Search

In this study, we have managed to model the energy surface of the aminophenol derivative azomethine molecule mathematically depending on two torsion angles SC1(C9C10C12N14) and SC2(C2C1C6C11). For this purpose, firstly, discrete data obtained from Density Functional Theory calculations have been converted into continuous data with the help of the Fuzzy Inference System. Thus, it is possible to calculate energy values for untested data, which are very costly in terms of time to obtain with other methods/experiments. Then, the continuous and non-smooth surface obtained from the fuzzy inference system and representing the energy values of the molecule has been transformed into a differentiable surface with the help of Bezier surfaces. Thus, an objective function has been obtained in which global optimization methods based on the derivative (or gradient) operator could be used. [ABSTRACT FROM AUTHOR]

Standard interpolatory subdivision schemes and their underlying interpolating refinable functions are of interest in CAGD, numerical PDEs, and approximation theory. Generalizing these notions, we introduce and study n s -step interpolatory M -subdivision schemes and their interpolating M -refinable functions with n s ∈ N ∪ { ∞ } and a dilation factor M ∈ N \ { 1 } . We completely characterize C m -convergence and smoothness of n s -step interpolatory subdivision schemes and their interpolating M -refinable functions in terms of their masks. Inspired by n s -step interpolatory stationary subdivision schemes, we further introduce the notion of r-mask quasi-stationary subdivision schemes, and then we characterize their C m -convergence and smoothness properties using only their masks. Moreover, combining n s -step interpolatory subdivision schemes with r-mask quasi-stationary subdivision schemes, we can obtain r n s -step interpolatory subdivision schemes. Examples and construction procedures of convergent n s -step interpolatory M -subdivision schemes are provided to illustrate our results with dilation factors M = 2 , 3 , 4 . In addition, for the dyadic dilation M = 2 and r = 2 , 3 , using r masks with only two-ring stencils, we provide examples of C r -convergent r-step interpolatory r-mask quasi-stationary dyadic subdivision schemes. [ABSTRACT FROM AUTHOR]

This study presents a piecewise non-linear binary subdivision scheme based on a four-point linear binary subdivision scheme. This scheme is first transformed into a piecewise linear subdivision scheme and then into a piecewise non-linear subdivision scheme. The non-linear subdivision approach removes artifacts and distortions from limit curves, producing better continuous curves. The study demonstrates that this technique is a workable technique for producing smooth curves, especially when avoiding bends, distortion, and oscillation.

Nonnegative matrix factorization (NMF) is a popular dimensionality reduction technique. NMF is typically cast as a non-convex optimization problem solved via standard iterative schemes, such as coordinate descent methods. Hence the choice of the initialization for the variables is crucial as it will influence the factorization quality and the convergence speed. Different strategies have been proposed in the literature, the most popular ones rely on singular value decomposition (SVD). In particular, Atif et al. (Pattern Recognit Lett 122:53–59, 2019) have introduced a very efficient SVD-based initialization, namely NNSVD-LRC, that overcomes the drawbacks of previous methods, namely, it guarantees that (i) the error decreases as the factorization rank increases, (ii) the initial factors are sparse, and (iii) the computational cost is low. In this paper, we improve upon NNSVD-LRC by using the low-rank structure of the residual matrix; this allows us to obtain NMF initializations with similar quality to NNSVD-LRC (in terms of error and sparsity) while reducing the computational load. We evaluate our proposed solution over other NMF initializations on several real dense and sparse datasets. [ABSTRACT FROM AUTHOR]

The connections between parameter surfaces enable the development of various geometric designs. However, these surfaces are generally connected along their boundaries in a rectangular domain. This study investigated the methods for connecting surfaces along a curve. To this end, we introduced two-variable degenerate functions and utilized their algebraic properties to characterize the form of the parameter surfaces for practical surface construction. The results were used to deform the surfaces along the curve. For application, we presented the examples of deformations using Bézier surfaces and extended them to general surfaces. [ABSTRACT FROM AUTHOR]

Composition detection and semantic line detection are important research topics in computer vision and play an important auxiliary role in the analysis of image esthetics. However, at present, few researchers have considered the internal relationship between these two related tasks for comprehensive research. In order to solve this problem, we propose a synchronous detection network of composition class and semantic lines based on cross-attention, which can realize the mutual supervision and guidance between composition class detection and semantic line detection, to improve the accuracy of each other’s detection. First, the pre-trained composition detection model and the pre-trained semantic line detection model as two teacher models to provide data labels of composition and semantic line information for the student model. Then, we train a student model with the help of the teacher model. The student model adopts the multi-task learning architecture by combining soft and hard parameter sharing, as we propose. At the same time, we develop a cross-attention module to ensure that both tasks get the help and supervision they need from each other. Experimental results show that our method can draw semantic lines while detecting composition classes, which increases the interpretability of composition class detection. Our composition detection accuracy reaches 92.57%, and for benchmark semantic lines, the accuracy of our AUC_A metric can reach 92.00%. [ABSTRACT FROM AUTHOR]

