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In order to effectively solve the problem of relatively large errors in individual positioning strategies in indoor environments, this paper applies the genetic optimization neural network algorithm to indoor location based on multi-source information fusion. The range of the geomagnetic fitness is constrained based on the results obtained by using the wireless WiFi positioning for combination and matching, which can reduce the value of the matching error effectively. Subsequently, the global optimal value of the indoor network is calculated based on the genetic algorithm, which can optimize the initial value and threshold of the neural network after genetic optimization so as to improve the accuracy of the network to the greatest extent possible while accelerating the convergence speed at the same time. After the optimization processing is completed, fusion training can be performed on the coordinates of the actual positions based on the obtained combination positioning situation and the predicted positioning result in the indoor network. Finally, the optimal positioning result can be obtained accordingly. Through the analysis of practical cases, it can be known that the mean square error predicted based on the genetic optimization neural network calculated by using the genetic algorithm can be effectively reduced by 76%, and the accuracy of the fusion positioning can be increased by 48% on average compared with the accuracy of a single positioning strategy. Hence, the method put forward in this paper has effectively improved the positioning accuracy, which suggests that its positioning performance is superior. [ABSTRACT FROM AUTHOR]

Nowadays, with the development of media technology, people receive more and more information, but the current classification methods have the disadvantages of low classification efficiency and inability to identify multiple languages. In view of this, this paper is aimed at improving the text classification method by using machine learning and natural language processing technology. For text classification technology, this paper combines the technical requirements and application scenarios of text classification with ML to optimize the classification. For the application of natural language processing (NLP) technology in text classification, this paper puts forward the Trusted Platform Module (TPM) text classification algorithm. In the experiment of distinguishing spam from legitimate mail by text recognition, all performance indexes of the TPM algorithm are superior to other algorithms, and the accuracy of the TPM algorithm on different datasets is above 95%. [ABSTRACT FROM AUTHOR]

As computer and multimedia technology advances and as the variety of knowledge display forms expands, there are more ways for people to obtain knowledge and information. The best course in musicology is voice performance. A crucial issue is how to select and categorize valuable curriculum materials of varying quality levels. This paper implements an automatic classification and integration algorithm for vocal performance learning materials using machine learning technology and tests it on the corresponding dataset, beginning with multidimensional association rule mining technology and the premise that multimedia data contain numerous useful characteristics. Experiments demonstrate the classification precision and data integration capacity of the proposed algorithm. Vocal performance is the most engaging course in musicology. To cultivate corresponding talents, we can rely on college and university offline courses and the rapidly developing multimedia technology to train talents online. An important topic is how to efficiently select and classify curriculum materials of varying quality in order to provide them to students with different learning needs. We can achieve the automatic classification of the course content by leveraging the superior learning capabilities of artificial intelligence and machine learning technology. Consequently, this paper implements an automatic classification and integration algorithm for vocal performance learning materials using machine learning-related technology and conducts an experimental test on the corresponding dataset, beginning with multidimensional association rule mining technology and the perspective that multimedia information itself contains a large number of useful characteristics. Experiments demonstrate that the proposed algorithm has a high level of classification accuracy and data integration capability. [ABSTRACT FROM AUTHOR]

The vigorous development of tourism has made rural tourism a highlight of the new era. In order to better realize the classification of rural tourism features, this paper proposes a knowledge recognition algorithm based on hierarchical clustering analysis. Firstly, the rationality of the optimization of the rural tourism feature algorithm is analyzed in this paper; secondly, the rural tourism feature classification index system is constructed based on the hierarchical clustering analysis; finally, the index weights are clearly divided according to the characteristics through the hierarchical clustering analysis of the knowledge identification algorithm. According to the specialties of hierarchical clustering analysis, the criteria of the algorithm are determined, and the characteristics of rural tourism are carefully classified. Rural tourists can visit different scenic spots in high density and improve the mobility of rural tourism. The experimental results show that: through the analysis of the characteristic classification data of the Rural Tourism College in this paper, it can be seen that the average daily income of the scenic spot is higher than that of the traditional rural scenic spot. The average daily income of rural tourism has increased by more than 261,900 yuan, which has largely promoted the development trend of rural tourism in my country. It is proved that the hierarchical clustering analysis method is helpful for rational zoning and serious thinking about the characteristics of rural tourism. This paper provides a reference for promoting the classification of rural tourism. [ABSTRACT FROM AUTHOR]

In order to solve the technical problem of fault signal recognition in the field of communication, this paper proposes an electronic communication fault signal recognition method based on data mining algorithm. Firstly, the K-means clustering algorithm is used to determine the cluster number k according to some attributes or class characteristics of the communication class samples, and the communication sample types are classified into a certain class so that the communication sample data in the cluster can be closely distributed and the data within a certain class range can be calculated by Euclidean distance formula. Then, this paper clusters the data. In the clustering data, BP neural network model is used to train and calculate the obtained clustering data again, which can map and deal with the complex nonlinear relationship between the fault information data of different clustering categories. The results show that the final error accuracy can be raised to about 20% by using the method in this paper. Conclusion. The algorithm designed in this paper can quickly predict the factors affecting the communication and find the communication fault information. [ABSTRACT FROM AUTHOR]

With the rapid development of we-media information dissemination, WeChat official accounts platform has become an important way for people to obtain health related knowledge. However, the platform information is redundant, miscellaneous, and overloaded. In order to meet the increasingly accurate and efficient knowledge service needs of users, reorganizing and aggregating document knowledge resources is effective. If we use the way of artificial recognition to filter information, it will inevitably cause huge labor and time cost, and the effect is very little in front of massive articles. This paper proposes a text summarization method for the WeChat platform based on improved TextRank that takes into account both user demands and sentence features during the summarization process. The data source crawled from the Sogou WeChat platform. The results show that the TextRank algorithm has obvious hints on the accuracy of text summarization extraction after fusing the Word2vec model. The improved TextRank method, integrating user demands and sentence features into the model, makes the results of text summarization closer to the theme of the article and more able to meet the user demand. According to the complexity of the algorithm, this method is not suitable for the automatic summarization of long or multiple documents. [ABSTRACT FROM AUTHOR]

In order to improve the development effect of private education, this paper analyzes the current situation of private education combined with the data mining algorithm and explores the problems existing in the development of private education. Moreover, this paper combines the semi-parametric product estimation method with parameter estimation and applies the estimation method to model-assisted sampling estimation. This work enhances the estimate accuracy of the sample estimation and increases the field of application of the model while enhancing the classic generalized regression estimation. It also modifies the estimation accuracy on the basis of the linear assumption. The experimental study reveals that the data mining algorithm-based analysis approach for private education development provided in this work has a certain impact, and the development strategy of private education is assessed on this premise. [ABSTRACT FROM AUTHOR]

