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.
Design of College English Teaching Activities in Application-Oriented Undergraduate Universities Oriented by the Needs of the Guangdong-Hong Kong-Macao Greater Bay Area
The high-quality development of the foreign trade industry in the Guangdong-Hong Kong-Macao Greater Bay Area has put forward new requirements for scenario-based, post-oriented and practical college English teaching in application-oriented undergraduate universities. Centered on demand orientation, this study adopts Needs Analysis Theory, the Production-Oriented Approach and Situated Learning Theory to construct a college English teaching design framework compatible with the industrial needs of the Greater Bay Area. Based on 52 corporate recruitment data, this study extracts core post competency requirements, designs two teaching cases: English communication for the Canton Fair and written communication for cross-border e-commerce, and forms a progressive practical teaching path of "basic simulation – advanced practical combat". The research shows that this teaching design can effectively connect classroom teaching with the competency requirements of foreign trade posts, improve students’ English application ability and workplace adaptability. Under the practical condition of limited school-enterprise cooperation resources, it can provide practical references for college English teaching reform and serving regional economic development in application-oriented undergraduate universities.
Guangdong-Hong Kong-Macao Greater Bay Area
Copyright (c) 2026 Frontiers in Computing and Intelligent Systems https://creativecommons.org/licenses/by-nc/4.0
Research on Configuration-Driven Dynamic Form Generation
Traditional static forms have two clear problems. They are not flexible enough. And they cost a lot to keep. They do not work well for complex business situations. And they cannot fit different devices well. These problems make traditional static forms hard to satisfy the needs of complex applications. This paper gives a configuration-driven way to make and improve dynamic forms. This method has two core technical parts. First, we build a standard form metadata model with JSON Schema. This model fully separates business logic from the front-end interface. It cuts the strong link between the two parts in traditional development. Second, we design a weight-driven adaptive layout algorithm. This algorithm lets forms change their layouts on their own. Forms can fit screens of different sizes well. We did comparative tests between this new method and traditional hard-coded forms. The test results show two clear advantages of the new method. It makes form rendering much faster. And it cuts down the time users need to fill out the form.
Sky Ranger: Industrial-Grade AI Drone Inspection Platform
Equipment inspection for power, petrochemicals and new energy is not just an operation; it is the lifeblood of industrial safety. But let's be honest, manual inspection is a bother. They are slow, risky, time-consuming and money-consuming. That is why we built Sky Ranger. With the help of artificial intelligence and the Internet of Things, a new kind of high-precision inspection technology that can cover all areas above, below and around structures has now been developed. The three main uses of SLAM 3D navigation are AI models that integrate all kinds of data and the NVIDIA Jetson edge device. It will find a fault in the field 98 per cent of the time. It is 8 times faster than a crew of people and saves 60% in costs. To be honest, the ROI is good. It will help the company go digital and will also be in line with China's "dual-carbon" goals.
Topology Optimization Design of Impellers Based on Selective Laser Melting Process
Aiming at the core contradiction between structural strength and material weight reduction in the lightweight design of impellers, this paper proposes a topology optimization method considering support-free forming and blade geometric shape retention based on selective laser melting (SLM) additive manufacturing technology. The variable density method is adopted for topology optimization design, and hollow structures and lattice filling strategies are integrated to optimize the internal material distribution of impellers. SLM process constraints are introduced in the optimization process to ensure that components can be formed without additional support structures. Static mechanical performance verification of the optimized model is conducted via finite element analysis. The results show that the overall weight of the optimized model is reduced by 36% while satisfying the requirements of impeller overall dimensions and mechanical properties, which effectively balances lightweight demand, structural strength and forming process adaptability.
