Search

Distributed multitarget tracking (MTT) is suitable for sensors with limited field of view (FoV). Generalized covariance intersection (GCI) fusion is used to solve the MTT problem based on label probability hypothesis density (PHD) filtering in this paper. Because the traditional GCI fusion only has good fusion performance for the targets in the intersection of each sensor's FoV, and the targets outside the intersection range would be lost, this paper redivides the Gaussian components according to the FoV and distinguishes the Gaussian components of the targets inside and outside the intersection. GCI fusion is sensitive to label inconsistency between different sensors. For label fusion in the intersection region, the best match of labels is found by minimizing label inconsistency index, and then GCI fusion is performed. Finally, the feasibility and effectiveness of the proposed fusion method are verified by simulation, and its robustness is proved. The proposed method is obviously superior to local sensor and traditional GCI algorithm. [ABSTRACT FROM AUTHOR]

The insulation resistance value is one of the important indexes for the safe operation of airport navigational lighting system. In this paper, a method based on deep learning and transfer learning is proposed to measure the insulation resistance value. To reduce the influence of high voltage environment and signal injection on the measurement accuracy, a multilayer LSTM model is established, in which the network convergence rate is accelerated by introducing a normalized layer in front of the first LSTM layer. Based on the constructed deep network, transfer learning is employed by sharing the weight parameters of the pretraining model to solve the problem of small data sample. The experimental results demonstrate that the proposed method can effectively improve the measurement accuracy of the insulation resistance value. [ABSTRACT FROM AUTHOR]

The constant expansion of higher education has led to the continuous increase of the teaching staff and the complexity of the work in the modern era of education, forcing the traditional colleges and universities (it is abbreviated as CU in this article) to participate in trade union activities to open up the road of innovation. What kind of method can effectively and quickly optimize trade union work (TUW) and improve satisfaction has become one of the research topics that has attracted much attention. Aiming at this problem, it has great research significance for the field of CU to participate in trade union activities. With the in-depth research on the work of trade unions in CU, the research on multivariate optimal design in the work of trade unions in CU is gradually carried out, and its advantages are of great significance for solving problems in the work of trade unions in CU. The purpose of this paper is to study the innovation of TUW mode in CU based on multivariate optimization. Through the analysis and research of the multivariate optimization idea, it can be applied to the innovation of the construction of the labor union work model in CU, and the multiobjective linear weighting method is used to solve the objective function, so as to solve the problem of improving the evaluation of labor union work satisfaction in CU. This paper analyzed the TUW in CU (it is abbreviated as TUW in this article) and the multivariate optimization design, conducted an experimental analysis on the improved TUW mode in CU, and used relevant theoretical formulas to explain. The results have shown that the overall evaluation score of the college TUW method proposed in this paper is higher than the traditional college TUW method, and the difference between the two is 18.7%. It can be seen that the university TUW based on mathematical modeling and multivariable optimal design can meet the innovation needs of university TUW, and the satisfaction and work efficiency are greatly improved. [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]

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]

Research on English teaching reform and model design is the focus of the current society. It is a novel idea to use artificial intelligence algorithm to design an English teaching platform, which combines the current field of English teaching reform with the field of artificial intelligence network. The current method is to use the sincent algorithm in artificial intelligence to design the model. Its defect is that the single network education learning makes the learners feel boring. In order to solve these problems, this paper proposes an English blended education method based on artificial intelligence, which aims to study the integration of the network teaching platform and traditional education. This paper uses the artificial intelligence sincent algorithm to establish a teaching platform framework and simulate application strategies for the development of blended education. Through the investigation process of blended English teaching, the results show that the proportion of students who are generally satisfied with English teaching has reached 53.67% and the proportion of students who feel generally satisfied has reached 27.83%. The results of this survey indicate that the majority of students approve of this type of English blended education method. [ABSTRACT FROM AUTHOR]

The analysis and design of key indicators of urban architecture has always been a frontier subject in the field of urban architecture. Building key indicator network area flow is challenging due to its high degree of nonlinearity and randomness. Based on the extraction method of key indicators of urban architectural design based on long and short-term convolutional memory network, this paper designs a data model of key indicators of urban architectural design. The model can effectively simulate the urban architectural design system to obtain the key index information of urban architectural design of objects step by step. The architecture of cascaded full convolutional long and short-term convolutional memory network is designed and improved. The experiment adopts the block method to ensure the output result with the same resolution as the input urban building image, which solves the problems of the traditional fully convolutional network such as small local receptive field, low output resolution. The simulation results show that the data prediction accuracy of the key indicators proposed in this paper reaches 92.3%, which is 16% and 11.5% higher than the prediction accuracy of the other two depth algorithms (76.3% and 80.8%) respectively, which promotes the key indicators of information flow within the network. [ABSTRACT FROM AUTHOR]

In engineering projects, the coordination and management of multiple objectives is an important part of project management, which has a direct impact on the realization of objectives in terms of project duration, cost, and quality. The current engineering project investment presents the characteristics of large scale, long period, and many risks, which puts forward higher requirements for the multiobjective coordinated management of the project. Therefore, the analysis and optimization of the various objectives of the engineering project is the basis for achieving multiobjective balance and coordination. The comprehensive consideration of the risks faced by the engineering project and the dynamics of the environment in which it is located can make the objective optimization more realistic. On the basis of risk identification and risk evaluation, this paper is committed to considering multiple objective factors that affect the outcome of risk decision-making to achieve the optimization of decision-making schemes and applies multiobjective genetic algorithm to the optimization of decision-making schemes, thus finding a way to optimize the decision-making scheme. This paper analyzes the research progress and status of construction project risk management decision and multiobjective evolutionary algorithm and points out the imperfections of the current research. Then it introduces the related theory of construction project risk management decision, the related terminology of multiobjective optimization problem and the method used in this paper —the principle, process, and characteristics of genetic algorithm and prepares for the following problem solving. In this paper, a mathematical model for multiobjective decision-making of engineering project risk management is established, and the multiobjective genetic algorithm is used to solve the model. Through the analysis of examples, a series of Pareto optimal solutions with good convergence and diversity are obtained, the best combination of various risk control measures is found, and three goals of risk evaluation value, management cost, and risk loss are achieved. The two models with or without risk correlation present certain differences, the model with correlation considered is more accurate and applicable than the model without correlation considered in the existing research. [ABSTRACT FROM AUTHOR]

