bluuebunny/crossref_metadata_2025 huggingface dataset:
Advancements and technical challenges in generative models of artificial intelligence painting
Authors: Chen Xuan
Published: November 2024

Abstract. Artificial Intelligence Painting, as a frontier field of AI technology application, has gained wide attention and rapid development in recent years. With the advancement of generative models, especially the emergence of variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models, the ability of AI in art creation has significantly increased. These techniques not only enable computers to generate realistic images, but also to simulate complex artistic styles, opening up new possibilities for artistic creation. Nevertheless, how to maintain a high degree of complexity and realism in the generated images, how to improve the data quality to enhance the generalization ability of the model, how to solve the pattern collapse problem to improve the stability of the generation, and how to improve the efficiency while ensuring the quality of the generation are all key issues in the current research. In addition, with the continuous evolution of AI technology, the future development direction and potential applications are also worth exploring in depth. The paper aims to systematically analyze the generative models and their applications in AI painting, to deeply discuss the technical challenges faced, and to look forward to the future development prospects.
Research on traffic flow prediction method based on LSTM model and PSO-LSTM model
Authors: Xu Fengyang
Published: November 2024

Abstract. With the acceleration of urbanization, the number of cars owned by residents has also significantly increased. The contradiction between the number of cars and the road carrying capacity has become increasingly severe, resulting in very serious congestion. This paper selects road data from 0:00 to 10:00 every morning in Beijing from April 2nd to April 12th, 2016, and uses the average speed of vehicles as a variable to measure road congestion. Based on these data, this article uses LSTM models to predict the speed of vehicles on two roads representing main and non main roads. Research has found that LSTM model has good predictive performance for the speed of vehicles on two roads and is very accurate in predicting the data trend. However, the accuracy of LSTM model in predicting non periodic and highly discrete data is not ideal. This article also uses the PSO-LSTM model to predict one of the roads, and the results show that the model is more accurate than the LSTM model in predicting non periodic data. The prediction of short-term traffic flow will greatly help the transportation department coordinate and manage resources, alleviate traffic congestion, and facilitate residents' travel.
Design of automatic sorting system based on capsule neural network
Authors: Ju Yanlin
Published: November 2024

Abstract. This paper proposes a vegetable disease classification system based on Capsule Neural Network (CapsNet), which uses the visual recognition mechanism of deep learning algorithms to improve the efficiency and accuracy of agricultural disease recognition. The CapsNet algorithm outperforms traditional Convolutional Neural Network (CNN) in few-shot learning and generalization due to its unique capsule structure and dynamic routing mechanism. Experimental results show that the proposed system performs well in the accuracy, robustness, and model interpretation of image recognition, and can accurately identify diseases even in the case of poor image quality or noise and occlusion. In addition, the embedded system of this experiment includes an image acquisition module, an algorithm core module, and a logic control module, which realizes the automation of the whole process from image acquisition to recognition result display. This study also discusses the scalability and adaptability of the system, as well as the future application prospects in agricultural intelligence and automation. It is expected that CapsNet will play a more critical role in artificial intelligence, especially in agrarian automation and smart agriculture.
Advancements in EEG-based BCI for cognitive decline diagnosis using deep learning and Transformers
Authors: Zhang Yuanyuan
Published: November 2024

Abstract. Deep learning and Transformer models have revolutionized medical diagnostics, particularly in the advanced analysis of large datasets. Given the individual variability in cognitive decline status, the accuracy of diagnosing elderly cognitive decline diseases is crucial. Since it allows for the initiation of treatment and interventions at the earliest possible stage, potentially slowing the progression of the disease. In this paper, the author reviews and summarizes studies on the use of Transformers and deep learning in Electroencephalography (EEG) signal decoding, which is the most prevalent signal-detecting method in Brain-Computer Interface (BCI) systems for elderly cognitive decline. The review indicates that deep learning and transformer models outperform traditional methods in classifying cognitive decline, offering effective methods and improved accuracy by extracting and analyzing in-depth features from EEG data. Differently, deep learning models are proficient in single modality learning, but transformer models offer a unified approach to process and integrate information from diverse data sources and, with the increase in data volume, Transformers have the potential to further improve the accuracy of diagnosis.
End-to-end system architectures in autonomous driving: Comparative analysis against modular design and technological exploration
Authors: Yuan Lisha
Published: November 2024

