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The quality of collision detection algorithm directly affects the performance of the whole simulation system. To address the low efficiency and low accuracy in detecting the collisions of flexible cloths in virtual environments, this paper proposes an oriented bounding box (OBB) algorithm with a simplified model, tree structure for a root-node double bounding box, and continuous collision detection algorithm incorporating an OpenNN-based neural network optimization. First, for objects interacting with the cloths with more complex modeling, the model is simplified with a surface simplification algorithm based on the quadric error metrics, and the simplified model is used to construct an OBB. Second, a bounding box technique commonly used for collision detection is improved, and a root-node double bounding box algorithm is proposed to reduce the construction time for the bounding box. Finally, neural networks are used to optimize the continuous collision detection algorithm, as neural networks can efficiently process large amounts of data and remove disjoint collision pairs. An experiment shows that the construction of an OBB using the simplified model is almost identical to that of the original model, but the taken to construct the OBB is reduced by a factor of approximately 2.7. For the same cloth, it takes 5.51%–11.32% less time to run the root-node double bounding box algorithm than the traditional-hybrid bounding box algorithm. With an average removal rate nearly identical to that of the traditional filtering method, the elapsed time is reduced by 7%–11% by using the continuous collision detection algorithm based on an OpenNN neural network optimization. The simulation results are realistic and in line with the requirements for real-time cloth simulations. [ABSTRACT FROM AUTHOR]

In the face of the batch, dynamic access data, or the flow of data that continuous changes over time, the traditional support vector machine algorithm cannot dynamically adjust the previous classification model. To overcome this shortcoming, the incremental support vector machine (ISVM) algorithm is proposed. However, many incremental support vector algorithms still have shortcomings such as low efficiency, memory limitation, and poor generalization. This paper puts forward the new ISVM algorithm, HDFC-ISVM ∗ algorithm, based on the high-dimensional distance and forgetting characteristics. This paper firstly proposes the original HDFC-ISVM algorithm that first learns the distribution characteristics of the samples according to the distance between the samples and the normative hyperplane. Then, it introduces the forgetting factor. In the incremental learning process, the classifier gradually accumulates the spatial distribution knowledge of samples, eliminates the samples that have no contributions to the classifier, and selectively forgets some useless samples according to the forgetting factor, which overcomes the shortcomings such as low efficiency and poor accuracy of some algorithms. But, the original HDFC-ISVM algorithm is sensitive to parameters, and different settings of the parameters have a great impact on the final classification accuracy of the algorithm. Therefore, on the basis of the original algorithm, an improved algorithm HDFC-ISVM ∗ based on the adjustments to the initialization strategy and updating rules of the forgetting factor is proposed. The initialization strategy and updating rules of the forgetting factor are adjusted to adapt datasets with different distributions in this improved algorithm. The rationality of the improved strategy about the forgetting factor is discussed theoretically. At the same time, the proposed algorithm has better classification accuracy, classification efficiency, and better generalization ability than other algorithms, which is verified by experiments. [ABSTRACT FROM AUTHOR]

Data integrity verification means that the data in the cloud are uploaded by the user. In addition to the user's own update of the data, any external factors including the cloud service provider's data are destroyed, tampered with, and lost, and the data are not updated in a timely manner. Any inconsistencies in the actual data required can be detected by the user. This article aims to study cloud data integrity verification algorithms based on data mining and accounting informatization. This article proposes data mining technology, accounting information to help business managers assist management work. The article uses Company H as an example to illustrate the accounting information system and proposes a new information management strategy based on it. The CBF algorithm and data integrity verification algorithm are used to study the cloud storage data integrity verification protocol, the cloud data integrity verification model is constructed, the data program flow design is analyzed, and the time-consuming operation of file data insertion is analyzed. The experiment in this paper uses 16 standard mathematical calculation formulas to strengthen the analysis. The results show that the study of cloud data integrity verification algorithms based on data mining and accounting information is beneficial to the integrity and protection of data. When the number of documents added increases from 0 to 400, the document agreement shows an upward trend, and the agreement in this paper basically fluctuates between 10 and 80. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet and wireless communications, smart multi-sensor networks are gradually being used in various fields, and the Internet of vehicles technology combined with smart multi-sensor network came into being. However, although the Internet of vehicles technology has greatly facilitated people's travel in traffic, its problems in security and privacy protection have also been amplified and became a major threat to the Internet of vehicles technology. Consistency algorithms are an important solution to the trust problem and are a prominent representative of distributed algorithms. In order to solve the problem of security and privacy protection of smart multi-sensors in car networking technology, in this paper, consistent algorithm and group signature verification technology are used to study the security and privacy protection performance of the Internet of vehicles in a connected random network composed of 300 nodes through artificially synthesized simulation data. Using the method in this paper, collect and analyze the experimental data of the number of messages, verification delay, traffic load, and message loss rate of randomly generated vehicles within 200 seconds and evaluate the performance of privacy protection in the maximum consistent algorithm; the results show that the verification delay will increase with the increase of the number of messages, and the more traffic load, the higher the message loss and verification delay, and the maximum consistent algorithm has good privacy protection performance. In terms of data, the RSU-based auxiliary scheme consumes 25.59% of the communication of the KPI-based signature scheme and 25.64% of the swarm scheme. [ABSTRACT FROM AUTHOR]

While facing complex surroundings, robots need to identify the same intention which is expressed in different ways. In order to solve the problem of assisting robots to get a better intention understanding, a self-tuning multimodal fusion algorithm is put forward in this paper, which is not restricted by the expressions of interacting participants and environment. The multimodal fusion algorithm can be transferred to different application platforms. Robots can own the understanding competence and adapt new tasks by changing the content of the robot knowledge base. Compared with other multimodal fusion algorithms, this paper attempts to transfer the basic structure of feed-forward neural networks on discrete sets, which has strengthened the consistency and perfect the complementary relations between multiple mode, and has realized the simultaneous operation of fusion operator's self-tuning and intention search. There are three kinds of modes selected in the paper: speech, gesture, and scene objects, where the single modal classifiers are trained separately. This method conducted a human-computer interaction experiment on the bionic robot Pepper platform, which proved that the method can effectively improve the accuracy and robustness of robots in aspects of understanding human intentions, and reduce the uncertainty about intention judgment in a single modal interaction. [ABSTRACT FROM AUTHOR]

