Search

As more and more surveillance cameras are deployed in the Internet of Things, it takes more and more work to ensure the cameras are not occluded. An algorithm of detecting whether the surveillance camera is occluded is proposed by comparing the similarity of the images in this paper. Firstly, the background modeling method based on frame difference is improved. The combination method of the background difference and frame difference is proposed, and the experimental results showed that the combination algorithm can extract the background image of the video more quickly and accurately. Secondly, the LBP (Local Binary Patterns) algorithm is used to compare the similarity between the background image and the reference image. By changing the window size of the LBP algorithm and setting an appropriate threshold, the actual demands can be satisfied. So, the algorithms proposed in this paper have high application value and practical significance. [ABSTRACT FROM AUTHOR]

As China becomes more and more international, the number of people traveling abroad is also increasing. The demand for English recognition is becoming more and more vigorous, and traditional translation software is time-consuming, laborious, and less accurate. This article optimizes the target detection model YOLOV3. Firstly, the image is divided into multiple model structures, and the K-means++ clustering algorithm is used to determine the target detection prior frame value and the high frame of the corresponding frame according to the characteristics of the English image. Then, by using K-means++ clustering algorithm to optimize the anchor parameters, the model structure is better adapted to the English identification dataset scene; finally, the feature information extracted by the DarkNet-53 model is spliced to improve the structure of the YOLOV3 convolutional layer, using 3090 graphics card GPU to perform multiscale training and testing. Experimental results show that the improved YOLOV3 algorithm in this paper has a mAP of 0.95 on the English identification dataset and a detection speed of 50fps, which is 0.11 higher than the mAP before optimization. Therefore, optimizing the YOLOV3 algorithm in this article has a good effect. In the future, English translation will become a necessary software program for Chinese people to go abroad. [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 combination of Spark distributed platform and High-Utility Itemset Mining can solve the problem of long running time issue of High-Utility Itemset Mining. In the experiment, we conclude that Spark-based parallel D2HUP and EFIM algorithms have a greater improvement in running time efficiency than serial algorithms. The existing research has shown that the EFIM and D2HUP algorithms are the two best algorithms for High-Utility Itemset Mining. This paper generates 118 datasets by generating and collecting the running time of the two algorithms in the real and simulated datasets, taking into account the characteristics of each dataset's length, sparse degree, and dataset size as characteristics with running time as the prediction target and then establishing a model. The accuracy of the prediction was evaluated through experiments, and a set of rules based on decision trees was generated. According to the rules, the fastest algorithm between EFIM and D2HUP can be predicted very well. [ABSTRACT FROM AUTHOR]

As a momentous part of marine data detection, modeling the marine geomagnetic field and detecting its magnetic data have momentous theoretical and practical value for obtaining its geomagnetic field parameters and characteristic distribution and then carrying out marine environment research. The detection of magnetic anomaly data of local marine geomagnetic field model has become a momentous prerequisite for obtaining key parameter info of local ocean and subsequent development and utilization. On account of this, this paper first analyzes the detection of local marine geomagnetic anomaly data, then studies the theoretical basis of local marine geomagnetic field model, as well as the measurement and modeling of local marine geomagnetic field data, and finally gives the analysis of the detection results of local marine geomagnetic anomaly data considering the robust trend surface. [ABSTRACT FROM AUTHOR]

In this paper, a sequence decision framework based on the Bayesian search is proposed to solve the problem of using an autonomous system to search for the missing target in an unknown environment. In the task, search cost and search efficiency are two competing requirements because they are closely related to the search task. Especially in the actual search task, the sensor assembled by the searcher is not perfect, so an effective search strategy is needed to guide the search agent to perform the task. Meanwhile, the decision-making method is crucial for the search agent. If the search agent fully trusts the feedback information of the sensor, the search task will end when the target is "detected" for the first time, which means it must take the risk of founding a wrong target. Conversely, if the search agent does not trust the feedback information of the sensor, it will most likely miss the real target, which will waste a lot of search resources and time. Based on the existing work, this paper proposes two search strategies and an improved algorithm. Compared with other search methods, the proposed strategies greatly improve the efficiency of unmanned search. Finally, the numerical simulations are provided to demonstrate the effectiveness of the search strategies. [ABSTRACT FROM AUTHOR]

In recent years, cloud computing has gained massive popularity in information technology and the industrial Internet of things. It provides facilities to the users over the wireless channel. Many surveys have been carried out in cloud security and privacy. The existing survey papers do not specify the classifications on the basis of cloud computing components. Therefore, they fail to provide the techniques with their specialities as well as the previously available literature review is outdated. This paper presents the security for cloud computing models with a new aspect. Unlike the previously existing surveys, the literature review of this paper includes the latest research papers in the field of cloud security. Also, different classifications are made for cloud computing security on the basis of different cloud components that are used to secure the cloud models. Furthermore, a total of eleven (11) classifications are considered, which includes cloud components to secure the cloud systems. These classifications help the researchers to find out the desired technique used in a specific component to secure the cloud model. Moreover, the shortcoming of each component enables the researchers to design an optimal algorithm. Finally, future directions are given to highlight future research challenges that give paths to researchers. [ABSTRACT FROM AUTHOR]

Multiview active learning (MVAL) is a technique which can result in a large decrease in the size of the version space than traditional active learning and has great potential applications in large-scale data analysis. This paper made research on MVAL-based scene classification for helping the computer accurately understand diverse and complex environments macroscopically, which has been widely used in many fields such as image retrieval and autonomous driving. The main contribution of this paper is that different high-level image semantics are used for replacing the traditional low-level features to generate more independent and diverse hypotheses in MVAL. First, our algorithm uses different object detectors to achieve local object responses in the scenes. Furthermore, we design a cascaded online LDA model for mining the theme semantic of an image. The experimental results demonstrate that our proposed theme modeling strategy fits the large-scale data learning, and our MVAL algorithm with both high-level semantic views can achieve significant improvement in the scene classification than traditional active learning-based algorithms. [ABSTRACT FROM AUTHOR]

