Analysis of maintenance policies for finite life-cycle multi-state systems

Maintenance policies for multi-state systems (MSS) are often analyzed under infinite horizon assumptions. In practice, it is important to consider maintenance policies under a finite horizon because the life cycles of most systems are finite. In this paper, we consider a finite life-cycle MSS that is subject to both degradation and Poisson failures. We study two classes of maintenance policies – preventive replacements and corrective replacements, and their effectiveness in controlling the customer’s expected discounted maintenance cost (EDMC). For both policies, replacement decisions are modelled via two control parameters – a threshold on the current system state and a threshold on the residual life cycle, which is measured as the time span from present to the end of life cycle. We derive close-to-explicit forms of the cost models under each of the policy. Methodologies for optimizing the maintenance thresholds are further proposed. Computational results verify that preventive replacements outperform corrective replacements typically when the downtime cost per failure is relatively high compared to the repair cost.     

Monte Carlo techniques for phrase-based translation

Recent advances in statistical machine translation have used approximate beam search for NP-complete inference within probabilistic translation models. We present an alternative approach of sampling from the posterior distribution defined by a translation model. We define a novel Gibbs sampler for sampling translations given a source sentence and show that it effectively explores this posterior distribution. In doing so we overcome the limitations of heuristic beam search and obtain theoretically sound solutions to inference problems such as finding the maximum probability translation and minimum risk training and decoding.     

Trends in youth’s videogame playing, overall computer use, and communication technology use: The impact of self-esteem and the Big Five personality factors

The objective of the current research is to model trends in video game playing, overall computer use, and communication technology use in a longitudinal sample of youths, aged 11-16 over a 3-year interval. In addition, individual difference characteristics that may be predictive of these trends were included, namely, socio-demographic characteristics (gender, ethnicity, and parental income) and personality characteristics (self-esteem, the Big Five personality factors). Findings suggested that youth increased their overall computer and communication technology use but decreased their videogame playing over time. Many individual differences predicted mean levels of these technologies with fewer predicting slopes. Conclusions, implications, and limitations are discussed.     

Indoor robot gardening: design and implementation

This paper describes the architecture and implementation of a distributed autonomous gardening system with applications in urban/indoor precision agriculture. The garden is a mesh network of robots and plants. The gardening robots are mobile manipulators with an eye-in-hand camera. They are capable of locating plants in the garden, watering them, and locating and grasping fruit. The plants are potted cherry tomatoes enhanced with sensors and computation to monitor their well-being (e.g. soil humidity, state of fruits) and with networking to communicate servicing requests to the robots. By embedding sensing, computation, and communication into the pots, task allocation in the system is de-centrally coordinated, which makes the system scalable and robust against the failure of a centralized agent. We describe the architecture of this system and present experimental results for navigation, object recognition, and manipulation as well as challenges that lie ahead toward autonomous precision agriculture with multi-robot teams.     

On Scene Segmentation and Histograms-Based Curve Evolution

We consider curve evolution based on comparing distributions of features, and its applications for scene segmentation. In the first part, we promote using cross-bin metrics such as the Earth Mover's Distance (EMD), instead of standard bin-wise metrics as the Bhattacharyya or Kullback-Leibler metrics. To derive flow equations for minimizing functionals involving the EMD, we employ a tractable expression for calculating EMD between one-dimensional distributions. We then apply the derived flows to various examples of single image segmentation, and to scene analysis using video data. In the latter, we consider the problem of segmenting a scene to spatial regions in which different activities occur. We use a nonparametric local representation of the regions by considering multiple one-dimensional histograms of normalized spatiotemporal derivatives. We then obtain semisupervised segmentation of regions using the flows derived in the first part of the paper. Our results are demonstrated on challenging surveillance scenes, and compare favorably with state-of-the-art results using parametric representations by dynamic systems or mixtures of them.     

Prioritizing agile benefits and limitations in relation to practice usage

In recent years, there has been significant shift from rigid development (RD) toward agile. However, it has also been spotted that agile methodologies are hardly ever followed in their pure form. Hybrid processes as combinations of RD and agile practices emerge. In addition, agile adoption has been reported to result in both benefits and limitations. This exploratory study (a) identifies development models based on RD and agile practice usage by practitioners; (b) identifies agile practice adoption scenarios based on eliciting practice usage over time; (c) prioritizes agile benefits and limitations in relation to (a) and (b). Practitioners provided answers through a questionnaire. The development models are determined using hierarchical cluster analysis. The use of practices over time is captured through an interactive board with practices and time indication sliders. This study uses the extended hierarchical voting analysis framework to investigate benefit and limitation prioritization. Four types of development models and six adoption scenarios have been identified. Overall, 45 practitioners participated in the prioritization study. A common benefit among all models and adoption patterns is knowledge and learning, while high requirements on professional skills were perceived as the main limitation. Furthermore, significant variances in terms of benefits and limitations have been observed between models and adoption patterns. The most significant internal benefit categories from adopting agile are knowledge and learning, employee satisfaction, social skill development, and feedback and confidence. Professional skill-specific demands, scalability, and lack of suitability for specific product domains are the main limitations of agile practice usage. Having a balanced agile process allows to achieve a high number of benefits. With respect to adoption, a big bang transition from RD to agile leads to poor quality in comparison with the alternatives.     

Hierarchical mapping of Northern Eurasian land cover using MODIS data

The Northern Eurasian land mass encompasses a diverse array of land cover types including tundra, boreal forest, wetlands, semi-arid steppe, and agricultural land use. Despite the well-established importance of Northern Eurasia in the global carbon and climate system, the distribution and properties of land cover in this region are not well characterized. To address this knowledge and data gap, a hierarchical mapping approach was developed that encompasses the study area for the Northern Eurasia Earth System Partnership Initiative (NEESPI). The Northern Eurasia Land Cover (NELC) database developed in this study follows the FAO-Land Cover Classification System and provides nested groupings of land cover characteristics, with separate layers for land use, wetlands, and tundra. The database implementation is substantially different from other large-scale land cover datasets that provide maps based on a single set of discrete classes. By providing a database consisting of nested maps and complementary layers, the NELC database provides a flexible framework that allows users to tailor maps to suit their needs. The methods used to create the database combine empirically derived climate–vegetation relationships with results from supervised classifications based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. The hierarchical approach provides an effective framework for integrating climate–vegetation relationships with remote sensing-based classifications, and also allows sources of error to be characterized and attributed to specific levels in the hierarchy. The cross-validated accuracy was 73% for the land cover map and 73% and 91% for the agriculture and wetland classifications, respectively. These results support the use of hierarchical classification and climate–vegetation relationships for mapping land cover at continental scales.     

Localized coarsening of conforming all-hexahedral meshes

Finite element mesh adaptation methods can be used to improve the efficiency and accuracy of solutions to computational modeling problems. In many applications involving hexahedral meshes, localized modifications which preserve a conforming all-hexahedral mesh are desired. Effective hexahedral refinement methods that satisfy these criteria have recently become available; however, due to hexahedral mesh topology constraints, little progress has been made in the area of hexahedral coarsening. This paper presents a new method to locally coarsen conforming all-hexahedral meshes. The method works on both structured and unstructured meshes and is not based on undoing previous refinement. Building upon recent developments in quadrilateral coarsening, the method utilizes hexahedral sheet and column operations, including pillowing, column collapsing, and sheet extraction. A general algorithm for automated coarsening is presented and examples of models that have been coarsened with this new algorithm are shown. While results are promising, further work is needed to improve the automated process.