Task scheduling in Cloud Computing paradigm poses new challenges for cloud provider as heterogeneous, diversified tasks arrived on to cloud console. To schedule these type of tasks efficiently on to virtual resources in cloud paradigm, an effective scheduler is needed, which precisely maps tasks to virtual machines by considering priorities of both tasks and VMs. Existing scheduling algorithms failed to map tasks precisely to virtual resources due to high dynamic nature in cloud environment which leads to increase of makespan and SLA violations will be increased. In this paper, authors proposed a task-scheduling mechanism, which considers task priorities and VMs. To model this scheduling paradigm we have chosen whale optimization through which our scheduler will take decisions for scheduling tasks precisely onto virtual resources in cloud environment. Entire simulation was carried out on CloudSim. Initially we have chosen random generated workload to run simulation and after that, we have considered a real-time workload named as BigDataBench and ran our simulation. Finally, we compared our proposed work with classical baseline mechanisms. From simulations we observed that proposed whale scheduler improved makespan for PSO, ACO, GA, and W-schedulers by 20.07%, 17.55%, 19.9%, and 6.35%, respectively, and 17.3%, 17.86%, 17.64%, and 5.93%, respectively, for BigDataBench workloads. SLA violations improved over PSO, ACO, GA, and W-Scheduler by 56.76%, 42.17%, 35.29%, and 24.53%, respectively, and 63.42%, 23.33%, 55.51%, and 40.1%, respectively, for BigDataBench workloads. From extensive simulation results, our proposed scheduler using whale optimization approach minimizes makespan and SLA violations to a great extent. [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]

With the rapid development of computer technology and electronic information technology, the sports training system no longer depends on the traditional algorithm for operation support, and various advanced posture algorithms are emerging. At the same time, it also further optimizes the intelligence and accuracy of the sports training algorithm. As an advanced algorithm combined with virtual reality technology, human posture estimation algorithm plays an obvious role in optimizing the effect of sports training. This paper will design a motion training system based on the optimized and improved human posture trajectory algorithm, use the depth image correlation theory to solve the problem of non-Gaussian noise crosstalk in the depth image of the traditional human posture algorithm in principle, improve the accurate feature extraction of the depth image by the algorithm, and solve the problem of human feature redundancy, so as to further improve the accuracy of the establishment of a single human model; on the problem of multi-person posture estimation algorithm, this paper proposes a high-resolution multi-person posture high-precision network model and adds the focus mechanism. Based on this, this paper realizes the high-precision and high-speed modeling of multi-person posture, so as to provide an accurate model for the multi-person function of sports training system and improve the efficiency of the algorithm. In the experimental part, this paper takes tennis as a typical case to design the sports training system and experiments based on the system designed in this paper. The experimental results show that the system under the proposed algorithm has obvious advantages in accuracy and training effect. [ABSTRACT FROM AUTHOR]

In order to effectively solve the problem that the radar detection system is difficult to detect the "low, small, slow" UAV, the high-performance computing early warning neural network is used to recognize the air UAV in real time and extract the target category and image space location information; the PSO algorithm is used to optimize the parameters of the anti-UAV to ensure that the anti-UAV not only relies on factors but also fully combines the dependence of the visual field factor to quickly obtain the optimal solution through analyzing the high-performance computing early warning neural network in this paper. This algorithm is used to initialize the anti-UAV resources and improve the global optimization capability of the algorithm proposed in this paper. Finally, the experimental results show that the proposed PSO algorithm has better high-performance computing early warning performance to meet the actual needs of network high-performance computing early-warning neural networks. [ABSTRACT FROM AUTHOR]

Attribute reduction is a popular topic in research on rough sets. In the classical model, much progress has been made in the study of the attribute reduction of indiscernibility and discernibility relations. To enhance the fault tolerance of the model, concepts of both indiscernibility and discernibility relations involving uncertain or imprecise information are proposed in this paper. The attribute reductions of the relative β -indiscernibility relation and relative β -discernibility relation and their algorithms are proposed. When the precision satisfies certain conditions, the reduction of two relation concepts can be converted into a positive region reduction. Therefore, the discernibility matrix is used to construct the reductions of the two relation concepts and the positive region. Furthermore, the corresponding algorithm of the relative β -indiscernibility (discernibility) relation reduction can be optimized when the precision is greater than 0.5, and this is used to develop an optimization algorithm that constructs the discernibility matrix more efficiently. Experiments show the feasibility of the two relation reduction algorithms. More importantly, the reduction algorithms of the two relations and the optimization algorithm are compared to demonstrate the feasibility of the optimization algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

Hierarchical analysis refers to the method of dividing the elements related to the result into different levels such as goals, methods, and processes and then performing quantitative and qualitative analysis according to different levels. It can hierarchize complex methods and make the solution process more scientific and reasonable. High-performance computing refers to computing systems and environments that usually use many processors or several computers organized in a cluster. In fact, there are many different types of high-performance computing systems on the market, most of which are used in conjunction with the network. For example, Ford built an online market with high-performance computers and connected to its more than 30,000 suppliers through the network. This kind of online procurement can not only lower prices and reduce procurement costs, but also shorten the procurement time. Ford estimates that this can save approximately $8 billion in procurement costs. In addition, fields such as manufacturing, logistics, and market research are also areas where high-performance computers show their talents. This article aims to study the analysis and evaluation of multitone piano transcription that rely on hierarchical analysis high-performance computing algorithms and hopes to use high-performance computing algorithms to make piano recordings more harmonious. This paper proposes a differential variable-multivariate curve resolution for accurate analysis of GC-MS overlapping peaks, mainly to solve the problem of inaccurate results caused by the difficulty in determining the components during the analysis of overlapping peaks; carrying out matrix calculation on the piano tone characteristic matrix, it realizes the electronic synthesis of the 25th harmonic of the piano tone. The experimental results in this paper show that when the number of Gaussians in the recording process becomes 5, the gap between the sound pressure amplitude and the sound pressure phase is significantly reduced; when the number of Gaussians is 10, the value of the sound pressure phase has an overshoot phenomenon; when the number of Gaussians is 15, the difference between the sound pressure amplitude and the sound pressure phase is the smallest, indicating that the error at this time is the smallest, and it is the most ideal recording environment. [ABSTRACT FROM AUTHOR]

Venture capital firms are always faced with insufficient information and insufficient time when evaluating whether startups are worth investing in. This paper focuses on how to combine the public information of startups with the decision tree algorithm to assist investors in project screening. By extracting the public information of 1104 AI and big data companies from January 2016 to June 2017 and the financing progress in the following 18 months, this paper finds that: the six indicators of having a working background in well-known companies, being reported by well-known media, having patents and being invested by excellent institutions, working experience is highly related to this venture, and having well-known financing consultants can effectively help investors screen out projects that can obtain sustainable financing, that is, high potential projects. Considering the problem of data availability in the real world, combined with industry experience, this paper makes a more detailed variable mining for semistructured information, such as the team resume of start-ups, and selects a decision tree algorithm that is insensitive to missing values and has strong interpretability to amplify the value of fragmented information. Finally, by the improvement of the algorithm, a project screening model that can meet the needs of investment practice is designed, and the fusion mode of public information and private information is discussed, which has a more complete guiding significance for optimizing investment work. [ABSTRACT FROM AUTHOR]

