusion can reach 98.1%, 99.3%, 98.6%, and 98.8% under the above working conditions, respectively. The intelligent-perception model proposed in this paper has higher accuracy and lower residual error than the traditional BP neural network perception and wavelet neural network models. The research in this paper provides a theoretical basis for the operation, maintenance management, and performance optimization of machine tool spindle systems. [ABSTRACT FROM AUTHOR]

Accurate identification of potential drug-target pairs is a crucial step in drug development and drug repositioning, which is characterized by the ability of the drug to bind to and modulate the activity of the target molecule, resulting in the desired therapeutic effect. As machine learning and deep learning technologies advance, an increasing number of models are being engaged for the prediction of drug-target interactions. However, there is still a great challenge to improve the accuracy and efficiency of predicting. In this study, we proposed a deep learning method called Multi-source Information Fusion and Attention Mechanism for Drug-Target Interaction (MIFAM-DTI) to predict drug-target interactions. Firstly, the physicochemical property feature vector and the Molecular ACCess System molecular fingerprint feature vector of a drug were extracted based on its SMILES sequence. The dipeptide composition feature vector and the Evolutionary Scale Modeling -1b feature vector of a target were constructed based on its amino acid sequence information. Secondly, the PCA method was employed to reduce the dimensionality of the four feature vectors, and the adjacency matrices were constructed by calculating the cosine similarity. Thirdly, the two feature vectors of each drug were concatenated and the two adjacency matrices were subjected to a logical OR operation. And then they were fed into a model composed of graph attention network and multi-head self-attention to obtain the final drug feature vectors. With the same method, the final target feature vectors were obtained. Finally, these final feature vectors were concatenated, which served as the input to a fully connected layer, resulting in the prediction output. MIFAM-DTI not only integrated multi-source information to capture the drug and target features more comprehensively, but also utilized the graph attention network and multi-head self-attention to autonomously learn attention weights and more comprehensively capture information in sequence data. Experimental results demonstrated that MIFAM-DTI outperformed state-of-the-art methods in terms of AUC and AUPR. Case study results of coenzymes involved in cellular energy metabolism also demonstrated the effectiveness and practicality of MIFAM-DTI. [ABSTRACT FROM AUTHOR]

A comprehensive assessment of the consequences of dam-break is a critical strategic necessity for guaranteeing socio-economic development and lives for individuals. The consequences of dam-break are affected comprehensively by a multitude of uncertainties, resulting in multi-source and inconsistent relationships between indicators. It is extremely tough to integrate information from different sources adequately under multiple uncertainties, which often limit the assessment reliability. In this work, a comprehensive uncertainty evaluation methodology for the consequences of dam-break was developed through multi-source information fusion. Firstly, cloud model was employed to deal with randomness and fuzziness in the quantification of the grading of indicators and constructed the basic probability assignment function of the evidence corresponding to each data source. Then, in order to address the issue that conflicting evidence cannot be effectively fused utilizing traditional evidence theory. The basic probability assignment function was fused by the improved evidence theory. Furthermore, due to the differences in the importance of each data source in the assessment process. The corresponding weights were determined employing trapezoidal fuzzy analytic hierarchy process and entropy weight method. Finally, the effectiveness of the method was verified by taking five reservoirs in the Haihe River Basin. It shows that multiple uncertainties from different sources of information are combined and handled and the severity grades of consequences of dam-break can be quantitatively analyzed with our assessment method. Meanwhile, multi-source information with conflicts and uncertainties can be approached to produce more reliable risk assessment results in the situation of highly conflicting evidence. [ABSTRACT FROM AUTHOR]

Introduction: Log anomaly detection is essential for monitoring and maintaining the normal operation of systems. With the rapid development and maturation of deep learning technologies, deep learning-based log anomaly detection has become a prominent research area. However, existing methods primarily concentrate on directly detecting log data in a single stage using specific anomaly information, such as log sequential information or log semantic information. This leads to a limited understanding of log data, resulting in low detection accuracy and poor model robustness. Methods: To tackle this challenge, we propose LogMS, a multi-stage log anomaly detection method based on multi-source information fusion and probability label estimation. Before anomaly detection, the logs undergo parsing and vectorization to capture semantic information. Subsequently, we propose a multi-source information fusion-based long short-term memory (MSIF-LSTM) network for the initial stage of anomaly log detection. By fusing semantic information, sequential information, and quantitative information, MSIF-LSTM enhances the anomaly detection capability. Furthermore, we introduce a probability label estimation-based gate recurrent unit (PLE-GRU) network, which leverages easily obtainable normal log labels to construct pseudo-labeled data and train a GRU for further detection. PLE-GRU enhances the detection capability from the perspective of label information. To ensure the overall efficiency of the LogMS, the second-stage will only be activated when anomalies are not detected in the first stage. Results and Discussion: Experimental results demonstrate that LogMS outperforms baseline models across various log anomaly detection datasets, exhibiting superior performance in robustness testing. [ABSTRACT FROM AUTHOR]

