Earticle

In order to solve non-linear errors caused by rotational movement of the rotary axis, firstly, it analyzes how non-linear errors are generated in the five-axis machining, and establishes the non-linear error model of complex surface in 3D space. Then it proposes a method of controlling and compensating for nonlinearity errors by introducing the interpolate point based on smoothing tool axis vector. In the method of surface interpolation, it is suggested to add an interpolation point, keep the tool axis vector on sector surface boundary within the start and end tool axis vector of the program block, and maintain the tool axis vector velocity and acceleration continuity. Simulation results show the nonlinearity errors of the fan blade machining are effectively controlled.

Design of a robust controller for multi input-multi output (MIMO) nonlinear uncertain dynamical system can be a challenging work. This research focuses on the design and analysis of a high performance chattering free PD plus PD partly sliding mode controller in presence of uncertainties. In this research, sliding mode controller is a robust and stable nonlinear controller, which selected to control of robot manipulator. The proposed approach effectively combines of design methods from switching sliding mode controller, and linear Proportional-Derivative (PD) control to improve the performance, stability and robustness of the sliding mode controller. To reduce the chattering with respect to stability and robustness; linear controller is added to the switching part of the sliding mode controller. The linear controller is to reduce the role of sliding surface slope and switching (sign) function. To improve the flexibility, design high speed and low cost controller, micro-electronic device (FPGA-Based) controller is introduced in this research. The proposed design is 30-bits FPGA-based controller for inputs and 35-bits for output. All joints of robot are used to test the controller in simulation environments, using VHDL code for the purpose of simulation in Xilinx. The maximum frequency in FPGA-based design is about 63.6 MHz and the delay time in this design is about 15.7 ns. It is observed that this controller is able to make as a fast response at 15.716 𝑛𝑠 clock period with 63.6 𝑀𝐻𝑍 of a maximum frequency and 4.407 𝑛𝑠 for minimum input arrival time after clock. From investigation and synthesis summary, 30.286 𝑛𝑠 for maximum input arrival time after clock with 33.018 𝑀𝐻𝑍 frequencies, this design has 15.716 𝑛𝑠 delays for each controller to 46 logic elements and the offset before CLOCK is 55.773 𝑛𝑠 for 132 logic gates.

Bat algorithm is an optimization method inspired by the echo-location bats to search in nature, hunt prey behavior, combining multi-agent system and evolution mechanism. To improve the search results of BA algorithm, this paper proposes a gauss perturbation bats optimization algorithm based on simulated annealing (SAGBA). Firstly, the bionic principle, optimization mechanism and characteristics of the bat algorithm are analyzed and the algorithm optimization process are defined; Then the idea of the simulated annealing is put into bat optimization algorithm, and Gaussian disturbance is carried out to some individuals using the bat algorithm and strengthen the ability of the bat algorithm jumping out of local optimal solution. Finally, conduct simulations are respectively compared in 20 typical benchmark test functions among bat optimization algorithm, simulates annealing particle swarm algorithm and SAGBA algorithm. The results show that SAGBA algorithm not only increases the global convergence, but convergence speed and accuracy are better than other two algorithms.

This paper deals with the duality for a class of multiobjective programming problems including inequality constraints. To establish and prove the dual results for the multiobjective programming problems, the dual models and the classes of generalized invexity functions so-called F J - d - αβ - Ρt - θ - pseudoinvex-I are introduced. Using the new concepts, the weak dual, strong dual and converse dual theorems are obtained for the multiobjective programming problems.

This research describes the design and implementation of nonlinear control strategies for three dimensions joint dental robot manipulators whose dynamic or kinematic models are uncertain. This technique describes the development of an adaptive task-space tracking controller for dental robot manipulators with uncertainty in the kinematic and dynamic models. The controller is developed based on the unit quaternion representation so that singularities associated with the otherwise commonly used three parameter representations are avoided. This controller requires the assumption that the manipulator models are nonlinearly parameterizable. However there might be scenarios where the structure of the manipulator dynamic model itself is unknown due to difficulty in modeling. These manipulators do not have rigid joints, hence, they are difficult to model and this leads to significant challenges in developing high-performance control algorithms. In this research to eliminate the hand tremors, PID controller is used and to improve the performance and reduce the steady state error PI based PD fuzzy rule base controller is used.

