Volume 95

Artificial Intelligence Generated Content (AIGC) has rapidly evolved, revolutionizing the creation of text, images, audio, and video content. Despite these advancements, research on the development process of AIGC technology remains scarce, necessitating a systematic discussion of its current state and future directions. So this paper delves into the significant advancements and foundational technologies driving AIGC, emphasizing the contributions of state-of-the-art models such as DALL-E 3 [1] and Sora [2]. We analyze the evolution of generative models from single-modal approaches to the current multimodal generative models. The paper further explores the application prospects of AIGC across various domains such as office work, art, education, and film, while addressing the existing limitations and challenges in the field. We propose potential improvement directions, including more efficient model architectures and enhanced multimodal capabilities. Emphasis is placed on the environmental impact of AIGC technologies and the need for sustainable practices. Our comprehensive review aims to provide researchers and professionals with a deeper understanding of AIGC, inspiring further exploration and innovation in this transformative domain.

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Object detection has always been an important and challenging task in the field of computer vision. In recent years, Vision Transformers (ViT) have achieved remarkable results on image classification tasks, demonstrating its potential in vision tasks. In this paper, we propose CN-ViT, a novel Vision Transformer based visual object detection model.CN-ViT effectively improves the accuracy and robustness of object detection by combining the advantages of self-attention mechanism and convolutional neural network, and introducing the GCCA (Global Context Block and Coordinate Attention) module. In this paper, CN-ViT is evaluated on the Mini COCO standard dataset. The experimental results suggest that CN-ViT may outperform current mainstream object detection methods in terms of detection accuracy and speed. This study sheds light on the potential of Transformer architectures for complex visual tasks and offers valuable insights for future research in this area.

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With the rapid development of world industrialization, the continuous increase of global carbon dioxide emissions has led to the gradual deterioration of the ecological environment and the obvious aggravation of the greenhouse effect. In this paper, the carbon dioxide content in the air over the European Alps is taken as the research object, and the RNN and LSTM neural network prediction models are respectively used to compare and predict it. The results show that the fitting effect of LSTM is better than that of RNN, and the fitting effect of the prediction model will also improve with the increase of iteration times and sample size. Since carbon monoxide and methane in the air will cause changes in carbon dioxide content, this paper adds the two factors into the LSTM prediction model as influencing factors, and the goodness-of-fit can reach 0.95, which is higher than the prediction results when there are only one influencing factor or no influencing factor. In order to reduce the running time, XGBoost, LightGBM and random forest algorithms are respectively used in this paper and Bayesian optimization algorithm is used to predict the carbon dioxide content. The results show that the prediction effect is slightly lower than LSTM. Therefore, this paper takes the above three algorithms as the base model. Linear regression experiments are carried out for the meta-model's Stacking fusion algorithm. The goodness-of-fit can reach 0.92, which significantly improves the prediction effect compared with the base model. Finally, the sensitivity analysis of the Stacking fusion model is carried out in this paper. The experimental results show that the model has strong stability.

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Firstly, the thesis extensively analysed and processed the vegetable sales data of a fresh food superstore. It integrated the product information and sales data of each vegetable category, and carried out data cleaning and analysis through Excel tools, including sorting out the outliers, listing the sales volume of each of the six categories, using pivot tables for statistical analysis, and establishing line graphs, heat maps, scatter plots, and other visual displays of the data characteristics and correlations. Secondly, the thesis investigated the relationship between sales volume and pricing, fitted the correlation between total sales volume and pricing using a random forest regression model, and predicted the daily replenishment volume for the coming week using an LSTM time series model. It also fitted quantitative fitting formulas for metrics for each vegetable category, classified profits into normal sales volume profits and discounted sales volume profits, and ultimately maximised profits through dynamic programming models and particle swarm optimisation algorithms. Finally, the thesis considered the impact of other factors such as weather, holidays, seasons and market environment on sales volume with specific data collection and analyses that help guide better pricing strategies. The findings will contribute to a better understanding of vegetable sales behaviour and optimise superstore operational strategies.

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Nowadays, with the rapid development of science and technology, the innovation and application of robot technology has become an important force to promote industrial development, which requires robots to have a high degree of autonomy and adaptability. Among them, terrain classification technology is one of the key technologies to achieve this goal. In order to improve the ground adaptability of robots in complex environments, this paper proposes a terrain classification algorithm based on improved Hilbert-Huang transform(HHT) combined with ensemble empirical mode decomposition(EEMD) and long short-term memory network(LSTM). Firstly, the signal data is processed by EEMD, and then the frequency domain features of the signal are extracted by Hilbert transform to expand the feature dimension. Finally, the features are learned and classified by the LSTM model, which effectively improves the classification accuracy. In this paper, we conducted sufficient experiments to compare the effects of EMD and EEMD and the effects of different neural network models, and verified the contribution of the Hilbert-Huang transform to improve the classification performance through ablation experiments, which proves the effectiveness and reliability of our proposed algorithm, and provided powerful technical support for the robot to adapt to the ground information in the complex environment.