Improved Sparse Principal Component Analysis Algorithm based on Non-convex Optimization
Convex Sparse Principal Component Analysis
Research on Cold Chain Logistics Route Optimization Based on Improved Genetic Algorithm
With the rapid growth in cold chain demand, cold chain logistics has become increasingly important in modern supply chain systems. However, in practical distribution processes, route optimization becomes more complex due to temperature control requirements, product perishability, and multiple operational constraints. This paper addresses the routing optimization problem in cold chain logistics distribution by developing a mathematical model with the objective of minimizing total distribution cost, comprehensively considering vehicle fixed costs, transportation costs, refrigeration costs, and cargo deterioration costs. On this basis, an improved genetic algorithm is designed to solve the model. The initial population is generated using a nearest-neighbor priority strategy, and a local search mechanism is incorporated to enhance the algorithm’s search capability and convergence performance. A small-scale case is used for simulation analysis. The results show that the proposed method can obtain reasonable distribution routes under vehicle capacity constraints, and exhibits good convergence and stability, thereby verifying the effectiveness of the model and algorithm.
Community Detection Algorithm based on Multi-objective Biogeographical Optimization with Community Belongingness
In community detection algorithms based on optimization, although single-objective optimization methods have been widely applied, they only consider a single objective function and ignore other factors related to community structure, making it difficult to effectively reveal complex networks with multiple potential characteristics. In contrast, multi-objective optimization methods can overcome these limitations and have received increasing attention in the field of community detection in recent years. For the problem of community detection in complex networks, this paper proposes a multi-objective biogeographical optimization-based community detection algorithm (MOBBO-CMD) based on community membership. Firstly, by combining non-dominated sorting and crowding distance, the biogeographical optimization (BBO) mechanism is improved, and a BBO model suitable for multi-objective optimization is established. Simultaneously, the proportion of internal connections within the community (NRA) and the proportion of external connections outside the community (RC) are selected as two optimization objectives, modeling the community detection problem as a multi-objective optimization problem to alleviate the limitation of modularity resolution. Secondly, in the migration operation, a one-way migration method based on community membership is proposed to avoid disrupting the existing relatively correct community division. Furthermore, utilizing node division information, a mutation strategy based on boundary nodes is designed to ensure that mutations can produce better solutions. On real network datasets, MOBBO-CMD is experimentally evaluated compared with four contrasting algorithms. The results show that compared to traditional single-objective community detection algorithms, MOBBO-CMD using multi-objective optimization can evaluate community division results more comprehensively and accurately. Compared to other multi-objective contrasting algorithms, MOBBO-CMD improves community detection accuracy and finds communities closer to the true network structure.
Prediction of SO2 solubility in ionic liquids based on machine learning and analysis of SHAP
Sulfur dioxide (SO₂) is a major atmospheric pollutant, and ionic liquids (ILs) have shown great potential for SO₂ capture due to their unique physicochemical properties. However, the complex and designable structures of ILs make it challenging to establish accurate structure–solubility relationships. In this study, a prediction model based on molecular descriptors and a Long Short-Term Memory (LSTM) neural network was developed to predict SO₂ solubility in ILs. A dataset of 381 experimental solubility data points covering 48 ILs was collected and augmented to 1905 points using a physics-rule-based method. Molecular descriptors for anions and cations were computed from SMILES strings using RDKit, and an intelligent feature selection method reduced the feature dimensionality from 89 to 50. The proposed Z-Score-LSTM model achieved excellent predictive performance, with a coefficient of determination (R²) of 0.9500, a root mean square error (RMSE) of 0.0521, and a mean absolute error (MAE) of 0.0252 on the test set. SHAP analysis revealed that temperature and cation partial charge distribution (Cation_PEOE_VSA13) are the most influential features, with anion polar surface area inhibiting solubility and cation shape indices promoting it. This work provides a high-precision, interpretable tool for the molecular design of novel SO₂-absorbing ionic liquids.
