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At present, object detectors based on convolution neural networks generally rely on the last layer of features extracted by the feature extraction network. In the process of continuous convolution and pooling of deep features, the position information cannot be completely transferred backward. This paper proposes a multiscale feature reuse detection model, which includes the basic feature extraction network DenseNet, feature fusion network, multiscale anchor region proposal network, and classification and regression network. The fusion of high-dimensional features and low-dimensional features not only strengthens the model's sensitivity to objects of different sizes but also strengthens the transmission of information, so that the feature map has rich deep semantic information and shallow location information at the same time, which significantly improves the robustness and detection accuracy of the model. The algorithm is trained and tested in Pascal VOC2007 dataset. The experimental results show that the mean average precision of the objects in the dataset is 73.87%. At the same time, compared with the mainstream faster RCNN and SSD detection models, the mean average precision of object detection algorithm based on DenseNet is improved by 5.63% and 3.86%, respectively. [ABSTRACT FROM AUTHOR]

Simultaneous Localization and Mapping (SLAM) has become a research hotspot in the field of robots in recent years. However, most visual SLAM systems are based on static assumptions which ignored motion effects. If image sequences are not rich in texture information or the camera rotates at a large angle, SLAM system will fail to locate and map. To solve these problems, this paper proposes an improved ORB-SLAM2 algorithm based on information entropy and sharpening processing. The information entropy corresponding to the segmented image block is calculated, and the entropy threshold is determined by the adaptive algorithm of image entropy threshold, and then the image block which is smaller than the information entropy threshold is sharpened. The experimental results show that compared with the ORB-SLAM2 system, the relative trajectory error decreases by 36.1% and the absolute trajectory error decreases by 45.1% compared with ORB-SLAM2. Although these indicators are greatly improved, the processing time is not greatly increased. To some extent, the algorithm solves the problem of system localization and mapping failure caused by camera large angle rotation and insufficient image texture information. [ABSTRACT FROM AUTHOR]

Small and micro enterprises play a very important role in economic growth, technological innovation, employment and social stability etc. Due to the lack of credible financial statements and reliable business records of small and micro enterprises, they are facing financing difficulties, which has become an important factor hindering the development of small and micro enterprises. Therefore, a credit risk measurement model based on the integrated algorithm of improved GSO (Glowworm Swarm Optimization) and ELM (Extreme Learning Machine) is proposed in this paper. First of all, according to the growth and development characteristics of small and micro enterprises in the big data environment, the formation mechanism of credit risk of small and micro enterprises is analyzed from the perspective of granularity scaling, cross-border association and global view driven by big data, and the index system of credit comprehensive measurement is established by summarizing and analyzing the factors that affect the credit evaluation index. Secondly, a new algorithm based on the parallel integration of the good point set adaptive glowworm swarm optimization algorithm and the Extreme learning machine is built. Finally, the integrated algorithm based on improved GSO and ELM is applied to the credit risk measurement modeling of small and micro enterprises, and some sample data of small and micro enterprises in China are collected, and simulation experiments are carried out with the help of MATLAB software tools. The experimental results show that the model is effective, feasible, and accurate. The research results of this paper provide a reference for solving the credit risk measurement problem of small and micro enterprises and also lay a solid foundation for the theoretical research of credit risk management. [ABSTRACT FROM AUTHOR]

Flight safety is of vital importance for tilt-rotor unmanned aerial vehicles (UAVs), which can take off and land vertically as well as cruise at high speed, especially in different kinds of complex environment. As being the executor of the flight control, the actuator failure will directly affect the controllability of the tilt-rotor UAV, and it has high probability of causing fatal personal injury and financial loss. However, due to the limitation of weight and cost, small UAVs cannot be equipped with redundant actuators. Therefore, there is an urgent need of fault detection and diagnosis method for the actuators. In this paper, an actuator fault detection and diagnosis (FDD) method based on the extended Kalman filter (EKF) and multiple-model adaptive estimation (MMAE) is proposed. The actuator deflections are added to the state vector and estimated using EKF. The fault diagnosis algorithm of MMAE could assign a conditional probability to each faulty actuator according to the residual of EKF and diagnose the fault. This paper is structured as follows: first, the structure and model of tilt-rotor UAV actuator are established. Then, EKF observers are introduced to estimate the state vector and to calculate residual sequences caused by different faulty actuators. The residuals from EKFs are used by fault diagnosis algorithm to assign a conditional probability to each failure condition, and fault type can be diagnosed according to the probabilities. The FDD method is verified by simulations, and the results demonstrate that the FDD algorithm could accurately and efficiently diagnose actuator fault without any additional sensor. [ABSTRACT FROM AUTHOR]

In recent years, more and more scientists have been interested in research on driving two-wheel bicycles. The problems in two-wheel bicycle control problem are self-balancing, uncertain models, and the impact of noise. In the paper, to solve the self-balancing problem, we use the flywheel method according to the inverted pendulum principle. To overcome the effects of the uncertain model, the impact of noise, we designed the vehicle balance controller according to the robust control algorithm. However, robust controllers often have a high order, which affects the quality during real control. To simplify the robust controller, we propose the use of a model order reduction algorithm. The simulation and experimental results have proved the correctness of the solutions given in the paper. [ABSTRACT FROM AUTHOR]

The traditional support vector machine algorithm is not enough to classify single-stranded DNA molecules, so this paper proposes an improved threshold extraction algorithm based on collaborative filter for the classification of single-stranded DNA. Firstly, according to the different characteristic curves of the blocking current signals formed by the four bases (A, T, C, and T) that make up DNA molecules crossing the nanopore, the collaborative filter feature extraction algorithm with improved threshold is proposed. Then, the feature information is reconstructed and sent to the SVM classifier for training. Finally, the unfiltered, collaborative filter, improved threshold collaborative filter, and Bessel filter data are, respectively, extracted and sent to the SVM classifier for classification and comparison research. The experimental results show that the improved collaborative filter algorithm has higher accuracy in single-stranded DNA molecular classification. [ABSTRACT FROM AUTHOR]

