Frontiers in Computing and Intelligent Systems
Frontiers in Computing and Intelligent Systems (FCIS) is a peer-reviewed journal published by Darcy & Roy Press. The mission of the journal is to provide an academic forum for discussing the issues of computing and intelligent systems across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards. FCIS is indexed by CNKI since 1 January 2024.
ECAAF-CenterNet: Efficient Channel Attention and Adaptive Fusion for Cephalometric Landmark Detection
In response to the challenges of complex local structures and significant differences in scale distribution in key point detection of lateral cranial films, this paper proposes an automatic key point detection method for lateral cranial films based on the improved CenterNet, ECAAF-CenterNet. Based on the CenterNet anchor-free detection framework, DLA34 is selected as the backbone network. Combined with the characteristics of dense distribution of key points and obvious scale differences in head images, the network structure is improved from the two aspects of feature enhancement and feature fusion. Introduce the Efficient Channel Attention Mechanism (ECA) in the backbone residual module to enhance the network's expression ability for key channel features. Meanwhile, in the multi-scale feature fusion stage, an adaptive weighted cross-scale feature fusion mechanism AF is designed to perform dynamic weighted fusion on features at different levels. Through experimental verification on the private dataset of 1603 lateral cranial films, the MRE reached 1.54mm, and the SDR could reach 94.51% at the 4.0mm threshold, which was superior to the baseline model.
Copyright (c) 2026 Frontiers in Computing and Intelligent Systems https://creativecommons.org/licenses/by-nc/4.0
Visual Analysis of North-South Clinical Treatment Differences Based on the "Disease-Physician-Prescription-Herb" Association
As a pivotal component of China's Intangible Cultural Heritage, Traditional Chinese Medicine(TCM) diagnosis and treatment holds unique value in the prevention and treatment of complex diseases and in health management, demonstrating significant North-South regional disparities. To deeply unravel the intrinsic patterns underlying these disparities, this paper constructs a visual analytics system based on medical records, prescriptions, and medicinal material data. First, data is categorized according to the characteristics of TCM diagnosis and treatment, and visualization tasks are defined. Then, by integrating text, spatial, and multidimensional visualization techniques, along with a multi-view linked interaction mechanism, the system enables the correlation analysis of textual features, geographical distribution, and prescription formulation patterns in north-south medical records. Comparative examination of prescriptions and herb combinations utilized by northern and southern practitioners reveals substantive divergences in TCM diagnostic, therapeutic strategies, and pharmacopeia applications. This research contributes both empirical foundations for standardizing TCM clinical practice and pharmaceutical protocols, and a new method for the digital research of TCM cultural heritage.
MFD-MoE: Multi-scale Fusion Denoising Mixture of Experts Network for Stable Recognition of Underwater Acoustic Targets
Stable recognition of underwater acoustic targets is a key technology for ensuring continuous and efficient maritime defense, resource exploration, and environmental monitoring. However, in complex marine environments, background noise, propagation disturbances, and dynamic changes in target features often lead to unstable recognition results. To address this issue, this paper proposes the Multi-scale Fusion Denoising Mixture of Experts (MFD-MoE) Network, with a focus on improving recognition stability. We design the Denoised Time-Frequency Feature Fusion module (DTFF), in which first performs soft-threshold denoising, followed by a feature decomposition strategy that includes key steps such as channel splitting, multi-scale feature extraction, and upsampling. We then design a multi-scale feature fusion strategy that combines multi-modal and pooling results, enhancing the stable expression of features in the presence of interference and target state changes, resulting in a feature extraction architecture consisting of four DTFF layers. Furthermore, we introduce a mixture of experts model, and based on the characteristics of underwater acoustic information recognition, we design an expert mixture mechanism with residual connections to improve the model's stability under target motion or interference. Experimental results show that the MFD-MoE model significantly outperforms existing methods in multi-class underwater acoustic target recognition tasks, offering both high stability and high accuracy, thus providing a new solution for intelligent underwater target perception in complex environments.