This paper is dedicated to the analysis and detailed study of a procedure to generate both the weighted arithmetic and harmonic means of n positive real numbers. Together with this interpretation, we prove some relevant properties that will allow us to define numerical approximation methods in several dimensions adapted to discontinuities. [ABSTRACT FROM AUTHOR]

This article is devoted to introducing new spline quasi-interpolants for the sharp approximation of data in one and several dimensions. The new construction aims to obtain accurate approximations close to singularities of the function from which the data are obtained. The technique relies in an accurate knowledge of the position of the singularity, which can be known or approximated, and that allows for the obtention of accurate approximations of the jumps of the function and its derivatives at this point. With this information, it is possible to compute correction terms for the B-spline bases of the spline that are affected by the presence of singularities. The result is a piecewise smooth reconstruction with full accuracy (meaning by full accuracy, the accuracy of the spline at smooth zones). The numerical experiments presented support the theoretical results obtained. [ABSTRACT FROM AUTHOR]

Linear approximation methods suffer from Gibbs oscillations when approximating functions with jumps. Essentially non oscillatory subcell-resolution (ENO-SR) is a local technique avoiding oscillations and with a full order of accuracy, but a loss of regularity of the approximant appears. The goal of this paper is to introduce a new approach having both properties of full accuracy and regularity. In order to obtain it, we propose a three-stage algorithm: first, the data is smoothed by subtracting an appropriate non-smooth data sequence; then a chosen high order linear approximation operator is applied to the smoothed data and finally, an approximation with the proper jump or corner (jump in the first order derivative) discontinuity structure is reinstated by correcting the smooth approximation with the non-smooth element used in the first stage. This new procedure can be applied as subdivision scheme to design curves and surfaces both in point-value and in cell-average contexts. Using the proposed algorithm, we are able to construct approximations with high precision, with high piecewise regularity, and without smearing nor oscillations in the presence of discontinuities. These are desired properties in real applications as computer aided design or car design, among others. [ABSTRACT FROM AUTHOR]

Trigonometric box splines can be considered as the multivariate generalizations of univariate B-splines. This means multivariate trigonometric box splines can be constructed from univariate trigonometric B-splines by suitable adaptive methods. Mainly, this paper is concerned with the construction of a new class of bivariate trigonometric box spline functions with the help of trigonometric B-splines through directional convolution method. These are refinable functions and some of their properties are also investigated. Secondly, a new effective non-stationary subdivision scheme is derived from the two-scale relationship of a particular trigonometric box spline. The non-stationary subdivision scheme is capable of producing such trigonometric box spline surfaces in regular regions. Some important properties along with the interactive modeling capability of this subdivision scheme are described in detail. [ABSTRACT FROM AUTHOR]

This paper analyzes the coupling relationship and coupling factors between ecological landscape planning and architectural design, combines ecological planning with landscape planning and design and explores the practical value of green architecture. The ecological landscape construction is divided into five systems, and three indicators with a high degree of influence on the health of the urban ecosystem are selected as the system influence factors to construct a healthy coupling synergy model of ecological landscape planning and architectural design. Taking Fujian Province as an example, we analyze the degree of coupling between ecological landscape planning and architectural design systems and the regional distribution of the level of coupling coordination in Fujian Province. The highest coupling coordination degree between ecological landscape planning and architectural design is R10 and Rl for green buildings, with coupling coordination degrees of 0.8341 and 0.8001, respectively, and the lowest is R = 59 0.3854. Thus, it is important to synergize between architectural design and ecological landscape planning and construction.

In this paper, according to the characteristics of parametric landscape design, practical landscape architecture is selected for parameter and rule sets, and then the program calculation is established by parametric software to realize the digital design of urban landscapes. The parametric model of urban architectural design is designed for site factor indexing, construction, and management of space and form. The automatic identification model based on the building plan for the construction of building functions is carried out to generate candidate target areas and location refinement analysis, and the parametric generation of the urban landscape is optimized for design. Compared to the initial scheme, the economic and technical indicators increased the volume ratio by 11.2%, the building density by 17.3%, and the green area ratio by 3.6%. The three performance indicators 1/VG̅, CHIS̅ and 1/VB̅ of Rop are 8.5%, 35.6% and 20.8%, respectively.

