bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the Influence of Syntactic Structures on English Syntactic Priming
Authors: He Yuge
Published: November 2024

Abstract: English syntactic structures are essential components in the process of learning English as a second language (ESL) and play a critical role in the acquisition process. Based on previous research, this paper discusses the impact of syntactic structures on English L2 syntactic priming among Chinese learners from three perspectives: research paradigms, priming structures, and the factors influencing priming effects. The findings indicate that, given syntactic structure interchangeability, different syntactic structures exhibit varying degrees of priming effects. Key factors such as L2 proficiency, syntactic usage preferences, and language structureincluding both structural differences within English and similarities between the learner's native language and Englishsignificantly affect the extent of these priming effects. These insights not only contribute to further research on English L2 syntactic priming but also provide practical guidance for ESL teaching, highlighting the importance of understanding these dynamics to improve language acquisition and instructional strategies. It also enables English as a second language learners to fully understand the mechanisms of syntactic priming effects, providing them with scientific learning methods, thereby allowing them to learn English more efficiently.
Research and Analysis of Smart Home Products Based on the Emotional Needs of Generation Z Living Alone
Authors: He Mengnan , Wang Zhenya
Published: November 2024

Abstract: With the rapid transformation of Chinas economic structure, the concentration of industrial resources, and the widespread access to education, the young Generation Z has undergone a conceptual metamorphosis, flocking to first- and second-tier cities, resulting in a phenomenon of living alone. In a sense, these individuals drive social development through their own efforts; however, their collective psychological characteristics often clash with the heterogeneous and transient environment in which they reside. The issue of unmet emotional needs stemming from their solitude is becoming increasingly pronounced. This paper examines the living conditions and consumption characteristics of Generation Z living alone and, from the perspective of their emotional needs, explores their preferences and demands in the application of smart home products. The goal is to provide meaningful insights for improving the quality of life for young urban dwellers living alone and to contribute to the research and development direction of smart home product design.
The Impact of Social Media Addiction on Teenagers
Authors: Wang Feier
Published: November 2024

Abstract: Electronic devices and social media platform usage has become so prevalent that it become almost indispensable for people to stay in contact and usage in daily life. Even though social media provides numbers of benefits, the overuse of social media can lead to problems such as social media addiction. In this research, the cause, impact, and solutions of social media addiction will be discussed. Due to the high susceptivity of teenagers to social media addiction, this research aims to figure out possible ways to interfere with teenagers addiction and discuss possible treatments for it. This research used document analysis as the method. This research found social media addiction is caused by triggers, reward systems, and emotional needs; the impact of social media addiction is impaired brain functions, mental health issues, and overall well-being, including the cycle of depression and addiction, and the high rates of suicidal thoughts. This research also poses solutions, including setting limits to social media usage, adopting coping strategies, seeking therapy, and incorporating physical exercise.
Survival and Life: A Study on Cultural Adaptation of Ethnic Minority Groups from the Perspective of Dietary Anthropology
Authors: Fu Zhiqin , Xu Jintao , Chen Sihan , Liu Wenbo
Published: November 2024

Abstract: In the process of globalization and modernization, the diet culture of minority groups is undergoing profound changes and adjustments. Therefore, through the perspective of food culture, this paper studies the identity of ethnic minorities in cultural adaptation, cross-cultural integration and their resistance to foreign cultures. It is found that diet is not only an important carrier of cultural inheritance of ethnic minorities, but also a core way of cultural self-expression and social interaction. In the process of cultural adaptation, ethnic minorities not only maintain the uniqueness of traditional culture through the inheritance and change of eating habits, but also carry out innovation and integration under the influence of foreign cultures to ensure the continuation of culture and self-identity. At the same time, eating rituals in festivals and religious occasions have also become an important way for ethnic minorities to express their cultural identity. In addition, the commercialization of diet and the development of tourism have enabled the ethnic diet culture to spread widely in the context of globalization and brought economic benefits. Studies have shown that diet plays a key role in ethnic minorities' response to the impact of globalization, maintaining cultural identity and promoting cultural inheritance, and is an important tool and symbol in cultural adaptation.
Research on automated defect detection system based on computer vision
Authors: Gao Xian
Published: November 2024