In order to solve the problem of information leakage in the process of college students' information security management, this paper proposes a design of college students' security management system based on a symmetric encryption algorithm. The system is based on the principle of the symmetric encryption algorithm and follows the principle of encryption independence to ensure the security and reliability of the system. The general framework of the system is analyzed in detail. Secondly, the security and database of system function modules are designed, and finally, the performance of the system is tested. The results are as follows: the safety management system designed in this paper has obtained satisfactory evaluation in the trial universities, accounting for 93% of the trial population. It is proved that this system has clear authority design, high efficiency, and security. Managers can query students' basic information, students' real-time location, alarm data information, and so on in real time, which can ensure students' safety to the greatest extent. [ABSTRACT FROM AUTHOR]

In this paper, we construct a model of convolutional neural network speech emotion algorithm, analyze the classroom identified by the neural network with a certain degree of confidence together with the school used in the dataset, find the characteristics and rules of teachers' control of classroom emotion nowadays using big data, find the parts of classroom emotion, and design a classroom emotion recognition model based on convolutional neural network speech emotion algorithm according to these characteristics. This paper will investigate the factors and patterns of teachers' emotional control in the classroom. In this paper, the existing neural network is adapted and improved, and some preprocessing is performed on the current dataset to train the network. The network used in this paper is a combination of convolutional neural network (CNN) and recurrent neural network (RNN), which takes advantage of both CNN for feature extraction and RNN for memory capability in the sequence model. This network has a good effect on both object labeling and speech recognition. For the problem of extracting emotion features of whole-sentence speech, we propose an attention mechanism-based emotion recognition algorithm for variable-length speech and design a spatiotemporal attention module for the speech emotion algorithm and a convolutional channel attention module for the CNN network to reduce the contribution of the spatiotemporal data of the speech emotion algorithm and the unimportant parts of the CNN convolutional channel feature data in the subsequent recognition by the attention mechanism. In turn, the weight of core key data and features is increased to improve the model recognition accuracy. [ABSTRACT FROM AUTHOR]

In order to improve the practical effect of the Chinese corpus, this paper combines the semantic mining algorithm to design the Chinese corpus, proposes an ontology adaptive algorithm based on content learning, and conducts in-depth research on the model of the algorithm. Firstly, in view of the heterogeneity of web information structure and the chaotic nature of information organization, this paper proposes a web content extraction method to effectively remove noise information. Moreover, this paper analyzes an ontology-based text content parsing method, which uses the semantic parsing capability of the ontology to improve the semantic parsing capability of the text. Secondly, this paper proposes the BFA algorithm to optimize the BP neural network. The experimental research shows that the Chinese corpus system based on semantic mining proposed in this paper has a good practical effect and meets the actual needs of Chinese teaching and translation. [ABSTRACT FROM AUTHOR]

The forecast of capital demand has the characteristics of uncertainty. There are known and unknown information about the capital demand for regional economic development. In fact, there are also some in between, that is, uncertain. Consumption is the ultimate goal of production and a key link in realizing a virtuous circle of economic development. This paper uses the GM (1, 1) model to compare the predicted value of the test area with the actual value in 5 years, and the loudness is as high as 90%. Under the guidance of the profit model, the regional economic capital demand has a decisive influence on the regional economic development. The predictive analysis model of capital needs is conducive to fully mobilizing the impact of infrastructure construction of all parties and is an important factor affecting economic development. The mathematical model proposed in this paper is helpful for deepening the research on the management of regional economic development and enriching the theoretical system of regional economic development. [ABSTRACT FROM AUTHOR]

In this paper, the long short-term memory with dense neural network (LSTM-DNN) is first introduced to calculate marine drifting trajectory. Based on the Internet of Things technology and the LSTM-DNN algorithm, the marine drifting trajectory model is established. In this model, the information such as wind field, temperature field, ocean current motion field, and target attributes are included, and the influences of the above information on the trajectory model are studied in detail. In order to verify the proposed model, the marine experiments are carried out in the end. The results show that the predicted trajectory data matches well with the experimental trajectory data. By introducing DNN into the algorithm, computational accuracy of drifting trajectory can be significantly improved compared with the conventional LSTM-based prediction model. A detailed comparison of the two algorithms has also been given in the paper. The proposed remote sensing of marine drifting trajectory model can provide a high accurate trajectory prediction and will lead an important guidance in the marine search and rescue work. [ABSTRACT FROM AUTHOR]

In recent years, commercial banks and rural financial institutions in and below the county area have developed rapidly, and the number of outlets has increased rapidly. But for the vast rural areas of the country, the number of outlets is still limited. At present, there are still a large number of areas with zero financial coverage in rural the country. The development of the financial industry in a region not only affects the level of local economic growth but also has a good effect on improving the income level of local residents and changing the way of local economic development. To better develop rural finance and county economy, this paper constructs an artificial neural network model and FA model to predict the rural finance industry, and four indicators are selected to test the performance measurement of the model. The hit rates of the five models are 54.65%, 53.75%, 59.78%, 65.1%, and 73.08%, respectively. The model proposed in this paper has the highest hit rate and can predict rural finance more accurately as well, allowing for clearer changes in county economic and industrial growth. [ABSTRACT FROM AUTHOR]

English writing is considered by English learners to be the portion of English learning with the greatest application, the most thorough understanding, and the most challenging instruction. It automatically detects and corrects (DAC) grammatical faults in English writing, which is critical in the English learning and teaching processes. The goal of this research is to investigate the sequence annotation model and the Seq2Seq NN model based on cyclic NN, and to use these two models to detect grammatical faults in English (EGE). This paper provides an EGE DAC approach based on sequence annotation with the aid of the sequence annotation model developed in this paper. Simultaneously, this work presents an EGE DAC approach based on Seq2Seq that integrates the sequence annotation model. The model is no longer trained on a single form of grammatical error, but rather on all types of errors combined, allowing it to respond to any EGE. This work considers the DAC of grammatical errors with fixed confusion sets, such as prepositions and articles. This model's F1 value for article error correction is 38.05 percent, which is 33.40 percent higher than the F1 value for UIUC article error correction. The F1 value for preposition error correction is 28.89 percent, which is 7.22 percent higher than the F1 value for UIUC preposition error correction. [ABSTRACT FROM AUTHOR]

