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Meta-heuristic algorithms have the advantages of resilience, global optimization capacity, and coding flexibility, making them helpful in tackling difficult optimization issues. The enhanced wind-driven optimization (CHTWDO) that was proposed in this paper coupled the chaotic map approach and the hyperbolic tangent with the T-distribution mutation method. The initial air particles are evenly distributed in the system space through a tent mapping strategy. Meanwhile, the variation probability of the hyperbolic tangent model and the T-distribution variation method are used to improve the comprehensive performance of the algorithm. In this way, the global search accuracy and the ability of avoiding the extreme value of the algorithm can be taken into account. Combining the three strategies, CHTWDO had higher global search accuracy and a stronger ability to jump out of local extremum. Comparing with the eight meta-heuristic algorithms (including WDO) and the single strategy improved WDO on 24 test functions, the experimental results show that CHTWDO with two improved strategies has better convergence precision and faster convergence speed. Statistical tests such as Friedman's and Wilcoxon's rank-sum tests are used to determine significant differences between these comparison algorithms. Finally, CHTWDO also obtains the best results on four classical optimization problems in engineering applications, which verifies its practicality and effectiveness. [ABSTRACT FROM AUTHOR]

This paper proposes an interactive two-archive method to solve many-objective optimization problems. Two updating strategies based on the aggregation-based framework are presented and incorporated into a two-archive framework. In addition, we further extend this method by introducing an interactive mechanism in which evolutionary information is passed from the diversity archive to the convergence archive. Given the requirement to balance convergence and diversity, a mating selection method is proposed to regulate the evolutionary speed of these two archives collaboratively. The proposed algorithm has been tested extensively on several problems with different peer algorithms to validate its effectiveness. The results show that the proposed method can outperform several state-of-the-art evolutionary algorithms for handling many-objective optimization. [ABSTRACT FROM AUTHOR]

To improve soldiers' combat capability, weapon arms have a good development prospect. However, due to special work scenarios and tasks, new requirements are exerted on. Based on the fast-expanding random tree algorithm (RRT), path algorithm optimization (RRT-H) is proposed for the path planning of weapon arms. Overall path optimization is achieved by reducing the local path length with a closer path point planning to the obstacle. In a complex environment, the RRT-H algorithm can avoid local traps by guiding the new path extension direction and exploring multiple different paths in the map. The superiority of this algorithm is verified with 2D plane obstacle avoidance and pathfinding simulation experiments. Compared to RRT ∗ , RRT ∗ smart, and information RRT ∗ , the RRT-H can obtain high-quality calculation results in a shorter time. After setting degrees of freedom (DOF) as that of variables, the algorithm is applied to the 4-DOF weapon arm, which confirms an effective reduction to the 4-DOF weapon arm's motion costs. [ABSTRACT FROM AUTHOR]

The minimum spanning tree (MST) is widely used in planning the most economical network. The algorithm for finding the MST of a connected graph is essential. In this paper, a new algorithm for finding MST is proposed based on a deduced theorem of MST. The algorithm first sorts the edges in the graph according to their weight, from large to small. Next, on the premise of ensuring the connectivity of the graph, it deletes the edges in this order until the number of edges of the graph is equal to the number of vertexes minus 1. Finally, the remaining graph is an MST. This algorithm is equivalent to an inverse Kruskal algorithm. For sparse graphs, the proposed algorithm has higher efficiency than the Kruskal algorithm. Experiments and analysis verify the effectiveness of the algorithm proposed in this paper. The algorithm provides a better choice for finding the MST of the sparse graph. [ABSTRACT FROM AUTHOR]

This paper takes the flying defense team as the operating unit and studies the route planning and tasks in the process of plant protection operations as system problems. In the route planning, the flight defense team's operating time is used as the optimization goal of route planning. Taking the UAV battery and drug load as constraints, the flight mission is divided into subtasks that can be completed by a single UAV in a single flight. On this basis, the particle swarm algorithm is used to optimize the task allocation, so as to achieve the goal of UAV plant protection route and task division with minimum time loss. In order to verify the effectiveness of the method in this paper, 3 and 4 UAVs flying defense teams were used as the experimental objects, and experiments were carried out in large and small field groups. In terms of time consumption, the method in this paper is compared with the unit operation area method and the single size method. The energy consumption advantage is weaker than time consumption. In terms of time consumption, the method in this paper can greatly improve the efficiency of large-area operation areas. Compared with the unit operation area method, 3 drones can increase the efficiency by 58.98%, which is increased by 10.22% compared with the unit size method. The efficiency of 4 units can be increased by 58.08% compared with the unit operation area method, and the efficiency is increased by 10.22% compared with the unit size method. [ABSTRACT FROM AUTHOR]

Neural network is a supervised classification algorithm which can deal with high complexity and nonlinear data analysis. Supervised algorithm needs some known labels in the training process, and then corrects parameters through backpropagation method. However, due to the lack of marked labels, existing literature mostly uses Auto-Encoder to reduce the dimension of data when facing of clustering problems. This paper proposes an RBF (Radial Basis Function) neural network clustering algorithm based on K-nearest neighbors theory, which first uses K-means algorithm for preclassification, and then constructs self-supervised labels based on K-nearest neighbors theory for backpropagation. The algorithm in this paper belongs to a self-supervised neural network clustering algorithm, and it also makes the neural network truly have the ability of self-decision-making and self-optimization. From the experimental results of the artificial data sets and the UCI data sets, it can be proved that the proposed algorithm has excellent adaptability and robustness. [ABSTRACT FROM AUTHOR]

In this paper, a novel UAV path planning algorithm based on improved cellular ant colony algorithm and dynamic window algorithm (ICACO-IDWA) is proposed to solve the problem of dynamically changing threat during actual flight. The main innovations of this paper are as follows. (a) The hexagon grid method is proposed to model the UAV flight space, which solves the problem of inconsistent simulation time step. (b) A novel ICACO-IDWA algorithm is proposed. In the first stage, the optimal path is obtained by the improved cellular ant colony algorithm (ICACO). In the second stage, the improved dynamic window algorithm (IDWA) is used to optimize the optimal path considering dynamic threat. Through the algorithm, the UAV path planning with dynamic threat change is realized. Finally, simulation results verify the effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

