Improved Fusion Algorithm Research Articles

Application of Depth Convolution Virtual Reality (VR) in Traditional Sculpture Design under Digital Modeling

Virtual reality technology continues to be used in other technical fields, art forms are becoming more and more abundant. Virtual reality technology provides convenience for art creation. The combination of art and technology can become a new art form, digital art, and has become a trend of art development. The earliest sculpture art in the art field is no exception, and has also blended with the virtual reality technology. Because the artistic presentation of traditional sculpture art is a relatively complex materialization process, the thinking presentation and creation process of sculpture art need to go through a series of processes to complete the final sculpture works of art; This makes sculpture art more complex and more difficult to operate than other arts when using virtual reality technology. If virtual reality technology wants to integrate perfectly with sculpture art, it needs to combine the process of sculpture art and provide different digital technology methods in different links. This document uses linear combination to solve two level problems, which is a very effective adaptive algorithm. The empirical results show that the improved fusion algorithm can re analyze the importance of the positive process in the rapid development of virtual reality technology, and the rapid development of virtual reality technology is crucial to shaping the value of this theme. Compared with traditional methods, the accuracy of this experiment is increased by 13.4%, and compared with the algorithm TIN, the time complexity and space complexity are 97% and 54% lower respectively.

Improved Fusion Algorithm for Fiber-Optic Gyroscope: Suppressing Intrinsic Noise and Environmental Disturbances

Improved Fusion Algorithm for Fiber-Optic Gyroscope: Suppressing Intrinsic Noise and Environmental Disturbances

Robot path planning using fusion algorithm of ant colony optimization and genetic algorithm

Aiming at the shortcomings of single ant colony optimization such as many redundant nodes, slow convergence and low efficiency, based on the idea of “selection-crossover” of genetic algorithm, an improved fusion algorithm of ant colony optimization and genetic algorithm is proposed. In this paper, the fusion algorithm includes “optimal strategy” and “genetic region strategy”. The optimal strategy is that high-quality parents are selected by roulette in the first [Formula: see text] paths of each generation; genetic region strategy is that according to the path information of the parents, the grid map is divided into genetic area and nongenetic area. Genetic area refers to the area where the offspring ants can pass, and nongenetic area refers to the area where the offspring ants can’t pass; finally, the offspring ant searches the path in the genetic region to reduce the search range of the offspring ant and improve the convergence speed. Simulation results show that the fusion algorithm has faster searching speed and more stable convergence than the basic ant colony optimization and other improved ant colony optimization.

A Robot Path Planning Method Based on Improved Genetic Algorithm and Improved Dynamic Window Approach

Intelligent mobile robots play an important role in the green and efficient operation of warehouses and have a significant impact on the natural environment and the economy. Path planning technology is one of the key technologies to achieve intelligent mobile robots. In order to improve the pickup efficiency and to reduce the resource waste and carbon emissions in logistics, we investigate the robot path optimization problem. Under the guidance of the sustainable development theory, we aim to achieve the goal of environmental social governance by shortening and smoothing robot paths. To improve the robot’s ability to avoid dynamic obstacles and to quickly solve shorter and smoother robot paths, we propose a fusion algorithm based on the improved genetic algorithm and the dynamic window approach. By doing so, we can improve the efficiency of warehouse operations and reduce logistics costs, whilst also contributing to the realization of a green supply chain. In this paper, we implement an improved fusion algorithm for mobile robot path planning and illustrate the superiority of our algorithm through comparative experiments. The authors’ findings and conclusions emphasize the importance of using advanced algorithms to optimize robot paths and suggest potential avenues for future research.

The design for supply chain management of intelligent logistics system using cloud computing and the internet of things

AbstractImage recognition is the key to smart logistics systems. Traditional handwriting feature extraction is difficult to meet the requirements of image recognition. Deep learning is used for image recognition. Firstly, convolutional neural network (CNN) and deep Boltzmann machines under deep learning are introduced. Second, cellular neural networks are used to perform feature recognition and extraction on images. Finally, a Parzen classifier is used to classify the obtained image features. The novelty is that through the structural design and research of the intelligent logistics system, the CNN is combined to construct a management system of supply chain logistics of image recognition and information processing. The experimental results show that the recognition accuracy time of the proposed improved fusion algorithm on the Mixed National Institute of Standards and Technology data set is 198.85 s. When the improved algorithm achieves the same recognition accuracy, it takes 159.65 s. The recognition efficiency of the improved algorithm is 19.71% higher than that of the unimproved algorithm. In addition, when the unimproved algorithm reaches the maximum number of iterations, the error rate is 2.47%. The error rate of the improved algorithm is only 0.74%. This study provides a basis for improving the image recognition accuracy and has certain practical value.