This paper constructs a morphological extraction model of Ganan Hakka architectural elements based on feature fusion using ResNet as the backbone network. Then, the extraction method of Ganan Hakka architectural colors based on the combination of multivariate fuzzy C-mean clustering algorithm and the K-mean++ algorithm is designed. The improved recommendation algorithm intelligently matches the extracted color and morphological elements and applies them to modern architectural interior design. The results show that the average time of interior design intelligent recommendation is 3.8s, the overall accuracy is above 0.94, and the degree of interior design intelligent matching is in the range of [0.78,0.99]. Based on this study, Ganan Hakka’s architectural elements can be well combined with modern architectural interiors to promote traditional culture.

First, This paper quantifies and ranks each constituent element using two methods of interior interaction design: extraction and weight determination. Secondly, the interaction experience mechanism of the scene space is proposed based on the user’s interaction behavior and feedback in the virtual reality interaction mechanism of Bayes’ theorem. The positioning of the virtual scene and the transformation of the user’s perspective in virtual reality are achieved through human spatial location mapping and the field-of-view range approach. The results show that in the virtual environment, the mean satisfaction value for the designer’s top view presentation is 5.876, the mean usability value is 22.52, the mean satisfaction value for the first view presentation is 4.929, and the mean usability value is 19.50. This paper provides new ideas and methods for the application of virtual reality technology in the field of interior design by providing a variety of interaction methods and immersive experiences for interior designers.

This paper aims to improve college students’ entrepreneurial abilities as its main goal. A platform for college students’ entrepreneurship development is constructed using cloud technology, and the entrepreneurship data on the platform is classified and divided by combining it with the KNN algorithm. The algorithm is optimized for distance calculation and distance sorting stages to reduce inter-program dependencies and shorten the program execution time. The distance between matrices is calculated by calling the functions in the cublas function library. The experimental data are used to analyze the feasibility of entrepreneurship training for college students. The results show that the KNN algorithm can be applied to data processing in cloud computing. When the data dimension is 128 and the number of sample points is 215, the algorithm can get a maximum speedup of 1.52 times. The server configuration of the platform needs to be in a 4-way dual-core or 4-core to ensure the platform’s normal operation. This study is beneficial in promoting the improvement and development of entrepreneurial skills for college students.

This paper first analyzes the development trend of environmental design education from two aspects of environmental design’s diversified beginnings and aesthetic foundation and creates a conceptual idea of environmental design teaching system design. The second step is to construct an environmental design teaching index evaluation system, then pre-process the evaluation data and construct the environmental design teaching evaluation model using principal component analysis. Finally, the principal component analysis is conducted by example to evaluate the index weights and the index evaluation. The structure showed that the aesthetic analysis and aesthetic enhancement ratings were 3.56 and 3.33 before environmental design teaching and 8.1 and 8.6 after environmental design teaching. When taken together, environmental design instruction improves students’ aesthetic level.

This paper is oriented to the all-media vision to run the database mechanism in a multidimensional interactive teaching system for environmental art and design majors to maximize the possibility of predicting the probability distribution of the next course. Installing the database on the server enables data interaction within the system. Use a wavelet neural network for training to achieve the balance of communication channels. The results show that the mean square error of the system designed in this paper is under 0.1, the minimum average response time is 120.6ms, and the score of the important factor of student course evaluation is 75. The all-media vision can provide teaching design ideas for the teaching process of environmental art design majors and nurture applied design talents, as demonstrated.

To improve the teaching effect of Civics courses in colleges and universities, this paper uses multi-attribute regression to compute the nonlinear representation ability, combined with the perception machine deep learning network, to get the teaching resources and interaction mechanism of Civics courses. The weighted k-shell decomposition method is used to determine the weighted undirected graph of the Civics and Political Science course, and the shortest path between nodes is used to map the link to complete the teacher’s teaching feedback and personalized teaching. The results show that the hit rate and normalized gain rate of the proposed method in the Civics course dataset in colleges and universities are as high as 0.58 and 0.39, respectively, and the performance of the Civics course in colleges and universities is excellent, which indicates that the proposed method can promote the cultivation of students’ quality of Civics and politics, and it has great significance in the field of teaching and learning.