Abstract. In the context of accelerated technological advancement, artificial intelligence is becoming an increasingly pivotal enabler of autonomous driving. With the integration of machine learning into autonomous driving systems, the traditional modular architecture has been disrupted, evolving into a more advanced end-to-end architecture. The end-to-end architecture has the advantages of simple structure and no cumulative error, especially under the current situation where a large amount of data and functions are continuously inputted, it demonstrates superior performance compared to the traditional modular system. However, the implementation of end-to-end is still mostly confined to academia and has not been widely applied to actual vehicles; the mainstream on actual vehicles is still the modular system architecture. As a result, the two system architectures are compared in this paper to ascertain their relative complexity, the extent to which errors are accumulated, their interpretability, the security they afford, and the extent of their data dependency. This is achieved through a comprehensive literature review. Furthermore, the paper delineates the deployment of the end-to-end system and outlines potential avenues for addressing the challenges that emerged during the implementation phase. The results indicate that the end-to-end system exhibits a certain degree of sophistication. However, a series of challenges pertaining to interpretability and security must be addressed to fully realize the potential of the end-to-end system.
A study of machine learning applications in healthcare
Authors: Zhou Xinnan
Published: November 2024

Abstract. Machine learning, a sub-field of artificial intelligence, has numerous algorithms with applications in image recognition and classification, natural language processing, data prediction, medical diagnosis, and more, achieving significant results. Nowadays, the application of many algorithms of machine learning in the medical field has become a research hotspot and attracted wide attention from society, which plays a key role in disease screening, disease prediction, and assisted diagnosis of diseases. In this paper, we have developed a new approach to the medical field that can be applied in the healthcare industry by using technologies such as k-nearest neighbor (KNN), recurrent neural network (RNN), plain Bayesian (NB), decision tree (DTT), random forest (RF), deep convolutional adversarial network (DCAN), generative adversarial network (GAN), deep convolutional generative adversarial network (DCGAN),deep convolutional neural network (CNN), and support vector machine (SVM). By referring to the existing related literature and application practice results, this paper provides a systematic introduction to the application and research of machine learning in the medical field. This paper also analyses and describes the challenges and difficulties in the implementation of machine learning in the medical field.
Comparison Distributed and Parallel Machine Learning: Evidence from Models, Principles and Application Scenarios
Authors: Zhang Zhefan
Published: November 2024

Abstract. Contemporarily, the demand for processing large-scale data has been rapidly increasing, prompting continuous advancements in the field of machine learning. This study examines the state of parallel and distributed machine learning at present, both of which aim to enhance computational efficiency when dealing with large datasets and demonstrate promising applications across various domains. As the scale of data escalates, traditional single-machine learning methods are becoming increasingly inadequate, which lead to the emergence of parallel and distributed machine learning. These approaches enable substantial computations to be performed more efficiently through the collaboration of multi-core CPUs or multiple computing nodes. This research conducts an in-depth analysis of the inherent challenges associated with these methods, including data transmission latency, synchronization requirements, and user privacy concerns. Ultimately, this research emphasizes the tremendous potential of both methods for future applications, a potential that is bolstered by ongoing advancements in hardware and algorithm optimization. These results provide valuable insights for practitioners in the field and offers guidance for future research directions.
Research on agricultural disease detection technology based on artificial intelligence
Authors: Li Chengru
Published: November 2024

Abstract. Pests and disease are two major factors that reduce the yield of agricultural products per acre of arable land. A report from the Food and Agriculture Organization of the United Nations suggests that agricultural disease and pests cause a loss of more than 1/3 of agricultural production every year. However, farmers cannot check their crops all day without a break. Artificial Intelligence (AI) is developing rapidly this year, it performs well in image recognition. Thus, it is suitable to integrate AI in agriculture. This paper shows the outcome of the accuracy of the outcome when a model is trained using the ResNet-50 model and by some crop pathogens datasets downloaded from Kaggle and using a platform of mmpretrain. It also discusses the pros, cons, and areas for improvement when using AI to detect agricultural disease. This research aims to enhance crop yields and agricultural stability by helping integrate the model in agricultural production.
Analysis of Gain Enhancement Techniques in Integrated Circuit Design
Authors: Chen Dingyi
Published: November 2024

Abstract. Today, the size of semiconductor devices is still decreasing, which makes the circuit more integrated, faster computing speed, and lower power consumption. But for a single transistor, some performance degrades. One of the important properties is voltage gain, which must be compensated with some special methods in the design. This paper will focus on the discussion and analysis of four methods, which are able to improve the voltage gain. These are cascode amplifier, cascade amplifier, gain boosting, and bootstrapping. In this letter, their respective advantages and disadvantages will be discussed. Furthermore current applications and possible external development of these methods are also important part of this paper. In this part, the discussion will be further expanded and summarized on the basis of previous studies. Hopefully, these studies can help others gain a more comprehensive understanding of these technologies and provide a theoretical basis for future high-performance analog integrated operational amplifier research and design.
A Review of Applications of Deep Learning Techniques in Recommender Systems
Authors: Liu Zhengyang
Published: November 2024