Cross-border e-commerce logistics and transportation system is important link in the cross-border e-commerce supply chain. How to choose a fast, safe, reliable, and low-cost logistics transportation mode is an urgent problem to be solved in cross-border e-commerce logistics. In view of this, this paper first analyzes the system characteristics of cross-border e-commerce logistics transportation systems and analyzes the factors affecting the choice of transportation mode from the perspective of reliability. Then, based on the ELECTRE method system, a multiattribute decision-making method for cross-border e-commerce logistics transportation mode selection is proposed. Finally, through a data example, it is verified that the model constructed in this paper and the proposed multiattribute decision-making method can effectively help cross-border e-commerce choose logistics transportation mode according to goods. [ABSTRACT FROM AUTHOR]

The variation of frequency is a significant indicator of the operation status of rotating machinery. Generally, the frequency is extracted and tracked in real-time based on the vibration signal produced by the rotating machinery. However, various interferences generated from contact sampling and transmitting result in difficulty in obtaining the frequency correctly in real-time from the vibration. To solve this problem, this paper presents an interpolated Kalman filter (IKF) based on the vibration and surface noise signals for real-time frequency tracking. First, the cross-correlation operation is performed on the vibration and surface noise signals sampled synchronously to enhance the energy concentration. After that, a frequency search procedure is carried out to calculate the input of the tracking task. Finally, an IKF-based frequency lock procedure is applied to eliminate the interferences and track the frequency in real time. Besides, a correction procedure is added to prevent the measurement process from tracking the frequency incorrectly. The performance of the proposed method is verified by the experiments based on an ARM-based test bench using standard signals and actual test signals. [ABSTRACT FROM AUTHOR]

Engineers strive to realize the goal of control in the best possible way based on a given quality criterion when developing control system. The well-known optimal control problem requires finding a solution as a function of time. Such a function cannot be directly used to control a real object because its application corresponds to an open-loop control system and engineers usually complete it with a feedback stabilization system. Hence, there is an obvious need to reformulate the optimal control problem so that its solution can be directly applied to real objects. The paper presents a new extended statement of the optimal control problem. An additional requirement for the control function is introduced to give the system describing the control object properties that will ensure the stability of solutions. The desired control function must provide for the optimal trajectory given the properties of the attractor in the neighbourhood. The solution to the extended optimal control problem can be directly used to control a real object. The paper presents a computational machine learning approach to solving the extended problem of optimal control based on the application of a synthesized optimal control technique. Examples of the practical solution to the stated problem are given to illustrate the efficiency of the approach, where the solution to the conventional optimal control problem is compared with the proposed extended one in the presence of perturbations in models and initial conditions. [ABSTRACT FROM AUTHOR]

A priori knowledge-enhanced particle filter (PKE-PF) method is proposed to solve the problem of particle impoverishment in bearing fault prognosis with incomplete data. Based on the existing bearing life degradation data and the parameter transfer method in the transfer learning theory, particle initialization optimization, which is very important in the PF method, is carried out to effectively improve particle effectiveness and avoid the problem of premature particle exhaustion. Based on the whole-life degradation experiment of rolling bearings, the validation results show that the traditional PF method and its improved method are prone to particle exhaustion, which seriously affects the fault prediction results. The PKE-PF method proposed in this paper can effectively avoid the problem of premature particle depletion and obtain a more ideal fault prognosis results. [ABSTRACT FROM AUTHOR]

With increasing developments in information technology, IT projects have received widespread attention. However, the success rate of large information technology projects is extremely low. Most current extension forecast models are designed based on a balanced number of samples and require a large amount of training data to achieve an acceptable prediction result. Constructing an effective extension forecast model with a small number of actual training samples and imbalanced data remains a challenge. This paper proposes a Meta-IP model based on transferable knowledge bases with few-shot learning and a model-agnostic meta-learning improvement algorithm to solve the problems of sample scarcity and data imbalance. The experimental results show that Meta-IP not only outperforms many current imbalance processing strategies but also resolves the problem of having too few samples. This provides a new direction for IT project extension forecasts. [ABSTRACT FROM AUTHOR]

When using traditional recommendation algorithms to solve the problems of course recommendation, such as data sparseness and cold start, the performance of recommendation cannot be significantly improved. In order to solve its limitations in capturing learners' preferences and the characteristics of courses, this paper first clarifies the research foundation of course recommendation based on autoencoder and analyzes the description of course relevance and recommendation methods. According to the timing characteristics of online learning, an online course recommendation model based on autoencoder is proposed where the long-term and short-term memory (LSTM) network is used to improve the autoencoder, so that it can extract the temporal characteristics of data. Then, the Softmax function is used to recommend courses. The experimental results show that, compared with recommendation model of collaborative filtering algorithm and traditional autoencoder, the proposed method has higher recommendation accuracy. [ABSTRACT FROM AUTHOR]