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Electrocardiogram signal (ECG) can directly reflect the health status of the heart, and is an important basis for prevention and treatment of heart disease. In order to realize ECG signal classification effectively, an ECG signal classification method based on discrete wavelet transform and Xgboost is proposed in this paper, which improves the accuracy of ECG signal classification. Specifically, we first divide, select and downsample the heart beat of the data, and then use the discrete wavelet transform to reduce the noise of the data set to improve the signal to noise ratio. Finally, we use Xgboost algorithm as the classifier to classify the data, and get 98.7% accuracy rate on the test set. In each module, we carried out comparative experiments to verify the correctness and rigor of our method. In addition, in order to make up for the lack of interpretability of traditional machine learning methods, we defined the importance of each feature according to the information gain generated by different features to the model during the training of XGBoost, and then got the key bands that should be paid attention to when distinguishing heart beats, which improved the interpretability of the model. It also provides a scientific basis for the classification of ECG signals and practical medical work.

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To accurately predict the occurrence of rock bursts during deep coal mining and ensure the safe and efficient operation of coal mines, a signal recognition and prediction system based on the Random Forest model is proposed. This system utilizes electromagnetic radiation (EMR) and acoustic emission (AE) signal data, employing feature extraction and point biserial correlation analysis to screen out the most relevant feature parameters. A Random Forest binary classification model is constructed to identify interference signals. Subsequently, by introducing new features such as moving average slope and exponentially weighted moving average (EWMA), a time series analysis of precursor feature signals is conducted. A real-time warning model based on the Random Forest algorithm is developed, dynamically calculating the probability of precursor feature signal occurrence by integrating historical data and real-time data changes. This approach improves the accuracy and recall rate of signal recognition and prediction, providing reliable data support for mine safety management.

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Skin disease image segmentation is a crucial component of computer-aided diagnosis, providing precise localization and delineation of lesions that enhance diagnostic accuracy and efficiency. Despite significant advancements in convolutional neural networks (CNNs), there remains substantial room for improvement in segmentation performance due to the diverse and complex nature of skin lesions. In this study, we propose DMDLK-Net, a dynamic multi-scale feature fusion network with deformable large kernels, specifically designed to address the challenges in skin disease segmentation. Our network incorporates a Dynamic Deformable Large Kernel (DDLK) module and a Dynamic Multi-Scale Feature Fusion (DMFF) module, enhancing the model's ability to capture intricate lesion features. We present the performance of DMDLK-Net on the ISIC-2018 dataset, highlighting its promising results. Key contributions of this work include the innovative use of deformable large kernels for adaptive feature extraction and the introduction of dynamic multi-scale fusion to balance local and global information. Our experimental results confirm the effectiveness of DMDLK-Net in delivering high-precision segmentation, thus providing a reliable tool for clinical applications.

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The time series prediction problem has a very wide range of applications in many fields. Most scholars use the LSTM class of algorithms for prediction. However, this method consumes a significant amount of computational power and presents several challenges. To address this issue, this paper proposes a time series forecasting method based on feature fusion and XGBoost. Specifically, we first utilize holiday information and the K-Means algorithm for feature extraction to expand the feature dimensions of the dataset, and then employ XGBoost as a model for training and prediction. Experiments demonstrate that the method proposed in this paper significantly reduces error compared to other traditional machine learning and deep learning methods, while the training time is much shorter than these methods. For example, compared with LSTM, the MSLE of this model decreases by 1.42%, while the training time is only 0.15% of that of LSTM. This greatly saves on training costs and computational power consumption. This confirms the effectiveness of using machine learning and clustering algorithms in time series prediction and provides new methods and practical application directions for future time series prediction models.

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This paper presents a summary of the progress of deep learning in the research of intelligent receivers and predicts its future development direction. First, the background of the era of intelligent receivers is introduced. With the rapid development of communication technology, intelligent receivers show insufficient flexibility, low accuracy, and spectral efficiency in detecting current radio signals. This is a challenge that is difficult to overcome in order to meet the needs of modern signal recovery. With the advent of artificial intelligence technologies such as deep learning, academia and industry began to explore the potential of applying these techniques to the field of communication receivers, leading to the emergence of intelligent receivers. This paper then introduces the fundamentals of wireless communication systems and deep learning neural networks, laying the groundwork for a deeper understanding of the subsequent content. Chapter 3 provides a comprehensive overview of the application of intelligent receivers in signal reception, including channel estimation, signal detection, modulation identification, demodulation and decoding. Finally, the paper considers the practical applications of intelligent receivers and speculates on their future development.

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