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Ant colony optimization (ACO) algorithm is a new heuristic algorithm which has been demonstrated a successful technology and applied to solving complex optimization problems. But the ACO exists the low solving precision and premature convergence problem, particle swarm optimization (PSO) algorithm is introduced to improve performance of the ACO algorithm. A novel hybrid optimization (HPSACO) algorithm based on combining collaborative strategy, particle swarm optimization and ant colony optimization is proposed for the traveling salesman problems in this paper. The HPSACO algorithm makes use of the exploration capability of the PSO algorithm and stochastic capability of the ACO algorithm. The main idea of the HPSACO algorithm uses the rapidity of the PSO algorithm to obtain a series of initializing optimal solutions for dynamically adjusting the initial pheromone distribution of the ACO algorithm. Then the parallel search ability of the he ACO algorithm are used to obtain the optimal solution of solving problem. Finally, various scale TSP are selected to verify the effectiveness and efficiency of the proposed HPSACO algorithm. The simulation results show that the proposed HPSACO algorithm takes on the better search precision, the faster convergence speed and avoids the stagnation phenomena.

Refer to this research, an integral criterion-based hybrid fuzzy controller is proposed for vibration reduction in sensitive actuators. The first problem of the fuzzy logic controller was stability in certain and uncertain systems. This problem can be reduced in certain system by using hybrid fuzzy control law. However fuzzy hybrid controller based on PID methodology has advantage in certain system, but it has challenge in presence of uncertainty and motor vibration. To solve this challenge direct adaptive methodology applied to hybrid fuzzy controller. This type of adaptive technique is model-reference variable structure control. Regarding to proposed method, chattering eliminate as well improve the stability and robustness. The simulation results exhibit that the proposed method works well in certain system.

Handwritten digit recognition is a task of great importance in many applications. There are different challenges faced while attempting to solve this problem. It has drawn much attention from the field of machine learning and pattern recognition. Minimax probability machine (MPM) is a novel method in machine learning and data mining. In this paper, we present an extension algorithm for MPM, which is named twin minimax probability machine (TWMPM). TWMPM generates two hyperplanes to improve the classification accuracy. Experiment results on several data sets from the UCI repository demonstrate that TWMPM can improve performance of MPM in most cases. The proposed method is used for recognizing the handwritten digits provided in the MNIST data set of images of handwritten digits (0-9). The testing accuracies are improved comparing with MPM.

Wood defect is due to the physiological process, genetic factor or affected by the external environment in the growth period. These defects will reduce the utilization value of wood. However, it is very difficult to determine whether there are defects exist, and the degree of defects. Therefore, the effective detection of wood defect information is particularly important. A new wood defect detection method by using RBF neural network was proposed in this paper. The new RBF defect detection method can be divided into the following main steps: (1) Detect wood defects by using X-ray nondestructive testing technology. (2) Deal with defect images by using digital image processing technology. (3) Analyze the information of different defects, and extract the characteristic value of wood defects. (4) Then, the RBF neural network model was constructed. (5) Finally, the RBF neural network is trained with the known samples and simulated with the unknown samples. The experimental results shown that the RBF neural network method was effectively detect the two typical wood defects. This method provides an important theoretical basis to realize the wood defect automatic detection.

Internal combustion (IC) engines are optimized to meet exhaust emission requirements with the best fuel economy. The mean-value engine model can be used to validate control strategies for different types of controllers that are model-based. The equations that are used to calculate the outputs of the model are approximated over an engine cycle. A significant advantage of the mean-value engine model is its low computational throughput which makes it possible for real-time simulation. In order to reduce engine emissions and improving engine fuel economy, closed loop combustion control, which requires cycleto-cycle combustion measurement such as cylinder pressure, is a necessity. The addition of a cylinder pressure signal to a mean value engine model will allow for developing closed loop combustion control strategies (or other strategies that involve cylinder pressure) to be validated. This is because the cylinder pressure model can produce a cylinder pressure signal for a complete engine operational map and a mean value engine model can produce real world engine parameters and conditions. The performance of the baseline computed fuel controller is compared with that of a baseline proportional, integral, and derivative (PID) controller.

