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The existing fault diagnosis methods for 12 kV ring main units face several limitations, including slow perception speed, low cross‐modal data fusion accuracy, and ineffective avoidance of signal interference. The accuracy of fault diagnosis is low, and the isolation speed is slow. This article proposes an integrated design method for the 12 kV intelligent ring main unit and constructs a fault diagnosis and isolation model. Firstly, an integrated hardware platform is constructed that significantly improves the perception speed and fusion potential. Then, an innovative fusion diagnostic model, named GAT‐Transformer‐DRSN, is proposed. The proposed model consists of three core components: a GAT‐based topology fault locator, which analyzes fault propagation paths; a spatiotemporal Transformer‐based cross‐modal evolution analyzer, which improves fusion accuracy; and a DRSN‐based anti‐interference feature extractor, which suppresses the impact of signal interference. Finally, a self‐optimizing closed loop is formed based on weighted fusion output diagnosis results and isolation instructions. The experimental validation utilizes fault data from the American Electric Power Research Institute (EPRI) and actual operational data from a provincial distribution network in China. The proposed GAT‐Transformer‐DRSN achieved the highest performance across all datasets, with fault diagnosis metrics including a Macro‐F1 score of 0.9531, recognition accuracies of 96.28% and 96.45%, and false alarm rates of 0.75% and 0.72%, outperforming all other comparative methods. This article proposes an integrated design method for a 12 kV intelligent ring main unit and constructs a fault diagnosis and isolation model. Firstly, building an integrated hardware platform can significantly improve perception speed and fusion potential. Then, an innovative fusion diagnostic model GAT‐Transformer‐DRSN is proposed, including a GAT‐based topology fault locator to analyze the fault propagation path; a cross‐modal evolution analyzer based on spatiotemporal Transformer to solve the problem of fusion accuracy; an anti‐interference feature extractor based on DRSN to suppress the influence of signal interference. Finally, based on weighted fusion output diagnosis results and isolation instructions, a self‐optimizing closed loop is formed.

Rolling bearing is the core component of industrial machines, but it is difficult for common single signal source-based fault diagnosis methods to ensure reliable results since sensor signals are vulnerable to the pollution of background noises and the attenuation of transmitted information. Recently, multi-source information-based fault diagnosis methods have become popular, but the information redundancy between multiple signals is a tough problem that will negatively impact the representational capacity of deep learning algorithms and the precision of fault diagnosis methods. Besides that, the characteristics of various signals are actually different, but this problem was usually omitted by researchers, and it has potential to further improve the diagnosing performance by adaptively adjusting the feature extraction process for every input signal source. Aimed at solving the above problems, a novel model for bearing fault diagnosis called multi-branch selective fusion deep residual network is proposed in this paper. The model adopts a multi-branch structure design to enable every input signal source to have a unique feature processing channel, avoiding the information of multiple signal sources blindly coupled by convolution kernels. And in each branch, different convolution kernel sizes are assigned according to the characteristics of every input signal, fully digging the precious fault components on respective information sources. Lastly, the dropout technique is used to randomly throw out some activated neurons, alleviating the redundancy and enhancing the quality of the multiscale features extracted from different signals. The proposed method was experimentally compared with other intelligent methods on two authoritative public bearing datasets, and the experimental results prove the feasibility and superiority of the proposed model.

To improve the accuracy of feature representation and abnormal condition identification, a new abnormal condition identification method, named integrating multiple binary neural networks based on condition-relevant information (CRI-MBNN), is presented for the electro-fused magnesia smelting process in this study. Firstly, the features related to each specific abnormal condition, which is named condition-relevant information (CRI), are analyzed and extracted from the multi-source heterogeneous information with the help of limited and consensus domain knowledge. Then, the CRI is fused at the feature-level to provide a comprehensive representation of each abnormal condition. Furthermore, for each abnormal condition, a binary neural network (BNN) is established based on the fused feature. They are further integrated according to the frequency of each condition in the actual production process to form the final abnormal condition identification network, i.e., CRI-MBNN. Finally, the effectiveness and feasibility of the proposed CRI-MBNN are verified by the electro-fused magnesia smelting process.

