Earticle

Surge current test data have been taken on as one important parameter among the process of the production and application of rectifier diode. According to the National Rectifying Diode Surge Current Testing Standards, surge current is usually generated from transformer and simultaneously tested by some testing special instruments, i.e. oscilloscopes. However, this kind of test can cause problems, such as extra high cost required, surge current waveforms distorted, and great influence made upon power grid fluctuations while high current tested. This paper addresses an efficient method to tackle those problems mentioned above. In this method, surge currents are generated by stored charges, then a microcomputer is adopted to control its surge waveform and the real time data are tested and displayed, such as the currents and voltages through the rectifier tube. Such technique can easily eliminate the effects of defects resulted from those surge current generated by transformer. Furthermore, it’s already proved to be much more accurate, controllable in surge time and peak. Compared with the method of surge current generated by transformer, such energy-stored method also saves more energy and occupies small volumes of space. Recently, instruments developed with this method have been applied in production testing systems in a number of related enterprises, and yield to a favorite effect.

The stochastic modeling of biological systems is gaining more and more attention when stochastic fluctuations need to be described. On the other hand, the biological systems under investigation become much more complex than before. Traditional modeling methods like stochastic Petri nets do not easily scale and thus become difficult to tackle this situation. This paper aims to present a colored stochastic Petri net approach for systems biology to modeling complex biological systems. Specifically, this paper describes a type of biochemically-interpreted colored stochastic Petri nets and their extensions and analysis techniques for systems biology and illustrates their applications using Ca2+ release sites that consist of a set of coupled Ca2+ channels.

A software system is built by a number of concerns where a concern is composed by one or several functional requirements or non-functional requirements or both. In the software system development one of the essential principles is separation of concerns. Generally in the software system, separation of concerns is the process of breaking into many modules with meager overlapping each other. On account of that there may be few specific concerns which are not to be located into a single module which are said to be crosscutting concerns. Aspect-oriented programming (AOP) provides techniques for managing cross-cutting concerns into a single manageable module called, Aspect. AOP is used to solve many problems such as tangling and scattering representations. But the identification and specification of crosscutting concerns and considering it as aspects is not a trouble-free job. This proposed method uses colored Petri Nets for defining the concerns and its requirements and also for executing the final modeled software system which monitors transitions based on that it will determine the join-points in a desired moment –Before, After, Around and Replace operators by the action of Aspect module. Fan in analysis techniques is used for finding the dominant aspect in the system.

Agro-meteorological index insurance is an innovative agricultural insurance product that has solved problems of conventional agricultural insurances including excessive cost and moral hazard. Agro-meteorological index insurance has become an effective agricultural insurance scheme. This paper primarily uses meteorological data and winter wheat yield data in the Jiangsu Province for the last 35 years to build a yield loss model based on which we designed an agro-meteorological index. Using production data and the experience rate method, we calculate pure premium rates for winter wheat in different cities for different deductible amounts. According to the calculation results, we divided the Jiangsu Province into three regions with different deductible amounts and determined pure premium rates and pure premiums for each city, thereby completing the design of a winter wheat meteorological index insurance product. Our study designed specific insurance products for different areas; it has a good guiding significance for the agricultural insurance in Jiangsu province research.

In recent years, the research of concrete based on CT Images has been popular. A new modified Ray-casting on VTK aims to solve the poor effect on 3-D reconstruction of concrete CT image, which has great influence on the construction of accurate 3-D meso-concrete numerical model. Owing to this result, A new tech-VTK system came into being to get improvement and development. What is more, it can achieve visualization. It is tested that the method in this dissertation is more effective and accurate meanwhile it will help to build the construction of concrete material numerical model.

In this paper, image steganography technique based on first component alteration technique using hybrid edge detector is proposed. There are lots of algorithms to hide data but they are also decreasing the quality of the image. In this work, edges of an RGB image has been detected by hybrid edge detector which is the combination of 3x3 matrix scanning and sobel edge detector, and then text will be embedded in to the first component of edges of the color image. Experimental results show that we have achieved not only high embedding capacity but also enhanced the quality of the stego image.

Simulate the computer-aided design of gymnastic device in the pre-development stage by applying CATIA V6 software, and explore the matching degree between gymnastic device design and human body database using the theory and data of Ergonomics. Build the improved model of gymnastic device based on CATIA V6 virtual human data and explore the best evaluation method of man-machine simulation for gymnastic device design after the analysis of man-machine operational processes for computer-aided gymnastic product design and the experiment of human data measurement specimens. The experiment of arm strength gymnastic device for shoulder shows that the application of CATIA V6 software platform not only further shortens the product development cycle and improves the degree of match between gymnastic device and human body, but also enhance the true integration of gymnastic device development and design system from 3D, design and simulation to manufacture.