Multimodal Fusion Lateral Stability Control Strategy for Autonomous Vehicles
In recent years, the rapid development of autonomous driving technology has placed increasingly high demands on vehicle lateral stability control. Traditional control methods often rely on single-source information, which may lead to insufficient adaptability and robustness in complex driving scenarios. To address this challenge, this paper proposes a multimodal fusion lateral stability control strategy for autonomous vehicles. By integrating data from multiple sensors such as vision, radar, and vehicle dynamics, the framework establishes a comprehensive perception of the driving environment and vehicle state. The proposed method processes and fuses heterogeneous information through a carefully designed fusion algorithm, enabling more accurate identification of potential instability risks. Based on the fused decision-making information, a coordinated control module adjusts steering and braking interventions in real-time to maintain lateral stability. Experimental validations demonstrate that this strategy significantly enhances the vehicle's ability to resist lateral disturbances, improves trajectory tracking accuracy, and ensures smoother and safer handling under various road conditions. The research provides a practical and effective solution for the lateral stability control of autonomous vehicles, contributing to the enhancement of driving safety and system reliability. Future work will focus on optimizing the fusion algorithm's computational efficiency and extending its application to more extreme and unpredictable traffic environments.
Autonomous Vehicles; Multimodal Fusion; Lateral Stability Control; Vehicle Dynamics; Sensor Fusion; Control Strategy.
Research on the Construction of an Automation Platform Based on Data Stream Processing
With the deepening of digital transformation, to address the problems of high latency and low accuracy in processing industrial production automation data using traditional methods, this paper proposes a type of research on the construction of an automation platform based on data stream processing. The research is analyzed in a four-layer structure. Firstly, a machine learning method is adopted to perform zero-mean standardization on the accessed data; secondly, the stream processing engine layer is constructed using Apache Flink technology to process data streams continuously with low latency; then, the storage service layer of the platform is built based on metadata; finally, it is explained that the data operation, maintenance and application layer is responsible for the stable operation of the system and data output. The research results provide theoretical support and practical reference for enterprises to build an efficient, reliable, and easy-to-use real-time data automation platform.
Explainable Prediction of Asphalt Pavement Rutting Depth Using XGBoost and SHAP: Insights from the LTPP Database
Rutting is one of the most critical distresses affecting the performance and durability of asphalt pavements. This study developed an explainable machine learning framework for predicting asphalt pavement rutting depth by integrating XGBoost and SHAP interpretation methods based on the Long-Term Pavement Performance (LTPP) database. A total of 623 valid records from 357 control asphalt pavement sections in warm climate zones with or without freezing conditions were selected. Six key influencing factors—including Annual Average Daily Traffic (AADT), average temperature, precipitation, freeze index, humidity, and radiation—were used as input variables. The XGBoost regression model achieved excellent predictive performance on the test set, with an R² of 0.9434, RMSE of 0.9278, and MAE of 0.4083. SHAP analysis revealed that AADT and average temperature are the dominant factors contributing 42.1% and 34.6% respectively to rut depth prediction, forming a clear “dual-core driving” mechanism. Precipitation showed moderate influence, while freeze-thaw, humidity, and radiation had relatively minor effects. The results highlight the strong nonlinear coupling between traffic load and high-temperature environment in rut development. This study not only provides a high-precision prediction model but also offers interpretable insights that can support scientific decision-making in anti-rutting pavement design, maintenance strategies, and regional management in high-traffic and high-temperature areas.
MBA-Net: Masked Background Alignment for Robust RGB-Thermal Invisible Gas Segmentation
Industrial safety monitoring requires reliable detection of fugitive gas emissions, which are typically invisible in RGB images but observable in thermal infrared imagery. RGB–thermal (RGB-T) fusion therefore provides a promising solution for area-based gas leak detection. However, existing CNN-based approaches are limited by local receptive fields, making it difficult to capture the diffuse and irregular structures of gas plumes. Although recent Transformer-based methods improve global context modeling, they often amplify background thermal artifacts, leading to high false-positive rates in complex industrial environments. To address these challenges, we propose MBA-Net, a dual-stream Transformer-based framework for robust RGB-T invisible gas segmentation. MBA-Net first employs a dual-stream backbone to extract multi-scale contextual features from RGB and thermal modalities. A Thermal Artifact Suppression Gate (TASG) is introduced to perform structure-guided suppression of thermally salient but structurally inconsistent background responses. To further reduce residual background bias, we design a Masked Background Alignment (MBA) loss that enforces cross-modal feature consistency in background regions during training, without introducing additional inference cost. Finally, a Confidence-Aware Refinement (CAR) module is proposed to adaptively enhance uncertain regions, improving the representation of diffuse gas boundaries and weak plume responses. Extensive experiments on the public Gas-DB benchmark demonstrate that MBA-Net achieves superior segmentation performance with competitive computational efficiency.