Abstract. With the intensification of market competition and consumer demand for product quality enhancement, people's development of industrialization is also more and more in-depth, the production scale and complexity of a variety of products is also increasing. However, in the process of automated production, product defects are unavoidable due to various factors such as equipment, environment, and human influence. The traditional human inspection method is inefficient, costly, and subjective, making it difficult to meet the needs of modern industrial production. Therefore, the need for an efficient, accurate and automated inspection system is urgent. Based on the current social situation, this paper will introduce the automated defect detection system based on computer vision, which is widely used in many fields, and has a great guarantee for people in the production of products with high quality and low cost, based on the needs in these areas, this paper will firstly introduce the relevant algorithms and their main contributions to the application scenarios of computer vision technology, and then introduce the key technologies of computer vision in defect detection in the key technology.
Advanced obstacle recognition and trajectory prediction for autonomous vehicles using 3D CNN and GRU
Authors: Wang Zhixiang
Published: November 2024

Abstract. Recent years have seen a rapid advancement in new energy vehicle technology, which has increased consumer demand for autonomous driving systems. However, current self-driving cars still face many technical barriers. Obstacle detection and obstacle trajectory prediction in dynamic environments is one of the main difficulties of the technique. To improve self-driving cars' capacity for route planning and decision-making in dynamic contexts, this thesis proposes an obstacle identification and trajectory prediction model that combines a gated recurrent unit (GRU) network with a three-dimensional convolutional neural network (3D CNN). The integrated framework utilises the feature recognition capability of 3D CNN and the prediction capability of GRU to accurately predict dynamic obstacles, thereby improving the performance of the model in various types of environments (e.g., visual SLAM). Experimental results based on publicly available datasets (e.g., KITTI and Argoverse) show that the proposed method has significant advantages in dynamic obstacle identification and prediction as well as overall model operation efficiency. The findings show that the integrated framework is useful and successful in advancing the creation of self-driving automobiles.
Reasons and solutions for device mismatch and structural mismatch in electronic circuits
Authors: Ruan Zihan
Published: November 2024

Abstract. Integrated circuits have become one of the most popular industries in recent years, and more and more fields are interacting with it. With the passage of time, various new circuit designs have been created, and layout designs continue to advance. People are increasingly concerned about the accuracy and stability of devices, and the use of devices is also increasing. Circuit mismatch is starting to be a concern. This article based on three levels, discussed the most basic device-level resistor and capacitor mismatches at first, then talked about the circuit structures that improve the accuracy of capable circuits at the very top of the manufacturing process, which include the Current mirror structure and operational amplifier structure. Structure-based approaches to mismatch reduction have also been mentioned. In the end, the mainstream chopping and auto-zeroing techniques in circuit compensation are summarized, and prospects for proposing a solution system for circuit mismatch, make the adjustment clearer when solving problems.
Research and optimization of the gain characteristics of cascode amplifiers
Authors: Jiang Yancheng , Zhao Lechen
Published: November 2024

Abstract. With the continuous development of IC technology, high-performance operational amplifiers are widely used in various circuit systems such as high-speed Analog-to-Digital Converters (ADCs), Digital-to-Analog Converters (DACs), switched capacitor filters, bandgap voltage reference sources, precision comparators. The amplifier has become the core unit circuit of analog IC and mixed-signal IC design, and its performance directly affects the overall performance of the circuit and system. The design of high-performance operational amplifiers has always been one of the hotspots in the research of analog IC design in order to meet the needs of various application fields. Cascode Amplifier is a classic structure widely used in high-frequency and high-gain electronic circuits. This project aims to study the gain characteristics of cascode amplifiers, explore the main factors affecting their gain, and propose methods to optimize the gain. Through theoretical analysis and simulation, the influence of different parameters on gain is verified. Then the optimization strategy is summarized to provide a reference for actual circuit design.
Enhancing English education with Natural Language Processing: Research and development of automated grammar checking, scoring systems, and dialogue systems
Authors: Zhang Jiazhen , Hu Junhui
Published: November 2024

Abstract. In this paper, we investigate the scope for transformational change that these technologies offer to English education. We examine how NLP can support English education through automated grammar checking having students text checked for grammatical errors in real time, automated scoring systems which evaluate written and verbal English against predefined criteria, and dialogue systems which interacts with learners in English to help develop their speaking and listening skills in an engaging and non-judgmental environment. We document the current status of these NLP applications, examine their educational benefits, highlight some of the challenges faced by the technologies and finally discuss the way forward for large-scale adoption of such technologies, bringing the research to a logical conclusion. We hope that this study will provide a comprehensive understanding of how NLP can be employed to transform education in English and also highlight some of the associated challenges and ethical dilemmas.
Application of machine learning algorithms in resource allocation for wireless communications
Authors: Lu Mengkai
Published: November 2024