Six-Phase Worm Drive Motor Design with Fault – Tolerant Control
With the continuous development of industrial technology, multi-phase motors are widely used in fields such as industrial robots, ships, and automobiles. The performance requirements for motors are increasing, making their integration and reliability particularly important. To address the issues of low integration and the need for additional braking devices in traditional externally-driven reducers, this paper designs a six-phase fault-tolerant motor based on a built-in drum-shaped worm reducer, integrating worm drive with the six-phase fault-tolerant motor to achieve drive, control, and transmission as a unified system. This study focuses on the fault-tolerant control strategies for a six-phase permanent magnet synchronous motor (PMSM) under a single-phase open-circuit fault. Based on the principle of constant total magnetomotive force, two fault-tolerant control methods: minimum current and maximum output torque are proposed. The effectiveness and stability of these strategies in maintaining stable operation under rated load after a phase-loss fault are verified through MATLAB/Simulink simulations and prototype experiments.
Typical Applications of Matrices in Cryptography and Their Theoretical Support for Efficient and Green Intelligent Computing
As the core content of linear algebra, matrices provide basic mathematical support for many algorithms in artificial intelligence (AI). The characteristic of efficient computation in matrix theory facilitates the dual improvement of the performance and energy efficiency of AI algorithms. This paper focuses on the typical applications of matrices in cryptography, aiming to provide theoretical support for constructing an efficient and green intelligent computing path. Research shows that as a bridge connecting mathematical theory and technological applications, matrices not only occupy a fundamental position in the development of AI but also possess extensive research value and profound social significance.
Research on Student Performance Prediction based on BKA-BiLSTM Algorithm
Based on the online resources and online data of Object Procedural Programming (OPP) course, this paper proposed the course dataset of students (Dataset of OPP: DOPP), and conducts a prediction study on students' final exam scores through this dataset. The hybrid algorithm of Black Kite Algorithm (BKA) and BiLSTM: BKA-BiLSTM algorithm model, is used for prediction, which can not only capture the data upstream and downward information, but also optimize the hyperparameters of the model through BKA. The results show that BKA-BiLSTM has better results in evaluation index than other regression analysis algorithms for the dataset DOPP of this course, and has good practical application value.
Research on the Design and Scene Adaptation of Remote Sensing Image Super-Resolution Models Based on CNN and Transformer Aggregation
In recent years, existing models in complex geographical scenarios (such as densely built urban areas and fragmented mountainous terrains), suffer from insufficient feature fusion accuracy and poor scene adaptability, leading to blurred edges and loss of details in super-resolved images. This limitation hinders their practical applications in fields like emergency disaster relief (e.g., rapid assessment of building damage in disaster areas) and agricultural yield estimation (e.g., precise identification of crop growth conditions). Many convolutional neural network (CNN)-based methods have made significant progress in single-image super- resolution tasks. However, CNNs only excel in local feature extraction while lacking in global feature modeling capabilities. To address this issue, we integrate CNNs(CNN), leveraging their local feature extraction strengths, with Transformers, which excel in global dependency modeling, to provide a novel approach for remote sensing image super-resolution. The CNN-Transformer aggregation mechanism enhances the model's adaptability to multi-scene remote sensing images, promoting the deep application of remote sensing super-resolution technology in practical operations (e.g., rapid emergency monitoring and precision agricultural yield estimation), thereby maximizing the value of remote sensing data.
Network Intrusion Detection for Intelligent Driving
The rapid advancement of intelligent driving technology has increased communication between vehicles, external networks, cloud platforms, and other vehicles, significantly elevating cybersecurity risks. Since intelligent driving systems rely heavily on in-vehicle networks and wireless communications, they are vulnerable to various network attacks. Traditional security measures often fail to meet the stringent real-time and accuracy requirements of such systems, making it urgent to develop efficient and intelligent intrusion detection systems to ensure safe and stable operation. This paper begins by analyzing the network architecture and security requirements of intelligent driving systems, and reviews mainstream network intrusion detection technologies—including feature matching, anomaly detection, and methods based on machine learning and deep learning. By comparing the strengths and weaknesses of different approaches, it highlights the challenges related to real-time performance and accuracy that conventional detection techniques face in intelligent driving environments. The study also discusses future trends in intelligent driving cybersecurity, emphasizing the importance of integrating AI technology with cybersecurity to enhance intrusion detection capabilities. Finally, the paper proposes research directions such as multi-level defense systems and cross-domain collaborative defense, offering both theoretical and practical support for securing intelligent driving systems.
