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"Kleidung"
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This is not fashion : streetwear past, present and future
\"This is the story of streetwear. Authors King ADZ and Wilma Stone recount how a long line of subcultural movements have taken over both the high street and high-end fashion, and explore just how a revolutionary sartorial trend has evolved to encompass a vast range of disparate tribes, offering a powerful sense of belonging and identity to all. The story begins in 1972, in Jersey City, USA, with the birth of the first ever streetwear shop, Trash and Vaudeville. The journey then encompasses punk, Ivy League preppies, the hip-hop kings and queens of Harlem, the dresser/casual movement born out of British football culture, the skater scene of California, the Paninari scooter-brats of Milan, and much more. Whether focusing on major brands such as Stèussy, Carhartt, Tommy Hilfiger and SHUT or today's up-and-comers from South African townships or downtown Seoul, this dynamic study surveys the scene. It also takes a look at how the internet era has changed the ways streetwear is sold and consumed, and how the field may evolve in the future. Packed with profiles of industry pioneers, Q & As with key figures and over 300 illustrations, this is the complete history of the fastest-growing and most influential movement in contemporary clothing.\"--Provided by publisher.
Book
Study on the Design of Intelligent Positioning Clothing for Preventing the Elderly from Getting Lost
In response to the increasing trend of population aging and the frequent loss of the elderly in China, the modern intelligent positioning technology is applied to clothing. Based on the modern electronic technology, Laut Air 530 positioning module is installed in clothing by means of special process to develop a type of Intelligent positioning clothing for preventing the elderly from getting lost, which works with an App. According to the research results, this type of Intelligent positioning clothing for preventing the elderly from getting lost is able to trace location of the elderly quickly and prevent the elderly from getting lost effectively.
Journal Article
Queen Elizabeth's wardrobe unlock'd : the inventories of the Wardrobe of Robes prepared in July 1600, edited from Stowe MS 557 in the British Library, MS LR 2/121 in the Public Record Office, London, and MS V.b.72 in the Folger Shakespeare Library, Washington DC
This work on the wardrobe of Queen Elizabeth I is illustrated with photographs of portraits, minatures, tomb sculptures, engravings, woven textiles and embroiders. Two indexes are provided: the first of paintings, persons, places and events; the second offering information on fashionable dress.
Book
Ensemble of a subset of kNN classifiers
Combining multiple classifiers, known as ensemble methods, can give substantial improvement in prediction performance of learning algorithms especially in the presence of non-informative features in the data sets. We propose an ensemble of subset of
k
NN classifiers, ES
k
NN, for classification task in two steps. Firstly, we choose classifiers based upon their individual performance using the out-of-sample accuracy. The selected classifiers are then combined sequentially starting from the best model and assessed for collective performance on a validation data set. We use bench mark data sets with their original and some added non-informative features for the evaluation of our method. The results are compared with usual
k
NN, bagged
k
NN, random
k
NN, multiple feature subset method, random forest and support vector machines. Our experimental comparisons on benchmark classification problems and simulated data sets reveal that the proposed ensemble gives better classification performance than the usual
k
NN and its ensembles, and performs comparable to random forest and support vector machines.
Journal Article
Forensic document examination system using boosting and bagging methodologies
Document forgery has increased enormously due to the progression of information technology and image processing software. Critical documents are protected using watermarks or signatures, i.e., active approach. Other documents need passive approach for document forensics. Most of the passive techniques aim to detect and fix the source of the printed document. Other techniques look for the irregularities present in the document. This paper aims to fix the document source printer using passive approach. Hand-crafted features based on key printer noise features (KPNF), speeded up robust features (SURF) and oriented FAST rotated and BRIEF (ORB) are used. Then, feature-based classifiers are implemented using K-NN, decision tree, random forest and majority voting. The document classifier proposed model can efficiently classify the questioned documents to their respective printer class. Further, adaptive boosting and bootstrap aggregating methodologies are used for the improvement in classification accuracy. The proposed model has achieved the best accuracy of 95.1% using a combination of KPNF + ORB + SURF with random forest classifier and adaptive boosting methodology.
Journal Article
A bootstrap-aggregated hybrid semi-parametric modeling framework for bioprocess development
Hybrid semi-parametric modeling, combining mechanistic and machine-learning methods, has proven to be a powerful method for process development. This paper proposes bootstrap aggregation to increase the predictive power of hybrid semi-parametric models when the process data are obtained by statistical design of experiments. A fed-batch
Escherichia coli
optimization problem is addressed, in which three factors (biomass growth setpoint, temperature, and biomass concentration at induction) were designed statistically to identify optimal cell growth and recombinant protein expression conditions. Synthetic data sets were generated applying three distinct design methods, namely, Box–Behnken, central composite, and Doehlert design. Bootstrap-aggregated hybrid models were developed for the three designs and compared against the respective non-aggregated versions. It is shown that bootstrap aggregation significantly decreases the prediction mean squared error of new batch experiments for all three designs. The number of (best) models to aggregate is a key calibration parameter that needs to be fine-tuned in each problem. The Doehlert design was slightly better than the other designs in the identification of the process optimum. Finally, the availability of several predictions allowed computing error bounds for the different parts of the model, which provides an additional insight into the variation of predictions within the model components.