The identification of disruptive technologies is intended to focus on training and incubation in advance and is an important means to accelerate the upgrading of industrial structure and the transformation of developmental mode and seize the commanding heights of future development. Based on summarizing the existing major identification methods of disruptive technologies, this paper concludes the rule that "disruptive technologies are always at the root node of a certain classification in the deeply classified technology development network". It also proposes a new algorithm to use term frequency-inverse document frequency technology and a patented Subject-Action-Object structure to extract technical features, develop networks based on similarity matrix generation technology, and identify subversive technologies based on the depth classification model. Using patent data, it is found that the technology development network generated by the algorithm proposed in this paper can effectively show the trajectory of technology development by fitting the patent citation relationship. Through this algorithm, we have successfully identified technologies that have had disruptive effects in the field and verified the effectiveness of this algorithm. [ABSTRACT FROM AUTHOR]

Recently, the same authors provided a switching algorithm for the preferred clock skew estimator appropriate for the precision time protocol (PTP) scenario. The algorithm chooses the one-way delay (OWD) clock skew estimator for the Forward path or the Reverse path or the two-way delay (TWD) clock skew estimator that has the best performance in the mean square error (MSE) perspective. However, the switching algorithm applies only to the Gaussian scenario. In a real system, the packet delay variation (PDV) can be characterized as an fractional Gaussian noise (fGn) process where the Hurst exponent parameter can also have values higher than 0.5. Thus, the Gaussian case-switching algorithm may not apply in a real system where after a small set of PTP measurements, the switching algorithm should be able to switch effectively to the preferred clock skew estimator with the best performance in the MSE perspective. In this paper, the PDV is characterized as an fGn process where the Hurst exponent (H) is in the range of 0.5 ≤ H < 1. We estimate the unknown PDV variances and the Hurst exponent parameters of the Forward and Reverse paths. These estimated parameters are used for switching to the preferred clock skew estimator from the MSE perspective, even in the presence of asymmetry in the PDV or in the Hurst exponent parameters. We also detect and alarm for the unexpected load, cyber-attack, or clock skew deviation that can occur in a real system. [ABSTRACT FROM AUTHOR]

Falls cause great harm to people, and the current, more mature fall detection algorithms cannot be well-migrated to the embedded platform because of the huge amount of calculation. Hence, they do not have a good application. A lightweight fall detection algorithm based on the AlphaPose optimization model and ST-GCN was proposed. Firstly, based on YOLOv4, the structure of GhostNet is used to replace the DSPDarknet53 backbone network of the YOLOv4 network structure, the path convergence network is converted into BiFPN (bidirectional feature pyramid network), and DSC (deep separable convolution) is used to replace the standard volume of spatial pyramid pool, BiFPN, and YOLO head network product. Then, the TensorRt acceleration engine is used to accelerate the improved and optimized YOLO algorithm. In addition, a new type of Mosaic data enhancement algorithm is used to enhance the pedestrian detection algorithm, improving the effect of training. Secondly, use the TensorRt acceleration engine to optimize attitude estimation AlphaPose model, speeding up the inference speed of the attitude joint points. Finally, the spatiotemporal graph convolution (ST-GCN) is applied to detect and recognize actions such as falls, which meets the effective fall in different scenarios. The experimental results show that, on the embedded platform Jeston nano, when the image resolution is 416 × 416, the detection frame rate of this method is stable at about 8.33. At the same time, the accuracy of the algorithm in this paper on the UR dataset and the Le2i dataset has reached 97.28% and 96.86%, respectively. The proposed method has good real-time performance and reliable accuracy. It can be applied in the embedded platform to detect the fall state of people in real time. [ABSTRACT FROM AUTHOR]

With the proliferation of portable digital products, image quality degradation has received a lot of attention. As the most common phenomenon in image degradation, the issue of image deblurring is the focus of much attention. Blind motion blur removal is the main target of this paper. The heavy-tailed distribution is the most dominant statistical feature of natural images. However, most image deblurring methods use a gradient prior with fixed parameters to recover a clear image, which leads to loss of details in the recovered clear image and does not consider the higher order prior of the natural image. Therefore, this paper proposes a new regularized image recovery model based on the Gaussian-scale mixture expert field(GSM-FOE) model. First, the GSM-FoE model learns filters and corresponding parameters with higher order prior information of images by training images in a natural image library; second, these learning results are used to guide the image recovery process. The GSM-FoE model and gradient-fidelity based image recovery model is proposed, which can be used with an iterative re-weighted least squares (IRLS) method. Experiments demonstrate that the suggested recovery approach is simple to use and successful at reducing blur and noise, as well as suppressing ringing effects while preserving image information. Moreover, the image restoration method performs well for large blurring kernels. The results fully reflect the effectiveness and robustness of the proposed method for complex noise scenarios. The quality of the generated images is significantly better than that of several classical methods. [ABSTRACT FROM AUTHOR]

Since there is a close relationship between network information security attack events and time complexity, it is necessary to count the degree of correlation between the current connection record and the connection record within a certain period of time before. Only in this way can the relationship between network connection data and network information security attack events be better reflected. In this paper, a rough Fourier fast algorithm based on rough set theory is proposed. Based on the characteristic attributes of the intrusion detection data set with the most value of character attributes as the data division basis, the computer network intrusion anomaly detection data set is intelligently divided into small data sets, so as to carry out attribute reduction. The network intrusion detection rule update experiment adopts the misuse detection method, extracts some samples from the KDD99 data set for training, obtains the computer network intrusion detection rules in the hierarchical decision table, and uses the incremental learning algorithm to update the rules, compared with the intrusion detection rules expressed by the decision table to test the feasibility and effectiveness of the rule update, and compared with the improved RSDB, RE-RFE algorithm, and KNN algorithm to evaluate the effect of the rough Fourier fast detection model applied to the problem of network intrusion anomaly detection. Using the attribute subset reduced by the rough Fourier algorithm to perform classification and modeling of computer network intrusion anomaly detection is significantly reduced, and the average time is reduced by 0.09 seconds, and this paper lays a good foundation for the application of network security intrusion detection algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the existing scenic spot recommendation algorithms ignore the implicit trust and trust transmission of users when dealing with user relationships, and the lack of historical browsing behavior data of users in new city scenes leads to an inaccurate recommendation. In this paper, a personalized scenic spot recommendation method combining user trust relationship and tag preference is proposed. Firstly, the trust degree is introduced when the recommendation quality is poor only considering the similarity of users. By mining the implicit trust relationship of users, the problem that the existing research cannot make recommendations when the direct trust is difficult to obtain is solved, and the data sparsity and cold start problems are effectively alleviated. Secondly, in the process of user interest analysis, the relationship between scenic spots and tags is extended to the relationship among users, scenic spots and tags, and users' interest preferences are decomposed into long-term preferences for different scenic spots tags, which effectively alleviates the problem of poor recommendation quality when users' historical tour records are lacking. The personalized tourism recommendation method proposed in this paper effectively integrates many features of social networks and effectively alleviates the problems of data sparseness and feature learning in tourism recommendation based on social networks by using vectorization and deep learning technology. Its research has very important usage scenarios and commercial value in the tourism industry. This model can efficiently mine the association rules between scenic spots in multisource information data. The experimental results show that mining the correlation between the scenic spots selected by tourists can provide effective information for tourism decision-making. [ABSTRACT FROM AUTHOR]