Applied Research on Face Recognition Based on Transformer and Deep Reinforcement Learning
The progress of deep learning technology has greatly enhanced the performance of face recognition system, but there are also limitations such as low sample efficiency, unstable training, etc. Transformer is a revolutionary deep learning model architecture with the advantages of parallel computing and powerful long-range dependent modelling capability. This paper analyses the advantages and disadvantages of classical methods, deep reinforcement learning, Transformer and other methods applied in face recognition after reviewing many literatures, and concludes that the application of deep reinforcement learning combined with Transformer in face recognition is still in the early stage, but it has also achieved certain results, and it is a direction full of development potential in the field of artificial intelligence today.
Diagnosis and Prediction Model for Alzheimer's Disease based on Random Forest Algorithm
Early diagnosis of Alzheimer's disease (AD) is crucial for delaying disease progression. Although machine learning methods such as random forests have shown potential in this field, effectively leveraging multi-source clinical data to construct high-accuracy models remains a research priority. This study aims to develop and validate a diagnostic and predictive model for AD based on the random forest algorithm. Using a multidimensional dataset encompassing demographic information, clinical test indicators, and lifestyle factors, the study first performed data cleaning and feature engineering, innovatively constructing composite features such as cardiovascular risk scores, cognitive impairment risk scores, and comprehensive health scores. Subsequently, a feature selection method incorporating domain knowledge was applied to identify a core set of features, including Activities of Daily Living (ADL), functional assessments, Mini-Mental State Examination (MMSE), and age. The final random forest model, optimized through cross-validation, achieved an accuracy of 94.95% and an F1-score of 0.9476 on the test set. Feature importance analysis not only validated the model’s interpretability but also highlighted the critical predictive roles of patients’ functional status and cognitive levels. The findings demonstrate that the random forest-based model can efficiently and accurately support the auxiliary diagnosis of AD, offering a low-cost, non-invasive solution with potential applications for early screening in primary healthcare settings.
A Study on the Lightweighting of YOLOv11-based Traffic Sign Detection Models
Accurate and real-time traffic sign detection is a critical component of environmental perception for Advanced Driver-Assistance Systems (ADAS) and autonomous vehicles, playing a vital role in ensuring driving safety. However, current mainstream lightweight object detection models often encounter performance bottlenecks when dealing with challenges prevalent in real-world road scenes, such as small objects and complex backgrounds, primarily due to the loss of feature information. To address this issue, this paper conducts an in-depth application and performance evaluation of an improved YOLOv11-based lightweight detection algorithm. The algorithm is characterized by two key features: first, it enhances the perceptual capabilities for tiny objects by introducing a higher-resolution P2 feature layer; second, it adopts an integrated fusion detection module, termed FPN-P2 in this paper, which applies a "shallow feature direct-path, deep feature fusion" strategy to efficiently integrate multi-scale features. Extensive experiments on the public TT100K dataset demonstrate that the optimized configurations of this architecture achieve significant breakthroughs in both performance and efficiency. Specifically, its performance-optimal configuration achieves a mAP@0.5 of 76.3%, substantially surpassing baseline models like YOLOv8n. Meanwhile, its efficiency-optimal configuration attains a high accuracy of 68.8% while having fewer parameters than the baseline model. This research not only provides and validates highly competitive solutions for high-precision lightweight traffic sign detection but also, through intensive ablation studies, offers valuable empirical insights into model architecture selection and optimization for specific tasks.
Intelligent Sensing Cloud Map: Research on Smart Scenic Area System Based on Cloud-Edge Collaborative Architecture
With the sharp increase in the number of tourists, scenic spots are facing challenges in environmental monitoring, passenger flow management and intelligent services, and it is difficult for tourists and managers to obtain real-time information about the scenic spots. This research designs a smart scenic area system based on the cloud-edge collaborative architecture, and enhances the intelligent management level and tourist experience through data analysis and visual display. The system acquires and analyzes data from edge sensors, builds a visualization system with the help of Echarts technology, and presents the data in the form of charts. The APP is developed using Android technology, supporting real-time data monitoring by users and managers. This system achieves efficient data processing and display through a cloud-edge collaborative architecture, providing a solution for the intelligent management of scenic spots.