Defect Detection of Can Cap Printing Based on Machine Vision
To address the problems of high cost, low efficiency, and high rates of missed and incorrect detections in the production of metal can lid printing products, an online inspection system for metal can lids was designed based on machine vision. Firstly, threshold segmentation was performed using the maximum inter-class variance method, and the detection area of the can lid was extracted through feature extraction and intersection operations. For surface printing detection, a printing defect detection method based on constructing a difference model was proposed. The Sobel operator was used to extract the edge information of the image to create a difference model, and template matching and the detection area were used to process the tested image using similarity measurement, affine transformation, and scaling of gray values, etc. Finally, the obtained images were input into the difference model for the final defect detection. Experiments were conducted by detecting a large number of tested images with different defects to verify the effectiveness of the scheme.
A Medication Recommendation Method Based on Multi-View Medication Representation and a Time-Aware Copy Mechanism
Electronic health records provide an important data foundation for intelligent medication assistance. Although existing medication recommendation methods have achieved notable progress in recommendation accuracy, they still suffer from insufficient medication representation learning and coarse-grained utilization of historical prescriptions. To address these limitations, this paper proposes a medication recommendation method based on multi-view medication representation and a time-aware copy mechanism. At the medication representation level, information from the EHR co-occurrence graph, drug--drug interaction graph, and drug molecular structure graph is integrated to construct a multi-view medication representation, thereby enhancing the structural and semantic expressiveness of medication embeddings. At the level of historical information utilization, a time-aware copy mechanism is introduced, which jointly considers temporal decay and diagnosis similarity during historical prescription reuse, so as to improve the specificity and reliability of historical prescription selection. Experiments conducted on the public MIMIC-III dataset demonstrate that the proposed method outperforms multiple baseline models in terms of Jaccard, F1, and PR-AUC, while also achieving better safety performance on DDI Rate. Furthermore, ablation studies verify the effectiveness of both the multi-view medication representation module and the time-aware copy mechanism. The results indicate that the proposed method can improve recommendation accuracy while simultaneously accounting for medication safety, thus providing an effective solution for intelligent medication assistance.
Deep Learning Model Fused with Attention Mechanism for Defect Detection of Electroplated NdFeB Products
Electroplated neodymium-iron-boron (NdFeB) products are widely used in electronics, automobiles, and new energy industries. Surface defects, such as cracks, pinholes, scratches, and edge chipping, can directly affect product performance and service life. To address the problems of insufficient detection accuracy, weak anti-interference capability, and difficulty in identifying tiny defects in traditional deep learning models for NdFeB surface defect detection, an improved defect detection model integrating an attention mechanism is proposed. Based on YOLOv8n, the proposed model introduces an improved Recurrent Channel Attention (RCA) mechanism into the backbone network to enhance the extraction of critical defect features and suppress complex background interference. Meanwhile, Switchable Atrous Convolution (SAConv) is incorporated to enlarge the receptive field and improve the model's perception and representation ability for multi-scale defects. Experimental results show that the proposed model achieves an mAP@0.5 of 85.3% on a self-built electroplated NdFeB defect dataset, representing an improvement of 6.7% over the original YOLOv8n model. The detection speed reaches 62 FPS, meeting the real-time detection requirements of industrial production lines. The proposed model can effectively identify various common surface defects in electroplated NdFeB products and demonstrates strong detection capability for tiny and subtle defects under complex lighting conditions, thereby providing reliable technical support for intelligent quality inspection of electroplated NdFeB products.