In order to realize intelligent identification of rail damage, this paper studies the extraction method of complete damage ultrasonic B-scan data based on the density-based spatial clustering of applications with noise algorithm (DBSCAN). Aiming at the problem that the traditional DBSCAN algorithm needs to manually set the Eps and Minpts parameters, a KMNN-DBSCAN (K-median nearest neighbor DBSCAN) algorithm is proposed. The algorithm first uses the dataset's own distribution characteristics to generate a list of Eps and Minpts parameters and then determines the optimal Eps and Minpts through an optimization strategy to achieve complete self-adaptation of the two parameters of Eps and Minpts. In order to further improve the clustering performance of the algorithm, the partition idea is introduced, and the partition KMNN-DBSCAN algorithm is proposed to solve the problem that the clustering results of the DBSCAN algorithm are inconsistent with the actual categories on datasets with uneven density. The experimental results show that the KMNN-DBSCAN algorithm has higher clustering accuracy and silhouette coefficient (SC) for the D037 dataset ultrasound information group (UIG) division; compared with the KMNN-DBSCAN algorithm, the proposed partition KMNN-DBSCAN algorithm has higher clustering accuracy, F-Measure, and SC values. The partition KMNN-DBSCAN algorithm achieves accurate division of all damage UIG on the damaged B-scan data with large density differences, and completes the effective extraction of complete damage data. [ABSTRACT FROM AUTHOR]

Buffer operators can effectively weaken the impact disturbance of the system and restore the original appearance of the system. However, the buffer strength of the existing buffer operator is fixed, and the buffer strength cannot be adjusted. So the buffer effect for the system impact disturbance is not flexible enough, and the accuracy of the prediction model established is not high. In order to solve this problem, this paper proposes weakened buffer operators with time-varying parameters, analyzes the properties of the newly constructed buffer operator, and proves the superiority of the new buffer operators theoretically. Finally, the new buffer operators proposed in this paper are applied to the prediction of the number of employees in China's air transport industry and the unsafe events in China's civil aviation. The results show that the proposed buffer operators are better than the existing buffer operators, which further verify the validity and practicability of the proposed buffer operators. [ABSTRACT FROM AUTHOR]

In order to solve the problem of maximizing the utilization of resources through reasonable deployment under limited resources, this paper studies from two aspects: one is to establish the mathematical model of maximum coverage of space detection, and the other is to improve the harmony algorithm. The exploration performance and convergence performance of the harmony search algorithm are analyzed theoretically, and the more general formulas of exploration performance and convergence performance are proved. Based on theoretical analysis, the algorithm called opposition-based learning parameter adjusting harmony search is proposed. By using the algorithm to test the functions of different properties, the value range of key control parameters of the algorithm are given. The proposed algorithm is applied to optimize the problem of radar deployment. This paper takes a certain area of the Shandong Peninsula as the deployment scope. The simulation results show that the proposed algorithm is effective and practical. Although there is a large amount of calculation, it provides ideas and ways for other problems, such as the site selection of new observation and communication post, the deployment of maneuvering radar stations, and the track planning of fleet. [ABSTRACT FROM AUTHOR]

This work employs deep reinforcement learning and multi-objective optimization algorithms to the allocation of English distance teaching resources in order to increase their allocation efficiency. Moreover, based on the analysis of current regression correction, this paper discusses the algorithm of partition regression correction in depth, and proposes two different neighborhood regression correction algorithms. The proposal of neighborhood further expands the original concept of partition and solves various problems in partition correction. In order to reduce the model complexity of the neighborhood regression algorithm, this paper proposes to solve the problem through structural risk minimization and principal component extraction. The simulation results suggest that the English distance teaching resource allocation approach described in this research, which is based on deep reinforcement learning and multi-objective optimization, may significantly enhance the English distance teaching resource allocation impact. [ABSTRACT FROM AUTHOR]

In the field of computer vision, image saliency target detection can not only improve the accuracy of image detection but also accelerate the speed of image detection. In order to solve the existing problems of the saliency target detection algorithms at present, such as inconspicuous texture details and incomplete edge contour display, this paper proposes a saliency target detection algorithm integrating multiple information. The algorithm consists of three processes: preprocessing process, multi-information extraction process, and fusion optimization process. The frequency domain features of the image are calculated, the algorithm calculates the frequency domain features of the image, introduces power law transform and feature normalization, improves the frequency domain features of the image, saves the information of the target region, and inhibits the information of the background region. On three public MSRA, SED2, and ECSSD image datasets, the proposed algorithm is compared with other classical algorithms in subjective and objective comparison experiments. Experimental results show that the proposed algorithm can not only accurately and comprehensively extract significant target regions but also retain more texture information and complete edge information while satisfying the human visual experience. All evaluation indexes are significantly better than the comparison algorithm, showing good reliability and adaptability. [ABSTRACT FROM AUTHOR]

The traditional microgrid electricity transactions face a low security, poor economic benefits, and weak stability. To solve these problems, this paper proposes a multimicrogrid cross-chain transaction model based on quantum blockchain. Specifically, a bidding strategy was developed for the noncooperative dynamic game of aggregator-multimicrogrid alliance, aiming to balance and optimize the benefits of all parties and effectively enhance the consumption rate of electric energy. To improve the transaction efficiency between aggregators, a consensus mechanism was designed for multimicrogrid cross-chain communication, realizing consistent self-adaptation to cross-chain information. In addition, the quantum threshold signature technology was adopted to ensure the reliability of transaction data and create an unconditional secure communication environment. Case analysis shows that our model proposed in this paper not only ensures the security and stability of transactions but also enhances the economic benefits, providing theoretical support and decision support for the optimization of multimicronetwork cross-chain transaction model. [ABSTRACT FROM AUTHOR]

In the order picking process of the warehouse center, considering the rapid increase in the volume of orders arriving at the picking center at the time of the promotional festival, a hybrid operation mode with multiple picking tables is used to meet the picking requirements of the huge number of real-time orders. Therefore, in this paper, a hybrid picking mode is proposed, taking into account both the idle degree of picking stations and their order item centers of gravity, and a new reinforcement learning algorithm embedding mechanism (PRL) with placeholder control is designed to solve the problem of a huge number of real-time item orders arriving at the picking center system on promotional holidays and in inconsistent quantities, and numerical simulations are performed for this algorithm. The experimental results show that the PRL algorithm in hybrid picking mode can handle a huge number of orders simultaneously and improve picking efficiency effectively. [ABSTRACT FROM AUTHOR]

