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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]

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]

In order to enhance the suppleness of a lower limb rehabilitation medical robot during the re-habilitation process, this study proposes a multi-source information fusion lower limb motion intention recognition method based on surface electromyographic signals (sEMG) and lower limb joint angles. To solve the problem of data traffic surge during the collection process, a multi-source current limiting sliding time window algorithm (MLS) is proposed. The MLS algorithm controls the data flow through a flow limiting and sliding time window mechanism to ensure the efficiency and stability of the system in handling large data volumes. On this basis, the study combines the Back Propagation Generalized Algorithm Neural-network (BPGN) to construct a prediction model for lower limb joint angles. The experimental results show that under the same conditions of the algorithm, the fusion of multi-source information reduces the average error of knee joint angle prediction by 10.8° and the average error of ankle joint angle prediction by 7.2° compared with the method using a single lower limb joint angle signal. Under the same condition of input signal, the multivariate flow-limiting sliding time-window BPGN reduced the average knee joint error by 13.6° and the average ankle joint angle error by 8.5° compared to the BPGN intent recognition. The multivariate flow-limited sliding time window BPGN reduced the mean knee error by 11.2° and the mean ankle angle error by 7.4° compared to Radial Basis Function (RBF) Neural-network intent recognition. By integrating the sEMG signal and lower limb joint angle information, the system can more accurately capture the patient’s movement intention and realize more precise lower limb rehabilitation training.

To address the challenges of feature fusion, real-time performance, and model complexity in the application of image and vibration signal fusion for coal-gangue identification, a multi-head attention (MA)-based multi-layer long short-term memory (ML-LSTM) model, i.e., MA-ML-LSTM, was proposed. The variational mode decomposition (VMD) algorithm, optimized by particle swarm optimization (PSO), was employed to process vibration signals. Features such as energy, energy moment, kurtosis, waveform factor, and matrix singular values were extracted. A one-dimensional convolutional network was used to acquire vibration information. For image feature extraction, the fully connected layer of the multi-classification network ResNet-18 was removed, enabling the extraction of deep features from coal-gangue images. Dual-channel feature fusion of images and vibration signals was achieved using the MA mechanism and the ML-LSTM network, enhancing the expression of significant features in each channel. Experimental results demonstrated that the MA-ML-LSTM model achieved an average recognition accuracy of 98.72%, which was 4.60%, 7.96%, 5.37%, and 6.11% higher than traditional single models ResNet, MobilenetV3, 1D-CNN, and LSTM, respectively. Compared to EMD-RF, IMF-SVM, and CSPNet-YOLOv7 models, accuracy improved by 4.18%, 4.45%, and 3.46%, respectively. These findings validate the effectiveness of the coal-gangue identification technology driven by multi-source fusion of image features and vibration spectrum.

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.)

In wireless sensor networks, accurate node localization is essential for ensuring the precision of data collection. The DV-Hop algorithm, a popular range-free localization method, estimates distances between nodes by multiplying hop counts with average hop distances obtained through distance vector routing. However, this algorithm often experiences localization errors in randomly distributed network environments due to considerable inaccuracies in average hop distance calculations and the approximation of actual paths by straight-line paths. This paper introduces an enhanced DV-Hop localization algorithm, which constructs a mathematical model to optimize the mean square error of the average hop distance for any anchor node. This optimization corrects the average hop distance across the network, bringing it closer to the actual value, thus reducing errors and enhancing accuracy. Simulation results indicate that with 150 nodes, a 30% beacon node ratio and a 100-m communication range, the localization error of the improved FuncDV-Hop model decreased from 0.3916 to 0.1705, and the Root Mean Square Error (RMSE) decreased from 24.78m to 14.39m, thereby improving localization accuracy by 56.46%. [ABSTRACT FROM AUTHOR]

Fruit industry is one of the important economic pillars of China’s agriculture and rural areas. The current orchard production management level,especially the mechanization and information level,is relatively backward. Orchard mobile robot based on multi-source information fusion can realize stable and high-precision autonomous navigation in complex environment,provide intelligent and efficient autonomous navigation means for orchard mobile platform,and strongly support the construction of smart orchard. By analyzing the research progress of orchard mobile robot autonomous navigation system based on multi-source information fusion,this paper proposes to combine the actual complex and diverse working conditions of orchard,focus on key technologies such as positioning and mapping,path planning and decision control strategy,and based on the existing mobile platform,study the multi-source sensor information fusion strategy to achieve autonomous navigation in complex environment. The performance of the autonomous navigation system is verified by field tests. [ABSTRACT FROM AUTHOR]

