Discrete & Continuous Dynamical Systems - S
August & September 2019 , Volume 12 , Issue 4&5
Issue on nonlinear dynamics tools for solving problems coming from engineering, physics, chemistry, ecology and medicine
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The objective of the present paper is to study in an analytical way the existence and the stability of the libration points, in the restricted three-body problem, when the primaries are triaxial rigid bodies in the case of the Euler angles of the rotational motion are equal to
The problem of data transmission in communication network can betransformed into the problem of fractional factor existing in graph theory. Inrecent years, the data transmission problem in the specificnetwork conditions has received a great deal of attention, and itraises new demands to the corresponding mathematical model. Underthis background, many advanced results are presented on fractionalcritical deleted graphs and fractional ID deleted graphs. In thispaper, we determine that $G$ is a fractional
for any independent subset
A self-similar set is described as the unique (nonempty) compact subset remaining invariant under the action of a finite collection of similitudes on a complete metric space. Among this kind of fractals, those satisfying the so-called Moran's open set condition are especially appropriate to deal with applications of Fractal Geometry since their Hausdorff dimensions can be easily computed. However, such a separation property depends on an external open set whose properties are not fully known. In this paper, we construct a self-similar set in the real line lying under the open set condition which does not admit a connected feasible open set. This answers an open question posed by Zhou and Li in 2009.
In this paper, to solve network delay and network tracking control problems in multi-agent system communication, a design method of network prediction controller was introduced based on state difference estimation and output tracking error. This design method not only effectively compensates for influences of network delay on the system but also ensures stability of the closed-loop system and realizes the same tracking performance among multi-agents. The effectiveness of the proposed method was proven by simulation experiment.The key innovation in the paper is that the influence of network delay on the system was actively compensated and a network prediction tracking control mechanism was proposed to guarantee the stability of the closed-loop system.The proposed method achieved the same tracking with the local tracking control system under certain conditions.
In this paper we focus on the study of coupled systems of ordinary differential equations (ODE's) describing the diffusion of messages between mobile devices. Communications in mobile opportunistic networks take place upon the establishment of ephemeral contacts among mobile nodes using direct communication. SIR (Sane, Infected, Recovered) models permit to represent the diffusion of messages using an epidemiological based approach.
The question we analyse in this work is whether the coexistence of a fixed infrastructure can improve the diffusion of messages and thus justify the additional costs. We analyse this case from the point of view of dynamical systems, finding and characterising the admissible equilibrium of this scenario. We show that a centralised diffusion is not efficient when people density reaches a sufficient value.
This result supports the interest in developing opportunistic networks for occasionally crowded places to avoid the cost of additional infrastructure.
This paper aims to solve the problem of Risk assessment for enterprise merger and acquisition (M&A), which is an important problem in modern company management. Firstly, we design an index system to assess risks of enterprise M&A behavior, and six risks are considered: 1) Systemic risk, 2) Law risk, 3) Financial risk, 4) Intermediary risk, 5) Integrated risk, and 6) Information risk. Furthermore, 18 indexes are chosen to cover these six aspects. Secondly, we illustrate how to utilize the proposed risk assessment in the decision system for enterprise M&A risk assessment. We separate the M&A risk assessment process to three steps, that is, 1) Before M&A, and 2) In M&A, and 3) After M&A. Particularly, after the risk assessment process, there are three decisions for enterprise managers, that is, 1) implement the original M&A plan, 2) modify the original M&A plan, and 3) refuse it. Thirdly, we propose the multiple classifier fusion based risk assessment algorithm, which aims to effectively combine the six support vector machines. To relax the limitation of the SVM classifier, we introduce the fuzzy theory in the multiple classifier fusion algorithm, and the category label assignment is determined by utilizing a maximum membership rule. Finally, we conduct an experiment to make performance evaluation by constructing a dataset which includes the M&A data of 200 enterprises, among which 185 enterprises are used as training dataset and others are regarded as testing dataset. Using ROC curve, MAE and MAPE as evaluation criterions, performance of the proposed method is compared with single SVM scheme. Experimental results demonstrate that combining multiple the SVM classifiers together, accuracy of M&A risk assessment is greatly enhanced.
This paper considers the worst-case regret portfolio optimization problem when the distributions of the asset returns are uncertain. In general, the solution to this problem is NP hard and approximation methods that minimise the difference between the maximum return and the sum of each portfolio return are often proposed. Applying the duality of semi-infinite programming, the worst-case regret portfolio optimization problem with uncertain distributions can be equivalently reformulated to a linear optimization problem, and the established solution approaches for linear optimization can then be applied. An example of a portfolio optimization problem is provided to show the efficiency of our method and the results demonstrate that our method can satisfy the portfolio risk diversification property under the uncertain distributions of the returns.
This study applied the combined methods of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Information Entropy Weights to evaluate the tourism destination competitiveness (TDC) of 13 cities in Sichuan Province. In the empirical study, IEW was used to determine the subjective weights of four aspects and 26 evaluation indexes, which have the influence on TDC. In addition, applying the essential ideas of TOPSIS, chosen alternative should have the shortest geometric from the positive ideal solution (PIS) and the longest geometric distance from the negative ideal solution (NIS), to conduct a comprehensive evaluation and sort-based analysis. In the end, the essay arranged the TDC of 13 cities in Sichuan Province from high to low, then produced policy recommendations. The results represent that IEW & TOPSIS were an efficient and effective way to evaluate TDC.
