Earticle

This paper proposes a new intelligent control strategy to meet the tracking requirement of the power itself considering the coupling impacts of the plasma cutting technology. The dynamic intense coupling impacts of the technologies can’t be simulated, and a novel style of PID neural network (PID-NN) is applied to obtain closed-loop controller’s reference cutting current via its nonlinear mapping effect. The PID algorithm parameters are self-adjusted through fuzzy logic, whose inputs are both the reference current error and its variation rate. Simulation and testing experiments have verified the greater improvements of this project in steady precision, response speed and robustness, and the controller’s advantage in dealing with discrete events and multivariate decoupling problems.

Choosing a correct main protection configuration schemes for stator internal short circuits in generator is the premise of safe and reliable operation of the large generator. This paper firstly analyzes the general large hydro-generator stator internal short circuit fault type and fault number. It mainly does some statistics works on SF600-42/1380 generator fault location and fault number. And then through the finite element method, calculate each phase and each branch current if the generator internal short circuit happens. In the paper, we calculate the sensitivity of various kinds of main protection scheme. Get the dead zone position which easily lets the main protection device take action. Finally we summarize various factors that determine SF600-42/1380 generator main protection scheme of configuration.

With the very fast development in today`s digital world, Information Retrieval on Internet is gaining importance, day by day. The web comprises of huge amount of data and search engine provides an efficient way to navigate the web and get the relevant information. The search engine has proven to be less efficient in providing relevant information from a query processed by a user. Fors olving this problem and getting accurate results there is need to categorize these web pages. Many optimizations have also done to speedup the classification process as it is required to be fast while maintaining the efficiency. To maintain the accuracy with the lesser time requirement, researchers have developed a SVM based Layered approach with the help of firefly feature selection method.

This paper introduces independent finance certification agency to supervise the vendor selection process under the circumstance of the dynamic and incomplete information market. Using the signaling theory under unsymmetric information of game theory, a multidimension signaling model of the strategic choice is established. The game model is set up and the equilibrium calculated out following the description of the key elements of the buyer and vendor in above market. Upon separating equilibrium, the provider dispatches the signal stands for his actual category to maintain the performance of the market. Upon pool equilibrium, the inefficient provider rather than efficient one occupies the core position which results in loss of the latter. Case result shows that the buyer can adopt the signals from the Independent finance certification agency as a significant element when making decision. As a result the introduction of independent finance certification agency can effectively and efficiently supervise the vendor selection process in incomplete information market.

With rapid development of computer science and network technology, the technology of electronic voting is widely applied in various fields. There are lots of security requirements in electronic voting such as privacy, verifiability, fairness and robustness etc. Previous works mainly discuss how to guarantee these requirements by using cryptographic tools, such as encryption, commitment etc. There, most works assume that parties are honest who will follow the protocol or malicious who will arbitrarily break the security of the protocol. In fact parties in real life are neither honest nor malicious, but instead they may consider the benefits when they decide to participate in voting. This paper discusses the motivations of parties to participate into voting towards the view of game theory. More specifically, motivations are quantized as the notion of utilities just as those in game theory. Then we prove how to assign people to have motivations to vote by achieving Nash equilibrium.

Dal Lake (located at 34° 07′ N, 74° 52′ E, 1584 m above MSL, in Srinagar, Jammu & Kashmir, India) has been a great tourist attraction in the past. However, once beautiful water body has been the worst victim of the anthropogenic pressures during the second half of the 20th Century and has undergone tremendous ecological changes. In spite of innumerable conservation measures taken by the management authorities over the past two three decades, the ecological condition of the lake has deteriorated and it is getting enriched with plant nutrients and other pollutants, becoming more and more infested with macrophytes, getting slowly shallower and shallower and shrinking gradually in size. An ecological study of the water body was conducted during March-April 2013 so as to gain insight into the flaws of the conservation endeavors. It was revealed that ecologically unsound management practices are the major cause for the plight of the lake. In response to the inshore dredging, skimming of free floating macrophytes and unplanned mechanical deweeding undertaken during the past several years, some previously sparsely distributed aquatic plants in the lake have reached nuisance levels, while hitherto unknown invasive Azollapinnata has got widely distributed throughout the water body. A thorough study encompassing all environmental aspects, including socioeconomics, needs to be undertaken so as to frame an ecologically well balanced conservation strategy for this important aquatic ecosystem of the region.

As a kind of renewable energy, solar power becomes more and more widely used as the power supply for large-scale datacenters to save the brown energy consumption and to reduce the overall cost. The prediction accuracy of solar energy generation becomes a fundamental issue in the research of how to efficiently manage the renewable energy resources. This paper explores the possible ways to predict the solar radiation intensity based on the assumption that it impacts the solar power generation proportionally. Through the analysis and research of photovoltaic power generation system, we explore the influence factors for solar radiation intensity, establish a relationship of solar radiation intensity and ambient temperature, time, humidity, wind speed in the forecasting model, and finally established the multivariate linear regression model and artificial neural network model. According to the two models, the environmental monitoring data measured at the Qinghai University are employed as the basis of the prediction of solar radiation intensity, and compared with the actual measurement data monitoring system. Experimental results show that, by using the BP neural network prediction model, the achieved accuracy is higher than other empirical model. The prediction method and good results provide a necessary foundation for future related research based on solar radiation values forecasts.

The inboard tourism demand forecasting is very important to the development of tourism industry. In this paper, the SVR model is adopted to forecast monthly inbound tourism demand of China. And the elitist Non-dominated Sorting Genetic Algorithm (NSGAII) is used to parameter optimization. The NSGAII algorithm can reduce complexity of the algorithm, keeps the diversity of population and increasing the forecasting accuracy. At last, the proposed NSGAII-SVR model is used to forecast monthly inbound tourism demand of China from July 2011 to December 2011. And the experimental results show that the NSGAII-SVR has the best performance on forecasting compared with other models.