In this paper, we introduce a system able to generate human-like linguistic descriptions of the spatial relation between two objects in intrinsic reference framework. The description includes directional relation terms, topological relations terms, and adverbs commonly found in daily communications. Each description is built based on fuzzy rules and the information extracted from F-Histograms. Excellent results show that the method of linguistic descriptions is feasible.

National Ministry of Manpower of Indonesia stated that statistically, the total of civil servants in January 2013 is 4,467,982 [2]. The total of regency or municipality in Indonesia, based on interior ministry of Indonesia, is 497. Therefore, the average number of civil servants in each regency or municipality is about 9909. Every year, the citizens who have been affected by taxes, including these civil servants, need to create incomes and taxes reports. This processing system was made to help the government to process all salary data for taxes income reports. In improving the speed process of this system can be done in two ways. The first way is by improving computer hardware or algorithm of this system to use effectively the existing hardware such as processor [3]. This research will observe the usage of multithread to process mass data, by considering the bottleneck effect on hard disk. An overlapping method is suggested on this research to exploit the processor resource. The analysis using mathematical method, empirical data, and visualization will be served in this research. The result of this research is that the overlapping method has improved the speed of taxes process system by showing the time reduction until 65.36%.

Most autonomous vehicles use GPS to determine vehicle location and heading. Using GPS for vehicle autonomous driving posts a few challenges. It does not look far ahead of the vehicle and requires frequent adjustment of vehicle heading. This results in an unstable control system and increases the chance of unstable driving behavior. Unlike this kind of passive or reactive control system, human drivers look far ahead of the road to determine the road curvature and actively adjust vehicle heading and turning angle in advance. Visual information is essential to enabling this kind of human-like driving behavior for autonomous vehicles and should be an important supplement to the GPS-based system, especially in GPS-denied environments. Road lanes are often curved, making vision-based detection of smooth and continuous curves in front of the vehicle a challenging task. Furthermore, commonly used computer vision algorithms such as edge detectors or Hough transform for line or curvature detection are not robust in changing lighting conditions. This paper presents a vision algorithm designed specifically for detecting and modeling road curvature for human-like active steering control and heading adjustment for autonomous vehicles. The proposed algorithm has been tested for paved and unpaved road conditions and shown very good results.

As the absence of effective depictive expression of uncertain information effects the effectively selection of optimum state, the selection method of optimum state of qualitative modeling and reasoning process in complex system based on QSIM and cloud model is proposed. Firstly the calculation method of similarity degree using the distance based on cloud model is given. Then the selection methods of optimum state of qualitative modeling using similarity degree based on cloud model are studied. The selection method of optimum state using the distance based on cloud model is used in constraint filter. Lastly the proposed method is applied to the case of modeling in the agile supply chain management for verifying the proposed methods. The methods proposed in this paper have the merit of objective expression the uncertain information. And the fusion of qualitative and quantitative information can be used in complex system simulation

Multilevel thresholding is one of the most important techniques for image processing and pattern recognition. The maximum entropy thresholding (MET) has been widely applied in multilevel thresholding. In this paper, a novel multilevel MET algorithm based on the hybrid of particle swarm optimization (PSO) and Genetic algorithm is presented. In standard PSO the non-oscillatory route can quickly cause a particle to stagnate and also it may prematurely converge on suboptimal solutions that are not even guaranteed local optimal solution. To overcome this problem, we used Genetic algorithm. To obtain an optimal solution in Genetic algorithm, operation such as selection, reproduction, and mutation procedures are used to generate next generations. The capability of this hybrid PSO that called HPGT is enhanced by cloning of fitter particles instead of worst particles that is determined based on their fitness values. The performance of HPGT algorithm and PSO algorithm compared. The results show the convergence of the HPGT is very good.

The paper introduces a new electronic tag device and describes system structure, design of software and hardware, critical circuit and communication protocols in details .Two-wire bus transmission is designed to replace the conventional RS485 four-wire transmission, which saves 50% of the conductive wire material. The experiment proves the feasibility of the system and the load capacity of the two-wire bus.

In order to research the problem of the gray edge and leaving out some edges during detecting the color edges, the color contrast enhancement algorithm is designed that it dependences on decomposing the interrelated color component of the color images based on RGB color model. And the edge detection improved method is proposed based on the above algorithm. Namely, the components edges are computed by the gradient operators after the components are enhanced and the color image edges are composed. The experimental results show that the color edges are detected through the proposed method and the algorithm has the advantage of keeping the more edge details than the other ways. Because the algorithm is an improved method of pixel processing based on three components of RGB color model, it needn’t transform one color model to the other color model, so it is a more simple method than the other methods.