Interpretations of Tarot Card Spreads by AI: Predictive Versus Introspective Questions
To explore whether AI-generated interpretations of Tarot card spreads are different when predicting external events versus introspection on one's own thoughts. Thirty items made up the balanced test questions for prediction and verification; after querying, card-specific analysis results would be given as a reference. For each of the question-spread pairs, this study obtained five answers from DeepSeek and five from GPT-4, along with a human-divination score classified as "no", "partly yes" or "yes". Convert all responses into numbers (0, 1 or 2) and then calculate the average chatbot's response per question; next, perform a two-factor ANOVA on questions divided by groups and people to answer levels. ANOVA showed a significant main effect of the human answer level (p=0.0017), indicating that AI scores are correlated with human certainty at this point. However, a one-year follow-up of the "partly-yes" group found that there was still a significant difference (P < 0.01). Predictive questions had an average score of 1.58 compared to 0.82 for introspection; the mean difference reached 2.13, indicating it was highly pronounced (Cohen's D). Based on these results, although AI interpretations generally conform to human judgement, question type affects their output under an ambiguous human reference. Predictive Questions may trigger stronger affirmations due to bias in the training data. A new method for measuring sensibility in divination Systems and its impact on the behaviour of AI Advisory System through questions framing is demonstrated here.
Exploration on the Empowerment of Digital Twin and AI Early Warning for Smart Fire Protection Based on BIM Technology
Against the background of accelerated urbanization and diversified architectural forms, the passive and lagging shortcomings of the traditional fire protection model have become increasingly prominent. As a core part of smart city construction, smart fire protection relies on AI early warning and machine learning algorithms to mine risk patterns from multi-source data, upgrade fire early warning and response prediction, and provide systematic solutions for smart fire protection. Based on the digital foundation of BIM technology and focusing on digital twin and AI early warning as two core technologies, this paper analyzes the technical integration path in the field of smart fire protection, expounds the empowered application scenarios from the dimensions of risk perception, emergency command and operation & maintenance management, discusses the current challenges and future development directions, so as to provide theoretical reference and practical reference for the construction of smart fire protection system.
Uyghur Keyword Spotting and Speech Representation Learning Based on an E-Branchformer Encoder-Decoder Architecture
Keyword spotting for Uyghur remains challenging because of limited labeled resources, agglutinative morphology, speaker diversity, and unstable boundary cues under partial observation. This paper presents a non-streaming E-Branchformer encoder-decoder framework that unifies Uyghur keyword spotting and speech representation analysis. Beyond a standard keyword spotting pipeline, the study explicitly investigates how hidden representations evolve when only a prefix of an utterance is available. To support this goal, the corpus is subjected to systematic data cleaning, including duplicate removal, damaged-file filtering, language-mix exclusion, and low-quality-sample screening. After unified preprocessing and normalization, a prefix-stage dataset is built by extracting the first 25%, 50%, 75%, and 100% of each utterance, which enables controlled analysis of completeness and discriminability across scanning stages. The proposed model employs an E-Branchformer encoder, an attention-based decoder, and joint CTC/attention training. A representation-oriented multi-task objective combines keyword classification with completeness prediction, while encoded features from different prefix stages are used for discriminability analysis. Experiments on a 134.1 h Uyghur speech corpus demonstrate that the proposed method improves keyword spotting performance over competitive baselines and yields more stable hidden representations under incomplete input. The model reaches an EER of 4.9% and an ATWV of 0.901, while the prefix-stage representation study shows consistent gains in 5-NN discrimination and decreasing completeness error as the observed speech grows. These results indicate that representation-oriented training is beneficial for both keyword spotting accuracy and interpretability.
Single-Frame LiDAR Bird's-Eye View with Lightweight Transformer for Self-Driving Vehicle Trajectory Prediction Baseline Research
Urban advanced driver-assistance systems (ADAS) need to predict the future trajectory of the ego-vehicle at a frequency of 10–20 Hz. Traditional methods rely on high-definition maps, which have pain points such as low freshness, high cost, and narrow coverage. This paper proposes an open-source ego-vehicle trajectory prediction baseline that uses only single-frame LiDAR and requires no high-definition maps or visual assistance. A 200×200×8 bird's-eye view pseudo-image is generated by height-sliced voxelization, and end-to-end trajectory regression is completed by a 6-layer Transformer encoder and uniform Token sampling. After 70% sparse pruning and INT8 quantization, the model size is compressed to 8 MB, and 20 future waypoints are output within 30 ms on Jetson Orin. The ADE on the nuScenes-mini test set reaches 0.54 m, which is comparable to the visual BEV baseline, with a parameter size of <12 M. The code and weights have been open-sourced to facilitate subsequent fusion and temporal expansion.
