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Optimization Algorithm for Reduction the Size of Dixon Resultant Matrix: A Case Study on Mechanical Application
Get PDFIn the process of eliminating variables in a symbolic polynomial system, the extraneous factors are referred to the unwanted parameters of resulting polynomial. This paper aims at reducing the number of these factors via optimizing the size of Dixon matrix. An optimal configuration of Dixon matrix would lead to the enhancement of the process of computing the resultant which uses for solving polynomial systems. To do so, an optimization algorithm along with a number of new polynomials is introduced to replace the polynomials and implement a complexity analysis. Moreover, the monomial multipliers are optimally positioned to multiply each of the polynomials. Furthermore, through practical implementation and considering standard and mechanical examples the efficiency of the method is evaluated
R<sup>2</sup>N: A Novel Deep Learning Architecture for Rain Removal from Single Image
No full textVisual degradation of captured images caused by rainy streaks under rainy weather can adversely affect the performance of many open-air vision systems. Hence, it is necessary to address the problem of eliminating rain streaks from the individual rainy image. In this work, a deep convolution neural network (CNN) based method is introduced, called Rain-Removal Net (R2N), to solve the single image de-raining issue. Firstly, we decomposed the rainy image into its high-frequency detail layer and low-frequency base layer. Then, we used the high-frequency detail layer to input the carefully designed CNN architecture to learn the mapping between it and its corresponding de-rained high-frequency detail layer. The CNN architecture consists of four convolution layers and four deconvolution layers, as well as three skip connections. The experiments on synthetic and real-world rainy images show that the performance of our architecture outperforms the compared state-of-the-art de-raining models with respects to the quality of de-rained images and computing efficiency
An Automated Player Detection and Tracking in Basketball Game
Get PDFVision-based player recognition is critical in sports applications. Accuracy, efficiency, and Low memory utilization is alluring for ongoing errands, for example, astute communicates and occasion classification. We developed an algorithm that tracks the movements of different players from a video of a basketball game. With their position tracked, we then proceed to map the position of these players onto an image of a basketball court. The purpose of tracking player is to provide the maximum amount of information to basketball coaches and organizations, so that they can better design mechanisms of defence and attack. Overall, our model has a high degree of identification and tracking of the players in the court. We directed investigations on soccer, basketball, ice hockey and pedestrian datasets. The trial comes about an exhibit that our technique can precisely recognize players under testing conditions. Contrasted and CNNs that are adjusted from general question identification systems, for example, Faster-RCNN, our approach accomplishes cutting edge exactness on three sorts of recreations (basketball, soccer and ice hockey) with 1000×fewer parameters. The all-inclusive statement of our technique is additionally shown on a standard passer-by recognition dataset in which our strategy accomplishes aggressive execution contrasted and cutting-edge methods
Satellite Cloud-Derived Wind Inversion Algorithm Using GPU
Get PDFCloud-derived wind refers to the wind field data product reversely derived through satellite remote sensing cloud images. Satellite cloud-derived wind inversion has the characteristics of large scale, computationally intensive and long time. The most widely used cloud-derived serial--tracer cloud tracking method is the maximum cross-correlation coefficient (MCC) method. In order to overcome the efficiency bottleneck of the cloud-derived serial MCC algorithm, we proposed a parallel cloud-derived wind inversion algorithm based on GPU framework in this paper, according to the characteristics of independence between each wind vector calculation. In this algorithm, each iteration is considered as a thread of GPU cores, and each thread block array of GPU allocates n*32 threads, and the many thread blocks are allocated to the thread grid. The parameters of the algorithm are passed from CPU to GPU global memory and the storage spaces are previously created on the GPU device before the functions of algorithm are executed. The test results of multiple sets of different inversion models on the NVIDIA Geforce GT and the 4-core 8-thread Core i7-3770 CPU show that the algorithm significantly improves the inversion efficiency. The acceleration ratio is up to 112, and the parallel experiment acceleration ratio is also impressive
Fuzzy C-Means Algorithm Automatically Determining Optimal Number of Clusters
Get PDFIn clustering analysis, the key to deciding clustering quality is to determine the optimal number of clusters. At present, most clustering algorithms need to give the number of clusters in advance for clustering analysis of the samples. How to gain the correct optimal number of clusters has been an important topic of clustering validation study. By studying and analyzing the FCM algorithm in this study, an accurate and efficient algorithm used to confirm the optimal number of clusters is proposed for the defects of traditional FCM algorithm. For time and clustering accuracy problems of FCM algorithm and relevant algorithms automatically determining the optimal number of clusters, kernel function, AP algorithm and new evaluation indexes were applied to improve the confirmation of complexity and search the scope of traditional fuzzy C-means algorithm, and evaluation of clustering results. Besides, three groups of contrast experiments were designed with different datasets for verification. The results showed that the improved algorithm improves time efficiency and accuracy to certain degree
A Novel Scene Text Recognition Method Based on Deep Learning