When mobile robots run in indoor environment, a large number of similar images are easy to appear in the images collected, probably causing false-positive judgment in loop closure detection based on simultaneous localization and mapping (SLAM). To solve this problem, a loop closure detection algorithm for visual SLAM based on image semantic segmentation is proposed in this paper. Specifically, the current frame is semantically segmented by optimized DeepLabv3+ model to obtain semantic labels in the image. The 3D semantic node coordinates corresponding to each semantic label are then extracted by combining mask centroid and image depth information. According to the distribution of semantic nodes, the DBSCAN density clustering algorithm is adopted to cluster densely distributed semantic nodes to avoid mismatching due to the close distance of semantic nodes in the subsequent matching process. Finally, the multidimensional similarity comparison of first rough and then fine is adopted to screen the candidate frames of loop closure from key frames and then confirm the real loop closure to complete accurate loop closure detection. Testing with public datasets and self-filmed datasets, experimental results show that being well adapted to illumination change, viewpoint deviation, and item movement or missing, the proposed algorithm can effectively improve the accuracy of loop closure detection in indoor environment. [ABSTRACT FROM AUTHOR]

When the three-phase LCL grid-connected inverter operates under the condition of unbalanced grid voltage, the influence of the negative sequence component in the grid voltage will cause the grid-connected inverter system to produce active and reactive dual-frequency interference. This will cause the grid-side current to have many low-order harmonics, mainly the third harmonic, which does not conform the IEEE-Std. 929–2000 standard. In order to solve this problem, this paper designs a current auto-disturbance-rejection controller, which speeds up the tracking response speed of the system and reduces the harmonic content of the grid-side current under unbalanced conditions. At the same time, optimize the phase-locked loop structure and introduce a notch filter in the modulation link to eliminate the third low-frequency harmonic components and further improve the quality of the grid-side current waveform. Finally, the correctness of the control strategy suggested in this paper is verified by MATLAB/Simulink simulation. [ABSTRACT FROM AUTHOR]

With the goal of maximizing the efficiency of automated container terminals, taking the loading and unloading time as the research object, the integrated optimization of automated container terminal scheduling with proportional fair priority is established by setting the priority parameters under the premise of balancing the operation efficiency of each container ship at the berth. The model considers separate assignments of general containers and special containers in the actual operation at the automated container terminal, which is never considered in other literatures. A proportional fairness algorithm (PFA) is designed to solve the problem, taking the maximum of the ratio of quantities of remaining loading and unloading containers of each container ship to the distance of empty AGVs/YTs as the proportional fair priority. The priority of all current tasks is ranked, and the highest priority task is fulfilled first. Different from the published literature, the study of this paper can be presented in two aspects. One is in the modeling. This includes (i) formalizing the description of the purpose of the model and (ii) conducting a real-world coordination of four types of equipment that incorporate at automated container terminals which considers different operations between general goods and special goods in the actual automated terminal operations. The other is that PFA is applied to deal with the integrated scheduling whose results are better with the balance of efficiency and fairness. [ABSTRACT FROM AUTHOR]

This paper considers the principle-agent conflict problem in a continuous-time delegated asset management model when the investor and the fund manager are all risk-averse with risk sensitivity coefficients γ f and γ m , respectively. Suppose that the investor entrusts his money to the fund manager. The return of the investment is determined by the manager's effort level and incentive strategy, but the benefit belongs to the investor. In order to encourage the manager to work hard, the investor will determine the manager's salary according to the terminal income. This is a stochastic differential game problem, and the distribution of income between the manager and the investor is a key point to be solved in the custody model. The uncertain form of the incentive strategy implies that the problem is different from the classical stochastic optimal control problem. In this paper, we first express the investor's incentive strategy in term of two auxiliary processes and turn this problem into a classical one. Then, we employ the dynamic programming principle to solve the problem. [ABSTRACT FROM AUTHOR]

With the rapid development of coal mine intelligent technology, the complexity of coal mine equipment has been continuously improved and the equipment maintenance resources have been continuously enriched. The traditional coal mine equipment maintenance knowledge management technology can no longer meet the current needs of equipment maintenance knowledge management, and the problems of low utilization rate, poor interoperability, and serious loss of knowledge have gradually emerged. It is urgent to study new knowledge system construction and knowledge management application technology for large-scale coal mine equipment maintenance resources. Knowledge graph is a technical method to describe the relationship between things in the objective world by using a graph model. It can effectively solve the problem of knowledge dynamic mining and management under large-scale data. Therefore, this paper focuses on the establishment of a coal mine equipment maintenance knowledge graph system by using knowledge graph technology. The main research contents are as follows: Firstly, based on the current situation that there is no unified basic knowledge system in the field of coal mine equipment maintenance, this paper establishes the coal mine equipment maintenance ontology (CMEMO) to effectively solve the problem that there are no unified representation, integration, and sharing of coal mine equipment maintenance knowledge in this field and provide support for the construction of coal mine equipment maintenance knowledge graph. Then, aiming at the problem that the traditional named-entity recognition method has a poor recognition effect and relies too much on artificial feature design, this paper proposes a named-entity recognition model for coal mine equipment maintenance based on neural network (BERT-BiLSTM-CRF) and applies the model to the coal mine equipment maintenance data set for verification. The experimental results show that, under the same data set, the entity recognition effect of this model is more leading than that of other models. Finally, through demand analysis and architecture design, combined with the constructed ontology model of coal mine equipment maintenance field, the entity identification of coal mine equipment maintenance is completed based on the BERT-BiLSTM-CRF model and the Django application framework is used to build the coal mine equipment maintenance knowledge graph system to realize the functions of each module of the knowledge graph system. [ABSTRACT FROM AUTHOR]