Recently, the role of competing endogenous RNAs in regulating gene expression through the interaction of microRNAs has been closely associated with the expression of circular RNAs (circRNAs) in various biological processes such as reproduction and apoptosis. While the number of confirmed circRNA–miRNA interactions (CMIs) continues to increase, the conventional in vitro approaches for discovery are expensive, labor intensive, and time consuming. Therefore, there is an urgent need for effective prediction of potential CMIs through appropriate data modeling and prediction based on known information. In this study, we proposed a novel model, called DeepCMI, that utilizes multi-source information on circRNA/miRNA to predict potential CMIs. Comprehensive evaluations on the CMI-9905 and CMI-9589 datasets demonstrated that DeepCMI successfully infers potential CMIs. Specifically, DeepCMI achieved AUC values of 90.54% and 94.8% on the CMI-9905 and CMI-9589 datasets, respectively. These results suggest that DeepCMI is an effective model for predicting potential CMIs and has the potential to significantly reduce the need for downstream in vitro studies. To facilitate the use of our trained model and data, we have constructed a computational platform, which is available at http://120.77.11.78/DeepCMI/. The source code and datasets used in this work are available at https://github.com/LiYuechao1998/DeepCMI. [ABSTRACT FROM AUTHOR]

Currently, surface EMG signals have a wide range of applications in human–computer interaction systems. However, selecting features for gesture recognition models based on traditional machine learning can be challenging and may not yield satisfactory results. Considering the strong nonlinear generalization ability of neural networks, this paper proposes a two-stream residual network model with an attention mechanism for gesture recognition. One branch processes surface EMG signals, while the other processes hand acceleration signals. Segmented networks are utilized to fully extract the physiological and kinematic features of the hand. To enhance the model's capacity to learn crucial information, we introduce an attention mechanism after global average pooling. This mechanism strengthens relevant features and weakens irrelevant ones. Finally, the deep features obtained from the two branches of learning are fused to further improve the accuracy of multi-gesture recognition. The experiments conducted on the NinaPro DB2 public dataset resulted in a recognition accuracy of 88.25% for 49 gestures. This demonstrates that our network model can effectively capture gesture features, enhancing accuracy and robustness across various gestures. This approach to multi-source information fusion is expected to provide more accurate and real-time commands for exoskeleton robots and myoelectric prosthetic control systems, thereby enhancing the user experience and the naturalness of robot operation. [ABSTRACT FROM AUTHOR]

The current industrial knowledge graph often faces the challenge of data sparsity, which can significantly impact its effectiveness and reliability in daily operational processes. To address this challenge and ensure the integrity of the knowledge graph, we propose a novel method for link prediction that leverages both entity descriptions and neighborhood structure information. Specifically, our method uses BERT pre-training to obtain meaningful embeddings from entity descriptions and the R-GCN model to capture the structural patterns within neighborhoods. Additionally, a CNN is employed to fuse and decode these two types of representations, ensuring high accuracy in predicting missing links. We have evaluated our method on publicly available datasets, and the experimental results show its superiority over baseline models. Furthermore, when tested on the SEFD dataset for steel fault diagnosis, our method effectively completes the knowledge graph for this industrial domain. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has been widely used to promote the development of intelligent fault diagnosis, bringing significant performance improvement. However, most of the existing methods cannot capture the temporal information and global features of mechanical equipment to collect sufficient fault information, resulting in performance collapse. Meanwhile, due to the complex and harsh operating environment, it is difficult to extract fault features stably and extensively using single-source fault diagnosis methods. Therefore, a novel hierarchical vision transformer (NHVT) and wavelet time–frequency architecture combined with a multi-source information fusion (MSIF) strategy has been suggested in this paper to boost stable performance by extracting and integrating rich features. The goal is to improve the end-to-end fault diagnosis performance of mechanical components. First, multi-source signals are transformed into two-dimensional time and frequency diagrams. Then, a novel hierarchical vision transformer is introduced to improve the nonlinear representation of feature maps to enrich fault features. Next, multi-source information diagrams are fused into the proposed NHVT to produce more comprehensive presentations. Finally, we employed two different multi-source datasets to verify the superiority of the proposed NHVT. Then, NHVT outperformed the state-of-the-art approach (SOTA) on the multi-source dataset of mechanical components, and the experimental results show that it is able to extract useful features from multisource information. [ABSTRACT FROM AUTHOR]