Today, when market competition becomes increasingly fierce, improving the efficiency of storage services becomes a key for chemical companies to win the trade war. Focusing on the improvement of internal storage efficiency of enterprises, aiming at the characteristics that production enterprises for primary chemicals rely on economies of scale to make profit, according to industry conditions and forecasts of demand for market of chemical products based on logistic curve, and analyzing the difference between the storage scales of different enterprises, this paper proposes that general-scale emerging chemical enterprises should make full use of outsourcing resources to achieve economies of scale of storage; while in respect of mature super large enterprises, through using AHP method, object programming with priority and other methods, this paper proposes that enterprises’ own storage systems should be constructed and improved so as to achieve significant improvement of storage efficiency.

In this research, manage the Internal Combustion (IC) engine modeling and a multi-input-multi-output artificial intelligence baseline sliding mode methodology scheme is developed with guaranteed stability to simultaneously control fuel ratios to desired levels under various air flow disturbances by regulating the mass flow rates of engine PFI and DI injection systems. Analytical dynamic nonlinear modeling of internal combustion engine is carried out using elegant Euler-Lagrange method compromising accuracy and complexity. A baseline estimator with varying parameter gain is designed with guaranteed stability to allow implementation of the proposed state feedback sliding mode methodology into a MATLAB simulation environment, where the sliding mode strategy is implemented into a model engine control module (“software”). To estimate the dynamic model of IC engine fuzzy inference engine is applied to baseline sliding mode methodology. The proposed tracking method is designed to optimally track the desired FR by minimizing the error between the trapped in-cylinder mass and the product of the desired FR and fuel mass over a given time interval.

Semantic annotation is the process based on ontology annotation concept class, attribute and other metadata for cyber source and its various parts. Ontology mapping is to calculate the similarity between two ontology elements. Ontology merging is two or more source ontology merging into a goal Ontology. The basic principle of the concept lattice isomorphic generating is isomorphic to the background of the isomorphic concept lattice, and as concept lattice isomorphic background can generate the concept lattice. This paper analyzes the methods of ontology mapping and merging based on rough concept lattice isomorphic model and presents semantic annotation of ontology by using rough concept lattice isomorphic model. Experiments show that this method is better than the traditional method in semantic annotation accuracy and breadth.

The detection of faces is one of the most requesting fields of research in image processing and Visual estimation of head pose is desirable for computer vision applications such as face recognition, human computer interaction, and affective computing. In this paper, we propose completed method for face pose estimation, face and face parts detection, feature extraction, tracking. This paper proposes using an improved AdaBoost algorithm, which is much better than normal AdaBoost. We use the de-facto Viola- Jones method for face and face part detection. From the robustness property of Haar-like feature, we first construct the strong classifier more effective to detect rotated face, and then we propose a novel method that can reduce the training time. We adopt affine motion model estimation as a tracking method. The combination enables efficient detection around the search area limited by tracking. Experimental results demonstrated its effectiveness and robustness against different types of detection and pose estimation in the input face images, including faces that appear in a wide range of image positions and scales, and also complex backgrounds, occlusions, illumination variations and multi-pose head images.

In order to solve the simulation problem of steady compressible turbulent combustion, a small scale high performance parallel cluster system was designed. It based on the open source CFD platform OpenFOAM, and was used to to simulate the bluff－body stabilized Sydney flame hm1 which refers to the detailed chemical kinetic mechanisms of coal gas and air combustion reaction. The flow of combustion reaction component concentration distribution and temperature curve diagram are plotted according to the computational results. The comparison between the simulation and the experimental results shows that this parallel cluster system has efficient computation for steady compressible turbulent combustion flow.

While dealing with students in making their projects, we found that students are ready to cope up the standard software defined by standard body like IEEE/ACM. In this paper we have set the quality value of standard software on the basis of quality function for each month and comparing with standard software and software being developed. The novel idea of this paper is to calculate the difference in quality of the software being developed and the standard software which has been decided upon as the criteria for comparison. The system also calculating how much effort needed and what should be the speed of software being developed. This work is the second part of a Understanding the State of Quality of Software on the basis of Time Gap, Quality Gap and Difference with Standard Model, which moves towards practical implementation from theoretical foundation [1]. In order to obtain results we have used an indigenous tool for calculating the quality gap and time gap at a particular point of time and for the graphical representation of data, we have used Microsoft office 2007 graphical chart. For finding the mean, standard deviation, T value and P value, we have used SPSS version 16.