In recent years, an increasing number of population-based metaheuristic algorithms have been proposed to solve the flexible job-shop scheduling problem (FJSP) according to its practicality and complexity. Most of these algorithms are single-population-based and hence are very susceptible to becoming trapped in local optimal values. Thus, multipopulation methods are widely used to improve these algorithms, resulting in multipopulation algorithms. These multipopulation algorithms have been widely studied in the context of single-layer complex networks recently. However, coupled networks used to control two (or more) algorithms simultaneously to get a better algorithm are always ignored in literature. Therefore, in this paper, using coupled scale-free networks (with different scaling exponents) to control the genetic algorithm (GA) and the invasive weed optimization (IWO) simultaneously, a multipopulation GA/IWO with coupled scale-free networks (MPGAIWO-SF) is proposed to solve FJSP. Then, we study how some parameters (e.g., subpopulation size, subpopulation number, and scaling exponent) of MPGAIWO-SF affect its performance, which is also ignored in literature. The simulation results illustrate that (1) the performance of MPGAIWO-SF is significantly improved compared with that of GA and IWO; (2) as the subpopulation size increases, the performance of MPGAIWO-SF first becomes better and then remains almost unchanged; (3) as the subpopulation number increases, the performance of MPGAIWO-SF first becomes better and then decreases rapidly; and (4) the performance of MPGAIWO-SF becomes worse slightly as the scaling exponent decreases. Finally, solving more FJSP instances, the MPGAIWO-SF with optimized parameters is compared with other related algorithms to verify its effectiveness. [ABSTRACT FROM AUTHOR]

It is very important for power grid development research and related technical improvement to obtain the disaster situation of fine-scale distribution network, such as the transportation condition evaluation of distribution network and the wind waterlogging disaster prediction of distribution network. Among them, the wind waterlogging disaster prediction of distribution network is the main one, and the prediction of its disaster degree often determines whether the distribution network can be prevented before and rescued after the disaster. Therefore, in view of the above problems, combined with the actual transmission situation of the distribution network, after collecting the measured disaster data of the distribution network in relevant areas, combined with the multi-source data fusion technology and neural network modeling technology, this paper analyzes the disaster degree indicators of different distribution networks and constructs the relevant fuzzy matrix through the fuzzy theory to evaluate the disaster degree, which is verified by the measured data. This distribution network disaster loss prediction model can effectively implement the disaster loss prediction of distribution network and compare its prediction results with the other two different common models. The comparison results show that the prediction accuracy of the multi-source data fusion prediction model constructed in this paper is more than 0.95 compared with the other two models, while the prediction accuracy of the other two models is not more than 0.9, which proves that the model constructed in this paper has smaller errors. It has the advantages of higher accuracy and faster convergence speed. [ABSTRACT FROM AUTHOR]

Lack of trust, lack of standards, and low efficiency are the three biggest problems in China's trade financing at present. With the development and application of new generation technologies such as big data, cloud computing, artificial intelligence, and blockchain technology, China is in the stage of financial technology 3.0 under the deep integration of finance and technology. In the field of financial technology, the most concerned is the application of blockchain technology in trade finance business. With the successive construction of various blockchain platforms and the acceleration of the internationalization process, the international trade credit risk behind it is also increasing. Among many financial services, trade finance is the most closely integrated field with blockchain technology. In this context, preventing the risks in the business process of international trade enterprises, so as to reduce the cost of financial transactions, improve the effectiveness of financial services, and better serve the real economy is not only the internal development needs of enterprises, but also the national financial strategy needs. In view of the above problems, this paper analyzes the risk factors faced by multinational trading enterprises in the transaction process through the transaction data of some multinational enterprises on mobile phones, and constructs a credit evaluation system of international trading enterprises based on blockchain, in order to enhance the trade risk resistance ability of international trading companies. [ABSTRACT FROM AUTHOR]

In order to solve the limitations of the traditional method of determining expert weights in AHP and improve the level of the regional economy, this paper proposes a neural network algorithm based on AHP. This paper first calculates the interest correlation coefficient among experts, then obtains the comprehensive evaluation value of all experts according to the evaluation value matrix, and then determines the evaluation weight of each expert in the expert group. Finally, the comprehensive weight of each index is obtained by weighting the index weight obtained from the traditional AHP and the expert evaluation weight. Then, the evaluation value obtained by the improved AHP is used as a prior sample to train and test the BP neural network, and a classifier that can be popularized is obtained. The experimental results show that the overall evaluation value of circular economy of 9 construction enterprises is at a medium level, only 2 enterprises have an evaluation value of more than 50, and one enterprise has an evaluation value of only 35, indicating that the overall implementation of circular economy of these 9 construction enterprises is still at a low level, with great room for development and improvement. Conclusion. The economic management method based on a neural network algorithm can make the evaluation results more accurate and reliable and has high popularization and application value. [ABSTRACT FROM AUTHOR]