Music curriculum fusion and super-resolution reconstruction based on musical elements have gradually attracted the attention of researchers. The traditional music element fusion and reconstruction algorithm is based on the fusion and reconstruction of all the pixel information of the source music element, which has the problem of high time and space complexity. Based on the advanced iterative reconstruction theory, this paper uses the measurement matrix to measure the dimensionality of the music signal, compresses the music element data while acquiring the music elements, reduces the sampling frequency, reduces the sampling amount of the music element data, and greatly reduces the data. It solves the problem of spatial resolution reduction caused by degradation and the problem of music element feature extraction, classification, and identification and can obtain more music element features and detailed parameters. Aiming at the back-projection algorithm of musical symbol filtering, the advanced iterative reconstruction algorithm of musical symbol filtering of a triangular line array is simulated. The experimental results show that the feasibility analysis factor of the scheme reaches 0.917, and the running time of the reconstruction algorithm is reduced to 0.131 s, which promotes a large amount of data in music curriculum element fusion and super-resolution reconstruction. [ABSTRACT FROM AUTHOR]

Excessive exercise can strengthen the body and make people happy, but it can also cause physical injury. To address this issue, this paper proposes the TFD-SE (Three-Frame Difference Spatiotemporal Entropy) algorithm and the LB (Label Propagation) algorithm, which are both based on SE (spatiotemporal entropy) and label technology. The TFD-SE algorithm calculates the difference image using the three-frame difference method, then calculates the SE of pixels in the difference image, and performs morphological filtering and threshold segmentation, allowing it to detect moving objects effectively. The significance value of unlabeled nodes in the image is calculated using the LB algorithm. In both qualitative and quantitative comparisons, the experimental results show that both algorithms outperform other classical algorithms in terms of detection performance. [ABSTRACT FROM AUTHOR]

Human pose estimation is an important task in physical education, which can provide a valuable reference for teachers and students. We propose a human pose estimation method based on part affinity field. Firstly, the correlation of position information and orientation information between limb regions is maintained by part affinity field. Then the key points of limb pose are localized by part confidence map, and finally, the part affinity field is integrated to correlate all the acquired feature key points to obtain the human pose estimation. With the aid of computer vision technology, the students' training movements can be compared with the standard movements. It enables the students to feel the standard movements and badminton hitting points more intuitively. In the experiment, we set up a comparison experiment to compare the teaching mode of the method in this paper with the traditional teaching mode. The experimental results prove that through the teaching mode of our method, students have more standard strokes, more smooth skill switching between badminton serves and strokes, and higher badminton stroke scores. At the same time, such a teaching system adds a lot of fun to the course and makes the students' participation higher. [ABSTRACT FROM AUTHOR]

Touch is one of the most important human senses. With the popularization of touch-screen mobile phones, tablet computers, and other devices, touch-screen interactive technology has become a norm in people's daily lives, and advertisements that were once dominated by vision and hearing have added an interactive experience in the dimension of touch. Traditional advertising media screens can only complete simple information dissemination functions and cannot interact with users in a two-way manner. They can only receive information one-way and passively and lack interactivity. Touch-screen interactive advertising forms a good interaction with the target audience, thereby disseminating advertising information to achieve the purpose of promotion or brand image building. This paper designs a set of advertising media screen interaction systems based on smart sensors, including a gesture interaction module, a remote interaction module, and a touch interaction module. The gesture interaction module can recognize 5 static gestures and send gesture commands to control the advertising media screen. The remote interaction module can remotely control the advertising media screen, and the touch interaction module can control the advertising media screen through the touch screen. According to the functional requirements, the overall design of software and hardware is given, and the technical background of each module of the software is introduced. Next, the depth image-based gesture recognition method is studied. The number of fingers and the center distance feature are fused as feature vectors, and the weighted template matching method is used to classify and recognize gestures. Finally, the design and implementation of the interactive system are introduced. [ABSTRACT FROM AUTHOR]

Texture fusion is the process of applying the style of one style image to another content image. It is an artistic creation and image editing technology. In recent years, the rapid development of deep learning has injected new power into the field of computer vision, and a large number of image style transfer algorithms based on deep learning have been proposed. At the same time, some current character conversion algorithms based on unsupervised learning also face the loss of the content and structure of the generated characters, and at the same time, they have not learned a good face deformation effect, resulting in poor image generation effects. This paper studies the relevant research background and research significance of image style transfer methods and summarizes them in the time sequence of their development; summarizes the style transfer algorithms based on deep learning, and analyzes the advantages and disadvantages of each type of algorithm. Based on the fast style transfer algorithm, this method adds a saliency detection network and designs a saliency loss function. In the training process, the difference between the saliency map of the generated image and the content image is additionally calculated, and the saliency loss is used as a part of the total loss for iterative training. Experiments show that the stylized image generated by this algorithm can better retain the salient area of the content image and has a good visual effect. Compared with the original network, the amount of parameters of the attention mechanism is very small, and there is almost no additional burden. [ABSTRACT FROM AUTHOR]

Batch processing machine (BPM) scheduling problem is a NP hard problem for it includes machine allocation, job grouping, and batch scheduling. In this paper, to address the BPM scheduling problem with unrelated parallel machine, a multiobjective algorithm based on multipopulation coevolution is proposed to minimize the total energy consumption and the completion time simultaneously. Firstly, the mixed integer programming model of the problem is established, and four heuristic decoding rules are proposed. Secondly, to improve the diversity and convergence of the algorithm, the population is divided into two populations: each of the populations evolves independently by using different decoding rules, and the two populations will communicate through a common external archive set every certain number of generations. Thirdly, an initialization strategy and a variable neighborhood search algorithm (VNS) are proposed to improve the overall performance of the algorithm. Finally, in order to evaluate the proposed algorithm, a large number of comparative experiments with the state-of-the-art multiobjective algorithms are carried out, and the experimental results proved the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose DSP (Distributed algorithm Simulation Platform), a novel process-based distributed algorithm simulation platform to simulate real distributed systems for the design and verification of distributed algorithms. DSP consists of computer processes, and each process simulates an individual distributed node. A DSP process is mainly composed of a communication module, a computation module, an internal storage module, and an external interaction module. DSP is a flexible, versatile, and scalable simulation platform. It supports the testing of applications in various fields. Small-scale experiments can be done with a single personal computer, while large-scale experiments can be carried out through cloud servers. The greatest highlight of DSP is that it is plug and play, where nodes can be freely added or deleted during the simulation process. DSP is now open-sourced on GitHub, https://github.com/Wales-Wyf/Distributed-Algorithm-Simulation-Platform-DSP--2.0. [ABSTRACT FROM AUTHOR]