This paper aims to promote the integration of animation design and cultural and creative industries. The shape gradient algorithm is used to analyze the features of the animated shapes. The corresponding interval distance of anime shapes is determined by calculating the curve sampling points of anime feature points. The deformation framework is created, and a sequence of anime shapes is generated using the linear interpolation method. In the deformation of anime design, the role of three algorithms, namely image segmentation module, association map of attributes, and mathematical description of EMD, on its generation is focused on the analysis. The results show that anime users from post-00 and post-10 are more inclined to anime peripherals, accounting for 30% and 35%, respectively. The annual growth rate of anime peripheral is 90.2%, and the annual growth rate of the overseas anime market is also gradually increasing, reaching a growth rate of 103.1%. A successful anime IP will certainly influence the development of its anime and cultural creation industry. The research in this paper is conducive to promoting better integration of animation design and cultural and creative industries and promoting the development of an innovation economy.

This paper is based on graph theory, combined with a particle swarm optimization algorithm to find the optimal solution in the whole particle swarm through continuous iteration and obtain the sum of squares of each variable in the particle cognitive domain. Searching for the global optimal position calculates the inertia weights in the particle swarm algorithm and changes the velocity and position of the particles based on the motion properties of graph theory to improve the global search ability. The domain competition operator is executed after particle swarm initialization, and a hybrid crossover strategy is used to select a crossover point, which improves the algorithm’s performance by exchanging information with the global optimum. Based on graph theory, the spatial configuration optimization of furniture combinations is scored, in which the average score of furniture space ranges from 3.3 to 6.2, and the average score of spatial configuration optimization ranges from 4.5 to 8. Therefore, based on graph theory, the reasonable furniture form for spatial layout optimization can fully reflect the practical function of the space, comfort, and the creation of a spatial atmosphere.

This paper utilizes the fuzzy numerical analysis method to display the customary activities using rich artistic elements and stylistic changes so that the value and quality of the works can be significantly improved. Folk art influences are divided by nature, adjacent structure weights are estimated, the scale of matrix elements is evaluated, and the art design vector is adjusted. Comprehensively evaluate and assign art design factors, establish the fusion judgment set and fuzzy evaluation matrix, and complete the research on the fusion of folk art elements and art design by adopting the principle of maximum affiliation. According to fuzzy numerical analysis, the fusion score is 0.9, the overall art design aesthetics is improved, and the fusion effect is better.

This paper firstly analyzes the specific application of velvet flowers on women’s dresses from the perspective of fashion aesthetics and with the understanding of the background and development trend of dresses. Secondly, based on 3D visual perception, we propose the optimization of the normalization method and loss function optimization, which can improve the migration speed and generation quality of velvet flower design, and then seamlessly fit it to the dress base map by Poisson fusion to realize the intelligent design of velvet flower in dresses driven by 3D visual perception. Then, the color features and attributes of the floral in the dress are studied, and the HSV color space model is used to extract the high-frequency colors and attributes of the dress. Then, the floral design is digitized, and the dress design is driven by 3D visual perception is studied and analyzed. The results show that the loss value of the dress design style migration network trained based on AdaN is the smallest, and the loss value is reduced by 0.19 compared to the dress design style migration network using IN and 0.31 compared to BN. The velvet flower design on the dress design base map can be implemented based on the foundation provided by this study.

In this paper, relevant cultural materials were first collected and extracted using knowledge mapping and retrieval tools, and the co-occurrence analysis method was used to construct the co-occurrence relationship matrix between keywords. Combined with the method of graphic vectorization, the traditional cultural and creative product design patterns and motifs are directly scanned to extract the design prototype’s outer contour and polygonal path. Next, the recommendation process of cultural and creative products was realized by combining collaborative filtering and the TransD-CF algorithm. Finally, the influence of cognitive attitude and purchase behavior indicators on cultural and creative products was analyzed through correlation. The results show that the difference between genders using essay on hobby collection of cultural and creative products is 0.203, and the practicality requirement of cultural and creative products decreases by 0.09 for every 100 yuan increase in price. This paper presents a fresh perspective on cultural and creative promotion and plays a guiding role in design practice with the aid of artificial intelligence.

This paper contributes to the visual communication design of traditional animal and plant patterns by recreating the traditional pattern graphics based on the specific skeletal laws of traditional patterns. Based on the demand for vectorization of traditional animal and plant tattoos, a vectorization algorithm based on edge structure extraction is proposed to pre-process the tattoos using the gradient minimization model, and the tattoos are processed by image vectorization using the edge structure extraction algorithm of clustered segmentation and the Bézier curve fitting algorithm of the least squares method. Based on the image vectorization rendering of traditional animal and plant patterns, the visual communication design is carried out, and the color matching effect is analyzed in terms of single-factor color matching and comprehensive color matching. 10 extracted colors are analyzed in terms of the hue angle difference on the color ring, and the two comparative colors are the contrasting colors when the angle difference is 120~180°. The contrast height is highest when the difference value is 180°.