In view of the poor performance of the original mathematical model of assembly construction project precost budget, a mathematical model of assembly construction project precost budget based on improved neural network algorithm is proposed. This paper investigates the cost content of assembly construction project and analyzes its early cost. It finds that the early cost of assembly construction project includes component production cost, transportation component cost, and installation component cost. Based on the improved neural network algorithm to build an improved neural network model, the improved neural network model to mine the cost data in the early stage of assembly construction project is used. In this paper, the earned value variable is introduced to transform the project duration and project cost in the early stage of the prefabricated construction project into quantifiable cost data, and the earned value analysis method is used to estimate the implementation cost of the prefabricated construction project. According to the result of cost estimation, the mathematical model of precost budget of prefabricated construction project is built based on the project parameters. In order to prove that the cost budget performance of the mathematical model based on the improved neural network algorithm in the early stage of assembly construction project is better, the original mathematical model is compared with the mathematical model, the experimental results show that the cost budget performance of the model is better than the original model, and the cost budget performance is improved. [ABSTRACT FROM AUTHOR]

The construction and retrieval of indoor maps are important for indoor positioning and navigation. It is necessary to ensure a good user experience while meeting real-time requirements. Unlike outdoor maps, indoor space is limited, and the relationship between indoor objects is complex which would result in an uneven indoor data distribution and close relationship between the data. A data storage model based on the octree scene segmentation structure was proposed in this paper initially. The traditional octree structure data storage model has been improved so that the data could be backtracked. The proposed method will solve the problem of partition lines within the range of the object data and improve the overall storage efficiency. Moreover, a data retrieval algorithm based on octree storage structure was proposed. The algorithm adopts the idea of "searching for a point, points around the searched point are within the searching range." Combined with the octree neighbor retrieval methods, the closure constraints are added. Experimental results show that using the improved octree storage structure, the retrieval cost is 1/8 of R-tree. However, by using the neighbor retrieval, it improved the search efficiency by about 27% on average. After adding the closure constraint, the retrieval efficiency increases by 25% on average. [ABSTRACT FROM AUTHOR]

With the rapid increase of the data size, there are increasing demands for feature selection which has been a powerful tool to handle high-dimensional data. In this paper, we propose a novel feature selection of niche particle swarm optimization based on the chaos group, which is used for evaluating the importance of feature selection algorithms. An iterative algorithm is proposed to optimize the new model. It has been proved that solving the new model is equivalent to solving a NP problem with a flexible and adaptable norm regularization. First, the whole population is divided into two groups: NPSO group and chaos group. The two groups are iterated, respectively, and the global optimization is updated. Secondly, the cross-iteration of NPSO group and chaos group avoids the particles falling into the local optimization. Finally, three representative algorithms are selected to be compared with each other in 10 UCI datasets. The experimental results show that the feature selection performance of the algorithm is better than that of the comparison algorithm, and the classification accuracy is significantly improved. [ABSTRACT FROM AUTHOR]

Person reidentification is aimed at solving the problem of matching and identifying people under the scene of cross cameras. However, due to the complicated changes of different surveillance scenes, the error rate of person reidentification exists greatly. In order to solve this problem and improve the accuracy of person reidentification, a new method is proposed, which is integrated by attention mechanism, hard sample acceleration, and similarity optimization. First, the bilinear channel fusion attention mechanism is introduced to improve the bottleneck of ResNet50 and fine-grained information in the way of multireceptive field feature channel fusion is fully learnt, which enhances the robustness of pedestrian features. Meanwhile, a hard sample selection mechanism is designed on the basis of the P2G optimization model, which can simplify and accelerate picking out hard samples. The hard samples are used as the objects of similarity optimization to realize the compression of the model and the enhancement of the generalization ability. Finally, a local and global feature similarity fusion module is designed, in which the weights of each part are learned through the training process, and the importance of key parts is automatically perceived. Experimental results on Market-1501 and CUHK03 datasets show that, compared with existing methods, the algorithm in this paper can effectively improve the accuracy of person reidentification. [ABSTRACT FROM AUTHOR]

When there are many suspected loss links, the links in the path with a higher pass rate are assumed to be nondrop packet links or assuming that the link with the largest number of shares is a loss link, but this assumption lacks valid proof. In order to overcome these shortcomings, this paper proposes a link loss inference algorithm with network topology aware. The network model is established based on the historical data of the network operation and network topology characteristics. A weighted relative entropy ranking method is proposed to quantify the suspected packet loss links in each independent subset. The packet loss rate of the packet loss link is obtained by solving the unique solution of the simplified nonsingular matrix. Through simulation experiments, it is verified that the proposed algorithm has achieved better results in terms of congestion link determination and link loss rate estimation accuracy. [ABSTRACT FROM AUTHOR]

An efficient storage strategy for retail e-commerce warehousing is important for minimizing the order retrieval time to improve the warehouse-output efficiency. In this paper, we consider a model and algorithm to solve the cargo location problem in a retail e-commerce warehouse. The problem is abstracted into storing cargo on three-dimensional shelves, and the mathematical model is built considering three objectives: efficiency, stability, and classification. An artificial swarm algorithm is designed to solve the proposed models. Computational experiments performed on a warehouse show that the proposed approach is effective at solving the cargo location assignment problem and is significant for the operation and organization of a retail e-commerce warehouse. [ABSTRACT FROM AUTHOR]