Functions such as Internet browsing and online shopping have a great impact on people's lives. The footprints of people browsing various information and news on the web page are also increasing year by year. More and more people begin to pay attention to the visual communication in web design. Network information is widely loved by people because of its convenience, quickness, and simplicity. In order to study the visual problem in network information, this paper proposes to use a feature matching algorithm to study the visual information transmission in web design. Using stereo vision and feature matching algorithm, the target recognition function in the visual communication of web design is realized. The content of web design is defined from the perspective of visual beauty and overall harmony. By extracting the feature points in the data, the two-dimensional vision is transformed into a three-dimensional vision. It not only can accurately extract feature points from data but also can convert 2D vision to 3D vision. Finally, in order to optimize the feature matching speed of web design images, the epipolar constraint algorithm is used to optimize the feature matching function. The experimental results show that the content of this paper can truly show the virtual effect in web design and can intuitively upload and transmit simple and rich information content. This paper not only meets the aesthetic needs of the public for web design but also improves the problem of fuzzy information in the process of visual information transmission. [ABSTRACT FROM AUTHOR]

Accurate recommendation of tourist attractions is conducive to improving users' travel efficiency and tourism experience. However, the choice of tourism feature factors and the difference of recommendation algorithm will affect the accuracy of scenic spot recommendation. Aiming at the problems of sparse data, insufficient tourism factors, and low recommendation accuracy in the existing tourism recommendation research, this paper puts forward a scenic spot recommendation method based on microblog data and machine learning by using the characteristics of personalized expression and strong current situation of microblog data and the intelligent prediction function of machine learning, so as to realize accurate and personalized scenic spot recommendation. This paper extracts rich tourism characteristic factors. Typical tourism recommendation algorithms choose tourism characteristic factors from scenic spots, tourists, and other aspects, without considering the travel time, tourism season, and other contextual information of tourists' destination, which can help understand users' tourism preferences from different angles. Aiming at the problem of sparse data and cold start of collaborative filtering recommendation algorithm, this paper introduces deep learning algorithm and combines the proposed multifeature tourism factors to build dynamic scenic spot prediction models (random forest preferred attraction prediction (RFPAP) and neural networks preferred attraction prediction (NNPAP)). The experimental results show that RFPAP and NNPAP methods can overcome the problem of data sparsity and achieve 89.61% and 89.51% accuracy, respectively. RFPAP method is better than NNPAP method and has stronger generalization ability. [ABSTRACT FROM AUTHOR]

Under the background of public health events, the government needs to adopt reasonable support strategies to help cultural enterprises tide over the crisis. However, the original analytic hierarchy process cannot be comprehensive and accurate analysis, resulting in that the government support strategy cannot play a role. Fish swarm algorithm is a comprehensive intelligent algorithm, which can comprehensively analyze the factors affecting the formulation of supporting strategies for cultural enterprises, and has the advantages of simple operation and strong analysis ability. Under this background, this paper puts forward a fish swarm model; this method is a comprehensive intelligent analysis method, which can help the government find the best support strategy from the perspectives of politics, economy, and society. In order to verify the effectiveness of the fish school model, this paper uses MATLAB software for verification; the results show that, in the support strategy classification, selection accuracy, and selection time, fish school model is superior to the analytic hierarchy process. Therefore, the fish swarm model can help the government to better choose supporting policies and help cultural enterprises tide over the crisis and improve the accuracy of formulating supporting strategies for cultural enterprises. [ABSTRACT FROM AUTHOR]

In order to improve the scientificity of sports action analysis, this paper constructs a sports action analysis model based on machine learning based on the greedy algorithm and the bat algorithm. According to the structural characteristics of the model, the structure of the model is reflected in the form of face order, that is, the face neighborhood structure. Moreover, this paper judges the degree of similarity between model faces through the pros and cons of the order and applies it to the structural similarity matrix between models. In addition, this paper establishes corresponding mathematical models for the shape and structure of the model and constructs the shape similarity matrix, the surface neighborhood structure similarity matrix, and the structure similarity matrix between the source model and the target model. Finally, this paper designs and implements CAD model retrieval methods based on greedy algorithm and bat algorithm and designs experiments to compare the performance of the algorithm proposed in this paper with traditional algorithms. The result of the experiment shows that the algorithm proposed in this paper has obvious advantages in sports action analysis compared with the traditional algorithm. [ABSTRACT FROM AUTHOR]

Existing image inpainting schemes generally have the problems of structural disorder and blurred texture details. This is mainly because, in the reconstruction process of the damaged area of the image, it is difficult for the inpainting network to make full use of the information in the nondamaged area to accurately infer the content of the damaged area. Therefore, the paper has proposed an image inpainting network driven by multilevel attention progression mechanism. The proposed network has compressed the high-level features extracted from the full-resolution image into multiscale compact features and then drives the compact features to perform multilevel order according to the scale size. Attention feature progression is to achieve the goal of the full progression of high-level features including structure and details in the network. To further realize fine-grained image inpainting and reconstruction, the paper has also proposed a composite granular discriminator to achieve image inpainting process performing global semantic constraints and nonspecific local dense constraints. The related experimental results in the paper can show that the proposed method can achieve higher quality repair results than state-of-the-art ones. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) prediction of mechanical components is of high research value in the field of prognostics and health management (PHM). However, RUL prediction problems are completely challenging due to the complicacy of bearings' operating environment. In this paper, we transform the vibration acceleration signal collected by sensors into a time-frequency domain matrix through continuous wavelet transform (CWT) and then extract the features of the time-frequency domain matrix through the proposed multiscale residual convolutional neural network (MRCNN), which enables the model to extract more local and global features while constructing more accurate health indicators (HI). In order to highlight the degradation trend of mechanical components, the obtained health indicators are smoothed by exponential moving average (EMA). Finally, linear regression is exploited to predict the RUL of the bearing. Performance evaluations based on the public dataset PRONOSTIA demonstrate the effectiveness of our proposed algorithm, which is superior to existing data-driven algorithms in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

This paper introduces a novel method to control the operation of the active suspension system. In this research, a quarter-dynamic model is used to simulate the vehicle's vibrations. Besides, the sliding mode-PID-integrated algorithm with five state variables is proposed to be used. This is a completely original and novel algorithm. The process of establishing the control algorithm is clearly described. The simulation is performed by the MATLAB software. The results of the paper have shown the advantages of the sliding mode-PID algorithm used in this research. Accordingly, the displacement and acceleration of the sprung mass were significantly reduced when this algorithm was used. The maximum and average values of the displacement of the sprung mass are only 1.31% and 1.29%, respectively, compared with the situation of the vehicle using a passive suspension. Similarly, the value of the acceleration is 6.98% and 2.94%, respectively. In addition, the phenomenon of "chattering" has also been significantly reduced when using this controller. In the future, some more complex algorithms can be proposed. [ABSTRACT FROM AUTHOR]

In the current era of information technology, people's requirements for English translation are gradually increasing, and the need for a computer to understand and translate English language is becoming more urgent. In order to accurately identify phrases, this paper proposes an English translation recognition model based on optimized GLR algorithm, which can improve the accuracy of recognition by locating phrases in the text. [ABSTRACT FROM AUTHOR]

