Interestingness Measures for Multi-Level Association Rules

Association rule mining is one technique that is widely used when querying databases, especially those that are transactional, in order to obtain useful associations or correlations among sets of items. Much work has been done focusing on efficiency, effectiveness and redundancy. There has also been a focusing on the quality of rules from single level datasets with many interestingness measures proposed. However, with multi-level datasets now being common there is a lack of interestingness measures developed for multi-level and cross-level rules. Single level measures do not take into account the hierarchy found in a multi-level dataset. This leaves the Support-Confidence approach, which does not consider the hierarchy anyway and has other drawbacks, as one of the few measures available. In this chapter we propose two approaches which measure multi-level association rules to help evaluate their interestingness by considering the database’s underlying taxonomy. These measures of diversity and peculiarity can be used to help identify those rules from multi-level datasets that are potentially useful.

Binary image steganographic techniques classification based on multi-class steganalysis

In this paper, we propose a new multi-class steganalysis for binary image. The proposed method can identify the type of steganographic technique used by examining on the given binary image. In addition, our proposed method is also capable of differentiating an image with hidden message from the one without hidden message. In order to do that, we will extract some features from the binary image. The feature extraction method used is a combination of the method extended from our previous work and some new methods proposed in this paper. Based on the extracted feature sets, we construct our multi-class steganalysis from the SVM classifier. We also present the empirical works to demonstrate that the proposed method can effectively identify five different types of steganography.

Interpretation of association rules with multi-tier granule mining

This study was a step forward to improve the performance for discovering useful knowledge – especially, association rules in this study – in databases. The thesis proposed an approach to use granules instead of patterns to represent knowledge implicitly contained in relational databases; and multi-tier structure to interpret association rules in terms of granules. Association mappings were proposed for the construction of multi-tier structure. With these tools, association rules can be quickly assessed and meaningless association rules can be justified according to the association mappings. The experimental results indicated that the proposed approach is promising.

Interpretation of association rules in multi-tier structures

Dealing with the large amount of data resulting from association rule mining is a big challenge. The essential issue is how to provide efficient methods for summarizing and representing meaningful discovered knowledge from databases. This paper presents a new approach called multi-tier granule mining to improve the performance of association rule mining. Rather than using patterns, it uses granules to represent knowledge that is implicitly contained in relational databases. This approach also uses multi-tier structures and association mappings to interpret association rules in terms of granules. Consequently, association rules can be quickly assessed and meaningless association rules can be justified according to these association mappings. The experimental results indicate that the proposed approach is promising

HFL micro inverter with front-end diode clamped multi-level inverter and half-wave cycloconverter

A high-frequency-link (HFL) micro inverter with a front-end diode clamped multi-level inverter and a grid-connected half-wave cycloconverter is proposed. The diode clamped multi-level inverter with an auxiliary capacitor is used to generate high-frequency (HF) three level quasi square-wave output and it is fed into a series resonant tank to obtain high frequency continuous sinusoidal current. The obtained continuous sinusoidal current is modulated by using the grid-connected half-wave cycloconverter to obtain grid synchronized output current in phase with the grid voltage. The phase shift power modulation is used with auxiliary capacitor at the front-end multi-level inverter to have soft-switching. The phase shift between the HFL resonant current and half-wave cycloconverter input voltage is modulated to obtain grid synchronized output current.

Performance of soft-hard-soft (SHS) cement based composite subjected to blast loading with consideration of interface properties

This paper presents a combined experimental and numerical study on the damage and performance of a soft-hard-soft (SHS) multi-layer cement based composite subjected to blast loading which can be used for protective structures and infrastructures to resist extreme loadings, and the composite consists of three layers of construction materials including asphalt concrete (AC) on the top, high strength concrete (HSC) in the middle, and engineered cementitious composites (ECC) at the bottom. To better characterize the material properties under dynamic loading, interface properties of the composite were investigated through direct shear test and also used to validate the interface model. Strain rate effects of the asphalt concrete were also studied and both compressive and tensile dynamic increase factor (DIF) curves were improved based on split Hopkinson pressure bar (SHPB) test. A full-scale field blast test investigated the blast behavior of the composite materials. The numerical model was established by taking into account the strain rate effect of all concrete materials. Furthermore, the interface properties were also considered into the model. The numerical simulation using nonlinear finite element software LS-DYNA agrees closely with the experimental data. Both the numerical and field blast test indicated that the SHS composite exhibited high resistance against blast loading.

