With the development of wireless technology, signals propagating in space are easy to mix, so blind detection of communication signals has become a very practical and challenging problem. In this paper, we propose a blind detection method for broadband signals based on a weighted bi-directional feature pyramid network (BiFPN). The method can quickly perform detection and automatic modulation identification (AMC) on time-domain aliased signals in broadband data. Firstly, the method performs a time-frequency analysis on the received signals and extracts the normalized time-frequency images and the corresponding labels by short-time Fourier transform (STFT). Secondly, we build a target detection model based on YOLOv5 for time-domain mixed signals in broadband data and learn the features of the time-frequency distribution image dataset of broadband signals, which achieves the purpose of training the model. The main improvements of the algorithm are as follows: (1) a weighted bi-directional feature pyramid network is used to achieve a simple and fast multi-scale feature fusion approach to improve the detection probability; (2) the Efficient-Intersection over Union (EIOU) loss function is introduced to achieve high accuracy signal detection in a low Signal-Noise Ratio (SNR) environment. Finally, the time-frequency images are detected by an improved deep network model to complete the blind detection of time-domain mixed signals. The simulation results show that the method can effectively detect the continuous and burst signals in the broadband communication signal data and identify their modulation types.

Insole blanking production technology plays a vital role in contemporary machining and manufacturing industries. Existing insole blanking production models have limitations because most robots are required to accurately position the workpiece to a predetermined location, and special auxiliary equipment is usually required to ensure the precise positioning of the robot. In this paper, we present an adaptive blanking robotic system for different lighting environments, which consists of an industrial robot arm, an RGB-D camera configuration, and a customized insole blanking table and mold. We introduce an innovative edge detection framework that utilizes color features and morphological parameters optimized through particle swarm optimization (PSO) techniques to Adaptive recognition of insole edge contours. A path planning framework based on FSPS-BIT* is also introduced, which integrates the BIT* algorithm with the FSPS algorithm for efficient path planning of the robotic arm.

The real-time and accurate detection of boiler faults is essential for the optimization of boiler operation and the reduction of energy consumption and carbon dioxide emissions in power generation. However, due to the scarcity and limited diversity of boiler fault data and the complex requirements of feature modeling, significant challenges remain in developing such models. In this study, data representation techniques are applied to the task of real-time boiler fault detection. A CNN-GRU-Attention (GCA) model is designed, where raw time-series data are first transformed using Gramian Angular Field (GAF) to enable effective data representation learning. Subsequently, 1D temporal features and 2D spatial features are extracted through Gated Recurrent Unit (GRU) and Convolutional Neural Network (CNN) branches, respectively. The integration of these features through a self-attention mechanism further improves the model’s performance in terms of fault detection accuracy and robustness. The global variable snow ablation optimization algorithm (GVSAO) has been developed by integrating the good point set initialization method and the periodic mutation strategy within the traditional snow ablation optimization framework. The GVSAO algorithm is employed for the parameter optimization of GCA models. The experimental results on industrial boiler data demonstrate that the fault detection accuracy of this model is 93.1% and that its overall performance and response speed are superior to traditional methods. This method provides an effective solution for real-time fault detection of complex boiler systems.

Methane pyrolysis is a promising route for hydrogen production without CO2 emissions. However, conventional catalysts often suffer from fast catalyst deactivation due to carbon deposition. Molten metal alloys can reduce this problem because the density difference leads to separation between the catalyst and solid carbon. In this work, a fourth element M (M = 3d–5d transition metals) was introduced into the NiMo–Bi system to form NiMoM–Bi alloys. Density functional theory calculations show that all NiMoM–Bi alloys have negative formation energies and cohesive energies, indicating good thermodynamic stability. The introduction of M changes the Ni–Bi radial distribution functions and increases the structural disorder of the alloy. Among different elements, Pt, Au, Re, and Ir lead to faster methane dehydrogenation from CH4 to CH3. Bader charge analysis shows that a more positive Ni charge is associated with faster dehydrogenation. A linear relationship is found between Ni Bader charge and the first C–H bond breaking rate, with an R2 value of 0.8505. This work reveals the electronic origin of methane activation and guides the design of efficient molten metal catalysts.