Collaborative filtering recommendation technology is by far the most widely used and successful personalized recommendation technology. However, the method currently faced with some problems such as sparse matrix, affecting the accuracy of the predicted results. This paper puts forward a new community detection algorithm based on topological potential theory, and combines it with collaborative filtering recommendation algorithm. The users with similar interests are put into the same community. When searching for the user’s nearest neighbor, it target to the users in a specific community or several communities instead of all users, which narrows the search and improves the prediction accuracy. Experimental results suggest that this approach effectively reduces the impact on the prediction accuracy of the sparse matrix, and significantly improves the prediction ability and recommendation quality.

The machine-part cell formation problem explains the topics surrounding the creation of part families based on the processing needs of the components, and the classification of machine groups based on their capacity to process exact part families in manufacturing. Allocate the specified machines and parts into cells so that the grouping efficiency is maximized. The objective of the paper is to cluster the parts based upon self-organizing map (SOM). After clustering, Minkowski distance is used to sort the machines and some bottlenecks are introduced during the clustering process which can be removed manually by rearranging. We are going to implement self-organizing map (SOM) which is a type of artificial neural network technique to solve this problem. The proposed Minkowski distance approach produced solution with a good group efficiency.

In order to ensure that wind turbines could be online under the condition of power grid transient fault, the technology of low voltage ride-through (LVRT) for wind turbine has become a research focus of experts and scholars at home and abroad. In current wind turbine control strategy, the PI parameters of the control system keep unchanged when the wind turbine is in the process of LVRT, which affects the performance of the control system. Therefore, the adaptive PI parameter adjustment system based on BP neural network is proposed, which realize the adaptive adjustment of PI parameters in the process of LVRT. The simulation model of the LVRT process was built, the simulation results show that this control algorithm can effectively restrain the current oscillation caused by the voltage sag, shorten the fault recovery time of the system, and has a good dynamic performance. Besides, the adaptability and robustness of the system has been increased, which has improved the low voltage ride-through capability of the system.

The present paper, we doing the statistical analysis of insertion sort program and compute its execution times with various parameters. The statistical analysis endorses the specific quadratic pattern of the execution time on the number of items to be sorted. Next, a cursor along the future scope is indicated.

Binary translation is an important step to solve the code migration, QEMU is more advanced and efficient binary translation system. It uses lighter TCG technology to achieve dynamic binary translation but analysis of the TCG internal process, we found that the excessive use of temporary variables meaningless in the TCG intermediate code, the backend generates host code does not take into account the efficient use of registers. Through these two aspects of improvement, especially increased a linear scan register allocation algorithm in the back-end, can be in an acceptable translation time, generates efficient host code. The experimental results show that the optimized program run time significantly reduced and the amount of generated host code reduced by an average of 8%.

To improve the reliability of a product performance the tolerancing problem may not be neglected especially for miniature precise assembly device. The purpose of this paper is to explore an approach to evaluate the reliability of gear system considering the influence of tolerance constraints. The paper describes the principle and ways of tolerance handling in the finite element control equations, and then introduces the implementation steps based on Monte-Carlo FEA method. The resonance reliability analysis of a gear-tooth system of port-cleaning-equipment gear pump is carried out to illustrate the mentioned method, and a sensitivity analysis is also performed to identify the key tolerances and improve them in order to achieve the anticipated reliability.

In this paper an Artificial Neural Network (ANN) model, for predicting the performance of a sophomore student enrolled in engineering majors in the Faculty of Engineering and Information Technology in Al- Azhar University of Gaza was developed and tested. A number of factors that may possibly influence the performance of a student were outlined. Such factors as high school score, score of subject such as Math I, Math II, Electrical Circuit I, and Electronics I taken during the student freshman year, number of credits passed, student cumulative grade point average of freshman year, types of high school attended and gender, among others, were then used as input variables for the ANN model. A model based on the Multilayer Perceptron Topology was developed and trained using data spanning five generations of graduates from the Engineering Department of the Al- Azhar University, Gaza. Test data evaluation shows that the ANN model is able to correctly predict the performance of more than 80% of prospective students.

Virtualization is a term that refers to the abstraction of computer resources. Virtualization has many applications within any organization. This makes possible virtual storage network and utilizing hardware resources efficiently. Virtualization also makes the foundations of cloud computing services, allowing users to use the hardware as an on-demand service. With all such advantages, there are also some security and privacy issues for utilizing any form of virtualization. The aim of this survey is to highlight such threats and techniques to solve these issues.

A semantic block is treated as a unit while analyzing the webpage. First, we implement the VTPS algorithm to partition a webpage into semantic blocks. Then, we propose an algorithm to extract the spatial and content features, and then construct the feature vector for each block. Based on these vectors, the SVM learning algorithm is applied to train and classify the various theme-oriented webpage blocks. At last, the classification experiments show the efficiency of this method.