This paper addresses the constrained multiobjective optimization problem of time-modulated sparse arrays. The synthesis objective is to find an optimal element arrangement and associated excitation strategy of sparse arrays, which realize the balance of radiation power and sideband suppression performance with minimum number of elements, and suppress side lobe level simultaneously. A novel hybrid algorithm based on orthogonal perturbation method and convex optimization (OPM-CVX) for the synthesis of time-modulated sparse antenna array is presented in this paper. In order to satisfy the main lobe beamforming and side lobe suppression of sparse arrays, the proposed method optimizes element positions with minimum array numbers by orthogonal perturbation method and optimizes excitations of array element with dynamic range ratio constraint by convex optimization. Furthermore, a trapezoidal pulse time-modulated switching function is proposed to find the balance of radiation power and sideband suppression performance. The numerical results indicate that the proposed algorithm can be an effective approach for synthesis problems of time-modulated sparse arrays. [ABSTRACT FROM AUTHOR]

HotSpot is an algorithm that can directly mine association rules from real data. Aiming at the problem that the support threshold in the algorithm cannot be set accurately according to the actual scale of the dataset and needs to be set artificially according to experience, this paper proposes a dynamic optimization algorithm with minimum support threshold setting: S_HotSpot algorithm. The algorithm combines simulated annealing algorithm with HotSpot algorithm and uses the global search ability of simulated annealing algorithm to dynamically optimize the minimum support in the solution space. Finally, the Inner Mongolia sandstorm dataset is used for experiment while the wine quality dataset is used for verification, and the association rules screening indicators are set for the mining results. The results show that S_HotSpot algorithm can not only dynamically optimize the selection of support but also improve the quality of association rules as it is mining reasonable number of rules. [ABSTRACT FROM AUTHOR]

The objective of this paper is to present novel algorithms for solving the multiple attribute decision-making problems under the possibility intuitionistic fuzzy soft set (PIFSS) information. The prominent characteristics of the PIFSS are that it considers the membership and nonmembership degrees of each object during evaluation and their corresponding possibility degree. Keeping these features, this paper presents some new operation laws, score function, and comparison laws between the pairs of the PIFSSs. Further, we define COmplex PRoportional ASsessment (COPRAS) and weighted averaging and geometric aggregation operators to aggregate the PIFSS information into a single one. Later, we develop two algorithms based on COPRAS and aggregation operators to solve decision-making problems. In these approaches, the experts and the weights of the parameters are determined with the help of entropy and the distance measure to remove the ambiguity in the information. Finally, a numerical example is given to demonstrate the presented approaches. [ABSTRACT FROM AUTHOR]

The restricted Boltzmann machine (RBM) is one of the widely used basic models in the field of deep learning. Although many indexes are available for evaluating the advantages of RBM training algorithms, the classification accuracy is the most convincing index that can most effectively reflect its advantages. RBM training algorithms are sampling algorithms essentially based on Gibbs sampling. Studies focused on algorithmic improvements have mainly faced challenges in improving the classification accuracy of the RBM training algorithms. To address the above problem, in this paper, we propose a fast Gibbs sampling (FGS) algorithm to learn the RBM by adding accelerated weights and adjustment coefficient. An important link based on Gibbs sampling theory was established between the update of the network weights and mixing rate of Gibbs sampling chain. The proposed FGS method was used to accelerate the mixing rate of Gibbs sampling chain by adding accelerated weights and adjustment coefficients. To further validate the FGS method, numerous experiments were performed to facilitate comparisons with the classical RBM algorithm. The experiments involved learning the RBM based on standard data. The results showed that the proposed FGS method outperformed the CD, PCD, PT5, PT10, and DGS algorithms, particularly with respect to the handwriting database. The findings of our study suggest the potential applications of FGS to real-world problems and demonstrate that the proposed method can build an improved RBM for classification. [ABSTRACT FROM AUTHOR]

Noncircular signals are widely used in the area of radar, sonar, and wireless communication array systems, which can offer more accurate estimates and detect more sources. In this paper, the noncircular signals are employed to improve source localization accuracy and identifiability. Firstly, an extended real-valued covariance matrix is constructed to transform complex-valued computation into real-valued computation. Based on the property of noncircular signals and symmetric uniform linear array (SULA) which consist of dual-polarization sensors, the array steering vectors can be separated into the source position parameters and the nuisance parameter. Therefore, the rank reduction (RARE) estimators are adopted to estimate the source localization parameters in sequence. By utilizing polarization information of sources and real-valued computation, the maximum number of resolvable sources, estimation accuracy, and resolution can be improved. Numerical simulations demonstrate that the proposed method outperforms the existing methods in both resolution and estimation accuracy. [ABSTRACT FROM AUTHOR]

In this paper, an effective multiuser detection (MUD) is proposed for direct sequence ultrawideband- (DS-UWB-) based space formation flying systems. The proposed method called GMM-MUD is based on Gaussian mixture models (GMMs) to suppress multiple access interference. The GMM describes probability distributions of the hypothesis testing problem which is used for bit classification. To reveal the difference between correct bits and error bits, the preprocessing operation applies a mapping function based on optimal multiuser detection. The parameters of GMM are estimated by using expectation-maximization (EM) algorithm. EM algorithm employs iterative operation to simplify the complexity of maximum likelihood estimation method and considers the mapping values of received bits as the observations. Simulation results demonstrate that the proposed GMM-MUD algorithm achieves good performances in terms of bit error rate performance, user capacity, and near-far resistance. Moreover, the computational complexity is low enough for space formation flying applications. [ABSTRACT FROM AUTHOR]