Analysis of Abnormal Brain Networks in Autism Based on fMRI
Autism spectrum disorder (ASD) is characterized by widespread neurodevelopmental impairments, with emotional and behavioral deficits linked to abnormal local brain function and functional connectivity. Investigating whether the intrinsic functional brain network topology in ASD is altered is therefore of critical diagnostic importance. To identify aberrant brain networks in ASD, this study constructed functional networks based on 90 brain regions defined by an automated anatomical atlas, followed by network-based statistics (NBS) and graph-theoretical analyses. The findings were further validated using support vector machine (SVM) classification between ASD and neurotypical participants. NBS revealed that abnormalities in functional connectivity in ASD were predominantly located within the default mode and sensorimotor networks, involving regions in the temporal and frontal lobes, basal ganglia, and parts of the limbic system. Graph-theoretical analysis further indicated that topological alterations in ASD likely stem from dysregulated connectivity within these subnetworks, demonstrating significantly impaired efficiency in both information transfer and integration compared to controls—a finding potentially linked to characteristic ASD behavioral profiles. Using these aberrant connections as biological features, a systematic evaluation of multiple classifiers identified SVM as optimal. Through cross-validation and parameter tuning, classification achieved an accuracy of 84.86% on the NYU dataset. To further validate the generalizability of the identified network features, corresponding functional connections were extracted from the larger UM dataset (sample size >80), yielding a classification accuracy of 72.64%, confirming the clinical relevance of the detected network abnormalities. These results highlight abnormal topological organization in functional brain networks in ASD and provide novel insights into the neural mechanisms underlying communication deficits and repetitive behaviors in autism.
Copyright (c) 2025 Frontiers in Computing and Intelligent Systems https://creativecommons.org/licenses/by-nc/4.0
Innovative Pathways and Practical Research on the Living Heritage Transmission of Jingdezhen Ceramic Intangible Cultural Heritage (ICH) in the Digital-Intelligent Era
Against the backdrop of the Digital-Intelligent Era, the digital wave not only reshapes the inherent cultural connotations of Jingdezhen ceramic Intangible Cultural Heritage (ICH) as a "living cultural memory" but also endows it with the potential for the development of regenerative cultural resources. The transmission of ICH thus faces unprecedented opportunities and challenges. Jingdezhen ceramic ICH is undergoing a paradigm shift from "static preservation" to "living heritage transmission." As the core carrier of Chinese ceramic culture, Jingdezhen has become the focus of attention for the living transmission of its ceramic ICH techniques in today’s interactive and diversified digital communication environment. By analyzing the artistic transformation and cultural reconstruction mechanisms of Jingdezhen ceramic ICH in the digital context, this study proposes an innovative practical paradigm for living heritage transmission, explores its construction logic and contemporary value, and verifies the promotional effects of pathways such as digital exhibitions, virtual immersive experiences, digital collectibles, and new media/live streaming platforms on the living transmission of ICH through empirical research. This research aims to provide methodological references for the digital transmission of ICH and promote the creative transformation and innovative development of the living transmission of Jingdezhen ceramic ICH techniques.
Intangible Cultural Heritage (ICH) Techniques
Spatio-Temporal Graph-Based Association Rule Mining Method for Anomaly Node in Internet of Vehicle
Abnormal behaviors in the Internet of Vehicles often manifest as sudden changes in communication behavior, abnormal fluctuations in communication volume, and other irregular variations. These anomalies may be signs of a cyberattack, such as attackers attempting to disrupt normal commu-nication between vehicles by severing connections, establishing unauthorized communication links, or sending a massive amount of data packets to exhaust resources or isolate specific nodes. Traditional association rule methods struggle to handle the complex spatio-temporal characteristics, large-scale datasets, and the dynamic communication behavior among vehicle nodes that change over temporal and spatial. This study introduces spatio-temporal features to capture the communication behavior of vehicles as they vary across temporal and spatial, proposes an association rule mining algorithm based on spatio-temporal graphs, which deeply combines the spatio-temporal features of the Internet of Vehicles, such as location, time, communication behavior, and communication volume, to precisely track and analyze changes in vehicle node communication with the goal of identifying anomalous behavior nodes. Utilizing spatio-temporal graph-based association rule mining technology reveals nodes with anomalous behaviors characterized by frequent communication and dramatic increases in communication volume. The algorithm is designed in two steps: discovering frequent rules and verifying the credibility of these rules. Thanks to the adoption of connection and elimination strategies, this algorithm demonstrates the efficiency and accuracy in rule mining sought after in the field. Compared to traditional methods, our proposed mining algorithm shows significant improvements in mining time and the accuracy of the number of rules mined, especially displaying a remarkable advantage in handling the complex spatio-temporal characteristics of the Internet of Vehicles.