Big data processing technology has attracted a lot of attention due to its forecasting and warning of Internet security situation. The current risk assessment system still has problems such as high false alarm rate and excessive reliance on expert knowledge in the security defense system. Based on the big data-driven principle, this paper constructs a hierarchical local area network security risk event prediction model and proposes a predictive complex event processing method. The model building process is evolved and improved on the basis of the scoring function. The establishment method of vulnerability database and vulnerability association database is introduced in detail. At the same time, the problem of the difference between the structure and identification method of the information in the information database and the vulnerability database is solved, and the effect of timely modification when the data do not match is realized. Experimental results show that the algorithm has an accuracy of 98.75% and a fault tolerance rate of 0.0035, which promotes the accuracy of the network risk assessment results based on multistage network attacks. [ABSTRACT FROM AUTHOR]

In this paper, we address a hybrid flow-shop scheduling problem with the objective of minimizing the makespan and the cost of delay. The concerned problem considers the diversity of the customers' requirements, which influences the procedures of the productions and increases the complexity of the problem. The features of the problem are inspired by the real-world situations, and the problem is formulated as a mixed-integer programming model in the paper. In order to tackle the concerned problem, a hybrid metaheuristic algorithm with Differential Evolution (DE) and Local Search (LS) (denoted by DE-LS) has been proposed in the paper. The differential evolution is a state-of-the-art metaheuristic algorithm which can solve complex optimization problem in an efficient way and has been applied in many fields, especially in flow-shop scheduling problem. Moreover, the study not only combines the DE and LS, but also modifies the mutation process and provides the novel initialization process and correction strategy of the approach. The proposed DE-LS has been compared with four variants of algorithms in order to justify the improvements of the proposed algorithm. Experimental results show that the superiority and robustness of the proposed algorithm have been verified. [ABSTRACT FROM AUTHOR]

Cluster analysis, which is to partition a dataset into groups so that similar elements are assigned to the same group and dissimilar elements are assigned to different ones, has been widely studied and applied in various fields. The two challenging tasks in clustering are determining the suitable number of clusters and generating clusters of arbitrary shapes. This paper proposes a new concept of "epsilon radius neighbors" which plays an essential role in the cluster-forming process, thereby determining both the number of clusters and the shape of clusters, automatically. Based on "epsilon radius neighbors," a new clustering algorithm in which the epsilon radius value is adapted to the characteristics of each cluster in the current partition is proposed. Recently, clustering has been widely applied in environmental applications, including underground water quality monitoring. However, the existing studies have simply applied conventional clustering techniques, in which the abovementioned two challenging tasks have not been solved already. Therefore, in this paper, the proposed clustering algorithm is applied in assessing the underground water quality in Phu My Town, Ba Ria-Vung Tau Province, Vietnam. The experimental results on benchmark datasets demonstrate the effectiveness of the proposed algorithm. For the quality of underground water, the new algorithm results in four clusters with different characteristics. Through this application, we found that the new algorithm might provide valuable reference information for underground water management. [ABSTRACT FROM AUTHOR]

With the rapid development of software-defined network and network function virtualization technology, the scale of infrastructure and the number of available resources in cloud platforms continue to grow. It is also be used in AD Visualiser. AD is a visualisation tool for displaying the atomic decomposition (AD) of OWL ontologies. As the size of ontologies increases, ontology engineers become more difficult to understand and reuse ontologies. Atomic decomposition (AD) is a modular structure to help ontology engineers modularly manage ontologies. It decomposes ontologies into sets of atoms, with dependency, based on modules that provide strong logical guarantees (such as locality-based modules). This paper describes the design and implementation process of AD Visualiser and discusses its usability for ontology engineers in their daily work. For example, using AD Visualiser, ontology engineers avoid choosing signatures and determining the extraction results. They can extract modules very simply and faster. Besides, the graph of AD's modular structure should be helpful for engineers to intuitively explore and comprehend ontologies. [ABSTRACT FROM AUTHOR]

In order to improve the effectiveness of national fitness programs, this article analyzes the method of national fitness items recommendation based on a neural network algorithm. By using the time and space characteristics of fitness users' sign-in, a novel POI recommendation model is proposed, and a novel fusion method is proposed to combine similarity and spatial similarity to achieve the final similarity calculation based on fitness users' temporal and spatial preferences. In addition, in order to model the spatial similarity of fitness users, the Voronoi diagram is constructed by using the geographic locations of all POIs. Finally, this paper constructs a recommendation system for national fitness items based on a neural network algorithm. The experimental research results show that the national fitness program recommendation system proposed in this article basically meets the expected demand. [ABSTRACT FROM AUTHOR]

The accurate monitoring of tool condition is of great significance to improve the machining quality and efficiency of parts, prolong the service life of tools and machine tools, and reduce the harm of manufacturing environment. In this dissertation, two methods based on synchronous compressed continuous wavelet transform and deep convolution neural network (SWT-DCNN) and synchronous compressed continuous wavelet transform and deep convolution neural network (SST-DCNN) are proposed to monitor tool wear. It is found that the recognition accuracy of SWT-DCNN method is 99.96%, and that of SST-DCNN method is 99.86%. The reason is that SWT method has good time-frequency energy aggregation. Compared with the SST-DCNN method, the recognition accuracy of the SWT-DCNN method is more stable. At the same time, it is found that the recognition rate of the SST-DCNN method in the process of normal tool monitoring is only 93.3%, which is easy to classify normal tools into the initial wear category. The experimental results show that the two methods proposed in this paper can effectively monitor the tool wear state. [ABSTRACT FROM AUTHOR]