During the last decade, Rapidly-exploring Random Tree star (RRT∗) algorithm based on sampling has been widely used in the field of unmanned aerial vehicle (UAV) path planning for its probabilistically complete and asymptotically optimal characteristics. However, the convergence rate of RRT∗ as well as B-RRT∗ and IB-RRT∗ is slow for these algorithms perform pure exploration. To overcome the weaknesses above, Biased Sampling Potentially Guided Intelligent Bidirectional RRT∗ (BPIB-RRT∗) algorithm is proposed in this paper, which combines the bidirectional artificial potential field method with the idea of bidirectional biased sampling. The proposed algorithm flexibly adjusts the sampling space, greatly reduces the invalid spatial sampling, and improves the convergence rate. Moreover, the deeply theoretical analysis of the proposed BPIB-RRT∗ algorithm is given regarding its probabilistic completeness, asymptotic optimality, and computational complexity. Finally, compared to the latest UAV path planning algorithms, simulation comparisons are demonstrated to show the superiority of our proposed BPIB-RRT∗ algorithm. [ABSTRACT FROM AUTHOR]

As the global pandemic rapidly spreads, distance learning is emerging as a new method of instruction. Particularly, traditional courses that must be taught in-person, such as vocal singing instruction, require immediate adaptation to the COVID-19 and the new distance learning model. However, in the process of distance learning, it is frequently impossible to tailor and personalize instruction, particularly for vocal singing courses. Educational researchers are confronted with the pressing issue of how to extract useful and personalized patterns from a large volume of learner data in order to customize and individualize instruction. In this paper, we propose applying the decision tree method from data mining technology to a vocal singing education system by categorizing students according to the model. Once the characteristics of various learners have been stored in the corresponding user database, teachers can access timely information regarding the learners' most recent learning situation. This can be used as the basis for differentiating instructional strategies for various learners. This allows the instructor to designate individualized teaching and learning strategies for each student. [ABSTRACT FROM AUTHOR]

Public finance plays an important role in the development and construction of the country. Public finance is derived from and used by the people. On the one hand, public finance mainly comes from national taxes and the income of some state-owned enterprises or state-owned assets. On the other hand, public finance is used for national infrastructure, military investment, scientific and technological research and development, national daily operation, and other expenses. Therefore, the state of public finance is closely related to people's lives, and it is also one of the basic symbols of a country's prosperity and strength. How to ensure that the country's public finance is in a good state, grasp the leverage balance of public finance revenue and expenditure, and avoid the situation of national "bankruptcy" is additional attention that the public finance department should pay in the process of operation. Therefore, we urgently need a set of public finance monitoring and early warning system that matches China's public finance operation mechanism and conforms to China's basic national conditions. At present, previous studies rely on the existing detailed data on public finance to measure the situation of China's public finance, but this method refers to fewer data and is not forward-looking enough. Therefore, this paper adopts a BP neural network algorithm to monitor and warn the situation of China's public finance based on computer big data. [ABSTRACT FROM AUTHOR]

Nowadays, various algorithms are widely used in the field of economy and trade, and economic and trade management laws also need to introduce scientific and effective data models for optimization. In this paper, support vector machine algorithm and logistic regression algorithm are used to analyze and process the actual economic and trade case data and bank loan user data, and a hybrid model of support vector machine and logistic regression is established. This study first introduces the basic definitions and contents of the support vector machine algorithm and logistic regression algorithm, and then constructs a hybrid model by randomly dividing the data, first using the support vector machine algorithm to calculate the results, and then inputting them into the logistic regression algorithm. The first mock exam is that the efficiency of the hybrid model is much higher than that of the single model. This study mainly optimizes and upgrades the legal system of economic and trade management from two aspects. In the prediction of economic and trade legal cases, the hybrid model is significantly better than FastText and LSTM models in accuracy and macro recall performance. In terms of credit risk prediction of economic and trade loan users, the subset most likely to default in the loan customer set is obtained. [ABSTRACT FROM AUTHOR]

Flexible articulated manipulators are often used for the task of tracking reference trajectories in space and have attracted much attention. Aiming at the uncertainty of the robot system, based on random matrix and screw theory, this paper constructs a trajectory control model of a flexible joint manipulator. In order to reduce the steady-state tracking error, a random matrix variable structure control method is introduced into the model, and a nonlinear spinor-like random matrix function is designed to improve the traditional random matrix surface. In the simulation process, the dynamic model of the series robot is firstly obtained according to the screw and random matrix equation, and the dynamic characteristics are analyzed. Combined with dynamic surface control technology, a neural network controller is designed to solve the problem of dimensional explosion and ensure that the tracking error converges to a small neighborhood of zero. The experimental results show that by using the observation value to replace the unmeasurable state of the system, combining it with the random matrix network to identify the unknown dynamics of the system, and designing the random matrix network controller, the tracking control of the system can be realized, and the tracking error can be ensured to converge to a small neighborhood of zero. The control system has a better control effect than traditional PID, the peak time is shortened by 45.83%, the adjustment time is shortened by 46.91%, the maximum overshoot is reduced by 51.35%, and the steady-state error after filtering is only 0.049, which is reduced by 47.31%. Effective interference signal and measurement noise are suppressed, and the quantitative observation performance of the flexible joint manipulator system is improved. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet, data information has begun to spread rapidly on the Internet, and the types and fields involved are becoming more and more diverse. With the deepening of internationalization, English information has also become a common entry for tourism. Relatively speaking, the translation of English documents is also on the agenda. The intricate literature information has put forward a more specialized and vertical demand for retrieval. Traditional retrieval systems usually use string matching algorithms to search, but this technology cannot perform semantic expansion of query conditions. In the case of "multiple words with one meaning" and "polysemy of one word," the retrieval accuracy of traditional retrieval systems decreases. In order to solve the previously mentioned problems, this study proposes semantic retrieval based on domain ontology technology. The purpose of semantic retrieval is to semantically expand the user's query conditions on the basis of accurately understanding the retrieval conditions. At the same time, with the help of the constructed domain ontology, it provides more accurate and comprehensive retrieval services for document retrieval. The experimental results of this study show that, for the clustering of the entire dataset, the number of ontology concepts in each article is 61% of that, before merging, which effectively reduces the complexity of the semantic network model. Therefore, the validity of the ontology concept merging algorithm proposed in this study is verified. [ABSTRACT FROM AUTHOR]