Night lighting is closely related to social and economic development. Inversion of socioeconomic parameters using nighttime light (NTL) remote sensing data is a research hot spot currently. In this paper, a calibration method based on adaptive clustering algorithm for the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data was proposed to remove background noise in the original imagery. The validity of the calibration method was evaluated through comparing the correlation between the corrected NTL data and the regional economic data. The result indicated that the NTL data obtained by this calibration method have higher correlation with the regional GDP data, and the values of R2 and the root mean square error (RMSE) were 0.8531 and 133.18, respectively. On this basis, the total nighttime light (TNL)-gross domestic product (GDP) regression model obtained from this paper was used to invert the GDP of Liaoning Province from 2012 to 2016. Using the TNL-GDP regression model established in high-quality regions to verify the fraudulent economic statistics of Liaoning Province, it can be proved that NTL data can be a reliable reference for reflecting regional economic development trends. [ABSTRACT FROM AUTHOR]

The concept of a "Smart City" emphasizes the need to employ information and communication technologies to strengthen the quality, connectivity, and efficiency of various municipal services. Cloud computing and the Internet of Things are shaping future tech. Both ideas greatly impact smart city application and solution development. Cloud computing is amazing at managing and storing remote service access. Several companies have switched to cloud leasing to reduce local resource burden. Due to the intricacy and flexibility of cloud-maintained services, selecting jobs that best suit client needs should be optimized. Quality of service criteria for each cloud service are the best tools for choosing and optimizing cloud carriers. Genetic algorithms (GAs) and ant colony optimization (ACO) are combined to make cloud computing. It is discovered that the recommended ACO + GA obtains an accuracy of 82% when compared to existing methods of energy- and reliability-aware multiobjective optimization method and the hybrid cuckoo particles swarm, artificial bee colony optimization (CPS + ABCO) where accuracy is 68% and 75%, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a multiview collaborative visual analysis system of network security based on a DPCA (clustering by fast search and find of density peaks) clustering algorithm called DPCANETVis, with network security analysis requirements for multisource heterogeneous data. Firstly, the system proposes an improved DPCA clustering algorithm based on the hierarchical relationship of mail sending and receiving to achieve the purpose of accurate classification. Secondly, a three-layer visual layout is designed to display relevant information such as data hierarchy, node relationship, behavior model, and other relevant information, by mixing a variety of interactive visual analysis methods such as tree diagram, word cloud, line diagram, subject river, and parallel coordinate. Lastly, based on the exploration of events, all suspicious nodes and their abnormal behaviors can be displayed in the system. Finally, the prototype system is used to analyze the network security log data set provided by the ChinaVis 2018, and the feasibility of the multilevel interactive visual analysis method for network security is verified through many experiments and discussions. [ABSTRACT FROM AUTHOR]

The dynamic time warping algorithm (DTW) has problems such as high computational complexity and "ill-conditioned matching." Aiming at the above two main problems, this paper proposes an improved DTW algorithm for the final wave recording of the primary and secondary deep fusion equipment detection platform. The terminal recorded waveform and the waveform with non-Gaussian noise added as the research object, the two sets of waveforms are divided into frames and windowed, and the short-term energy entropy ratio of the two sets of waveforms is input into the DTW as the test vector. Using the optimal matching paths and distances of the two input vectors, the common substring lengths of the two sets of short-term energy entropy ratio sequences are calculated. Then, we define the optimal matching coefficient and correct the waveform similarity. The experimental data show that the improved DTW algorithm can accurately quantify the similarity between terminal waveforms, which can provide effective data support for the health status assessment of power distribution terminals. [ABSTRACT FROM AUTHOR]

Small samples are prone to overfitting in the neural network training process. This paper proposes an optimization approach based on L2 and dropout regularization called a hybrid improved neural network algorithm to overcome this issue. The proposed model was evaluated based on the Modified National Institute of Standards and Technology (MNIST, grayscale-28 × 28 × 1) and Canadian Institute for Advanced Research 10 (CIFAR10, RGB - 32 × 32 × 3) as the training data sets and data applied to the LeNet-5 and Autoencoder neural network architectures. The evaluation is conducted based on cross-validation; the result of the model prediction is used as the final measure to evaluate the quality of the model. The results show that the proposed hybrid algorithm can perform more effectively, avoid overfitting, improve the accuracy of network model prediction in classification tasks, and reduce the reconstruction error in the unsupervised domain. In addition, employing the proposed algorithm without increasing the time complexity can reduce the effect of noisy data and bias and improve the training time of neural network models. Quantitative and qualitative experimental results show that the accuracy of using the proposed algorithm in this paper with the MNIST test set has an improvement of 2.3% and 0.9% compared to L2 regularization and dropout regularization, respectively, and based on the CIFAR10 data set, the accuracy improvement of 0.92% compared with L2 regularization and 1.31% concerning dropout regularization. The reconstruction error of using the proposed algorithm in this paper with the MNIST data set has an improvement of 0.00174 and 0.00398 compared to L2 regularization and dropout regularization, respectively, and based on the CIFAR10 data set, the accuracy improvement of 0.00078 compared with L2 regularization and 0.00174 concerning dropout regularization. [ABSTRACT FROM AUTHOR]

This research aims to improve the rationality and intelligence of AUTOMATICALLY HIGHER MATHEMATICALLY EXAM SYSTEM (AHMES) through some AI algorithms. AHMES is an intelligent and high-quality higher math examination solution for the Department of Computer Engineering at Pai Chai University. This research redesigned the difficulty system of AHMES and used some AI algorithms for initialization and continuous adjustment. This paper describes the multiple linear regression algorithm involved in this research and the AHMES learning (AL) algorithm improved by the Q-learning algorithm. The simulation test results of the upgraded AHMES show the effectiveness of these algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a data-driven differential dynamic programming (DDP) algorithm is presented for the optimal tracking problems of nonlinear affine systems with unknown drift dynamics. Optimal tracking dynamics are established between the system states and expected trajectory. By using the DDP method and test data, a second-order data-driven framework of the Hamilton–Jacobi–Bellman (HJB) equation is constructed, in which system approximation and value function approximation are used. By using this framework, a data-driven iteration algorithm is proposed to achieve the optimal tracking controller. A simulation example is provided to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