This paper first investigates the method of extracting features from Chinese embroidery art images, including preprocessing, filtering, and enhancement steps. Then, the multi-classification recognition technique based on an improved support vector machine is used to classify and recognize different modern dress designs. Non-iterative and conjugate orthogonal algorithms are proposed for a feature fusion method based on partial least squares analysis. In the performance analysis part, the performance of feature extraction classification, data fusion energy, and fused data noise are evaluated. Lastly, the impact of the dress style generation map incorporating fused Chinese embroidery elements is analyzed. The study shows that the method in this paper can save 33.4 J of energy consumption after running for 250 s compared with the popular learning-based method. At least a 10% improvement in the number of dress style graphs that interest social event participants, etiquette service practitioners, and performers was achieved after multiple training iterations.

This paper firstly discusses the value significance of emotional packaging design, outlines its design principles and methods, and explains the thinking structure of emotional design. Secondly, the feature detection of symbolic graphics is realized using a mixture of regression and segmentation, and a multilevel cross-modal perceptual emotion recognition model is constructed based on the collaborative attention mechanism of the cross-modal Transformer and recurrent neural network. Finally, a dataset test and empirical analysis were conducted to verify the model’s effectiveness in this paper. The results show that the multimodal fusion model has better recognition performance than the unimodal and bimodal models, with F1 values of 0.914, 0.923, and 0.902 for neutral, positive, and negative emotions, respectively. Symbolic graphics in the emotional design of gift packages are mainly expressed in the form of intuition, vividness, and validity to emphasize the diversity of symbolic graphics in the emotional design of gift packages. This shows that multimodal emotional fusion can be realized to analyze the emotional design of symbolic graphics of gift packaging and promote the emotional innovation design of symbolic graphics of gift packaging.

The purpose of this paper is to analyze and research the practical activities of ideological and political education in colleges and universities using causal thinking design. Firstly, it studies the meaning and characteristics of ideological and political education and proposes a causal hypothesis. Secondly, the structural causal model is used to complete the basic structure of the causal thinking model, and the structural causal model is optimized using the HSIC Lasso method. Then, based on the Bayesian generative causal directional algorithm, the functional causal model is constructed, and the model is optimized. Based on the model, the Interaction Network Method for Civic Education Practice in Colleges and Universities is framed. Finally, based on the SCM algorithm, the learning effect of Civics in colleges and universities is analyzed. The Y → X and X → Y causal inference accuracy of the SCM algorithm is 100% and 90%, respectively. More than 70% of the students think that causal thinking exercises help to improve the learning effect of the Civics course.

Because the English vocabulary of water resources majors is difficult to understand and awkward to speak, this paper constructs a teaching model combined with pronunciation error detection. With the help of the feature engineering method, we extracted the pronunciation features of English for water resources majors and then used a smooth LSTM network to learn and recognize the pronunciation features so as to detect the errors in pronunciation habits and complete the correction of students’ professional English pronunciation. The pronunciation error detection rate reflects the effect of error detection, and the feasibility of the teaching model is verified according to the mastery degree of specialized vocabulary. The post-test mean value of the experimental class is 76.18 points, and the mean value of the control class is 71.12 points, which is 5.06 points higher than that of the experimental class. From the results of the independent samples t-test, the Sig value is 0.102, which is greater than 0.05, indicating that there is a significant difference in the post-test scores of the two classes. This study has a significant impact on students’ mastery of English vocabulary related to water resources.

This paper adopts multi-data fusion technology to design urban environmental intelligence. First, store the information collected by the reader communication module and receive and analyze the urban environmental monitoring data sent by the handheld RF reader. Secondly, calculate the mean value of environmental data by weighted average, summarize the sample information by combining it with Bayesian estimation, and fuse the values of the multi-sensor data. Finally, D-S evidence inference is utilized to expand Bayes and enhance the degree of trust in the focal element. The results show that the utilization value of multi-data fusion technology can reach 1000, with the growth of time, the public urban environment concern reaches 900 times, the level of intelligent design of the urban environment is improved faster, and the overall effect is good.

This paper combines the three elements of industry-teaching fusion with the competency characteristics of art design professionals and puts forward a specific cultivation path for art design professionals under the industry-teaching fusion mode with respect to the cultivation value of industry-teaching fusion for art design professionals. The association rule algorithm is selected to set up art design professional courses, and the neural network algorithm is selected to establish the talent cultivation mechanism model of industry-teaching fusion under the background of big data. According to the association rules for talent training path effect prediction, and then use big data is used to analyze the demand characteristics of art design professional talent training under the fusion of industry and education based on the demand characteristics for targeted talent training. The average difference between the student’s scores and the actual scores is 17.38, the accuracy rate of the model is 87.15% in the case of the allowable error range of 10 points, and the accuracy rate of the model is 90.55% in the case of the allowable error range of 15 points. A series of interventions, such as promotion rate prediction and academic warning, can be implemented based on this.