Robot Path Planning Navigation for Dense Planting Red Jujube Orchards Based on the Joint Improved A* and DWA Algorithms under Laser SLAM

High precision navigation along specific paths is required for plant protection operations in dwarf and densely planted jujube orchards in southern Xinjiang. This study proposes a robotic path planning and navigation method for dense planting of red jujube orchards based on the improved A* and dynamic window approach (DWA) algorithms using Laser Radar to build maps. First, kinematic and physical robot simulation models are established; a map of the densely planted jujube orchard is constructed using Laser Radar. The robot’s position on the constructed map is described using an adaptive Monte Carlo positioning algorithm. Second, a combination of the improved A* and DWA algorithms is used to implement global and real-time local path planning; an evaluation function is used for path optimisation. The proposed path planning algorithm can accurately determine the robot’s navigation paths, with the average error U, average linear path displacement error, and L-shaped navigation being 2.69, 2.47, and 2.68 cm, respectively. A comparison experiment is set up in the specific path navigation section. The experimental results show that the improved fusion algorithm reduces the average navigation positioning deviation by 0.91cm and 0.54 cm when navigating L and U-shaped specific paths. The improved fusion algorithm is superior to the traditional fusion algorithm in navigation accuracy and navigation stability. It can improve the navigation accuracy of the dense planting jujube garden and provide a reference method for the navigation of the plant protection operation in the densely planted jujube orchards.

Research on facial expression recognition based on an improved fusion algorithm

Abstract This article puts forward a facial expression recognition (FER) algorithm based on multi-feature fusion and convolutional neural network (CNN) to solve the problem that FER is susceptible to interference factors such as non-uniform illumination, thereby reducing the recognition rate of facial expressions. It starts by extracting the multi-layer representation information (asymmetric region local binary pattern [AR-LBP]) of facial expression images and cascading them to minimize the loss of facial expression texture information. In addition, an improved algorithm called divided local directional pattern (DLDP) is used to extract the original facial expression image features, which not only retains the original texture information but also reduces the time consumption. With a weighted fusion of the features extracted from the above two facial expressions, new AR-LBP-DLDP facial local features are then obtained. Later, CNN is used to extract global features of facial expressions, and the local features of AR-LBP-DLDP obtained by weighted fusion are cascaded and fused with the global features extracted by the CNN, thereby producing the final facial expression features. Ultimately, the final facial expression features are input into Softmax for training and classification. The results show that the proposed algorithm, with good robustness and real-time performance, effectively improves the recognition rate of facial expressions.

UAV Path Planning of Combining Ant Colony and Beetle Antennae Algorithm Using Intelligent Wireless Communication

In order to settle the problem of UAV path planning under mountain, an algorithm which based on the combination of ant colony algorithm and beetle antennae algorithm is proposed. Three dimensional environment model is established and objective function is constructed. It used ant colony algorithm to initialize the search path and the particle coordinates of all the next steps are updated by the beetle antennae algorithm. The improved algorithm adopted a new step update rule to speed up the convergence of the algorithm and used third-order B-spline interpolation method to smooth the path. Simulation results show that improved fusion algorithm has faster convergence speed and high stability by comparing with other algorithms under the same conditions, which verifies its effectiveness.

An Improved Universal Fusion Algorithm for Constructing 3D Multiscale Porous Media

AbstractVarious types of porous media materials inherently contain pore structures of different scales, ranging from nanoscale to millimeter scale. Due to the limitations of the existing imaging technology, it is challenging and intractable for any single method to obtain and characterize the multiscale pore structure features of porous media accurately and comprehensively. To address the issue, according to the inherent logical correspondence and mutual conversion relationship between different‐scale image pixels, we propose an improved universal fusion algorithm for constructing three‐dimensional (3D) multiscale porous media. We successfully applied this algorithm to the construction of multiscale pore structure model of carbonate rock, sandstone and coal, and subsequently made quantitative extractions and characterizations of their pore structure characteristics. In addition, the finite element method (FEM) was used to calculate their absolute permeability. The results show that the improved fusion algorithm can effectively solve the problem of pore bias of the existing algorithm, which reduces the porosity of the multiscale model to a certain extent, while maintaining good pore interconnectivity. Besides, the multiscale model obtained by the improved fusion algorithm has a wider pore size distribution interval than that of the existing algorithm, and the absolute permeability of the former, computed using the FEM, is closer to the laboratory‐measured value than that of the latter.