This paper proposes a novel approach for full duplex using chaotic sequences which is known as the asynchronous code-division duplex (Async-CDD) system. The Async-CDD system can transmit and receive signals at the same time and in the same frequency channel without time slot synchronization. The data rate of the Async-CDD system is 8 times higher than the conventional CDD system and is the same as a non-spreading system. The property of low block cross-correlation of the chaotic sequence allows the Async-CDD system achieve duplex interference suppression at any duplex delay. And the huge number of available code words/blocks of the chaotic sequence allows the Async-CDD system increase the data rate by increasing the number of multiplexed sub-channels. When both of the code length of the orthogonal chaotic code and the number of multiplexed sub-channels are 128, the orthogonal chaotic code provides 30.40 dBc self-interference suppression in average, which is 6.99 dB better than the orthogonal Gold code.
The fast growing telecommunications industry in China has been experiencing dramatic technological change and substantial productivity growth. The actual productivity growth pattern in the sector, however, need to be empirically examined. In this paper, using input and output data at the provincial level, we employ DEA-based Malmquist productivity index to estimate productivity change, technological change and relative efficiency change in China's telecommunications industry for the period spanning the years from 2011 to 2015. The results show that based on our sample, the productivity improved by 22.9% per annum, which was exclusively due to an average of 25.5% technological progress in the industry production function, while the average efficiency change is slightly negative. Our results also indicate that regions with relatively low levels of telecommunications (and economic) development have a greater chance and ability of enhancing telecommunications productivity growth through technological catch-up. In addition, we find that the industry experienced significantly higher productivity growth and technological progress in the later sample period between 2013 and 2015 than in the early period between 2011 and 2013.
Collaborative filtering recommendation algorithm is a successful and widely used recommendation method in recommender system. In the collaborative filtering recommendation algorithm, the key step is to find the nearest neighbor. Combined with the application scenario of the intelligent community, Pearson Correlation Coefficient is introduced to improve the accuracy of similarity calculation. At the same time, considering that the residents are relatively fixed, the K-means clustering algorithm can be combined with the user-based collaborative filtering recommendation algorithm to improve the sparsity of the matrix and improve the speed of recommendation. Validation results on MovieLens dataset show that the collaborative filtering recommendation algorithm integrating with K-means clustering algorithm and community factors can more effectively predict the actual user rating in the community application scenario, and improve the recommendation accuracy and recommendation speed, compared with the traditional collaborative filtering recommendation algorithm.
As a fundamental research in the field of natural language processing, the Uyghur morphological analysis is used mainly to determine the part of speech (POS) and segmental morphemes (stem and affix) of a word in a given sentence, as well as to automatically annotate the grammatical function of the morphemes based on the context. It is necessary to provide various information for other tasks of natural language processing including syntactic analysis, machine translation, automatic summarization, and semantic analysis, etc. In order to increase the morphological analysis efficiency, this paper puts forward a hybrid approach to create a statistical model for Uyghur morphological tagging through a small-scale corpus. Experimental results show that this plan can obtain an overall accuracy of 92.58 % with a limited training corpus.
With the continuous and quick development of Chinese tourism industry over years, ecological environmental problems emerge consequently. The contradiction between the development of tourism economy and the protection of ecological environment has become the focus of scientific experts and Chinese government, and accordingly it is of vital importance to predict tourism carrying capacity accurately. In this paper, a new forecast approach is proposed for government staff and scenic spot management staff on tourist carrying capacity, which promotes the effective, healthy and sustainable development of the tourism country.
In the framework of the perturbed photo-gravitational restricted three-body problem, the first order exterior resonant orbits and the first, third and fifth order interior resonant periodic orbits are analyzed. The location, eccentricity and period of the first order exterior and interior resonant orbits are investigated in the unperturbed and perturbed cases for a specified value of Jacobi constant C.
It is observed that as the number of loops increases successively from one loop to five loops, the period of infinitesimal body increases in such a way that the successive difference of periods is either 6 or 7 units. It is further observed that for the exterior resonance, as the number of loops increases, the location of the periodic orbit moves towards the Sun whereas for the interior resonance as the number of loops increases, location of the periodic orbit moves away from the Sun. Thereby we demonstrate that the location of resonant orbits of the given order moves away from the Sun when perturbation is included.
The evolution of interior first order resonant orbit with three loops is studied for different values of Jacobi constant C. It is observed that when the value of C increases, the size of the loop decreases and degenerates finally into a circle, the eccentricity of periodic orbit decreases and location of the periodic orbit moves towards the second primary body.
As a basic and fundamental problem in wireless sensor network (WSN), the network coverage greatly reflects the performance of information transmission in WSN. In order to achieve a good balance between target coverage and energy consumption, in this paper, we propose a novel wireless sensor network energy efficient coverage method based on genetic algorithm. Particularly, the goal of this work is cover a 2D sensing area via selecting a minimum number of sensors. Moreover, the deployed wireless sensors should be connected to let each sensor be connected a path to the base station. Afterwards, genetic algorithm is used to compute the minimum number of potential position to let all target be k-covered and all sensor nodes be m-connected, and each chromosome is set to be the number of potential positions. Finally, we provide a simulation to test the performance of the proposed method, and simulation results demonstrate that the proposed method can achieve high degree of target coverage without wasting extra energy.
This paper concentrates on the problem of human face recognition problem, which is a crucial problem in computer vision. In this paper, the semi-supervised learning based convolutional neural network is used to implement the face recognition system with high efficiency. Convolutional neural networks denote a multi-layer neural network, in which each layer is made up of multiple two-dimension planes and each plane consists of a lot of independent neurons. To extract the rich and discriminative information of human face images, the sparse Laplacian filter learning is utilized to learn the filters of the network with a large scale unlabeled human face images. Afterwards, a softmax classifier layer is trained by multi-task learning using only a small number of labeled human face images as the output layer. In the end, a series of experiments are conducted to test the performance of our proposed algorithm. Experimental results show that face recognition accuracy of the proposed improved CNN method performs better than other methods.