Effective fault diagnosis for diaphragm pumps is crucial. This paper proposes a diaphragm pump fault diagnosis method based on deep learning and multi-source information fusion (DMF). The time-domain features, frequency-domain features, and modulation features are extracted from the vibration signals from eight different positions. After feature enhancement and data preprocessing, the features are input into auto encoders (AE), convolutional neural networks (CNN), and support vector machines (SVM) to obtain the diagnostic results. The results indicate that the DMF method achieves a fault diagnosis accuracy of 99.98%, which is on average 9.09% higher than using a single diagnostic model. The demodulation method is more suitable for vibration signal feature extraction of the diaphragm pump, while the CNN is more suitable for identification of diaphragm pump faults. Specifically, it outperformed the sampling point 1-DPCA-AE model by 13.98% and the sampling point 4-DPCA-SVM model by 8.98%. [ABSTRACT FROM AUTHOR]

Applications in industrial production have indicated that the challenges of sparse fault samples and singular monitoring data will diminish the performance of deep learning-based diagnostic models to varying degrees. To alleviate the above issues, a multi-source information fusion meta-learning network with convolutional block attention module (CBAM) is proposed in this study for bearing fault diagnosis under limited dataset. This method can fully extract and exploit the complementary and enriched fault-related features in the multi-source monitoring data through the designed multi-branch fusion structure and incorporate metric-based meta-learning to enhance the fault diagnosis performance of the model under limited data samples. Furthermore, the introduction of CBAM can further assist the model to trade-off and focus on more discriminative information in both spatial and channel dimensions. Extensive experiments conducted on two bearing datasets that cover multi-source monitoring data fully demonstrate the validity and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

As the profundity of open-pit mining operations has increased, so has the frequency of geological disasters. The complex interaction of factors causing these disasters presents technical challenges for early warning and control systems. However, emergent technologies such as the internet, 5G networks, and artificial intelligence provide new opportunities for constructing integrated digital early warning platforms that synthesise multifaceted monitoring data to predict and mitigate open-pit mine hazards. Using efficient Internet-mediated information integration, data from various sources can be consolidated for enhanced disaster management. This paper reviews the current state of digital early warning platforms for open-pit mines using a Web of Science database search for pertinent literature. The framework, data layer, technology layer, and application layer of these platforms are investigated in order to identify associated technologies and obstacles. Important results include: (1) Inconsistent data formats and monitoring software diminish platform workflow efficiency. Robust data exchange protocols and feature-rich software could increase efficiency. (2) Platforms rely on limited data types as opposed to intelligent algorithms that integrate diverse monitoring inputs into global disaster predictions. The underutilization of advanced technologies such as artificial intelligence, the internet of things, and cloud computing. Mining calamity mechanisms and rock mechanics require additional study. [ABSTRACT FROM AUTHOR]

Tunnel collapses are common hazards in construction, and they significantly constraint construction progress and safety. Currently, research on the risk assessment of tunnel collapses primarily relies on a single source of information, which leads to distorted evaluations owing to the limitations of data sources. In contrast, using multi-source information offers strong adaptability, high credibility, and complementarity. Therefore, to enhance the accuracy of tunnel collapse risk assessments, this study proposes a novel approach that combines three types of information sources: historical engineering cases, expert knowledge, and on-site practical information. First, artificial neural networks, knowledge evaluation matrices, and cloud models are used to extract evidence from the three types of information sources, thereby acquiring preliminary evidence of collapse risk. When extracting expert knowledge information, an improved similarity aggregation method that comprehensively considers judgment ability and recognition is proposed to reduce the impact of expert subjectivity. Next, to address evidence conflicts in the fusion process, a distance metric based on belief intervals is constructed to calculate evidence credibility, and evidence importance is incorporated to reconstruct the multi-source evidence information. Subsequently, Dempster's synthesis rule is used to fuse the reconstructed evidence, and the collapse risk is calculated by deblurring the fusion results. Finally, the proposed method is applied to the Yanglin Tunnel in China, and the results are consistent with the onsite construction situation. Therefore, the proposed method is feasible and practical, and it can provide a valid reference for risk assessment in similar projects. [ABSTRACT FROM AUTHOR]

