Power-domain non-orthogonal multiple access (NOMA) is one of the key technologies in 5G communica-tions, enabling efficient multi-user transmission over the same time-frequency resources through power multiplexing. In this study, an improved max-min relay selection strategy was proposed for NOMA cooperative communication systems to address the issue of insufficient channel fairness in conventional strategies. The proposed strategy optimizes the relay selection process with the objective of ensuring channel fairness. Theoretical derivations and simulation analyses were conducted to comprehensively evaluate the proposed strategy from the perspectives of user throughput and system outage probability. The results demonstrate that, compared to the conventional max-min strategy and other commonly used relay selection methods, the proposed strategy significantly reduces the system outage probability while enhancing user throughput, thereby verifying its superiority in improving system reliability and stability.
A wide range of safety hazards exist in underground coal mines, characterized by unpredictability, randomness, and coupling effects. The increasing structural complexity and diversity of underground equipment present new challenges for fault state monitoring and diagnosis. To address the unique characteristics of underground equipment fault diagnosis, a characterization model of vibration hazards was proposed, integrating a time-frequency mask-based non-stationary filtering technique and sparse representation. Experimental analysis demonstrates that the time-frequency mask algorithm effectively filters out sharp non-stationary noise, restoring the original stationary healthy signal. Compared to Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Principal Component Analysis (PCA), the sparse representation algorithm exhibits superior performance in characterizing vibration hazards, achieving the highest accuracy.
The detection of image defects under low-illumination conditions presents significant challenges due to unstable and uneven lighting, which introduces substantial noise and shadow artifacts. These artifacts can obscure actual defect points while simultaneously increasing the likelihood of false positives, thereby complicating accurate defect identification. To address these limitations, a novel defect detection method based on machine vision was proposed in this study. Low-illumination images were captured and decomposed using a noise assessment-based framework to enhance defect visibility. A spatial transformation technique was then employed to distinguish between target regions and background components based on localized variations. To maximize the contrast between these components, the Hue-Saturation-Intensity (HSI) color space was leveraged, enabling precise segmentation of low-illumination images. Subsequently, an energy local binary pattern (LBP) operator was applied to the segmented images for defect detection, ensuring improved robustness against noise and illumination inconsistencies. Experimental results demonstrate that the proposed method significantly enhances detection accuracy, as confirmed by both subjective visual assessments and objective performance evaluations. The findings indicate that the proposed approach effectively mitigates the adverse effects of low illumination, thereby improving the accuracy and reliability of defect detection in challenging imaging environments.
Click fraud is a deceptive malicious strategy that relies on repetitive mimicking of human clicking on online advertisements, without actual intention to complete a purchase. This fraud can result in significant financial loses for both advertising companies and marketers, and at the same time destroying their public images. Nevertheless, detection of these illegitimate clicks is very challenging as they closely resemble to authentic human engagement. This study examines the utilization of artificial intelligence approaches to detect deceptive clicks, by identifying subtle correlations between the timing of the clicks, taking into account their geographical or network sources and linked application sources as indicators to separate legitimate from malicious activity. This study highlights the application of recurrent neural networks (RNNs) for this task, keeping in mind that the process of selection and tuning of the model's hyperparameters plays a vital role in the performance. An adapted implementation of crayfish optimization algorithm (COA) was consequently proposed in this paper, and used to optimize RNN models to enhance their general performance. The developed framework was evaluated utilizing actual operational datasets and yielded encouraging outcomes.
Accurate smoke detection in complex industrial environments, such as chemical plants, remains a significant challenge due to the inherently low contrast, transparency, and weak texture features of smoke, which often exhibits blurred boundaries and diverse spatial scales. To address these limitations, YOLOv8n-AM, an enhanced lightweight detection framework belonging to the YOLO (You Only Look Once) series, was developed by integrating advanced architectural components into the baseline YOLOv8n model. Specifically, the conventional Spatial Pyramid Pooling-Fast (SPPF) module was replaced with an Attention-based Intra-scale Feature Interaction (AIFI) Convolution Synergistic Feature Processing Module (SFPM), i.e., AIFC-SFPM, enabling more effective semantic feature representation and an improvement in detection accuracy. In parallel, the original convolutional module was optimized using a Multi-Scale Downsampling (MSDown) module, which reduces model redundancy and computational overhead, increasing the detection speed. Experimental evaluations demonstrate that the YOLOv8n-AM model achieves a 1.7% improvement in mean Average Precision (mAP), accompanied by a 9.1% reduction in Giga Floating-point Operations Per Second (GFLOPs) and a 15.4% decrease in parameter count when compared to the original YOLOv8n framework. These improvements collectively underscore the model’s suitability for real-time deployment in resource-constrained industrial settings where rapid and reliable smoke detection is critical. The proposed architecture thus provides a computationally efficient and high-precision solution for safety-critical visual monitoring applications.
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