With the vigorous development of China's sports industry, the rules and number of events are increasing, and the competition pressure on the playground is also increasing. The increase of competition pressure will bring many negative effects to athletes. In order to relieve the pressure of athletes in sports competition and eliminate the negative significance of pressure to athletes, this paper mainly introduces the clustering algorithm of sports source and competition. The clustering algorithm uses the similarity of attributes between data objects to calculate the clustering structure of fractional clustering. In this paper, the original data of sports competition pressure are obtained through the questionnaire survey, using clustering and recommendation algorithms to calculate and analyze the original data, the data utilization rate is as high as 98%, and the analysis efficiency is as high as 97%. Dividing athletes into three categories, the magnitude and source of stress are analyzed, respectively, and application methods are recommended according to their respective stress distributions, so as to assist psychologists in the diagnosis, and the corresponding height is 80%; this enables athletes to receive good counseling advice and remain mentally healthy. [ABSTRACT FROM AUTHOR]

In this paper, a single English text is taken as the research object, and the automatic extraction method of text summary is studied using data-driven method. This paper takes a single text as the research object, establishes the connection relationship between article sentences, and proposes a method of automatic extraction of text summary based on graph model and topic model. The method combines the text graph model, complex network theory, and LDA topic model to construct a sentence synthesis scoring function to calculate the text single-sentence weights and output the sentences within the text threshold in descending order as text summaries. The algorithm improves the readability of the text summary while providing enough information for the text summary. In this paper, we propose a BERT-based topic-aware text summarization model based on a neural topic model. The approach uses the potential topic embedding representation encoded by the neural topic model to match with the embedding representation of BERT to guide topic generation to meet the requirements of semantic representation of text and explores topic inference and summary generation jointly in an end-to-end manner through the transformer architecture to capture semantic features while modelling long-range dependencies by a self-attentive mechanism. In this paper, we propose improvements based on pretrained models on both extractive and generative algorithms, making them enhanced for global information memory. Combining the advantages of both algorithms, a new joint model is proposed, which makes it possible to generate summaries that are more consistent with the original topic and have a reduced repetition rate for evenly distributed article information. Comparative experiments were conducted on several datasets and small uniformly distributed private datasets were constructed. In several comparative experiments, the evaluation metrics were improved by up to 2.5 percentage points, proving the effectiveness of the method, and a prototype system for an automatic abstract generation was built to demonstrate the results. [ABSTRACT FROM AUTHOR]

There are few studies for the classification detection and dynamic multitarget detection of the targets in front of vehicles. In order to solve this problem, a dynamic multitarget detection algorithm is proposed. First, a dynamic multitarget detection with displacement at any time is suggested; secondly, a multitarget detection algorithm based on improved You Only Look Once version 3 (YOLOv3) is proposed for the detection of multitarget high probability risk events in front of the vehicle. The YOLOv3 algorithm model is a lightweight backbone network that uses embedded real-time detection technologies. In this paper, we use a lightweight Mobilenetv2 to replace Darknet-53 for feature extraction. Moreover, an optimizer is used for multiobjective feature extraction, group normalization, and multiobjective feature extraction. The results show that in comparison with the original YOLOv3 algorithm, the detection leakage rate of the improved YOLOv3 multitarget detection algorithm is less than 5%, and the amount of model parameters in this paper is reduced by 95% as compared to the traditional data and CPU intertime is reduced to 78%. [ABSTRACT FROM AUTHOR]

The mobile screening of digital movies can fully take into account the viewing experience of scattered areas. As a public cultural service system, it is playing a pivotal role. The consistency of the film screened with the tastes of the audience in the service area of the screening team has largely affected the quality of rural public culture services. Traditional recommendation algorithms directly use raw data to make predictions, leading to deviations in predictions. This article draws on the principles of immune recognition, clone selection, immune mutation, and self-adaptation of the artificial immune system to improve the recommendation effect of single-type data, the recommendation effect of sparse data, and the recommendation effect of project cold start problems and discusses the recommendation based on artificial immunity. For the single type of data, there are only positive samples, which leads to the problem that the training results are all positive. This paper proposes a single-class recommendation algorithm based on artificial immunity. The algorithm uses the positive and negative sample addition method proposed in this paper to add positive and negative samples related to user selection, so as to effectively solve the problem of difficult definition of data negative samples. Then, the artificial immune network is used to cluster the users of various activities, reduce the size of the candidate neighbor set, calculate the user's nearest neighbor set, and give recommendations. [ABSTRACT FROM AUTHOR]

In this paper, we consider the Vennia algorithm to conduct in-depth research and analysis on the traceability of dual-chain blockchain agricultural products' E-commerce information. This paper adds a collaborative verification module to the traceability system and carries out a detailed design of information storage, traceability consensus algorithm, and smart contract for agricultural products according to the characteristics of the agricultural products supply chain, among which the collaborative verification module adopts dynamic data storage technology; the ConsiderVinia consensus algorithm is improved by introducing the way of integral penalty mechanism to ensure the block data validity. After a comparative study of the features and differences of the three major blockchain technology platforms, this paper selects the super ledger to implement the agricultural traceability system based on blockchain technology, introduces the partitioning and credit mechanism into the ConsiderVinia algorithm, and elaborates the improvement process of the algorithm. The improved algorithm reduces the malicious behavior of nodes and maintains the system security through a credit mechanism while maintaining the consistency of blockchain. In the event of a transaction dispute, the third-party platform will determine the party at fault based on the transaction records and other evidence and make corresponding punishments and compensations. The experiment proves that the algorithm proposed in this paper can reduce the amount of network data transmission in the process of node consensus, which is better than the ConsiderVinia algorithm in terms of both throughput and latency, improves the consensus efficiency, and alleviates the communication bottleneck caused by the increase of users in blockchain applications, and the solution of applying the blockchain technology to the agricultural products traceability system is practical and feasible. The blockchain-based agricultural products information traceability system solves the problems of information asymmetry, difficult sharing, easy tampering, and storage centralization in the traditional IoT-based agricultural products traceability system and truly realizes the credible and reliable traceability of the whole chain of agricultural products information. The research content and results of this paper have certain theoretical and practical values. [ABSTRACT FROM AUTHOR]

Improved SVM algorithm improves the efficiency of College English teaching effect evaluation and meets the requirements of College English teaching evaluation. Based on the relevant theories, this paper constructs the evaluation index system with teachers and students as the main body and takes the questionnaire survey results as the input samples of the LSSVM algorithm. Compared with the evaluation accuracy of an optimized BP neural network and the category weighted gray target decision-making method, the results show that the evaluation accuracy of optimized LSSVM algorithm is 96.26%. Taking SIT as an example, this paper uses the optimized LSSVM algorithm to evaluate its teaching effect and obtains that teachers' literature and teaching contents are important factors to improve the effect of English teaching. Therefore, this paper introduces the intelligent voice system to optimize the English teaching design of SIT. The teaching design is optimized from the dimensions of teaching objectives, learning situation, teaching content, teaching media and curriculum materials, and teaching procedures. [ABSTRACT FROM AUTHOR]

