This paper focuses on the optimization strategy based on the Blinn-Phong lighting model. It begins with an introduction to the research background the Blinn-Phong lighting model, which plays an important role in computer graphics as a widely used lighting model, but its performance is not ideal enough in complex and specific material lighting effects. This paper proposes and analyzes the particle swarm algorithm(PSO) optimization strategy and explores the application of this strategy in practical scenarios, including the experimental effects performance comparisons of different models. Finally, the research conclusions are summarized. By optimizing the parameters of the Blinn-Phong lighting model using the particle swarm algorithm, lighting parameters are optimized as the particle position, and the mean square error of the image is used as the fitness function. In the iterative process, the lighting parameters are adjusted to make the rendering results as close to the target image as possible. Using the global search capability of PSO, it avoids falling into local optima and can the appropriate lighting parameters well. Combined with the image similarity (MSE) as the optimization goal, the rendering results are intuitive and the rendering effect is better.

Thoroughly and accurately identifying various defects on concrete surfaces is crucial to ensure structural safety and prolong service life. However, in actual engineering inspections, the varying shapes and complexities of concrete structural defects challenge the insufficient robustness and generalization of mainstream models, often leading to misdetections and under-detections, which ultimately jeopardize structural safety. To overcome the disadvantages above, an efficient concrete defect detection model called YOLOv11-EMC (efficient multi-category concrete defect detection) is proposed. Firstly, ordinary convolution is substituted with a modified deformable convolution to efficiently extract irregular defect features, and the model’s robustness and generalization are significantly enhanced. Then, the C3k2module is integrated with a revised dynamic convolution module, which reduces unnecessary computations while enhancing flexibility and feature representation. Experiments show that, compared with Yolov11, Yolov11-EMC has improved precision, recall, mAP50, and F1 by 8.3%, 2.1%, 4.3%, and 3% respectively. Results of drone field tests show that Yolov11-EMC successfully lowers false and under-detections while simultaneously increasing detection accuracy, providing a superior methodology to tasks that require identifying tangible flaws in practical engineering applications.

This paper presents an investigation into the modeling and prediction of the density of supercritical carbon dioxide (SC-CO2) and mole fraction of Erlotinib hydrochloride using machine learning techniques. The dataset consists of temperature and pressure as input variables, with density of SC-CO2 and mole fraction of Erlotinib hydrochloride as output variables. The process is supercritical-based operation which can be used for production of nanomedicines with improved aqueous solubility. Three models, Multilayer Perceptron (MLP), Orthogonal Matching Pursuit (OMP), and Extreme Gradient Boosting (XGB) were trained and optimized using the Firefly Optimization Algorithm (FA). The models’ performance in correlating solubility and density were evaluated via statistical analyses to measure their accuracy. The MLP model achieved high accuracy, with scores of 0.99714 and 0.97749 for density and mole fraction prediction, respectively in term of coefficient of determination (R2). The OMP and XGB models also provided reasonably accurate results. These findings demonstrate the effectiveness of machine learning in understanding and predicting SC-CO2 behavior, with potential applications in industrial processes and pharmaceutical formulations.

The passive distribution network is expected to transform into a complex active distribution network with multiple layers, levels, loops, and modes. The physical characteristics of the distribution network system should reflect the dynamic imitation of the real system configuration mode, which requires a shift from manual wiring to automatic, multimodal configuration methods. This paper proposes a fully programmable and flexible configuration modelling technology system based on a 400 V dynamic simulation platform. It designs a matrix flexible network frame and configuration system that can achieve one-click reconstruction of typical network frames with different wiring modes and topologies on the dynamic simulation platform, which can reduce the workload and operational errors of manual wiring operation and better support the testing and validation of the dynamic simulation system for power distribution networks.

With the continuous progress of space technology, space in-orbit service has become an important means to ensure the stable operation of spacecraft in complex space environments. However, the quality of non-cooperative spacecraft ISAR images acquired is low, the image edge is not clear, the resolution is poor, and the data provided is also limited, and the data is sparse. To solve this problem, a deep neural network architecture based on improved U-Net is proposed: PFU-Net. The close-connected structure can effectively extract in-orbit spacecraft features from images captured by inverse synthetic aperture radar (ISAR) and be used for pose estimation of non-cooperative in-orbit spacecraft. Specifically, the network first uses transfer learning to alleviate the problem of data sparsity through pre-training models. Secondly, aiming at the sparse features of spacecraft ISAR imaging, a dense residual structure combining channel attention mechanism is proposed in the coding and decoding part to achieve adaptive feature thinning, thereby reducing feature loss in the process of downsampling. In addition, the spectral gated network and multi-head self-attention mechanism are integrated to introduce additional frequency domain information, which is combined with spatial features to capture more global information. The experimental results show that compared with other methods, the improved network structure reduces the spacecraft Angle prediction error from the original 2.7° to 0.55°~0.58°, which is better than other advanced spacecraft attitude estimation models, and has a good performance in practicality and applicability.