Acoustic features for multi-level classification of Australian frogs

Over past few decades, frog species have been experiencing dramatic decline around the world. The reason for this decline includes habitat loss, invasive species, climate change and so on. To better know the status of frog species, classifying frogs has become increasingly important. In this study, acoustic features are investigated for multi-level classification of Australian frogs: family, genus and species, including three families, eleven genera and eighty five species which are collected from Queensland, Australia. For each frog species, six instances are selected from which ten acoustic features are calculated. Then, the multicollinearity between ten features are studied for selecting non-correlated features for subsequent analysis. A decision tree (DT) classifier is used to visually and explicitly determine which acoustic features are relatively important for classifying family, which for genus, and which for species. Finally, a weighted support vector machines (SVMs) classifier is used for the multi- level classification with three most important acoustic features respectively. Our experiment results indicate that using different acoustic feature sets can successfully classify frogs at different levels and the average classification accuracy can be up to 85.6%, 86.1% and 56.2% for family, genus and species respectively.

The accuracy of varietal selection using factor analytic models for multi-environment plant breeding trials

Modeling of cultivar x trial effects for multienvironment trials (METs) within a mixed model framework is now common practice in many plant breeding programs. The factor analytic (FA) model is a parsimonious form used to approximate the fully unstructured form of the genetic variance-covariance matrix in the model for MET data. In this study, we demonstrate that the FA model is generally the model of best fit across a range of data sets taken from early generation trials in a breeding program. In addition, we demonstrate the superiority of the FA model in achieving the most common aim of METs, namely the selection of superior genotypes. Selection is achieved using best linear unbiased predictions (BLUPs) of cultivar effects at each environment, considered either individually or as a weighted average across environments. In practice, empirical BLUPs (E-BLUPs) of cultivar effects must be used instead of BLUPs since variance parameters in the model must be estimated rather than assumed known. While the optimal properties of minimum mean squared error of prediction (MSEP) and maximum correlation between true and predicted effects possessed by BLUPs do not hold for E-BLUPs, a simulation study shows that E-BLUPs perform well in terms of MSEP.

Distributed Scheduling for Multi-Hop Wireless Networks

We study the performance of greedy scheduling in multihop wireless networks where the objective is aggregate utility maximization. Following standard approaches, we consider the dual of the original optimization problem. Optimal scheduling requires selecting independent sets of maximum aggregate price, but this problem is known to be NP-hard. We propose and evaluate a simple greedy heuristic. We suggest how the greedy heuristic can be implemented in a distributed manner. We evaluate an analytical bound in detail, for the special case of a line graph and also provide a loose bound on the greedy heuristic for the case of an arbitrary graph.

Multi-GNSS precise point positioning with raw single-frequency and dual-frequency measurement models

The emergence of multiple satellite navigation systems, including BDS, Galileo, modernized GPS, and GLONASS, brings great opportunities and challenges for precise point positioning (PPP). We study the contributions of various GNSS combinations to PPP performance based on undifferenced or raw observations, in which the signal delays and ionospheric delays must be considered. A priori ionospheric knowledge, such as regional or global corrections, strengthens the estimation of ionospheric delay parameters. The undifferenced models are generally more suitable for single-, dual-, or multi-frequency data processing for single or combined GNSS constellations. Another advantage over ionospheric-free PPP models is that undifferenced models avoid noise amplification by linear combinations. Extensive performance evaluations are conducted with multi-GNSS data sets collected from 105 MGEX stations in July 2014. Dual-frequency PPP results from each single constellation show that the convergence time of undifferenced PPP solution is usually shorter than that of ionospheric-free PPP solutions, while the positioning accuracy of undifferenced PPP shows more improvement for the GLONASS system. In addition, the GLONASS undifferenced PPP results demonstrate performance advantages in high latitude areas, while this impact is less obvious in the GPS/GLONASS combined configuration. The results have also indicated that the BDS GEO satellites have negative impacts on the undifferenced PPP performance given the current “poor” orbit and clock knowledge of GEO satellites. More generally, the multi-GNSS undifferenced PPP results have shown improvements in the convergence time by more than 60 % in both the single- and dual-frequency PPP results, while the positioning accuracy after convergence indicates no significant improvements for the dual-frequency PPP solutions, but an improvement of about 25 % on average for the single-frequency PPP solutions.

Total cross-section at LHC from minijets and soft gluon resummation in the infrared region

A model for total cross-sections incorporating QCD jet cross-sections and soft gluon resummation is described and compared with present data on pp and pp cross-sections. Predictions for LHC are presented for different parameter sets. It is shown that they differ according to the small x-behaviour of available parton density functions.