Dynamic window algorithm (DWA) is a local path-planning algorithm, which can be used for obstacle avoidance through speed selection and obtain the optimal path, but the algorithm mainly plans the path for fixed obstacles. Based on DWA algorithm, this paper proposes an improved DWA algorithm based on space-time correlation, namely, space-time dynamic window approach. In SDWA algorithm, a DWA associated with obstacle position and time is proposed to achieve the purpose of path planning for moving obstacles. Then, by setting the coordinates of the initial moving obstacle and identifying safety distance, we can define the shape of the obstacle and the path planning of the approach segment in thunderstorm weather based on the SDWA model was realized. Finally, the superior performance of the model was verified by setting moving obstacles for path planning and selecting the aircraft approach segment in actual thunderstorm weather. The results showed that SDWA has good path-planning performance in a dynamic environment. Its path-planning results were very similar to an actual aircraft performing thunderstorm-avoidance maneuvers, but with more smooth and economical trajectory. The proposed SDWA model had great decision-making potential for approach segment planning in thunderstorm weather. [ABSTRACT FROM AUTHOR]

With the evolution of the Internet and information technology, the era of big data is a new digital one. Accordingly, animation IP has been more and more widely welcomed and concerned with the continuous development of the domestic and international animation industry. Hence, animation video analysis will be a good landing application for computers. This paper proposes an algorithm based on clustering and cascaded SSD for object detection of animation characters in the big data environment. In the training process, the improved classification Loss function based on Focal Loss and Truncated Gradient was used to enhance the initial detection effect. In the detection phase, this algorithm designs a small target enhanced detection module cascaded with an SSD network. In this way, the high-level features corresponding to the small target region can be extracted separately to detect small targets, which can effectively enhance the detection effect of small targets. In order to further improve the effect of small target detection, the regional candidate box is reconstructed by a k-means clustering algorithm to improve the detection accuracy of the algorithm. Experimental results demonstrate that this method can effectively detect animation characters, and performance indicators are better than other existing algorithms. [ABSTRACT FROM AUTHOR]

Considering task dependencies, the balancing of the Internet of Health Things (IoHT) scheduling is considered important to reduce the make span rate. In this paper, we developed a smart model approach for the best task schedule of Hybrid Moth Flame Optimization (HMFO) for cloud computing integrated in the IoHT environment over e-healthcare systems. The HMFO guarantees uniform resource assignment and enhanced quality of services (QoS). The model is trained with the Google cluster dataset such that it learns the instances of how a job is scheduled in cloud and the trained HMFO model is used to schedule the jobs in real time. The simulation is conducted on a CloudSim environment to test the scheduling efficacy of the model in hybrid cloud environment. The parameters used by this method for the performance assessment include the use of resources, response time, and energy utilization. In terms of response time, average run time, and lower costs, the hybrid HMFO approach has offered increased response rate with reduced cost and run time than other methods. [ABSTRACT FROM AUTHOR]

To retain valuable information to the maximum extent and enhance the ability to mine the crude oil trade purchase price demand, this paper proposes a crude oil trade purchase model based on the DEA-Malmquist algorithm. The intranet of the management and control platform shall share the same database, and the intranet shall only allow managers to access and manage the system and only allow all registered users to access and realize data exchange between the intranet and the intranet through two-dimensional code scanning; moreover, due to the resource sharing between the intranet and the intranet for crude oil trade procurement, suppliers and other registered users can immediately grasp the procurement trends of enterprises. Under the DEA-Malmquist algorithm, the uncertainty of procurement management is analyzed by fuzzy theory, and the refined procurement decision model with fuzzy parameters is established. The optimal order time and purchase quantity are determined through the symbol distance and the method of the center of gravity. Experimental results show that the method can effectively retain valuable information in the initial sequence and has better practical application value of material procurement demand intelligent mining. The proposed model obtained the highest accuracy of 98.62%. [ABSTRACT FROM AUTHOR]

The existing social talent governance algorithms have a number of issues such as slow convergence rate, relatively low data accuracy, recall rate, and low anti-interference. To address these problems, this paper proposes a research on social talent governance algorithm based on genetic algorithm. We discuss the difference between the traditional and the genetic algorithms and determine the implementation process of genetic algorithm. On this basis, the excellent individuals are determined by independent computing fitness, and the initialization population is designed according to the individual similarity threshold. After the population is defined, the roulette and deterministic sampling selection method are integrated to clarify the selection calculation process. Based on the calculation results, we design the crossover operator by segmented single-point crossover between individuals. The mutation operator is designed by segmented mutation of different gene segments according to the calculation results. The results are incorporated into the simulated annealing acceptance probability to conduct simulated annealing for the individuals after the cross-mutation operation and set relevant conditions after the end of the algorithm. We seek the optimal solution of the data within the number of iterations and finally realize the whole process of social talent governance algorithm. The experimental results show that the proposed algorithm has fast convergence rate, high data precision and recall, and has certain feasibility. [ABSTRACT FROM AUTHOR]

Image fusion is to effectively enhance the accuracy, stability, and comprehensiveness of information. Generally, infrared images lack enough background details to provide an accurate description of the target scene, while visible images are difficult to detect radiation under adverse conditions, such as low light. People hoped that the richness of image details can be improved by using effective fusion algorithms. In this paper, we propose an infrared and visible image fusion algorithm, aiming to overcome some common defects in the process of image fusion. Firstly, we use fast approximate bilateral filter to decompose the infrared image and visible image to obtain the small-scale layers, large-scale layer, and base layer. Then, the fused base layer is obtained based on local energy characteristics, which avoid information loss of traditional fusion rules. The fused small-scale layers are acquired by selecting the absolute maximum, and the fused large-scale layer is obtained by summation rule. Finally, the fused small-scale layers, large-scale layer, and base layer are merged to reconstruct the final fused image. Experimental results show that our method retains more detailed appearance information of the fused image and achieves good results in both qualitative and quantitative evaluations. [ABSTRACT FROM AUTHOR]