Compared with traditional monolithic carbide ceramics, Cu-based composite ceramics have the advantages of higher hardness, good wear resistance and oxidation resistance and are a new type of structural ceramics with wide application prospects. This paper first proposes a novel bi-directional freezing technique to construct a three-dimensional long-range ordered layer network hierarchical structure based on the pressure infiltration process. Secondly, a composite finite element model based on fuzzy numerical analysis is proposed for the vibroacoustic analysis of the periodic composite coupled system (PCSAS). Finally, thermal analysis experiments and mechanical property tests verify the effectiveness of Cu-based composites applied to ceramic matrices. The experimental results show that the toughness of Cu-based composite ceramics increases with the increase of TiC content, and the toughness increases from 2.15±0.27Mpa.m1/2 to 4.21±0.25Mpa.m1/2 compared with that of conventional monolithic carbide ceramics. The Cu-based composite ceramics designed in this paper give full play to the advantages of the high strength of ceramic particles and high toughness of metal matrix and develop an effective method to prepare structural metal-ceramic composites. This paper has developed an efficient route for preparing structural metal-ceramic composites, which can provide a reference for materials researchers to develop high-toughness composite ceramics.

Anomaly detection for Internet egress is to enhance the user experience of browsing the Internet. Firstly, the five functional modules of the system are described, and the pre-processing data module is used to extract the Internet topology data for Internet anomaly detection. The linear regression algorithm is also introduced in detail, including the definition of linear regression and its parameter estimation method and the optimization of linear regression parameters by variance and squared error. Finally, the performance evaluation of the anomaly detection system proposed in this paper is carried out to verify the system’s feasibility. From the performance evaluation, the detection rate of the system in this paper is 2.93 and 5.33 percentage points higher than that of SVM and SNN detection methods, respectively, and the false alarm rate is 2.85%. Regarding the impact of different packet lengths, the system in this paper is relatively stable when the packet length is 600, with an accuracy rate of 99.94% and a false alarm rate of only 1.93%. The above data show that the Internet egress anomaly detection system proposed in this paper can effectively detect the anomalies existing in the Internet egress and accurately grasp the data can timely deal with the abnormal nodes, thus improving the user browsing experience.

The big energy data and carbon emission monitoring system is designed to collect carbon emission-related data for pollution gas management. This paper constructs a carbon emission monitoring system in the context of carbon neutrality and peaking. A multi-layer perceptron algorithm is introduced based on the principle of perception, and a BP-MLP neural network model is proposed by optimizing the perceptron weights using BP neural network. For the sensors in the carbon emission monitoring system, the node redundancy is processed, and the optimal sensor distribution is proved by using the correlation coefficient. Finally, the evaluation analysis of the carbon monitoring system was carried out in three aspects: relevance coefficient de-redundancy, number of iterations and daily emissions. The results show that when the correlation threshold is 0.8, the sensor distribution of the monitoring system can satisfy the monitoring under various wind conditions, and when the number of iterations is 600, the difference between the real value and the monitored value is only 3.63% and the daily emission peaks at 5.243 mg/m3 at 14:00 a.m. This shows that the carbon emission monitoring system constructed based on the BP-MLP model can effectively collect and analyze carbon emission data. Data collection and analysis, and provide corresponding data support for the management of gas pollution.

Exploring the influence of the ADDIE education model on teaching cultural and creative product design to promote the improvement of the quality of teaching cultural and creative product design. Starting from the ADDIE education model, this paper analyzes the five components of the model and the specific functions of each component. The logistic regression algorithm that constitutes the analysis module of the ADDIE education model is introduced, and a sparse prior factor is introduced to optimize the logistic regression algorithm; then, a sparse logistic regression algorithm is constructed to solve the likelihood function of the weight vector. Finally, the ADDIE education model is used to analyze the indicators of cultural and creative product design teaching in two aspects: the teaching objectives and teaching principles of cultural and creative product design. From the teaching objectives, the influence of cognitive, skill, and emotional objectives accounted for 47.11%, 44.41% and 51.26%, respectively. From the teaching principles, the percentages of uniqueness, practicality, and attractiveness are 51.5%, 43.7%, and 40.3%, respectively. Based on the ADDIE education model can affect the emotional goals and uniqueness in the teaching of cultural and creative product design, and it is necessary to focus on the emotional teaching of product design as well as diversified and differentiated teaching in design teaching in order to enhance better the learners’ interest degree in cultural and creative product design.