Abstract. The accelerated advances of wireless communication technologies in recent years has highlighted the necessity of efficient resource allocation in 5G and upcoming 6G networks, particularly in large-scale, high-density network implementations such as the Internet of Things (IoT) and ultra-dense cellular networks. The application of machine learning offers a number of significant advantages over traditional resource allocation algorithms, including enhanced adaptability, robustness, scalability, and predictive power. Therefore, the paper aims to examine the process of selecting the optimal machine learning algorithm for a specific resource management task. To this end, it provides an overview of the fundamental concepts of machine learning, including deep reinforcement learning (DRL), graph neural networks (GNN), and joint learning. Furthermore, this paper examines the potential applications of machine learning in the field of wireless resource management. The research presented in this paper provides a crucial theoretical foundation and guidance for further exploration and application of machine learning capabilities in the domain of wireless communication resource management. Overall, the research elucidates the potential of machine learning in wireless communication resource management and its applications, thereby advancing knowledge in this field and providing valuable references for the development of efficient and intelligent wireless communication networks.
Application status and prospect of machine learning in the field of enterprise development planning
Authors: Yang Fengrui
Published: November 2024

Abstract. In today's digital information background, data is already an important means of production, various kinds of data continue to grow, and it is difficult to rely on the manpower to deal with such a huge amount of data. Machine learning can learn the law of data change from a large number of data and build a model for analysis, to give accurate and efficient program opinions. With the development of machine learning, deep machine learning in daily life is gradually widespread, and several successful commercial applications such as Google Translate have been born. Based on machine learning technology, this paper studies its application status and prospects in enterprise development planning. The research results show that more and more enterprises now apply machine learning to the planning and summary of the enterprise, at the same time, there are many functions of machine learning to be developed in the enterprise, through more development, enterprises can carry out more reasonable development and planning. The significance of this research is that it can provide specific application programs and solutions for enterprises, and also provide important references and guidance for the future development of enterprises, which is conducive to the healthy development of enterprises. In the future, with the continuous increase in the amount of data, the continuous progress of algorithms and the improvement of computing power, the application of machine learning in enterprise development planning will be more extensive and in-depth.
Integrating computer technology and policy management for sustainable smart city development
Authors: Qiu Kuifu
Published: November 2024

Abstract. This paper presents the development and application of a machine learning-based Environmental, Social, and Governance (ESG) performance evaluation model. The model quantifies and assesses the ESG performance of companies, enabling them to identify strengths and weaknesses in their sustainability efforts. By leveraging advanced machine learning techniques, the model offers accurate and comprehensive insights into ESG performance, guiding companies in formulating effective improvement strategies. The study involves detailed data collection and preprocessing, model construction and training, and rigorous evaluation metrics to ensure reliability and effectiveness. Additionally, the paper discusses practical applications of the model in strategic decision-making, resource allocation, and enhancing stakeholder engagement. The findings highlight the importance of integrating technology with ESG considerations to drive sustainable business practices. This research provides a robust framework for ESG performance assessment and sets the stage for future advancements in this critical area.
Cutting-edge techniques in 3D modeling and animation: Leveraging mathematical models and advanced software tools
Authors: Zhang Qiongxian
Published: November 2024

Abstract. This paper explores the advancements in computer animation technology, focusing on 3D modeling and animation. It delves into the core aspects of character design, environment modeling, and animation rigging, emphasizing the mathematical models and algorithms that enhance realism and efficiency. Key techniques discussed include Bezier curves and spline interpolation for anatomy and proportions, Perlin noise and fractal algorithms for texturing, and Inverse Kinematics (IK) and Forward Kinematics (FK) for rigging. Additionally, the paper examines procedural generation and fluid dynamics simulations for environment modeling and the integration of motion capture data. The use of software tools like Blender, Autodesk Maya, and Houdini is highlighted. This study aims to provide a comprehensive understanding of the current state of 3D animation technology and its future directions.
Federated machine learning in finance: A systematic review on technical architecture and financial applications
Authors: Kang Ruixin , Li Qihui , Lu Haodong
Published: November 2024

Abstract. With the advancement of technology, an increasing amount of data is stored online, including substantial amounts of personal information. Especially in the financial industry, where large sums of money are often involved, protecting personal privacy is crucial. Federated learning, as a privacy-preserving distributed machine learning method, offers a solution to these challenges by enabling data privacy while addressing the difficulties of data sharing in the financial sectorissues that frequently impede innovation, risk management, and fraud detection. This paper delves into the principles of federated learning algorithms, exploring their mechanisms in detail. Besides, applications and case studies in applications such as financial fraud detection, supply chain financing prediction, and other financial services are examined. Furthermore, sample dataset will be introduced. Moreover, the potential benefits and challenges of implementing federated learning within financial contexts are also assessed. Finally, promising research directions for the application of federated learning in the financial industry are outlined.
Design and optimization of PD control system based on saturation
Authors: Duan Yibo
Published: November 2024

