Accurate assessment of the aging state of transformer oil-barrier insulation is crucial for ensuring the safe and reliable operation of power systems. This study presents the development of indoor accelerated thermal aging experiments to simulate the degradation of oil-immersed barrier insulation within transformers. A series of samples reflecting various aging states was obtained and categorized into six distinct groups. Raman spectroscopy analytical technology was employed to characterize the information indicative of different aging states of the oil-immersed barrier insulation. The raw Raman spectra were processed using asymmetric reweighted penalty least squares to correct baseline shifts, Savitzky–Golay (S-G) smoothing to eliminate fluctuation noise, and principal component analysis (PCA) to reduce data dimensionality by extracting principal components. A support vector machine (SVM) classifier was developed to discriminate between the Raman spectra and category labels. The SVM parameters were optimized using grid search, particle swarm optimization (PSO), and genetic algorithm (GA), yielding the optimal parameters (C and gamma). Notably, the grid search method demonstrated high efficiency in identifying the best combination of SVM parameters (c and g). Comparative analyses with varying numbers of principal components in SVM classifiers revealed that incorporating an optimal subset of PCA features achieved the highest classification accuracy of 94.44% for external validation samples, with only eight samples being misclassified into adjacent categories. This study offers technical support and a theoretical foundation for the effective assessment of the aging state of oil-barrier type insulation in transformers, contributing to the advancement of condition monitoring and maintenance strategies in power systems.

Surface electromyographic (sEMG) signals are weak physiological electrical signals, which are highly susceptible to coupling external noise and cause major difficulties in signal acquisition and processing. The study of using sEMG signals to analyze human motion intention mainly involves data preprocessing, feature extraction, and model classification. Feature extraction is an extremely critical part; however, this often involves many manually designed features with specialized domain knowledge, so the experimenter will spend time and effort on feature extraction. To address this problem, deep learning methods that can automatically extract features are applied to the sEMG-based gesture recognition problem, drawing on the success of deep learning for image classification. In this paper, sEMG is captured using a wearable, flexible bionic device, which is simple to operate and highly secure. A multi-stream convolutional neural network algorithm is proposed to enhance the ability of sEMG to characterize hand actions in gesture recognition. The algorithm virtually augments the signal channels by reconstructing the sample structure of the sEMG to provide richer input information for gesture recognition. The methods for noise processing, active segment detection, and feature extraction are investigated, and a basic method for gesture recognition based on the combination of multichannel sEMG signals and inertial signals is proposed. Suitable filters are designed for the common noise in the signal. An improved moving average method based on the valve domain is used to reduce the segmentation error rate caused by the short resting signal time in continuous gesture signals. In this paper, three machine learning algorithms, K-nearest neighbor, linear discriminant method, and multi-stream convolutional neural network, are used for hand action classification experiments, and the effectiveness of the multi-stream convolutional neural network algorithm is demonstrated by comparison of the results. To improve the accuracy of hand action recognition, a final 10 gesture classification accuracy of up to 93.69% was obtained. The separability analysis showed significant differences in the signals of the two cognitive-behavioral tasks when the optimal electrode combination was used. A cross-subject analysis of the test set subjects illustrated that the average correct classification rate using the pervasive electrode combination could reach 93.18%.

Research on wireless sensor network video surveillance system is a hot field of media classroom education and teaching. Based on the 5G communication network theory, this paper constructs a wireless sensor network video monitoring system for multimedia classroom education and teaching, systematically analyzes the new distance education model under the condition of the integration of the two networks, and improves the interactive live classroom education model. For the classroom teaching mode, the model studies the isochronous transmission technology of data collection and video encoding and decoding, adopts hardware compression encoding, integrates key technologies such as reliable multicast and conditional access, and proposes the design of the system scheme, which solves the problem of multimedia classroom education and teaching digital issues. During the simulation process, the MATLAB software platform was used to study the effect of the number of 5G communication network nodes and node attributes (such as node position, node perception angle, instantaneous perception direction, or direction difference between neighboring nodes, node speed, etc.) on the target area or target point. The experimental results show that the performance of the joint optimization of transmission video quality and network lifetime performs well under multipath conditions. The total utility under multipath finally converges to 8.3, while the single path finally converges to 6.5, which further promotes the real-time performance of the multimedia network classroom teaching system.

This study presents an in-depth study and analysis of IoT semantic association and decision-making using a partial differential fuzzy unsupervised approach. It focuses on a semantic annotation framework for device metadata and a knowledge base construction method to further improve the interoperability of IoT domain knowledge by building a unified IoT domain knowledge base and designing and implementing a semantic IoT knowledge management and application generation system. The main proposal is an IoT generic domain ontology, which reuses the existing excellent ontologies of IoT as much as possible, extracts the commonly used concepts of the domain and combines them, and provides a unified semantic template for IoT applications. On the other hand, by applying the entity linking technique to the extension of the knowledge base and linking the structured metadata of devices to the corresponding entities of the background knowledge base, the domain knowledge base can be made to share the rich background knowledge. At the same time, the interoperability of heterogeneous IoT metadata between applications is enhanced by unifying data and concepts from different device applications to the same background knowledge base through entity alignment techniques. The semantic representation of events applicable to IoT application scenarios is investigated, and an IoT event ontology for representing abstract events and event relationships in IoT is designed; next, a domain ontology with IoT sensing and control event representation capability is constructed based on the IoT event ontology, in which the typical domain ontology (SSN) that can be used for IoT applications is followed by the ontology reuse principle is improved and extended to support the description of event types and interevent relationships, and the IoT event model is associated with the improved IoT base ontology through an ontology alignment approach. Finally, the IoT sensing and control ontology are validated by semantic modeling of device composition, component relationships, and operational processes based on the IoT sensing and control ontology.

Methanol–gasoline blends have emerged as a promising and environmentally friendly bio-fuel option, garnering widespread attention and promotion globally. The methanol content within these blends significantly influences their quality and combustion performance. This study explores the qualitative and qualitative analysis of methanol–gasoline blends using Raman spectroscopy coupled with machine learning methods. Experimentally, methanol–gasoline blends with varying methanol concentrations were artificially configured, commencing with initial market samples. For qualitative analysis, the partial least squares discriminant analysis (PLS-DA) model was employed to classify the categories of blends, demonstrating high prediction performance with an accuracy of nearly 100% classification. For the quantitative analysis, a consensus model was proposed to accurately predict the methanol content. It integrates member models developed on clustered variables, using the unsupervised clustering method of the self-organizing mapping neural network (SOM) to accomplish the regression prediction. The performance of this consensus model was systemically compared to that of the PLS model and uninformative variable elimination (UVE)–PLS model. Results revealed that the unsupervised consensus model outperformed other models in predicting the methanol content across various types of methanol gasoline blends. The correlation coefficients for prediction sets consistently exceeded 0.98. Consequently, Raman spectroscopy emerges as a suitable choice for both qualitative and quantitative analysis of methanol–gasoline blend quality. This study anticipates an increasing role for Raman spectroscopy in analysis of fuel composition.