Teaching system is an efficient teaching method, and it is also the comprehensive management of teaching cases. Teachers can use the education system to standardize the data transmission of teaching plans. Therefore, communication is very important for teachers’ research. Forget it. Therefore, what schools say can relate the data in teaching, realize the comprehensive judgment of data, and finally determine the best teaching method. Therefore, the integration of teaching methods and decision tree can improve the effectiveness and accuracy of existing methods, and better identify teaching content.

Aiming at the problem that ISAR images have low resolution and serious noise and non-cooperative spacecraft lack cooperative information, resulting in large attitude estimation errors, a space target attitude estimation method based on contours and linear structures on ISAR images and models is proposed. This method uses an enhanced Frost filter to filter out image noise and preprocess the image to improve image quality. UNet3+ with the CBAM attention mechanism is used to segment the spacecraft contour from the ISAR image, and then the linear structure of the target contour is extracted. The target model is then rotated and projected to establish a target attitude template library, and the template library is clustered using the linear structure of the model. The Softassign ICP algorithm and local direction information are used to optimize shape context matching, and finally the attitude information of the target spacecraft is returned. Experimental results on real ISAR images show that this method can effectively reduce the attitude estimation error, and the errors of the three attitude angles are all within 1 degree.

The identification of GUI elements in a user interface design diagram is fundamental to many software engineering tasks. In order to solve the problems of low detection accuracy of GUI element recognition, low recognition accuracy and systematic extraction of GUI element information, this paper proposes a GUI element extraction method based on user interface design diagram. Firstly, the improved object detection model YOLOv5s-Shuffle-CIOU was used to detect GUI elements in the image user interface. For GUI elements classified as text elements, the optical character recognition (OCR) model is used for character extraction. Finally, relative positions of text GUI elements and non-text GUI elements within the graphical user interface are performed, and the resulting integration is encapsulated as an integrated JSON object representing GUI elements. This method can effectively improve the detection and classification accuracy of different categories of GUI elements, and can systematically extract all GUI element attribute information in graphical user interface. This approach is platform-independent and helps with model building in code generation and GUI testing.

With the wide application of distributed systems, complex transaction processing involving multiple nodes has become an important challenge. The difficulty lies in how to ensure the data consistency of each node and how to reduce the system delay to increase the transaction throughput. In order to solve the distributed transaction problem, the 2PC(two-phase commit) solution has been proposed by predecessors, but this solution brings some problems such as resource blocking, which has a great impact on the performance of distributed system. This paper proposes an asynchronous transaction processing scheme on the basis of 2PC protocol and BASE theory. It has been verified and evaluated that although 2PC scheme still performs well in solving distributed transaction problems, compared with 2PC scheme, the proposed scheme in this paper has slightly larger transaction throughput and less resource consumption, and the effectiveness of the solution has been verified in experiments.

The development and utilization of urban underground space is an important way to solve the “great urban disease”. As one of the most important types of urban underground foundations, utility tunnels have become increasingly popular in municipal construction. The investigation of utility tunnels is a general task and three-dimensional laser scanning technology has played a significant role in surveying and data acquisition. However, three-dimensional laser scanning technology suffers from noise and occlusion in narrow congested utility tunnel spaces, and the acquired point clouds are imperfect; hence, errors and redundancies are introduced in the extracted geometric elements. The topology of reconstructed BIM objects cannot be ensured. Therefore, in this study, a hierarchical segmentation method for point clouds and a topology reconstruction method for building information model (BIM) objects in utility tunnels are proposed. The point cloud is segmented into facades, planes, and pipelines hierarchically. An improved mean-shift algorithm is proposed to extract wall line features and a local symmetry-based medial axis extraction algorithm is proposed to extract pipelines from point clouds. A topology reconstruction method that searches for the neighbor information of wall and pipeline centerlines and establishes collinear, perpendicular, and intersecting situations is used to reconstruct a topologically consistent 3D model of a utility tunnel. An experiment on the Guangzhou’s Nansha District dataset successfully reconstructed 24 BIM wall objects and 12 pipelines within the utility tunnel, verifying the efficiency of the method.