Artificial Bee Colony (ABC) for multi-objective design optimization of composite structures

In this paper, we present a generic method/model for multi-objective design optimization of laminated composite components, based on Vector Evaluated Artificial Bee Colony (VEABC) algorithm. VEABC is a parallel vector evaluated type, swarm intelligence multi-objective variant of the Artificial Bee Colony algorithm (ABC). In the current work a modified version of VEABC algorithm for discrete variables has been developed and implemented successfully for the multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria: failure mechanism based failure criteria, maximum stress failure criteria and the tsai-wu failure criteria. The optimization method is validated for a number of different loading configurations-uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences, as well fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented. Finally the performance is evaluated in comparison with other nature inspired techniques which includes Particle Swarm Optimization (PSO), Artificial Immune System (AIS) and Genetic Algorithm (GA). The performance of ABC is at par with that of PSO, AIS and GA for all the loading configurations. (C) 2009 Elsevier B.V. All rights reserved.

Computer simulation of multi-elemental fusion reactor materials

Thermonuclear fusion is a sustainable energy solution, in which energy is produced using similar processes as in the sun. In this technology hydrogen isotopes are fused to gain energy and consequently to produce electricity. In a fusion reactor hydrogen isotopes are confined by magnetic fields as ionized gas, the plasma. Since the core plasma is millions of degrees hot, there are special needs for the plasma-facing materials. Moreover, in the plasma the fusion of hydrogen isotopes leads to the production of high energetic neutrons which sets demanding abilities for the structural materials of the reactor. This thesis investigates the irradiation response of materials to be used in future fusion reactors. Interactions of the plasma with the reactor wall leads to the removal of surface atoms, migration of them, and formation of co-deposited layers such as tungsten carbide. Sputtering of tungsten carbide and deuterium trapping in tungsten carbide was investigated in this thesis. As the second topic the primary interaction of the neutrons in the structural material steel was examined. As model materials for steel iron chromium and iron nickel were used. This study was performed theoretically by the means of computer simulations on the atomic level. In contrast to previous studies in the field, in which simulations were limited to pure elements, in this work more complex materials were used, i.e. they were multi-elemental including two or more atom species. The results of this thesis are in the microscale. One of the results is a catalogue of atom species, which were removed from tungsten carbide by the plasma. Another result is e.g. the atomic distributions of defects in iron chromium caused by the energetic neutrons. These microscopic results are used in data bases for multiscale modelling of fusion reactor materials, which has the aim to explain the macroscopic degradation in the materials. This thesis is therefore a relevant contribution to investigate the connection of microscopic and macroscopic radiation effects, which is one objective in fusion reactor materials research.

On optimal timer-based distributed selection for rate-adaptive multi-user diversity systems

We develop an optimal, distributed, and low feedback timer-based selection scheme to enable next generation rate-adaptive wireless systems to exploit multi-user diversity. In our scheme, each user sets a timer depending on its signal to noise ratio (SNR) and transmits a small packet to identify itself when its timer expires. When the SNR-to-timer mapping is monotone non-decreasing, timers of users with better SNRs expire earlier. Thus, the base station (BS) simply selects the first user whose timer expiry it can detect, and transmits data to it at as high a rate as reliably possible. However, timers that expire too close to one another cannot be detected by the BS due to collisions. We characterize in detail the structure of the SNR-to-timer mapping that optimally handles these collisions to maximize the average data rate. We prove that the optimal timer values take only a discrete set of values, and that the rate adaptation policy strongly influences the optimal scheme's structure. The optimal average rate is very close to that of ideal selection in which the BS always selects highest rate user, and is much higher than that of the popular, but ad hoc, timer schemes considered in the literature.

Fault-Tolerant Average Execution Time Optimization for General Purpose Multi-Processor System-on Chips

Fault-tolerance is due to the semiconductor technology development important, not only for safety-critical systems but also for general-purpose (non-safety critical) systems. However, instead of guaranteeing that deadlines always are met, it is for general-purpose systems important to minimize the average execution time (AET) while ensuring fault-tolerance. For a given job and a soft (transient) error probability, we define mathematical formulas for AET that includes bus communication overhead for both voting (active replication) and rollback-recovery with checkpointing (RRC). And, for a given multi-processor system-on-chip (MPSoC), we define integer linear programming (ILP) models that minimize AET including bus communication overhead when: (1) selecting the number of checkpoints when using RRC, (2) finding the number of processors and job-to-processor assignment when using voting, and (3) defining fault-tolerance scheme (voting or RRC) per job and defining its usage for each job. Experiments demonstrate significant savings in AET.