In recent years, the amount of household waste has increased sharply, and there is an urgent need to use intelligent classification equipment assist in completing tasks. However, existing garbage algorithms have large parameter counts poor real-time performance, which are not suitable for embedded devices. Therefore, order achieve a lightweight efficient model, this article uses self-built dataset pre-trained MobileNetV3 model PyTorch framework recognition classification. We introduce CBAM...

The method of acoustic radiation signal detection not only enables contactless measurement but also provides comprehensive state information during equipment operation. This paper proposes an enhanced feature extraction network (EFEN) for fault diagnosis rolling bearings based on learning. EFEN comprises four main components: the data preprocessing module, selection module (IFSM), channel attention mechanism (CAMM), and convolutional neural (CNNM). Firstly, one-dimensional is transformed...

Wind turbine rolling bearings are crucial components for ensuring the reliability and stability of wind power systems. Their failure can lead to significant economic losses equipment downtime. Therefore, accurate diagnosis bearing faults is great importance. Although existing deep learning fault methods have achieved certain results, they still face limitations such as inadequate feature extraction capabilities, insufficient generalization complex working conditions, ineffective multi-scale...

The imperative to address the burgeoning challenge of industrial water pollution has catalyzed pursuit sophisticated treatment methodologies capable neutralizing non-biodegradable contaminants. This investigation focuses on optimization Ti/SnO2-Sb-Ni anode composition, leveraging a synergistic hybrid machine learning strategy that integrates Simulated Annealing (SA), Differential Evolution (DE), and Random Forest (RF) algorithms. triad algorithms is meticulously applied ascertain optimal...

Low-frequency noise absorbers often require large structural dimensions, constraining their development in practical applications. In order to improve space utilization, an acoustic metamaterial with a spatial double helix, called helix resonator (SDHR), is proposed this paper. An analytical model of the double-helix established and verified by numerical simulations impedance tube experiments. By comparing absorption coefficients resonator, it shown that results model, experiments are good...

In the context of achieving two-carbon target, this study utilized a wastewater treatment plant in Shenyang City as case to accurately calculate indirect emissions related energy and chemical consumption within energy-intensive industry. Sumo software was employed for precise mathematical modeling. Considering operational characteristics plants cold regions, innovatively divided annual operation cycle into two periods, namely normal temperature low temperature, determined optimal parameters...

The measurement of chemical oxygen demand (COD) is very important in the process sewage treatment. value COD reflects effectiveness and trend treatment to a certain extent, but obtaining accurate data requires high cost labor intensity. To1 solve this problem, paper proposes an online soft method for based on Convolutional Neural Network-Bidirectional Long Short-Term Memory Network-Attention Mechanism (CNN-BiLSTM-Attention) algorithm. Firstly, by analyzing mechanism aerobic tank stage...

<title>Abstract</title> The high operating costs and energy requirements of the nitrification-denitrification method in municipal wastewater treatment cannot meet goal sustainable development. Therefore, it has become a research hotspot to achieve efficient energy-saving nitrogen removal through an Anammox-based autotrophic denitrification process mainstream treatment. In this study, single-stage partial nitrification-anaerobic ammonia oxidation with continuous anaerobic/anoxic feeding...

Sewage treatment plants face significant problems as a result of the annual growth in urban sewage discharge. Substandard discharge can also be caused by rising expenses and unpredictable procedures. The most widely used process areas is Anaerobic–Anoxic–Oxic (A2O) process. Therefore, modeling predicting effluent quality are great significance. A method based on Kernel Principal Component Analysis–Particle Swarm Optimization–Stochastic Configuration Network (KPCA-PSO-SCN) proposed for A2O...

To extract valuable characteristic information from the acoustic radiation signal of rolling bearings, a novel mathematical morphological network (MMNet) is proposed. First, layer constructed by leveraging advantages multi-scale enhanced top-hat operator (MEAVGH) that can positive and negative pulses, which are then integrated into deep learning network. Second, input undergoes processing with different scale structural elements (SEs) to obtain multi-branch data. This followed channel...