When the software system evolves, its scale is increasingly growing to the degree where it is very hard to handle. Measuring the internal quality of the source code is one of the goals of making software development an engineering practice. Source Lines of Code (SLOC) and Cyclomatic Complexity (CC) are usually considered indicators of the complexity of a software system. Software complexity is an essential characteristic of a software system where it plays an important role in its success or failure. Although understanding the complexity is very important, yet it is not clear how complexity evolves in open source systems. In this paper, we study the complexity evolution of five open source projects from different domains. We analyze the growth of ten releases of these systems and show how complexity evolves over time. We then show how these systems conform to the second Lehman's law of software evolution.

The damage of cylinder and equipment due to the pile detaching can be avoided by using the buffer device with large flow, which adopts throttle buffer inside the cylinder combining with the control of the relief valve outside the cylinder. To investigate the thermodynamic characteristics of the cylinder chambers and the law of movement of piston, the accurate mathematic model need to be established so the method based on pseudo-bond graph is introduced. According to the conservation equations for mass and energy, the thermal-hydraulic pseudo bond graph C and R elements are developed, and the connection rule for the two elements are determined based on their input and output variables. The bond graph model of the buffer system containing the coupled of multiple energy domains is obtained in a unified way based on the structure and working principle of buffer device, then the system state equations have been derived from the model. The model can be used in variable parameters’ simulation with the parameters in the experiment, the pressure simulation data of two chambers of impact cylinder agree well with the experimental results, which indicate that the model can be used for the design and optimization of buffer device of hydraulic pile hammer.

A data model is a set of concepts that can be used to describe the structure and operations of the database. It represents as a diagram that illustrates all the possible relationships between data elements. Many models are used to represent data. The most famous of these models are ER model and UML class diagrams. Each model has many uses and features different from the other model. The database designers when choosing the right model to represent their data is a hard task for them. In this paper, we have applied an experiment to compare the ER model with UML class diagram. The task of experiment is to determine the strengths and weaknesses points of each model. It is based on matching a given textual specification for a simple application domain against collection of diagrams. The results indicate that ER model is generally better in relationship name and participation representation than the UML class diagram. On the other hand, UML class diagrams provide a better representation for the cardinality relationship compared with ER diagrams.

Due to the rapid development of computer technology, the combination between college sports and the computer technology becomes a trend. In the national physical standards, sprint is a very important sport. Sprint has attracted the majority of physical education teachers and college students. In order to exercise better and more scientific, predicting the results of sprint becomes a meaningful job. Grey prediction model is one of the commonly prediction model. In the traditional Grey prediction model, we face many problems, such as the boundary value, background value problem etc. In order to solve the above problem, we propose an improved Grey prediction model. This model is ant colony-Grey prediction model (Ant-Grey prediction model). The prediction model overcomes these problems. And then we apply this model to predict the results of the sprint for the college students. In addition, we use the computer simulation.

SnIClustering Algorithm is put forward to deal with the large number of intermediate values when processing MapReduce. SnIClustering Algorithm picks up a few representative data through cluster sampling, and then retains the useful data through filtration according to the distribution characteristics. By doing so, intermediate values of MapReduce can be reduced sharply, saving time and easing network load. The last step is to cluster the selected data and samples. Experimental results show that SnIClustering is suitable to process large-scale data, since it can both process large-scale data within a short time and maintain fine clustering effect.

Security is a peak significant quality element in the pitch of software engineering. Software security improvement is easily done with the support of factors, models and metrics of security. Software security should be analyzed with the help of its security factors. Security dimension is the main attribute in evaluation, executing, and calculating security in the way to organize and develop quality of software. It is to be identified that qualifications of security factors increased through inspecting damages, discriminating susceptibility and attacks in design development process. This review is discussing the description and categorization of accessible security properties. Durability is an attribute of security that refers to the capability of software to conclude of a creation on time. Software security is affected with security attributes as well as durability. A stable state of the secure software enhances additional security.

RFC2544 is an international standard to test network interconnect devices. The study provided the design idea of the instrument based on the LINUX system platform for testing network performance and the detail test schedule. This paper introduces the RFC2544 relevant standard and testing condition. And also discusses the key technique which concerns the design of the system’s hardware and software. The test results indicate that the design idea and test schedule are viable which being effective for testing performance of network．