Wireless sensor networks (WSNs) are essential in environmental monitoring, surveillance systems, and smart cities. Achieving optimal coverage in WSNs is a fundamental challenge for effective event detection and monitoring. This research introduces an adaptive crossover mutation differential evolution (ADE) approach to address the node coverage planning problem in WSNs. The ADE method optimises sensor placement to maximise coverage while minimising the number of required sensors. It incorporates enhanced adaptive strategies for crossover, mutation, and re-initialisation to improve coverage efficiency. The ADE approach is evaluated using test suite functions and compared against existing optimisation strategies using various metrics for validation. Experimental results demonstrate the superior performance of the proposed approach in maximising node coverage in WSNs. The ADE approach presents a novel and effective solution for optimising coverage in WSNs with potential practical applications.
Embroidery cultural relics often suffer from missing stitches and colour fading due to environmental and human factors. Traditional manual restoration is time-consuming (over 200 hours per piece) and prone to secondary damage. Existing deep learning methods struggle with structural distortion (>15%) and poor semantic consistency in complex textures like gold/silver threads and three-dimensional embroidery. To address these issues, we propose an advanced embroidery image restoration method. A DenseNetwork with channel and spatial attention achieves 94.25% accuracy, 93.47% recall, and 96.59% specificity for classification. Restoration uses an improved GAN with dilated convolutions, attention modules, a mask-guided discriminator, and joint loss. On datasets with 20-30% masking, the SSIM reached 0.971 (vs. 0.873 for traditional GANs). At 40-50% masking, the FID dropped to 17.33 (vs. 20.14). The model is efficient, requiring only 10.52G FLOPs, 5.58M parameters, and 0.62s per image. This method enables high-quality, efficient restoration of embroidery artefacts.
Soft set theory is a useful mathematical paradigm for dealing with imprecise information and controlling uncertainty. By offering parameterised family of subsets defined over a universal set of parameters, it allows for flexibility in handling uncertainty in a variety of situations. Using soft set theory to express ambiguous problem parameters is still difficult, despite its benefits. With an emphasis on parameter reduction to expedite the selection of ideal objects, this paper explores the function of soft set theory in decision making. Parameter reduction increases efficiency by streamlining decision-making procedures while preserving crucial information. Additionally, a comparison table built using fuzzy soft set is used in a novel approach to object detection from imprecise data. In unpredictable situations, this approach increases decision-making accuracy and dependability. The study illustrates the potential of soft and fuzzy soft sets to improve decision-making processes through real-world situations, showcasing their adaptability and resilience in dealing with uncertainty.
In order to address the drawbacks of conventional proportional-integral (PI) controllers, including their inability to effectively handle nonlinearity, parameter fluctuations, and external disturbances, this research proposes a deep learning (DL)-based controller for DC motor drives in electric vehicles (EVs). The development of a custom neural network (CNN) controller is contrasted with neural net fitting (NNF) and PI controllers. The CNN controller is implemented and evaluated under various failure conditions and speed fluctuations after the DC motor drive system has been modelled. The CNN controller reduces overshoot by 25%, settling time by 30%, and speed tracking accuracy by 20%, according to the results. It keeps the system stable during high-resistance faults with a torque variation of only 15 Nm, whereas the PI controller becomes unstable when the torque variation reaches 25 Nm. This research comes to the conclusion that DL-based CNN controllers notably improve EV motor driving performance and dependability.