In recent years, region features extracted from target detection networks have played an important role in visual question answering. The region features only extract the areas that are related to the target, but they lose a lot of nontarget context information and fine-grained details. On the contrary, the grid feature does not lose the details of nontargets but is not conducive to the recognition of the counting question of multiple small targets in the image. To solve this problem, this paper proposes a visual question answering network via joint grid-region features (JGRCAN), which consists of a feature extraction layer, co-attention layer, and fusion layer. The feature extraction layer includes extracting grid features and region features from the image and text features from the question and extracting multivisual feature representation and question feature representation through the co-attention layer to output attention weight and attention feature representation, respectively. The proposed approach effectively integrates grid features and region features, realizes the complementary advantages of region features and grid features, and is able to accurately focus on areas of the image that are relevant to the answer to the question. The results show that the overall classification accuracy of the algorithm on the test-dev and test-std subsets of VQA-v2 is 70.87% and 71.18%, respectively. Compared with baseline models, our proposed JGRCAN has good performance. [ABSTRACT FROM AUTHOR]

With the increasing emphasis on sustainability, more and more environmentally friendly new materials and structures are developed, but they are difficult to be applied in engineering practice due to the lack of mature force resistance models. The design assisted by the testing method proposed by ISO2394:2015 and EN1990:2002 can solve this problem effectively. The aim of this paper was to analyze the characteristics of the method from a mathematical and statistical point of view based on the data and to suggest improvements. The resistance of each concrete member was derived by the design assisted by testing methods. The derivation results showed that the p values, the number of tests n , the coefficient of variation θ , and the coefficient of variation of resistance V R known or unknown have a large impact on the derived results. Also, the derivation by the Bayesian procedure or interval estimation methods might be negative. This indicates that the theory of the method is not rigorous and has some disadvantages. [ABSTRACT FROM AUTHOR]

Attitude measurement is an important core technology of vehicle flight. It is of great significance to ensure the vehicle's accurate orbit entry and orbit change, high-performance flight, reliable ground communication, high-precision ground observation, and successful completion of various space missions. Star sensor is the core component to realize an autonomous attitude measurement of the vehicle. Autonomous star map recognition is a key technology in star sensor technology, and it is also the focus and difficulty of research. When the star sensor enters the initial attitude acquisition mode, according to the algorithm, the star sensor can quickly obtain the initial attitude and enter the normal working mode. This paper proposes a star pattern recognition algorithm based on the radial companion-circumferential feature with a noise compensation code to address the low recognition rate caused by position noise in the process of constructing star patterns in the traditional star pattern recognition algorithm based on the radial feature. In order to solve the problem of slow matching search speed, a maximum matching number algorithm has been innovatively adopted, which can improve the search efficiency in the process of star pattern recognition. Thus, the capacity of the star pattern recognition feature library is effectively reduced, and the stability and recognition rate of the improved star pattern recognition algorithm are further improved. The improved star pattern recognition algorithm first establishes radial and circumferential feature vectors based on the bit vector, then adds the noise compensation code according to the companion star position error, then modifies the radial and circumferential feature vectors, and finally calculates the minimum similarity difference between the feature vector of the star pattern observed by the star sensor and the feature vector of the navigation star in the feature library to obtain the unique star pattern recognition result. The identification star database adopts the maximum matching number algorithm, which can improve the search efficiency, reduce the amount of redundant matching, and shorten the matching time. The simulation results show that even in the presence of star position and magnitude noise, the improved star pattern recognition algorithm with radial companion-circumferential feature maintains a high recognition rate of more than 97 percent, demonstrating that the algorithm's robustness is superior to other algorithms. The revised method described in this work outperforms the classic triangle algorithm and the radial feature star pattern recognition algorithm without compensation code in terms of algorithm robustness, recognition success rate, and recognition time. [ABSTRACT FROM AUTHOR]

Since the traditional multiple-error locating method in software testing is difficult to achieve, and its information analysis is inaccurate, a software multiple-error locating method based on path clustering and failure weighting is proposed in this paper. In an environment with complete test cases, the program execution context information is dynamically captured by running test cases, and a path matrix of execution trajectory information is constructed. The cluster analysis is used to divide clusters and expand the weight of failed execution and added to suspicious in the process of degree evaluation to troubleshoot multiple errors. Experiments are implemented on four benchmark programs. The results show that compared with the five methods based on the equivalent evaluation function, the error location cost of the proposed method was reduced by 19.15% on average and effectively improved the efficiency of error location. [ABSTRACT FROM AUTHOR]

In this paper, we develop a multiattribute group decision-making (MAGDM) method to solve problems with interactive attributes under interval-valued q-rung orthopair fuzzy set (IVq-ROFS) environment. Firstly, the interval-valued q-rung orthopair fuzzy Choquet integral average (IVq-ROFCA) operator is proposed to aggregate interval-valued q-rung orthopair fuzzy information. Then, we investigate the interval-valued q-rung orthopair fuzzy Choquet integral geometric (IVq-ROFCG) operator and offer several related properties. More importantly, for handling problems with interdependence between attributes for IVq-ROFS, a MAGDM method is developed based on the IVq-ROFCA operator. Finally, an example of the warning management system for hypertension is given to illustrate the proposed method, and parameter analysis and comparison analysis further verify the feasibility and validity of the proposed method. [ABSTRACT FROM AUTHOR]

This paper puts forward the thinking mode of problems solved by self, which can turn complaints into wishes that resembles needs beyond user's expectation and imagination. Inferring the framework for solving problems from existing problem materials, the proposed technique employs literal logic to manipulate mathematical strategies, so that rational thinking can guide the development of affectional contexts, and automatically generate solution concepts, systematically and systemically. Through the demonstration of a design case study and comparative analysis of the effectiveness of related creative techniques the feasibility, novelty, advancement, and practicality of the proposed model are verified. The proposed tool can assist its users to think outside the black box and create "needs that adopters yet to aware." [ABSTRACT FROM AUTHOR]