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]

Objective To effectively perceive and assess safety risks during tunnel construction process, a tunnel safety risk assessment method based on multi-source information fusion is proposed. Method A tunnel safety risk assessment method is introduced, with a total of 20 tunnel safety risk influencing factors selected from four categories: geology, design, environment, and construction management. Then a tunnel safety risk assessment index system is established, and safety levels are determined. The basic probability assignment of the safety risk assessment index is constructed and calculated based on fuzzy matter-element method. Fusion calculations of the multi-source data are performed based on Dempster-Shafer evidence theory, and the safety levels are classified according to the principle of maximum membership degrees. Taking Nanping Sta.-Houba Sta. interval (Nan-Hou Interval) of Chongqing Rail Transit Line 10 Phase II project as example, the effectiveness of the proposed method in practical engineering is verified. Result & Conclusion Tunnel construction safety risk levels can be classified from low to high into five levels: the geological risk level of Nan-Hou Interval tunnel project is Level II; the design risk level is Level III; the environmental risk level is Level IV; the construction management risk level is Level II; the overall tunnel engineering risk level is Level II. The overall tunnel project is in a relatively low-risk state. Through verification in actual projects, it is found that the tunnel risk assessment results are consistent with the actual risk situation on-site.

ObjectiveThe dynamic prediction of carbon emission from cold chain distribution is an important basis for the accurate assessment of carbon emission and its green credit grade. Facing the fact that the carbon emission of vehicles is affected by multiple factors, such as road condition information, driving characteristics, refrigeration parameters, etc., a dynamic prediction model of carbon emission was proposed from refrigerated vehicles that integrates multi-source information.MethodsThe backbone feature extraction network, neck feature fusion network and loss function of YOLOv8s was firstly improved. The full-dimensional dynamic convolution was introduced into the backbone feature extraction network, and the multidimensional attention mechanism was introduced to capture the contextual key information to improve the model feature extraction capability. A progressive feature pyramid network was introduced into the feature extraction network, which reduced the loss of key information by fusing features layer by layer and improved the feature fusion efficiency. The road condition information recognition model based on improved YOLOv8s was constructed to characterize the road condition information in terms of the number of road vehicles and the percentage of pixel area. Pearson's correlation coefficient was used to compare and analyze the correlation between carbon emissions of refrigerated vehicles and different influencing factors, and to verify the necessity and criticality of the selection of input parameters of the carbon emission prediction model. Then the iTransformer temporal prediction model was improved, and the external attention mechanism was introduced to enhance the feature extraction ability of iTransformer model and reduce the computational complexity. The dynamic prediction model of carbon emission of refrigerated vehicles based on the improved iTransformer was constructed by taking the road condition information, driving characteristics (speed, acceleration), cargo weight, and refrigeration parameters (temperature, power) as inputs. Finally, the model was compared and analyzed with other models to verify the robustness of the road condition information and the prediction accuracy of the vehicle carbon emission dynamic prediction model, respectively.Results and DiscussionsThe results of correlation analysis showed that the vehicle driving parameters were the main factor affecting the intensity of vehicle carbon emissions, with a correlation of 0.841. The second factor was cargo weight, with a correlation of 0.807, which had a strong positive correlation. Compared with the vehicle refrigeration parameters, the road condition information had a stronger correlation between vehicle carbon emissions, the correlation between refrigeration parameters and the vehicle carbon emissions impact factor were above 0.67. In order to further ensure the accuracy of the vehicle carbon emissions prediction model, The paper was selected as the input parameters for the carbon emissions prediction model. The improved YOLOv8s road information recognition model achieved 98.1%, 95.5%, and 98.4% in precision, recall, and average recognition accuracy, which were 1.2%, 3.7%, and 0.2% higher than YOLOv8s, respectively, with the number of parameters and the amount of computation being reduced by 12.5% and 31.4%, and the speed of detection being increased by 5.4%. This was due to the cross-dimensional feature learning through full-dimensional dynamic convolution, which fully captured the key information and improved the feature extraction capability of the model, and through the progressive feature pyramid network after fusing the information between different classes through gradual step-by-step fusion, which fully retained the important feature information and improved the recognition accuracy of the model. The predictive performance of the improved iTransformer carbon emission prediction model was better than other time series prediction models, and its prediction curve was closest to the real carbon emission curve with the best fitting effect. The introduction of the external attention mechanism significantly improved the prediction accuracy, and its MSE, MAE, RMSE and R2 were 0.026 1 %VOL, 0.079 1 %VOL, 0.161 5 %VOL and 0.940 0, respectively, which were 0.4%, 15.3%, 8.7% and 1.3% lower, respectively, when compared with iTransformer. As the degree of road congestion increased, the prediction accuracy of the constructed carbon emission prediction model increased.ConclusionsThe carbon emission prediction model for cold chain distribution under multi-source information fusion proposed in this study can realize accurate prediction of carbon emission from refrigerated vehicles, provide theoretical basis for rationally formulating carbon emission reduction strategies and promoting the development of low-carbon cold chain distribution.

