Extracting the optimal dimensionality for local tensor discriminant analysis

Supervised dimensionality reduction with tensor representation has attracted great interest in recent years. It has been successfully applied to problems with tensor data, such as image and video recognition tasks. However, in the tensor-based methods, how to select the suitable dimensions is a very important problem. Since the number of possible dimension combinations exponentially increases with respect to the order of tensor, manually selecting the suitable dimensions becomes an impossible task in the case of high-order tensor. In this paper, we aim at solving this important problem and propose an algorithm to extract the optimal dimensionality for local tensor discriminant analysis. Experimental results on a toy example and real-world data validate the effectiveness of the proposed method.     

Multiple costs based decision making with back-propagation neural networks

The current research investigates a single cost for cost-sensitive neural networks (CNN) for decision making. This may not be feasible for real cost-sensitive decisions which involve multiple costs. We propose to modify the existing model, the traditional back-propagation neural networks (TNN), by extending the back-propagation error equation for multiple cost decisions. In this multiple-cost extension, all costs are normalized to be in the same interval (i.e. between 0 and 1) as the error estimation generated in the TNN. A comparative analysis of accuracy dependent on three outcomes for constant costs was performed: (1) TNN and CNN with one constant cost (CNN-1C), (2) TNN and CNN with two constant costs (CNN-2C), and (3) CNN-1C and CNN-2C. A similar analysis for accuracy was also made for non-constant costs; (1) TNN and CNN with one non-constant cost (CNN-1NC), (2) TNN and CNN with two non-constant costs (CNN-2NC), and (3) CNN-1NC and CNN-2NC. Furthermore, we compared the misclassification cost for CNNs for both constant and non-constant costs (CNN-1C vs. CNN-2C and CNN-1NC vs. CNN-2NC). Our findings demonstrate that there is a competitive behavior between the accuracy and misclassification cost in the proposed CNN model. To obtain a higher accuracy and lower misclassification cost, our results suggest merging all constant cost matrices into one constant cost matrix for decision making. For multiple non-constant cost matrices, our results suggest maintaining separate matrices to enhance the accuracy and reduce the misclassification cost.     

Fault detection and isolation in nonlinear systems

This paper deals with fault detection and identification in dynamic systems when the system dynamics can be modeled by smooth nonlinear differential equations including affine, bilinear or linear parameter varying (LPV) systems. Two basic approaches will be considered, these apply differential algebraic and differential geometric tools.In the differential algebraic approach the state elimination methods will be used to derive nonlinear parity relations. In the specific case when a reconstruction of the fault signal is needed the dynamic inversion based approach will be investigated. This approach will also be studied from geometric point of view. The geometric approach, as proposed by Isidori and De Persis, is suitable to extend the detection filter and unknown input observer design approaches (well elaborated for LTI systems) to affine nonlinear systems.Beyond the development of the theory of fault detection and identification it is equally important to offer computable methods and to analyze the robustness properties against uncertainties. Both the observer based and the inversion based approaches will be elaborated for LPV systems that may offer computational tools inherited from linear systems and also allow to design for robustness utilizing results from robust filtering and disturbance attenuation.     

Resistance of headed studs subjected to fatigue loading: Part I: Experimental study

In steel-concrete composite structures the transfer of longitudinal shear forces at the interface between steel and concrete is mostly realized by headed shear studs. Especially in bridges due to traffic loads these shear connectors are subjected to high-cycle loading and the fatigue resistance governs the design. In this first part of two companion papers a series of experimental work with standard EC4 push-out specimens is presented. The main purpose of these tests was to determine the fatigue life and a possible reduction of the static strength of the headed shear studs subjected to unidirectional cyclic loading. A further aspect was to examine the effects of the loading sequence and damage accumulation on the fatigue life. The results of the experimental investigations show that due to a crack initiation at the stud foot at 10%-15% of the fatigue life, an early reduction of the static strength is caused. Furthermore tests to examine the effects of the loading sequence on the fatigue life revealed that the linear damage accumulation hypothesis according to Palmgren and Miner on which the present design codes are based do not describe the real behaviour.     

The effect of IEC grouping algorithms on frequency domain noise

In this paper, noise propagation through a two-stage nonlinear algorithm for measurement of harmonic distortion in power systems according to IEC 61000-4-7 is analyzed. Noise generated in the instrument’s input circuitry is considered as an aggregated noise source without detailed characterization of its origin. The first stage of the measurement algorithm is the discrete Fourier transform (DFT) of the input signal. Noise propagation through the DFT is analyzed by means of probability theory. The result of this analysis, the probability density function (pdf) of the DFT spectrum amplitudes, is used for characterization of the second stage of the measurement algorithm – the root-sum-square grouping of several spectrum components into a single harmonic distortion index. In this way, pdfs of the most common harmonic indices are derived and analyzed.     

The application of ionizing radiation in reversible addition-fragmentation chain transfer (RAFT) polymerization: Renaissance of a key synthetic and kinetic tool

Ionizing radiation, such as γ, ultraviolet, microwave and X-ray radiation, has long been used in polymer chemistry as a means of initiating polymerization, crosslinking gels and decomposing particular polymer components. More recently, ionizing radiation has found application in tandem with living radical polymerization to form novel polymeric materials with defined molecular weight and narrow molecular weight distribution. In particular, γ-rays and ultraviolet light both have shown promise as sources of initiation in reversible addition-fragmentation chain transfer (RAFT) polymerization. The ability to apply these sources of initiation at low temperatures is useful in applications where elevated temperature is likely to be detrimental to the system, for instance, in preparing protein-polymer conjugates. Similarly, the use of these initiating sources at low temperature is particularly suitable for some monomers, such as allyl compounds, which have not been synthesized using any other living radical approach. The current review examines the development of ionizing radiation as a tool in RAFT polymerization, with particular reference to the elucidation of the polymerization mechanism, the synthesis of high functionality polymers and probing the kinetic parameters of the RAFT process.