This research proposed an application of swarm inspired new meta heuristic algorithm Grey Wolf Optimization to solve active power dispatch problem imposing valve point effect and generator constraints. Grey Wolf optimization is based on mathematical approach whose solution convergence inspired by the leadership hierarchy and hunting mechanism of grey wolves. It explores search space as a multi-level decision mechanism and does not require gradient for search path. This approach converged to global optimal solution in spite of the non linearity added by valve point effect while solving the fitness function. Optimal scheduling of generators to minimize the total operating cost coupled with generator constraints and valve point effect to match load demand is implemented with proposed method and. Exploration, Computation and Convergence power are evaluated to track the computational efficiency of the proposed technique. The presented technique is tested on different test cases comprises three, six and thirteen test systems incorporating valve point effect. Test results are compared with other nature and bio inspired algorithms presented in literature .Analysis shows cut throat results as total operating cost turns out to be minimum as compared to other techniques which infers the effectiveness of proposed method and encourage to further explore the potential of proposed method to solve complex optimization problems in active power dispatch planning area.

Wood plastic composites (WPCs) were prepared and used as pencil board. The primary raw materials were corn straw fiber powder, high-density polyethylene, and polystyrene. The tensile strength, surface roughness, hardness, and roll cutting performance of the prepared composites were tested and compared with those of linden wood, which is typically used for pencil board. A back propagation (BP) neural network was adopted to build the prediction model of process technical parameters of the composite. An improved particle swarm (modified particle swarm optimizer, MPSO) was adopted to optimize the BP neural network, and the advantages of the PSO algorithm’s global optimization ability and the BP neural network algorithm’s high processing speed were realized. The results show that the composite had the best performance, with a mass ratio of 3.8 (corn straw fiber powder): 47.6 (high-density polyethylene): 28.6 (polystyrene). The composites can be used as a substitute for linden wood in the production of pencil board.

The extension of ant colony algorithm was proposed by Dorigo, the founder of ant colony algorithm, which is the latest ant colony algorithm for solving a continuous space optimization problem. Considering the blindness of man-made choice of initial solution and initial parameters of the algorithm, and according to the algorithm converging slowly and easily falling into local optimum, this paper has provided improvement strategy for this optimization. It has introduced quantum computing and genetic algorithm, chaos optimization to carry out combination and comparison, and it has carried out improvement on the weight internally solved by memory in the algorithm. The effectiveness of various combined algorithms was determined through the optimization of numerous multi-dimensional continuous functions.

In the realistic situation, there are many areas which contain imprecisely specified data. This imprecise data indicates the presence of vagueness, incompleteness and uncertainty which causes the problem during important decision-making task. The present paper focuses on the problem of mining important inference from supermarket basket data (in the presence of vagueness). The paper specifically studies the usefulness of vague set theory for the exploration of hesitation information and vague association rules. The hesitation information of an item plays a vital role in making selling strategies for the exhilaration of business. For this purpose, the vague set concept can be used as an important tool which can assist in the identification of hesitated item. The vague set theory with its two membership function provides more intuitive way to interact with the vague situation that causes the hesitation for any item. The effectiveness of the hesitated pattern and rule provide advanced decision-making capabilities that transform ‘almost sold’ items to ‘sold items’.

Aiming at the problem that the traditional Collaborative Filtering algorithm has low recommendation accuracy, in the paper, we propose a collaborative filtering recommendation algorithm based on the global trust degree integrating the direct trust information in the social networks. We first transform the local trust relationships to the global trust relationship by the rules in the trust network, and get the trust rank of all users in the trust networks; Then we use the global trust value to instead of the similarity information value as the weights of a predicted formula in the traditional collaborative recommendation algorithm, and integrate the weights to the matrix factorization-based recommendation model.