In order to predict the thermal deformation of CNC machine tool spindle system more accurately, a method based on multi-source heterogeneous information fusion is proposed. Aiming at the problem that it is difficult to obtain the global information of thermal deformation with a single type of information source, the effective prediction of thermal deformation of spindle system in machining center is realized under the fusion of temperature and vibration signals. First, the combined denoising method of empirical mode decomposition (EMD) and wavelet threshold is used to preprocess the vibration data. Then, the time-domain, frequency-domain, and time-frequency features of vibration signals are extracted, and the feature dimension is reduced based on correlation analysis and kernel principal component analysis (KPCA). After dimensionality reduction, the vibration data and temperature information are fused in the eigenmatrix. The nonlinear prediction of thermal deformation is studied by support vector regression for grid search parameter optimization (GS-SVR) and support vector regression for particle swarm optimization (PSO-SVR) methods. In order to realize the information acquisition of multi-channel temperature, vibration, and verify the effectiveness of the prediction model in the case of information fusion, a specific machining center is taken as the research object and experiments are performed based on multi-channel and heterogeneous signal acquisition. Finally, the prediction results based on different information sources are compared and analyzed. The results show that the thermal deformation of the machine tool obtained by the multi-source heterogeneous information fusion method is consistent with the actual test results. As compared with the predicted performance using only temperature information, the mean square error (MSE) decreased by 0.1663 µm. Therefore, the temperature - vibration information fusion model has higher accuracy in terms of predicting the thermal deformation of the spindle system model.

A motion control strategy based on multi-source heterogeneous motion information fusion and motion decoupling parallel washout algorithm (WA) is proposed for the control of a rehabilitation robot designed for stroke-related balance disorders. The robot features a serial-parallel hybrid structure and humanoid gait functionality, with its output being the pre-defined trajectory motion of the guiding pedals. The WA algorithm is widely applied in motion simulation and control. In this study, the filter parameters of the WA are optimized using Multi-Objective Genetic Algorithm (MOGA), aiming to minimize the motion perception error introduced by the robot, thereby optimizing the robot’s motion trajectory to better align with the human perception threshold and the dynamic response characteristics of the device. A custom-built multi-source heterogeneous sensing system is employed to capture human gait features, enabling the WA to generate specific motion trajectories pre-defined for the rehabilitation robot. To ensure that the optimization search space for each WA channel remains independent and to more accurately reproduce motion details, motion decoupling and dual parallel filtering control strategies are introduced. Through the optimization of the WA filter parameters, the system aims to minimize the theoretical motion perception error experienced by the user during robot-assisted motion training, with the potential to provide a more realistic motion experience and enhanced training outcomes. In the future, long-term follow-up and monitoring of the effectiveness will also be conducted.

To address the limitations in predictive capabilities of thermal error models built from single-source, single-structure data, this paper proposes a thermal error prediction model based on decision-level fusion of multi-source heterogeneous information to enhance prediction accuracy. First, an experimental platform for multi-source heterogeneous information acquisition was constructed to collect thermal error data from different signal sources (multi-source) and different structures (heterogeneous). Next, based on the characteristics of the multi-source and heterogeneous data, relevant features were extracted to construct the feature set. Then, using the feature information set of the multi-source and heterogeneous data, thermal error prediction sub-models were established using Nonlinear Autoregressive models with exogenous inputs (NARX) and Gated Recurrent Units (GRUs) for a vertical machining center spindle. Finally, the entropy weight method was employed to assign the weights for the linear-weighted fusion rule, achieving decision-level fusion of multi-source heterogeneous information to obtain the final prediction result. This result was then compared with experimental results and the prediction results of single-source models. The findings indicate that the proposed thermal error prediction model closely matches the actual results and outperforms the single-source and single-structure data models in terms of Root-Mean-Square Error (RMSE), Coefficient of Determination (R2), and Mean Absolute Error (MAE).