Influence of Dynamic Characteristics of Servo System for Wheeled Mobile Robots on Path Planning Accuracy and Collaborative Improvement
The precise navigation of wheeled mobile robots depends on the interaction between servo systems and path planning algorithms. This paper investigates the influence of servo system dynamics on path planning accuracy and proposes collaborative improvement strategies. Traditional approaches often design path planners and servo controllers in isolation, leading to suboptimal performance as the dynamic limitations of the actuation system are neglected. This disconnect causes tracking errors, path deviations, and instability, especially during high-speed maneuvers. To address this, the study systematically analyzes how key dynamic characteristics—servo bandwidth, positioning precision, response speed, and load disturbance rejection—impact path planning accuracy. Findings reveal that insufficient bandwidth and slow response times cause tracking errors, while poor positioning accuracy and disturbance rejection lead to path deviation. In response, this paper introduces a collaborative framework for co-design and co-optimization. Key strategies include implementing dynamic compensation in the servo system based on path demands, developing an adaptive path planning algorithm that incorporates servo constraints, and co-optimizing controller and planning parameters. Simulations validate these strategies, demonstrating that integrating servo dynamics into the path planning process is crucial for high-precision and reliable navigation, enhancing the robustness of autonomous operations.
Wheel Mobile Robot Servo System Dynamic Characteristics
Servo-Path Planning Interaction Mechanism
Research on State of Health (SOH) of Power Batteries Based on Multi-source Data Fusion and Random Forest Algorithm
Against the backdrop of the global energy transition and the rapid development of the new energy vehicle industry, the SOH of power batteries is directly related to vehicle safety, driving range, and service life, and its accurate estimation has become a critical demand of the industry. However, the internal electrochemical degradation process of power batteries is complex and affected by the coupling of multiple factors. Traditional estimation methods relying on a single data source or simple models suffer from drawbacks such as insufficient accuracy and weak generalization ability. This paper proposes an SOH estimation framework based on multi-source data fusion and the random forest algorithm. The framework integrates time-series data (e.g., voltage, current, and temperature) during battery charging and discharging cycles as well as indirect health indicators. Through efficient data fusion and a structured feature engineering process, key features are screened to construct a random forest estimation model. Experimental validation demonstrates that the proposed model achieves high-precision and robust SOH estimation of power batteries, with a significant reduction in the mean absolute error. It provides an effective technical approach for online state estimation in battery management systems (BMS) and also offers a valuable reference for research in the field of condition monitoring and life prediction for complex systems.
A Review of Green Workshop Scheduling Problems Based on Algorithmic Structures
As a major energy consumer, China faces significant challenges from environmental pollution and resource scarcity, which critically impact sustainable social development. With the national "dual carbon" goals established, the manufacturing sector must integrate green transformation principles into corporate development. Currently, under the framework of green manufacturing, workshop green scheduling has emerged as a prominent research focus in this field. Therefore, this paper integrates recent cutting-edge research and future prospects in this field. It addresses green scheduling in assembly lines, flexible manufacturing workshops, and distributed scheduling systems. The discussion primarily focuses on green workshop scheduling within algorithmic frameworks, thoroughly examining the strengths and weaknesses of different algorithms. It highlights innovative developments such as novel algorithms and hybrid approaches tailored to solve specific problems. The aim is to tackle challenges in green workshop scheduling, including energy conservation and emission reduction, efficient resource utilization, and effective coordination among complex workshops. This paper provides a systematic review of past research achievements and summarizes current mainstream algorithms. It identifies shortcomings in existing studies and points toward future research directions.