Vibration Feature Fusion and Machine Learning Based Road Surface Identification Method
To address the demand for real-time road condition monitoring in intelligent transportation systems, a pavement roughness classification method based on multi-feature fusion and machine learning is proposed. By constructing vibration datasets for four typical types of road surfaces, 17-dimensional feature vectors including time-domain, frequency-domain, and statistical features are extracted. Feature importance evaluation and classification are performed using Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and other algorithms. Experimental results show that the proposed method achieves a classification accuracy of over 95.2% on the test set. Random Forest outperforms comparative methods in both training efficiency and feature interpretability, providing reliable technical support for real-time road condition monitoring and automated classification.
Facial Expression Recognition with Hybrid Features Leveraging DINO Prior Knowledge
Facial expression recognition plays a crucial role in smart education. To address the over-reliance on single prior image features or the ineffective integration of multiple image features in facial recognition tasks, as well as the poor generalization of facial expression recognition in natural environments. This study utilizes the large-scale visual model DINOv2 as a pre-training model, with its pre-trained weights frozen, leveraging its learned experience from natural image datasets to acquire more universal image features, thereby enhancing the generalization performance of feature extraction. Furthermore, this work proposes a Hybrid Feature Facial Expression Recognition model (HFFER). The model utilizes two different pre-trained models to acquire distinct features, and effectively integrates them through cross-attention mechanisms and multiple convolutions. Experimental results demonstrate that the model achieved accuracies of 92.18% on the RAF-DB datasets, respectively, surpassing or being comparable to existing models. This study introduces a novel approach to facial expression recognition, while its application in real classroom images demonstrates its feasibility and potential in practical educational settings.
Hybrid Feature Facial Expression Recognition Model
Optimizing BP Neural Network Image Restoration Based on an Improved Particle Swarm Optimization Algorithm
To address the shortcomings of BP neural networks, such as sensitivity to initial weights and thresholds and susceptibility to local optima, this paper proposes a particle swarm optimization (PSO) algorithm to optimize the BP neural network image restoration model. By searching for the initial weights and thresholds of the BP neural network, the dependence on these parameters is eliminated. The particle swarm optimization algorithm is modified, and the improved algorithm is used to search for the initial weights and thresholds of the BP neural network. The optimized network is then applied to restore the Cameraman image. Experimental results demonstrate that for the Cameraman image, the PSO-BP neural network achieves a peak signal-to-noise ratio (PSNR) improvement of 18.07% and 1.25%, respectively, compared to the standard BP neural network. Additionally, the structural similarity (SSIM) improves by 4.18% and 0.95%, respectively. These findings indicate that the optimized BP neural network delivers superior image restoration performance.
Continuous Lower Bounds for Moments of the Mixed Product of Twisted L-functions
Moments of central values in family of L-functions are important subjects in analytic number theory. The twisted L -functions is an important class of automorphic L-functions. Recently, using the lower bound principle of Heap and Soundararajan, Chen et al. have obtained the sharp lower bounds for all positive real k-th (K>1) moments of the mixed product of two twisted L-functions at the central value, when there exists a divisor q0 such that q0 |q and qη q0 q1/2-η Here q is the modulus of Dirichlet character and η is a small real number. Based on Chen et al. and Blomer et al. the work of on the first moment of the mixed product of two twisted L-function at the central value, when the modulus q of the Dirichlet character is prime, in this paper, we study lower bounds for all positive real k-th (K>1) moments of the mixed product of two distinct twisted L-function at the central value for the prime modulus case and obtain the sharp lower bounds.