With the rapid development of capital market and the great increment of people's income, more and more people want to invest money to the stock market and increase their wealth. Hence, the stock market has been an important part of the modern market economy. In this paper, we propose a novel stock price fluctuation prediction method based on the time series analysis technology. The main idea of this paper lies in that stock price of the future is predicted by mining and analyzing the historical data. Particularly, 16 technical indicators are chosen as input variables to the proposed model. Afterwards, we propose a hybrid ARIMA-ANN model to solve the stock price prediction problem, and the stock price is predicted by both the low volatility component and the high volatility component. Finally, experimental results demonstrate that the proposed can predict the stock price fluctuation with lower error rate.
A series of phenomena including lower circulation efficiency of Chinese fresh agricultural products, postharvest decay, damage and waste of agricultural products, regional and structural contradiction of supply and demand, drastic fluctuation in price and difficulty in buying and selling, etc. are serious, which has restricted the sound development of Chinese fresh agricultural product industry. To analyze and discuss main factors affecting circulation efficiency of Gannan navel orange, the methods, such as AHP (analytic hierarchy process) and Delphic method, etc., have been used for empirical analysis on Gannan navel orange, and it is found that fruit factors (including single structure and centralized mature period of navel orange, etc), infrastructure factors (including the lack standardization for construction of trading place, outdated warehousing facility and technology, insufficient input of infrastructure of cold chain, etc) and policy environment factors (including food safety, absence of relevant laws and regulations of market supervision, etc) are the existing main factors restricting high-efficient circulation of Gannan navel orange. Based on the conclusion of empirical research and beginning with main circulation links of production, storage and transportation as well as marketing, etc and supporting measures of brand building, product safety and policy service system, etc, the countermeasures and suggestions are proposed to improve circulation efficiency of Gannan navel orange.
Atmospheric environmental quality significantly influences the environment of people's life and the development of modern society. Therefore, in this paper, we aim to accurately assess atmospheric environmental quality. Firstly, we propose and analyze two indicator systems for atmospheric environmental quality assessment, that is, 1) the standard air pollution index, and 2) an index system which covers different types of influencing factors related to air quality. In particular, the proposed index system is made up of seven parts, that is, 1) Climate and meteorology, 2) Economic and social development, 3) Industrial structure, 4) Energy consumption, 5) Industrial pollutant discharge, 6) The greening of the city, and 7) Environmental protection construction. Secondly, we propose a hybrid AHP-Fuzzy Comprehensive Evaluation Model to evaluate the atmospheric environmental quality. In the hybrid model, AHP is used to calculate the weight coefficient of the index system, and fuzzy comprehensive evaluation model is exploited to describe the vagueness of remark in the evaluation process. Finally, experimental results demonstrate that using the proposed index system, the hybrid AHP-Fuzzy comprehensive evaluation model is able to assess air quality more accurately than the modified API.
The issue of searching missing aircraft is valuable after the event of MH370. This paper provides a global optimal model to foster the efficiency of maritime search. Firstly, the limited scope, a circle whose center is the last known position of the aircraft, should be estimated based on the historical data recorded before the disappearance of the aircraft. And Bayes' theorem is applied to calculate the probability that the plane falling in the region can be found. Secondly, the drift of aircraft debris under the influence of wind and current is considered via Finite Volume Community Ocean Model(FVCOM) and Monte Carlo Method(MC), which make the theory more reasonable. Finally, a global optimal model about vessel and aircraft quantitative constraints is established, which fully considers factors including the area of sea region to be searched, the maximum speed, search capabilities, initial distance of the vessels by introducing 0-1 decision variables.
In option pricing, backward stochastic differential equation (BSDE) has wide application and Black-Scholes model is one of the classic pricing model. However, the model needs many preconditions which causes the implementing environment of model to approach perfection, leading to large deviation in actual application. Therefore, this article study the optimization problem of option pricing model under limited conditions intensively. It means that when random volatility is given, the option pricing formula with random interest rate is proposed and corresponding revision is also provided. Then we adopt call option and put option of Standard Poor's 500 index options to perform empirical research. The results indicate the assumption of random volatility is closer to reality. Compared to tradition models, the approach proposed in this article has enough theoretical basis. It is proved to own simple modeling method and higher accuracy which also shows certain reference significance to option pricing.
TSP is a classic problem in the field of logistics, and ant colony algorithm is an important way to solve the problem. However, the ant colony algorithm has some shortcomings in practical application. In this paper, the ant colony algorithm is improved by particle swarm optimization algorithm, and the ant colony algorithm is obtained by giving the ant colony a certain ''particle property''. Finally, an example is given to demonstrate the effectiveness of the improved ant colony algorithm.
We model environmental games with stochastic data based on an imprecise distribution which is assumed to be attached to an a-priori known set. Our model is different from previous games where the probability distribution of the uncertain data is precisely given. Our model is also different from the robust games which presents a robust optimization approach to game models with the uncertain data in a compact convex set without probabilistic information which can lead to overly conservative solutions. A distributionally robust approach is used to cope with our setting in the games by combining the stochastic optimization and robust optimization approaches which can be termed as the distributionally robust environmental games. We show that the existence of an equilibrium for the distributionally robust environmental games under mild assumptions. The computation method for equilibrium, with the first- and second-information about the probability of uncertain data, can be reformulated as a semidefinite programming problem which can be tractably realized. Numerical tests are given to show the efficiency of the proposed methods.