Multi-sources information fusion analysis of water inrush disaster in tunnels based on improved theory of evidence

Water inrush is one of the most serious geological disasters threatening tunnel construction. Generally, complexity and multi-sources feature of physical information existing in tunnel construction make disaster prediction very difficult, how to accurately predict the disaster becomes a hot topic in the field of tunnel engineering. Dempster-Shafer (DS) theory of evidence is a widely used method for reasoning with multiple evidences, however some unbelievable results usually appear in dealing with highly conflicting evidences by its traditional combination rule. Thus an improved fusion algorithm based on weighted average of evidence conflict probability was firstly introduced into risk prediction of water inrush disaster. Through the improved algorithm, multi-sources precursor information measured from previous model test were fused to predict quantitative risk levels of water inrush for different excavation step of subsea tunnel in the model test. The predicted high risk at the 12th excavation step by improved algorithm agreed well with actual phenomenon of intensive seepage observed in the test, while the traditional method gave a lower level. Moreover, the improved algorithm predicted a more accuracy result in the phase of water inrush (at 16th excavation step shown in test). In brief, the improved algorithm can make more accuracy prediction for water inrush disasters and will provide valuable reference for similar engineering.

An improved evidence fusion algorithm in multi-sensor systems

Multi-sensor information fusion plays an important role in practical application. Although D-S evidence theory can handle this information fusion task regardless of prior knowledge, counter-intuitive conclusions may arise when dealing with highly conflicting evidence. To address this weakness, an improved algorithm of evidence theory is proposed. First, a new distribution distance measurement method is first proposed to measure the conflict between the evidences, and the credibility degree of the evidences can be obtained. Next, a modified information volume calculation method is also introduced to measure the effect of the evidence itself, and the information volume of the evidences can be generated. Afterwards, the credibility degree of each evidence can be modified based on the information volume to obtain the weight of each evidence. Ultimately, the weights of the evidences will be used to adjust the body of evidence before fusion. A numerical example for engine fault diagnosis exhibits the availability and effectiveness of the proposed method, where the BPA of the true fault is 89.680%. Furthermore, an application for target recognition is given to show the validity of the proposed algorithm, where the BPA of the true target is 98.948%. The experimental results show that the proposed algorithm has the best performance than other methods.

Effective storage location assignment model based on a genetic simulation annealing algorithm

Automated warehouses have become the main application equipment in logistics due to their access automation and simple operation. In order to adapt to the increasingly rapid logistics speed, it is necessary to optimise the location assignment of items in the automated warehouse. Firstly, according to the characteristics of the automated warehouse operation environment, the storage location assignment optimisation model with the shortest time of items travelling the warehouse, the minimum distance between related items and the lowest orthocentre of the shelf is proposed. Then according to the characteristics of the optimisation model and the shortcomings of the traditional genetic algorithm (GA), the defects of the GA are improved and the fusion with the simulated annealing algorithm (SA) is completed, so as to form an improved genetic simulation annealing algorithm (SAGA) for the model. Finally, the effectiveness and superiority of the improved fusion algorithm are verified by comparing the SA, the SAGA and the improved SAGA.

Multi-focus image fusion method based on improved VGG network

In order to better retain the original image information and improve the image fusion performance, an improved fusion algorithm combining visual geometry group(VGG) convolutional neural network with edge pixel statistical features was proposed. Firstly, this algorithm divided the complete image into image blocks, preprocessed the image blocks to obtain a higher image classification accuracy of 0.985 or more, and used the improved VGG convolutional neural network to accelerate the convergence of the model. When the image blocks were input into the network, the weight matrix of binary classification could be preliminarily obtained. Secondly, in the high-frequency detail part, the clear blur modules of left-focus image and right-focus image were respectively subjected to blurring processing, and the weights matrix with obvious boundaries was obtained by the threshold segmentation based on the statistical feature between the pixel points. Finally, combined with the two-step weight matrix, the fusion strategy of weighted sum was used to obtain the clear focus image. In order to illustrate the effectiveness of the algorithm, the experimental part shows the objective evaluation of its fusion subjective visual effect diagram and information entropy, which is outstanding compared with other algorithms and can better retain the information of the original image.

Effective storage location assignment model based on a genetic simulation annealing algorithm

Automated warehouses have become the main application equipment in logistics due to their access automation and simple operation. In order to adapt to the increasingly rapid logistics speed, it is necessary to optimise the location assignment of items in the automated warehouse. Firstly, according to the characteristics of the automated warehouse operation environment, the storage location assignment optimisation model with the shortest time of items travelling the warehouse, the minimum distance between related items and the lowest orthocentre of the shelf is proposed. Then according to the characteristics of the optimisation model and the shortcomings of the traditional genetic algorithm (GA), the defects of the GA are improved and the fusion with the simulated annealing algorithm (SA) is completed, so as to form an improved genetic simulation annealing algorithm (SAGA) for the model. Finally, the effectiveness and superiority of the improved fusion algorithm are verified by comparing the SA, the SAGA and the improved SAGA.