In order to improve the copyright protection effect of literary works and improve the healthy dissemination of digitized literary works, this paper combines data mining technology to conduct research on the copyright protection of literary works and constructs a literary copyright protection system. In digital literary works, watermarking algorithms can be used to watermark the characteristics of literary works to obtain digital literary works that have been watermarked. After that, this paper can combine data mining algorithms to perform text feature recognition and feature classification and improve the copyright protection effect of literary works. The experimental research results verify that the effect of the copyright protection system of literary works based on data mining algorithms is very good. [ABSTRACT FROM AUTHOR]

Synthetic aperture Radar (SAR) uses the relative movement of the Radar and the target to pick up echoes of the detected area and image it. In contrast to optical imaging, SAR imaging systems are not affected by weather and time and can detect targets in harsh conditions. Therefore, the SAR image has important application value in military and civilian purposes. This paper introduces the classification of Gaussian process. Gaussian process classification is a probabilistic classification algorithm based on Bass frame. This is a complete probability expression. Based on Gaussian process and SAR data, Gaussian process classification algorithm for SAR images is studied in this paper. In this paper, we introduce the basic principle of Gaussian process, briefly analyze the basic theory of classification and the characteristics of SAR images, provide the evaluation index system of image classification, and give the SAR classification model of Gaussian process. Taking Laplace approximation as an example, several classification algorithms are introduced directly. Based on the two classifications, we propose an indirect multipurpose classification method and a multifunction classification method for two-pair two-Gaussian processes. The SAR image algorithm based on the two categories is relatively simple and achieves certain results. [ABSTRACT FROM AUTHOR]

With the improvement of living standards, more and more people are pursuing personalized routes. This paper uses personalized mining of interest points of ethnic minority tourism demand groups, extracts customer data features in social networks, and constructs data features of interesting topic factors, geographic location factors, and user access frequency factors, using LDA topic models and matrix decomposition models to perform feature vectorization processing on user sign-in records and build deep learning recommendation model (DLM). Using this model to compare with the traditional recommendation model and the recommendation model of a single data feature module, the experimental results show the following: (1) The fitting error of DLM recommendation results is significantly reduced, and its recommendation accuracy rate is 50% higher than that of traditional recommendation algorithms. The experimental results show that the DLM constructed in this paper has good learning and training performance, and the recommendation effect is good. (2) In this method, the performance of the DLM is significantly higher than other POI recommendation methods in terms of the accuracy or recall rate of the recommendation algorithm. Among them, the accuracy rates of the top five, top ten, and top twenty recommended POIs are increased by 9.9%, 7.4%, and 7%, respectively, and the recall rate is increased by 4.2%, 7.5%, and 14.4%, respectively. [ABSTRACT FROM AUTHOR]

Energy efficiency optimization of mobile edge computing e-commerce clients and reasonable management of server computing resources are worth further study. The participant of the algorithm game model proposed in this paper is mobile e-commerce customer management. The decision space is a two-dimensional space composed of unloading decision and power control, and the benefit function is the energy efficiency function and delay function. The existence and uniqueness of the multidimensional game model are proved theoretically. The simulation results show that the proposed multidimensional game based energy efficiency optimization algorithm of mobile edge computing can reduce the energy consumption and delay of mobile terminals and improve the energy efficiency of unloading calculation under the same task compared with the game scheme without considering power consumption control when the number of e-commerce customer management is larger. This paper deduces the optimal load migration decision of mobile e-commerce customer management and the optimal pricing strategy of mobile edge cloud service providers and proves that the optimal decision and optimal pricing constitute the Starkberg equilibrium. The semidistributed and decentralized task transfer decision-making mechanisms are designed, respectively, and the management decision-making behaviors of mobile e-commerce customers in the mobile edge cloud energy trading market are studied by numerical analysis, as well as the time efficiency of the two mechanisms. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence, handicraft design has developed from artificial design to artificial intelligence design. Traditional handicraft design has the problems of long time consumption and low output, so it is necessary to improve the process technology. Artificial intelligence technology can provide optimized design steps in handicraft design and improve design efficiency and process level. Handicrafts are regarded as important social products and exist in people's daily life. In the current society, many people do handicrafts and there are major exhibitions. Furthermore, the display of handicrafts is also very grand and shocking. In the design of handicrafts, the traditional design method cannot completely keep up with the production speed and efficiency of handicrafts. Therefore, this paper adopts the fusion multi-intelligent decision algorithm of multi-node branch design in the design method of handicraft. The algorithm model combination is used to analyze and design the layout of the handicraft, which speeds up the design efficiency and production of the handicraft. In this paper, two intelligent algorithms will be used for fusion; they are genetic algorithm and GA-PSO fusion algorithm obtained by particle swarm optimization and they are embedded in handicraft design method for application through mathematical model construction and function construction. After comparing the performance parameter index data of three intelligent algorithms and GA-PSO fusion algorithm, it is obtained that GA-PSO fusion algorithm is 97% correct and has 82% readability, 72% robustness, and 61% structure, making it have better important indicators. Four algorithms optimize each design problem in all aspects of handicraft design at present. Design efficiency, image distribution rate, image optimization degree, and image clarity are compared by simulation experiments. Compared with three intelligent algorithms, traditional design methods, and manual design methods, GA-PSO fusion algorithm can effectively improve the design method and design effect of handicrafts with 92.1% design efficiency, 82.7% image distribution rate, 94.3% image optimization degree, and 84% layout void rate. Finally, the space complexity experiment of four algorithms shows that GA-PSO algorithm can achieve 9.73 dispersion with 11.42 space complexities, which makes the dimension reduction relatively stable, and the algorithm can maintain stability in the design and application of handicrafts. [ABSTRACT FROM AUTHOR]