In this paper, we will study the uniform $L^1$ stability of the inelastic Boltzmann equation. More precisely, according to the existence result on the inelastic Boltzmann equation with external force near vacuum, we obtain the uniform $L^1$ stability estimates of mild solution for the hard potentials under the assumptions on the characteristic generated by force term which can be arbitrarily large. The proof is based on the exponentially decay estimate and Lu's trick in [
Inefficient investment will affect enterprise's survival and long-term development, and ultimately lead to the decline in corporate value. In order to promote the efficiency of the enterprise investment, in this paper, we aim to effectively analyze enterprise inefficient investment behavior, which has great significance in both enterprise management and social resources allocation. Firstly, we propose and analyze some typical enterprise investment theories, such as 1) MM enterprise investment theory, 2) Jorgensen investment theory, and 3) Tobin's q theory. Secondly, we propose a novel enterprise inefficient investment behavior analysis method based on regression analysis. Finally, to demonstrate the effectiveness of the proposed method, we conduct a series of experiments based on the CCER database. Experimental results show that the economy fluctuates across states due to the aggregate cash-flow shock driving the level of aggregate liquidity. Furthermore, we also can see that the particular sample path starts with a series of positive shocks, which can increase the capital value and decrease the cash value.
In this paper, a hybrid positioning method based on global optimization for difference of convex functions (D.C.) with time of arrival (TOA) and angle of arrival (AOA) measurements are proposed. Traditional maximum likelihood (ML) formulation for indoor localization is a nonconvex optimization problem. The relaxation methods can?t provide a global solution. We establish a D.C. model for TOA/AOA fusion positioning model and give a solution with a global optimization. Simulations based on TC-OFDM signal system show that the proposed method is efficient and more robust as compared to the existing ML estimation and convex relaxation.
A dynamic simulation approach for performing emulation experiments on vehicle driveline test bench is discussed in this paper. In order to reduce costs and shorten new vehicle development cycle time, vehicle simulation on the driveline test bench is an attractive alternative at the development phase to reduce the quantity of proto vehicles. This test method moves the test site from the road to the bench without the need for real chassis parts. Dynamic emulation of mechanical loads is a Hardware-in-the-loop (HIL) procedure, which can be used as a supplement of the conventional simulations in testing of the operation of algorithms without the need for the prototypes. The combustion engine is replaced by a electric drive motor, which replicates the torque and speed signature of an actual engine, The road load resistance of the vehicle on a real test road is accurately simulated on load dynamometer motor. On the basis of analyzing and comparing the advantages and disadvantages of the inverse dynamics model and the forward model based on speed closed loop control method, in view of the high order, nonlinear and multi variable characteristics of test bench system, a load simulation method based on speed adaptive predictive control is presented. It avoids the complex algorithm of closed loop speed compensation, and reduces the influence of inaccurate model parameters on the control precision of the simulation system. The vehicle start and dynamic shift process were simulated on the test bench.
In order to prevent risks in major projects, it is of great importance to accurately assess risks in advance. Therefore, in this paper, we propose a novel major project risk assessment method with the BP neural network model. Firstly, we propose an index system for major project risk assessment, which is made up of four parts: 1) Schedule risk, 2) Cost risk, 3) Quality risk, and 4) Resource risk. Secondly, we propose a hybrid BP neural network and particle swarm optimization (PSO) model to evaluate risks in major projects. Especially, major project risk assessment results are achieved from the output layers of the BP neural network which is optimized by the PSO algorithm. In our proposed hybrid model, the fitness for each particle is computed through an optimal function, and then the particle can improve its velocity for the next cycle by searching the optimal value. Furthermore, this process should be repeated when the end condition is satisfied. Finally, experimental results demonstrate that the proposed method is able to evaluate risk level of major projects with high accuracy.
In this paper, we propose a 81-point face feature points template that used for face attraction analysis. This template is proposed that based on the AAM model, according to the geometric characteristics and the illumination model. The experimental results demonstrate that, the attraction of human face can be analyzed by the feature vector analysis of human face image quantification and the influence of light intensity on the attraction of human face. By taking the appropriate algorithm, the concept of facial beauty attractiveness can be learned by machine with numeric expressions.
The distribution of mineral resources in China is mainly concentrated in minority areas. However, the technology of mineral resources development in minority areas is relatively backward and the utilization rate isn't high. Unreasonable exploitation for mineral resources has caused tremendous damage of mining environment, which restricts the sustainable, healthy and stable development of mining areas. Therefore, how to construct the ecological industrial chain of mineral resources in minority areas has become an important issue of mining sustainable development. In this paper, a SD model with the characteristic of minority areas is established by constructing the dynamical system flowchart that takes mineral resources-environment-economy-society (MEES system) as the main research object based on system dynamics simulation, combination determining weights, and fuzzy sets, etc. In addition, taking Tibetan minority areas for an example, this paper predicts the tendency of the MEES system in the region. Meanwhile, this paper designs four different development modes to provide the operable choice and reference for exploiting the mineral resources in minority areas.
Taking carbonation depth uncertainty into account is key to approach durability analysis of concrete girder bridges in a probabilistic way. The Normal distribution has been widely used to represent the probability distribution of carbonation depth. In this study, two new methods such as Least Squares method and Bayesian Quantile method, are used to estimate the parameters of the Normal distribution. These two considered methods are also compared with the commonly used Maximum Likelihood method via an extensive numerical simulation and three real carbonation depth data examples based on performance measures such as, K-S test, RMSE and
Stowage operations in container terminals are an important part of a port's operational system, as the quality of stowage operations will directly affect the efficiency of port loading and discharge operations, and the scheduling of container shipping liners. The intelligent stowage of containers in container ships was studied in this work. A multi-objective integer programming model was constructed with the minimization of container rehandling, yard crane movements, and the sum of weight differences between stacked container pairs as its objective functions, to address the need for intelligent optimization of single bay export container stowage on a ship's deck. This model also satisfies the stability requirements of preliminary stowage plans drawn by shipping companies, and the operational requirements of container terminals. Linear computational methods were then constructed to transform non-linear constraints into linear ones for better AIMMS solution. Through numerous case analyses and systematic tests, it was shown that our system is able to rapidly solve for stowage planning optimization problems with complex preliminary stowage data, thus proving the applicability and effectiveness of this model. In particular, the application of this model will simultaneously address the safety of ship voyages, the transportation quality of shipping containers and other forms of cargo, and the cost efficiency of ship operations. In addition, this model will also contribute to the optimization of loading and discharge processes in container terminals. Therefore, our model has immense practical value for improving port productivity, as it will contribute to the organization of port operations in a rational, orderly and effective manner.