Abstract. Designing PI, PD, and PID controllers is challenging, but it is an inevitable step if engineers want their systems to perform well. Consequently, control theory was purposed and the system became more complicated. Unfortunately, saturation is hard to prevent. The degree of saturation is an important aspect of the electrical signal process. This paper investigates the concepts of saturation for PD,PI, and PID control. It involves the discussion of properties of passive components, excessive input/output concerning the design maximum or minimum, and the saturation function. The main purpose of the paper is to give an insight into how to prevent control saturation. The investigation will be mainly based on current control theory, research processes from other papers, and the online simulations. To avoid saturation, applying analysis on the gain of all controllers and saturation function is crucial.
Review of Talking Head Synthesis for Driving Mechanisms and Portrait Rendering
Authors: Liu Ziwei
Published: November 2024

Abstract. Talking head synthesis has emerged as a vital area of research, enabling the generation of realistic and expressive digital avatars. This paper explores the primary mechanisms driving talking head synthesis, categorized into video-driven and audio-driven methods. Video-driven techniques manipulate facial movements using key points, 3D meshes, and latent spaces, while audio-driven approaches focus on synchronizing lip movements and facial expressions with audio inputs. Recent advances in each method, highlighting key innovations and the challenges faced, such as occlusion, identity preservation, and lip synchronization are reviewed. The technology's applications span smart customer service, online education, telemedicine, and video creation. Future research directions focus on overcoming challenges like handling large-angle poses, ensuring temporal consistency, and improving multilingual performance.
Comprehensive Analysis of the Impact of Learning Rate and Dropout Rate on the Performance of Convolutional Neural Networks on the CIFAR-10 Dataset
Authors: Peng Changyu
Published: November 2024

Abstract. This study investigates the impact of two crucial hyperparameters, learning rate and dropout rate, on the performance of Convolutional Neural Networks (CNNs) on the CIFAR-10 dataset. Learning rate controls how quickly a model adapts to new data, while dropout rate helps prevent overfitting by randomly omitting neurons during training. These parameters are critical because they directly influence model convergence speed, accuracy, and generalization ability. By experimenting with various combinations of learning rates (0.01, 0.001, 0.0001) and dropout rates (0.3, 0.5, 0.7), we analyze the model's performance in terms of accuracy and overfitting. Our results suggest that a learning rate of 0.001 combined with a dropout rate of 0.5 yields the best balance between learning efficiency and generalization. This study offers important insights for optimizing hyperparameters in CNN training.
Processing and Comparison of GBoost, XGBoost, and Random Forest in Titanic Survival Prediction
Authors: Huang Shiyuan
Published: November 2024

Abstract. Contemporarily, the machine learning has evolved from its early concepts to a sophisticated field consisting of advanced algorithms and diverse applications. Tree-based classification models have become powerful tools for complex predictive challenges. In this study, the effectiveness of tree-based classification models, such as Random Forest, XGBoost, and Gradient Boosting, is examined on the Titanic survival prediction challenge, which originates from the 1912 Titanic disaster. Passengers survival and death were influenced by various factors in this disaster. By using features such as gender, age, and class, and the survival outcome as the target variable, a binary classification model is developed to predict each passenger's survival status. The study includes data preprocessing, feature selection based on a foundational model, and model training. After the construction, hyper-parameters tuning, and cross-validation of three classifiers, this research compares and analyzes the performance scores to evaluate the characteristics of these tree-based learning methods, aiming to provide a reference for the similar applications.
Analysis of Accelerating Ideation Process with Large Language Model
Authors: Wei Yuhan
Published: November 2024

Abstract. Large Language Models (LLMs) have rapidly advanced, demonstrating near-human intelligence in language comprehension, problem-solving, and autonomous decision-making, which can be leveraged in scientific discovery. Previous research of LLM for science often focuses on experiment execution part and the first stage of research, i.e., idea generation with large language models is lack of research. This paper fills this gap by surveying the use of LLM-based agent systems in research idea generation. This research first proposes three stages in research idea generation pipeline, pre-ideation (knowledge preparation), ideation (generation and iteration), post-ideation (evaluation), and provide detailed summary in different methods in each stage, then it summarizes different metrics and angles of evaluating generated research ideas, finally it discusses limitations and ethical concerns of existing works and suggest potential solutions and future directions. These results aim to provide a solid foundation for future research in improving LLM-based ideation systems and fostering responsible AI usage in scientific discovery.
Application Research of Infrared Technology in Safety Detection in Ship Field
Authors: Kong Bin
Published: November 2024