Journal Article
A Review of Contemporary Techniques for Measuring Ergonomic Wear Comfort of Protective and Sport Clothing
Protective and sport clothing is governed by protection requirements, performance, and comfort of the user. The comfort and impact performance of protective and sport clothing are typically subjectively measured, and this is a multifactorial and dynamic process. The aim of this review paper is to review the contemporary methodologies and approaches for measuring ergonomic wear comfort, including objective and subjective techniques. Special emphasis is given to the discussion of different methods, such as objective techniques, subjective techniques, and a combination of techniques, as well as a new biomechanical approach called modeling of skin. Literature indicates that there are four main techniques to measure wear comfort: subjective evaluation, objective measurements, a combination of subjective and objective techniques, and computer modeling of human–textile interaction. In objective measurement methods, the repeatability of results is excellent, and quantified results are obtained, but in some cases, such quantified results are quite different from the real perception of human comfort. Studies indicate that subjective analysis of comfort is less reliable than objective analysis because human subjects vary among themselves. Therefore, it can be concluded that a combination of objective and subjective measuring techniques could be the valid approach to model the comfort of textile materials.
Journal Article
Usability Study of a Wireless Monitoring System among Alzheimer's Disease Elderly Population
Healthcare technologies are slowly entering into our daily lives, replacing old devices and techniques with newer intelligent ones. Although they are meant to help people, the reaction and willingness to use such new devices by the people can be unexpected, especially among the elderly. We conducted a usability study of a fall monitoring system in a long-term nursing home. The subjects were the elderly with advanced Alzheimer’s disease. The study presented here highlights some of the challenges faced in the use of wearable devices and the lessons learned. The results gave us useful insights, leading to ergonomics and aesthetics modifications to our wearable systems that significantly improved their usability and acceptance. New evaluating metrics were designed for the performance evaluation of usability and acceptability.
Journal Article
Optimization of public resources through an ensemble-learning model to measure quality perception in the social protection system in health of Mexico
In order to optimize the use of public resources, a model of ensemble learning was proposed to measure the perception of quality in the medical care granted to the people affiliated to the social protection in health system of Mexico. Which allows a more efficient allocation of resources based on the main areas of opportunity identified in the measurement of service quality. Identify the effect of the main factors that are directly related to the satisfaction level and perception of quality of health services. A satisfaction index was built using an ensemble model using principal component analysis, logistic model and bagging meta-estimator, to identify the effect of a group of factors in the perception of quality of health services and monitor the perceived quality of users in real time. The survey data collected for the “Social Protection System in Health-SPSS 2014” was used, considering a sample of 28,290 users. The proposed index shows, in general, the positive perception of quality of health services, the national average index was of 0.0756, 95% CI [− 9.714 to 2.027]. There are factors statistically significant (
P
< 0.05) that influence these results, among the most important that can be highlighted is the good perception of infrastructure OR 2.12; CI [95% 1.9–2.36]; the gratuity of the service provided OR 1.98; CI [95% 1.42–2.76]; and full medicines supply OR 1.81; CI [95% 1.91–2.36]. The key factors identified that determine the perception of quality allow to define focused strategies and lines of action to improve service quality as well as better allocation of resources.
Journal Article
Computing efficient features using rough set theory combined with ensemble classification techniques to improve the customer churn prediction in telecommunication sector
Rough set theory (RST) can be viewed as one of the classical set theory for handling with imprecision knowledge. The theory has discovered applications in numerous areas, for example, engineering, industries, environment and others. Churn in telecommunication sector, customer switching from one service provider to another. Predicting telecom customer churn is challenging due to the huge and inconsistent nature of the data. Churn prediction is crucial for telecommunication companies in order to build an efficient customer retention plan and apply successful marketing strategies. In this article, a methodology is proposed using RST to identify the efficient features for telecommunication customer churn prediction. Then the selected features are given to the ensemble-classification techniques such as Bagging, Boosting, Random Subspace. In this work the duke university-churn prediction data set is considered for performance evaluation and three sets of experiments are performed. Finally the performance of the proposed model is evaluated based on the following metrics such as true churn, false churn, specificity, precision and accuracy and it is identified that Proposed system designed with combining attribute selection with ensemble classification techniques works fine with classification accuracy of 95.13% compared to any single model.
Journal Article