The emergence of portable devices provides great convenience for image acquisition, but the image resolution is too low, which affects the identification and use of the image. Multiframe super-resolution algorithm makes a great contribution to extracting image features, but there is a problem with too much computation. Based on this, this paper proposes a low-resolution image fusion algorithm based on deep learning. Based on the introduction of extracting image features from low-resolution images, this paper proposes to fuse multiple low-resolution images, reduce the time of calculation error per frame, reduce the amount of calculation, extract feature information, and improve the alexnet network model to realize image extraction. The convolutional self-coding fusion network is used to realize image fusion and complete the reconstruction and fusion of low-resolution images. In the simulation analysis of the fusion algorithm, objective indexes such as network structure and loss function are selected to evaluate the effectiveness of the algorithm. This paper reduces the computational complexity of image acquisition and ensures that the amount of calculation can be reduced in each iteration. Analyze the effectiveness of the fusion algorithm, comprehensively evaluate the objective indicators, and verify the advantages of the algorithm through indicators such as gradient change and information entropy. [ABSTRACT FROM AUTHOR]

To improve the effect of English translation teaching, this paper combines the differential evolution algorithm to construct and simulate the interactive English translation teaching mode and analyzes the differential enumeration attack. Moreover, this paper studies the algorithm structure of the LowMC algorithm and evaluates the rationality of the LowMC algorithm round number setting by analyzing the actual attack effect of the key initial round number of a specific linear layer. Additionally, by studying whether the key initial round number can reach the theoretical value under the actual linear layer, the effect of differential evolution algorithm processing teaching information is evaluated. Through expert evaluation, it can be seen that the interactive English translation teaching model based on the differential evolution algorithm proposed in this paper has good effects and can improve the quality of English translation teaching. [ABSTRACT FROM AUTHOR]

In recent years, with changing conditions in global markets and competition between supply chains, organizations have realized that in order to grow and survive in today's turbulent environment, they must make every effort to create and maintain a sustainable competitive advantage. Therefore, in this research, a multiobjective, multilevel, multiperiod, and multicommodity mathematical model is presented. The proposed model has five levels in the direct direction and three levels in the reverse direction. This research is one of the few studies that has considered the sustainable closed-loop supply chain of cellulosic-based products (paper and corrugated board) in the model and has considered it at all levels of the chain. Also, the quality of returned products in the reverse supply chain and its impact on the collection and recycling of products and returning to the supply chain are other contributions of this research. The results show, by increasing the transportation costs, the number of established distribution centers increases to a certain extent and finally remains constant. As it is shown, the reduction of transportation costs to the point of establishing four centers in the recession scenario and five centers in the boom scenario becomes saturated and is no longer able to minimize the number of established centers. [ABSTRACT FROM AUTHOR]

Chinese traditional culture is a typical "moral culture," and traditional morality is the core and essence of culture. However, in recent years, the phenomenon of following the trend of rural construction has been particularly serious, and many villages have lost their original features. To solve the above problems, the genetic algorithm can be used to further explore the traditional culture of urbanization construction. A genetic algorithm is a natural evolutionary process that imitates natural selection and genetic operation in nature to obtain optimal solution, in which genetic operation mainly includes the processes of gene replication, crossover, and mutation. This paper studies the traditional culture of urbanization construction based on the genetic algorithm under the concept of environmental protection. Among the accuracy of urban construction land expansion, in 2018, the accuracy of ant colony algorithm, data mining algorithm, and particle swarm optimization algorithm is 58%, 51.8%, and 56.7%, respectively. The accuracy of this genetic algorithm is as high as 58.8%. It can be seen that the genetic algorithm in this paper has the highest accuracy in the expansion of urban construction land. Therefore, in the process of large-scale urbanization based on the genetic algorithm, we should pay attention to not being separated from traditional culture, not letting farmers lose their regional culture, local culture, and grassroots culture, and protecting the cultural-ecological environment on which these cultures depend. [ABSTRACT FROM AUTHOR]

e-commerce mode shows great modern commercial value. In particular, online shopping has become a fashion and trend for people because of its convenience and rapidness. How to find the information users that need accurately and quickly in the increasing network information and recommend products is a big problem. Although precision marketing was mainly used in e-commerce activities in the past, due to factors such as the technical basis and data analysis ability at that time, there was not enough technical ability and theoretical basis to deeply mine and make use of the existing data. The collaborative filtering algorithm is one of the most widely used and successful recommendation techniques, but it has obvious defects. In this paper, the nearest neighbor collaborative filtering recommendation algorithm based on statistical eigenvalue classification is proposed in the collaborative filtering algorithm. By calculating the similarity between items, the user's rating of unrated items is preliminarily predicted, the nearest neighbor of items is formed, and the classified cluster of items is formed. The matrix is filled by the similarity between related items. The cold treatment problem is solved under the optimization of the ant colony algorithm. In the experiment of the model, the optimization rate for the cold start problem is 87.3%. [ABSTRACT FROM AUTHOR]

The research on artistic psychological intervention to judge emotional fluctuations by extracting emotional features from interactive vocal signals has become a research topic with great potential for development. Based on the interactive vocal music instruction theory of emotion recognition, this paper studies the design of artistic psychological intervention system. This paper uses the vocal music emotion recognition algorithm to first train the interactive recognition network, in which the input is a row vector composed of different vocal music characteristics, and finally recognizes the vocal music of different emotional categories, which solves the problem of low data coupling in the artistic psychological intervention system. Among them, the vocal music emotion recognition experiment based on the interactive recognition network is mainly carried out from six aspects: the number of iterative training, the vocal music instruction rate, the number of emotion recognition signal nodes in the artistic psychological intervention layer, the number of sample sets, different feature combinations, and the number of emotion types. The input data of the system is a training class learning video, and actions and expressions need to be recognized before scoring. In the simulation process, before the completion of the sample indicators is unbalanced, the R language statistical analysis tool is used to balance the existing unbalanced data based on the artificial data synthesis method, and 279 uniformly classified samples are obtained. The 279 ∗ 7 dataset was used for statistical identification of the participants. The experimental results show that under the guidance of four different interactive vocal music, the vocal emotion recognition rate is between 65.85%-91.00%, which promotes the intervention of music therapy on artistic psychological intervention. [ABSTRACT FROM AUTHOR]