Abstract. Nowadays, with tons of information brought by the development of the Internet, recommender systems are becoming more and more popular, and they are affecting many aspects of peoples daily lives. When people use the search engine, shop on e-commerce platforms or watch videos on video software, they are enjoying the content provided by recommender systems. Deep learning techniques also gained a rapid development recently due to their strengths in dealing with complex tasks. Deep learning techniques have triumphed in many areas. Also, there are more efforts contributed to the deep learning based recommender system. However, very few studies focused on giving a detailed review of deep -learning-based recommender system , and these reviews were usually conducted several years ago, which cannot reflect the latest research trends. Hence, the aim of this paper is to summary the deep learning techniques used in recommender systems. A classification scheme is given, and some possible future directions are pointed out.
A Study on the Relationship Between Spiral Motion Illusions and Anxiety Levels: A Comprehensive Analysis of Neural Activity and Visual Stimuli
Authors: Gui Yuxuan , Hao Yifei , Zhu Pengxu
Published: November 2024

Abstract. Spiral Motion Illusions (SMIs) are a fascinating phenomenon where static images create a compelling sense of motion, tricking the brain into perceiving movement where there is none. This study investigates the relationship between SMIs and anxiety levels, aiming to uncover how these visual illusions might influence emotional states. The experimental design involved exposing thirty-one participants to two distinct types of SMIsthe "rotating snakes" and "rotating ad-lib" imageswithin a controlled environment. Anxiety levels were assessed both before and after exposure using standardized questionnaires, while neural activity was continuously monitored through the ThinkGear ASIC Module (TGAM) to capture real-time brainwave patterns. The results revealed significant changes in anxiety levels, with each type of SMI eliciting unique neural responses. The "rotating snakes" illusion, characterized by dynamic color contrast, and the "rotating ad-lib" images, defined by contrasting colors, produced distinct patterns of anxiety-related brainwave activity. These findings provide valuable insights into the intricate ways that visual illusions can influence emotional states, particularly anxiety, and highlight the potential of SMIs as tools for both psychological research and therapeutic applications. This study advances our understanding of the neural mechanisms underlying anxiety and offers a new perspective on how visual stimuli can affect mental health.
Personal Loan Default Prediction Analysis based on TabNet and Logistic Regression
Authors: Guan Yuqi , Wang Yifan , Yang Yuehao
Published: November 2024

Abstract. With the continuous development of the global economy, the demand for loans from individuals and enterprises is growing. However, loan defaults have gradually become a major challenge facing the financial industry. Loan defaults not only directly affect the profitability of financial institutions, but may also trigger systemic risks and pose a potential threat to the entire economic system. Therefore, improving the accuracy of loan default prediction is crucial for financial institutions to effectively manage credit risks. This study built a system for personal loan default prediction by combining TabNet with the Logistic regression model. Through feature engineering, this study extracts potential credit risk features by utilizing datasets from personal and Internet loans. The accuracy of default prediction is improved by combining TabNet's feature learning capabilities with Logistic Regression's interpretability. An AUC value of 0.89 was achieved by the integrated model, which achieved a notable performance. The results indicate that a system that relies on machine learning to predict defaults can significantly enhance the quality of credit approval decisions and lower the likelihood of bad debts for financial institutions. Future studies could aim to optimize the feature selection process, experiment with more advanced machine learning algorithms, or apply the model to diverse loan datasets, thereby enhancing both its generalization and accuracy. In conclusion, this study offers a novel approach to loan default prediction, demonstrating significant practical value and providing substantial support for the risk management and decision-making processes of financial institutions.
A Novel Defect Segmentation Model via Integrating Enhanced U-Net with Convolutional Block Attention Mechanisms
Authors: Zhang Junyi
Published: November 2024