Image text description is a multimodal data processing problem in the computer field, which involves the research tasks of computer vision and natural language processing. At present, the research focus of image text description task is mainly on the method based on deep learning. The work of this paper is mainly focused on the imprecise description of visual words and nonvisual words in the description of image description tasks in the image text description. An adaptive attention double-layer LSTM (long short-term memory) model based on coding-decoding is proposed. Compared with the algorithm based on the adaptive attention mechanism based on the coding-decoding framework, the evaluation index BLEU-1 is improved by 1.21%. The METEOR was 0.75% higher and CIDEr was 0.55%, while the indexes of BLEU-4 and ROUGE-L were not as good as those of the original model, but the index was not different. Although it cannot surpass all the performance indicators of the original model, the description of visual words and nonvisual words is more accurate in the actual image text description. [ABSTRACT FROM AUTHOR]

Many optimization algorithms have been applied to solve high-dimensional instances of the 0-1 knapsack problem. However, these algorithms often fall into a local optimization trap and thus fail to obtain the global optimal solutions. To circumvent this shortcoming, a hybrid harmony search algorithm with distribution estimation is proposed in this paper. A few important features of the proposed algorithm are as follows: (i) the idea of probability distribution estimation is employed to design the adaptive search strategy, (ii) a fixed improvisation process is presented to improve the algorithm searching ability, (iii) a new method of initialization is used to ensure that the initialization is feasible harmony and (iv) an improved remediation approach is proposed to effectively repair the infeasible solutions. To assess the effectiveness of the proposed algorithm, some experiments are carried out. The experimental results reveal that the proposed algorithm is a reliable and promising alternative for solving the 0-1 knapsack problem. [ABSTRACT FROM AUTHOR]

In recent years, there have been constant disputes over college student cadres. Starting from the public opinion crisis of College Students' autonomy and using the "K-core" algorithm, this paper found that the college student cadres could not realize their own correct roles. The fuzzy cognitive map (FCM) model was used to analyze 49 influencing factors of N University's retreat action. It is found that the five factors of mass support, ability, ideology and morality, work tasks, and performance evaluation are important factors leading to the retreat action. This research proposes role reconstruction and system rectification strategies for the five variables in order to achieve a true feeling of student autonomy and good governance. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy and performance of traditional image threshold segmentation algorithm, this paper proposes a multithreshold segmentation method named improved Harris hawk optimization (IMHHO). Firstly, IMHHO adopts Tent map and elite opposition-based learning to initialize population and enhance the diversity. Secondly, IMHHO uses quadratic interpolation to generate new individuals and enhance the local search ability. Finally, IMHHO adopts improved Gaussian disturbance method to disturb optimal solution, which coordinates the local and global search ability. Then, the performance of IMHHO is tested based on 14 benchmark functions. In image segmentation, different algorithms are tested to compare the comprehensive performance based on Otsu and Renyi entropy. Experiments show that IMHHO performs better in the three kinds of benchmark functions; the segmentation effect is directly proportional to the number of thresholds; compared with other algorithms, IMHHO has better comprehensive performance. [ABSTRACT FROM AUTHOR]

To address automatic detection of pedestrian fall events and provide feedback in emergency situations, this paper proposes an attention-guided real-time and robust method for pedestrian detection in complex scenes. First, the YOLOv3 network is used to effectively detect pedestrians in the videos. Then, an improved DeepSort algorithm is used to track by detection. After tracking, the authors extract effective features from the tracked bounding box, use the output of the last convolutional layer, and introduce the attention weight factor into the tracking module for final fall event prediction. Finally, the authors use the sliding window for storing feature maps and SVM classifier to redetect fall events. The experimental results on the CityPersons dataset, Montreal fall dataset, and self-built dataset indicate that this approach has good performance in complex scenes. The pedestrian detection rate is 87.05%, the accuracy of fall event detection reaches 98.55%, and the delay is within 120 ms. [ABSTRACT FROM AUTHOR]

Scalar multiplications are considered an essential aspect of implementations of isogeny-based cryptography. The efficiency of scalar multiplication depends on the equation of the underlying elliptic curves and the addition chain employed. Bos and Friedberger recently stated that, for larger scalar multiplication, addition-subtraction chains will become more useful for twisted Edwards curves because of the differential restriction on Montgomery curves in the setting of isogeny-based cryptosystem. Motivated by these comments, we attempt to increase the efficiency of scalar multiplication in twisted Edwards curves in terms of the memory of algorithms. In this paper, we present a double-base addition-subtraction chain algorithm with memory efficiency for scalar multiplication. The memory usage of this part is O log n 2 / log log n , which is better than the result of the tree-based approach, which is O log n 2 . [ABSTRACT FROM AUTHOR]

In order to improve the retrieval efficiency and security of the cloud server, an encrypted cloud data mark and group search method (MGSM) based on singular value decomposition is proposed in this paper. Firstly, all documents are clustered, then indexes and query marks are constructed according to classes, and then documents with low correlation are filtered according to their matching degree. Secondly, the reserved document index vector is expressed as an index matrix, and the singular value decomposition (SVD) algorithm is employed to reduce the dimensionality of the matrix. Then, the corresponding threshold is set to improve the search efficiency while ensuring accuracy. Thirdly, the reduced-dimensional indexes are grouped to reduce the high-dimensional encryption key to multiple low-dimensional keys, further reducing the index encryption time. Theoretical analysis and experimental results show that the proposed method is more feasible and more effective than the compared schemes. [ABSTRACT FROM AUTHOR]