With the continuous updating and advancement of artificial intelligence technology, it gradually begins to shine in various industries, especially playing an increasingly important role in incremental music teaching and assisted therapy systems. This study designs artificial intelligence models from the perspectives of attention mechanism, contextual information guidance, and distant dependencies combined with incremental music teaching for the segmentation of MS (multiple sclerosis) lesions and achieves the automatic and accurate segmentation of MS lesions through the multidimensional analysis of multimodal magnetic resonance imaging data, which provides a basis for physicians to quantitatively analyze MS lesions, thus assisting them in the diagnosis and treatment of MS. To address the highly variable characteristics of MS lesion location, size, number, and shape, this paper firstly designs a 3D context-guided module based on Kronecker convolution to integrate lesion information from different fields of view, starting from lesion contextual information capture. Then, a 3D spatial attention module is introduced to enhance the representation of lesion features in MRI images. The experiments in this paper confirm that the context-guided module, cross-dimensional cross-attention module, and multidimensional feature similarity module designed for the characteristics of MS lesions are effective, and the proposed attentional context U-Net and multidimensional cross-attention U-Net have greater advantages in the objective evaluation index of lesion segmentation, while being combined with the incremental music teaching approach to assist treatment, which provides a new idea for the intelligent assisted treatment approach. In this paper, from algorithm design to experimental validation, both in terms of accuracy, the operational difficulty of the experiment, consumption of arithmetic power, and time cost, the unique superiority of the artificial intelligence attention-based combined with incremental music teaching adjunctive therapy system proposed in this paper can be seen in the MS lesion segmentation task. [ABSTRACT FROM AUTHOR]

Objective. On the basis of inheriting the classical aesthetic orientation of Japanese literature, contemporary Japanese literature changes the creative methods of traditional Japanese writers, strives to transcend the national limitations of Japanese literature, and strives to capture literary materials with a "modern feel." Combined with modern western literary techniques to express the "modern self," and integrating popular literature with pure literature, Japanese writers created a "healing" literature that is popular all over the world. There is some value in exploring the correlation between Japanese literature and the influence of psychotherapy. Method. In this paper, an equation diagnosis algorithm based on iterative learning for a class of linear discrete systems is studied, and the existing discrete iterative learning diagnostics are improved. We exchange the order of time domain and iterative domain to achieve the purpose of point-by-point equation diagnosis. On this basis, a virtual influence initial value estimation algorithm with a sliding window is proposed. The impact that has not been diagnosed is estimated and used as the initial value of the impact, so as to reduce the number of iterations and improve the efficiency of equation diagnosis. This paper takes the public and readers as different perspectives, based on the use and satisfaction theory and prospect theory, and uses interviews and questionnaires to study the communication effect of Japanese literature from the three dimensions of cognition, emotion, and behavior. The cognitive part includes the public's understanding and readers' understanding and reading; the emotional part includes the public's and readers' impressions and emotional attitudes; the behavioral part includes the public's reading situation and readers' behavior changes. Results/Discussion. In the survey of loyal readers, although readers' understanding of Japanese literature has improved compared with the general public, it is not high, and their liking and influence on behavior are slightly lower than the preset level. This paper believes that the existing problems of Japanese literature dissemination are mainly concentrated in three aspects: the market is somewhat biased, which narrows the cognitive scope of users; as a cross-cultural literary work, Japanese literature still makes readers feel that it exists; there is some sense of estrangement, which will affect their enjoyment in reading; as a niche interest, readers lack timely communication and sharing objects after reading, so it is difficult to enhance their sense of identity. [ABSTRACT FROM AUTHOR]

In order to improve the analysis ability of three-dimensional reconstruction of ceramic crack computer VR image corners, a ceramic crack computer VR image corner detection based on three-dimensional reconstruction algorithm is proposed. Taking the celadon tea cup with cracked glaze as the research object, multiple-side images of porcelain were extracted. Harris corner detection, image registration, and stitching are used to splice the multiple-side image into a complete unfolded image. In order to determine the generalization ability of the algorithm, 10 cracked porcelain cups with the same type but different glaze color are used for image acquisition and stitching, and the effect of the stitching method proposed in this paper is evaluated. The experimental results show that the image mosaic accuracy is more than 92%, which has high accuracy and effectiveness. Through image graying, binarization and morphological thinning, the crack is displayed in three dimensions, which can provide support for subsequent crack morphological display and topology analysis. The processing flow of this paper improves the automation of image stitching of cracked porcelain, and provides support for image stitching, texture detection, and three-dimensional crack analysis of cracked porcelain and other split porcelain. It has strong innovation and practicability. [ABSTRACT FROM AUTHOR]

At present, e-commerce drives the logistics industry to develop greatly, but at the same time, there is a huge demand in this field, such as lower cost and higher efficiency. Facing the needs of logistics management development, it needs the blessing of intelligent technology, which involves countless fields at present. Intelligent logistics management has become a hot spot at present. What needs to be solved in this respect is how to shorten the transportation distance and save costs. To solve this problem, this paper proposes to introduce the shortest path algorithm. This paper compares the Dijkstra algorithm with the A ∗ algorithm under the background of logistics management and finds that the latter is more suitable for this field with huge amount of information. In order to improve the performance of the A ∗ algorithm, this paper introduces ant colony algorithm, which can better avoid obstacles. Combining these two algorithms, a ∗ -ant colony algorithm is obtained. The algorithm absorbs the advantages of the two algorithms, while maintaining high efficiency and good stability. These characteristics are very satisfying in the field of logistics management. Through the performance test and simulation experiment, it is concluded that the algorithm has excellent optimization ability and can reduce the cost for this field. [ABSTRACT FROM AUTHOR]

In the era of big data, text as an information reserve database is very important, in all walks of life. From humanities research to government decision-making, from precision medicine to quantitative finance, from customer management to marketing, massive text, as one of the most important information carriers, plays an important role everywhere. The text data generated in these practical problems of humanities research, financial industry, marketing, and other fields often has obvious domain characteristics, often containing the professional vocabulary and unique language patterns in these fields and often accompanied by a variety of "noise." Dealing with such texts is a great challenge for the current technical conditions, especially for Chinese texts. A clustering algorithm provides a better solution for text big data information processing. Clustering algorithm is the main body of cluster analysis, K-means algorithm with its implementation principle is simple, low time complexity is widely used in the field of cluster analysis, but its K value needs to be preset, initial clustering center random selection into local optimal solution, other clustering algorithm, such as mean drift clustering, K-means clustering in mining text big data. In view of the problems of the above algorithm, this paper first extracts and analyzes the text big data and then does experiments with the clustering algorithm. Experimental conclusion: by analyzing large-scale text data limited to large-scale and simple data set, the traditional K-means algorithm has low efficiency and reduced accuracy, and the K-means algorithm is susceptible to the influence of initial center and abnormal data. According to the above problems, the K-means cluster analysis algorithm for data sets with large data volumes is analyzed and improved to improve its execution efficiency and accuracy on data sets with large data volume set. Mean shift clustering can be regarded as making many random centers move towards the direction of maximum density gradually, that is, moving their mean centroid continuously according to the probability density of data and finally obtaining multiple maximum density centers. It can also be said that mean shift clustering is a kernel density estimation algorithm. [ABSTRACT FROM AUTHOR]