This paper aimed to study the clinical characteristics of patients with pulmonary embolism (PE) with syncope (PE + S) based on the three-dimensional (3D) reconstruction algorithm in computed tomography (CT) pulmonary angiography (CTPA). In this study, 857 patients with acute PE (APE) who were treated in hospital were selected as the research objects and divided into syncope group (group S) and nonsyncope group (group NS). The 3D reconstruction marching cubes (3DR-MC) algorithm was compared with the traditional MC (T-MC) algorithm and the mesh simplification MC (MMS-MC) algorithm, and the results proved that the running time of the 3DR-MC algorithm on the platform was shorter than that of the other two algorithms. The incidence of syncope in group S in women was higher than that in group NS (51.7% vs. 38.2%). The incidence of syncope classified as high risk in group S was higher than that in group NS, and the mortality rate of pulmonary embolism patients with syncope was higher, and the difference was statistically significant (χ2 = 113.332, P < 0.05). The incidence of syncope in group S was higher than that in group NS (χ2 = 4.074, P < 0.05). In short, hypertension was an independent risk factor for syncope. PE + S patients could be diagnosed and treated as early as possible based on the clinical characteristics, so as to reduce the adverse consequences of misdiagnosis. [ABSTRACT FROM AUTHOR]

Aiming at the problems of poor image tracking effect, low precision, and long time in the process of image tracking of volleyball player's arm hitting, a volleyball player's arm hitting image tracking method based on D-P algorithm is proposed. This paper analyzes the basic concept, basic principle, and basic equation of D-P algorithm and collects the arm stroke trajectory image of volleyball players under the three-dimensional visual model. Using wavelet multiscale decomposition method, the arm stroke trajectory of volleyball players is filtered, and the edge contour feature points of the arm stroke image of volleyball players are extracted. Using the gray histogram feature extraction method, the gray information of volleyball player's arm hitting trajectory image is enhanced. Combined with pixel adaptive enhancement technology, the key action feature points of volleyball player's arm hitting image trajectory are located. Based on D-P algorithm, the volleyball player's arm hitting image trajectory is adjusted and modified to realize the correct tracking of volleyball player's arm hitting image trajectory. The experimental results show that the trajectory tracking effect of volleyball player's arm hitting image is better, which can effectively improve the tracking accuracy and shorten the tracking time. [ABSTRACT FROM AUTHOR]

Accurate quantitative evaluation of the supervision effect of the smart pension industry can reduce the cost of social pension. The traditional methods cannot effectively classify the regulatory risk levels of the smart pension industry. Therefore, this paper proposes a multisource information intelligent fusion algorithm based on the intelligent pension industry optimization path research. Firstly, we establish the principal model of the supervision effect system of the intelligent elderly care industry optimization path and describe the risk level of the supervision effect from different levels. We build the intelligent service platform of the intelligent elderly care training, calculate the weight vector of the supervision risk of the optimization path at all levels, and determine the attribute type of the supervision effect at all levels. Finally, we calculate the maximum influence value of the supervision effect of the intelligent elderly care industry optimization path and use this value to complete the quantitative evaluation of its supervision effect. Simulation results show that the proposed method can evaluate the regulatory effect of smart pension industry and improve the precision of the regulatory effect of smart pension industry effectively. [ABSTRACT FROM AUTHOR]

The sharing of English teaching resources has always been a concern. In order to further improve the value of different English teaching resources, this paper proposes a resource management system based on an improved collaborative recommendation algorithm. The proposed model can predict user behavior based on deep learning models of graph neural network (GNN) and recurrent neural network (RNN). The graph neural network can capture the hidden state of local user behavior and be used as a preprocessing step. Recurrent neural networks can capture time series information. Therefore, the model is constructed by combining GNN and RNN to obtain the advantages of both. In order to prove the effectiveness of the model, we used CNGrid's real user behavior dataset in the experiment and finally compared the results with other methods. The different deep learning-based models achieved a precision of up to 88% and outperformed other traditional models. The experimental results show that this new deep learning model has good sharing value. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has strongly affected education in China, even if education departments and corresponding schools took a series of measures to manage online education of the school's new semester in China, including maneuver, learning platform allocation, and teacher training. In this paper, edge computing is used to optimize online education, and a task offloading algorithm is designed to minimize the computing delay of terminal tasks. Through preparation, practice, and reflection of this online education, this study aims to comprehensively demonstrate the learning condition of online education in China and present the real adjustment impact based on the problems encountered during the process. Although several schools gradually reopened to students in 3 months, several improvements are warranted in various ways. This study proposes the construction of education infrastructure, the adjustment of teaching organization, and the learning methods of teachers and students, providing a clear guiding significance for the development and enhancement of online education in the future. [ABSTRACT FROM AUTHOR]

Detection of substation equipment can promptly and effectively discover equipment overheating defects and prevent equipment failures. Traditional manual diagnosis methods are difficult to deal with the massive infrared images generated by the autonomous inspection of substation robots and drones. At present, most of the infrared image defect recognition is based on traditional machine learning algorithms, with low recognition accuracy and poor generalization capability. Therefore, this paper develops a method for identifying infrared defects of substation equipment based on the improvement of traditional ones. First, based on the Faster RCNN, target detection is performed on 6 types of substation equipment including bushings, insulators, wires, voltage transformers, lightning rods, and circuit breakers to achieve precise positioning of the equipment. Afterwards, different classes are identified based on the sparse representation-based classification (SRC), so the actual label of the input sample can be obtained. Finally, based on the temperature threshold discriminant algorithm, defects are identified in the equipment area. The measured infrared images are used for experiments. The average detection accuracy achieved by the proposed method for the 6 types of equipment reaches 92.34%. The recognition rate of different types of equipment is 98.57%, and the defect recognition accuracy reaches 88.75%. The experimental results show the effectiveness and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