As a nonuniform structure, tailings dam undergoes complex and chaotic nonlinear changes, under the joint influence of multiple dynamic or nondynamic factors. These changes make it difficult to predict the deformation of tailings dam accurately with a numerical model. To solve the problem, this paper proposes a grey deformation prediction model optimized by double coefficient (GDPM-DC). Unlike a single grey prediction model, the GDPM-DC does not mutate significantly but adapts well to specific scenarios. Besides, the model can smoothen and stabilize the original data and thus achieve accurate prediction of the deformation of tailings dam. The main results are as follows. The GDPM-DC made more accurate predictions than the traditional grey model (1, 1) (GM (1, 1)), the grey model based on logarithmic transformation (GM-LT), and the grey model optimized by weight coefficient (GM-WC). It significantly improved the overall prediction accuracy of the vertical and transverse deformations of the dam and controlled the relative error of the predicted seepage pressure to 2.79%–3.71%. Moreover, the model could forecast the trend component and random fluctuation component of seepage pressure effectively, fit the increasing trend in stages 1–3 and the decreasing trend in stages 3–9, and realize the quantitative prediction of deformation law for the operating tailings dam. The research results provide a meaningful reference for the instability analysis and safety management of tailings dam. [ABSTRACT FROM AUTHOR]

How to determine a suitable security algorithm for a special application scenario is a complex problem. In this paper, this complex problem is formulated as a multicriteria decision-making (MCDM) problem, and we propose a novel MULTIMOORA (multiobjective optimization on the basis of a ratio analysis plus the full MULTIpevaluation information in the security algorithms evaluation problem. The MULTIMOORA method is an excellent decision method, which owns strong robustness. However, it has not been used to process the complex information structure of q-rung orthopair fuzzy sets. Moreover, it cannot solve the problem that the extreme values negatively influence the ranking results, and it also cannot capture the interrelationship hiding behind the criteria. To overcome the above challenges, we propose novel q-rung orthopair fuzzy Dombi power Heronian mean (DPHM) operator and q-rung orthopair fuzzy Dombi power geometric Heronian mean (DPGHM) operator. Based on these two operators, the MULTIMOORA method is improved for solving the security algorithms' evaluation problem. Finally, a practical example for evaluating five security algorithms is used to illustrate the decision process of the proposed q-rung orthopair fuzzy MULTIMOORA method. [ABSTRACT FROM AUTHOR]

The researching, designing, calculating, and controlling cable-driven parallel robots (CDPRs) are being promoted in recent years. The researches focus on optimizing the design of CDPRs configuration, computing workspace, calculating cable tension distribution, designing the mechanical structure, and developing controller. However, due to the complexity of the structure and unidirectional characteristic of cable, many computational methods have been applied to solve the above problems. To facilitate the performance of theoretical studies on important issues in the design and control of the CDPRs, a summary of computational methods is needed for important issues related to the functionality and accuracy of CDPRs such as defining the workspace, distributing the cable tensions, and calculating the sagging of the cable. This paper summarizes published studies on CDPRs and focuses on classifying and analyzing methods used to calculate important issues in the process of calculating and designing CDPRs. The efficiency of these calculation methods is also analyzed and evaluated based on the mathematical theories of kinematics and dynamics of CDPRs. The content of this study is an effective reference for studies on important problems in the process of designing and implementing CDPRs, helping researchers shorten the time to review related topics. [ABSTRACT FROM AUTHOR]

Accurate counting in dense scenes can effectively prevent the occurrence of abnormal events, which is crucial for flow management, traffic control, and urban safety. In recent years, the application of deep learning technology in counting tasks has significantly improved the performance of models, but it still faces many challenges, including the diversity of target distribution between image and background, the drastic change of target scale, and serious occlusion. To solve these problems, this paper proposes a spatial context feature fusion network, abbreviated as SCFFNet, to understand highly congested scenes and perform accurate counts as well as produce high-quality estimated density maps. SCFFNet first uses rich convolutions with different scales to calculate scale-aware features, adaptively encodes the scale of contextual information needed to accurately estimate density maps, and then calibrates and refuses the fused feature maps through a channel spatial attention-aware module, which improves the model's ability to suppress background and focus on main features. Finally, the final estimated density map is generated by a dilated convolution module. We conduct experiments on five public crowd datasets, UCF_CC_50, WorldExpo'10, ShanghaiTech, Mall, and Beijing BRT, and the results show that our method achieves lower counting errors than existing state-of-the-art methods. In addition, we extend SCFFNet to count other objects, such as vehicles in the vehicle dataset HBR_YD, and the experimental results show that our proposed method significantly improves the output quality with higher accuracy than previous methods. [ABSTRACT FROM AUTHOR]

To collect full-labeled data is a challenge problem for learning classifiers. Nowadays, the general tendency of developing a model is becoming larger to be able to obtain more potential capacity to effectively predict unknown instances. However, imbalanced datasets still are not able to meet the needs for training a robustness classifier. A convincing guidance to extract invariance features from images is training in augmented input datasets. However, selecting a proper way to generate synthetic samples from a larger quality of feasible augmentation methods is still a big challenge. In the paper, we use three types of datasets and investigate the merits and demerits of five image transformation methods—color manipulate methods (color and contrast) and traditional affine transformation (shift, rotation, and flip). We found a common experiment result that plausible color transformation methods perform worse against traditional affine transformations in solving the overfitting problem and improve the classification accuracy. [ABSTRACT FROM AUTHOR]