Mesh simplification is an important task in the fields of computer graphics and 3D model processing. However, the traditional quadratic error measurement method may lead to the loss of geometric features, abnormal topological changes, and even unstable results in the simplified model. To solve these problems, this paper proposes an improved quadratic error measurement method based on Newton's method. As an iterative optimization algorithm, Newton's method is known for its fast convergence speed and high accuracy and is particularly suitable for solving problems involving quadratic error metric matrices. This method also fully considers the influence of different simplified vertices on the mesh simplification results. By using the Euclidean distance metric, the simplified vertices with the smallest error from the original vertices are selected, thereby making the details of the simplified model closer to the original model. Experimental results show that the quadratic error measurement method based on Newton's method can more effectively retain the detailed features of the original model at the same simplified scale. What is even more encouraging is that this method performs well when handling model simplifications with different curvature characteristics, while the simplification results are more stable. This provides strong support for further research in the field of 3D model simplification.

With the popularity of single-page application comes a new problem: a modular application needs to be split and merged. The challenges are how to integrate technology-independent applications, how to implement data communication between micro applications, how to manage the resources of the micro application runtime, and how to isolate the micro-applications. On the basis of existing micro-frontend framework, this paper proposes a scheme of componentized micro-frontend architecture base based on Web Components. According to the empirical evaluation, the scheme in this paper solves the above problems and the loading speed of the first screen is faster than that of the Single-spa scheme, and the effectiveness of the solution is verified in actual projects.

The Multilevel Texture Technique (Mipmap) is one of the most commonly used techniques for addressing the relationship between viewpoint distance and texture resolution. Its advantages lie in its speed and its ability to ensure high-quality texture details at different distances and angles, reducing the occurrence of aliasing and mosaicking artifacts. However, employing Mipmap technology for generating and storing multilevel textures requires additional memory space and computational resources. This paper introduces a novel texture rendering approach based on Mipmap technology. The algorithm utilizes bicubic interpolation to create textures of varying resolutions, incorporating the Level of Detail (LOD) technique. As objects move farther away from the camera, the algorithm employs lower-resolution textures for rendering. Conversely, as objects approach the camera, it gradually switches to higher-resolution textures. This approach effectively reduces computational workload and memory bandwidth requirements while delivering enhanced visual quality.

This paper proposes an adaptive subdivision method for triangular meshes based on approximation error of edge. This method first calculates the position of the new point obtained after a subdivision of the two old points on the current edge, and then uses the average of the Euclidean distances between the two old points and the corresponding new points as the basis for subdivision. This average Euclidean distance is also known as approximation error of edge. This method can improve the determination rate of smooth areas of the model, thereby reducing the number of unnecessary patches. The experimental results show that this method not only ensures the smoothness of the subdivided model, but also results in fewer generated vertices and faces when the given threshold is small. Additionally, it leads to a reduction in the number of detected live edges. This method can be applied to the subdivision of triangle mesh models to improve rendering efficiency.

With the development of web technology, web pages become more and more complex. Using the virtual DOM method can make the web page have efficient update performance without reducing development efficiency. An essential part of this method is the ability to quickly calculate the differences between the old and new virtual DOMs and update them into the old DOM. This paper presents a multi-threaded virtual DOM difference method based on web workers. This method improves the performance of difference method by offloading tasks to worker threads and using a multi-threaded parallel method based on worker thread pools, and presents a method based on SharedArrayBuffer to reduce the consumption of data communication between threads. Experimental results show that compared with the original main thread difference method, the new method can obtain a speedup ratio of 1.3x-2.4x in the scenario of 10k scale nodes and four threads.

In enterprise level web applications, request scheduling usually aims to minimize average response time and maximize application benefits by fully utilizing existing server resources. It is necessary to prioritize the processing of critical requests and high-priority user requests to enable the enterprise to obtain more benefits. Aiming at the multiple objectives of enterprise Web request scheduling, this paper proposes a multi-objective request scheduling algorithm based on cuckoo search. Firstly, based on the characteristics of the request task and the scheduling objectives, a multi-objective evaluation model is established, and the decision variable is determined as the execution order of the request queue on the server side. Then the non-dominated sort cuckoo search algorithm with elite strategy is introduced to solve the decision variables and the global optimal Pareto solution set is found by combining the multi-objective evaluation model. From this set, an optimal solution is selected as the request execution sequence, and the server executes the request based on this sequence. Experiments show that the proposed algorithm has more obvious advantages than FIFO and SJR in achieving the goal of minimizing the average response time and maximizing the benefits.