An approach based on online RBFNN is proposed to predict the ball-carrier's behavior shooting, passing and dribbling in basketball matches. In order to describe the factors affecting the behavior of ball carrier, artificial potential field (APF)-based player information is introduced to model the court situation of all players after tracking and vision range determination, then a feature vector is formed as the input of the online RBF neural network. The behavior prediction of the ball carrier is solved by the online RBF neural network based on GIRAN learning algorithm. Compared with the offline RBF neural network, the online neural network can adjust both structure and parameters to basketball matches, thus the prediction accuracy is improved to some extent.

This article deals with solving the flexible job shop scheduling problem in dynamic environment (DFJSSP). In this problem, environment may face with many real time events such as random arrival of jobs or breakdown machine efficiency. Jobs and their operations are processing the machines according to static scheduling in which environment might face with such events. Regarding being NP – hard of the problem , a hybrid of artificial immune and virus evolutionary algorithm are offered to solve it which use the technique of stable action – reaction scheduling . In these algorithms two objective functions are minimized: Efficiency and stability. Efficiency is the objectives value in static scheduling, whereas stability is presented in dynamic scheduling because its purpose is to improve static scheduling, reduce the deviation from the first scheduling, and increase system stability.

In Object-oriented paradigm, classes are the basic units to organize the small applications. The concept of ‘package’ introduced when programmer found it difficult to organize the large application using classes. A good organization of classes into identifiable and collaborating packages eases the understanding, maintenance, testing and evolution of the software. In order to assess and improve the quality of an application during development process, developers and designers use various software metrics. Robert C. Martin presented a set of software metrics to identify poorly designed packages on the basis of certain features like stability, abstraction and coupling of the packages. According to Robert C. Martin, the Packages which are highly interdependent tend to be rigid, not reusable and hard to maintain. These metrics have been used to predict the quality of the packages early in the design phase and hence helps the programmers to reduce the overall cost of development. But these Software metrics neither give the exact value nor defines the sharp boundaries to categorize the packages. In this research work, a decision making system based on Fuzzy inference mechanism is used to categorize the good and bad packages that depends on various factors which are vague in nature. The proposed model is finally validated using the Distance from the Main Sequence ‘D’ metric intended by Robert Martin.

A framework of granular computing clustering algorithms is proposed in the paper. Firstly, granules are represented as the normal forms, such the diamond granule in 2-domensional space and hyperdiamond granule in N-dimensional space, sphere granule in 2-dimensional space and hypersphere granule in N-dimensional space. Secondly, operations between two granules are designed to realize the transformation between two spaces with different granularities. Thirdly, the threshold of granularity is used to control the join process between two granules. The performance of granular computing algorithms is evaluated by the experimental results on the data sets selected from machine learning repository.

Private colleges in China have the dual natures of public products and profit-driven entities. Since the late 1970s, private higher education began, the positioning and development model are in the process of constantly updated and improved. In this paper, I try to describe the current situation of the development through the development scale, development model and development characteristics of the private higher education in China, analyze the internal and external influencing factors in the development process of private higher education emphatically. In the end, propose the countermeasures and suggestions for the future development of private colleges. Conclusions: Private colleges should design strategy under the guidance of national policy, choose the future development mode actively; Strive for government support and subsidies actively; Guide the whole society’s investment in private higher education actively; Improve the ability and level of teaching staff; Pay special attention to students’ recruitment, cultivating and employment, to improve students' quality.

Benchmarking is the decisive instrument for enhancement which proficient over assessment from former organizations predictable as the best within reason. Benchmarking provides an external focus which forces the organization to seem what the competitors are adopting. The manufacturing industries are focused in this study such as service, professionalism, replace ability, credibility and many more while bringing other processes up to stain and highlights different types of Benchmarking process used for different industries. Thus, the attempt has been made by authors to present an evaluation model for Benchmarking by using ANP approach. Here, ANP approach is used on the basis of software where, group of experts were consulted to establish interrelations and to provide weight age for pair wise comparison. Mutually spirit of experts will provide the identical results for the study and reasonably cooperative for managers to give assurance for internationally manufacturing industries.