With large-scale, integrated, intelligence for ports, many ports begin to use intelligent detection systems to make their operations more efficient. The container truck recognition and positioning system is also beginning to apply into container quayside to assist the joint operations between quay cranes and container trucks. However, the traditional vehicle detection by using motion region detection cannot recognize the type of moving object, and the traditional pattern recognition method cannot meet the requirements in real-time operation. In order to solve these problems, an algorithm fused by regional clustering and two-stage SVM classifier is proposed in this paper. The method consists of two phases, which are independently executed in two camera systems on quay cranes. In the first stage, a fast motion regional clustering algorithm is used to detect moving image patches as the truck candidate sub-windows. In the second stage, the container trucks will be recognized in these sub-windows by an optimized two-stage SVM classifier. Compared with existing traditional algorithm, experimental results in container terminal show that the fusion algorithm with regional clustering and two-stage SVM has higher efficiency and better truck recognition performance.
Multiplications in finite fields are playing a key role in areas of cryptography and mathematic. We present approaches to exploit systolic architecture for multiplications in composite fields, which are expected to reduce the time-area product substantially. We design a pipelined architecture for multiplications in composite fields
This paper addresses a new variant of the location routing problem (LRP), namely the heterogeneous fleet LRP with simultaneous pickup and delivery and overloads (HFLRPSPDO) which has not been previously tackled in literatures. In this problem, the heterogeneous fleet is comprised of vehicles with different capacities, and the vehicle overloads up to a specified upper bound is allowed. This paper proposes a polynomial-size mixed integer linear programming formulation for the problem in which a penalty function, allowing capacity violations of vehicles, is integrated into objective function. Furthermore, two heuristic algorithms, respectively based on tabu search and simulated annealing, are proposed to solve HFLRPSPDO. Computational results on simulated instances show the effectiveness of the proposed problem formulation and the efficiency of the proposed heuristic algorithms.
This study was carried out to obtain visual simulations of twill woven fabrics on a computer screen using certain fabric characteristic. Based on the Peirce model, the polynomial curve fitting method is utilized to simulate the buckling configuration of twill weave yarns. Polynomial mathematical model was never used in constructing twill weave woven fabric structure in the past studies. In polynomial model, each point on yarn buckling track is calculated through the curvature, the radius of the warp and weft yarn, the geometric density, and the buckling curve height. Moreover, the twill weave structure is displayed through the arrangement of the warp and weft yarns. The polynomial mathematical model method was applied to convert the yarn path to a smooth curve and will be provided for three-dimensional computer simulation of satin weave fabric. Different twill weave is displayed by changing fabric parameters. In the VC++6.0 development environment, according to polynomial mathematical model, the three-dimensional simulation of twill fabric structure was given in details through the OpenGL graphics technology.
In real game problems not all players can cooperate directly, games with communication structures introduced by Myerson in 1977 can deal with these problems quite well. More recently, this concept has been introduced into fuzzy games. In this paper, games on (fuzzy) communication structures were studied. We proved that if a coalitional game has a nonempty core, then the game restricted on an n-person connected graph also has a nonempty core. Further, the fuzzy game restricted on the n-person connected graph also has a nonempty core. Moreover, we proved the above two cores are identical and the core of the coalitional game is included in them. In addition, optimal fuzzy communication structures of fuzzy games were studied. We showed that the optimal communication structures do exist and proposed three allocating methods. In the end, a full illustrating example was given.
The damaged area of the hyperchaotic image is prone to lack of texture information. It needs to make image restoration design to improve the information expression ability of the image. In this paper, an iterative restoration algorithm of hyperchaotic image based on support vector machine is proposed. The sample blocks in the damaged region of hyperchaotic images are divided into smooth mesh structures according to block segmentation method, and the neighborhood pixels of which points need to repair are ranked efficiently according to gradient values. According to the edge fuzzification features, the position of the important structural information of the damaged area is located. A multi-dimensional spectral peak search method is applied to construct the information feature subspace of image texture, so as to find the best matching block for restoring the damaged region of hyperchaotic image. Considering the features of structural information and texture information, the maximum likelihood algorithm is used to reconstruct the pixel elements in the image region by piecewise fitting. Through the support vector machine algorithm, the image iterative restoration is carried out. The simulation results show that the restoration method for hyperchaotic image can achieve effective restoration of image damaged area, the quality of restorationed image is better, and the computation speed is fast. The image restoration method can effectively ensure the visual effect of the reconstructed image.
At present, the data filtering quality of reversible hidden data access algorithm based on column store database is not guaranteed, and the location accuracy and data access security of reversible hidden data are low. In this paper, the whitening vector is obtained by processing the sample length of the observed data signal. By using the nonlinear robust function, the data projection is realized, the judged threshold of projection data is constructed, an matrix with adaptive filter characteristic is set up, and the high quality of filtering results are output; the parameters between three anchor nodes and the location of reversible hidden data are measured, and the artificial bee colony optimization neural network is used for modeling and forecasting the ranging error, and determine the weights according to the results, so that on the basis of the three edge location algorithm, the positioning accuracy of the data is to further improve; through the establishment of authorized institutions, producing key, off-line encryption, online encryption, ciphertext conversion, decrypt ion and other aspects, the security of access data is completed. The experiment shows that the algorithm can effectively improve the quality of data filtering and positioning accuracy and the security of data access is also better than that of the current algorithm.