With the development of science and technology, it has had a great impact on the manufacturing industry. The manufacturing industry has higher and higher requirements for industrial manipulators, and the expectations for the quality and function of the industrial manipulators in the manufacturing industry are increasing day by day. At the same time, the requirements for the working accuracy of the manipulators are also getting higher and higher. In this paper, a new method is proposed. An error compensation and calibration algorithm based on parameter separation and integration of the standard DH model (SDH) is used to construct the kinematic model of the manipulator. The six groups of structural parameters of the six-axis manipulator are calibrated once every two groups, thereby eliminating the influence of the singular value of the position of the manipulator. Finally, the feasibility of this scheme is verified by simulation experiments, the accuracy is ensured, and the expected purpose is achieved. Because the method in this paper has high requirements on the posture of the manipulator, it reduces the overall calibration calculation and saves time, which has a certain effect on the improvement of the industrial manipulator in the manufacturing industry. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

The paper investigates DOA estimation of coherent Signals with the limited aperture sparse array. Mutual coupling between the sensors of the array cannot be ignored in practical radar with a limited aperture of array sensors, which will result in a degradation in the performance of Direction of Arrival (DOA) estimation. This paper proposes a Mutual-coupling-optimized array (MCOA) with a limited aperture in this scenario to reduce the mutual coupling effect. Firstly, we prove the sparse uniform linear array (SULA) has the smallest mutual coupling leakage when the array aperture and the number of sensors is determined. Secondly, we modify the spacing of the array sensors in SULA to make sure that the spacing between all array sensors and the reference sensor are coprime aiming to estimate DOA without spatial aliasing. Thirdly, we give an expression for the array element spacing arrangement with reduced mutual coupling leakage. Finally, the coherent signals are well resolved by the Sparse Bayesian Learning (SBL) algorithm. Numerous simulations are conducted to validate the advantages of the proposed array compared to several sparse arrays for estimating coherent signals in the presence of mutual coupling. [ABSTRACT FROM AUTHOR]

Because most of the production and transmission components of the power system are exposed to the natural environment, they are vulnerable to the threat of natural disasters for a long time and the resulting circuit damage is also large. With the improvement of people's living standards and the improvement of electricity demand, more and more attention has been paid to the normal operation of the power system. Improving the disaster resistance ability of the power system is an important prerequisite to ensure the national economy and life. In view of the above problems, in order to solve and improve the power supply recovery ability of the distribution network in the face of various disasters, this paper focuses on the analysis of the disaster response stage of the intelligent distribution network under natural disasters and the comprehensive data and related indicators of the power grid system, so as to establish the evaluation index system of the power grid's predisaster tolerance and postdisaster resilience and, through the introduction of the fuzzy comprehensive evaluation theory, quantifying the disaster response capability of the distribution network. Finally, through the introduction of case analysis, the case results show that the intelligent distribution network disaster response ability evaluation algorithm based on fuzzy comprehensive evaluation constructed in this paper can accurately calculate the disaster response ability of the distribution network and has an important guiding role in the disaster prevention and reduction of the distribution network. [ABSTRACT FROM AUTHOR]

In this paper, the clustering algorithm of big data fuzzy K-means is used to research and analyze the teaching management system. Since the values of the indicator variables of bad units are usually significantly different in smooth areas and discontinuous areas, we cluster the indicators of local areas through K-means clustering, so that only good units or bad units are included in each class. The server is deployed on the campus and is networked with the teaching computers of each classroom. Teachers log in to the class rate management system before or during class, log in to their respective users, select the corresponding class, and make corresponding attendance records according to the attendance of students in the class. The system automatically counts the number of expected arrivals, attendance, late arrivals, leave requests, and absenteeism for the class. Teachers can also fill in information feedback for this class: a list of absenteeism, classroom discipline, student learning status, teaching suggestions, and teaching testimonials. The development environment built by this system is a combination of PHP + MYSQL, which reasonably planned according to the overall needs. The development mode adopts the top-down model, which runs through the whole process from system development to testing to application. The most used floating point number encoding is the decimal floating point number encoding. The relationship of each module of the virtual reality teaching management system is sorted out, the overall framework of the virtual reality teaching management system is established, and the design of the business flow diagram of the management system is completed. This paper also uses Oracle database technology to further explain the design process of designing the system database. Finally, combined with the system design and implementation, the system test is completed from two aspects: system function test and performance test. [ABSTRACT FROM AUTHOR]

In the era of "Internet +," the world economy is increasingly globalized and informatized, the development of China's B2C e-commerce is facing unprecedented opportunities, but it is also constrained by consumption risks. Consumption risk will make consumers have a crisis of trust in e-commerce, which brings uncertainty to the development of B2C. Therefore, it is very necessary to predict and prevent consumption risks in B2C e-commerce and take corresponding legal countermeasures. It is well known that neural networks (NNs) have strong predictive ability, but there are also problems such as lack of stability. As a result, in order to improve the prediction ability of neural network, principal component analysis and particle swarm technology are proposed in this paper as well as its stability and prediction error. The risk prediction accuracy of the BP NN (BPNN) technique was the lowest at 60% and the maximum at 70%, according to the experimental results of this research. The GA-BP technique has the lowest risk prediction accuracy of 80 percent. The risk prediction accuracy of the PSO-BP method is the lowest with 90% and the highest with 100%. Although the NN before the improvement can effectively predict the consumption risk, the risk prediction ability of the improved NN combined with principal component analysis and particle swarm algorithm is higher. Therefore, in life, the relevant personnel can apply the GA-BP and PSO-BP methods to the consumption risk prediction in B2C e-commerce to reduce the risk and make the e-commerce develop better. [ABSTRACT FROM AUTHOR]

This paper proposes a nonelliptic extended English teaching ability evaluation algorithm based on an adaptive random matrix model. The algorithm models a nonelliptical expansion target as multiple elliptical sub-objectives, and the expansion state of each sub-target is described by an inverse Wishart distribution. The new method is combined to improve the robustness problem caused by the initialization after the algorithm expansion. This research uses the smart teacher education platform to conduct research and analysis on the evaluation of teaching practice ability of intern normal students. From the perspective of data evaluation, we explore the influence of normal students' curriculum training on the teaching practice ability of normal students. By analyzing the correlation between normal students' course grades and practice grades, this paper explores the influence of normal students' prepractice training on normal students' teaching practice ability. This paper explores the influence of the training of normal students' course learning on the teaching practice ability of normal students. The results show that the learning level of normal students' professional courses has a significant impact on the development of normal students' practice performance and teaching practice ability; the impact of normal students' pedagogical course level on normal students' practice performance and teaching practice ability is relatively low. [ABSTRACT FROM AUTHOR]