Plastic Parts Surface Defect Detection Algorithm Based on Path-Augmentation Multi-Level Feature Pyramid Network
Due to the characteristics of plastic parts surface defects such as variable scales and shapes, and a large number of small defect targets, existing algorithms cannot fully extract and utilize defect features. A plastic surface defect detection algorithm based on Path-Augmentation Multi-Level Feature Pyramid Network (PA-MLFPN) is proposed. On the basis of extracting multi-level and multi-scale defect features, to further enhance the representation ability of small defect targets, firstly, dilated convolution is adopted in the encoder of Thinned U-shape Modules (TUM) to fully retain shallow detail features. Meanwhile, a bottom-up feature augmentation path is added to the original TUM to interpolate shallow features into deep layers. Secondly, the Efficient Channel Attention (ECA) module is introduced in the second stage of the Scale-wise Feature Aggregation Module (SFAM) to achieve more reasonable weight allocation across channels. Finally, Focal Loss is used to calculate classification loss, which alleviates the problem of imbalance between positive and negative samples to a certain extent. Embedding the proposed PA-MLFPN into SSD and conducting experiments on the plastic parts surface defect dataset show that the algorithm achieves a mean Average Precision (mAP) of 84.89% and effectively solves the missing detection problem of small defect targets.
Multi-Level Feature Pyramid Network (MLFPN)
A Knowledge Distillation-Based Triple-Branch Framework for Malicious Domain Detection via Cross-Source Feature Fusion
With the continuous expansion of internet applications, malicious domains have become a significant entry point for cyberattacks, posing severe threats to user privacy and corporate security. In practical scenarios, the limited number and complex distribution of malicious domain samples render traditional detection methods—relying on large-scale labelled datasets—ineffective under smallsample conditions, struggling to adapt to rapidly evolving attack techniques. To address this, this paper proposes a malicious domain detection model integrating knowledge distillation with tri-branch cross-source features. Designed for small-sample learning scenarios, this model comprehensively utilises domain semantic features (BERT vectors), manually derived statistical features, and SSL certificate features. It achieves deep semantic representation through branch encoders and employs attention mechanisms for adaptive weighted fusion of multi-source information. Notably, certificate features exhibit significant discriminative power between malicious and benign domains, providing structured security clues that enhance the model’s recognition of novel attack patterns. To further improve generalisation performance and deployment eﬀiciency, this paper introduces a knowledge distillation mechanism. A pre-trained large model (RoBERTa) serves as the teacher model, guiding the student model to learn high-level semantic relationships with limited data through semantic representations and soft labels. Concurrently, Triplet Loss constraints are applied to optimise intra-class cohesion and inter-class separation within the feature space. Experimental results demonstrate that the proposed model achieves outstanding performance on balanced few-shot datasets, attaining Precision, Recall, F1, AUC, and Accuracy scores of 0.9857, 0.9988, 0.9922, 0.9968, and 0.9921 respectively, significantly outperforming conventional methods. This research validates the effectiveness and practical feasibility of a lightweight architecture integrating knowledge distillation and cross-source features for malicious domain detection with small samples, offering novel insights for rapid deployment and continuous protection in cybersecurity systems.
From Pixel Fidelity to Task Performance: Image Quality Challenges and Paradigm Shift in Public Security Video Surveillance
As public safety video surveillance enters the era of intelligence, image quality has become a bottleneck limiting the performance of visual perception systems. This paper reviews the development of image quality research in this field: from the early focus solely on pixel-level fidelity evaluation to evolving into evaluation guided by high-level visual task performance. In this paper, we first analyze the image quality challenges faced in public security scenarios. Then, focusing on how to ensure the perception reliability of intelligent systems, we summarize three technical approaches: first, using image preprocessing technologies such as image enhancement, restoration, and super-resolution to directly improve input quality; second, designing targeted detection and recognition models to enhance system robustness against quality degradation; third, leveraging multimodal information fusion, such as visible-infrared integration, to compensate for the limitations of a single modality. This review aims to clarify that the core of image quality research has shifted toward building a task-oriented and degradation-insensitive robust visual perception system.