Although many kinds of intelligent toys, interactive toys that can mobilize children’s sensory experience in many aspects and cultivate their creative thinking are still scarce. In this paper, interactive experience elements are built based on virtual reality technology, image recognition technology, human-computer interaction theory, and ergonomics theory. A brain interaction network based on multimodal sensory modulation is constructed to allow intelligent toys to perceive their surroundings effectively. And the dynamic causal model (DCM) is proposed to infer the connection strength and direction of neural activities among pre-defined cortical regions of the brain. The results showed that the classification accuracy of noisy audiovisual (AVd) modality in the BA18 region was 87.87% significantly higher than that of the STS region (81.41%). The average classification accuracy of the mono-visual noise-added (Vd) modality in the BA18 ROI was 79.40%, which was also significantly higher than that of the STS ROI at 65.12%. It indicates that the interactive design based on multimodal senses can effectively stimulate children’s brain interest areas, which makes children experience better and communicate more interactions. The research results of this paper provide new perspectives and have important implications for the application of interactive intelligence to toy art.

The purpose of exploring the application of computer-aided design technology in industrial product design is to realize the diversification of industrial product design and meet the personalized customization needs of customers. Starting from computer-aided technology, this paper illustrates the optimization of CAD design by using a genetic algorithm and constructs a bipolar progressive interactive genetic algorithm using bipolar progressive ranking and fuzzy fitness jointly. Genetic algorithm genotypes in CAD process product design are defined, and a comparative evaluation experimental analysis of industrial product design is performed with the AR-IGA algorithm. Regarding the number of iterations, the average number of iterations for the AR-IGA algorithm to complete the color-matching design of process products among ten testers was 9.34, which was 36.42% lower than that of the TIGA algorithm. The average evaluation elapsed time of the AR-IGA algorithm was 246.9s, which was 24.12% less than the average evaluation elapsed time of the TIGA algorithm. This shows that computer-aided technology optimized by genetic algorithms can effectively improve the efficiency of industrial product design and satisfy more customers to realize personalized design simultaneously.

As an emerging product of the rapid development of science and technology in the era of big data, digital media art is the application of various technologies, such as computer processing and digital special effects in film and television animation creation. This paper firstly takes digital media art as the research base and discusses the path of advancing digital media art in film and television animation. Secondly, the Web 3D interaction technology in digital media art is applied to the model building and animation generation of film and television animation, and the human body movements are captured to get the form reconstruction to generate SMPL human model parameters. Finally, a three-factor repeated measurement experiment and ablation experiment verified the feasibility and effectiveness of applying digital media art to film and television animation. The results show that the interaction of AOS and TT has a significant main effect on the total interaction time MT and F(15,43.213) = 62.329. In the ablation experiment, the increase points are 1.98, 1.63, and 1.52, which are greater than 1.5 on average. The application of digital media art to film and television animation proposed in this paper verifies the scheme’s feasibility through the ablation experiment and the three-factor repetition test. It also lays the foundation for the international development of Chinese film and animation.

This paper first defines the conceptual scope of intellectual property, and the intellectual property evaluation system analyzes the special characteristics of enterprise intellectual property evaluation and compares the differences between several intellectual property management models. Secondly, an open adaptive enterprise IPR evaluation system is constructed based on a linear regression model, and the system structure and the relationship between subsystems are analyzed in depth. Finally, based on the theory of adaptive evaluation management, the adaptive IPR evaluation system is constructed. The adaptive enterprise IPR evaluation model based on linear regression was constructed mainly from three dimensions, and the method to determine the development coordination index and early warning degree of the three dimensions was deduced. The results show that the average efficiency of the typical enterprise IPR evaluation system calculated based on the linear regression model is 0.86, which is 21.3% more efficient than the traditional model. Four of the decision units’ DEA is effective, 63% of the inputs are effective, and 37% of the input resources are wasted, which aligns with the actual enterprise. The adaptive IPR evaluation system based on the linear regression model proposed in this paper has theoretical innovation value and practical significance for enterprises to realize the transformation of IPR achievements.