Abstract. Defect segmentation in industrial quality control is pivotal for maintaining high product surface standards, but it remains a challenge due to complex defect patterns and varying backgrounds. Traditional segmentation methods often struggle with sensitivity and specificity, leading to either missed defects or false positives. In order to address the above issues this paper presents an advanced defect segmentation model that integrates a Convolutional Block Attention Module (CBAM) with an enhanced U-Net architecture. Our method leverages the spatial and channel-wise attention mechanisms to focus more accurately on subtle defects and remove background interference, enhancing the discriminative power of the feature maps generated by the U-Net. Experimental results on SD-saliency-900 industrial datasets demonstrate significant improvements in segmentation accuracy and robustness compared to existing state-of-the-art methods, confirming the effectiveness of integrating attention mechanisms in deep learning models for complex defect segmentation tasks. Specifically, our model achieved a mean Intersection over Union (mIoU) of 0.87, which is an increase of 5% over the traditional U-Net model and a 4% improvement compared to the DeepLabv3+ model. These results indicate a substantial enhancement in defect detection performance, validating the superiority of our approach.
Leaf disease detection and classification with a CNN-LSTM hybrid model
Authors: Pan Yibing
Published: November 2024

Abstract. Smart agriculture refers to the use of modern information technology, Internet of Things technology, and artificial intelligence to achieve accurate management, efficient operation and sustainable development of the entire process of agricultural production. Smart agriculture mainly includes the application of data collection and analysis, intelligent agricultural machinery, precise fertilization, disease and pest detection and agricultural products traceability. Leaf disease detection and classification are considered as challenging yet important tasks in smart agriculture. Deep leaning methods have been proven effective for these image-based recognition tasks. In this study, two advanced deep learning methods, namely, Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) are combined together to achieve a further improvement. Numerical results demonstrate that the proposed method outperforms both CNN and LSTM variants.
Research on gesture recognition technology based on machine learning
Authors: Xiong Yuang
Published: November 2024

Abstract. Hand Gesture Recognition (HGR), as a significant technological advancement in the field of Human-Computer Interaction (HCI), aims to develop systems capable of accurately recognizing and interpreting human gestures for a diverse range of applications, including device control, virtual reality, gesture passwords, and gesture interaction. With the continuous advancement of machine learning algorithms, especially deep learning techniques, machine learning-based gesture recognition has garnered widespread attention. This paper presents a review of the development of gesture recognition techniques from traditional approaches to the current mainstream deep learning-based methods, and outlines the challenges and technical difficulties encountered. It analyzes several of the most popular classification techniques, including Naive Bayes, K-Nearest Neighbors (KNN), Random Forests, XGBoost, Support Vector Classifiers (SVCs), and Convolutional Neural Networks (CNNs). Furthermore, this paper examines the application of these algorithms in both dynamic and static gesture recognition and compares their performance and suitability in different scenarios. The results demonstrate that the accuracy and robustness of gesture recognition systems can be markedly enhanced through the prudent selection and optimization of the algorithms, which serves as a valuable reference for future research and applications.
Optimizing embedded AI systems for autonomous driving: Challenges and solutions using bayesian networks
Authors: Yuan Pan
Published: November 2024

Abstract. Embedded Artificial Intelligence (AI) systems are important components of autonomous vehicles. However, incorporating AI into autonomous vehicles is technically complex, due to the constraints of computation, real-time processing of data, uncertainty handling, and hardware limitations. Bayesian Networks (BNs) are a promising method that allows probabilistic modelling in adaptive learning and environment perception. Here we report on an overview of the application of BNs on autonomous driving, with an emphasis on how BNs can be optimized for embedded system resource constraints, including both computational and energy. Various optimization techniques are discussed, such as model pruning, approximation, acceleration using hardware accelerators, such as Field-Programmable Gate Arrays (FPGA) and Application Specific Integrated Circuit (ASICs), and advanced cooling and power management to ensure AI reliability under high computational load. By reviewing these approaches, we aim to contribute to the development of more robust and green AI systems for autonomous driving.
A Research on Defects and Improvements to Multi-Armed Slots
Authors: Wu Shengyu
Published: November 2024

In recent years, various large language models have sprung up, which are constantly being trained and updated. Most of the time, these models can provide answers more accurately. However, in some specific cases, the answers generated by training on a large corpus of non-emotion text may not be correct. This paper proposes a feasible method to reduce the sum of squares as much as possible so that the fitting function is closer to the true value. By adding the concept of emotion to deep learning, combined with the fusion model of multi-layer linear regression and neural network learning, it was found that adding emotion is more helpful to the language model through multiple trainings, so that the "arm" pulled by the AI each time gets a larger expectation than the original, which significantly improves the reliability and provides a new idea and direction for the problem in this direction.
Advancing Text-to-Image Synthesis with GANs: Integrating CNNs, LSTMs, and Style Transfer Techniques
Authors: Zhou Kexin
Published: November 2024

