Chronological categorization and decomposition of customer loads

The majority of distribution utilities do not have accurate information on the constituents of their loads. This information is very useful in managing and planning the network, adequately and economically. Customer loads are normally categorized in three main sectors: 1) residential; 2) industrial; and 3) commercial. In this paper, penalized least-squares regression and Euclidean distance methods are developed for this application to identify and quantify the makeup of a feeder load with unknown sectors/subsectors. This process is done on a monthly basis to account for seasonal and other load changes. The error between the actual and estimated load profiles are used as a benchmark of accuracy. This approach has shown to be accurate in identifying customer types in unknown load profiles, and is used in cross-validation of the results and initial assumptions.

The effect of poser race on the happy categorization advantage depends on stimulus type, set size, and presentation duration

The question as to whether poser race affects the happy categorization advantage, the faster categorization of happy than of negative emotional expressions, has been answered inconsistently. Hugenberg (2005) found the happy categorization advantage only for own race faces whereas faster categorization of angry expressions was evident for other race faces. Kubota and Ito (2007) found a happy categorization advantage for both own race and other race faces. These results have vastly different implications for understanding the influence of race cues on the processing of emotional expressions. The current study replicates the results of both prior studies and indicates that face type (computer-generated vs. photographic), presentation duration, and especially stimulus set size influence the happy categorization advantage as well as the moderating effect of poser race.

Recommendations for the categorization of germ cell mutagens

Germ cell mutagens are currently classified into three categories in the German List of MAK- and BAT-Values. These categories have been revised and extended in analogy to the new categories for carcinogenic chemicals. Germ cell mutagens produce heritable gene mutations, and heritable structural and numerical chromosome aberrations in germ cells. The original categories 1 and 2 for germ cell mutagens remained unchanged. Two new categories 3 A and 3 B are proposed for chemicals which are suspected to be germ cell mutagens. A new category 5 is proposed for germ cell mutagens with low potency which contribute negligibly to human genetic risk provided the MAK value is observed. The following categories are presented for further discussion. 1. Germ cell mutagens which have been shown to increase the mutant frequency among the progeny of exposed humans. 2. Germ cell mutagens which have been shown to increase the mutant frequency among the progeny of exposed animals. 3 A. Substances which have been shown to induce genetic damage in germ cells of humans or animals, or which are mutagenic in somatic cells and have been shown to reach the germ cells in their active forms. 3 B. Substances which are suspected of being germ cell mutagens because of their genotoxic effects in mammalian somatic cells in vivo or, in exceptional cases in the absence of in vivo data, if they are clearly mutagenic in vitro and structurally related to in vivo mutagens. 4. not applicable (Category 4 was introduced for carcinogenic substances with nongenotoxic modes of action. By definition, germ cell mutagens are genotoxic. Therefore, a Category 4 for germ cell mutagens cannot exist.) 5. Germ cell mutagens, the potency of which is considered to be so low that, provided the MAK value is observed, their contribution to genetic risk is expected not to be significant.

Bottleneck assignment problems under categorization

This paper considers two special cases of bottleneck grouped assignment problems when n jobs belong to m distinct categories (m < n). Solving these special problems through the available branch and bound algorithms will result in a heavy computational burden. Sequentially identifying nonopitmal variables, this paper provides more efficient methods for those cases. Propositions leading to the algorithms have been established. Numerical examples illustrate the respective algorithms.

Young at heart, old at work? Relations between age, (meta-)stereotypes, self-categorization, and retirement attitudes

The aims of this study were to examine how workers' negative age stereotypes (i.e., denying older workers' ability to develop) and negative meta-stereotypes (i.e., beliefs that the majority of colleagues feel negative about older workers) are related to their attitudes towards retirement (i.e., occupational future time perspective and intention to retire), and whether the strength of these relationships is influenced by workers' self-categorization as an “older” person. Results of a study among Dutch taxi drivers provided mixed support for the hypotheses. Negative meta-stereotypes, but not negative age stereotypes, were associated with fewer perceived opportunities until retirement and, in turn, a stronger intention to retire. Self-categorization moderated the relationships between negative age (meta-)stereotypes and occupational future time perspective. However, contrary to expectations, the relations were stronger among workers with a low self-categorization as an older person in comparison with workers with a high self-categorization in this regard. Overall, results highlight the importance of psychosocial processes in the study of retirement intentions and their antecedents.

Place categorization and semantic mapping on a mobile robot

In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot with-out environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot’s behaviour during navigation tasks. The system is made available to the community as a ROS module.

Similarity relations in visual search predict rapid visual categorization

How do we perform rapid visual categorization?It is widely thought that categorization involves evaluating the similarity of an object to other category items, but the underlying features and similarity relations remain unknown. Here, we hypothesized that categorization performance is based on perceived similarity relations between items within and outside the category. To this end, we measured the categorization performance of human subjects on three diverse visual categories (animals, vehicles, and tools) and across three hierarchical levels (superordinate, basic, and subordinate levels among animals). For the same subjects, we measured their perceived pair-wise similarities between objects using a visual search task. Regardless of category and hierarchical level, we found that the time taken to categorize an object could be predicted using its similarity to members within and outside its category. We were able to account for several classic categorization phenomena, such as (a) the longer times required to reject category membership; (b) the longer times to categorize atypical objects; and (c) differences in performance across tasks and across hierarchical levels. These categorization times were also accounted for by a model that extracts coarse structure from an image. The striking agreement observed between categorization and visual search suggests that these two disparate tasks depend on a shared coarse object representation.

Smoothing LDA Model for Text Categorization

Abstract. Latent Dirichlet Allocation (LDA) is a document level language model. In general, LDA employ the symmetry Dirichlet distribution as prior of the topic-words’ distributions to implement model smoothing. In this paper, we propose a data-driven smoothing strategy in which probability mass is allocated from smoothing-data to latent variables by the intrinsic inference procedure of LDA. In such a way, the arbitrariness of choosing latent variables'priors for the multi-level graphical model is overcome. Following this data-driven strategy,two concrete methods, Laplacian smoothing and Jelinek-Mercer smoothing, are employed to LDA model. Evaluations on different text categorization collections show data-driven smoothing can significantly improve the performance in balanced and unbalanced corpora.

Comparing Student Software Designs Using Semantic Categorization

Eckerdal, A. McCartney, R. Mostr?m, J.E. Ratcliffe, M. Zander, C. Comparing Student Software Designs Using Semantic Categorization. Proceedings of the Fifth Finnish/Baltic Sea Conference on Computer Science Education, 2005

Fuzzy-Rough Attribute Reduction with Application to Web Categorization.

R. Jensen and Q. Shen, 'Fuzzy-Rough Attribute Reduction with Application to Web Categorization,' Fuzzy Sets and Systems, vol. 141, no. 3, pp. 469-485, 2004.