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]

[Objective] To effectively perceive and assess safety risks during tunnel construction process, a tunnel safety risk assessment method based on multi-source information fusion is proposed. [Method] A tunnel safety risk assessment method is introduced, with a total of 20 tunnel safety risk influencing factors selected from four categories: geology, design, environment, and construction management. Then a tunnel safety risk assessment index system is established, and safety levels are determined. The basic probability assignment of the safety risk assessment index is constructed and calculated based on fuzzy matter-element method. Fusion calculations of the multi-source data are performed based on Dempster-Shafer evidence theory, and the safety levels are classified according to the principle of maximum membership degrees. Taking Nanping Sta. Houba Sta. interval (Nan-Hou Interval) of Chongqing Rail Transit Line 10 Phase II project as example, the effectiveness of the proposed method in practical engineering is verified. [Result & Conclusion] Tunnel construction safety risk levels can be classified from low to high into five levels: the geological risk level of Nan-Hou Interval tunnel project is Level II; the design risk level is Level III; the environmental risk level is Level IV; the construction management risk level is Level II; the overall tunnel engineering risk level is Level II. The overall tunnel project is in a relatively low-risk state. Through verification in actual projects, it is found that the tunnel risk assessment results are consistent with the actual risk situation on-site. [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.)

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]

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.

Multi-source sensor information fusion technology plays an important role in many application fields in the era of “Industry 4.0”. Generally, in practical engineering applications, the obtained information is inevitably uncertain due to the degradation of sensor performance, environmental interference, abnormal communication transmission process, etc. Compared with the strict requirements of probability density function for data quantity, the use of interval numbers to characterize the uncertainty of information has obvious advantages. Classical Dempster-Shafer(D-S) evidence theory can effectively deal with uncertain information, whereas its evidence structure is single-valued, making it difficult to fuse interval uncertainty information. Therefore, it is necessary to adopt an interval-valued evidence structure to carry out interval uncertainty information fusion. Current researches on interval-valued belief structure mainly focus on the modification of evidence combination rules but neglect the key problem of how to generate interval-valued evidence structure from original data obtained from sensors. Aiming at the problem of classification and discrimination based on multi-source interval-valued sensor information fusion, this paper adopts the theory of interval-valued evidence and focuses on the problem of generating a reasonable belief structure based on original interval-valued sensor information by using a multistage correction framework. Firstly, an interval-valued Lance and Williams Distance(ILWD) is proposed, and an initial belief structure is generated based on the proposed ILWD using the original sensor information. Secondly, by considering the interval length effect and based on Jaccard similarity coefficient, the initial interval-valued belief structure is corrected by a two-stage process. Then, in order to alleviate the impact of evidence conflict on the combination results, the weight of each evidence is assigned by an optimization method (the third stage of the belief structure modification). Further, the evidence is combined based on the method proposed by Wang et al. (2007). Finally, a practical case study is carried out to verify the rationality and effectiveness of the proposed method.

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.

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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]

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]

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]