Heuristic evolutionary optimization algorithms are the solutions to many engineering optimization problems. Differential evolution (DE) is a real stochastic evolutionary parameter optimization in current use.DE does not require more control parameters compared to other evolutionary algorithms. Master Production Scheduling (MPS) is posed as one of multi objective parameter optimization problems and often require an optimal solution for the success of a business organization by balancing demand and supply. This work reviews some of the fundamental theory of differential evolution, the methodology for master production scheduling calculation and most important results. The results available for the existing algorithms are compared with results obtained by the proposed evolutionary algorithm. The analysis reveals that the DE algorithm provides a better solution with reasonable computational time.

There are plenty of applications about real-time simulation of realistic clouds. In this paper, the model of the clouds is established based on cellular automata and the method of how to deal with boundary grid points is developed. The efficiency is enhanced and some of the dynamic aspects of clouds are described by using promoted transition rules and introduced ascending air current. Multiple forward scattering model is used in illumination calculation, and Henyey-Greenstein phase function is embedded into forward scattering. The simulation results show that real-time rendering of the clouds is implemented realistically.

This paper presents the use of a novel evolutionary algorithm called Biogeography-based optimization (BBO) for the solution of the optimal power flow problem. The objective is to minimize the total fuel cost of generation and environmental pollution caused by fossil based thermal generating units and also maintain an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, shunt capacitors/reactors and power flow of transmission lines. BBO searches for the global optimum mainly through two steps: Migration and Mutation. In the present work, BBO has been applied to solve the optimal power flow problems on IEEE 30-bus test system with six generating units to test the effectiveness of the proposed method. Satisfactory results obtained from the proposed method were compared to conventional and evolutionary optimization methods.

Some progress has been made in container crane simulated training system research and development globally, but there are still serious problems such as faultiness in training function and effect, less coordination in synchronicity of visual and tactile simulation, etc. The lack of accurate system dynamics model is the main reason. The existing model takes the consideration of real-time motion simulation so simplified that it’s unable to accurately reflect the dynamic characteristics of structure or mechanism in starting and braking stage, and the coupled vibration characteristics in operation process, etc. In this paper, the geometric model of the entire container crane structure was established by using Solidworks, and used ADAMS and ANSYS for joint simulation of start-brake and coupled vibration characteristics, which provides a solution to establishing kernel model of container crane simulated training system and achieving real-time simulation.

An efficient multimedia data management process is very important in multimedia system application. Huge size of multimedia data that distributed in multi locations makes multimedia data management more complicated. Rapid development of multimedia applications created a vast volume of multimedia data and it is exponentially incremented from time to time. This situation requires for efficient data classification and organization technique for providing effective multimedia data manipulation process. Using rough sets theory and web services technology, this paper proposed a new rules generation for multimedia data classifying in collaborative environment. ROSETTA tool is applied to verify the reliability of the generated results. The experiments show that the rough sets theory based for multimedia data classifying is suitable to be executed in web services environment.

The prediction of wind farm output power is considered as an effective way to increase the wind power capacity and improve the safety and economy of power system. It is one of the hot research topics on wind power. The wind farm output power is related to many factors such as wind speed, temperature, etc., which is difficult to be described by some mathematical expression. In this paper, Back Propagation (BP) neural network algorithm and genetic algorithm (GA) are combined to establish the prediction model of the short-term wind farm output power based on meteorological data collected by Wireless Sensor Network (WSN). The Meteorological data is used to determine the input variables of the BP neural network. Meanwhile, the GA is used to adjust the value of BP's connection weight and threshold dynamically. Then the trained BP neural network is used to predict the wind power. The experiment results show that our method has better prediction capability compared with that using BP neural network alone or using wind power formulas.

Open-space emergency evacuation is the movement of people away the threat in the short time. Task allocation is a fundamental research challenge in emergency evacuation domain of multi-agent systems (MAS). This paper proposes the multi-agent task allocation model which is called EV-GAP. Accordingly, the capability of agent was redefined to satisfy the requirement of open-space emergency evacuation. Corresponding the task allocation algorithm based on EV-GAP has is also given. Finally, the experiment of the task allocation of open-space emergency evacuation for a chlorine leakage accident is completed to validate this model.