Abstract. This research pioneers a novel methodology for generating images from text by integrating Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks, effectively bridging the semantic disparity between textual inputs and their visual representations. The proposed model integrates attention mechanisms to enhance semantic precision, making sure that generated images closely align with the provided text. Additionally, style transfer techniques are employed to infuse the images with artistic elements, thereby enriching their visual appeal and diversity. The methodology involves a multi-stage process: CNNs are utilized for feature extraction, LSTMs encode textual descriptions into contextually rich vectors, and style transfer is applied to incorporate artistic styles into the generated images. Extensive experiments demonstrate that the model excels in producing high-fidelity images that not only capture the essence of textual descriptions but also exhibit significant visual diversity. This research makes substantial contributions to the field of GAN-based image synthesis, offering a framework that advances both semantic accuracy and creative expression. The findings provide a solid foundation for future research and innovations in automated image generation, highlighting the potential for further improvements and applications across various domains.
Harnessing Machine Learning to Model Intuitive Physics: Insights from Game Engines and Cartoon Simulations
Authors: Tang Jiazhen
Published: November 2024

Abstract. This paper explores the application of machine learning (ML) in modeling intuitive physics, focusing on insights derived from game engines and cartoon simulations. Intuitive physics refers to the human capability to predict physical phenomena in everyday situations, a skill that can be replicated and enhanced in machines using ML algorithms. By integrating ML with game engines, which provide dynamic, controllable environments, and cartoon simulations, known for their exaggerated physical scenarios, the research aims to develop models that better understand and predict physical interactions. The study analyzes the efficiency of current ML approaches, including deep learning and reinforcement learning, in capturing the complex, often non-intuitive physics depicted in cartoons, and how these models can be generalized to understand real-world physics. The findings suggest potential improvements in the algorithms' ability to interpret and anticipate physical events, leading to advancements in robotics and AI-driven simulation systems.
A survey of offline precision calibration of industrial robots
Authors: Yang Xiaodong
Published: November 2024

Abstract. As the need for precision and efficiency grows in industrial manufacturing, the accuracy of industrial robots assumes paramount importance. Nevertheless, factors, such as mechanical structures and assembly processes often introduce errors, compromising robots' positioning and repeatability accuracy. Consequently, precise calibration and compensation of these errors are paramount for optimizing robots performance. A thorough analysis of error sources and the establishment of error models in industrial robots are conducted in the present study, with special attention paid to offline calibration methods. This paper reviews the robot error model building methods and offline accuracy calibration techniques, summarizes the technical difficulties of the calibration task, and proposes the future development direction for the difficulties such as the complexity of error modeling, the difficulty of non-motor calibration calculation, and the traditional stiffness compensation that does not meet the needs of the robot's operation process. The importance of this research lies in its potential to improve the accuracy and reliability of industrial robots, thus contributing to the development of industrial automation.
Exploiting EEG to identify confused students in online learning: A machine learning model comparison
Authors: Chen Junjie
Published: November 2024

Abstract. The COVID-19 pandemic has forced most schools, including universities, to adopt online courses for teaching. Due to the breaking of the constraints of time and space, online courses still occupy a large proportion in education until now, and large-scale open online education courses will become a major trend in the future. Of course, problems also arise in this process. As teachers cannot receive feedback from students anytime and anywhere in online courses, such as micro expressions, body movements, etc., they cannot know whether students are confused. Therefore, how to effectively detect students' learning status has grown in popularity. In the last few years, machine learning has developed rapidly, and a large number of artificial intelligence have emerged, making it possible to detect whether students are confused by combining electroencephalography with machine learning. In order to determine whether students were confused, this study used a wireless single channel Mindset device to collect Electroencephalography (EEG) signals. Six machine learning modelsrandom forests, eXtreme Gradient boosting, K-nearest neighbors, gradient boosting machines, logistic regression, and support vector machineswere then chosen. The findings indicate that other machine learning models have a high accuracy rate in classifying students' bewilderment, with the exception of the logistic regression model. With a 99.69% accuracy rate, the eXtreme gradient boosting model performs better than a number of other models.
Impact of RAW vs. RGB images on low-light object detection in autonomous driving
Authors: Xie Zifan
Published: November 2024