Abstract. This paper studies infrared technology and its application in the field of ship detection, mainly studying infrared thermal imaging of failure equipment refueling oil spill and ship safety problems because infrared technology is a high precision contact thermal measurement method through infrared camera infrared objects, the infrared spectrum wavelength range of infrared radiation heat, on the receiver into electronic signals, through electronic attenuation and electronic scanning form the corresponding heat map or temperature field image, to intuitively reflect the temperature distribution and heat focus of the object. In addition, the infrared detection device can be reasonably installed in the ship without affecting the work of other equipment, and it has been found that infrared technology has significantly improved the solution to the safety problems of ships.
Application of AI-Driven Medical Image Recognition in Precision Medicine and Healthcare
Authors: Lee Yuting
Published: November 2024

Abstract. Artificial Intelligence (AI) has been playing an important role in medical image recognition, especially in precision medicine and health management, where AI can improve the accuracy and efficiency of diagnosis by utilizing advanced technologies such as deep learning. This paper explores in detail the application of AI techniques in the early diagnosis of cancer, image analysis of cardiovascular diseases, and assessment of neuropsychiatric disorders. We also identify challenges in technical standards, data security and privacy protection, and interdisciplinary collaboration, and emphasize the importance of establishing effective technical standards and evaluation systems, as well as fostering collaboration among experts in various fields.
Advances in flight control systems for modern commercial aircraft
Authors: Du Yihao
Published: November 2024

Abstract. In consequence of the accelerated evolution of aviation technology, the flight control systems of commercial aircraft have undergone substantial technological advancement and system optimization. This paper presents a comprehensive analysis of the recent advancements and future trends in flight control systems for commercial aircraft. The fundamental concepts and historical evolution of flight control systems are delineated, with a particular emphasis on the evolution of autopilot systems and the technological advancements of flight control systems (FCS). Furthermore, it examines innovations in flight control algorithms, including adaptive control and intelligent algorithms, and investigates the impact of fault-tolerant design on flight safety. In terms of system integration, the integration of flight control systems with navigation and communication systems, as well as performance enhancements resulting from system optimization, is explored. In addition, it examines the potential impact of intelligence and networking on future flight control systems, with a particular focus on the prospects for the application of artificial intelligence, quantum computing, and new material technologies. The synthesis of these areas provides a systematic understanding of the technological evolution and future development of flight control systems for commercial aircraft.
Research on robot path planning and obstacle avoidance algorithm in dynamic environment based on deep reinforcement learning
Authors: Liu Zhaolin
Published: November 2024

Abstract. In dynamic environments, robot path planning and obstacle avoidance are critical tasks, especially in applications such as autonomous driving, industrial automation, and mobile robotics. These tasks are inherently challenging due to the unpredictability of the environment and the need for real-time decision-making. This paper seeks to address these challenges by developing and analyzing both traditional and optimized models for robot navigation. The initial model utilizes a basic Q-learning algorithm, which provides a straightforward approach to learning from the environment but often struggles with the complexity of dynamic scenarios. To this end, an optimized model is developed that combines the Double Deep Q-Learning algorithm (Double DQN) in conjunction with heuristic strategies. The research employs the MATLAB Reinforcement Learning Toolbox to implement and train these models, and utilizes a simulated environment with dynamic obstacles as a testing site. The simulation generates the necessary data to allow for comprehensive testing and evaluation of the models performance. The results show that the optimized model greatly exceeds the initial model in terms of path planning efficiency and obstacle avoidance capabilities, and that the combination of advanced reinforcement learning techniques and heuristic strategies is extremely important for enhancing the performance and reliability of robotic systems in complex, dynamic environments, offering valuable insights for future applications in various fields of robotics.
The Application of Secondary Effects in Optimizing Analog Circuits
Authors: Ma Yujie
Published: November 2024

Abstract. This study delves into the application of secondary effects, specifically body effect, channel length modulation effect, and subthreshold conduction effect, in the optimization of analog integrated circuits. The research highlights the critical role these effects play in modern circuit design, especially as device dimensions continue to shrink. Through a systematic analysis, the study presents key strategies for mitigating these effects to enhance circuit performance, reliability, and power efficiency. Notably, the implementation of a transient BD scheme in partially analog-assisted D-LDOs (Digital Low Dropout Regulators) has shown a performance improvement of over 10% compared to schemes without this enhancement, while also significantly reducing total coupling. Techniques such as DIBL effect compensation have proven effective in reducing load sensitivity and power consumption. This study provide valuable insights and practical approaches for the design and optimization of high-performance, energy-efficient electronic systems, making a significant contribution to the field of integrated circuit design.
CNN-Based Distracted Driver Recognition: Enhancing Accuracy Through Optimized Training Techniques
Authors: Ma Chuanzhi
Published: November 2024