Traditional insurance policy settlement is a manual process that is never hassle-free. There are many issues, such as hidden conditions from the insurer or fraud claims by the insured, making the settlement process rough. This process also consumes a significant amount of time that makes the process very inefficient. This whole scenario can be disrupted by the implementation of blockchain and smart contracts in insurance. Blockchain and innovative contract technology can provide immutable data storage, security, transparency, authenticity, and security while any transaction process is triggered. With the implementation of blockchain, the whole insurance process, from authentication to claim settlement, can be done with more transparency and security. A blockchain is a virtual chain of data blocks that is a decentralized technology. Any transaction or change in the blocks is done after the decentralized validator entity, not a single person. The smart contract is a unique facility stored on the blockchain that gets executed when the predetermined conditions are met. This paper presents a framework where smart contracts are used for insurance contracts and stored on blockchain. In the case of a claim, if all the predetermined conditions are met, the transaction happens; otherwise, it is discarded. The conditions are immutable. That means there is scope for alteration from either side. This blockchain and intelligent contract-based framework are hosted on a private Ethereum network. The Solidity programming language is used to create smart contracts. The framework uses the Proof of Authority (PoA) consensus algorithm to validate the transactions. In the case of any faulty transaction request, the consensus algorithm acts according to and cancels the claim. With blockchain and smart contract implementation, this framework can solve all the trust and security issues that rely on a standard insurance policy. [ABSTRACT FROM AUTHOR]

The three-dimensional reconstruction of outdoor landscape is of great significance for the construction of digital city. With the rapid development of big data and Internet of things technology, when using the traditional image-based 3D reconstruction method to restore the 3D information of objects in the image, there will be a large number of redundant points in the point cloud and the density of the point cloud is insufficient. Based on the analysis of the existing three-dimensional reconstruction technology, combined with the characteristics of outdoor garden scene, this paper gives the detection and extraction methods of relevant feature points and adopts feature matching and repairing the holes generated by point cloud meshing. By adopting the candidate strategy of feature points and adding the mesh subdivision processing method, an improved PMVS algorithm is proposed and the problem of sparse point cloud in 3D reconstruction is solved. Experimental results show that the proposed method not only effectively realizes the three-dimensional reconstruction of outdoor garden scene, but also improves the execution efficiency of the algorithm on the premise of ensuring the reconstruction effect. [ABSTRACT FROM AUTHOR]

To improve the performance for distributed blockchain system, a novel and effective consensus algorithm is designed in this paper. It firstly constructs a more random additive constant through the generation matrix of the error correction code and uses the value of the hash entropy to prove that the constructed hash function can meet the requirements of high throughput and fast consensus in performance. In addition, a distributed consensus coordination service system is used in the blockchain system to realize the synchronization of metadata and ensure the consistency of block data, configuration information, and transaction information. The experiment results show that our proposed strategy can reduce the waste of computing resources, increase the block generation speed, and ensure the fairness of nodes participating in the competition, which is an effective solution to ensure the stable operation of the blockchain system. [ABSTRACT FROM AUTHOR]

To study the classification and evolution of key technologies in the transportation field, the data of 36 authoritative SCI journals in the transportation field were collected from the Web of Science core collection database from 2001 to 2020. Based on the bibliometric method, this study used Python to process and visualize data, combined with bibliometric software VOSviewer to assist data visualization. Firstly, a preprocessing data algorithm was designed to deduplicate the collected data, merge synonyms, and extract key technologies. Then the paper records that contained the key technology lexicon were filtered out. Next, the annual number of publications and the distribution of key technologies over time were counted. The least squares method was used to fit the distribution of the annual proportion of the publications, and the slope k1 of the fitted linear regression equation was used to determine the research interest trend of key technologies. The key technologies were divided into "hot technology," "cold technology," and "other technologies," according to the research heat trend. In order to further explore the research hotspots, the least squares method was also used to fit the citations of all technologies to obtain the slope k2. We use the Gaussian mixture model (GMM) algorithm to cluster k1 and k2 of each technology. As a result, the 144 technologies were divided into 13 super-key technologies, 60 key technologies, 59 relative key technologies, and 12 lower-key technologies. Then, the evolution of key technologies was analyzed from two perspectives of weighted evolution and cumulative evolution. And the technology evolution trend in the transportation field in the past 20 years was explored. Finally, the cooccurrence clustering method was adopted to divide key transportation technologies into five categories: vehicle technology and control, optimization algorithms and simulation techniques, artificial intelligence and big data, Internet of Things and computing, and communication technology. The research results can provide references for different people in the transportation field, including but not limited to researchers, journal editors, and funding agencies. [ABSTRACT FROM AUTHOR]

Current data has the characteristics of complexity and low information density, which can be called the information sparse data. However, a large amount of data makes it difficult to analyse sparse data with traditional collaborative filtering recommendation algorithms, which may lead to low accuracy. Meanwhile, the complexity of data means that the recommended environment is affected by multiple dimensional factors. In order to solve these problems efficiently, our paper proposes a multidimensional collaborative filtering algorithm based on improved item rating prediction. The algorithm considers a variety of factors that affect user ratings; then, it uses the penalty to account for users' popularity to calculate the degree of similarity between users and cross-iterative bi-clustering for the user scoring matrix to take into account changes in user's preferences and improves on the traditional item rating prediction algorithm, which considers user ratings according to multidimensional factors. In this algorithm, the introduction of systematic error factors based on statistical learning improves the accuracy of rating prediction, and the multidimensional method can solve data sparsity problems, enabling the strongest relevant dimension influencing factors with association rules to be found. The experiment results show that the proposed algorithm has the advantages of smaller recommendation error and higher recommendation accuracy. [ABSTRACT FROM AUTHOR]