RETRACTED ARTICLE: Improved swarm intelligence algorithm for protein folding prediction

Swarm intelligence algorithm is to simulate the behaviour of groups of organisms to solve the target problem heuristic search algorithm. This paper briefly describes four swarm intelligence algorithms, focus on analysis of the ant colony algorithm and artificial fish algorithm and how to merge the two algorithms. Based on ACO and AFSA, a new fusion algorithm is proposed. The fusion algorithm has the robustness and parallelism of the ant colony algorithm, and has the advantages of strong global search ability and fast search speed of artificial fish swarm algorithm. The convergence of the improved fusion algorithm is proved by analysis, and the algorithm is applied to the field of protein folding prediction. It can be found that the new fuse algorithm, which in the case of find out the minimum energy value, the time required for the algorithm is less than that of the basic algorithm. It not only ensures the quality of the optimal value, but also greatly saves the calculation time. It has great practical value.

Equipment Condition Monitoring and Diagnosis System Based on Evidence Weight

To ensure the safety and reliability of equipment and effectively prevent the occurrence of major accidents, a monitoring and diagnosis system of equipment condition is proposed in this paper. First, a perceptual model of four layers, which can collect the original data of equipment by sensors and analyze the real-time information with intelligent algorithms and display the decision making on the screen, is designed. Second, a method of condition monitoring and diagnosis based on evidence weight is proposed. The basic probability assignment of evidence is corrected by Mahalanobis distance and sensor weight. A threshold is introduced to select the appropriate fusion rule by comparing the relationship between threshold and conflict factor. In order to verify the effectiveness and practicability of the method, the improved fusion algorithm is applied to the monitoring and diagnosis of centrifugal pumps. Finally, a prototype system is implemented to illustrate the validity of the system in practice.

Research of intelligent professional search engine based on agent

Owing to the increasing growth of network information, users cannot quickly query. Especially for professional information, the query time needed is longer. In order to improve precision of the professional search engine, after analysing the meta-search engine technology, combined the rtf algorithm and the PageRank algorithm, an improved fusion algorithm of search results based on PageRank is proposed, which can query according to the synonym and the relevant word. The algorithm was applied to the search engine for coal geology, and the experimental results showed it can fuse the independent search engine results together to some degree, and increase the precision and the recall ratio of professional engine.

Research of intelligent professional search engine based on agent

Owing to the increasing growth of network information, users cannot quickly query. Especially for professional information, the query time needed is longer. In order to improve precision of the professional search engine, after analysing the meta-search engine technology, combined the rtf algorithm and the PageRank algorithm, an improved fusion algorithm of search results based on PageRank is proposed, which can query according to the synonym and the relevant word. The algorithm was applied to the search engine for coal geology, and the experimental results showed it can fuse the independent search engine results together to some degree, and increase the precision and the recall ratio of professional engine.

A new prediction model based on multi-block forecast engine in smart grid

By changing the electricity market in smart grids, the consumers will be able to react to the electricity price. As close correlation of price and load, the density of this reaction can affect to demand curve and shift it in market. For this purpose, an accurate prediction model is demanded for optimal operation as well as planning in power system. For this purpose, we proposed a new hybrid forecast model based on dual-tree complex wavelet transform and multi-stage forecast engine (MSFE). In this model at first, the signal entered to proposed wavelet transform and then, it is filtered by new feature selection. After that, the signal predicted by proposed MSFE in three steps. An intelligent algorithm is applied to the forecast engine to increase its ability and prediction accuracy during the process. Finally, the improved fusion algorithm gather the outputs of MSFE. Effectiveness of the proposed method has been implemented over Australia’s and New England electricity market data. Obtained results compared with several prediction models which demonstrate the validity of proposed model.

Based on the Improved Fusion Algorithm of Wavelet Image Contrast and Canny Operator

Aiming at the visual effect and the fusion quality is poor of the traditional image fusion algorithm, the new fusion algorithm is adopted that combining the Canny operator with taking big window contrast. The algorithm uses wavelet transform to the original images are decomposed firstly, the process will be based on the frequency characteristics of the image information to form two pieces of information, low frequency and high frequency information. Low frequency subband uses weighted average fusion algorithm, while high frequency subband uses the algorithm which combines Canny operator with taking big window contrast, the last fusion image is got from the wavelet reconstruction in contrast to the principle of wavelet decomposition . The corresponding simulation analysis was carried out on the fusion algorithm, and the visual effect is more clear. Also the object evaluation standard increased significantly.