Because of the limitations of the technical conditions, the traditional algorithms can not be mapped with many kinds of color, and the treatment effect is poor, which is not conducive to human eye observation. A clustering fusion algorithm based on D-S evidence theory is proposed in this paper to make salt denoising and Gauss denoising operation for the multi-band color image, to improve the image recognition, and better reflect the objective reality, which is not limited by technical conditions; the denoised images are made texture features and edge features extraction; these two kinds of features are fused and carried out the probability distribution to solve the probability of that the each pixel belongs to each class; Based on the DS evidence combination, the probability of four channels is fused, and according to the probability of what kind of each pixel belonging is the largest, it is clustered. Experimental results show that the proposed algorithm can combine different bands of color images to different levels of target features, and retain more effective information, which is conducive to target recognition and detection.
Nowadays, when moving targets are located in complex environment, the positioning algorithm takes longer time, and the result is not consistent with the actual positioning of the moving target, which has the problem of low positioning efficiency and inaccurate positioning results. In this paper, a moving target automatic tracking and positioning algorithm is proposed in the complex environment, which establishes the geodetic coordinate system and the space rectangular coordinate system, and completes the transformation between the geodetic coordinate system and the rectangular coordinate system, so as to improve the accuracy of the positioning result. The signal is rebuilt and the MIMO radar positioning model is used to complete the automatic tracking and positioning of the moving target in complex environment, to reduce the time consuming. The experimental results show that the proposed method can quickly and accurately track and locate the moving target in complex environment.
The current global enhancement algorithm for medical X-ray image has problems of poor de-noising and enhancement effect and low reduction of the enhanced medical X-ray image. To address the problems, a global enhancement algorithm for X-ray image in medical image classification is proposed in this paper. The medical X-ray image is gray scaled, which provides the basis for the further processing of the image. The noise in medical X-ray image is removed by using multi-wavelet transform to improve the enhancement effect of the method. With the curve-let domain the medical X-ray image is enhanced, the reduction degree of medical X-ray image is improved and the global enhancement of the medical X-ray image is completed. Experimental results show that the de-noising effect of the proposed method is effective, the enhanced medical X ray image is better, and the reduction degree of medical X-ray image is high.
Aiming at the poor extraction effect of the current extraction algorithm for local fuzzy features of dynamic images and the low extraction accuracy, a new algorithm based on FAST corner is proposed to extract the local fuzzy feature of dynamic images efficiently. Through analyzing the mode distortion existing in the local fuzzy features of dynamic images, and processing the spatial domain of dynamic images by using point processing and neighborhood processing, and processing the image frequency domain by filtering, the preprocessing of dynamic images and the effect of local fuzzy feature extraction of dynamic images are improved. On the basis of this, aiming at the shortcomings of FAST corner extraction of local fuzzy features of dynamic images, this paper puts forward the idea of algorithm optimization, and analyzes the realization process of the improved algorithm to achieve the algorithm optimization processing and complete the local fuzzy feature extraction of dynamic images. Based on the least squares method, the inaccurate local fuzzy features in the dynamic images are removed to ensure the accuracy of feature extraction. Experimental results show that the proposed algorithm can accurately extract the local fuzzy features of dynamic images, and the extraction results are better.
At present, the MD5 based data encryption algorithm for wireless network mobile communication cannot effectively detect the intrusion data in the mobile communication. Redundant data is not removed, the efficiency of data encryption is low, and the overall communication security is poor. In this paper, a MDEA based data encryption algorithm for wireless network mobile communication is proposed. By applying normalization of communication data to DBN model, using the way of changing one parameter while keeping others, the optimal DBN detection model is built to achieve high-precision detection of intrusion data. Using the signal intensity at different times, the speed and process time of the data level movements are estimated. By estimating the results, the redundant data and inappropriate data are removed, and performed the MDEA operation based on the secret data, introduced random numbers and timestamps to prevent the foreign infiltrations. Experiments show that the algorithm can not only improve the detection quality of intrusion data, but also enhance the cleaning effect of redundant data and in the communication, and enhance data security.
At present, the remote video monitoring system has the problem of weak anti-interference ability and poor response of the system. Therefore, the video image is not clear. On the basis of the Internet of things (IOT), a design method of embedded Linux remote video monitoring system is proposed. The method is based on ARM+Linux development platform, the 301V USB camera of Vimicro is used to collect images, to make preprocessing, and improve the system's response. The embedded Linux operating system is used to realize the functions of data acquisition and transmission of video image. The fractal wavelet of multivariate statistical model is used to denoise the video image so as to improve the anti-interference of the system. The experimental results show that the method has strong anti-interference ability and good response to the system.
The traditional drive response synchronization control method has the poor robustness, which results in the low security performance of the optical chaotic secure communication. To address this problem, an optical chaotic secure algorithm based on space laser communication is proposed in this paper. With the advantage of space laser communication and full consideration of the influence of complex environment on signal transmission of laser wireless communication, a laser wireless communication channel model is built. Based on this, a hybrid self-synchronization chaotic system model is proposed. It can reduce the information needed for transmission, and only need to transmit a small amount of error correction signals on the channel to achieve synchronization of the receiving and the transmitting system, which greatly suppress the drawback of the traditional method. On the basis of chaotic synchronization, the erbium-doped fiber laser is used for information transmission. Different encryption techniques are used to achieve optical chaotic secure communication within the allowable range of error. Numerical simulation results show that the proposed algorithm has good security and robustness, and can realize the secure communication for different signals.