Target detection is a complex process that is important as an important module in computer vision applications. In particular, in many occasions where the real-time requirements are extremely high, it is very important to achieve fast and accurate detection of targets. But at this stage, there are still many problems in the research on rapid target detection, such as inefficiency and high is the first phase of automatic target recognition (ATR). For the performance of SAR image target detection, this paper proposes a CFAR fast detection algorithm based on Rayleigh. CFAR detection is divided into two steps: horizontal and vertical CFAR detection. The efficiency of parameter estimation is improved by the coincidence of adjacent point reference windows and the distribution characteristics of images. The algorithm in this paper combines the target variance characteristics to reduce the false alarm rate. The experiment was performed on the MSTAR dataset. Fast target detection algorithm based on CFAR and target variance feature has the characteristics of high detection rate, low false alarm, and high speed, and its detection performance is good. The experimental results show that the recognition efficiency of the proposed algorithm is higher than that of the traditional algorithm on different target datasets, the time is shortened by 30%, and the accuracy rate is equal to that of the traditional algorithm. [ABSTRACT FROM AUTHOR]

With the development of computer technology, digital image processing, as an emerging field, has become an indispensable tool in the information society. The action interference effect refers to the phenomenon that an individual reacts to a dangerous object longer than a safe object in the state of preparation for action. Currently, there is no effective anti-jamming technology to recognize dance movements. In order to solve these problems, this paper proposes a dance movement interference suppression algorithm based on contour model and AdaBoost algorithm, which aims to realize the application of gesture-based behavior recognition technology in the field of dance steps and complete the recognition of some dance steps. The method of this paper is to study the contour model based on the image and then propose to use the AdaBoost algorithm to detect body movements and finally use the radio frequency interference suppression algorithm to ensure the smooth operation of the technology. The function of the method is to propose a dance action recognition and anti-interference algorithm based on contour model and AdaBoost algorithm. This article describes the contour model and then uses the AdaBoost algorithm to recognize the action and adjust the action interference effect. The results show that the algorithm proposed in this paper can accurately identify dance movements, with an average recognition accuracy rate of 99.2%. [ABSTRACT FROM AUTHOR]

In view of the problems of low printing efficiency and low accuracy of 3D printers currently developed in the domestic market, based on fused deposition modeling technology, this paper constructs a processing data model with a cylindrical coordinate data structure by 3D printing and processing models according to the cylindrical coordinate slicing rules. The data model can be used to search in adjacent cylinders to obtain the cylindrical coordinate information of the printing model and use the depth-first traversal method of the slicing model to establish a cylindrical coordinate slicing function. Aiming at the problem of "pointcut" of the model cylinder in cylindrical coordinate slice, a high-performance calculation method of machining accuracy based on the cylindrical coordinate slice algorithm is proposed. This algorithm is used to perform 3D printing processing through high-performance computing of cylindrical coordinate slices. After obtaining the intersection points obtained according to the order of the cylindrical coordinate slices of the model, through the automatic generation of the section profile corresponding to the printed model in OpenGL software, the profile direction of the model section can be determined according to the first cylindrical coordinate slice data generated by cutting each contour. The software and hardware of the 3D printer control system are designed, and the actual model printing test is conducted at the same time. By debugging and testing each module of software and hardware, the system is guaranteed to run stably under scientific and reasonable design. Finally, the experimental analysis results show that the algorithm proposed in this paper can effectively reduce the topology time required for cylindrical coordinate slicing, and the operation is simple, stable, and reliable. [ABSTRACT FROM AUTHOR]

Throughout the world, the reliability-based approach to safety design of aircraft systems is quite mature and widely used. However, there are still shortcomings in the reliability-based aircraft system safety analysis method. It cannot dynamically analyze the accident evolution process and lack consideration of the complex situation of multifactor coupling. On the basis of the original aircraft system safety analysis method, this paper innovatively proposes a functional hazard analysis (FHA) method based on the analytic hierarchy process (AHP) and multifactor fuzzy comprehensive assessment (FCA). The purpose is to improve the objectivity and quantification of the FHA method in the safety design of aircraft systems. At the same time, in the terminal airworthiness verification, this paper proposes a repeatable and controllable virtual test flight verification method, which aims to reduce the cost and cycle of the terminal airworthiness verification and expand the coverage of the envelope verification. Finally, combined with the clauses in MIL-HDBK-516B, a case calculation is carried out to verify the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

At present, the reform of electricity marketization has become a new trend in the development of the world's electric power industry and electricity retailers have a huge role in further activating power market competition. Simulation of the bidding behavior of electricity sales companies is helpful to analyze the rationality of the transaction behaviors of electricity sales companies and predict the potential risks of market operation. Considering the complex and diverse behaviors of electricity retailers and the immaturity of existing simulation technologies in the power sales market, this paper proposes a Q -learning algorithm-based simulation method for the bidding behavior of electricity retailers. Firstly, we analyze the factors influencing the bidding behavior of electricity retailers, then, we construct a simulation model of electricity retailers' bidding behavior based on the Q -learning algorithm, and finally, we simulate the bidding process of medium- and long-term trading in provincial power markets by using simultaneous learning from both sides of the generation and consumption. The results show that the behavior of different types of electricity retailers meets the expected level of the model and the simulation method has generalization and extension significance. On this basis, this paper also proposes a bid simulation system architecture based on the bid line simulation model of electricity retailers, which provides a certain reference value for market operation agencies to carry out the corresponding deduction work. [ABSTRACT FROM AUTHOR]

People may quickly employ imagig devices to acquire and use image data thanks to the rapid development of computer networks and communication technologies. However, imaging devices obtain massive data through real-time acquisition, and a large number of invalid images affect the imaging device system's endurance on the one hand while also requiring a significant amount of time for analysis on the other hand, so there is a critical need to find a way to automate the mining of valuable information in the data. In this paper, we propose an intelligent imaging device system, which embeds a target intelligent recognition algorithm, improves the YOLOv3 model by using a method based on depth-separable convolutional blocks and inverse feature fusion structure, and finally achieves fast target detection while improving detection accuracy through the design of distance-based nonextreme suppression and loss function. By preprocessing the images and automatically identifying and saving images containing target animals, the range of the imaging device system equipment can be improved and the workload of researchers searching for target animals in images can be reduced. In this paper, we propose a method for intelligent preservation of contained target images by deploying lightweight target recognition algorithms on edge computing hardware with the intelligence of the intelligent imaging device system as the research goal. After simulation experiments, the use of this method can improve the endurance of the imaging device system and reduce the time of manual processing at a later stage. [ABSTRACT FROM AUTHOR]