This paper builds a spatial design model based on the reconstruction of 3D visual spatial points in an indoor environment and combines the minimum Euclidean distance to calculate the vector relationship between spatial points and realize the corresponding spatial point matching. For the exploration of traditional elements in spatial design, an extraction method based on the improved cuckoo algorithm is proposed. By adding a dynamic inertia weight adjustment method, one inertia weight value is randomly selected from the Gaussian distribution each time to balance the algorithm’s local and global search. By extracting traditional elements, calligraphy is the most frequent in the home space, with 79.4% integration. The tea set is the most frequent traditional element in the dining space, with 84.53% integration. A couplet is the most frequent traditional element in the public open space, with 71.98% integration. The extraction based on intelligent algorithms can help designers better integrate traditional elements in the design of environmental spaces.

Under the digitalization of big data information, how to use big data technology to combine with visual communication design teaching has become a key topic of concern in the current education sector. Firstly, this paper proposes using a deep learning model based on big data technology for teaching practice research of visual communication design majors in colleges and universities and constructs a deep learning model by combining perceptron and bp neural algorithm for data mining property and accurate prediction. Secondly, the deep learning model is optimized by using the activation function and knowledge distillation function in response to the limitation problem of the deep learning model. Then the teaching evaluation data of visual communication design majors in colleges and universities are obtained through questionnaire survey work, and evaluation indexes are determined according to the evaluation data. Finally, students of visual design majors at Guizhou Vocational and Technical College were selected as research samples, and the practice performance of communication design majors was analyzed based on the deep learning model. The results showed that the deep learning model: from week 1 to week 20, the score improved from 52 to 88, which improved the score by 69.23%. The traditional method: from week 1 to week 20, the score improved from 52 to 80, improving the score by 53.84%. This study is conducive to promoting the healthy development of the visual communication profession and cultivating design talents that meet the needs of the information age and is of great significance to the development of the visual communication profession in China.

At present, the call for information reform in education teaching is increasing, and art education is also facing the demand for online. This paper analyzes the demand for online art education according to the characteristics of existing online education, proposes an online education system based on virtual simulation technology for the special needs of art education, and studies the structure distribution of learning resources, teaching organization and online teaching practice under 3D virtual simulation scenarios respectively. At the same time, a virtual simulation explanation robot is added using Unity3D technology to realize online learning interaction in art education. In the continuity test of the system, the system throughput per second accounted for 42.31% of the maximum throughput on average, with the lowest accounting for only 28.65% and the highest accounting for 56.98%, and the operating efficiency of the system met the teaching needs of art education. In the concurrency test, the success rate of system interface calls remained above 80% as the number of concurrent users increased. In practice, the overall satisfaction rate of students with the art online education system reached 88.3%. The system in this paper can enhance the interest in art teaching and provide a feasible new path for the development of art education in the information era.

In this paper, firstly, in the context of intelligence, using techniques such as hierarchical features, local perceptual field, weight sharing, and spatial subsampling make the convolutional neural network with representational learning ability, certain translation, scaling, and deformation invariance, which can accurately extract the features of target objects in graphic design elements. Next, the network structure, loss function, and optimization methods of MaskR-CNN are analyzed, and the MaskR-CNN convolutional neural network is implemented based on the TensorFlow framework, and the network is applied to the extraction of buildings from graphic design elements. Then the borrowed landscape technique in graphic design is used to enrich the combination form of elements of architectural design through the mirror effect of water landscape. The selected IALD dataset is composed of 180 data images and 180 corresponding labeled images, and the effect of high-resolution building extraction of graphic design elements based on the MaskR-CNN model is verified by simulation experiments. The simulation results show that Mask R-CNN improves the accuracy of building extraction from graphic design elements by 5.11%, mAP also improves by about 10.09%, and the inference time is less than 1 second; Mask R-CNN has good robustness and accuracy for building extraction from graphic design elements. This study has important research significance for the depth and development of the commercial architectural design.

This paper first explores the color emotion analysis of visual communication art, using linear equations and correlations to analyze the correlation between color emotion and image color space and classifying the degree of warmth and coldness and emotional expression of various colors. Then a multi-model hybrid color emotion calculation method is proposed, using positive-negative attribute values to get color positivity-taking values, using heavy-light to get color weight-taking values, and using warm-cold to get color heat-taking values, respectively. And the global color emotion feature of the image is calculated by using the extraction algorithm of central area weighted features. Finally, the influence of visual communication art on tea packaging is analyzed in terms of graphic design, color and layout of the packaging. In terms of packaging focus, 64 people think that they should focus on the embodiment of humanistic customs and culture, 87 people think they should focus on aesthetic aesthetics, and 75 people think they should highlight the characteristics of the product. In terms of the cultural and emotional orientation of packaging, 70.9% of people think that packaging diversity is the main tendency, and 50.9% think that retro style can better reflect the cultural characteristics of tea. The art of visual communication makes tea packaging design more design and aesthetic.