Volume 175

Machine learning (ML) has become a key driver of innovation in industrial manufacturing, enhancing quality control, predictive maintenance, and process optimization. Manufacturers can achieve improved efficiency, reduced costs, and enhanced operational reliability by leveraging advanced ML algorithms, such as deep learning and traditional models. However, challenges remain in the large-scale deployment of ML, including issues with data privacy, legacy system interoperability, and the need for high-quality datasets. This paper investigates three core research questions: the enhancement of manufacturing processes via ML algorithms, the technical impediments to ML implementation, and the resolution of these challenges through emerging technologies such as digital twins and IoT. The study reveals that ML has significantly improved fault diagnosis, reduced downtime, and optimized energy use. However, it also highlights ongoing concerns around data privacy and system integration. The paper concludes by discussing the potential of future technologies to advance ML adoption in manufacturing further while emphasizing sustainability and innovative manufacturing initiatives.

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The transformer model was used to train and generate story text this time because certain parts or endings of the original story were not satisfactory. This study tried to use the model training to obtain other story paths. The main purpose is to study two paths: one is how to use pre-trained models for fine-tuning to achieve the desired effect, and the other is how to build a model trained from scratch to achieve the desired effect. DeepSeek R1 will be used as a control group to evaluate the generation effect.According to the results, the pre-trained model performs better on smaller datasets, generating logical sentences and paragraphs, while the model trained from scratch has not yet achieved good results on smaller datasets. As an improvement measure, a larger dataset will be used to enhance the model's generation performance, while adjusting new hyperparameters to fit the dataset.

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With the rapid advancement of wireless communication technologies, the increasing diversity of modulation schemes poses significant challenges for traditional modulation recognition methods in complex communication environments. To address this, this research proposes a hybrid deep learning model that integrates Convolutional Neural Networks (CNN) and Transformers. The CNN module is employed to extract local time-frequency features from the modulated signals, enhancing the model's capacity to capture short-term dependencies. Meanwhile, the Transformer module leverages its self-attention mechanism to model global temporal dependencies, improving recognition accuracy for complex modulation patterns. The model is trained and validated using the publicly available DeepSig RadioML 2018.01A dataset across various Signal-to-Noise Ratio (SNR) conditions, ranging from -20 dB to 30 dB. Experimental results demonstrate that our hybrid model achieves a remarkable recognition accuracy of up to 91% in environments with SNRs above 10 dB, highlighting its robustness and effectiveness in modulation recognition tasks.

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With the rapid development of artificial intelligence technology, reinforcement learning (RL) has emerged as a core research direction in the field of intelligent decision-making. Among numerous reinforcement learning algorithms, Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) have gained widespread attention due to their outstanding performance. These two algorithms have been extensively applied in areas such as autonomous driving and game AI, demonstrating strong adaptability and effectiveness. However, despite numerous application instances, systematic comparative studies on their specific performance differences remain relatively scarce. This study aims to systematically evaluate the differences between DQN and PPO algorithms across four performance metrics: convergence speed, stability, sample efficiency, and computational complexity. By combining theoretical analysis and experimental validation, we selected classic reinforcement learning environments—CartPole (for discrete action testing) and CarRacing (for continuous action evaluation)—to conduct a detailed performance assessment. The results show that DQN exhibits superior performance in discrete action environments with faster convergence and higher sample efficiency, whereas PPO demonstrates greater stability and adaptability in continuous action environments.

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