Many important computational applications in science, engineering, industry, and technology are represented by PSE (parameter sweep experiment) applications. These applications involve a large number of resource-intensive and independent computational tasks. Because of this, cloud autoscaling approaches have been proposed to execute PSE applications on public cloud environments that offer instances of different VM (virtual machine) types, under a pay-per-use scheme, to execute diverse applications. One of the most recent approaches is the autoscaler MOEA (multiobjective evolutive algorithm), which is based on the multiobjective evolutionary algorithm NSGA-II (nondominated sorting genetic algorithm II). MOEA considers on-demand and spot VM instances and three optimization objectives relevant for users: minimizing the computing time, monetary cost, and spot instance interruptions of the application's execution. However, MOEA's performance regarding these optimization objectives depends significantly on the optimization algorithm used. It has been shown recently that MOEA's performance improves considerably when NSGA-II is replaced by a more recent algorithm named NSGA-III. In this paper, we analyze the incorporation of other multiobjective optimization algorithms into MOEA to enhance the performance of this autoscaler. First, we consider three multiobjective optimization algorithms named E-NSGA-III (extreme NSGA-III), SMS-EMOA (S-metric selection evolutionary multiobjective optimization algorithm), and SMPSO (speed-constrained multiobjective particle swarm optimization), which have behavioral differences with NSGA-III. Then, we evaluate the performance of MOEA with each of these algorithms, considering the three optimization objectives, on four real-world PSE applications from the meteorology and molecular dynamics areas, considering different application sizes. To do that, we use the well-known CloudSim simulator and consider different VM types available in Amazon EC2. Finally, we analyze the obtained performance results, which show that MOEA with E-NSGA-III arises as the best alternative, reaching better and significant savings in terms of computing time (10%–17%), monetary cost (10%–40%), and spot instance interruptions (33%–100%). [ABSTRACT FROM AUTHOR]

It was proposed to develop a better multiscale learning dictionary picture de-noising technique. The approach improves the adaptive threshold curvilinear transform, which can divide an image into different scale information and be used to build a curvilinear multiscale learning dictionary. The method finished the dictionary and sparse coefficient updates in the picture through circular iterations and then superimposed the matching curvilinear wave domain image blocks and performed the curvilinear inverse transform to generate the denoised image. The test was carried out by adding additive Gaussian noise to a standard grayscale image, and the results revealed that the peak signal-to-noise ratio of the grayscale image de-noising result of this paper's method was improved by 56.6% on average, and the structural similarity was improved by 0.44 on average, compared to the conventional de-noising algorithm. It was determined that the enhanced approach preserved the picture's edge and texture information well, that image quality was greatly improved, and that the algorithm's execution efficiency was superior to that of the conventional de-noising algorithm. [ABSTRACT FROM AUTHOR]

In order to improve the effectiveness of college student management and promote the integration of college student management information, this paper applies intelligent sensor algorithms to student management. Moreover, this paper combines uncertainty theory with multisensor data fusion technology to establish a complete set of multisensor data processing tools for student information and provides a complete mathematical theoretical framework for the principles of student management information fusion. In addition, in view of the problem of comparing a large number of mixed data of information sources, it is necessary to transfer the information fragments obtained by each sensor to a common set so that the information fragments expressed in different sets can be integrated. Finally, this paper constructs an intelligent student management model and conducts research in combination with simulation experiments. Through simulation research, it can be known that the method proposed in this paper can effectively improve the effect of student management. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence and big data, the concept of "Internet plus education" has gradually become popular, including automatic scoring system based on machine learning. Countries all over the world vigorously promote the deep integration of information technology and discipline teaching in various fields. English is a medium of communication in the current era of education information development trend. English composition automatic scoring mode is gradually accepted by the majority of educators and applied in the actual classroom teaching. However, the research of English composition automatic grading in teaching space is not perfect. Most systems have used traditional algorithms. Therefore, this paper constructs the automatic scoring algorithm and sentence elegance feature scoring algorithm of English composition based on machine learning, explores the influence of the algorithm on English writing teaching, and proves the correctness of the design idea and algorithm of this paper through a lot of experiments. [ABSTRACT FROM AUTHOR]

With the rapid development of computer science, a large number of images and an explosive amount of information make it difficult to filter and effectively extract information. This article focuses on the inability of effective detection and recognition of English text content to conduct research, which is useful for improving the application of intelligent analysis significance. This paper studies how to improve the neural network model to improve the efficiency of image text detection and recognition under complex background. The main research work is as follows: (1) An improved CTPN multidirectional text detection algorithm is proposed, and the algorithm is applied to the multidirectional text detection and recognition system. It uses the multiangle rotation of the image to be detected, then fuses the candidate text boxes detected by the CTPN network, and uses the fusion strategy to find the best area of the text. This algorithm solves the problem that the CTPN network can only detect the text in the approximate horizontal direction. (2) An improved CRNN text recognition algorithm is proposed. The algorithm is based on CRNN and combines traditional text features and depth features at the same time, making it possible to recognize occluded text. The algorithm was tested on the IC13 and SVT data sets. Compared with the CRNN algorithm, the recognition accuracy has been improved, and the detection and recognition accuracy has increased by 0.065. This paper verifies the effectiveness of the improved algorithm model on multiple data sets, which can effectively detect various English texts, and greatly improves the detection and recognition performance of the original algorithm. [ABSTRACT FROM AUTHOR]

In order to explore the relationship between sports and health and improve the scientific nature of sports, this paper uses cluster analysis algorithm as the basis, adopts the entropy estimation method for small sample sets to estimate the information entropy value, and improves the mutual information estimation to propose a mutual information estimation method based on entropy estimation. Moreover, this paper uses a clustering algorithm to combine sports and health intelligent diagnosis requirements to construct a system structure. The system recommends better sports suggestions to the user according to the user's physical condition, makes sports plans according to the user's health, and can also analyze the user's sports process. In addition, on the basis of demand analysis, this paper designs experiments to test the performance of the system constructed in this paper. From the experimental statistical results, it can be seen that the system constructed in this paper can basically meet the actual needs of sports and health intelligent diagnosis. At the same time, this paper proves that there is a strong correlation between sports and health. [ABSTRACT FROM AUTHOR]

In order to explore the economic development trend in the postepidemic era, this paper improves the traditional clustering algorithm and constructs a postepidemic economic development trend analysis model based on intelligent algorithms. In order to solve the clustering problem of large-scale nonuniform density data sets, this paper proposes an adaptive nonuniform density clustering algorithm based on balanced iterative reduction and uses the algorithm to further cluster the compressed data sets. For large-scale data sets, the clustering results can accurately reflect the class characteristics of the data set as a whole. Moreover, the algorithm greatly improves the time efficiency of clustering. From the research results, we can see that the improved clustering algorithm has a certain effect on the analysis of economic development trends in the postepidemic era and can continue to play a role in subsequent economic analysis. [ABSTRACT FROM AUTHOR]

To solve the problems of warehouse explosion and delay of logistics distribution network under the sudden explosion of demand on "double 11" and "618," this paper proposes a logistics distribution network location algorithm that can consider reliability, green environmental protection, and path optimization. Firstly, the transportation model of logistics distribution network location-route optimization is established. Under the condition that the transportation model satisfies the vehicle path reliability constraint, it can minimize the total cost, including logistics distribution cost, transportation oil consumption, and CO2 emission cost. This paper designs an improved imperial competition algorithm to solve it according to the characteristics of the transportation model. Firstly, the competition mechanism of "United Lian Heng" was introduced in the initial national stage, enhancing the information exchange and retaining the superior population. Secondly, in the process of empire assimilation, we can learn from the colonial rule strategy, which is gradually infiltrated and assimilated by all levels of the country to enhance the development ability of the algorithm. Finally, the algorithm designs a mechanism to judge and jump out of local optimum, so as to avoid "premature" affecting the optimization performance. The rationality of the model and the effectiveness of the improved imperial competition algorithm are verified by simulation experiments of different scales, while the influence of reliability level is analyzed. Experimental results show that the proposed method can effectively solve problems of different scales and maintain stable performance under different reliability levels. Moreover, its algorithm performance is better than that of the standard imperial competition algorithm. [ABSTRACT FROM AUTHOR]