Multiobjective flexible workshop scheduling is an important subject to improve resource utilization and production efficiency and enhance the competitiveness of enterprises. As the situation of resource constraints becomes more and more severe, the problem of companies rationally allocating limited resources in production is becoming more and more serious. Today, the manufacturing industry widely adopts advanced manufacturing modes such as computer-integrated manufacturing and intelligent manufacturing, but in these semi-intelligent manufacturing modes with a high degree of uncertainty and a high degree of personnel dependence, it is difficult to adapt to the work of large-scale production. Therefore, suitable clustering algorithms are urgently needed to help solve these problems, and this paper selects a clustering algorithm based on the genetic simulation annealing algorithm. This article is aimed at studying the problem of efficiency improvement in the production process of large-scale manufacturing and at finding a stronger and more effective production mode for the manufacturing industry. Firstly, this paper introduces the basic principles of simulated annealing genetic algorithm and regularized clustering algorithm. These algorithms have excellent performance in searching for global optimal solutions. They can be constantly tested and computed to keep the calculation results close to the global optimal solution. In this paper, the K -means clustering algorithm is used to select the shortest completion time to represent the clustering target. According to the minimum distance principle, the machine, workpiece, and other objects are input into the clustering of the algorithm, and the K -means algorithm will send out the sorting plan. Therefore, a multiobjective flexible job shop scheduling model based on genetic simulated annealing algorithm and clustering algorithm is established. Then, by using hypothetical production data to simulate the operation of the workshop, the scheduling model was applied to conduct a deduction and empirical comparative study. The experimental results showed that the model shortened the completion time of the workpiece by 4.4% and increased the average load rate of the machine by 10%. [ABSTRACT FROM AUTHOR]

Financial and economic series prediction was very important for the study of economic time series. Because the traditional time series analysis method was mainly suitable for stable data and cannot be applied to unstable economic variables, this paper proposed a knowledge model of financial and economic series prediction time slot algorithm, which provided the basis for solving unstable financial data. Firstly, based on the analysis of relevant financial and economic series prediction methods at home and abroad, this paper applied the time slot allocation algorithm to economic series analysis and prediction and designed a path level time slot allocation algorithm, which introduced a fixed time slot to shorten the time required to complete the path level time slot allocation. Secondly, this paper abstracted the calculation of slot allocation as a convex optimization problem and proposed a round slot allocation algorithm based on constraints and optimal solution. The algorithm can improve the end-to-end throughput and the effective utilization of slots. Finally, the model proposed in this paper was used to predict the indicators of relevant industries, and the simulation results were compared with the standard data. The experimental results showed that the financial and economic series prediction system based on time slot allocation algorithm proposed in this paper can effectively improve the accuracy of financial and economic series prediction, which verified the effectiveness and feasibility of this model. [ABSTRACT FROM AUTHOR]

Computer vision-based motion target detection and tracking, which is widely used in video surveillance, human-computer interaction, range interpretation, and other fields, is one of the current research hotspots in the field of computer vision. In engineering scenarios, the two are inseparable and need to work together to accomplish specific tasks. The related research is progressing rapidly, but there is still room for improving its timeliness, accuracy, and automation. In this paper, we summarize and classify some classical target detection methods, analyze the basic principles of convolutional neural networks, and analyze the classical detection algorithms based on region suggestion and deep regression networks. After that, we improve the SSD algorithm for the shortage of low-level feature convolution layers, which has insufficient feature extraction and leads to poor detection of small targets. For the motion target tracking problem, this paper studies the motion target tracking method based on support vector machine and proposes the tracking method of support vector regression and the corresponding online support vector regression solution method based on the analysis of support vector tracking method and structural support vector tracking method. In this paper, we propose a tracking method that fuses structural support vector machines and correlation filtering. The structure is based on the idea of Inception, which adds and replaces some feature convolution layers of the original network while maintaining the original lightweight backbone. The final experiments on the VOC data set demonstrate that the improved algorithm improves the average detection accuracy by 2.6% compared to the original algorithm and basically maintains the real-time speed as well. Experimental simulations on a subset of VOC data (human set) show a significant improvement in AP values and more effective and reliable detection tracking of moving targets. The stability and accuracy of motion target detection and tracking are improved by setting parameters, such as confidence level; the effectiveness and continuity of detection and tracking are judged by setting the interframe centroid distance. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic segmentation and weighted comprehensive matching algorithm based on neural networks for cross-media piano performance audio recognition and retrieval. The 3D convolutional neural network process is separated to compress the network parameters and improve the computational speed. Skip connection and layer-wise learning rate solve the problem that the separated network is challenging to train. The piano performance audio recognition is facilitated by shuffle operation. In pattern recognition, music retrieval algorithms are gaining more and more attention due to their ease of implementation and efficiency. However, the problems of imprecise dynamic note segmentation and inconsistent matching templates directly affect the accuracy of the MIR algorithm. We propose a dynamic threshold-based segmentation and weighted comprehensive matching algorithm to solve these problems. The amplitude difference step is dynamically set, and the notes are segmented according to the changing threshold to improve the accuracy of note segmentation. A standard score frequency is used to transform the pitch template to achieve input normalization to enhance the accuracy of matching. Direct matching and DTW matching are fused to improve the adaptability and robustness of the algorithm. Finally, the effectiveness of the method is experimentally demonstrated. This paper implements the data collection and processing, audio recognition, and retrieval algorithm for cross-media piano performance big data through three main modules: the collection, processing, and storage module of cross-media piano performance big data, the building module of audio recognition of cross-media piano performance big data, and the dynamic precision module of cross-media piano performance big data. [ABSTRACT FROM AUTHOR]

In order to improve the psychological teaching quality of college students, this paper combines image denoising algorithm and data mining algorithm to construct a psychological teaching quality evaluation system. The denoising algorithm is mainly used to identify the behavior and expressions of the students so as to explore the psychological state of the students. Moreover, this paper combines the data mining algorithm to carry out the quantitative analysis of students' psychological state and analyzes and introduces the calibration principle of Dirckx method in detail to improve it and then forms a new calibration method. In addition, based on the arctangent function, this paper proposes a new two-frame random phase-shift fringe image phase shift extraction algorithm. The research results show that the image denoising algorithm and data mining proposed in this paper have a good effect in the evaluation method of psychological teaching quality and can play a good role in the improvement of students' mental health. [ABSTRACT FROM AUTHOR]