Due to the lack of domain and interface knowledge, it is difficult for users to create suitable service processes according to their needs. Thus, the paper puts forward a new service composition recommendation method. The method is composed of two steps: the first step is service component recommendation based on recurrent neural network (RNN). When a user selects a service component, the RNN algorithm is exploited to recommend other matched services to the user, aiding the completion of a service composition. The second step is service composition recommendation based on Naive Bayes. When the user completes a service composition, considering the diversity of user interests, the Bayesian classifier is used to model their interests, and other service compositions that satisfy the user interests are recommended to the user. Experiments show that the proposed method can accurately recommend relevant service components and service compositions to users. [ABSTRACT FROM AUTHOR]

With the development of computer science, especially the application of 3D scanning technology in garment design, intelligent modeling is realized, which is impossible to achieve in traditional design methods. In this paper, we propose the 3D model construction of human garments based on the motion recovery structure method. The eigenmatrix is obtained from the camera parameters, and the transformation matrix is calculated by matching the image feature points with the help of scale-invariant feature conversion algorithm to realize the 3D reconstruction technology of human garments based on multiview image sequences. The effectiveness of this method is verified through experiments, and it has good robustness and accuracy. Through the form of style modeling, the design thinking and method can be extended to form a more reasonable garment structure and guide the innovation of garment production mode. [ABSTRACT FROM AUTHOR]

Nowadays, opinion texts are quickly published on websites and social networks by various users in the form of short texts and also in high volumes and various fields. Because these texts reflect the opinions of many users, their processing and analysis, such as clustering, can be very useful in a variety of applications including politics, industry, commerce, and economics. High dimensions of the text representation decrease efficiency of clustering, and an effective solution for this challenge is reducing dimensions of texts. Manifold learning is a powerful tool for nonlinear dimension reduction of high-dimensional data. Therefore, in this paper, for increasing efficiency of opinion texts clustering, by manifold learning, dimensions of the represented opinion texts are reduced based on sentiment and semantics, and their intrinsic dimensions are extracted. Then, the clustering algorithm is applied to dimension-reduced opinion texts. The proposed approach helps us to cluster opinion texts with simultaneous consideration of sentiment and semantics, which has received very little attention in the previous works. This type of clustering helps users of opinion texts to obtain more useful information from texts and also provides more accurate summaries in applications, such as the summarization of opinion texts. Experimental results on three datasets show better performance of the proposed approach on opinion texts in terms of important measures for evaluating clustering efficiency. An improvement of about 9% is observed in terms of accuracy on the third dataset and clustering based on sentiment and semantics. [ABSTRACT FROM AUTHOR]

In this paper, a route-planning approach is proposed based on the region-segmentation Dynamic Programming (DP) algorithm for Automated Guided Vehicles (AGVs) in large Smart Road Network Systems (SRNSs) to deal with the problem of low route computation efficiency of the classical DP algorithm. We introduced an improved Markov Decision Process (MDP) to describe SRNSs, in which the SRNSs are divided into several regions according to the AGVs' start nodes and their goal nodes to improve the route-planning efficiency. Moreover, the route with the minimum number of turns is selected to reduce the system running time and energy cost in the following way: first, all the equidistant shortest routes are acquired from the AGVs' start nodes to their goal nodes using the improved DP algorithm; then, the routes are screened by calculating the angular deviation between all feasible routes and AGVs' initial directions, and the route with the fewest number of turns is taken as the shortest-time route. The simulation results verified that the proposed method can effectively solve the route-planning problem of AGVs in current SRNSs. [ABSTRACT FROM AUTHOR]

Currently, there is less research on how to improve the efficiency of the application of computer graphics technology in the creation of public sculpture. Therefore, this paper will focus on how computer graphics algorithms can enable systems for the creation of public sculpture with the intervention of computer graphics technology to create more accurate and completed works of public sculpture. It will explore and analyze how computer image algorithms can help creators apply computer image technology to finish complete and accurate public sculptures, and individual studies, computer imagery, and model analysis are also used. In systems for the creation of public sculpture, the point cloud data of the model is obtained through 3D laser scanning technology; then the algorithm of the point cloud model is integrated and the Statistical Outlier Removal algorithm of the point cloud model intervention is processed. By this way, the point cloud model of the work is optimized, and then a more completed and accurate public sculpture work can be produced by 3D sculpting or 3D printing. The research shows that, in the creation of public sculptures with the intervention of computer graphics technology, the computer graphics algorithm acquires the basis of the high-definition public sculpture data model. The computer graphics algorithm improves the accuracy and completeness of the creator using computer graphics technology; it is also the key to transform the accurate enlargement and transformation of the sculptural model into the actual sculptural work. [ABSTRACT FROM AUTHOR]

Blockchain, the underlying technology of Bitcoin, has been deeply studied in various fields after its development in recent years. As a typical decentralized distributed data storage system, consensus reached among all participants in a blockchain system requires a consensus mechanism to be realized. In order to make blockchain applicable to different application scenarios, different consensus mechanisms have been proposed. With the further development of blockchain applications, more and more studies have been conducted on the consensus mechanism. However, some existing consensus mechanisms still have some problems in various aspects. Therefore, this paper proposes a trade deal algorithm based on the blockchain mechanism of consensus. First of all, according to PBFT, the lack of a dynamic problem in the VPBFT voting mechanism was introduced. The node system is divided into four types with different responsibilities and gives the number of relations between nodes. When the number of nodes is changed, it can be calculated according to the quantity relation, ensuring dynamic. Second, a data anonymous transaction and authentication protocol is designed. In the protocol, when the seller sells data, the mapping relationship between the real identity and the false identity of the data owner is blinded and sent to the buyer. When the buyer wants to verify their identity, the seller's identity can only be verified with the authentication of the blockchain. The proposed algorithm is superior to the current consensus in terms of time and energy consumption, throughput, and fault tolerance methods, which is proven through experimental tests and simulation analysis. [ABSTRACT FROM AUTHOR]