Although the theory of two-sided markets is popular nowadays, little attention was paid on self-network externalities and UGC (User Generated Content) produced by buyers which are the major features of social commerce two-sided markets. This article studies percentages of buyers who generated UGC, self-network externalities of buyers and cross-group externalities between two groups. The three factors influence the pricing strategy of social commerce platforms based on two-sided markets. With analyzing the special characteristics of social commerce, the study unveils that the optimal price of buyers charged by social commerce platform shows a negative correlation with self-network externalities and it also shows a negative correlation with the probability of UGC generated by buyers. The study also shows that the equilibrium price of buyers declines with the augment of cross-group externalities of buyers to sellers, and the equilibrium price of sellers declines with the augment of cross-group work externalities of sellers to buyers. The social commerce platforms on the basis of interest graph, lower UGC threshold and more interactive among customers gain the competitive advantages. Compared to other platforms, the two-sided social commerce platforms give consumers and retailers more subsidies.

A large number of electronic documents are labeled using human-interpretable annotations. High-efficiency text mining on such data set requires generative model that can flexibly comprehend the significant of observed labels while simultaneously uncovering topics within unlabeled documents. This paper presents a novel and generalized on-line labeled topic model based on global and local topic (GL-OLT) tracking the time evolution of topics in a sequentially organized multi-labeled corpus. GL-OLT topic model has an incrementally update principle based on time slices by an on-line fashion, and each label has not only a set of local topics, but also has several global topics. Empirical results are presented to demonstrate significant improvements accuracy of label predictive, and lower perplexity and high performance of our proposed model when compared with other models.

This paper applies a novel hybrid rough set theory-particle swarm optimizer technique, namely rough particle swarm optimization (RPSO) algorithm, for solving non-convex economic load dispatch (NELD) problem. The RPSO algorithm is based on the notion of rough patterns that uses rough values defined with upper and lower intervals in which represent a set of values. This RPSO method is suggested to deal with the practical constraints such as valve point loading effect, generation limitation, ramp rate limits and prohibited operating zones in the NELD problems. Simulations were performed on four different power systems with 3, 6, 15 and 40 generating units and the results are compared with classical PSO and crazy PSO algorithms. The results of this study reveal that the proposed approach is able to find appreciable NELD solutions than those of previous algorithms.

Evidence theory is an effective method for uncertainty reasoning, which is widely used in areas like expert system, artificial intelligence, pattern recognition and system decision. But traditional DS combination rule will produce the result contrary to intuition on the condition of high confliction. To solve the problem, this paper proposes a modified method based on the option of distance function and correction of support degree. Firstly, it introduces the Minkowsky distance as distance function of evidences and finds the support degree of each evidence in system, then corrects the support degree on the basic of its distribution, finally, it gets the weighted average of evidence by the normalized evidence credibility, and uses the DS combination rule to synthesis evidences. The simulation results demonstrate the effectiveness and reliability of modified method.

Modern education technology has changed the teaching mode, and brings a huge impact to physical education. With the development of multimedia technology, the impact of network education is unprecedented, not only changes the teaching content and teaching methods, but makes the fundamental reform of the modern education system. In this paper, we test the correlation of red sports culture and modern physical education by using network data. The result shows that after red sports cultural has been integrated; college student’s outlook on life, values and world outlook has a very significant increase, and it will enhance innovative thinking ability of college student. So that, network education has formed a new growth point, and will become the main direction.

The lane keeping problem of automatic driving for vehicles was studied based on the simplified linear lateral dynamic model. Three kinds of sliding mode methods were proposed by adopting integral sliding mode, anti-saturation and adaptive method. The stability of each method was proved by constructing Lyapunov method. What is worthy pointing out is that the anti-saturation method can not only reduce the oscillations by using soft function and sigmoid function, but also it can solve the saturation problem of front wheels by using the bounded characteristics of above two functions. And this method is also very simple and it has a reasonable physical meaning which makes the chosen of control parameter very easy. At last, detailed numerical simulations were done for proposed three methods and simulation results were compared with other three methods in the past references to testify the rightness of the proposed method.

