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Li J.-R.,Henan Vocational and Technical Institute | Zhang S.-Q.,Henan Vocational and Technical Institute
Communications in Computer and Information Science | Year: 2012

Message-Digest Algorithm 5, a hash function which is widely used in the field of computer security, can provide the message integrity protection. But to the field following certain rules, for example, by date of birth as a password, using the MD5 encryption algorithm, can still quickly decipher by MD5 decoding procedures of list form. In view of this situation, on the basis of MD5, putting forward a improved algorithm which has greatly security in data safety, and the improved algorithm is applied to the online shopping system based on ajax framework, for digital signature function of users on the system provides powerful safety security. © 2012 Springer-Verlag.

Zhang D.,Henan Vocational and Technical Institute | Li J.,Henan Vocational and Technical Institute
Telkomnika (Telecommunication Computing Electronics and Control) | Year: 2015

Image registration, as one of the basic tasks of image processing, is the process to register two images about the same objective or background which are acquired in different times, different sensors, different perspectives and different shooting conditions. In the image registration, because of the problems that the image information is complicated, they have strong correlation and incompleteness, inaccuracy and non-construction occur in different levels in the processing, to apply the method of computational intelligence information processing in the image registration can have better results than the traditional computation methods. This paper proposes an image registration method based on wavelet decomposition and ant colony optimization, which divides the process of image registration into coarse registration and refined registration through wavelet decomposition technique. In the coarse registration, the transformation parameter value of the image approximation component is acquired through ant colony optimization while the changing parameter value of the original image is obtained by the ant colony search method in the refined registration. The simulation experiment shows that this registration method has the characteristics of anti-noise, fast speed, high accuracy and high registration success rate.

Wang Y.,Henan Vocational and Technical Institute
Metallurgical and Mining Industry | Year: 2015

Image feature classification is one of the basic questions of image processing and computer vision and it is also a key step of image analysis. BP neural network has been extensively applied in feature classification and it can classify specific objects or features through early learning; however, BP algorithm also has many defects, including slow convergence speed and easiness to be trapped in local optimum. This paper proposes an image feature classification method based on particle swarm optimization (PSO). It takes the gray image with specific object as the object to be segmented, studies the samples with PSO neural network and gets the training network. Then it takes the pixel matrix of the image as the input vector and puts in the well-trained network for classification. Finally, it realizes the segmentation. The experiment shows that the method of this paper is a feasible one and it has higher convergence speed and stronger robustness. Through the highly-efficient processing, this method can obtain important information and achieve excellent effect when used in the segmentation of the objects in complicated scenes.

Wang Y.,Henan Vocational and Technical Institute
Revista Tecnica de la Facultad de Ingenieria Universidad del Zulia | Year: 2016

During photographing, relative motion between the object and camera can cause image blur. Image restoration technology is able to recover original image from its degraded version as far as possible, and in image restoration, motion blur image restoration is very important. Genetic algorithm (GA), as a global optimization search algorithm based on Darwin's biological theory of evolution, can quickly and efficiently calculate the complicated nonlinear multidimensional data space. However, the standard GA and the inverse filter algorithm are easy to cause the problem of "premature convergence" to restoring the degraded image, in this paper, combining with the characteristics of strong image information correlation, an improved GA(IGA) is proposed. Experiments show IGA can overcome the "premature convergence", reduce the computational complexity, especially show the strong robustness in global optimization, and improve the quality of restored image to a certain extent.

Wang J.,Henan Vocational and Technical Institute | Zhang D.,Henan Vocational and Technical Institute
Telkomnika (Telecommunication Computing Electronics and Control) | Year: 2015

Image is often subject to noise pollution during the process of collection, acquisition and transmission, noise is a major factor affecting the image quality, which has greatly impeded people from extracting information from the image. The purpose of image denoising is to restore the original image without noise from the noise image, and at the same time maintain the detailed information of the image as much as possible. This paper, by combining artificial bee colony algorithm and BP neural network, proposes the image denoising method based on artificial bee colony and BP neural network (ABC-BPNN), ABC-BPNN adopts the "double circulation" structure during the training process, after specifying the expected convergence speed and precision, it can adjust the rules according to the structure, automatically adjusts the number of neurons, while the weight of the neurons and relevant parameters are determined through bee colony optimization. The simulation result shows that the algorithm proposed in this paper can maintain the image edges and other important features while removing noise, so as to obtain better denoising effect.