Cybersecurity Policy Research Driven by Data
To support data-driven national cybersecurity policy formulation, we developed a multidimensional analytical framework focusing on global cybercrime distribution, policy effectiveness, and the correlation between demographic characteristics and criminal patterns. First, we collected authoritative data from multiple sources including the International Telecommunication Union (ITU) and conducted preprocessing. Using a K-means clustering model based on Euclidean distance with Python tools, we revealed the global distribution patterns of cybercrime. Second, by integrating time series analysis with Difference-in-Differences (DID) models, we visualized changes in policy-related indicators before and after implementation, quantified policy impacts, and identified key effective policies. Finally, we constructed a multiple linear regression model incorporating interaction effects to visualize correlations between demographic factors and cybercrime, utilizing statsmodels tools for precise predictions. This study demonstrates how the integration of multi-methods and efficient computation provides scientific decision-making support for optimizing cybersecurity policies and global cybersecurity governance.
Lang2Vision Diffusion: Language-Driven Diffusion for Robotic Action Frame Prediction
We address the challenge of enabling robots to predict the visual outcomes of their actions through Lang2Vision Diffusion (L2V-Diff), a novel adaptation of InstructPix2Pix for robotic action frame prediction. Our framework takes an initial RGB observation paired with natural language instructions and generates photorealistic images of anticipated future states via vision-language conditioned diffusion. The method is fine-tuned on synthetic RoboTwin data (300 episodes across hammering, handover, and stacking tasks), demonstrating strong quantitative performance (mean Structural Similarity: 0.971, Peak Signal-to-Noise Ratio: 37.1 dB). By bridging high-level instructions with pixel-accurate visual prediction, L2V-Diff advances safety-critical robotic applications while eliminating the need for explicit 3D reconstruction or physics simulation.
The Mechanism of Holistic Smart Service Based on Digital Twin in the Metaverse Scenario
The Metaverse, as the next evolutionary stage of the internet, is redefining interaction paradigms between humans, objects, and environments. In this context, traditional smart service models face challenges such as insufficient immersion, delayed personalization, and physical-virtual disconnection. This study explores how to construct a new "Holistic Smart Service (HSS)" mechanism in the Metaverse using Digital Twin (DT) technology. We first define HSS as a closed-loop service system integrating high-fidelity perception, real-time intelligent decision-making, immersive interaction, and value co-creation. Then, we propose a three-layer M-DT-HSS (Metaverse-Digital Twin-Holistic Smart Service) model and formally define its core components—service state space, user intention recognition function, and physical-digital synergy optimization function. Based on this model, we design a five-layer system architecture (perception, twin construction, AI engine, interaction presentation, and value co-creation layers) and elaborate on the implementation path from data fusion to service generation. Finally, we validate the proposed mechanism through a virtual smart community service prototype system. Experiments show that our approach outperforms traditional models in critical indicators: user satisfaction increases by 28.7%, service response latency decreases by 63.2%, and resource utilization efficiency improves by 41.5%. This research provides a theoretical framework for smart services in the Metaverse era and guides their engineering implementation.
A Non-contact Real-time Diameter Measurement Method for Steel Wire Ropes via W-EDSR and W-DeblurNet
Steel wire ropes are widely used in hoisting, transportation, mining and marine engineering, and their complex braided structure provides excellent tensile strength and fatigue resistance. To overcome the limitations of conventional methods for detecting steel wire rope diameters—particularly in terms of accuracy, robustness, and adaptability to complex working environments—this study proposes a non-contact, real-time detection approach based on super-resolution (SR) technology. The method employs an RGB-Depth camera to simultaneously acquire both visual and depth data of the steel wire rope, and leverages an enhanced super-resolution network (W-EDSR) in combination with a self-supervised deblurring network (SRN-WDeblur) to enhance image clarity and mitigate motion blur. Following this, a semantic segmentation and edge extraction algorithm, augmented with depth information, is applied to derive continuous and smooth rope boundaries. Experimental results indicate that this method achieves 99.35% accuracy for static steel wire rope measurements and maintains 99.24% accuracy when the rope moves at 10 m/s, significantly outperforming traditional approaches and demonstrating strong robustness against motion blur. This research provides a reliable and efficient solution for online monitoring of mine hoisting steel wire ropes, contributing to enhanced intelligence and safety in mining operations.