A new kind of novel control method which is named backstepping synchronous control is firstly proposed in this paper. It is a kind of method that can applied in any general system as widely as PID control, variable structure control and any other control method. Synchronization was most frequently used in secure communication and else in the past, but here its concept was successfully used to construct a new control method. It is similar to model referenced adaptive control but it is still different from that. An auxiliary system with several virtual synchronous inputs and a real input is constructed to cope the uncertainties of original system. The synchronous inputs are designed to realize the synchronization between auxiliary system and original system. Since the real input is designed according to the auxiliary system without uncertainty by using backstepping method, so it is easy to design. Because the synchronous input is decoupling so it is easy to cope with uncertainties. In this paper, simplified supersonic missile system was taken as an example to show the design process and stability analysis of the new method. Also, it was compared with traditional PID control and backstepping control method. At last, detailed numerical simulations were done to testify the effectiveness of the proposed method.

The design of the fuzzy logic controller is the control accomplishment as a feedback for significantly improving the dynamic performance of drive. The switches affecting in Switched reluctance motor (SRM) drive is receiving increasing attentions from various researchers as well as variable candidate for adjustable speed applications. Switched Reluctance Motors having simple structure and inherent mechanical strength without rotor winding. Torque-ripple reduction in switched reluctance motors (SRM) has become major research design. In servo control applications or when smooth control is entails at low speeds, reduction of torque ripple becomes the main concern in an adequate control strategy. In this intelligent controller such as Fuzzy Logic Controller (FLC) current compensating technique is employed for minimizing the torque ripples in SRM. For the purpose of comparison and the performance of conventional Proportional- Integral controller is also considered. With its flux linkage profile, SRM could be well modeled and simulated by using MAT Lab/Simulink.

Recommendation technology is used to help people solve the problem of information overload. Recent years, it has been widely applied to the movie ratings, e-commerce and many other fields. Researchers have noticed its powerful application prospect. But with the exponential growth of information data, the recommendation systems also have to improve the ability of data processing and this leads to that the traditional collaborative filtering recommendation algorithms cannot meet the needs of the users. To solve the problem, we designed an algorithm based on the theory of statistical analysis. This algorithm classified the data simply firstly, and then system could give users the relatively satisfactory personalized recommendations by the statistical analysis of different attributes on the data sets.

With the fast development of cloud computing and computer science technology, the combination of the IOT and clod computing in the medical-assisted environment is urgently needed. The prior research focus more on individual development of the single technique, quite a less research on the field of medical monitoring and managing service application have been conducted. Therefore, in this paper, we study and analyze the application of cloud computing and the Internet of Things on the field of medical environment. We are trying to make the combination of the two kinds of technology monitoring and management information system in hospital. Remote monitoring cloud platform architecture model (RMCPHI) set up medical information in the first place. Then the RMCPHI architecture was analyzed. Eventually, the last effective PSOSAA algorithm proposed the hospital medical information service cloud system monitoring and management application. Experimental simulation illustrates that the proposed algorithm outperforms the other state-of-the-art algorithms. Further potential research areas are discussed.

In this days, organizations invest in management information systems because they provide economic value to the business. While the adoption of management information systems in the organizations, especially in Yemen is still dealing with issues in field of successful adoption. In other hands, the Telecommunications industry today is a key enabler of productivity across economies and societies. In context of Yemen, the telecommunication sector is a strategic and vital sector being of direct link with the citizen and public and private institutions alike. The sector has witnessed substantial change and developments during the last ten years. However, There are a lot of issues in telecommunication companies in Yemen. This paper aims to shed some light on issues related to employees at telecommunication companies in Yemen, and the role of management information systems in solving these issues.

The abundant information available in hyperspectral image has provided important opportunities for land-cover classification and recognition. However, “Curse of dimensionality” and small training sample set are two difficulties which hinder the improvement of computational efficiency and classification precision. In this paper, we present a co-training based method on hyperspectral image classification. Firstly, two views of samples are generated through two kinds of dimensionality reduction methods. After that, the co-training process is viewed as combinative label propagation over two independent views. Experimental results on real hyperspectral image show that the proposed method has better performance than the other state-of-the-art methods.