Abstract. In the field of autonomous driving, object detection in low-light conditions presents a critical challenge. Traditional detection algorithms, reliant on RGB imagery, often suffer from diminished performance under inadequate illumination. In contrast, RAW images, which retain a higher level of intrinsic image information, have the potential to enhance detection accuracy. Therefore, the paper aims to assess whether models trained on RAW images exhibit enhanced object detection performance in low-light scenarios. To this end, experiments are conducted utilizing the Low-light Object Detection (LOD) dataset, which includes paired RAW and RGB images, with variations in lighting simulated through different exposure times. Two distinct datasets are constructed: one consisting of RAW-normal and RAW-dark images, and another comprising RGB-normal and RGB-dark images. The models evaluated in this research include YOLOv8, Faster R-CNN (Region-based Convolutional Neural Network), and EfficientDet, selected to ensure robustness and generalizability of the findings. The results demonstrate that models trained on RAW images significantly outperform those trained on RGB images, thus exhibiting higher accuracy both in general and under low-light conditions. The preservation of detailed image information in RAW format facilitates enhanced feature extraction, thereby improving detection accuracy and resilience under low-light conditions. These findings provide novel insights into advancing object detection methodologies for autonomous driving systems operating in challenging lighting environments.
A Review of Facial Generation Technology Research
Authors: Wang Haohua
Published: November 2024

Abstract. Face generation, as a cutting-edge generative technology, has made significant strides in various image synthesis tasks, including facial inpainting, text-to-image translation, and video-based facial animation. Generating realistic and diverse human faces is a critical task in computer vision, with a wide array of real-world applications. Given the remarkable success of face generation models, there has been a growing interest in leveraging advanced techniques to further improve the quality, diversity, and control of generated faces. This paper will provide a comprehensive overview of the state-of-the-art face generation techniques. Specifically, the paper reviews the key approaches in this field, including Generative Adversarial Networks (GANs), Vector Quantized methods, and the more recent Diffusion Models, each of which has contributed significantly to the advancement of face generation. The paper discusses how these models have evolved to handle the complexity of face generation, from capturing subtle facial details to enabling fine-grained control over facial attributes. The paper also explores the major applications of face generation technologies, with a particular emphasis on their use in entertainment, virtual reality, and security. Finally, the paper identifies promising directions for future research in face generation, such as improving the interpretability of models, addressing ethical concerns, and enhancing the ability to generate faces that are both highly realistic and diverse.
SOVAR: System of Visual Assistance and Recognition
Authors: Huang Ken
Published: November 2024

Abstract. Around 295 million individuals globally suffer from moderate to severe vision impairment. They struggle with daily activities and depend heavily on others for assistance. Leveraging augmented reality (AR) and artificial intelligence (AI), we have developed SOVAR, a mobile application that enables greater independence for visually impaired individuals in their daily lives. SOVAR includes two modules: navigation and scene understanding. Navigation involves two phases: mapping and guidance. During mapping, SOVAR builds and optimizes the maps with key locations labeled by users via voice input. During guidance, SOVAR plans the path and guides users to requested key locations, with visual and audio assistance and realtime obstacle avoidance. The scene understanding module includes a Large Vision Language Model (LVLM) to help the users through image captioning and visual question answering. For navigation, our user study shows that participants successfully navigated to the key locations from three separate locations in 86.67% of trials without intervention. The success rate improves with increased user familiarity with the application. For scene understanding, our study shows that leveraging LVLMs to help the visually impaired allowed the participants to answer the visual-related questions with an accuracy of 100%. In this work, we developed SOVAR, a mobile application leveraging AR and AI to assist the visually impaired in navigation and scene understanding. The promising results from the user study on SOVAR demonstrate the effectiveness of AR and AI for visual assistance and indicate their potential impact on general assistive technologies.
Enhancing Semantic Consistency in Image-to-Image Translation with an Improved CycleGAN Framework
Authors: Fan Runze
Published: November 2024

Abstract. This paper presents an enhanced Cycle-Consistent Adversarial Networks (CycleGAN) model aimed at preserving semantic consistency during image-to-image translation, with a focus on complex tasks such as autonomous driving and scientific simulations. The study's key contribution is the incorporation of a pre-trained semantic segmentation model to preserve important characteristics during translation, such as license plates, traffic signs, and pedestrian structures. By introducing a semantic consistency loss alongside the traditional cycle-consistency loss, the proposed approach ensures that key features are retained, even in challenging scenes. Extensive experiments conducted on the Cityscapes dataset demonstrate the effectiveness in maintaining both visual fidelity and semantic accuracy, significantly improving upon the traditional CycleGAN. This method proves particularly valuable in domains where precision is essential, such as cross-domain image generation for autonomous systems and medical imaging. Future research will focus on optimizing the model for real-time applications and exploring multi-domain frameworks to further enhance its performance in diverse environments. Overall, this study offers an efficient image style-transfer solution for preserving semantic integrity without sacrificing translation accuracy.
Neural Machine Translation (NMT): Deep learning approaches through Neural Network Models
Authors: Hu Junhui
Published: November 2024