The spherical fuzzy soft set is a generalized soft set model, which is more realistic, practical, and accurate. It is an extended version of existing fuzzy soft set models that can be used to describe imprecise data in real-world scenarios. The paper seeks to introduce the new concept of spherical fuzzy soft topology defined on spherical fuzzy soft sets. In this work, we define some basic concepts including spherical fuzzy soft basis, spherical fuzzy soft subspace, spherical fuzzy soft interior, spherical fuzzy soft closure, and spherical fuzzy soft boundary. The properties of these defined set are also discussed and explained with an appropriate examples. Also, we establish certain important results and introduce spherical fuzzy soft separation axioms, spherical fuzzy soft regular space, and spherical fuzzy soft normal space. Furthermore, as an application, a group decision-making algorithm is presented based on the TOPSIS (Technique of Order Preference by Similarity to an Ideal Solution) method for solving the decision-making problems. The applicability of the proposed method is demonstrated through a numerical example. The comprehensive advantages of the proposed work have been stated over the existing methods. [ABSTRACT FROM AUTHOR]

This paper proposes a stochastic search algorithm called improved hypercube optimisation search (HOS+) to find a better solution for optimisation problems. This algorithm is an improvement of the hypercube optimisation algorithm that includes initialization, displacement-shrink and searching area modules. The proposed algorithm has a new random parameters (RP) module that uses two control parameters in order to prevent premature convergence and slow finishing and improve the search accuracy considerable. Many optimisation problems can sometimes cause getting stuck into an interior local optimal solution. HOS+ algorithm that uses a random module can solve this problem and find the global optimal solution. A set of experiments were done in order to test the performance of the algorithm. At first, the performance of the proposed algorithm is tested using low and high dimensional benchmark functions. The simulation results indicated good convergence and much better performance at the lowest of iterations. The HOS+ algorithm is compared with other meta heuristic algorithms using the same benchmark functions on different dimensions. The comparative results indicated the superiority of the HOS+ algorithm in terms of obtaining the best optimal value and accelerating convergence solutions. [ABSTRACT FROM AUTHOR]

The existing fuzzy clustering algorithms are mostly fuzzy comprehensive evaluation algorithms based on specific elements, but the main problem of such fuzzy algorithms is the lack of overall research on the responsible individuals and the lack of hierarchy in the algorithms. It is suitable for data mining of academic early warning systems. Therefore, an improved fuzzy algorithm based on fuzzy performance evaluation based on composite elements is proposed, and it is applied to the performance evaluation system to solve the complex problems in performance evaluation. In the process of building smart campuses in colleges and universities, academic prewarning, as the main part of smart campuses, mainly uses data mining technology to ensure students complete their studies smoothly and at the same time provides certain decision-making support for colleges and universities. Based on the research topic of the relevant departments of a certain school, this paper aims to build an academic early warning system suitable for the school to ensure that students can successfully complete their studies. The main research contents are divided into two parts: "study early warning model research" and "design and implementation of an academic early warning system." Through analysis and experiments, it is proved that the model evaluation effect based on the algorithm improvement is the best, with recall reaching 85%, precision reaching 78.96%, and AUC reaching 80.25%. [ABSTRACT FROM AUTHOR]

Exploring the information interaction between users can provide a new way to solve open data ecosystem (ODE) sustainable development. In this paper, an information diffusion model, ILSOC, is proposed to analyze the information spreading among users within the ODE, and we use this model to inspect the impact on the sustainability of the ODE. First, we establish an information diffusion model that considers the public swing mentality. As a result, the relevant parameter variables are defined, and their influence is evaluated. Finally, this article proposes countermeasures to solve the sustainability problem within ODEs from the perspectives of users. Results demonstrate that (1) simply increasing the probability of interaction between users cannot effectively improve the sustainability of the ODE. (2) Parameters, such as contact rate, conversation rate, silent rate, and reversal rate, have constant effects on the ODE. (3) To maintain this ecosystem stability, some incentives and punishments for users in the public event are necessary. [ABSTRACT FROM AUTHOR]

In order to develop the dynamic effectiveness of the structures such as trusses, the application of optimisation methods plays a significant role in improving the shape and size of elements. However, conjoining two heterogeneous variables, nodal coordinates and cross-sectional elements, makes a challenging optimisation problem that is nonlinear, multimodal, large-scale with dynamic constraints. To handle these challenges, evolutionary and swarm optimisation algorithms can be robust and practical tools and show great potential to solve such complex problems. This paper proposed a comparative truss optimisation framework to solve two large-scale structures, including 314-bar and 260-bar trusses. The proposed framework consists of twelve state-of-the-art bio-inspired algorithms. The experimental results show that the marine predators algorithm (MPA) performed best compared with other algorithms in terms of convergence speed and the quality of the proposed designs of the trusses. [ABSTRACT FROM AUTHOR]

In computational mathematics, the iterative method is a mathematical procedure. This method uses an initial value to generate a sequence of improving approximate solutions for a class of problems, in which the n-th approximation is derived from the previous ones. The iterative method is widely used to solve complex problems in engineering. In this paper, the iterative method is applied to inverse the subsurface interface with the gravity anomaly. First, the classical Parker-Oldenburg interface inversion formula was introduced and analogized to the downward continuation formula. Then, combined with the regular-integral downward continuation method, the iterative inversion formula of the gravity interface is derived. The iterative mode of the improved method suppresses high-frequency signals effectively. At the same time, there is no need to perform forward calculations in the iterative process. The model test shows that the proposed method can accurately calculate the depth of the interface. Finally, the proposed interface inversion method is applied to the Qinghai-Tibet Plateau, and the calculated Moho interface provides some geophysical data support for the geological interpretation of the area in the future. [ABSTRACT FROM AUTHOR]