Research on Intelligent Path Planning for Unmanned Aerial Vehicles in Complex Environments
Aiming at the problems of insufficient real-time performance and safety in path planning for unmanned aerial vehicles (UAVs) in dynamic and complex environments, this study proposes a collaborative path planning method that integrates binocular vision environmental perception, ant colony algorithm for global optimization, and artificial potential field method for local dynamic obstacle avoidance, with the goal of improving the accuracy and robustness of autonomous navigation. This method precisely captures dynamic environmental information through binocular vision, optimizes the global path search using the ant colony algorithm, and combines the artificial potential field method to achieve real-time local obstacle avoidance. It not only enables rapid identification and response to complex environments but also significantly enhances the accuracy and robustness of UAV autonomous navigation.
Obstacle Avoidance for Unmanned Aerial Vehicles
Research and Implementation of a Human-Computer Interaction AI Intelligent Robot Based on Speech
With the advancement of deep learning, language model training, and edge computing, speech-based human-robot interaction (HRI) systems are expanding their applications in manufacturing and medical escort service robots. In this paper, a modular speech-driven human-robot interaction system is proposed and implemented, which integrates speech recognition (ASR), natural language understanding (NLU), dialogue management, and action execution modules. To evaluate the engineering trade-offs of different technology options, three sets of comparative trials were designed and implemented: ASR (cloud-based commercial vs. local models), NLU (traditional statistical methods vs. Transformer fine-tuning), and dialogue management (rule-driven vs. reinforcement learning). Simulation experiments were conducted in the ROS/Gazebo environment. Subjective tests were also performed, with evaluation metrics including word error rate (WER), accuracy, F1-score, response latency, task completion rate, and user satisfaction. The results show that the Transformer-based NLU is significantly better than the traditional methods in semantic parsing. ASR in the cloud has obvious advantages in recognition quality, but the local model is more suitable for real-time control scenarios in terms of delay and privacy. Dialogue management is recommended to adopt a hybrid strategy of "rule first + reinforcement learning enhancement." Finally, the problems of system engineering deployment, model compression, edge-cloud collaboration, and ethical compliance were discussed.
Personalized Game Character Design via Fast Style Transfer API Integration
As player expectations for personalized visual identities continue to rise, traditional game character design workflows struggle to efficiently support diverse artistic customization. Manual character styling is time-consuming, costly, and difficult to scale in real-time interactive environments. To address this challenge, this study proposes a fast neural style transfer (FST) framework for real-time game character customization. The proposed system integrates a feed-forward convolutional neural network with perceptual loss and Adaptive Instance Normalization (AdaIN) to enable efficient and flexible artistic style transfer for both 2D characters and 3D texture maps. The framework supports arbitrary and interpolated styles while preserving character structure and visual consistency. To evaluate system performance, we conduct quantitative experiments using SSIM, FID, and inference latency, alongside user-centered evaluations measuring usability and aesthetic satisfaction. Experimental results demonstrate that the proposed method achieves real-time performance with an average inference latency of 43.2 ms, while maintaining high visual fidelity and strong content preservation. User studies further indicate high usability and personalization satisfaction. Overall, this work demonstrates the feasibility of integrating fast neural style transfer into interactive game pipelines and provides practical insights for AI-assisted character customization systems.
Design and Feasibility Analysis of Visual Monitoring Scheme for Tri-Fold Logistics Box
Our project team has developed the tri-fold logistics box which can employ multi-level folding to adjust the height and expand the capacity with dual-box splicing. This study proposes a monitoring framework with machine vision to track the cargo status in the box during transportation. The monitoring unit is designed to integrate a three-camera system with a rotatable brackets to fulfill the requirement of the multi folding status. The lightweight YOLOv8-nano network combined with spatial attention mechanisms is proposed to detect the cargo status. We propose to use an STM32 + NPU embedded hardware which has compact size and low power consumption. It will not interfere with the normal operation of the box. The study proposes a monitoring plan for the tri-fold logistics box to serve as a reference for research on foldable logistics equipment.