Get PDFScene text recognition is one of the most important techniques in pattern recognition and machine intelligence due to its numerous practical applications. Scene text recognition is also a sequence model task. Recurrent neural network (RNN) is commonly regarded as the default starting point for sequential models. Due to the non-parallel prediction and the gradient disappearance problem, the performance of the RNN is difficult to improve substantially. In this paper, a new TRDD network architecture which base on dilated convolution and residual block is proposed, using Convolutional Neural Networks (CNN) instead of RNN realizes the recognition task of sequence texts. Our model has the following three advantages in comparison to existing scene text recognition methods: First, the text recognition speed of the TRDD network is much fast than the state-of-the-art scene text recognition network based recurrent neural networks (RNN). Second, TRDD is easier to train, avoiding the problem of exploding and vanishing, which is major issue for RNN. Third, both using larger dilated factors and increasing the filter size are all viable ways to change receptive field size. We benchmark the TRDD on four standard datasets, it has higher recognition accuracy and faster recognition speed based on the smaller model. It is hopefully used in the real-time application
A Multi-Objective Decision-Making Approach for the Optimal Location of Electric Vehicle Charging Facilities
Get PDFElectric vehicles (EVs) are recognized as one of the most promising technologies worldwide to address the fossil fuel energy resource crisis and environmental pollution. As the initial work of EV charging station (EVCS) construction, site selection plays a vital role in its whole life cycle. In this paper, a multi-objective optimization model for the location layout of EVCSs is established when considering various factors such as user demand, investment cost, soil locations, the emergency charging mileage limit, the actual road condition and service network reliability. The model takes the minimum investment cost and the minimum user charging cost as the dual objectives. On the basis of satisfying the user’s charging demand and the capacity constraints of EVCSs, the redundant design of the charging pile and station is considered to ensure the reliability of the service network. In the allocation of user charging demand, in this paper, two factors of time and distance are considered, and the equal time load distance method is adopted to distribute traffic flow under the limitation of emergency charging mileage. When calculating the average travel speed of a road section, an accounting method based on the land price level is proposed considering the congestion. Then, the linear weighting method is applied to normalizing the multi-objective function, and the genetic algorithm is employed to solve the problem. Finally, a computational experiment is presented to demonstrate the applicability and effectiveness of the proposed approach. The results show that the proposed approach is a useful, practical, and effective way to find the optimal location of EVCSs
Three-Dimensional Numerical Analysis of Blast-Induced Damage Characteristics of the Intact and Jointed Rockmass
Get PDFThis article reports numerical results investigating the damage evolution and spatial distribution characteristics of intact and jointed rockmass subjected to blast loading. The behaviors of rock material are described by the Holmquist- Johnson-Cook (HJC) constitutive model incorporated in the finite element software LS-DYNA. Results indicate that the damage distribution shows a reverse S-shape attenuation with the increase of the distance from borehole, and a better goodness of fit with the Logistic function is observed. In the single-hole blasting of jointed rockmass, there are two types of regions around the intersection of borehole and joint in which the damage degree is quite different. The crushing damage develops in a Ψ-shape path along the joint. In the radial direction, the crushing damage and cracking damage of rock show different distribution forms with the increase of joint dip angle. As for the double-hole blasting, due to the superposition of the blast waves, the damage degree in the region between the two boreholes of intact rockmass is significantly large. For jointed rockmass, the joint has local enhancement or inhibition effect on the blast damage in the region between the two boreholes
Influence of Ground Stress on Coal Seam Gas Pressure and Gas Content
Get PDFThe influence of ground stress was quantitatively analyzed on coal seam gas pressure and gas content in this paper. Mining activities in coal mine can result in stress concentration in the coal (rock) body around the mining space, but porosity of the coal seam would not change too much. Therefore, gas pressure and gas content in the coal seam are slightly affected. Studies showed that the free gas was gradually transformed into adsorbed gas, and the gas adsorption volume was small, and then gas pressure increases roughly linearly when the porosity decreased because of stress influence. Additionaly, when porosity of coal seam reduced to 40%, the amount of adsorbed gas accounted for no more than 10% of coal seam gas content, and the increase of gas pressure did not exceed 15% of the original gas pressure
A Study on the Properties of Resin Transfer Molding Cyanate Ester and Its T800 Grade Carbon Fiber Composites
Get PDFThe properties of resin transfer molding (RTM) cyanate ester and its T800 grade carbon fiber composites were studied with the rheometer, differential scanning calorimetry (DSC), FT-IR, dynamic mechanical analyzer (DMA), thermal gravimetric analysis (TGA), mechanical property testing, and scanning electron microscopy (SEM). The results showed that the temperature of cyanate ester suitable for RTM process was 70℃. Curing process of the resin was 130℃/2 h+160℃/2 h+200℃/2 h+220℃/4 h. Glass transition temperature and heat decomposition temperature of the cured resin are 289℃ and 415℃, respectively. Mechanical properties of T800/RTM cyanate composites are 13.5% higher than that of T700/RTM cyanate composites and equal to that of T800/Prepreg cyanate composites. Tg of T800/RTM cyanate composites was proved to be 291℃. Fracture pattern of the composites was flat, which proved excellent interface properties between fiber and resin in this composite