To address the issue of integrity monitoring for a multi-source information fusion navigation system, a theoretical framework of two-level integrity monitoring is proposed. Firstly, at the system level, a system-integrity-monitoring method based on the Kalman filter weighted least-squares form is established to detect and isolate faulty navigation sources. Secondly, at the sensor level, considering the redundancy of the faulty navigation sources, this paper presents the design of a multi-mode comprehensive fault-detection method for non-redundant navigation sources. Additionally, an extended-dimension matrix optimized sensor-fault detection and verification method for redundant navigation sources is proposed. Finally, integrity risk allocation criteria are established based on the effectiveness of navigation sources to calculate the system protection level and trigger integrity alarms. The two-level integrity-monitoring method was tested on a multi-source information fusion navigation system integrated with an inertial navigation system (INS), global positioning system (GPS), BeiDou satellite navigation system (BDS), Doppler velocity log (DVL), barometric altimeter (BA), and terrain-aided navigation (TAN). Test results demonstrate that the proposed method can effectively isolate the faulty navigation source within 10 s. Furthermore, it can detect the faulty sensors within the faulty navigation sources, thereby enhancing the reliability and robustness of the multi-source information fusion navigation system. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Central South University is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To obtain multi-source information generated during the friction process, environment-based design methodology was used to analyze the current situation and demand of tribological test research. A recursive object model of the multi-source information acquisition system was constructed, and by identifying key conflicts and seeking solutions, the scheme design of the system was realized. Through the deployment of multiple sensors and the development of Lab VIEW software, a multi-source tribological information acquisition system was developed on a general tribometer, which realized the real-time acquisition, storage and display of multi-source information such as vibration, sound, and sound pressure during the friction process.The system was further integrated into a digital twin virtual entity of the tribometer based on Unity 3D. Meanwhile, in order to facilitate the subsequent utilization of multi-source information, a corresponding signal preprocessing method was proposed. The test verifies that the system meets the requirements of multi-source information acquisition during friction tests, and can provide a data basis for subsequent multi-source information fusion and multi-disciplinary information association mapping, as well as the realization of tribological state monitoring based on derived state information. [ABSTRACT FROM AUTHOR]

As one of the most important mobile inspection equipment for daily inspection and testing and commissioning of high-speed railway, the high speed comprehensive inspection train lacks efficient information integration, on-site decision-making and command management tools. Combined with the characteristics of daily inspection and testing and commissioning, the construction goal of multi-source information fusion integration platform for high speed comprehensive inspection train is proposed, and the overall architecture, function, interface and hardware architecture of the platform are designed. The relationship between information fusion and information integration is analyzed, and the realization process of multi-source information fusion technology in high speed comprehensive inspection train is expounded. Research and innovative applications of the key technologies such as BIM information fusion, GIS information fusion, infrastructure equipment fusion, multi-map collaborative linkage, dynamic loading of high-volume BIM models, cross-platform interconnection and information sharing, and multi-dimensional visualization are implemented. The multi-source information fusion integration platform of high speed comprehensive inspection train realizes multi-dimensional information integration and sharing, multi-disciplinary data fusion analysis, integrated decision-making and visual display based on 'One screen + M pictures', improves the efficiency of multi-party collaborative communication, improves the intelligent management level of infrastructure, and provides technical support for high-speed railway operation safety assessment and maintenance guidance. [ABSTRACT FROM AUTHOR]

