Document Clustering Based On Max-Correntropy Non-Negative Matrix Factorization

Nonnegative matrix factorization (NMF) has been successfully applied to many areas for classification and clustering. Commonly-used NMF algorithms mainly target on minimizing the $l_2$ distance or Kullback-Leibler (KL) divergence, which may not be suitable for nonlinear case. In this paper, we propose a new decomposition method by maximizing the correntropy between the original and the product of two low-rank matrices for document clustering. This method also allows us to learn the new basis vectors of the semantic feature space from the data. To our knowledge, we haven't seen any work has been done by maximizing correntropy in NMF to cluster high dimensional document data. Our experiment results show the supremacy of our proposed method over other variants of NMF algorithm on Reuters21578 and TDT2 databasets.Comment: International Conference of Machine Learning and Cybernetics (ICMLC) 201

Resilience Informed Performance Assessment of Infrastructure Systems

Over the recent decade increased research efforts have been directed on the modeling of resilience of infrastructure systems in their context, i.e. as socio-technical systems. The present paper presents a generic resilience model framework for the support of design and integrity management of such systems. The starting point is the general system representation framework by JCSS (2008) with special consideration of the modeling of uncertainties and dependencies. Furthermore, the evolution of the performance, together with the expected value of benefits and losses, as well as the capacity of infrastructure systems over time is described. On this basis the resilience modeling is formulated considering the performances of and the interactions between infrastructure systems, the organization responsible for integrity management and regulations. Finally, an example is presented considering the modeling and analysis of the resilience of one wind turbine park for the purpose of optimizing resilience management. Parameter studies are presented illustrating how the resilience performance may be optimized by means of adjusting the reliability of subsystems as well as through allocation of income for coverage of costs of future inspections, maintenance and renewal works. Moreover, it is illustrated how performance relevant indicators such as the down time and the stock keeping of essential spare parts can be assessed through the proposed resilience analysis framework

Extrapolation Method for System Reliability Assessment: A New Scheme

The present paper presents a new scheme for probability integral solution for system reliability analysis, which takes basis in the approaches by Naess et al. (2009) and Bucher (2009). The idea is to evaluate the probability integral by extrapolation, based on a sequence of MC approximations of integrals with scaled domains. The performance of this class of approximation depends on the approach applied for the scaling and the functional form utilized for the extrapolation. A scheme for this task is derived here taking basis in the theory of asymptotic solutions to multi-normal probability integrals. The scheme is extended so that it can be applied to cases where the asymptotic property may not be valid and/or the random variables are not normally distributed. The performance of the scheme is investigated by four principal series and parallel systems and some practical examples. The results indicate that the proposed scheme is efficient and adds to generality for this class of approximations for probability integrals. </jats:p

On Decision Support for Sustainability and Resilience of Infrastructure Systems

An overview of selected contributions across the different sciences to sustainability and resilience research is provided and discussed. A general frame-work for supporting decisions for sustainable and resilient design and management of societal infrastructures is then proposed taking basis in Bayesian decision analysis and probabilistic systems performance modelling. A principal example for decision support at regulatory level is presented for a coupled system comprised of infrastructure, social, hazard and environmental subsystems. The infrastructure systems is modelled as multi-component Daniels system generating benefits over time after deduction of potential losses due to disturbance events. The societal system is represented in terms of the preparedness level with respect to respond, reorganize and rehabilitate functionality after disturbances and the environmental system is represented in terms of local and global scale constraints concerning acceptable emissions

Objectives and Metrics in Decision Support for Urban Resilience

A holistic framework for the representation of systems resilience in the context of decision support on societal developments at urban, national and global scales is presented with emphasis on the identification of objectives and corresponding metrics of systems resilience performances in the context of technical, social and environmental systems. The proposed framework facilitates for inclusion of specific policies and stakeholder interests that might be relevant as boundary conditions for the ranking of decision alternatives. The application of the proposed framework and metrics is illustrated through a principal example considering an interconnected system comprised by the subsystems infrastructure, governance and environment. It is shown how decision alternatives for the management of urban systems can be related to societal welfare and capacity to cope with disturbances in the long run and thereby facilitating a systems resilience optimization

Combining remote sensing and ground census data to develop new maps of the distribution of rice agriculture in China

Large-scale assessments of the potential for food production and its impact on biogeochemical cycling require the best possible information on the distribution of cropland. This information can come from ground-based agricultural census data sets and/or spaceborne remote sensing products, both with strengths and weaknesses. Official cropland statistics for China contain much information on the distribution of crop types, but are known to significantly underestimate total cropland areas and are generally at coarse spatial resolution. Remote sensing products can provide moderate to fine spatial resolution estimates of cropland location and extent, but supply little information on crop type or management. We combined county-scale agricultural census statistics on total cropland area and sown area of 17 major crops in 1990 with a fine-resolution land-cover map derived from 1995–1996 optical remote sensing (Landsat) data to generate 0.5° resolution maps of the distribution of rice agriculture in mainland China. Agricultural census data were used to determine the fraction of crop area in each 0.5° grid cell that was in single rice and each of 10 different multicrop paddy rice rotations (e.g., winter wheat/rice), while the remote sensing land-cover product was used to determine the spatial distribution and extent of total cropland in China. We estimate that there were 0.30 million km2 of paddy rice cropland; 75% of this paddy land was multicropped, and 56% had two rice plantings per year. Total sown area for paddy rice was 0.47 million km2. Paddy rice agriculture occurred on 23% of all cultivated land in China

On the Probabilistic Characterization of Robustness and Resilience

Over the last decade significant research efforts have been devoted to the probabilistic modeling and analysis of system characteristics. Especially performance characteristics of systems subjected to random disturbances, such as robustness and resilience have been in the focus of these efforts and significant insights have been gained. However, as much of the undertaken research and developments aim to fulfill the particular needs of specific application areas and/or societal sectors somewhat diverging perspectives and approaches have emerged. In the present paper we take basis in recent developments in the modeling of robustness and resilience in the research areas of natural disaster risk management, socio-ecological systems and social systems and we propose a generic decision analysis framework for the modeling and analysis of systems across application areas. The proposed framework extends the concept of direct and indirect consequences and associated risks in probabilistic systems modeling formulated by the Joint Committee on Structural Safety (JCSS) to facilitate the modeling and analysis of resilience in addition to robustness and vulnerability. Moreover, based on recent insights in the modeling of robustness, a quantification of resilience is formulated utilizing a scenario based systems benefit modeling where resilience failure is associated with exhaustion of the capital accumulated by the system of time. The proposed framework and modeling concepts are illustrated with basis in a simple interlinked system model comprised by an infrastructure system, a governance system, a regulatory system and a geo-hazards system. It is shown how the robustness and the resilience of the interlinked system may be modeled and quantified, how robustness and resilience are influenced by the stochastic dependency structure of the disturbance events and corresponding resistances, how robustness and resilience depends on the capacity of the social system to plan for and respond to disturbances over time and how robustness and resilience interrelate