In order to improve the effect of classroom teaching and realize the improvement of mathematics teaching quality, this article combines data-driven decision-making technology to design a mathematics teaching plan system and applies data-driven decision-making technology to the design of mathematics teaching plan by improving the big data algorithm. In addition, this paper designs a mathematics teaching plan design system based on data-driven decision-making technology. The system learning module displays the knowledge points in a chapter-sequential navigation mode and stores data or information in the nodes, which are connected to form a network structure through chains. Finally, this paper verifies the designed system with experiments. From the results of the experimental research data, it can be seen that the mathematical teaching plan design system based on the data-driven decision-making technology constructed in this article has good practical effects. On this basis, the system constructed in this article can be verified through further practice. [ABSTRACT FROM AUTHOR]

The use of neural machine algorithms for English translation is a hot topic in the current research. English translation using the traditional sequential neural framework, which is too poor at capturing long-distance information, has its own major limitations. However, the current improved frameworks, such as recurrent neural network translation, are not satisfactory either. In this paper, we establish an attention coding and decoding model to address the shortcomings of traditional machine translation algorithms, combine the attention mechanism with a neural network framework, and implement the whole English translation system based on TensorFlow, thus improving the translation accuracy. The experimental test results show that the BLUE values of the algorithm model built in this paper are improved to different degrees compared with the traditional machine learning algorithms, which proves that the performance of the proposed algorithm model is significantly improved compared with the traditional model. [ABSTRACT FROM AUTHOR]

In order to improve the teaching effect of programming courses, this article applies network topology to the construction of programming course teaching platform system and constructs a teaching framework combined with intelligent teaching mode. In addition, this article takes the opportunistic social network composed of mobile smart terminal devices as the application background and briefly introduces the concept, characteristics, and application scenarios of the opportunistic social network. In addition, the typical message forwarding algorithm currently studied in this paper is analyzed. Taking into account the social attributes and forwarding willingness of nodes, in order to increase the message transmission rate and reduce message transmission delay and network overhead, two message transmission strategies are proposed to improve the operating effect of the programming course teaching platform. Finally, this paper verifies the effectiveness of the network topology in the teaching of the algorithm and programming of the intelligent teaching mode through experimental research. [ABSTRACT FROM AUTHOR]

Traditional sports aid systems analyze sports data via sensors and other types of equipment and can support athletes with retrospective analysis, but they require several sensors and have limited data. This paper examines a sports aid system that uses deep learning to recognize, review, and analyze behaviors through video acquisition and intelligent video sequence processing. This paper's primary research is as follows: (1) With an eye on the motion assistance system's application scenarios, the network topology and implementation details of the two-stage Faster R-CNN and the single-stage YOLOv3 target detection algorithms are investigated. Additionally, training procedures are used to enhance the algorithm's detection performance and training speed. (2) To address the issue of target detection techniques' low detection performance in complicated backgrounds, an improved scheme from Faster R-CNN is proposed. To begin, a new approach replaces the VGG-16 network in the previous algorithm with a ResNet-101 network. Second, an expansion plan for the dataset is provided. (3) To address the short duration of action video and the high correlation of image sequence data, we present an action recognition method based on LSTM. To begin, we will present a motion decomposition scheme and evaluation index based on the key transaction frame in order to simplify the motion analysis procedure. Second, the spatial features of the frame images are extracted using a convolutional neural network. Besides, the spatial and temporal aspects of the image sequence are fused using a two-layer bidirectional LSTM network. The algorithm suggested in this research has been validated using a golf experiment, and the results are favorable. [ABSTRACT FROM AUTHOR]

This paper proposes an intention understanding algorithm (KDI) based on an elderly service robot, which combines Neural Network with a seminaive Bayesian classifier to infer user's intention. KDI algorithm uses CNN to analyze gesture and action information, and YOLOV3 is used for object detection to provide scene information. Then, we enter them into a seminaive Bayesian classifier and set key properties as super parent to enhance its contribution to an intent, realizing intention understanding based on prior knowledge. In addition, we introduce the actual distance between the users and objects and give each object a different purpose to implement intent understanding based on object-user distance. The two methods are combined to enhance the intention understanding. The main contributions of this paper are as follows: (1) an intention reasoning model (KDI) is proposed based on prior knowledge and distance, which combines Neural Network with seminaive Bayesian classifier. (2) A set of robot accompanying systems based on the robot is formed, which is applied in the elderly service scene. [ABSTRACT FROM AUTHOR]

To address the limitations of the university digital teaching quality assessment algorithms as well as the large evaluation mistakes in the existing algorithms, this paper presents a unique university digital teaching quality evaluation method based on multilevel analysis. First, the existing state of digital teaching quality evaluation in colleges and universities is studied to develop an evaluation index for digital teaching quality. Then, to identify and compute the weight of digital teaching quality indicators, an index weight evaluation matrix is built and the weight of digital teaching quality assessment indicators is plotted using a multilevel structure tree model. Then, from the top to the bottom of the tree, this paper computes the hierarchical ranking of assessment indicators. Additionally, this paper computes the membership degree of index evaluation, normalises the evaluation indicators, and completes the digital teaching quality assessment with the digital teaching confidence calculation. The experimental results demonstrate that the proposed method's digital teaching quality assessment index has a high degree of accuracy and low evaluation error. [ABSTRACT FROM AUTHOR]

Construction project cost prediction is an important function in construction-related fields; it can provide an important basis for project feasibility study and design scheme comparison and selection, and its accuracy will directly affect the investment decision of the project. The successful realization of construction cost prediction can bring great convenience to the control and management of construction cost. The purpose of this paper is to study a fast, accurate, convenient, deducible, and rational construction project cost prediction method, to provide a basis for the cost management of the whole life cycle of the project. Therefore, this paper uses particle swarm optimization algorithm to improve BP neural network and proposes a novel construction project cost prediction algorithm based on particle swarm optimization-guided BP neural network. Aiming at the defects of BP neural network updating weights and thresholds with the gradient descent method, this paper uses the advantages of particle swarm optimization in the field of parameter optimization to optimize BP neural network with PSO algorithm. The structure of BP neural network weights and the threshold of each neuron in the coding, through intelligent search for each particle, find the most suitable weights and thresholds, so that the BP neural network has faster convergence speed, better generalization ability, and higher prediction precision. Simulation results also show that the proposed algorithm is competitive enough. [ABSTRACT FROM AUTHOR]