Abstract. This paper explores the significant advancements in Neural Machine Translation (NMT) models, focusing on the impact of different architectures, training methodologies, and optimization techniques on translation quality. The study contrasts the performance of Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and the Transformer model, highlighting the superior capabilities of the Transformer in handling long-range dependencies and providing contextually accurate translations. Key optimization techniques, such as learning rate scheduling, dropout regularization, and gradient clipping, are discussed in detail, emphasizing their roles in enhancing model performance and training efficiency. Furthermore, the paper presents a comparative analysis of NMT and traditional Statistical Machine Translation (SMT) systems, showcasing NMT's superior BLEU scores and fluency. The application of model distillation is also examined, demonstrating how smaller models can achieve high performance with reduced computational resources. These findings underscore the transformative potential of NMT in achieving state-of-the-art translation quality and efficiency.
Digital Rights Management (DRM) technologies and legal research: Applications and regulations of encryption, digital watermarking, and copyright protection systems
Authors: Han Lu , Liu Mohong
Published: November 2024

Abstract. This paper explores the intersection of technology and law in Digital Rights Management (DRM) systems. By examining the application and regulation of encryption technologies, digital watermarking, and comprehensive copyright protection systems, this study provides a detailed understanding of how these technologies prevent unauthorized copying, distribution, and use of digital content. The paper introduces mathematical models to quantify the effectiveness of encryption and watermarking, offering insights into their practical applications and implications for intellectual property law. The comprehensive DRM effectiveness model combines these individual models, accounting for user inconvenience, to provide a holistic view of DRM system performance. Key case studies illustrate the implementation of DRM technologies in various industries, highlighting best practices and regulatory compliance. The study concludes with recommendations for future research and policy development to enhance the effectiveness and legal robustness of DRM technologies. This work contributes to the academic and practical understanding of DRM, offering a framework for optimizing DRM strategies in a dynamic digital landscape.
Advancements in deep learning for visual object tracking
Authors: Dai Jingyi
Published: November 2024

Abstract. Since contemporary information-retrieval systems rely heavily on the content of titles and abstracts to identify relevant articles in literature searches, great care should be taken in constructing both. This comprehensive review delves into the transformative impact of deep learning on the domain of visual object tracking. Since the inception of AlexNet in 2012, deep learning has revolutionized feature extraction, leading to significant advancements in tracking accuracy and robustness. The article explores the integration of deep learning with various tracking algorithms, including deep correlation filters, classification-based approaches, Siamese networks, gradient-based methods, and the innovative application of Transformer architectures. Moreover, the role of tracking datasets in fostering algorithm development and innovation is highlighted, with an emphasis on the expansion in scale, diversity, and annotation quality. Furthermore, the article also examines the multifaceted evaluation metrics for tracking algorithms, encompassing precision, robustness, efficiency, generalization, and real-time capabilities. Looking ahead, the review outlines future research directions, such as algorithm optimization for lightweight and accelerated performance, enhancing generalizability, leveraging multimodal data fusion, and refining Transformer models for improved temporal information processing. The challenges of long-term tracking and the growing importance of algorithm interpretability and transparency are also discussed. In summary, the article underscores the promising trajectory of deep learning in visual object tracking, with ongoing research poised to make tracking technologies smarter, more efficient, and robust, catering to a wide array of practical applications and environments.
AI-driven automatic generation and rendering of game characters
Authors: Xie Yaopeng
Published: November 2024

Abstract. This paper provides a comprehensive review of AI-driven techniques for the automatic generation and rendering of game characters, with a particular focus on Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). GANs, using adversarial training approaches, have revolutionized character development by producing incredibly realistic and aesthetically pleasing gaming characters. VAEs, despite frequently encountering issues like image blurriness, provide an alternate strategy that emphasizes diversity and originality in the generated content. Additionally, conditional models that enable more individualized and regulated character generation are explored, as well as hybrid models that combine the best features of both GANs and VAEs. The difficulties with mode collapse in GANs and the requirement for big datasets for both GANs and VAEs are also covered, along with some possible fixes like transfer learning and semi-supervised learning strategies. This analysis emphasizes the growing significance of AI-driven game character generation in the gaming industry by highlighting its current state, problems, and future directions.
Research on Graphitic Carbon Nitride (g-C3N4) prepared by template-based method and its photocatalytic properties
Authors: Li Chengkun
Published: November 2024