Modeling and Simulation of Thermal Management System for New Energy Vehicle Battery Pack in Vehicle Engineering
In the context of the global transition towards new energy vehicles, the thermal management of battery packs has become a critical technical challenge, particularly under fast-charging and extreme operating conditions. This study aims to address the thermal runaway risks associated with battery packs by developing a refined thermal management system model based on multi-physics coupling simulation technology. A three-dimensional geometric model incorporating battery cells, thermal interface materials, and cooling plates was constructed using CT scanning data to accurately represent the internal structure of the battery module. Through fluid-solid-thermal coupling simulations conducted with STAR-CCM+ software, the temperature distribution and heat dissipation patterns within the battery pack were analyzed under varying coolant flow rates and environmental temperatures. Furthermore, an optimization of the cooling plate channel structure was performed employing the particle swarm optimization algorithm, leading to the proposal of a dynamic thermal control strategy. The simulation results demonstrate that the optimized thermal management system effectively reduces the maximum temperature during fast charging and maintains a minimal temperature difference across the battery pack. These findings provide a reliable simulation basis for the engineering design of battery thermal management systems in new energy vehicles, contributing to enhanced safety and performance.
New Energy Vehicle; Battery Pack; Thermal Management System; Multi-Physics Coupling Simulation; Particle Swarm Optimization; Thermal Control Strategy.
Research on Cooperative Obstacle Avoidance Method for Multi-Rotor UAV Swarms Based on Adaptive Sliding Mode Control
In the context of complex urban environments, the demand for dynamic obstacle avoidance and formation control in multi-rotor unmanned aerial vehicle (UAV) swarms has become increasingly prominent. This study addresses these challenges by proposing a collaborative control method that integrates lidar perception with adaptive sliding mode control. An enhanced YOLOv8 algorithm is employed to achieve real-time detection and trajectory prediction of dynamic obstacles, thereby constructing a constrained space for safe avoidance. Subsequently, an adaptive sliding mode controller is designed, which, combined with an adjacency graph communication topology, enables distributed cooperative control within the swarm. This approach effectively mitigates the chattering issues commonly associated with traditional sliding mode control. Validation through hardware-in-the-loop simulations and actual flight experiments demonstrates that the proposed method significantly reduces the response time for obstacle avoidance in dense obstacle scenarios while maintaining formation position errors within a low range. The results indicate a marked improvement in both operational safety and efficiency for UAV swarms engaged in urban logistics and emergency rescue missions, offering a practical and reliable solution for real-world applications.
Multi-Rotor UAV Swarm; Adaptive Sliding Mode Control; Cooperative Obstacle Avoidance; YOLOv8; Distributed Control; Urban Logistics; Emergency Rescue.
The Application Scenarios of Energy Storage Technology Based on the Integrated System of Light, Storage and Charging
The case that this paper examines is the use of the energy storage technology on the basis of the integrated photovoltaic, storage and charging system in various situations. To begin with, the current research stasis of the integrated photovoltaic-storage-charging systems and the energy storage technologies in the country and internationally was systematically reviewed. The principle of the structure and functioning of the system were examined and the peculiarities of the common types of energy storage technologies, as well as their detailed interpretation, were given. The present paper is a systematic investigation of the application features and usefulness of energy storage technology in the urban environment (residential and commercial), rural one (agricultural production and household electricity consumption) and in park-based areas (industrial and technological parks), and in the connection of power station, finding out the most important technical issues of each of the scenarios. The study also determines the technical and market-level issues that the development of energy storage technologies is facing currently and the respective optimization strategies and policy recommendations. According to the findings, energy storage technology in integrated photovoltaic-storage-charging systems has a high adaptability and a potential to develop at a great extent in a wide range of application inspections. However, some technical improvements and localized market promotion strategies are required to take advantage of the specificity of the given case to the maximum. Lastly, energy storage technology future development trends and prospects of its industrial application are considered.
Integrated photovoltaic-storage-charging system, Energy storage technology, Urban applications, Rural applications.