The existing trustworthiness attestation methods are not only difficult to be applied to the embedded system because they are mainly based on virtual machine technology, but have some problems such shat evidence is not obtained in time, protecting the privacy need trusted third party and trust measurement efficiency is low. In this paper, an embedded system dynamic trustworthiness attestation method based on dual-kernel (super kernel and normal kernel) operating system architecture is proposed. Super kernel is non-changeable, and it verifies the integrity of the critical data structures and kernel file in normal kernel. Super kernel can serve as a trusted third party which can dynamically verify whether the code segment changes in runtime. A system implementation is given in this paper, and the experimental data show that the behavior of the system can dynamically verify the behavior of program whether meets embedded trusted application demand or not.

This letter presents a wideband tri-passband filter in which tri-section stepped-impedance shunt-stubs are used as tri-mode resonators, connected by transmission lines. Developed from the topology of quarter-wavelength stub filters, these filters have much wider passbands compared to many other designs published. The stepped-impedance stubs also contribute transmission zeros between the passbands, thus increasing the isolation. A prototype filter with passbands located at 0.6-1, 1.5-1.8 and 2.2-2.4 GHz is designed by an optimization method. Good agreement between simulation and measurements in respect of low insertion loss and high isolation is demonstrated.

Much effect has been devoted over the past decade to inference of gene regulatory networks (GRNs). However, the previous methods infer GRNs containing large amount of false positive edges, which could result in awful influence on biological analysis. In this study, we present a novel hybrid framework to improve the accuracy of GRN inference. In our method, network topologies from linear and nonlinear ordinary differential equation (ODE) models are integrated. The additive tree models are proposed for identification of linear/nonlinear models. We also propose a new criterion function that sparse and relevant terms are considered while inferring linear and nonlinear models. Benchmark datasets from Dialogue for Reverse Engineering Assessments and Methods challenge and real biological dataset from SOS DNA repair network in Escherichia coli are used to test the validity of our method. Results reveal that our proposed method can improve the prediction accuracy of GRN inference effectively and performs better than other popular methods.

Gas turbines can be found in many industrial application areas. Gas turbine generation is limited by some undesirable effects which can be incorporated as operational constraints. Because of importance of energy, optimization of power generation systems is necessary. In order to achieving higher efficiencies, some propositions are preferred such as recovery of heat from exhaust gases in a regenerator, utilization of intercooler in a multistage compressor, steam injection to combustion chamber and etc. In this article multi-objective particle swarm optimization are employed for Pareto approach optimization of Gas Turbine cycle. In the multi-objective optimization a number of conflicting objective functions are to be optimized simultaneously. Multi-objective optimization offers a candidate scheme whose solution can satisfy the foregoing major requirements. At the first stage single objective optimization has been investigated and then MOPSO has been used for multi-objective optimization. The sets of selected decision variables based on this Pareto front, will cause the best possible combination of corresponding objective functions. The obtained results show that the output of multi-objective optimization scheme confirms that of single objective results.

This paper explores the contributions of all reconstruction combinations of the synthesis delay filters in adaptive synthesis filter banks and their effects on objective performance in image compression. It presents a novel comprehensive approach to manage all reconstruction combinations and their placing configuration in the adaptive synthesis filter banks. Based on the contributions of reconstruction combinations, it suggests the most suitable reconstruction combinations in order to reduce the computation complexity of adaptive synthesis filter banks. The simulations are carried out by using popular bi-orthogonal 9/7 filters on a number of standard test images with different design examples, which show that suggested reconstruction combinations of the synthesis filters play a very vital role on objective performance.

With the development of cloud computing, mobile device and industry automation, virtualization plays an important role today. The core of virtualization is hypervisor which directly determines the performance of platform. Therefore, how to allocate resource effectively becomes an important issue. Xen, one of widely used open source projects, is a virtual machine monitor. Simple EDF (Earliest Deadline First) scheduler that is a dynamic-priority real-time scheduler in Xen implements the famous EDF scheduling algorithm. Due to EDF scheduling strategy has miss the deadline and inefficiency in overloaded condition, we have improved the D_EDF scheduling algorithm that combines Deadline-Monotonic scheduling strategy with EDF scheduling in virtualized environment. And we have extended the Simple EDF scheduler which provided by Xen using improved D_EDF scheduling algorithm. Our experiment demonstrates that the proposed scheduling algorithm increased the performance under CPU-intensive and memory-intensive workload in overloaded condition.

Internet has developed into dynamic network where users use wired/wireless access technologies. The diversity of bandwidths, delays and error rates observed on Internet have increased. TCP is the common denominator for many services therefore by modifying TCP the need for applying solutions locally can be reduced. We focus on making TCP more efficient in a way to speed up its slow start phase. Different Slow Start algorithms will be analyzed and new variant will be proposed.