The conventional SINS/CNS integrated navigation system equipped on ballistic missiles is typically equipped with attitude measurement, which can only estimate the gyro drift and has no effect on accelerometer bias. To address the issue, an improved multi-source information fusion method containing a new nonlinear framework called SINS/RKCNS with the indirect horizon reference and kinematic constraint is proposed, and a MAP-based modified iterated CKF is involved to increase positioning accuracy and system robustness. Furthermore, to reduce the influence of correlated noise, state augmentation is employed in the iterative process. Eventually, experiments are conducted, and the results confirm the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Dempster-Shafer theory (DST), as a generalization of Bayesian probability theory, is a useful technique for achieving multi-source information fusion under uncertain environments. Nevertheless, when a high degree of conflict exists between pieces of evidence, unreasonable results are often generated using Dempster's combination rule. How to fuse highly conflicting information is still an open problem. In this study, we first propose an improved belief Hellinger divergence measure, which can fully consider the uncertainty in basic probability assignments, to quantify the conflict level between evidence. Second, some properties (i.e., nonnegativity, nondegeneracy, symmetry, and trigonometric inequality) of the proposed divergence measure are discussed. Then, we present a novel multi-source information fusion strategy, in which the credibility of the evidence is determined based on external discrepancy and internal ambiguity. Additionally, we consider the decay of credibility when fusing evidence across different times. Finally, applications in fault diagnosis and Iris dataset classification are presented to demonstrate the effectiveness of our method. The results indicate that our approach is more reasonable and can identify the target with a higher belief degree. [ABSTRACT FROM AUTHOR]

A multi‐source information fusion model is proposed to assess the safety and reliability of bamboo‐integrated products, which, unlike metal materials, hold different properties in longitudinal and transverse surfaces/directions. First, a weight factor is proposed to estimate the importance of each data source. Subsequently, a multi‐source information fusion model is proposed to compute the weight factors of the bamboo‐based materials in longitudinal and transverse directions. The reliability and failure probability of a bamboo‐integrated product, in longitudinal and transverse directions, are computed according to the multi‐source information fusion model‐based safety assessment approach with the assistance of the Stress and Strength interface model. Finally, a comparison between the proposed safety assessment method and the conventional Stress and Strength interface model is finalized, and the results indicate that the proposed method computes more reliable results than that of the conventional method. Overall, the proposed method provides a framework for safety and reliability assessment under multiple data sources and contributes to the comprehensive application of diverse data. [ABSTRACT FROM AUTHOR]

Underground coal fires are a widespread disaster prevailing in major coal-producing countries globally, posing serious threats to the ecological environment and restricting the safe exploitation of coal mines. The accuracy of underground coal fire detection directly affects the effectiveness of fire control engineering. In this study, we searched 426 articles from the Web of Science database within 2002–2022 as the data foundation and visualized the research contents of the underground coal fire field using VOSviewer and CiteSpace. The results reveal that the investigation of "underground coal fire detection techniques" is currently the focal area of research in this field. Additionally, the "underground coal fire multi-information fusion inversion detection methods" are considered to be the future research trend. Moreover, we reviewed the strengths and weaknesses of various single-indicator inversion detection methods, including the temperature method, gas and radon method, natural potential method, magnetic method, electric method, remote sensing, and geological radar method. Furthermore, we conducted an analysis of the advantages of the multi-information fusion inversion detection methods, which possesses high precision and wide applicability for detecting coal fires, while highlighting the challenges in handling diverse data sources. It is our hope that the research results presented in this paper will provide valuable insights and ideas for researchers involved in the detection and practical research of underground coal fires. [ABSTRACT FROM AUTHOR]

The failure mode and effects analysis (FMEA) is a commonly adopted approach in engineering failure analysis, wherein the risk priority number (RPN) is utilized to rank failure modes. However, assessments made by FMEA experts are full of uncertainty. To deal with this issue, we propose a new uncertainty management approach for the assessments given by experts based on negation information and belief entropy in the Dempster–Shafer evidence theory framework. First, the assessments of FMEA experts are modeled as basic probability assignments (BPA) in evidence theory. Next, the negation of BPA is calculated to extract more valuable information from a new perspective of uncertain information. Then, by utilizing the belief entropy, the degree of uncertainty of the negation information is measured to represent the uncertainty of different risk factors in the RPN. Finally, the new RPN value of each failure mode is calculated for the ranking of each FMEA item in risk analysis. The rationality and effectiveness of the proposed method is verified through its application in a risk analysis conducted for an aircraft turbine rotor blade. [ABSTRACT FROM AUTHOR]