A vehicle's oscillation can be improved by using an active suspension system to replace the conventional passive suspension system. The active suspension system operates on the control signal from the controller. This paper introduces the Fuzzy-PI integrated control algorithm to control the system's efficiency. The Fuzzy algorithm adjusts the KP and KI parameters of the controller. This response depends on the oscillation state of the vehicle. A quarter model with a hydraulic actuator was used in this research. The simulation process takes place in the MATLAB-Simulink environment. In the first case, the maximum and average values of displacement and acceleration of the sprung mass when the vehicle uses the Fuzzy-PI algorithm are only 2.68%, 1.34%, and 1.65%, 0.66%, respectively, compared to the situation of vehicles using the passive suspension system. For the second case, these values are 3.33%, 1.69%, and 2.01%, 0.73%, respectively. As a result, the vehicle's stability and comfort are greatly enhanced when the integrated controller is used. Besides, the change of KP and KI parameters is also completely suitable for the vehicle's driving conditions. This research only leads to calculations and simulations; the experimental process is expected to occur soon. [ABSTRACT FROM AUTHOR]

In the Internet of Things (IoT) ecosystem, localization is critical for tracking and monitoring targets via nodes. The distance vector-hop (DV-Hop) technique is a good choice for localizing neighborhood in IoT networks. The conventional DV-Hop algorithm is a distributed localization approach that does not consider the distribution of the nodes into full deliberation when calculating the hop count from the source to destined nodes. The transfer distance and node positions thus do not attain higher efficiency while ascertaining the distance between sources and destined nodes. The study aims to resolve the pitfalls in the traditional algorithm by making enhancements in controlling the original DV-Hop algorithm's hop count and transfer distance method by utilizing the particle swarm to estimate the node positions. Error rate in the distance between beacon nodes and unseen nodes is effectively reduced with the proposed technique that calculate error factors with corrections in a reversed fashion to revise hop counts. An escape factor is introduced to take control of updating particles' velocity in the system, and the inertia weight is defined by a piecewise function to enlarge search space. This mechanism increases the diversity of the particle populations and mitigates the tendency of estimations on node positions to be trapped into local optima under stationary state. Also, the improved DV-Hop algorithm described in the paper has a better convergence speed due to the presence of random inertia weight logarithmic method. Finally, the problem of premature convergence is also tackled as a variation factor is adopted in collaboration with a fitness function that affects the particles' movement range and assists in global convergence. The overall performance of improved DV-Hop is evaluated by statistical metrics and also compared with the traditional DV-Hop algorithm under simulated environment with the data collected from real-world scenarios. Industry 4.0 is fully dependent upon IoT and the count of hops is very important for deciding the routing from the source to destination for speedy transmission of data. The improved DV-hop algorithm can achieve better results and has reduced error rate by more than 30%. The DV-Hop algorithm plays an important role in IoT-enabled environment especially in Industry 4.0. [ABSTRACT FROM AUTHOR]

Post competency assessment has become an important part of modern recruitment, and teaching is one of the indispensable occupations. Assessing the competence of teachers in teaching positions has become a hot topic. This paper takes English teachers as the research object and uses K-means clustering algorithm to evaluate the competence of teachers. This paper first analyzes the references related to this research and finds suitable materials for this research and then mainly describes in detail the main point of English teaching post competency. Next, this paper uses the formula to illustrate the K-means clustering algorithm used in this paper. Finally, it uses experiments to verify the influencing factors of the four dimensions of English job competency. And the final survey results show that the influencing factors have different significant differences on the four dimensions. And the experiment proves the effectiveness and robustness of the proposed algorithm in evaluating the competence of English teaching positions. In addition, from the perspective of the composition of professional titles, it is consistent with the age structure, accounting for 63.3% of the total. Associate senior researchers are the backbone of colleges and universities. This research provides a theoretical basis and reference for measurement tools for college English teacher recruitment and selection, vocational training, performance evaluation, salary reform, and other related educational practices. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of current music recommendation algorithms, such as low accuracy and poor timeliness, a personalized hybrid recommendation algorithm incorporating genetic features is proposed. The user-based collaborative filtering (UserCF) algorithm analyzes the degree of users' preference for music genes. The improved neural matrix decomposition collaborative filtering (B-NCF) algorithm calculates the correlation between similar users and constructs the adjacency relationship between users. The results of the two algorithms are fused by using a weighted hybrid approach to generate the recommendation list. Finally, the hybrid recommendation model is built on the Spark platform. The paper's traditional and hybrid recommendation algorithms are validated using the Yahoo Music dataset. The experimental results show that the advantages of the algorithm in this paper are more significant under the MAE and F1-measure indexes, and the recommendation accuracy and precision have been greatly improved; the hybrid algorithm can ensure the diversity of the recommended contents, the recommendation hit rate is higher, and the timeliness meets the demand of personalized music recommendation. [ABSTRACT FROM AUTHOR]

Algorithms are ubiquitous in nature and human society, and algorithms in national sports are the internal mechanisms for the creation and development of national sports. Algorithms from nature, society, and culture act as the external driving force for the development of ethnic sports. Different ethnic sports are based on physical behaviors, including physical recreation, social interaction, and life-shaping behaviors. In this paper, we suggest an algorithm for health and wellness elevation of ethnic sports in the context of body-medicine integration, examine the fall situation in sports life, and propose a bidirectional LSTM fall detection model, which can automatically extract deeper data features within the fall behavior for the input data (taken from inertial sensors) and realize the processing of data from preprocessing to detection results. Medical disciplines provide scientific ideas and pathways that are founded on a rigorous medical way of thinking and knowledge system to summarize sports, so that they can be prescribed to explore new pathways of exercise for health, to carry out deliberate, planned, and scientific exercise. Finally, the superiority of the proposed algorithm in this paper is verified on a relevant dataset. [ABSTRACT FROM AUTHOR]