Infrared Imaging-Based Object Detection and Tracking for UAV Systems: Principles, Algorithms, and Advances
Infrared sensing has become a critical perception modality for unmanned aerial vehicles (UAVs), enabling robust operation under low illumination, night-time conditions, and adverse weather. This survey provides a systematic and comprehensive review of the infrared UAV perception pipeline, covering imaging principles, preprocessing techniques, deep-learning-based object detection, small-target enhancement strategies, and state-of-the-art object tracking algorithms. We first describe the physical foundations of infrared radiation and summarize key preprocessing procedures such as nonuniformity correction and deep-learning-based denoising. We then examine the evolution of infrared object detection, including CNN- and Transformer-based frameworks, with particular attention to modern YOLO variants and methods designed for small and tiny targets commonly observed in aerial platforms. Furthermore, we review traditional correlation-filter-based tracking, advanced Siamese and discriminative learning trackers, reinforcement-learning-based approaches, and recent Transformer-driven trackers, followed by an overview of multispectral and graph-based multi-object tracking strategies. Typical UAV applications and widely adopted evaluation metrics are also discussed. This survey aims to provide a unified reference for researchers and practitioners developing high-performance infrared perception systems for UAVs.
Research on Task Assignment and Path Planning Algorithm for Multi-UAV Collaboration
This paper investigates the co-optimization problem of task allocation and path planning in multi-UAV collaborative systems. Addressing the challenges of strong coupling, high dimensionality, and real-time requirements in dynamic complex environments, a distributed co-optimization framework integrating hierarchical planning concepts with spatiotemporal constraints is proposed. By constructing a multi-objective mixed-integer programming model, designing a lightweight event-triggered task allocation algorithm (improved CBBA), and integrating a hierarchical path planning strategy (global meta-heuristic search + local model predictive control), the framework enhances task execution efficiency and system robustness while reducing communication overhead. Simulation and experimental results demonstrate its superior performance in typical scenarios such as disaster relief and precision agriculture. This research provides an efficient, reliable, and scalable solution for practical applications involving large-scale heterogeneous UAV swarms, holding significant theoretical implications and engineering value.
Research on the Auxiliary Learning Mechanism of BIM (Building Information Modeling) Course in the Curriculum System of Architectural Engineering Technology Major
The BIM (Building Information Modeling) course is based on 3D digital technology and integrates various types of information throughout the entire life cycle of buildings. This paper explores the value of integrating various subjects into the BIM course system, seeks BIM solutions in the process of integrating theoretical teaching and practical skills in the cultivation of talents in the field of architectural engineering technology, and explores a new mechanism for evaluating the professional course system. Provide reference opinions for the cultivation of talents in the architectural engineering technology major of vocational education.
Architectural Engineering Technology Curriculum System
Rapid and Non-destructive Detection of Sucrose Content in Tea by Near-Infrared Spectroscopy
This study aims to establish a rapid and non-destructive method for determining sucrose content in green tea using near-infrared (NIR) spectroscopy. A total of 164 representative tea samples were collected from Yiling District, Wufeng County, and Yidu City in Hubei province as well as major tea-producing areas like Yunnan, Sichuan, Anhui, and Jiangxi. The research systematically verified and compared a variety of spectral preprocessing combinations (such as SG smoothing, SNV and D1) and characteristic wavelength selection algorithms (SPA, iPLS, CARS) to build a robust quantitative model. Among the tested models (PLSR, SVR and RFR), the Random Forest Regression (RFR) model demonstrated superior performance. Its key construction steps involved initial SG+SNV preprocessing, followed by characteristic wavelength screening using iPLS. During calibration, the model achieved a correlation coefficient (R²c) of 0.9656, and a root mean square error (RMSEC) of 1.0316. For validation, the correlation coefficient (R²p) reached 0.9043 and the root mean square error (RMSEP) was 1.7806. These results demonstrate that the model efficiently enables non-destructive sucrose testing in tea and provides a reliable technical solution for quality control.