Abundant researches have consistently illustrated the crucial role of microRNAs (miRNAs) in a wide array of essential biological processes. Furthermore, miRNAs have been validated as promising therapeutic targets for addressing complex diseases. Given the costly and time-consuming nature of traditional biological experimental validation methods, it is imperative to develop computational methods. In the work, we developed a novel approach named efficient matrix completion (EMCMDA) for predicting miRNA-disease associations. First, we calculated the similarities across multiple sources for miRNA/disease pairs and combined this information to create a holistic miRNA/disease similarity measure. Second, we utilized this biological information to create a heterogeneous network and established a target matrix derived from this network. Lastly, we framed the miRNA-disease association prediction issue as a low-rank matrix-complete issue that was addressed via minimizing matrix truncated schatten p-norm. Notably, we improved the conventional singular value contraction algorithm through using a weighted singular value contraction technique. This technique dynamically adjusts the degree of contraction based on the significance of each singular value, ensuring that the physical meaning of these singular values is fully considered. We evaluated the performance of EMCMDA by applying two distinct cross-validation experiments on two diverse databases, and the outcomes were statistically significant. In addition, we executed comprehensive case studies on two prevalent human diseases, namely lung cancer and breast cancer. Following prediction and multiple validations, it was evident that EMCMDA proficiently forecasts previously undisclosed disease-related miRNAs. These results underscore the robustness and efficacy of EMCMDA in miRNA-disease association prediction.

In view of the suitability assessment of forest land resources, a consistent fuzzy assessment method with heterogeneous information is proposed. Firstly, some formulas for transforming large-scale real data and interval data into fuzzy numbers are provided. To derive the unified representation of multi-granularity linguistic assessment information, a fuzzy quantitative transformation for multi-granularity uncertain linguistic information is proposed. The proofs of the desirable properties and some normalized formulas for the trapezoidal fuzzy numbers are presented simultaneously. Next, the objective weight of each assessment indicator is further determined by calculating the Jaccard–Cosine similarity between the trapezoidal fuzzy numbers. Moreover, the trapezoidal fuzzy numbers corresponding to the comprehensive assessment values of each alternative are obtained. The alternatives are effectively ranked according to the distance from the centroid of the trapezoidal fuzzy number to the origin. Finally, based on the proposed consistent fuzzy assessment method, the suitability assessment of forest land resources is achieved under a multi-source heterogeneous data setting.

Constructing scientific evaluation system and evaluation methods to make timely quantitative evaluation for regional industrial operation quality is of great practical significance for expediting the new industrialization process and promoting the improvement of national economic operation quality. Aiming at the problem of evaluating the industrial operation quality, this paper constructs a new evaluation system from the perspective of industrial operation performance and industrial development potential, and then proposes a multi-source heterogeneous multi-attribute decision-making method based on the linguistic 2-tuple model to evaluate the industrial operation quality. In this method, the original multi-source heterogeneous data whereby real numbers, interval numbers, and linguistic fuzzy numbers coexist are all transformed into linguistic 2-tuples, then a new ranking method based on grey relational degree of linguistic 2-tuple matrix is presented to rank the level of industrial operation quality for the given cities. Further, a decision-making example of evaluating the industrial operation quality for 14 cities in Hunan Province of China is provided to highlight the implementation, availability, and feasibility of the proposed evaluation model.

Heterogeneous cross-project defect prediction (HCPDP) is aimed at building a defect prediction model for the target project by reusing datasets from source projects, where the source project datasets and target project dataset have different features. Most existing HCPDP methods only remove redundant or unrelated features without exploring the underlying features of cross-project datasets. Additionally, when the transfer learning method is used in HCPDP, these methods ignore the negative effect of transfer learning. In this paper, we propose a novel HCPDP method called multi-source heterogeneous cross-project defect prediction (MHCPDP). To reduce the gap between the target datasets and the source datasets, MHCPDP uses the autoencoder to extract the intermediate features from the original datasets instead of simply removing redundant and unrelated features and adopts a modified autoencoder algorithm to make instance selection for eliminating irrelevant instances from the source domain datasets. Furthermore, by incorporating multiple source projects to increase the number of source datasets, MHCPDP develops a multi-source transfer learning algorithm to reduce the impact of negative transfers and upgrade the performance of the classifier. We comprehensively evaluate MHCPDP on five open source datasets; our experimental results show that MHCPDP not only has significant improvement in two performance metrics but also overcomes the shortcomings of the conventional HCPDP methods.