Submodularity in Batch Active Learning and Survey Problems on Gaussian Random Fields

Many real-world datasets can be represented in the form of a graph whose edge weights designate similarities between instances. A discrete Gaussian random field (GRF) model is a finite-dimensional Gaussian process (GP) whose prior covariance is the inverse of a graph Laplacian. Minimizing the trace of the predictive covariance Sigma (V-optimality) on GRFs has proven successful in batch active learning classification problems with budget constraints. However, its worst-case bound has been missing. We show that the V-optimality on GRFs as a function of the batch query set is submodular and hence its greedy selection algorithm guarantees an (1-1/e) approximation ratio. Moreover, GRF models have the absence-of-suppressor (AofS) condition. For active survey problems, we propose a similar survey criterion which minimizes 1'(Sigma)1. In practice, V-optimality criterion performs better than GPs with mutual information gain criteria and allows nonuniform costs for different nodes

The Implementation of Financing Strategies in Urban Rapid Transit Infrastructure: How Could Chinese Cities Do Better?

Urban rapid transit infrastructure have been expanding at an exploding speed in Mainland China. Government subsidy used to be the sole fiscal support to the transit development for a long time. Now the government is facing enormous burden to support the expansion on its own. The major problems are the shortage of construction and revenue-expenditure gap in funding. Therefore, the municipalities are seeking to introduce foreign financing strategies to the transit funding to build a more profitable financing platform. Hong Kong MTR has been a popular model among mainland cities for its “Rail + Property” model and efficient internal management. However, under current context, it is really difficult to completely copy the success of Hong Kong. Therefore, the implementation has shown several variations. Beijing Metro adds a parallel system to the existing one, which are under the operation of Public-Private Partnership with Hong Kong MTR Company. Wuhan, while paving the avenue for “Rail + Property”, employed Finance Lease to utilize the existing facilities to collect construction funding, which made time for property auction and development adjunct to and atop of the transit line. In analyzing the implementation process of the two cases, the thesis found out the strategies of implementing financing strategies in Chinese mainland cities, and illustrated the context and the incentives for the variations. By reviewing the case of Wuhan and Beijing, the thesis analyzed how the two cities made localized adaptions, their contracts in the two implementations, and the key part that contributed to reaching the expected effects. In finding a working principal from these two cases, the thesis also analyzed the problems in implementation caused by the government role, the financing platform of metro company, and the mechanism of implementation

Condition monitoring of wind turbines based on extreme learning machine

Wind turbines have been widely installed in many areas, especially in remote locations on land or offshore. Routine inspection and maintenance of wind turbines has become a challenge in order to improve reliability and reduce the energy of cost; thus adopting an efficient condition monitoring approach of wind turbines is desirable. This paper adopts extreme learning machine (ELM) algorithms to achieve condition monitoring of wind turbines based on a model-based condition monitoring approach. Compared with the traditional gradient-based training algorithm widely used in the single-hidden layer feed forward neural network, ELM can randomly choose the input weights and hidden biases and need not be tuned in the training process. Therefore, ELM algorithm can dramatically reduce learning time. Models are identified using supervisory control and data acquisition (SCADA) data acquired from an operational wind farm, which contains data of the temperature of gearbox oil sump, gearbox oil exchange and generator winding. The results show that the proposed method can efficiently identify faults of wind turbines

INDEMICS: An Interactive High-Performance Computing Framework for Data Intensive Epidemic Modeling

We describe the design and prototype implementation of Indemics (_Interactive; Epi_demic; _Simulation;)—a modeling environment utilizing high-performance computing technologies for supporting complex epidemic simulations. Indemics can support policy analysts and epidemiologists interested in planning and control of pandemics. Indemics goes beyond traditional epidemic simulations by providing a simple and powerful way to represent and analyze policy-based as well as individual-based adaptive interventions. Users can also stop the simulation at any point, assess the state of the simulated system, and add additional interventions. Indemics is available to end-users via a web-based interface. Detailed performance analysis shows that Indemics greatly enhances the capability and productivity of simulating complex intervention strategies with a marginal decrease in performance. We also demonstrate how Indemics was applied in some real case studies where complex interventions were implemented

Reducing sensor complexity for monitoring wind turbine performance using principal component analysis

Availability and reliability are among the priority concerns for deployment of distributed generation (DG) systems, particularly when operating in a harsh environment. Condition monitoring (CM) can meet the requirement but has been challenged by large amounts of data needing to be processed in real time due to the large number of sensors being deployed. This paper proposes an optimal sensor selection method based on principal component analysis (PCA) for condition monitoring of a DG system oriented to wind turbines. The research was motivated by the fact that salient patterns in multivariable datasets can be extracted by PCA in order to identify monitoring parameters that contribute the most to the system variation. The proposed method is able to correlate the particular principal component to the corresponding monitoring variable, and hence facilitate the right sensor selection for the first time for the condition monitoring of wind turbines. The algorithms are examined with simulation data from PSCAD/EMTDC and SCADA data from an operational wind farm in the time, frequency, and instantaneous frequency domains. The results have shown that the proposed technique can reduce the number of monitoring variables whilst still maintaining sufficient information to detect the faults and hence assess the system’s conditions

How Does Risk Management Improve Farmers’ Green Production Level? Organic Fertilizer as an Example

With increases in the frequency of various natural and social risks, effectively coping with uncertainty is necessary for the sustainable development of individuals and the society, particularly smallholder farmers with vulnerable livelihoods. Using survey data from farmers in China, we constructed a risk management capability index system for farmers at the individual, collective, and government levels to empirically analyze the impact of risk management on green production behavior through the Heckman model for two-stage sample selection. The results showed that risk management is a key factor affecting green production behavior. Membership status (membership in an organization), government subsidies, and income levels significantly promote green production levels. Moreover, risk management not only directly affects the green production level but also promotes green production behavior by expanding the scale of operation, improving the sense of responsibility, and enhancing the behavioral responsibility. Additionally, the mediating effect of these factors on farmers in the low-risk perception group was more obvious. Therefore, the risk management level of farmers should be improved at the individual, collective, and government levels to promote sustainable agriculture