Including shared bicycles, shared chefs, shared ideas, shared printing, and even shared human resources, the vocabulary of sharing has quietly entered people's lives, affecting everyone's necessities. "Everyone can participate" and "everything can be shared" are all familiar words in the sharing economy field. The implementation of the sharing economy is carried out by relying on the sharing platform. Knowledge, as a special resource, can exert its maximum benefit only when it is widely disseminated and shared, and the process of knowledge sharing and transformation itself contains knowledge innovation. By linking the multiparticipants of the platform, sharing, cocreation, and win-win are realized. The research value of this paper lies in the use of microservices to solve the problems of high coupling, poor scalability, and difficulty in rapid iteration in traditional monolithic applications, effectively identifying and blocking spam that may appear on the platform through a relatively simple solution. Meanwhile, it has been expected that under the trend of sharing economy, with the development of blockchain and 5G technology, an Internet life and health platform can be built. The upstream and downstream in the medical and health industry can be linked to share resources between the upstream and downstream in the industry, which can create a common nakedness, thus improving the operational efficiency and profitability of the entire medical and health industry. Finally, a spam identification scheme combining the improved Aho-Corasick algorithm (AC) and the naive Bayesian model (NBM) has been proposed, and a comparative experiment was conducted between this scheme and the scheme directly using NBM. The experimental results showed that the macro F1 value of the improved scheme on the bad evaluation dataset of platform A was 3.6% higher than that of NBM alone, and the macro F1 value of the improved scheme was 1.7% higher than that of NBM alone on the B platform review dataset. The overall performance of the improved NBM algorithm was stable and better than the traditional algorithm, which verified the feasibility of the scheme. [ABSTRACT FROM AUTHOR]

This paper uses adaptive BP neural networks to conduct an in-depth examination of eye movements during reading and to predict reading effects. An important component for the implementation of visual tracking systems is the correct detection of eye movement using the actual data or real-world datasets. We propose the identification of three typical types of eye movements, namely, gaze, leap, and smooth navigation, using an adaptive BP neural network-based recognition algorithm for eye movement. This study assesses the BP neural network algorithm using the eye movement tracking sensors. For the experimental environment, four types of eye movement signals were acquired from 10 subjects to perform preliminary processing of the acquired signals. The experimental results demonstrate that the recognition rate of the algorithm provided in this paper can reach up to 97%, which is superior to the commonly used CNN algorithm. [ABSTRACT FROM AUTHOR]

The traditional English teaching system has certain problems in the acquisition of teaching resources and the innovation of teaching models. In order to improve the effect of subsequent English online teaching, this paper improves the machine learning algorithm to make it a core algorithm that can be used by artificial intelligence systems. Moreover, this paper combines the WBIETS system to expand the system function, analyzes the needs of the English network teaching system, and constructs the system function modules and logical structure. The data layer, logic layer, and presentation layer in the system constructed in this paper are independent of each other and can be effectively expanded when subsequent requirements change. In addition, this paper solves the problem of acquiring English teaching resources through the WBIETS system. To evaluate the performance of the English network teaching system, this paper performs comprehensive mathematical and experimental analysis. The experimental results show that the system constructed in this paper basically meets the actual teaching requirements. [ABSTRACT FROM AUTHOR]

The advancement in technology is taking place with an accelerating pace across the globe. With the increasing expansion and technological advancement, a vast volume of text data are generated everyday, in the form of social media platform, websites, company data, healthcare data, and news. Indeed, it is a difficult task to extract intriguing patterns from the text data, such as opinions, summaries, and facts, having varying length. Because of the problems of the length of text data and the difficulty of feature value extraction in news, this paper proposes a news text classification method based on the combination of deep learning (DL) algorithms. In order to classify the text data, the earlier approaches use a single word vector to express text information and only the information of the relationship between words were considered, but the relationship between words and categories was ignored which indeed is an important factor for the classification of news text. This paper follows the idea of a customized algorithm which is the combination of DL algorithms such as CNN, LSTM, and MLP and proposes a customized DCLSTM-MLP model for the classification of news text data. The proposed model is expressed in parallel with word vector and word dispersion. The relationship among words is represented by the word vector as an input of the CNN module, and the relationship between words and categories is represented by a discrete vector as an input of the MLP module in order to realize comprehensive learning of spatial feature information, time-series feature information, and relationship between words and categories of news text. To check the stability and performance of the proposed method, multiple experiments were performed. The experimental results showed that the proposed method solves the problems of text length, difficulty of feature extraction in the news text, and classification of news text in an effective way and attained better accuracy, recall rate, and comprehensive value as compared to the other models. [ABSTRACT FROM AUTHOR]

Green development is the hot spot of cold chain logistics today. Therefore, this paper converts carbon emission into carbon emission cost, comprehensively considers cargo damage, refrigeration, carbon emission, time window, and other factors, and establishes the optimization model of location of low-carbon cold chain logistics in the Beijing-Tianjin-Hebei metropolitan area. Aiming at the problems of the fish swarm algorithm, this paper makes full use of the fireworks algorithm and proposes an improved fish swarm algorithm on the basis of the fireworks algorithm. By introducing the explosion, Gaussian mutation, mapping and selection operations of the fireworks algorithm, the local search ability and diversity of artificial fish are enhanced. Finally, the modified algorithm is applied to optimize the model, and the results show that the location scheme of low-carbon cold chain logistics in Beijing-Tianjin-Hebei metropolitan area with the lowest total cost can be obtained by using fireworks-artificial fish swarm algorithm. [ABSTRACT FROM AUTHOR]

Automatic marking of English compositions is a rapidly developing field in recent years. It has gradually replaced teachers' manual reading and become an important tool to relieve the teaching burden. The existing literature shows that the error of verb consistency and the error of verb tense are the two types of grammatical errors with the highest error rate in English composition. Hence, the detection results of verb errors can reflect the practicability and effectiveness of an automatic reading system. This paper proposes an English verb's grammar error detection algorithm based on the cyclic neural network. Since LSTM can effectively retain the valid information in the context during training, this paper decided to use LSTM to model the labeled training corpus. At the same time, how to convert the text information in English compositions into numerical values for subsequent calculation is also an important step in automatic reading. Most mainstream tools use the word bag model, i.e., each word is encoded according to the order of each word in the dictionary. Although this encoding method is simple and easy to use, it not only causes the vector to lose the sequence information of the text but also is prone to dimensional disaster. Therefore, word embedding model is adopted in this paper to encode the text, and the text information is sequentially mapped to a low-dimensional vector space. In this way, the position information of the text is not lost, and the dimensional disaster is avoided. The proposed work collects some corpus samples and compares the proposed algorithm with Jouku and Bingguo. The verification results show the superiority of the proposed algorithm in verb error detection. [ABSTRACT FROM AUTHOR]