To a large extent, classical boosting denoising algorithms can improve denoising performance. However, these algorithms can only work well when the denoisers are linear. In this paper, we propose a boosting algorithm that can be used for a nonlinear denoiser. We further implement the proposed algorithm into a shrinkage curve learning denoising algorithm, which is a nonlinear denoiser. Concurrently, the convergence of the proposed algorithm is proved. Experimental results indicate that the proposed algorithm is effective and the dependence of the shrinkage curve learning denoising algorithm on training samples has improved. In addition, the proposed algorithm can achieve better performance in terms of visual quality and peak signal-to-noise ratio (PSNR). [ABSTRACT FROM AUTHOR]

This paper demonstrates an effective method of performing network reconfiguration (NR) and distributed generation placement (DGP) simultaneously called NR-DGP for the multi-goal function of reducing power loss and voltage deviation as well as enhancing load balance and feeder balance with the least number of changing switches. These membership goals are combined by the fuzzy decision-making relied on the max-min technique. The results for the NR and NR-DGP on two test system including of 33 and 84 nodes are gained by the improved moth swarm algorithm (IMSA). Wherein, IMSA is developed based on the original moth swarm algorithm (MSA) with improvement of the exploration mechanism using Lévy distribution. The calculated results show that NR-DGP method has gained the better improvement of the test systems' technical indicators than the NR only. The obtained results also show that IMSA has better performance for the NR and NR-DGP problems than the MSA in term the quality of the obtained optimal solution. Thus, IMSA can be an effective approach for determining the optimal solution of the NR and NR-DGP problems. [ABSTRACT FROM AUTHOR]

In the current situation of university education in China, there are some problems that need to be solved urgently in college Chinese teaching in China, which seriously restrict the realization and implementation of college Chinese teaching objectives. The emergence and development of cloud computing technology can integrate different information media, and through the integration of traditional text, pictures, videos, and other resource media, it can be presented in a clearer and more vivid way. In this paper, a cloud computing resource scheduling model is established for the realization of the platform for the development and utilization of university Chinese cloud computing curriculum resources. The multi-QoS (Quality of Service) objective constraint problem is transformed into a single objective constraint solving problem. The improved GA (genetic algorithm) is used to solve the single objective constraint problem, and the square of the objective function is used as a fitness function, and a better resource allocation strategy is obtained. Experimental results show that the allocation time of the algorithm proposed in this paper is greatly reduced, and the QoS guarantee ability of cloud computing service providers is effectively improved. It has certain reference value for the further research of cloud computing technology in the development and utilization of college Chinese curriculum resources. [ABSTRACT FROM AUTHOR]

This article will focus on how to reduce the negative information in the main theme report. Negative information contains subjectivity, deviation, interference, and cancellation characteristics, which will influence the primary theme report's communication effect, interfere with the audience's interpretation of the report, and cancel out the report's positive energy. The original intention of the theme report is to promote social harmony and safeguard social justice, but the appearance of negative information makes the reported effect fail to reach the expected purpose. The concept of theme reports and negative information is defined in this work. This study examines the primary topic report's qualities, such as The Times' mainstream, good content, and strong report. In addition, the form and characteristics of negative information are also described. In this paper, a collaborative filtering recommendation method based on non-neighbor user contributions is suggested, which uses a linear fitting formula to apply the responsibilities of both neighbor and non-neighbor users to the recommendation system. The results show that the accuracy and diversity of our algorithm are better than those of traditional collaborative filtering algorithms. The diversity of several common recommendation algorithms is studied. The findings reveal that the diversity of recommendation algorithms is linked to the sparsity of data as well as the algorithm's suggestion mechanism. In general, the more scarce the data, the higher the recommendation algorithm's variety. At the same time, we also study the diversity of recommendation systems, and the results show that although the overall diversity of the system is gradually decreasing, user behavior is becoming more and more diverse. [ABSTRACT FROM AUTHOR]

Multimodal biometrics fusion plays an important role in the field of biometrics. Therefore, this paper presents a multimodal biometrics fusion algorithm using deep reinforcement learning. In order to reduce the influence of user behavior, user's personal characteristics, and environmental light on image data quality, data preprocessing is realized through data transformation and single-mode biometric image region segmentation. A two-dimensional Gobar filter was used to analyze the texture of local sub-blocks, qualitatively describe the similarity between the filter and the sub-blocks and extract the phase information and local amplitude information of multimodal biometrics features. Deep reinforcement learning was used to construct the classifier of different modal biometrics, and the weighted sum fusion of different modal biometrics was implemented by fractional information. The multimodal biometrics fusion algorithm was designed. The Casia-iris-interval-v4 and NFBS datasets were used to test the performance of the proposed algorithm. The results show that the fused image quality is better, the feature extraction accuracy is between 84% and 93%, the average accuracy of feature classification is 97%, the multimodal biometric classification time is only 110 ms, the multimodal biometric fusion time is only 550 ms, the effect is good, and the practicability is strong. [ABSTRACT FROM AUTHOR]

In view of the poor effect of current sports dance movement detection, this paper puts forward a sports dance movement detection method based on posture recognition. By using the principle of posture recognition to obtain the characteristic information of sports dance movement, construct the action characteristic evaluation system, optimize the action detection algorithm combined with posture recognition technology, and realize the classified detection of sports dance movements. Finally, experiments show that the sports dance movement detection based on pose recognition has high practicability and effectiveness in the process of practical application and fully meets the research requirements. [ABSTRACT FROM AUTHOR]

In the process of wireless communication, the transmission of signals will be subject to various kinds of interference, which will affect the quality of communication. Interference detection is an important part of improving the reliability of communication. When the interfering signal has the same frequency as the original communication signal, with traditional methods, it is difficult to extract feature parameters. Aiming at this special cofrequency interference signal, this paper proposes a time series signal prediction model based on deep learning and uses the difference between the predicted signal and the received signal as the eigenvalue to detect interference. In order to improve the detection rate, the eigenvalues predicted by LSTM and Bi-LSTM networks are subjected to windowing experiments. The Support Vector Machine (SVM) is used to detect the interference of eigenvalues, and the comparison results are visualized by the confusion matrix. The experimental simulation results show that the Bi-LSTM model has better feature extraction ability for time series signals, and the prediction ability of the signal and the accuracy of interference detection are higher than those of the LSTM model. [ABSTRACT FROM AUTHOR]

