Plastic waste identification based on multimodal feature selection and cross-modal Swin Transformer.

The classification and recycling of municipal solid waste (MSW) are strategies for resource conservation and pollution prevention, with plastic waste identification being an essential component of waste sorting. Multimodal detection of solid waste has increasingly replaced single-modal methods constrained by limited informational capacity. However, existing hyperspectral feature selection algorithms and multimodal identification methods have yet to leverage cross-modal information exhaustively.

Research on an Equivalent Heat Source Model of the AC Arc in the Short Gap of a Copper-Core Cable and a Fire Risk Assessment Method.

The magnetohydrodynamics (MHD) model of the alternating current (AC) arc is complex, so a simplified equivalent heat source (EHS) model can be used to replace the complex model in studying the AC arc's thermal characteristics and cable fire risk. A 2D axisymmetric AC arc MHD simulation model in the short gap of a copper-core cable is established in this paper. The AC arc voltage and current obtained by the model are consistent with experiments.

Rapid dataset generation methods for stacked construction solid waste based on machine vision and deep learning.

The development of urbanization has brought convenience to people, but it has also brought a lot of harmful construction solid waste. The machine vision detection algorithm is the crucial technology for finely sorting solid waste, which is faster and more stable than traditional methods. However, accurate identification relies on large datasets, while the datasets from the field working conditions are scarce, and the manual annotation cost of datasets is high.

A security technology of power relay using edge computing.

The purposes are to find the techniques suitable for the safety relay protection of intelligent substations and discuss the applicability of edge computing in relay protection. Regarding relay protection in intelligent substations, edge computing and optimized simulated annealing algorithm (OSAA) are combined innovatively to form an edge computing strategy. On this basis, an edge computing model is proposed based on relay fault traveling waves.

Series AC Arc Fault Detection Method Based on High-Frequency Coupling Sensor and Convolution Neural Network.

Arc faults induced by residential low-voltage distribution network lines are still one of the main causes of residential fires. When a series arc fault occurs on the line, the value of the fault current in the circuit is limited by the load. Traditional circuit protection devices cannot detect series arcs and generate a trip signal to implement protection.

A Novel Methodology for Series Arc Fault Detection by Temporal Domain Visualization and Convolutional Neural Network.

AC arc faults are one of the most important causes of residential electrical wiring fires, which may produce extremely high temperatures and easily ignite surrounding combustible materials. The global interest in machine learning-based methods for arc fault diagnosis applications is increasing due to continuous challenges in efficiency and accuracy. In this paper, a temporal domain visualization convolutional neural network (TDV-CNN) methodology is proposed.

An arc fault diagnosis algorithm using multiinformation fusion and support vector machines.

Arc faults in low-voltage electrical circuits are the main hidden cause of electric fires. Accurate identification of arc faults is essential for safe power consumption. In this paper, a detection algorithm for arc faults is tested in a low-voltage circuit.

An experimental study on the thermal characteristics and heating effect of arc-fault from Cu core in residential electrical wiring fires.

The characteristics of a series direct current (DC) arc-fault including both electrical and thermal parameters were investigated based on an arc-fault simulator to provide references for multi-parameter electrical fire detection method. Tests on arc fault behavior with three different initial circuit voltages, resistances and arc gaps were conducted, respectively. The influences of circuit conditions on arc dynamic image, voltage, current or power were interpreted.

A Novel Arc Fault Detector for Early Detection of Electrical Fires.

Arc faults can produce very high temperatures and can easily ignite combustible materials; thus, they represent one of the most important causes of electrical fires. The application of arc fault detection, as an emerging early fire detection technology, is required by the National Electrical Code to reduce the occurrence of electrical fires. However, the concealment, randomness and diversity of arc faults make them difficult to detect.

[Research on process character information in early fire based on Fourier transform infrared spectrum detection].

CO was chosen as an early fire detection factor through analyzing all kinds of characters in the process of fires, and an experiment system was established based on Fourier transform infrared spectrometer. Through this system, lots of early fire experiments were carried out, and the authors got the CO concentrations of all kinds of materials. Using the concentration of CO, an autoregressive integrated model was established by time series analysis, then the process characters phi1 and phi2 were extracted from them.

[Research on early fire detection with CO-CO2 FTIR-spectroscopy].

A new fire detection method is put forward based on the theory of FTIR spectroscopy through analyzing all kinds of detection methods, in which CO and CO2 are chosen as early fire detection objects, and an early fire experiment system has been set up. The concentration characters of CO and CO2 were obtained through early fire experiments including real alarm sources and nuisance alarm sources. In real alarm sources there are abundant CO and CO2 which change regularly.