Lou S.,Henan Vocational and Technical Institute | Zhang H.,Zhengzhou University
International Journal of Earth Sciences and Engineering | Year: 2014

Wireless sensor network (WSN) has been widely used in soil monitoring. The localization algorithm is a hot research topic in wireless sensor network. From different perspective, many scholars have put forward some localization algorithm about ranging and non-ranging, when ranging, ranging algorithm causes easily ranging error by environment factor influence, thus damages localization accuracy; The defect of non-ranging algorithm is need to collect center nodes of entire network information in order to estimate position, the communication overhead is large. Aiming at the shortcomings of these localization algorithms, in this paper, proposes a semi-central location algorithm based on support vector regression, the simulation experiments show that the algorithm can relieve the disadvantage of centralized non ranging algorithm which communication overhead is large and the defect of ranging algorithm which is the influence of error accumulation. © 2014 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.

Tang H.,Henan Vocational and Technical Institute
Metallurgical and Mining Industry | Year: 2015

Artificial neural network and the intelligent optimization algorithm are hotpots and the cutting-edge ones of the current information science technology, which are of great theoretical and application significance for the fields of pattern classification and identification as well as prediction, etc. This paper put forward a learning method, which is EACONET (Elitist Ant Colony Optimization NET), to optimize the feedforward neural network based on the elitist strategy ant colony optimization, targeting at the weight optimization of the feedforward neural network, in order to solve problems like prematurity and slow rate of convergence of the ant colony optimization in training the neural network. This method combines the global parallel search with the local certainty of BP network, to search for optimal points. At the same time, it can enhance the rate of convergence, avoid the local extremum, can be applied to the functional approximation and the nonlinear system identification, etc. The results of simulation experiment show the effectiveness of the proposed algorithm.

Zhang S.,Henan Vocational and Technical Institute | Wang A.,Henan Vocational and Technical Institute
Telkomnika (Telecommunication Computing Electronics and Control) | Year: 2015

Image compression is to compress the redundancy between the pixels as much as possible by using the correlation between the neighborhood pixels so as to reduce the transmission bandwidth and the storage space. This paper applies the integration of wavelet analysis and artificial neural network in the image compression, discusses its performance in the image compression theoretically, analyzes the multiresolution analysis thought, constructs a wavelet neural network model which is used in the improved image compression and gives the corresponding algorithm. Only the weight in the output layer of the wavelet neural network needs training while the weight of the input layer can be determined according to the relationship between the interval of the sampling points and the interval of the compactly-supported intervals. Once determined, training is unnecessary, in this way, it accelerates the training speed of the wavelet neural network and solves the problem that it is difficult to determine the nodes of the hidden layer in the traditional neural network. The computer simulation experiment shows that the algorithm of this paper has more excellent compression effect than the traditional neural network method.

Pan X.,Henan Vocational and Technical Institute
Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu | Year: 2016

Purpose: Swarm intelligence is the intelligent behaviour represented by a kind of individuals with no or simple intelligence through any form of cluster and collaboration. The research consems the dual-population self-adaptive hybrid algorithm based on genetic algorithm (GA) and artificial bee colony (ABC). We have obtained some important performance measures, which are helpful for swarm intelligence algorithms. Methodology: We proposed a genetic-bee colony dual-population self-adaptive hybrid algorithm based on information entropy, which uses dual-population structure and independent evolution and which conducts information exchange through information entropy to maintain population diversity and accelerate the evolution process between the two populations when appropriate. Findings: We first analysed the basic structure and characteristics of GA and ABC, and then the dual-population based on GA and ABC, which joined the information entropy, was presented, in the parallel operation of two relatively independent populations to accelerate the emergence of a new individual by competition between the populations; it lias better effects in complex function optimization problems. Originality: We made a combinational study of GA and ABC. Although the current biological intelligent evolutionary algorithm has greatly improved its convergence speed, it is not ideal when optimizing complicated functions. This aspect of research is still relatively few at present. Practical value: We researched the optimization algorithm, which is applied to various research fields. Nowadays, it is a development trend to improve the original algorithm by integrating the intelligent algorithm. Dual-population algorithm can overcome the shortage of separate algorithm, and become more suitable for complex optimization problems. We provided the foundation to search for complex distributed problems without centralized control or global model. © Xiaomeng Pan, 2016.

Han M.,Henan Vocational and Technical Institute | Wang A.,Henan Vocational and Technical Institute
International Journal of Earth Sciences and Engineering | Year: 2015

Remote-sensing image segmentation is the foundation and the key step to process and apply remotesensing image. Because remote-sensing image has many grayscales and much information and in order to improve the segmentation result and speed of the image, this paper develops the coding method, fitness function as well as the operations of crossover and mutation of the single-threshold segmentation algorithm based on genetic algorithm, based on which, it designs and realizes the multi-threshold remote-sensing image segmentation algorithm based on genetic algorithm. It compares the image segmentation result and the differences in the threshold selection efficiency through experimental analysis, the result of which verifies that the algorithm proposed in this paper is an effective and ideal remote-sensing image multi-threshold segmentation method. © 2014 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.

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