A class of optimal fault tolerant Jacobian for minimal redundant manipulators based on symmetric geometries

Design of locally optimal fault tolerant manipulators has been recently addressed via using the constraints of the desired null space for the Jacobian matrix of the manipulators. In the present paper the Jacobian matrices for optimal fault tolerance are presented based on geometric properties of column vectors instead of the null space. They are equally fault tolerant to a single joint failure from the worst-case relative manipulability and worst-case dexterity points of view. The optimality is achieved through a symmetric distribution of points on spheres.

Organic ionic plastic crystal electrolytes; a new class of electrolyte for high efficiency solid state dye-sensitized solar cells

Dye-sensitized solar cells are an increasingly promising alternative to conventional silicon solar cells as a method of converting solar energy to electricity and thus providing an effectively inexhaustible energy source. However, the most efficient of these devices currently utilize liquid electrolytes, which suffer from the associated problems of leakage and evaporation. Hence, significant research is currently focused on the development of solid state alternatives. Here we report a new class of solid state electrolyte for these devices, organic ionic plastic crystal electrolytes, that allow relatively rapid diffusion of the redox couple through the matrix, which is critical to the cell performance. A range of different organic ionic plastic crystal materials, utilizing different cation and anion structures, have been investigated and the conductivities, diffusion rates and photovoltaic performance of the electrolytes are reported. The best material, utilizing the dicyanamide anion, achieves efficiencies of more than 5%.

Multi-class pattern classification in imbalanced data

The majority of multi-class pattern classification techniques are proposed for learning from balanced datasets. However, in several real-world domains, the datasets have imbalanced data distribution, where some classes of data may have few training examples compared for other classes. In this paper we present our research in learning from imbalanced multi-class data and propose a new approach, named Multi-IM, to deal with this problem. Multi-IM derives its fundamentals from the probabilistic relational technique (PRMs-IM), designed for learning from imbalanced relational data for the two-class problem. Multi-IM extends PRMs-IM to a generalized framework for multi-class imbalanced learning for both relational and non-relational domains.

Supervised subspace learning with multi-class Lagrangian SVM on the Grassmann manifold

Learning robust subspaces to maximize class discrimination is challenging, and most current works consider a weak connection between dimensionality reduction and classifier design. We propose an alternate framework wherein these two steps are combined in a joint formulation to exploit the direct connection between dimensionality reduction and classification. Specifically, we learn an optimal subspace on the Grassmann manifold jointly minimizing the classification error of an SVM classifier. We minimize the regularized empirical risk over both the hypothesis space of functions that underlies this new generalized multi-class Lagrangian SVM and the Grassmann manifold such that a linear projection is to be found. We propose an iterative algorithm to meet the dual goal of optimizing both the classifier and projection. Extensive numerical studies on challenging datasets show robust performance of the proposed scheme over other alternatives in contexts wherein limited training data is used, verifying the advantage of the joint formulation.

Microphone identification using one-class classification approach

Rapid growth of technical developments has created huge challenges for microphone forensics - a subcategory of audio forensic science, because of the availability of numerous digital recording devices and massive amount of recording data. Demand for fast and efficient methods to assure integrity and authenticity of information is becoming more and more important in criminal investigation nowadays. Machine learning has emerged as an important technique to support audio analysis processes of microphone forensic practitioners. However, its application to real life situations using supervised learning is still facing great challenges due to expensiveness in collecting data and updating system. In this paper, we introduce a new machine learning approach which is called One-class Classification (OCC) to be applied to microphone forensics; we demonstrate its capability on a corpus of audio samples collected from several microphones. Research results and analysis indicate that OCC has the potential to benefit microphone forensic practitioners in developing new tools and techniques for effective and efficient analysis.

Reliability and validity of the modified Chinese version of the children's leisure activities study survey (CLASS) questionnaire in assessing physical activity among Hong Kong children

This study aimed to examine the reliability and validity of the modified Children’s Leisure Activities Study Survey (CLASS) Chinese-version questionnaire in assessing physical activity among Hong Kong Chinese Children. Test-retest reliability was examined in 84 boys and 136 girls aged 9–12 years by comparing data from two administrations of the survey conducted one week apart. Validity was determined by comparing data from the second administration with accelerometer estimates. The results suggested that the questionnaire provided reliable and valid estimates in overall physical activity patterns in Hong Kong Chinese children. However, substantial overestimation was observed in vigorous activity.

Identifying microphone from noisy recordings by using representative instance one class-classification approach

Rapid growth of technical developments has created huge challenges for microphone forensics - a sub-category of audio forensic science, because of the availability of numerous digital recording devices and massive amount of recording data. Demand for fast and efficient methods to assure integrity and authenticity of information is becoming more and more important in criminal investigation nowadays. Machine learning has emerged as an important technique to support audio analysis processes of microphone forensic practitioners. However, its application to real life situations using supervised learning is still facing great challenges due to expensiveness in collecting data and updating system. In this paper, we introduce a new machine learning approach which is called One-class Classification (OCC) to be applied to microphone forensics; we demonstrate its capability on a corpus of audio samples collected from several microphones. In addition, we propose a representative instance classification framework (RICF) that can effectively improve performance of OCC algorithms for recording signal with noise. Experiment results and analysis indicate that OCC has the potential to benefit microphone forensic practitioners in developing new tools and techniques for effective and efficient analysis.

Sesamin modulation of lipid class and fatty acid profile in early juvenile teleost, Lates calcarifer, fed different dietary oils

Sesamin, a major sesame seed lignan, has diverse biological functions including the modulation of molecular actions in lipid metabolic pathways and reducing cholesterol levels. Vertebrates have different capacities to biosynthesize long-chain PUFA from dietary precursors and sesamin can enhance the biosynthesis of ALA to EPA and DHA in marine teleost. Early juvenile barramundi, Lates calcarifer, were fed for two weeks on diets rich in ALA or SDA derived from linseed or Echium plantagineum, respectively. Both diets contained phytosterols and less cholesterol compared with a standard fish oil-based diet. The growth rates were reduced in the animals receiving sesamin regardless of the dietary oil. However, the relative levels of n-3 LC-PUFA in total lipid, but not the phospholipid, increased in the whole body by up to 25% in animals fed on sesamin with ALA or SDA. Sesamin reduced the relative levels of triacylglycerols and increased polar lipid, and did not affect the relative composition of phospholipid subclasses or sterols. Sesamin is a potent modulator for LC-PUFA biosynthesis in animals, but probably will have more effective impact at advanced ages. By modulating certain lipid metabolic pathways, sesamin has probably disrupted the body growth and development of organs and tissues in early juvenile barramundi.