The rapid development of three-dimensional (3D) laser scanning technology has provided a new technical means for the geometric accuracy evaluation of subway stations. With high precision and high efficiency, laser scanning technology can present the construction site condition in a panoramic way, which is essential for achieving high precision and all-round geometric accuracy evaluation. However, when the survey coordinate system of the design building information modeling (BIM) predefined in the design stage is not applied during the laser scanning data acquisition or the BIM loses the survey coordinate system during the interaction, the objects will have different coordinate positions in the point cloud and BIM, which will limit the accuracy comparison between the two data sources. Meanwhile, the existing methods mainly focus on the above overground buildings, and the accuracy evaluation of underground structures mainly focuses on the overall deformation monitoring. So far, the existing methods do not constitute a hierarchical index system to assess the geometric accuracy of various objects in the subway station. This study proposes a method to evaluate the geometric accuracy of subway stations based on laser scanning technology. A coarse-to-fine coordinate registration from point cloud to the design BIM is used to unify coordinates in different reference systems; and geometric accuracy evaluation of different structures in subway stations is achieved by developing geometric accuracy evaluation indexes and technical systems. The method is applied to the geometric accuracy monitoring of the Hongqi Road subway station, and the experimental results verify the reliability of the method.

With the unprecedented development of big data, it is becoming hard to get the valuable information hence, the recommendation system is becoming more and more popular. When the limited Boltzmann machine is used for collaborative filtering, only the scoring matrix is considered, and the influence of the item content, the user characteristics and the user evaluation content on the predicted score is not considered. To solve this problem, the modified hybrid recommendation algorithm based on Gaussian restricted Boltzmann machine is proposed in the paper. The user text information and the item text information are input to the embedding layer to change the text information into numerical vector. The convolutional neural network is used to get the latent feature vector of the text information. The latent vector is connected to rating vector to get the item and the user vector. The user vector and the item vector are fused together to get the user-item matrix which is input to the visual layer of Gaussian restricted Boltzmann Machine to predict the ratings. Some simulation experiments have been performed on the algorithm, and the results of the experiments proved that the algorithm is feasible.

With the advent of the big data era, the privacy-preserving data mining is gaining a significant importance. The present study envisaged the development of a privacy-preserving data mining, based on the proximal support vector regression (PPSVR). The algorithm was based on a distributed system, and it was shown that the global kernel could be calculated by the local kernel. In order to protect the data privacy, the stochastic noise was added to the original data on each data set, and each participant had to provide only the disturbed local kernel. Furthermore, simulation experiments were performed on the algorithm and the results indicated that the accuracy of the PPSVR was almost equal to the proximal support vector regression algorithm (PSVR). The algorithm takes advantage of the speed of the PSVR, and the experimental validation showed that the speed of the PPSVR was faster than the support vector regression (SVR).

The drive system of switched reluctance motor (SRM) is a complex nonlinear system that is composed of many links. The delay in the measurement of the speed and position signal of SRM is caused by the factors that affect the measuring sensor. To effectively improve the influence of the SRM rotor position and speed signal delay on the system performance, a sliding mode position tracking method based on output delay observation was proposed in this study. First, the model was discretized according to the structure and characteristics of SRM and the mathematical parameters of the system were identified using a multi-innovation stochastic gradient (MISG) algorithm. Second, a delay state observer was constructed on the basis of an SRM system model with output delay. Then, the sliding mode tracking control method based on the delay state observation compensation was proposed and combined with sliding mode control theory. Lastly, the effectiveness of the designed model parameter identification, delay state observation, and output delay control methods were compared through numerical simulation. Results show that when uncertain factors, such as noise, are present in the system, the MISG identification method can rapidly and accurately identify the parameters of the SRM model compared with the stochastic gradient identification method; the identification accuracy of the former is four times higher than that of the latter. Similarly, the sliding mode position tracking control method based on output delay observer can rapidly and accurately track the position and speed within 0.5 s. However, its position (0.2 rad) and velocity (0.233 rad/s) tracking exhibit large steady-state errors when no delay observation compensation is present. The proposed method not only demonstrates high position tracking accuracy, but also possesses strong robustness to output delay.