The search range and search position of the particle are closely related to the shrinkage-expansion factor of the particle and the value of the center of the potential well. As the number of iterations increases, its search will gradually fall near the center of the potential well, and the search range will gradually decrease. Therefore, when the center of the potential well and the search range gradually approach the global optimum, the final result of the algorithm can be guaranteed to be the global optimum. Therefore, we need to optimize the shrinkage-expansion factor and the potential well center, improve the individual development ability of the group in the later stage of the algorithm and the expansion ability of the group, so that the potential well center is easier to fall near the global optimal point. Based on the chaotic strategy, a particle swarm initialization method is proposed. Using the unique ergodicity of the chaotic system, the method can make the particles spread well in the solution space of the search problem. Based on the antecedent normal cloud model, an adaptive method for determining control parameters and the center of the potential well is proposed. Based on the consequent normal cloud model, a particle adaptive mutation method is proposed. The comprehensive application of these improvement measures to the improved QPSO effectively improves the performance of the original algorithm. In this paper, the principle and method of multioptical axis parallelism measurement are studied. Based on the hardware equipment of the parallelism measurement system based on the large-diameter off-axis parabolic mirror collimator, the measurement accuracy and automation of the system are improved through image processing technology. The degree and anti-interference ability of the system are analyzed in detail. According to the characteristics of the system, an optimized focus evaluation function and a center positioning algorithm are proposed to improve the measurement speed and accuracy of the system. Through the improvement of the system DCT focus evaluation coefficient method and the optimization of the least squares ellipse fitting process, the measurement accuracy and anti-interference ability of the system are improved, the calculation time is shortened, and the real-time performance and automation of the system are enhanced. [ABSTRACT FROM AUTHOR]

The three-dimensional organic growth features of buildings have been given more and more attention according to the unique view representation regulations of the indoor construction industry, in order to optimize the usage and allocation efficiency of building space. To explore the configuration structure of building function space, the three-dimensional box dimension algorithm is used to measure the fractal dimension of the indoor and outdoor form of building space, and the two-dimensional and three-dimensional fractal characteristics of building space are compared and analyzed; the method of the second derivative is utilized for the automatic recognition of fractal scale-free areas, aiming at the imprecise problem of the artificial judgment of scale-free areas in earlier fractal research of interior and outdoor forms of architectural space. The experimental results show that the second derivative method can effectively and automatically identify the scale-free region. The coefficients of linear fitting are above 0.990, and the indoor and outdoor forms of architectural space have clear fractal characteristics. After applying the method, the three-dimensional fractal dimension grows, indicating that the interior and outdoor form usage of architectural space in this area is generally effective and compact. [ABSTRACT FROM AUTHOR]

To estimate and analyze the orbit of a spacecraft in the ascent phase, three algorithms are proposed in this paper: a two-satellite point-by-point intersection positioning algorithm, single-satellite positioning algorithm, and multiple-point intersection collaborative positioning algorithm. Meanwhile, two mathematic models of the spacecraft orbit are presented based on the kinematics characteristic and the least square method. Besides, by combining the results of the proposed satellite positioning algorithms with mathematic models of the spacecraft orbit, the particle filtering based on the Monte Carlo algorithm is applied to filtering of the final results of the spacecraft. Finally, the effectiveness of the two-satellite point-by-point intersection positioning algorithm and the single-satellite positioning algorithm is verified through the experiment simulation on the real data. [ABSTRACT FROM AUTHOR]

In this paper, the theory of viscoelastic material relaxation modulus testing, testing method, and material relaxation modulus parameter solution method is studied. Based on the IoT sensor test devices and the Hertzian mathematics theory, the relationship between the compression amount, pressure, and relaxation modulus of a rigid spherical indenter pressed into a flat viscoelastic material is studied. In combination with the Boltzmann superposition principle, the relaxation modulus test method and theory for viscoelastic materials are proposed. A good fitting curve was obtained to solve the relaxation modulus parameters of various materials under the three-element Maxwell model. The experimental setup and the results of the parameter fitting were analyzed to verify the feasibility and accuracy of the results. [ABSTRACT FROM AUTHOR]

Conventional ambiguity resolution (AR) strategy of directly fixing all raw frequency ambiguities for uncombined precise point positioning (PPP) generally requires a long initial time to ensure fixing reliability. The rich signals from multi-frequency GNSS can provide new opportunities for rapid PPP AR. In this paper, based on a unified multi-frequency uncombined PPP model, the raw frequency ambiguities are linearly transformed to the between-satellite-single-differenced extra-wide-lane (EWL), wide-lane (WL), and narrow-lane (NL) ambiguities, and a cascading ambiguity resolution (CAR) method of fixing EWL/WL/NL ambiguities sequentially is proposed. Meanwhile, a partial ambiguity fixing (PAF) strategy with ambiguity subset adaptively selected based on the successively increased elevations is also adopted in each step to improve the fixing rate. Further, experiments with globally distributed stations are carried out to verify the algorithm. With the constraints of EWL/WL AR, the precision of NL ambiguity and its variance-covariance matrix can be effectively optimized, so the time to first fix (TTFF) is significantly shortened, and the ratio value is also improved to varying degrees. As for BDS-only solution, the average TTFF is shortened from 23.7 min to 10.9 min, with an improvement over 50%; for GPS/BDS, GPS/Galileo, and GPS/Galileo/BDS combined solutions, the TTFF is shortened from 14.3 min, 9.4 min, and 6.7 min to 8.1 min, 2.6 min, and 1.8 min, which are, respectively, shortened by 43.4%, 72.3%, and 73.1%. In general, the proposed CAR strategy can shorten the TTFF of multi-GNSS multi-frequency PPP to about 2 min. The performance of EWL/WL/NL ambiguity-fixed solutions is also analyzed. The NL solution is generally at centimeter-level accuracy over the entire period; however, limited by the atmosphere errors during the convergence stage, the EWL/WL solutions can only obtain decimeter-level accuracy, and the difference between them and NL solution gradually decreases with the continuous improvement of atmosphere accuracy. [ABSTRACT FROM AUTHOR]