This paper presents a backpropagation neural network algorithm for data compression and data storage. Data compression or establishing model ten coding is the most basic idea of traditional data compression. The traditionally designed ideas are mainly based on reducing the redundancy in the information and focus on the coding design, and its compression ratio has been hovering around dozens of percent. After the traditional coding compression of information, it is difficult to further compress by similar methods. In order to solve the above problems, the information that takes up less signal space can be used to represent the information that takes up more signal space to realize data compression. This new design idea of data compression breaks through the traditional limitation of relying only on coding to reduce data redundancy and achieves a higher compression ratio. At the same time, the information after such compression can be repeatedly compressed, and it has a very good performance. This is the basic idea of the combination of neural network and data compression introduced in this paper. According to the theory of multiobjective function optimization, this paper puts forward the theoretical model of multiobjective optimization neural network and studies a multiobjective data compression method based on neural network. According to the change of data characteristics, this method automatically adjusts the structural parameters (connection weight and bias value) of neural network to obtain the largest amount of data compression at the cost of small information loss. This method has the characteristics of strong adaptability, parallel processing, knowledge distributed storage, and anti-interference. Experimental results show that, compared with other methods, the proposed method has significant advantages in performance index, compression time and compression effect, high efficiency. and high-quality robustness. [ABSTRACT FROM AUTHOR]

Corner detection is a common method to obtain image features, and the detection effect influences the performance of matching and tracking directly. A FAST-Harris fusion corner detection algorithm is proposed to improve the shortcomings of the Harris algorithm, such as the low detection accuracy and low positioning accuracy, and a corner detection fusion model is established. First, the detected target image is padded, and then the FAST algorithm is used with a 25% reduced contrast points to achieve fast capture roughly; in this way, a candidate corner set is obtained. Then, screening the candidate corner is set one by one by calculating the response function of the Harris with Scharr operator to achieve capture accurately. Finally, the real corners are obtained using SAD for nonmaximum suppression. The positioning error, error detection rate, robustness, and running time of corner detection are obtained by the PyCharm platform. Compared with Harris, the error detection rate and localization error of the algorithm are reduced by 16.89% and 42.04%, respectively. Compared with 8 popular corner detection algorithms, the error detection rate and localization error of the algorithm in this paper are the lowest, which are 24.60% and 1.42 pixels. The robust performance in lossy JPEG compression is the best, with 17.37% shorter running time than Harris algorithm. The method in this paper can be used in scenarios such as autonomous driving and image search services. [ABSTRACT FROM AUTHOR]

Natural language processing is an important direction in the field of computer science and artificial intelligence. It can realize various theories and methods of effective communication between humans and computers using natural language. Machine learning is a branch of natural language processing research, which is based on a large-scale English-Chinese database. Due to the relatively poor alignment corpus of English and Chinese bilingual sentences containing unknown words, machine translation is unprofessional and unbalanced, which is the problem studied in this paper. The purpose of this paper is to design and implement a length-based system for sentence alignment between English and Chinese bilingual texts. The research content of this paper is mainly divided into the following parts. First, the evaluation function of bilingual sentence alignment is designed, and on this basis, the bilingual sentence alignment algorithm based on the length and the optimal sentence pair sequence search algorithm is designed. In this paper, China National Knowledge Infrastructure (CNKI) is selected as an English-Chinese bilingual candidate website and English-Chinese bilingual web pages are downloaded. After analyzing the downloaded pages, nontext content such as page tags is removed, and bilingual text information is stored so as to establish an English-Chinese bilingual corpus based on segment alignment and retain English-Chinese bilingual keywords in the web pages. Second, extract the dictionary from the software StarDict, analyze the original dictionary format, and turn it into a custom dictionary format, which is convenient and better to use the double-sentence sentence alignment system, which is conducive to expanding the number of dictionaries and increasing the professionalism of vocabulary. Finally, we extract the stems of English words from the established corpus to simplify the complexity of English word processing, reduce the noise caused by the conversion of word parts of speech, and improve the operation efficiency. A bilingual sentence alignment system based on length is implemented. Finally, the system parameters are adjusted for comparative experiments to test the system performance. [ABSTRACT FROM AUTHOR]

In this paper, the Gram matrix is used to calculate the correlation of the filter response sets under different scale kernels learned by each layer of the network in the deconvolution, and the loss between the corresponding feature response correlations in the multilayer network is calculated. Linear summation is used to obtain a stable, multiscale image model representation. This paper extracts the contours of the salient areas of the image and adjusts the parameters of the deconvolution network to learn the salient area patterns of the image. At the same time, for the image to be generated, a shape template is used to limit the range of the area to be generated in order to obtain a shape image with similar patterns. When the spatial relative relationship characteristics of the image constituent objects are obvious, we appropriately add high-level semantic feature activation values for reinforcement. This paper solves the estimation of the unknown blur kernel by using image prior knowledge, filtering and gradient domain algorithms and other different technologies to obtain image jitter or scene movement information and estimate the size, location, and density of the blur kernel. This paper studies a relatively robust deconvolution model, which is insensitive to random noise, has stable effects, and can overcome the water ripple effect caused by the usual convolution process. This paper attempts to study the fuzzy model with variable space. The usual blur is a spatial invariant model; that is, a single kernel is used to describe the motion of all pixels on the image. By selecting different characteristic parameters, this paper conducts experimental research on some existing hydrophobic indicator function methods and calculates the relationship between characteristic parameters and hydrophobicity when different hydrophobic indicator functions are adopted. One characteristic of the hydrophobic image of composite insulators is low contrast. The traditional method of converting color images to grayscale images cannot improve the image contrast. This paper analyzes the hydrophobic image of the composite insulator, and the extracted B channel component image of the hydrophobic image improves the contrast of the image and facilitates the subsequent segmentation of water traces and background. In this paper, the water repellent image's watermark area is counted, and connected-domain wave processing is used to limit the area of water droplets retained, thereby improving the efficiency of filtering water droplets without having a big impact on the image as a whole. The problem of uneven illumination is an unavoidable problem in the field of image processing, and the resulting reflection problem brings difficulties to image processing. This article regards the reflective area of the watermark as a "hole" and uses the idea of "hole filling" to eliminate the reflective point, which weakens the reflection problem to a certain extent. [ABSTRACT FROM AUTHOR]