With the rapid development of electronic information technology and Internet technology, people's ability to generate and collect data is also increasing. With the rapid development of international exchanges, the number of literary translation texts has also increased dramatically. The information contained in the huge data of literary translation texts is huge, but these data are disorganized at this stage. Therefore, the classification of literary translation texts has become the key to efficient management of translated text information. Literary translation text classification is the process of classifying a given text as one or several of several predetermined text categories according to the content of the text. As one of the key steps in processing huge amounts of text data, text classification is generally regarded as the orderly organization of text sets, that is, grouping similar and related texts together. In this way, the problem of information clutter can be solved to a greater extent, and the efficiency of users' discovery, filtering, and analysis of text information resources can be effectively improved. At present, the basis of text classification is mainly based on the characteristics of words in the article, to analyze the correlation between words and categories. This approach ignores information such as word order and collocations in literary translation texts. To solve this problem, this paper introduces the distributed incremental sequence data mining algorithm into the classification of literary translation texts. This method can fully mine the features of syntactic order and other features based on considering the words and phrasing characteristics of articles in different application fields. The text classification effect is strengthened by discovering more effective information. The experimental results show that the method can improve the classification performance of literary translation texts. [ABSTRACT FROM AUTHOR]

The speech enhancement effect of traditional deep learning algorithms is not ideal under low signal-to-noise ratios (SNR). Skip connections-deep neural network (Skip-DNN) improves the traditional deep neural network (DNN) by adding skip connections between each layer of the neural network to solve the degradation problem of DNN. In this paper, the Multiresolution Cochleagram (MRCG) features in the gammachirp transform domain are denoised to obtain the improved MRCG (I-MRCG). The noise reduction method adopts the Minimum Mean-Square Error Short-Time Spectral Amplitude Estimator (MMSE-STSA) and takes I-MRCG as the input feature and Skip-DNN as the training network to improve the speech enhancement effect of the model. This paper also proposes an improved source-to-distortion ratio (SDR) loss function. When the loss function uses the improved SDR, it will improve the performance of Skip-DNN speech enhancement model. The experiments in this paper are performed on the Edinburgh dataset. When using I-MRCG as the input feature of Skip-DNN, the average perceptual evaluation of speech quality (PESQ) is 2.9137, and the average short-time objective intelligibility (STOI) is 0.8515. Compared with MRCG as Skip-DNN input features, the improvements are 0.91% and 0.71%, respectively. When the improved SDR is used as the loss function of the speech model, the average PESQ is 2.9699 and the average STOI is 0.8547. Compared with other loss functions, the improved SDR has a better enhancement effect when used as the loss function of the speech enhancement model. [ABSTRACT FROM AUTHOR]

Ocean economy is the sum of various industrial activities for developing, utilizing, and protecting the ocean, as well as the activities associated with it. As the globe gradually pays more attention to ocean economic growth, China, as a significant maritime country, has begun to pay attention to the sector and put up ocean economic development policies. The marine economy has great potential, and the development of the marine economy is of great significance to the economic development of China. The Fujian Province is located at the junction of the Belt and Road and the Yangtze River Economic Belt, with a unique geographical location and natural resource advantages. Analyzing the state of the ocean economy in the Fujian Province, identifying challenges, and developing ocean economic development strategies to promote the long-term growth of the ocean economy in the Fujian Province has become a pressing issue that must be addressed. Because there is currently limited research on ocean economic systems and a paucity of ocean economic data, this paper uses a deep neural network to forecast the ocean production output value of the Fujian Province. The experimental results show that the model proposed in this paper can predict the ocean invention value of the Fujian Province well, which confirms the effectiveness of the model. [ABSTRACT FROM AUTHOR]

On the basis of the analysis of the evolution dynamics and the process of smart tourism service, this paper constructs the evolutionary game model of smart tourism service and reveals the evolution mechanism of smart tourism service based on the network platform. Based on the strategic main line of "advantages," it proposes the design ideas and overall framework of the smart tourism service model based on the network platform, including the smart tourism information interactive service model, the element collaborative service model, and the value cocreation service model. The comparison of recommendation results shows that the recommendation error of the genetically improved generalized regression neural network algorithm is reduced, and the recommendation accuracy is better than that of the unimproved generalized regression neural network algorithm. In the recommendation scenario of click-through rate recommendation, the existing recommendation models are difficult to meet the functions of memory and generalization at the same time and cannot fully mine and combine low-level features, and the model parameters of the deep learning model are difficult to learn under the high-dimensional sparse data set of the recommendation system. To solve the problem of generalization, this paper proposes a deep CTR recommendation model based on the gradient boosting tree and factorization machine. It can fully mine low-level feature information and automatically realize low-level feature combination, which can better learn model parameters on high-dimensional sparse data sets, and the recommendation results are no longer overgeneralized. In this paper, simulation experiments are carried out on the data set, and the related recommendation models are compared. The experimental results show that the model proposed in this paper achieves better results in both the AUC (area under ROC curve) evaluation index and the cross-entropy evaluation index. [ABSTRACT FROM AUTHOR]

Stimuli from the road surface cause car vibrations. The suspension system ensures the vehicle's stability and smoothness when moving. In this research, the author introduces the use of active suspension with a hydraulic actuator to improve the comfort of cars. Car oscillations are described based on a quarter-dynamics model with five state variables. Besides, the nonlinear system's SM (sliding mode) control algorithm is used to control the system. The SM algorithm's parameters are optimally selected using the multiple loop algorithm. The multiple loop algorithm allows the values to vary within a predefined limit and provides an optimal parameter based on the vehicle's vibration conditions. This is a new point in the paper compared to previous studies. The output of the simulation problem includes displacements and accelerations of both the sprung and unsprung masses. In addition, the change of dynamics force at the wheel is also considered when evaluating the interaction between the wheel and the road surface. According to the paper's results, the vehicle body's acceleration and displacement values are strongly reduced when using the SM algorithm, compared with cars with only traditional suspension. In addition, the change in dynamics force at the wheel is not significant when using this algorithm. As a result, the car's smoothness has been improved while the vehicle's stability has not been lost. The phenomenon of "chattering" still occurs in some cases; however, its effect is negligible. [ABSTRACT FROM AUTHOR]

Mechanical properties are important indicators for evaluating the quality of strips. This paper proposes a mechanical performance prediction model based on the Gray Wolf Optimization (GWO) algorithm and the Extreme Learning Machine (ELM) algorithm. In the modeling process, GWO is used to determine the optimal weights and deviations of ELM and experiments are used to determine the model's key parameters. The model effectively avoids manual intervention and significantly improves aluminum alloy strips' mechanical property prediction accuracy. This paper uses processed data from the aluminum alloy production plant of Shandong Nanshan Aluminum Co., Ltd. as experimental data. When the prediction deviation is controlled within ±10%, the GWO-ELM model can achieve a correct rate of 100% for tensile strength, 97.5% for yield strength, and 77.5% for elongation on the test set. The RMSE of the tensile strength, yield strength, and elongation of the GWO-ELM model was 5.365, 11.881, and 1.268, respectively. The experimental results show that the GWO-ELM model has higher accuracy and stability in predicting aluminum alloy strips' tensile strength, yield strength, and elongation. The GWO-ELM model effectively avoids the defects of the traditional model. It has a special guiding significance for producing aluminum alloy strips. [ABSTRACT FROM AUTHOR]