This paper mainly studies the formation control problem of multiple quadrotor aircraft via fixed-time control theory. First, based on the bilimit homogeneous theory and the framework of multiagent theory, for multiaircraft, a fixed-time formation control strategy is proposed. Considering the external disturbance existing on the attitude loop of the aircraft, the corresponding fixed-time disturbance observer is designed with the observer technology. Then, a fixed-time attitude controller is designed based on the accurate observation and fast compensation from the disturbance observer. Finally, some simulations are performed to verify the effectiveness of the proposed theoretical method. [ABSTRACT FROM AUTHOR]

This paper proposes a modified fuzzy C-means (FCM) algorithm, which combines the local spatial information and the typicality of pixel data in a new fuzzy way. This new algorithm is called bias-correction fuzzy weighted C-ordered-means (BFWCOM) clustering algorithm. It can overcome the shortcomings of the existing FCM algorithm and improve clustering performance. The primary task of BFWCOM is the use of fuzzy local similarity measures (space and grayscale). Meanwhile, this new algorithm adds a typical analysis of data attributes to membership, in order to ensure noise insensitivity and the preservation of image details. Secondly, the local convergence of the proposed algorithm is mathematically proved, providing a theoretical preparation for fuzzy classification. Finally, data classification and real image experiments show the effectiveness of BFWCOM clustering algorithm, having a strong denoising and robust effect on noise images. [ABSTRACT FROM AUTHOR]

The imbalance data refers to at least one of its classes which is usually outnumbered by the other classes. The imbalanced data sets exist widely in the real world, and the classification for them has become one of the hottest issues in the field of data mining. At present, the classification solutions for imbalanced data sets are mainly based on the algorithm-level and the data-level. On the data-level, both oversampling strategies and undersampling strategies are used to realize the data balance via data reconstruction. SMOTE and Random-SMOTE are two classic oversampling algorithms, but they still possess the drawbacks such as blind interpolation and fuzzy class boundaries. In this paper, an improved oversampling algorithm based on the samples' selection strategy for the imbalanced data classification is proposed. On the basis of the Random-SMOTE algorithm, the support vectors (SV) are extracted and are treated as the parent samples to synthesize the new examples for the minority class in order to realize the balance of the data. Lastly, the imbalanced data sets are classified with the SVM classification algorithm. F-measure value, G-mean value, ROC curve, and AUC value are selected as the performance evaluation indexes. Experimental results show that this improved algorithm demonstrates a good classification performance for the imbalanced data sets. [ABSTRACT FROM AUTHOR]

The accurate measurement of roll angular rate for high spinning projectile has long been a challenging problem. Aiming to obtain the accurate roll angular rate of high spinning projectile, a novel extraction and filter algorithm, BSCZT-KF, is proposed in this paper. Firstly, a compound angular motion model of high spinning projectile is established. According to the model, we translate the roll angular rate measurement problem into a frequency estimation problem. Then the improved CZT algorithm, BSCZT, was employed to realize an accurate estimation of the narrowband signal frequency. Combined with the peak detection method, the BSCZT-KF algorithm is presented to further enhance the frequency estimation accuracy and the real-time performance. Finally, two sets of actual flight tests were conducted to verify the effectiveness and accuracy of the algorithm. The test results show that the average error of estimated roll angular rate is about 0.095% of the maximum of roll angular rate. Compared with the existing methods, the BSCZT-KF has the highest frequency estimation accuracy for narrowband signal. [ABSTRACT FROM AUTHOR]

We consider the problem of the optimization of the functioning of a pumped-storage hydroplant. The problem can be mathematically formulated as an optimal control problem, and when the considered hydromodel is of the fixed-head type, an added complication arises: the solution is of the bangsingular-bang type. In this paper, we propose a simple and efficient optimization algorithm to find the solution. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of convolutive blind source separation in frequency domain and introduce a solution to the problem in an independent vector analysis (IVA) framework. IVA utilizes both the statistical independence of different sources in each frequency bin and the statistical dependence of the same source in different frequency bins. However, most of previous works impose orthogonality constraint on the rows of each separation matrix which may undermine the separation performance. In this work, we propose a nonorthogonal IVA algorithm based on decoupled relative Newton method. This proposed algorithm updates the separation matrices row by row, and unlike deflation separation algorithm, there is no separation error accumulation arising. Simulation results are provided to show the superior convergence behavior and separation performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In 2004, Macharis et al. proposed a consistent comparison matrix. In this study, we provide a patch work for discussing the construction and restrictions of their consistent comparison matrix. Their study is important in academia and has been cited by over 200 papers. However, in the article, the authors neither provide a detailed explanation on how to construct the proposed revised consistent comparison matrix nor mention any restriction when constructing it. In this study, we not only propose an algorithm to construct the consistent matrix, but also prove that two restrictions exist when developing a sequence of their consistent comparison matrices, which are: (a) the changing of entries should follow a decreasing order with respect to the row index and (b) there is at most one entry in each row that can be revised by experts. The results of this study will help researchers properly apply the consistent matrices proposed by Macharis et al. (2004) in the future. [ABSTRACT FROM AUTHOR]