Abstract. Distracted driving is a significant contributor to vehicle accidents worldwide, underscoring the need for an effective recognition model that can identify risky driver behaviors in real-time and provide timely alerts. This study proposes a Convolutional Neural Network (CNN)-based model designed to accurately detect distracted drivers. To optimize the model's performance, this paper implemented several strategic enhancements. During the preprocessing phase, 90% of the dataset was allocated for training, ensuring a comprehensive learning process, while the remaining 10% was used for validation to assess the model's accuracy. The Adam optimizer was chosen for its ability to dynamically adjust the learning rate, facilitating faster convergence to an optimal solution. Additionally, the Cross-Entropy loss function was employed to amplify errors during training, driving the model to correct inaccuracies more effectively. The model was trained over 25 epochs, resulting in an accuracy rate of nearly 80%. This level of performance demonstrates the models viability for real-world applications, where it can play a critical role in reducing accidents caused by distracted driving. Future research may focus on further refining the model by exploring advanced loss functions, optimizers, and CNN architectures, as well as incorporating more sophisticated data preprocessing techniques.
A Study of Techniques to Increase Amplifier Gain
Authors: Xu Ruihong
Published: November 2024

Abstract. Improving amplifier gain has been one of the key topics in electronics. Although many techniques or structures have been created to increase amplifier gain, the increase in gain largely leads to changes in other parameters such as stability and bandwidth. Therefore, the aim of this paper is to investigate basic common-source amplifiers, common-source common-gate structures and current cancellation techniques, a total of five basic circuit structures for increasing amplifier gain, and to compare and analyse them based on some of their parameters. This paper uses a combination of theoretical derivation and simulation and experimental verification to carry out the study. Finally, the advantages and disadvantages of each structure as well as the application scenarios and development prospects are listed. Through this paper, researchers can not only have a basic understanding of the above structures, but also have a more intuitive understanding of the design of amplifiers as a multi-dimensional optimisation problem. It is essential to design the right amplifier for different circuit requirements.
A Novel Filter Bank and Fourier Transform Convolutional Neural Network for SSVEP Classification
Authors: Xie Jiacheng
Published: November 2024

Abstract. Classifying Steady-State Visual Evoked Potentials (SSVEPs) is crucial for enhancing the performance of Brain-Computer Interface (BCI) systems. This study introduces a new method for SSVEP classification within BCI frameworks, called the Filter Bank and Fourier Transform Convolutional Neural Network (FBFCNN). The FBFCNN model effectively extracts key harmonic features by first segmenting SSVEP signals into multiple sub-bands through a filter bank. Subsequently, the Fast Fourier Transform (FFT) is applied to convert these signals from the time domain into the frequency domain. The generated spectrum's real and imaginary components are then fed into a convolutional neural network (CNN). This method improves SSVEP feature extraction and increases classification accuracy by fusing the advantages of CNNs with filter banks. Experimental results, derived from publicly available SSVEP datasets, show that the FBFCNN model surpasses traditional techniques in both accuracy and Information Transfer Rate (ITR). Offering a reliable and efficient solution for real-time brain signal decoding, the FBFCNN model represents a significant advancement in SSVEP-based BCI systems.
Adaptive Multi-layer Attention Double Dueling Deep Q-Network for Muti-agent Reinforcement Learning
Authors: Jiang Jiyu
Published: November 2024

Abstract. In multi-agent fields, traditional muti-agent DQN methods often suffer from overestimation bias and overestimation of unimportant actions, especially when state-action Q-value differences are slight. To deal with such issue, we present an adaptive Multi-layer Attention Double Dueling Deep Q-Network (MAD-D3QN) model, aiming to improve decision-making accuracy in complex multi-agent environments. The proposed model utilizes two attention layers that dynamically calculate state value and action advantage weights, facilitating more precise Q-value estimation and reducing the common overestimation bias. Related experiments carried out in StarWar II scenarios show that the MAD-D3QN model obviously outperforms traditional methods (IQL,DQN), achieving higher decision efficiency and robustness. Our findings demonstrates that the MAD-D3QN framework not only promotes the state-of-the-art in multi-agent reinforcement learning but also provides potential applications in real-world cooperative tasks. Future research will delve into the integration of advanced multi-agent communication structures to further enhance model adaptability.
A comparative study of KAN transformer and traditional vision transformer for ultrasound image-based diagnosis
Authors: Li Zonghan
Published: November 2024