At present, when detecting intrusive interference signals in classified form, the effect of channel denoising is very poor, and the characteristics of the extracted signals are not clear, which can not achieve effective detection of intrusion signals. An algorithm based on wavelet packet frequency hopping estimation for complex network intrusion detection is proposed in this paper. The soft and hard threshold method is used for wavelet coefficient decomposition, threshold processing, and signal reconstruction; according to probability statistics, a new sequence is composed of the spectral amplitude corresponding to the same frequency of each random variable in a random process and the spectrum matrix of intrusion interference signal is formed, so as to extract the characteristic spectrum of intrusion interference signal; by using the energy balance method, Gauss stochastic wavelet characteristics of intrusion signal can be simulated. The results of network intrusion detection are obtained by the Gauss additivity of the high-order cumulants of the network intrusion. The three edge centroid positioning method is applied to achieve the high-precision location of the intrusion point. Experiments show that the algorithm effectively improves the network channel denoising and the feature extraction effect of the intrusion signal, and it is also better than the current algorithm for the detection and location of the interference signals.
In the social network, there is the problem of network sensitive information with low accuracy rate of information recognition. To effectively improve the accuracy of intelligent identification of sensitive information, an intelligent recognition algorithm for sensitive information based on improved fuzzy support vector machine is proposed in this paper. The information is collected. The trajectory of the best movement of the information node is found in the low energy cache. In the limited time, the performance of information acquisition is improved by using the mobility of information nodes. According to DFS criterion, the features are added into the feature subset or eliminate the sensitive information. The feature selection algorithm based on multi-label is applied to feature selection of the collected information, so that the information gain between information feature and label set can be used to measure the importance. The improved support vector machine classification algorithm is used to classify the information selected by feature selection, and select effective candidate support vector, reduce the number of training samples, and improve the training speed. The new membership function is defined to enhance the effect of support vector on the construction of fuzzy support vector machine. Finally, the nearest neighbor sample density is applied to the design of membership function to reduce the noise, and achieve intelligent recognition of the sensitive information in the social network. Experimental results show that the accuracy rate of sensitive information intelligent recognition can be effectively improved by using the proposed algorithm.
At present, the high frequency signal processing algorithm of capacitive sensor based on RBF has the problems of poor filtering effect and high level of signal detection and poor quality of signal separation. In this paper, an optimization algorithm for blind processing of high frequency signal of capacitive sensor is proposed. Based on the gradient method, and the calculation way of improved variance gradient estimation, the gradient of square single- error sample is taken as the estimation of mean square error to filter the capacitive sensor signal, and adjust the filtering step by adjusting the threshold, which can enhance the filtering effect of the sensor signal; The detection threshold is calculated by determining the false alarm probability. The decision condition is used to detect the target signal and get the high accuracy sensor signal. The initialization separation matrix is set according to the number of observation signals, and the correlation matrix of the source signal can be calculated, so as to achieve the efficient separation of high frequency signals. The experiment shows that the algorithm can effectively solve the problems existing in the current signal processing algorithm, and it is reliable.
In cloud computing environment, in order to optimize the deployment scheduling of resources, it is necessary to improve the accuracy of the optimal solution, guarantee the convergence ability of the algorithm, and improve the performance of cloud computing. In this paper, a multi-objective optimization algorithm based on improved particle swarm is proposed. A multi-objective optimization model is built. Improved multi-scale particle swarm is used to optimize the built multi-objective model. The combination of the global search capability and the local search capability of the algorithm is realized by using Gaussian variation operator with varied scales. The large scale Gaussian variation operator with concussion characteristics can complete fast global search for decision space, so that particles can quickly locate the surrounding area of the optimal solution, which enhances the ability to escape the local optimal solution of the algorithm and avoids the occurrence of precocious convergence. The small scale variation operator gradually reduces the area near the optimal solution. Experimental results show that the improved particle swarm optimization algorithm can effectively improve the precision of the optimal solution and ensure the convergence of the algorithm.
Aiming at the shortcomings of current algorithms due to the fixed steps, which is easy to fall into local optimum, with robustness and poor transparency, and cannot be balanced against various common attacks, an optimization algorithm of digital image watermarking algorithm based on Drosophila was proposed. In the support of the virtual reality technology, the original color host image was transformed from the RGB space to YCrCb space, and the pixel block of the Y component was divided into a certain size; according to the principle of forming DC coefficients in the DCT domain, the DC coefficient of each block is calculated directly in the airspace, and the amount of modification for each DC coefficient is determined based on the watermark information and the quantization step size; according to the distribution characteristics of DC coefficient, watermarks are embedded directly in the airspace; the type of digital watermarking and digital watermarking pretreatment methods were determined by using Drosophila optimization algorithm. At the same time, digital watermark embedding, extraction rules and initial steps were selected and identified. The Drosophila optimization algorithm with step size reduces the balance between global and local search ability, which makes up for the shortcomings of traditional algorithm. The experimental results showed that the proposed algorithm can effectively balance the invisibility and robustness of the watermark, and can resist all kinds of common attacks, which with a better visual extraction effect.