Irony recognition is an important research direction in text sentiment analysis, which contributes to discover ironic tone to judge text emotion correctly. The object of this paper is the application of deep learning to irony recognition, and the research aims to solve the problem that existing machine learning algorithms have difficulty in discriminating ironic tones. Aiming at the irony recognition of Chinese social comments, DC-BiGRU-CNN is proposed from the perspective of structural optimization rather than a text vectorization mechanism point of view, which is a dual-channel CNN combined with BiGRU, and incorporates attention mechanism and multigranularity convolutional neural network as the main framework. This paper first briefly discusses the difficulties of irony recognition and provides an overview of existing text sentiment analysis algorithms. This is followed by a detailed discussion of DC-BiGRU-CNN. Furtherly, it is compared with the main irony recognition methods on a social comment dataset containing Chinese ironic comments, and the experimental results show that DC-BiGRU-CNN can improve the accuracy of irony recognition. [ABSTRACT FROM AUTHOR]

At present, FPGA (field-programmable gate array) architecture has made great progress in the requirements of hardware volume, which can meet common needs. However, for the increasing number of resources, it is difficult to significantly reduce the delay of process mapping. Therefore, this paper proposes FDMAP (fit descending map) algorithm from the perspective of the LUT number to reduce the delay. This paper proposes a method of FPGA mapping and debugging for heterogeneous multicore high-performance processors based on isomorphic symmetric FPGA architecture, which effectively utilizes the architectural features of heterogeneous multicore processors and the symmetric features of isomorphic FPGA, divides FPGA functions from top to bottom in a hierarchical way, and constructs FPGA architecture from bottom to top. Using differential bridge and adaptive delay adjustment sampling technology, combined with the embedded virtual logic analyzer debugging tool, FPGA architecture can be lightened and deployed quickly. Multicore complementary core-to-core replacement simulation mapping methods such as debug shells can be used to effectively complete the mapping of the target's high-performance heterogeneous multicore processor to the entire SOC (system on-ship) chip system-level FPGA. In the aspect of algorithm, the fdmap algorithm is mainly implemented, and the low latency mapping of resources is realized with FPGA architecture. In order to verify the effectiveness of mapping the fdmap algorithm, this paper compares the fdmap algorithm with the vector VM algorithm. The research shows that when the wavelength resolution is 7 pm and the temperature error is less than 1°C, the shell is debugged, and 10 mapping examples are simulated with the fdmap algorithm. In the experiment, the LUT with the most critical 20% is selected, and the closed value of the LUT search type is set to 0.86. Compared with the original data, the number of LUTs increased by 15.2%, and the criticality decreased by 35.21%. Compared with the vector VM algorithm with the biggest gap, the number of LUTs decreased by 14.25%, the criticality improved by 14.21%, and the overall delay decreased by 65%. Therefore, the isomorphic symmetric FPGA architecture proposed in this paper can improve the structural criticality and significantly reduce the latency while reducing the number of LUTs. [ABSTRACT FROM AUTHOR]

In this paper, the problem of direction-of-arrival (DOA) estimation for strictly noncircular sources under the condition of unknown mutual coupling is concerned, and then a robust real-valued weighted subspace fitting (WSF) algorithm is proposed via block sparse recovery. Inspired by noncircularity, the real-valued coupled extended array output with double array aperture is first structured via exploiting the real-valued conversion. Then, an efficient real-valued block extended sparse recovery model is constructed by performing the parameterized decoupling operation to avoid the unknown mutual coupling and noncircular phase effects. Thereafter, the WSF framework is investigated to recover the real-valued block sparse matrix, where the spectrum of real-valued NC MUSIC-like is utilized to design a weighted matrix for strengthening the solutions sparsity. Eventually, DOA estimation is achieved based on the support set of the reconstructed block sparse matrix. Owing to the combination of noncircularity, parametrized decoupling thought, and reweighted strategy, the proposed method not only effectively achieves high-precision estimation, but also efficiently reduces the computational complexity. Plenty of simulation results demonstrate the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Many clustering algorithms are in favour of node-based methods, but a link between nodes has one single feature, so link-based clustering is sometimes easier than node-based methods. Being dependent on the characteristics of links, a detection algorithm for a non-overlapping link community is put forward in this paper. The method proposed also distinguishes the differences between nodes with a high degree of accuracy and detects communities with a minimal number of overlapping nodes. On the basis of three different datasets, experiments were conducted to compare the proposed algorithm with different non-overlapping and overlapping clustering algorithms, and the results show that our algorithm generates the least number of overlapping nodes and achieves a good community partition. [ABSTRACT FROM AUTHOR]

For the complex system modeling of operating underwater robots, various modules are built in Simulink to simulate the interaction of various forces and torques inside the underwater vehicle. According to the characteristics of the ROV itself, the ROV dynamics model is reasonably designed. In view of the problems of the position tracking delay, underwater environment interference, and unstable operation of the ROV, the fuzzy fractional PID control algorithm is adopted in the controller in this paper, and the control effect of the fuzzy PID and PID algorithm is compared and analyzed. [ABSTRACT FROM AUTHOR]

As a highly dense area for passengers, the airport departure hall is particularly characterized by the easy gathering of passengers, irregularity, and difficulty in command, which has become a high incidence of emergencies. For the evacuation problem in case of emergencies, the vast majority of passengers randomly choose the shortest path from the exit to escape, which will lead to congestion at some nodes and prolonging of the evacuation time. Therefore, this study proposes a better evacuation method considering the dynamic change in the spatiotemporal congestion of passengers. First, this paper constructs an evacuation network diagram for the departure hall of the Shenzhen Bao'an International Airport terminal building, simplifies the evacuation process into point-to-point evacuation, and proposes a time-varying network algorithm to optimize the emergency evacuation of the airport. Second, on the premise of obtaining the number and dynamic distribution of passengers in each area in real time, a linear programming model with the shortest evacuation time as the optimization objective is established, and the current optimal evacuation path of passengers is dynamically calculated. Finally, the two evacuation methods, random evacuation and evacuation, based on a time-varying network algorithm are simulated and analyzed by AnyLogic software. The results show that the evacuation scheme based on the time-varying network algorithm can effectively relieve the passenger congestion of some evacuation paths and safety exits. The evacuation time is reduced by 74 s, the evacuation efficiency is improved by 14%, passenger congestion at some evacuation routes and safety exits can be effectively relieved, and the global optimization of the emergency evacuation of passengers is achieved. [ABSTRACT FROM AUTHOR]