Abstract. In recent years, the MLP architecture has almost been monopolized in the field of deep learning, and its success is undeniable, but at the same time there are some problems. Kolmogorov-Arnold Network (KAN) is a new neural network architecture based on Kolmogorov-Arnold theory implementation. Compared to traditional MLPs, KANs have higher interpretability, faster training, and more efficient usability. In this paper, based on the theory of KAN, the author try to replace the Multi-Layer Perceptron (MLP) architecture in Vision Transformer (ViT) with the better performing KAN,author conducted scoring classification experiments on clinical acute respiratory distress syndrome (ARDS) and pneumonia image datasets provided by the Emergency Department of Changzheng Hospital in Shanghai, China, in order to validate the feasibility of the application of KAN+ViT in assisting clinical medical ultrasound diagnosis as well as its high efficiency in comparison with the traditional MLP+ViT structure. And classification experiments on the Cifar-10 dataset are used to validate the superiority of this new architecture over the traditional ViT architecture.
Exploration and Application Analysis of Autonomous Driving Technology
Authors: Wu Zhe
Published: November 2024

Abstract. In recent years, with the acceleration of urbanization and the increase in vehicle ownership, traffic congestion has become an increasingly prominent issue. As vehicle technology advances, autonomous driving vehicles have gradually entered the public eye. Autonomous driving technology, seen as an important solution to the problem of traffic congestion, has garnered widespread attention and application. This paper employs a literature review and case analysis method to comprehensively explore the development history, core components, and challenges faced in the practical application of autonomous driving technology. It details the practical examples of companies such as Tesla, Google Waymo, and Baidu Apollo, fully showcasing the key role and vast potential of autonomous driving technology in intelligent transportation systems. Through a comprehensive analysis of the application of autonomous driving technology, this paper provides an in-depth introduction to its current applications, aiming to offer a more systematic knowledge framework and address the limitations of a single-perspective approach.
The Latest Development of Synthetic Aperture Radar: An Overview
Authors: Guo Jiuyi
Published: November 2024

Abstract. Synthetic Aperture Radar (SAR) technology enables high-resolution imaging regardless of weather conditions, offering immense potential for target detection and recognition. This paper reviews the latest advancements in SAR technology, highlighting key developments and applications. The limitations of traditional SAR imaging techniques, particularly the trade-off between resolution and swath width, have been addressed by High Resolution Wide Swath (HRWS) SAR, enabling simultaneous acquisition of high-resolution and wide-swath data. Multi-polarimetric SAR provides richer scattering information for improved target recognition accuracy, while deep learning algorithms enhance image quality and recognition performance. Emerging technologies like 3D-ISAR and holographic imaging offer novel approaches to target recognition and tracking. We discuss the development history, performance advantages, and limitations of these technologies, providing a comprehensive overview of their practical applications. We identify current challenges and future trends in SAR target imaging and recognition, including computational efficiency, adversarial attack defense, data acquisition and processing, and feature extraction and classification. Potential research directions, such as integrated multi-mode SAR systems, multi-source data fusion, and adversarial attack defense strategies, are outlined to fully harness the potential of SAR technology in target imaging and recognition.
Data mining in AI: Evolution, applications, and future directions
Authors: Wu Zongjian
Published: November 2024

Abstract. This paper provides a comprehensive analysis of the evolution and impact of data mining in the field of artificial intelligence (AI), with a particular focus on its application within social and information networks. It traces the origins of AI back to the 1956 Dartmouth Conference, highlighting the subsequent advancements in technologies such as machine learning and data mining that have fueled AI's growth. The paper explores the multifaceted applications of data mining in various sectors including healthcare, transportation, and industrial manufacturing, and delves into the challenges and innovations in recommendation systems, matrix factorization, and intelligent control of autonomous vehicles in intelligent transportation systems. The study emphasizes the significance of distributed algorithms and big data processing frameworks in enhancing the efficiency and applicability of data mining techniques.
Empirical methods for enhancing user experience in human-computer interaction design with digital media integration
Authors: Wang Yidan , Li Chenshu
Published: November 2024