Multi-source information fusion technology has been widely used because it can maximize the use of information that collected from multiple data sources for decision fusion. As an uncertain information processing theory, Dempster-Shafer (D-S) evidence theory is prevalent in the field of multi-source information fusion. However, there is still room for improvement in the handling of uncertain problems involving highly conflicting evidence sources. For the purpose of improving the practicality and efficiency in handling highly conflicting evidence sources, a new belief Rényi divergence is defined to measure the discrepancy between evidences in D-S evidence theory. The proposed belief Rényi divergence takes belief function (Bel) and plausibility function (Pl) into account, thus allowing it to provide a more rational and telling approach for measuring differences between evidence. Moreover, some important properties of belief Rényi divergence have been studied, the belief Rényi divergence regards to Hellinger distance, Kullback-Leibler divergence and χ2 divergence, which ensures that the metric has a wider range of application scenarios. Based on the proposed belief Rényi divergence measure, a novel multi-source information fusion method is designed. The proposed belief Rényi divergence is used to measure difference between evidence; Deng entropy is used to quantify the uncertainty, thereby calculating information volume of the evidence. Accordingly, the proposed method can fully assess relationship among evidences and information volume of each evidence. Through a comprehensive analysis and experiments, practicality and effectiveness of the proposed method for multi-source information fusion are verified. Finally, an iris dataset-based experiment is implemented to verify the new proposed divergence measure and the multi-source information fusion algorithm has a more extensive applicability. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Compared to traditional measurement devices, the micro-synchrophasor measurement unit (D-PMU or μPMU) in the distribution power system has great differences in data acquisition frequency, data format, data dimension, time-stamped information, etc. Hence, it is imperative to research the integration mechanism of heterogeneous data from multiple sources. Based on the analysis of the current technology of multi-source information fusion, this paper proposes a novel approach, which considers two aspects: the interoperability of multi-source data and the real-time processing of large-scale streaming data. To solve the problem of data interoperability, we have modified the model of D-PMU data and established a unified information model. Meanwhile, an advanced distributed processing technology has been deployed to solve the problem of real-time processing of streaming data. Based on this approach, a smart distribution power system wide-area measurement and control station can be established, and the correctness and practicality of the proposed method are verified by an on-field project. [ABSTRACT FROM AUTHOR]

Multi-source information fusion is a sophisticated estimating technique that enables users to analyze more precisely complex situations by successfully merging key evidence in the vast, varied, and occasionally contradictory data obtained from various sources. Restricted by the data collection technology and incomplete data of information sources, it may lead to large uncertainty in the fusion process and affect the quality of fusion. Reducing uncertainty in the fusion process is one of the most important challenges for information fusion. In view of this, a multi-source information fusion method based on information sets (MSIF) is proposed in this paper. The information set is a new method for the representation of granularized information source values using the entropy framework in the possibilistic domain. First, four types of common membership functions are used to construct the possibilistic domain as the information gain function (or agent). Then, Shannon agent entropy and Shannon inverse agent entropy are defined, and their summation is used to evaluate the total uncertainty of the attribute values and agents. Finally, an MSIF algorithm is designed by infimum-measure approach. The experimental results show that the performance of Gaussian kernel function is good, which provides an effective method for fusing multi-source numerical data. [ABSTRACT FROM AUTHOR]

An early warning method of compressor valve fault based on multi-parameter signals (vibration, pressure, and temperature) is presented in this work. Due to the complexity working condition, the run data of the compressor are of problems like noise and feature aliasing, which makes it difficult to extract useful features and find out the running law from the original signals. In this work, an improved deep learning network Multi-Level Fusion long short-term memory based on Component Evaluating Empirical Mode Decomposition and Fuzzy C-Means (CEEMD-FCM & MLF-LSTM) for parameter prediction of reciprocating compressor and an information fusion strategy is proposed for compressor valve fault warning. The CEEMD-FCM & MLF-LSTM network consists of data processing block, information learning block, and prediction output block, which is mainly responsible for parameter prediction. In the data processing block, the CEEMD-FCM algorithm is used for parameter decomposition, noise removal, and fuzzy mode (FM) reconstruction, which generates the input for the information learning block to ensure the predicting accuracy and reduce model complexity. MLF-LSTM is constructed to predict the parameter in the future by learning the temporal and spatial characteristics of FMs of the run data. Then, an early warning strategy for compressor valve fault based on multi-source information fusion is developed. Experimental results have verified that the proposed CEEMD-FCM & MLF-LSTM model and early warning strategy could realize early warning of compressor valve fault effectively. [ABSTRACT FROM AUTHOR]