At present, in reversible information hiding algorithm of image, the difference expansion idea is used. After the carrier image encryption, in encrypted image, information bits are embedded in the low value, resulting in the fact that in the image embedded with watermarking information, a part of the boundary pixel value has flipped. After being extracted, the carrier image cannot be recovered completely that is not only a large quantity of calculation, and the image quality has also been some damage. A algorithm for reversible information hiding of encrypted the medical image in the homomorphic encryption domain is proposed. Combining wavelet and fast fuzzy algorithm, the image edge is extracted from the high frequency and low frequency parts of medical image, and the medical image is reconstructed in the compressed boundary part. Combined with the thought of block compressed sensing and block edge pixels, the reconstructed medical image is divided to multiple non-overlapping blocks. The pixel in the right lower edge of the block is made homomorphic encryption operation, the remaining pixels are made compressed sensing operation, and the two parts are combined to a ciphertext to be sent to the owner of the channel According to the information hiding key the secret information is embedded into the ciphertext by the channel owners. The receiver can extract the information and restore the original image based on the encryption key and the information hiding key. The experimental results show that the proposed algorithm has high embedding capacity, the image quality after recovery is high, and the computational complexity is low.
Aiming at the poor encryption effect existing in the data encryption algorithm of e-commerce platform, and the data lost and distorted easily after encrypting, a data encryption algorithm based on blockchain technology is proposed in this paper. By analyzing the symmetric key algorithm and the public key algorithm, the DES encryption algorithm is described in detail. The two related technologies of digital envelopes and message authentication are analyzed to ensure the accuracy of the data and the one time encryption of the data. Based on this, in order to ensure the effectiveness of encryption, the process of asymmetric encryption algorithm based on chaotic sequence of neural network and asymmetric encryption algorithm based on neural network chaotic attractor are analyzed, and the security is tested. While ensuring the accuracy of data, it improves the effect of data encryption and realizes the encryption of e-commerce platform data, which is to realize data encryption algorithm based on blockchain technology. Experimental results show that the~proposed algorithm can encrypt the data of e-commerce platform, and the encryption process is relatively simple, the encryption effect is better, and the accuracy of the encrypted data is relatively high, which provides a theoretical basis for further research of the subject.
Rolling bearings are the most prone components to failure in urban rail trains, presenting potential danger to cities and their residents. This paper puts forward a rolling bearing fault diagnosis method by integrating empirical mode decomposition (EMD) and genetic neural network adaptive boosting (GNN-AdaBoost). EMD is an excellent tool for feature extraction and during which some intrinsic mode functions (IMFs) are obtained. GNN-AdaBoost fault identification algorithm, which uses genetic neural network (GNN) as sub-classifier of the boosting algorithm, is proposed in order to address the shortcomings in classification when only using a GNN. To demonstrate the excellent performance of the approach, experiments are performed to simulate different operating conditions of the rolling bearing, including high speed, low speed, heavy load and light load. For de-nosing signal, by EMD decomposition is applied to obtain IMFs, which is used for extracting the IMF energy feature parameters. The combination of IMF energy feature parameters and some time-domain feature parameters are selected as the input vectors of the classifiers. Finally, GNN-AdaBoost and GNN are applied to experimental examples and the identification results are compared. The results show that GNN-AdaBoost offers significant improvement in rolling bearing fault diagnosis for urban rail trains when compared to GNN alone.
In order to address the anonymous batch authentication problem of a legal reader to many tags in RFID (Radio Frequency Identification) system, an efficient RFID anonymous batch authentication protocol was proposed based on group signature. The anonymous batch authentications of reader to many tags are achieved by using a one-time group signature based on Hash function; the authentication of the tag to the reader is realized by employing MAC (Message Authentication Code). The tag's anonymity is achieved via the dynamic TID (Temporary Identity) instead of the tag's identity. The proposed protocol can resist replay attacks by using random number. Theoretical analyses show that, the proposed protocol reaches the expected security goals. Compared with the protocol proposed by Liu, the proposed protocol reduces the computation and storage of the server and tag while improving the security.
Aiming at the problems of asymmetric information and unreasonable emergency allocation schemes in the current cross-regional emergency operation, the emergency deployment process of multi-machine and multi-task is analyzed, and the emergency allocation model with the goal of minimizing the allocation cost and loss is established in the paper. Emergency allocation algorithm based on rule of nearest-distance-first, which allocate machinery for the nearest farmland firstly, and emergency allocation algorithm based on rule of max-ability-first, by which machinery with maximum ability to farmland is allocated firstly, are proposed. The operational data of farmland and agricultural machinery generated randomly are calculated and analyzed. The results show that when the amount of agricultural machinery is sufficient, the algorithm based on the maximum contribution capacity priority is better. When the agricultural machinery is insufficient, the calculation results of the emergency allocation algorithm based on the nearest distance priority are better. When the number of farmland is not more than 30, the average operation time of the two algorithms in this paper is not more than 3.8 seconds, and both two algorithm have good performance.
Remotely sensed data are widely used in disaster and environment monitoring. To complete the tasks associated with processing these data, it is a practical and pressing problem to match the resources for these data with data processing centers in real or near-real time and complete as many tasks on time as possible. However, scheduling remotely sensed data processing tasks has two phases, namely, task assignment and task scheduling. This paper presents a model using bilevel optimization, which considers task assignment and task scheduling as a single problem. Using this architecture, a mathematical model for both levels of the problem is presented. To solve the mathematical model, this paper presents a cooperative coevolution algorithm that combines the advantages of a very fast simulated annealing algorithm with a learnable ant colony optimization algorithm. Finally, the effectiveness and feasibility of the proposed approach compared with the conventional method is demonstrated through empirical results.
Fractal dimension and specifically, box-counting dimension, is the main tool applied in many fields such as odontology to detect fractal patterns applied to the study of bone quality. However, the effective computation of such invariant has not been carried out accurately in literature. In this paper, we propose a novel approach to properly calculate the fractal dimension of a plane subset and illustrate it by analysing the box dimension of a trabecular bone through a computed tomography scan.
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