Abstract. The Graphitic Carbon Nitride (g-C3N4), a non-metallic polymer photocatalytic material, has a wide range of raw materials and a simple preparation process. Due to its unique electronic structure and excellent photocatalytic performance, g-C3N4 has attracted wide attention in the field of environmental purification and energy conversion in recent years. However, g-C3N4 faces a lot of challenges, including a wide band gap, many interlayer defects, and poor interlayer electron transport. Nevertheless, its unique semiconductor structure and chemical stability still provide the possibility to explore the effect of its piezoelectric properties on photocatalysis, which paves the way for a novel approach to environmentally friendly hydrogen production. Therefore, the paper delves into the application of different template methods in the preparation of g-C3N4 by reviewing the related literature, and analyzes the role of different template materials in the preparation. In addition, in this paper, the photocatalytic properties of g-C3N4 are explored, which are mainly reflected in the photocatalytic water separation performance and photocatalytic degradation of organic dyes. It is found that the g-C3N4 shows great potential in the fields of photocatalytic water decomposition for hydrogen production and degradation of organic pollutants due to its unique band gap, nontoxicity, low cost and wide range of sources. Future research should continue to explore more kinds of template materials and their preparation processes, with the aim of realizing further enhancement of the performance of g-C3N4 photocatalysts and promoting their practical applications in the fields of environment and energy.
A review of advances in image recognition models: From K-NN to Vision Transformer
Authors: Liu Shuhao
Published: November 2024

Abstract. In the digital age, visual data are everywhere. Image recognition has become a vital branch of artificial intelligence and is used in a wide range of fields. From medical diagnostics to autonomous vehicles, its impact is profound and far-reaching. This review visits the significant advancements in the field of image recognition, summarizes and analyzes the progress from traditional methods like K-Nearest Neighbors (K-NN) to newer models like Vision Transformer. This paper analyzes image recognition models like AlexNet, GoogLeNet, ResNet, and Vision Transformer, with the principles, strengths, weaknesses, and the significance of these models to deep learning fields, along with their applications in image recognition tasks. These models not only push the boundaries of image recognition but also find extensive applications across various domains. In the future, image recognition can continue to develop with larger and better datasets, increased computing power, and more advanced network structures.
Review of Self-Stabilization Algorithms in UAV Electro-Optical Pods
Authors: Liu Yuheng
Published: November 2024

Unmanned Aerial Vehicles (UAVs) have significantly impacted various industries, with electro-optical (EO) pods playing a crucial role in enhancing their functionality. These pods require advanced stabilization algorithms to maintain a stable line of sight (LOS) in dynamic environments. This paper reviews the evolution of self-stabilization algorithms used in UAV EO pods, from traditional Proportional-Integral-Derivative (PID) controllers to advanced Kalman filters and machine learning techniques. Through a comprehensive analysis, the paper explores the application of these algorithms in different scenarios, highlighting their importance in both military and civilian domains. The findings provide insights into optimizing EO pod performance, ensuring high-quality imaging and precise targeting in various UAV operations.
The influence of the gap on the air intake grille on the downforce of a car
Authors: Zhao Runpu , Lin Jiaxuan , Li Sizhe , Li Dongyu
Published: November 2024

Abstract. This study investigates the impact of varying air intake grille intervals on the downforce of vehicles. The analysis is conducted using fluid finite element analysis with ANSYS Fluent software. Various grille intervals are tested to determine their influence on airflow and downforce. Results indicate that grille intervals significantly affect downforce, with the optimal interval identified for achieving maximum aerodynamic efficiency. The findings provide insights for optimizing grille design to enhance vehicle performance.
Research on the Anchoring Performance of Green and Low-Carbon Anchoring Materials for Urban Rail Transit Foundation Pit Engineering
Authors: Shun Yao , Liu Guoliang , Wang Jinlong , Wang Gang , Gao Qi , Zhou Huabo
Published: November 2024

Abstract. Currently, most anchoring materials used in engineering are ordinary cement mortar, which has a slow hardening process, a prolonged early strength development, and high carbon emissions. In response to this, this paper proposes a new type of green anchoring material. The anchoring performance of this material is verified through indoor and field tests. Indoor tests on the basic properties of the green anchoring material were conducted, determining the optimal water-material ratio to be between 0.25 and 0.30 through tests on fluidity, setting time, and compressive strength. Field tests studied the ultimate bond strength of the new green anchoring material in typical soil-rock composite strata in Qingdao, confirming its rapid-setting and high-strength anchoring characteristics. The application of this anchoring material can reduce carbon emissions and realize the concept of green and low-carbon environmental protection.