With the rapid growth of population, more diverse crowd activities, and the rapid development of socialization process, group scenes are becoming more common, so the demand for modeling, analyzing, and understanding group behavior data in video is increasing. Compared with the previous work on video content analysis, factors such as the increasing number of people in the group video and the more complex scene make the analysis of group behavior in video face great challenges. Therefore, a group behavior pattern recognition algorithm based on spatio-temporal graph convolutional network is proposed in this paper, aiming at group density analysis and group behavior recognition in the video. A crowd detection and location method based on density map regression-guided classification was designed. Finally, a crowd behavior analysis method based on density grade division was designed to complete crowd density analysis and video group behavior detection. In addition, this paper also proposes to extract spatio-temporal features of crowd posture and density by using the double-flow spatio-temporal map network model, so as to effectively capture the differentiated movement information among different groups. Experimental results on public datasets show that the proposed method has high accuracy and can effectively predict group behavior. [ABSTRACT FROM AUTHOR]

With the rapid development of the information age, the development of industrial robots is also advancing by leaps and bounds. In the scenes of automobile, medicine, aerospace, and public service, we have fully enjoyed the convenience brought by industrial robots. However, with the continuous development of industrial robot-related concepts and technologies, human-computer interaction and cooperation have become the development trend of industrial robot. In this paper, the human-machine cooperation and path optimization of industrial robot in a complex road environment are studied and analyzed. At the theoretical modeling level, firstly, the industrial robot is modeled and obstacle avoidance is analyzed based on the kinematics of industrial robot; thus, an efficient and concise collision detection model of industrial robot is proposed. At the algorithm level, in view of the complex road conditions faced by industrial robots, this paper will study and analyze the obstacle avoidance strategy of human-computer cooperation and real-time path optimization algorithm of industrial robots. Based on the virtual target point algorithm, this paper further improves the problem that the goal of the traditional path planning algorithm cannot be fully covered, so as to propose the corresponding improved path planning algorithm of industrial robots. In the experimental part, based on the existing industrial robot system, the human-machine cooperation and path planning system proposed in this paper are designed. The experimental results show that the algorithm proposed in this paper improves the accuracy of obstacle avoidance by about 10 points and the corresponding convergence speed by about 5% compared with the traditional algorithm and the experimental effect is remarkable. [ABSTRACT FROM AUTHOR]

Support vector machines (SVMs) are designed to solve the binary classification problems at the beginning, but in the real world, there are a lot of multiclassification cases. The multiclassification methods based on SVM are mainly divided into the direct methods and the indirect methods, in which the indirect methods, which consist of multiple binary classifiers integrated in accordance with certain rules to form the multiclassification model, are the most commonly used multiclassification methods at present. In this paper, an improved multiclassification algorithm based on the balanced binary decision tree is proposed, which is called the IBDT-SVM algorithm. In this algorithm, it considers not only the influence of "between-classes distance" and "class variance" in traditional measures of between-classes separability but also takes "between-classes variance" into consideration and proposes a new improved "between-classes separability measure." Based on the new "between-classes separability measure," it finds out the two classes with the largest between-classes separability measure and uses them as the positive and negative samples to train and learn the classifier. After that, according to the principle of the class-grouping-by-majority, the remaining classes are close to these two classes and merged into the positive samples and the negative samples to train SVM classifier again. For the samples with uneven distribution or sparse distribution, this method can avoid the error caused by the shortest canter distance classification method and overcome the "error accumulation" problem existing in traditional binary decision tree to the greatest extent so as to obtain a better classifier. According to the above algorithm, each layer node of the decision tree is traversed until the output classification result is a single-class label. The experimental results show that the IBDT-SVM algorithm proposed in this paper can achieve better classification accuracy and effectiveness for multiple classification problems. [ABSTRACT FROM AUTHOR]

Software defect prediction (SDP) is an important technology which is widely applied to improve software quality and reduce development costs. It is difficult to train the SDP model when software to be test only has limited historical data. Cross-project defect prediction (CPDP) has been proposed to solve this problem by using source project data to train the defect prediction model. Most of CPDP methods build defect prediction models based on the similarity of feature space or data distance between different projects. However, when the target project has a small amount of label data, these methods usually do not consider this part of data information. Therefore, when the distribution between source project and target project is quite different, these methods are difficult to achieve good prediction performance. To solve this problem, this paper proposes a CPDP method based on a semisupervised clustering (namely, Tsbagging). Tsbagging has two stages; in the first stage, we cluster to the source project data based on the limited labeled data in the target project and assign different weights to these source project data according to the clustering results. In the second stage, we use bagging method to train the prediction model based on the weight assigned in the first stage. The experimental results show that the performance achieved by Tsbagging is better than other existing SDP methods. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet technology, the use and sharing of data have brought great opportunities and challenges to mankind. On the one hand, the development of data sharing and analysis technology has promoted the improvement of economic and social benefits. On the other hand, protecting private information has become an urgent issue in the Internet era. In addition, the amount and type of information data are also increasing. At present, most algorithms can only encrypt a single type of small-scale data, which cannot meet the current data environment. Therefore, it is very necessary to study the privacy protection algorithm of multimodal data fusion. To improve the security of privacy protection algorithm, combined with the idea of multimode, this paper combines the improved traditional spatial steganography algorithm LSB matching method and the improved traditional transform domain steganography algorithm DCT with AES encryption algorithm after modifying the S-box and then combines it with image stitching technology, so as to realize a safe and reliable privacy protection algorithm of multimode information fusion. The algorithm completes the hidden communication of private information, which not only ensures that the receiver can accurately recover private information in the process of information transmission but also greatly improves the security of private information transmission. [ABSTRACT FROM AUTHOR]

Due to the current nonlinear traffic noise and changing natural environment, accurate prediction of traffic noise has become a major challenge. In order to improve the accuracy of traffic noise prediction, this paper constructs a short-term traffic noise prediction model based on PSO algorithm and BRF network structure. The model can predict the noise state of future traffic according to historical traffic information. Particle swarm optimization (PSO) network is used for short-term traffic prediction to deal with the problems of low accuracy and long-time consumption. A BRF (one-dimensional convolution)-PSO short-term traffic noise prediction model is proposed. BRF method is used to extract the spatial features of traffic noise and compared with PCA (principal component analysis)-PSO model and PSO model. The results show that the prediction accuracy of BRF-PSO model is 2% higher than that of PCA-PSO model and 6% higher than that of PSO model, which proves the effectiveness of BRF-PSO model. This study provides a technical reference for the short-term traffic noise prediction of intelligent city and the effective improvement of urban traffic environment. [ABSTRACT FROM AUTHOR]