Aiming at the requirements of offline high-precision navigation and positioning in the mapping of ocean floor, survey, and other fields for autonomous underwater vehicle (AUV)integrated navigation system, a post-processing algorithm based on factor graph optimization is proposed in this paper. First, the factor graph model is established by the factor graph formulation to address the asynchronous and heterogeneous problem of multi-source information fusion. Then the equivalent inertial measurement unit (IMU) factor is introduced to replace several consecutive IMU factors between two adjacent measurements to further aid high-rate navigation solutions. Second the forward and backward message passing processes are performed independently using the sum-product algorithm in the factor graph model. Finally, the optimal navigation solution is obtained by weighted smoothing the results of the forward and backward message passing processes. Simulation results show this algorithm proposed in this paper provides more accurate navigation accuracy and smoothness, especially in the case of signals loss or sensors fault. Compared with the federal Kalman filtering method, the horizontal positioning errors of the un-simplified factor graph, the simplified factor graph, and the factor graph optimization are improved by about 30%, 34%, and 80%, respectively. The semi-physical environment results also clearly verify the reliability and effectiveness of the proposed method, and its horizontal positioning accuracies are kept within −2∼2 m. [ABSTRACT FROM AUTHOR]

Leaf mildew is a common disease of tomato leaves. Its detection is an important means to reduce yield loss from the disease and improve tomato quality. In this study, a new method was developed for the multi-source detection of tomato leaf mildew by THz hyperspectral imaging through combining internal and external leaf features. First, multi-source information obtained from tomato leaves of different disease grades was extracted by near-infrared hyperspectral imaging and THz time-domain spectroscopy, while the influence of low-frequency noise was removed by the Savitzky Golay (SG) smoothing algorithm. A genetic algorithm (GA) was used to optimize the selection of the characteristic near-infrared hyperspectral band. Principal component analysis (PCA) was employed to optimize the THz characteristic absorption spectra and power spectrum dimensions. Recognition models were developed for different grades of tomato leaf mildew infestation by incorporating near-infrared hyperspectral imaging, THz absorbance, and power spectra using the backpropagation neural network (BPNN), and the models had recognition rates of 95%, 96.67%, and 95%, respectively. Based on the near-infrared hyperspectral features, THz time-domain spectrum features, and classification model, the probability density of the posterior distribution of tomato leaf health parameter variables was recalculated by a Bayesian network model. Finally, a fusion diagnosis and health evaluation model of tomato leaf mildew with hyperspectral fusion THz was established, and the recognition rate of tomato leaf mildew samples reached 97.12%, which improved the recognition accuracy by 0.45% when compared with the single detection method, thereby achieving the accurate detection of facility diseases. [ABSTRACT FROM AUTHOR]

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As a single feature parameter cannot comprehensively evaluate the health status of a battery, a multi-source information fusion method based on the Gaussian mixture model and Bayesian inference distance is proposed for the health assessment of vehicle batteries. The missing and abnormal data from real-life vehicle operations are preprocessed to extract the sensitive characteristic parameters which determine the battery performance. The normal state Gaussian mixture model is established using the fault-free state data, whereas the Bayesian inference distance is constructed as an index to quantitatively evaluate the battery performance state. In order to solve the problem that abnormal data may exist in the measured data and introduce errors into evaluation results, the determination rules of abnormal data are formulated. The verification of real-life vehicle operation data reveals that the proposed method can accurately evaluate the onboard battery state and reduce safety hazards of electric vehicles during the normal operation process. [ABSTRACT FROM AUTHOR]

The existing motor fault classification methods mostly use sensors to detect a single fault feature, which makes it difficult to ensure high diagnostic accuracy. In this paper, a motor fault classification method based on multi-source information fusion Naive Bayes classification algorithm is proposed. Firstly, this paper introduces the concept and advantages of multi-source information fusion, as well as its problems of miscellaneous information and inconsistent data magnitude. For example, as this paper classifies the fault of generators, there are many physical quantities, such as voltage, current and temperature, which are not in the same dimension, therefore it is difficult to fuse. Then, aiming at the corresponding problems, this paper uses a PCA dimension reduction method to remove redundant information and reduce the dimension of multi-dimensional complex information. Aiming at the problem of unequal data magnitude, the interval mapping method is adopted to effectively solve the misjudgment caused by unequal data magnitude. After the initial multi-source information processing, the classical Naive Bayes classification algorithm is used for fault classification, and the algorithm diagnosis and verification are carried out according to the statistical fault data. Use of the algorithm increases accuracy to more than 97%. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)