Automatic reasoning is a very challenging research area in the field of artificial intelligence. Automatic inference based on lattice-valued logic is an important basic research content in the field of intelligent information processing. It is of great academic significance to deal with incomparability, linguistic valued information, and formal verification of the correctness of corresponding software and hardware systems in various applications. Based on the extensive work of α-resolution automated reasoning in linguistic truth-valued lattice-valued logic, this paper discusses the structure and its α-resolvability of the generalized literal. For further improving the efficiency, a topology graph deduction is proposed as well as its soundness, completeness, and universal algorithm. The use of distributed computing and resource sharing is the core idea of this deduction. In the decision process, as long as one satisfiability solution is found by subtask, the decision result can be given directly, shortening the decision time. The algorithm reduces the time complexity from factorial level to square. [ABSTRACT FROM AUTHOR]

Soft open point (SOP) can improve the flexibility and reliability of power supplies; thus, they are widely used in distribution network systems. Traditional single-vector model predictive control (SV-MPC) can quickly and flexibly control the power and current at both ports of the SOP. However, SV-MPC can only select one voltage vector in a sampling time, producing large current ripples, and power fluctuations. In order to solve the above problems, this paper proposes a three-vector-based low complexity model predictive control (TV-MPC). In the proposed control method, two effective voltage vectors and one zero voltage vector are selected in a sampling time. For the two-port SOP, methods are given to judge the sectors on both sides and select the voltage vectors. Furthermore, the calculation method of the distribution time is proposed as well. Finally, the effectiveness of the proposed method is verified by steady-state and dynamic-state simulation results compared with the SV-MPC. [ABSTRACT FROM AUTHOR]

Peaking parameter is the key content in the regular inspection of the pressure pipeline. Solving the problem of the peaking measurement method defined by a standard cannot be applied to a situation in which there exists a weld surface with reinforcement and misalignment. In this paper, a peaking estimation method based on data prediction was proposed to estimate the contour information of the base metal at the weld joint using the contour point set data of the base metal part of the weld. Herein, the longitudinal weld peaking estimation method based on a piecewise logistic regression (PLR) and the girth weld peaking estimation method based on a piecewise Bayesian linear regression (PBLR) were studied, and the midpoint of the two symmetrical points of the base metal on either side of the weld was used as a reference for calculating the peaking. Finally, we collected the surface profile data of longitudinal weld pressure pipes with diameters of 155 mm, 255 mm, 550 mm, and 600 mm and the surface profile data of girth weld pressure pipes with diameters of 120 mm, 130 mm, 140 mm, and 170 mm. This weld seam data used the data estimation method proposed in this article and traditional long short-term memory and fitting methods. The results showed that the proposed data prediction method could accurately predict the position of the base metal, and the theoretical mean absolute error of the peaking estimation based on the PBLR and PLR could attain 0.06 mm and 0.07 mm, respectively, which meets the parameter measurement requirements of related verification fields. [ABSTRACT FROM AUTHOR]

Induction heating systems are characterized by model uncertainty, nonlinearity, and external disturbances, and the control accuracy of the system directly affects the performance of the heated workpiece. In order to improve the temperature control accuracy and anti-interference performance of induction heating systems, this paper proposes a composite control strategy combining fractional-order PID (FOPID) and active disturbance rejection control (ADRC). Meanwhile, for the problem of too many controller tuning parameters, an improved quantum behavior particle swarm optimization (QPSO) algorithm is used to transform the nine parameters to be tuned in fractional-order PID active disturbance rejection control (FOPID-ADRC) into a minimization value optimization problem for solving. The simulation results show that the FOPID-ADRC controller improves the anti-interference capability and control accuracy of the temperature control system, and the improved QPSO algorithm has better global search capability and local optimal adaptation value. [ABSTRACT FROM AUTHOR]

In order to solve the problem of road roughness identification, a study on the nonlinear autoregressive with exogenous inputs (NARX) neural network identification method was carried out in the paper. Firstly, a 7-DOF plane model of vehicle vibration system was established to obtain the vertical acceleration and elevation acceleration of the body, which were set as ideal input samples for the neural network. Then, based on the plane model, with common speed, the road roughness was solved as the ideal output sample of the NARX neural network, and the road roughness of B-level and C-level was identified. The results show that the proposed method has ideal identification accuracy and strong antinoise ability. The relative error of C-level road roughness is larger than that of B-level road roughness. The identified road roughness can provide a theoretical basis for analyzing the dynamic response of expressway roads. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the subspace-like direction of arrival (DOA) estimation performs poor due to the error of sources number, this paper proposes a new super-resolution DOA estimation algorithm based on the diagonal-symmetric loading (DSL). Specifically, orthogonality principle of the minimum eigenvector of the specific covariance matrix and the source number estimation based on the improved K-means method were adopted to construct the spatial spectrum. Then, by considering the signal-to-interference-to-noise ratio (SINR), the theoretical basis for selecting parameters was given and verified by numerical experiment. To evaluate the effectiveness of the proposed algorithm, this paper compared it with the methods of minimum variance distortionless response (MVDR) and new signal subspace processing (NSSP). Experimental results prove that the proposed DSL has higher resolution and better estimation accuracy than the MVDR and NSSP. [ABSTRACT FROM AUTHOR]

To realize an automatic diagnosis of rotating machinery structure faults, this paper presents a novel fault diagnosis model based on adaptive multiband filter and stacked autoencoders (SAEs). First, to solve the problem where the actual rotating frequency and its harmonics cannot be accurately extracted in engineering applications, an improved adaptive multiband filtering method is designed. This method takes the theoretical rotating frequency as the search center, extracts the maximum within the positive and negative deviation as the actual rotating frequency, and sets a threshold according to the actual value to realize multiband filtering. This method can effectively remove background noise and accurately extract the actual rotating frequency and its harmonics. Second, an unsupervised SAE multiclassification model is established to realize an automatic diagnosis of fault types. This model can automatically extract the in-depth features of the filtered signal and improve the fault classification accuracy. Third, engineering and comparative experiments were carried out to verify the effectiveness and superiority of this model. Results show that the proposed automatic diagnosis model can extract the characteristic components abundantly and accurately recognize rotating machinery structural faults. [ABSTRACT FROM AUTHOR]