Abstract. This study rigorously examines the application of empirical methodologies aimed at augmenting the user experience (UX) within the domain of human-computer interaction (HCI) design, with a pronounced emphasis on the seamless integration of digital media. In an era where digital media intricately intertwines with our daily lives, crafting interfaces that are both intuitive and engaging is becoming increasingly essential. This paper embarks on an in-depth analysis of a variety of empirical research techniques, including but not limited to, user studies, A/B testing, and comprehensive analytics. These methodologies are pivotal in providing critical insights and feedback that inform and refine the HCI design process. By judiciously incorporating these empirical methods throughout the design and development phases of digital media applications, designers and developers are equipped to forge more effective, accessible, and immersive user experiences. This approach ensures that digital media interfaces are not only functional but also highly engaging and responsive to user needs and preferences. The research findings underscore the critical role of user-centered design practices in significantly enhancing user engagement, satisfaction, and usability. It emphasizes that understanding the end-user's perspective and integrating their feedback into the design process is fundamental in creating digital media interfaces that resonate with users. Through a detailed exploration of these empirical methods, the study provides a comprehensive framework for improving digital media experiences, highlighting the necessity for a synergistic approach to HCI design that prioritizes user satisfaction and usability. This body of work contributes valuable insights into the ongoing discourse on the optimization of digital media interfaces through empirically informed design strategies, advocating for a user-centric approach in the rapidly evolving landscape of digital media technology..
Overview of object detection based on deep learning
Authors: Zhang Dantong
Published: November 2024

Abstract. In recent years, computer vision technology has developed rapidly, as one of its important research directions, object detection has received widespread attention due to its high accuracy. Meanwhile, object detection has many application fields, such as intelligent transportation, medical and health, security systems, etc. Traditional object detection methods have limitations when applied to complex real-world scenarios. To improve the shortcomings of conventional methods, deep learning based object detection algorithms have significantly improved the efficiency of object detection and become a research hotspot in object detection. This article summarizes the algorithms into two-stage and one-stage object detection algorithms based on the technical processes and structural differences in handling object detection tasks. Firstly, several common two-stage and one-stage object detection algorithms and their applications in real-world scenarios are introduced. Then their data sets and algorithm performance are analyzed and compared. Finally, according to the results of the comparison, the existing problems of the two-stage object detection algorithm and the one-stage object detection method are discussed, and their future development directions are pointed out.
A Review of Facial Expression Recognition Based on Convolutional Neural Networks
Authors: Zhu ChenXuan
Published: November 2024

Abstract. The intelligent processing of human facial expressions has gained popularity due to the growth of big data and the expanding knowledge of AI and machine learning. In this paper, the author reviews the current mainstream methods for face recognition using convolutional neural network (CNN) models in deep learning, providing insights and future directions. By collecting and analyzing research findings, the characteristics, strengths and weaknesses of each model are discussed. The results indicate that CNN-based face recognition systems typically involve several steps: face detection, face alignment, face representation, face matching, and post-processing. By examining expression recognition with models like LeNet-5, AlexNet, VGGNet, and GoogleNet, the author concludes that CNNs possess features such as being data-driven, capable of feature learning, multi-level structured, adaptable to different domains, capable of real-time processing, and supporting multi-task learning. This technology has made significant strides and offers considerable potential for further development.
Analysis of the Hyperparameter Selection in Machine Learning
Authors: Qian Defu
Published: November 2024

Abstract. A Machine learning is now playing a role in various fields. With the development of the internet, the volume of data is increasing, and the algorithms of machine learning are becoming more complex. Adjusting hyperparameters is a challenge for beginners who are new to machine learning. This research implemented a simple neural network model through code. Moreover, this study has changed some of the most basic parameters of the model and trained different models. This study has used visualization methods to show how the model training time and performance change with the alteration of hyperparameters, and derived their respective accuracy rates. This article provides entry-level data and code for newcomers to the field of machine learning. Since this experiment uses a federated learning model, employs local_update SGD, and also utilizes parallel methods, it is quite comprehensive, covering many algorithm codes for machine learning beginners. Testing the basic parameters of the model can help newcomers to machine learning understand more clearly the impact of hyperparameters on the model. It also aids in the development of more algorithms for adjusting hyperparameters.
Movie Recommendation System Based on Word Embedding
Authors: Tian Jiale
Published: November 2024

Abstract. Amidst the swift progression of Internet and big data technologies, recommendation systems have emerged as crucial conduits linking users to products across various digital platforms. This study delves into the deployment of neural collaborative filtering within the realm of movie recommendation systems, with the objective of constructing a system of high precision. Utilizing the MovieLens dataset, this investigation applies one-hot encoding and embedding techniques within the PyTorch-Lightning framework to effectively model user behaviors and predict cinematic preferences. The neural collaborative filtering (NCF) model leverages deep learning to extract latent features of users and items, exhibiting a notable enhancement in performance compared to traditional collaborative filtering approaches. Empirical results indicate that the NCF model attains an accuracy of 86% on the validation dataset, underscoring its efficacy in extensive recommendation scenarios.