In order to help badminton players make reasonable training plans and realize a comprehensive grasp of the training process, this paper mainly recognizes and perceives the posture of badminton athletes based on the method of moving edge calculation. Firstly, from the perspective of moving edge motion analysis, considering the vector field formed by moving edge vector as movable spatial distribution information, the spatial distribution model of moving edge field is realized. Secondly, while athletes interact with the computer through limb movement, the overall posture of athletes is divided into several parts, and each part is perceived separately. Finally, in the human posture evaluation module, an algorithm for human posture evaluation in the image pixel plane is proposed. Through comparative experiments, the motion recognition algorithm can effectively recognize the three typical swing movements of badminton players in the video and improve the overall performance of the existing recognition algorithms. [ABSTRACT FROM AUTHOR]

Considering the problems of fuzzy repair and low pixel similarity matching in the repair of existing tomb murals, we propose a novel tomb mural repair algorithm based on sequential similarity detection in this paper. First, we determine the gradient value of tomb mural through second-order Gaussian Laplace operator in LOG edge detection and then reduce the noise in the edge of tomb mural to process a smooth edge of tomb mural. Further, we set the mathematical model to obtain the edge features of tomb murals. To calculate the average gray level of foreground and background under a specific threshold, we use the maximum interclass variance method, which considers the influence of small cracks on the edge of tomb murals and separates the cracks through a connected domain labelling algorithm and open and close operations to complete the edge threshold segmentation. In addition, we use the priority calculation function to determine the damaged tomb mural area, calculate the gradient factor of edge information, obtain the information entropy of different angles, determine the priority of tomb mural image repair, detect the similarity of tomb mural repair pixels with the help of sequential similarity, and complete the tomb mural repair. Experimental results show that our model can effectively repair the edges of the tomb murals and can achieve a high pixel similarity matching degree. [ABSTRACT FROM AUTHOR]

This paper aimed to explore pelvic lymphadenectomy for gynecological malignant tumors guided by computed tomography angiography (CTA) images under region-growing algorithm (RGA). 100 cases of malignant tumor patients who received pelvic lymphadenectomy in hospital from January 2018 to January 2020 were analyzed. Patients were classified into control group (CTA image) and experimental group (RGA-based CTA image), each with 50 cases. The overall accuracy (OA) of the pelvic CT image segmentation parameters under RGA, the watershed segmentation algorithm (WA), and the swarm intelligence optimization algorithm (SIOA) was compared. Comparisons of segmentation parameters, denoising performance, and CT imaging of patients as well as diagnosis rate and total efficiency rate were carried out. The results showed that overall accuracy (OA) of RGA was considerably higher versus watershed segmentation algorithm (WA) and swarm intelligence optimization algorithm (SIOA). However, false positive rate (FPR) and false negative rate (FNR) of RGA were greatly lower than those of other algorithms. RGA greatly improved the accuracy of pelvic tumor detection. The peak signal-to-noise ratio (PSNR) of RGA was superior to that of WA and SIOA, but differences in edge preservation index (EPI) value were not significant. The diagnosis rate of the experimental group was 48/50 (96%), while the diagnosis rate by manual means was 38/50 (76%). For the diagnosis rate and total efficiency, results of the experimental group were evidently higher in contrast to the control group (P < 0.05). In conclusion, under RGA, CTA image-guided pelvic lymphadenectomy had good segmentation accuracy and denoising performance, and it was superior in total efficiency and diagnosis rate, which was worthy of clinical promotion. [ABSTRACT FROM AUTHOR]

Evaluation of music teaching is a highly subjective task often depending upon experts to assess both the technical and artistic characteristics of performance from the audio signal. This article explores the task of building computational models for evaluating music teaching using machine learning algorithms. As one of the widely used methods to build classifiers, the Naïve Bayes algorithm has become one of the most popular music teaching evaluation methods because of its strong prior knowledge, learning features, and high classification performance. In this article, we propose a music teaching evaluation model based on the weighted Naïve Bayes algorithm. Moreover, a weighted Bayesian classification incremental learning approach is employed to improve the efficiency of the music teaching evaluation system. Experimental results show that the algorithm proposed in this paper is superior to other algorithms in the context of music teaching evaluation. [ABSTRACT FROM AUTHOR]

Most network pruning methods rely on rule-of-thumb for human experts to prune the unimportant channels. This is time-consuming and can lead to suboptimal pruning. In this paper, we propose an effective SuperPruner algorithm, which aims to find optimal pruned structure instead of pruning unimportant channels. We first train a VerifyNet, a kind of super network, which is able to roughly evaluate the performance of any given network structure. The particle swarm optimization algorithm is then used to search for optimal network structure. Lastly, the weights in the VerifyNet are used as the initial weights of the optimal pruned structure to make fine-tuning. VerifyNet is a network performance evaluation; our algorithm can quickly prune the network under any hardware constraints. Our algorithm can be applied in multiple fields such as object recognition and semantic segmentation. Extensive experiment results demonstrate the effectiveness of SuperPruner. For example, on CIFAR-10, the pruned VGG16 achieves 93.18% Top-1 accuracy and reduces 74.19% of FLOPs and 89.25% of parameters. Compared with state-of-the-art methods, our algorithm can achieve higher pruned ratio with less accuracy cost. [ABSTRACT FROM AUTHOR]

Hyperspectral information can be used to express the material properties of objects, which has a strong effect on camouflage recognition. However, it is difficult to process it directly because of the huge hyperspectral image data. Therefore, this paper proposes a new band selection algorithm to achieve band selection by simulating visual perception. The subspace clustering self-attention adversarial network is constructed to realize the initial selection of band. According to the visual chromatic aberration principle, a model is constructed to determine the band that combines the strongest response intensity of a particular material, and then this band is selected as the final band, therefore realizing the algorithm of material demarcation in this way. [ABSTRACT FROM AUTHOR]