Super-resolution spectral analysis and signal reconstruction of magnetic Barkhausen noise

An investigation was performed based on frequency domain analysis and reconstruction for the magnetic Barkhausen noise (MBN). The power spectrum density (PSD) of MBN was analyzed based on autoregressive (AR) model. The relations between PSD peaks and mechanical properties of high strength steel sheet were studied. The energy of low frequency signal components decreases as hardness increases, and increases as tensile stress increases. The energy of high frequency signal components increases as hardness increases, and decreases as tensile stress increases. After identification, the MBN signal was reconstructed by discrete Gabor expansion which remains desired signal components and removes undesired ones.     

Decision support system of unplanned dilution and ore-loss in underground stoping operations using a neuro-fuzzy system

Unplanned dilution and ore-loss are the most critical challenges in underground stoping operations. These problems are the main cause behind a mine closure and directly influencing the productivity of the underground stope mining and the profitability of the entire operation. Despite being aware of the significance of unplanned dilution and ore-loss, prediction of these phenomena is still unexplained as they occur through complex mechanisms and causative factors. Current management practices primarily rely on similar stope reconciliation data and the intuition of expert mining engineers. In this study, an innovative unplanned dilution and ore-loss (uneven break: UB) management system is established using a neuro-fuzzy system. The aim of the proposed decision support system is to overcome the UB phenomenon in underground stope blasting which provides quantitative prediction of unplanned dilution and ore-loss with practical recommendations simultaneously. To achieve the method proposed, an uneven break (UB) prediction system was developed by an artificial neural network (ANN) considering 1076 datasets covering 10 major UB causative factors collected from three underground stoping mines in Western Australia. In succession, the UB consultation system was established via a fuzzy expert system (FES) in reference to surveyed results of fifteen underground-mining experts. The UB prediction and consultation system were combined as one concurrent neuro-fuzzy system that is named the ‘uneven break optimiser’. Because the current UB prediction systems in investigated mines were highly unsatisfactory with correlation coefficient (R) of 0.088 and limited to only unplanned dilution, the performance of the proposed UB prediction system (R of 0.719) is a remarkable achievement. The uneven break optimiser can be directly employed to improve underground stoping production, and this tool will be beneficial not only for underground stope planning and design but also for production management.     

A semi-analytical approach for linear and non-linear analysis of unstiffened laminated composite cylinders and cones under axial,torsion and pressure loads

A semi-analytical model for the non-linear analysis of simply supported, unstiffened laminated composite cylinders and cones using the Ritz method and the Classical Laminated Plate Theory is proposed. A matrix notation is used to formulate the problem using Donnell׳s and Sanders׳ non-linear equations. The approximation functions proposed are capable to simulate the elephant׳s foot effect, a common phenomenon and a common failure mode for cylindrical and conical structures under axial compression. Axial, torsion and pressure loads can be applied individually or combined, and solutions for linear static, linear buckling and non-linear buckling analyses are presented and verified using a commercial finite element software. The presented non-linear buckling analyses used perturbation loads to create the initial geometric imperfections, showing the capability of the method for arbitrary imperfection patterns. The linear stiffness matrices are integrated analytically and for the conical structures an approximation is proposed to overcome the non-integrable expressions.     

Hierarchical multi-reservoir optimization modeling for real-world complexity with application to the Three Gorges system

High dimensionality in real-world multi-reservoir systems greatly hinders the application and popularity of evolutionary algorithms, especially for systems with heterogeneous units. An efficient hierarchical optimization framework is presented for search space reduction, determining the best water distributions, not only between cascade reservoirs, but also among different types of hydropower units. The framework is applied to the Three Gorges Project (TGP) system and the results demonstrate that the difficulties of multi-reservoir optimization caused by high dimensionality can be effectively solved by the proposed hierarchical method. For the day studied, power output could be increased by 6.79 GWh using an optimal decision with the same amount of water actually used; while the same amount of power could be generated with 2.59 × 10⁷ m³ less water compared to the historical policy. The methodology proposed is general in that it can be used for other reservoir systems and other types of heterogeneous unit generators.     

Optimal-correlation-based reconstruction for distributed compressed video sensing

Distributed compressed video sensing (DCVS) is a framework that integrates both compressed sensing and distributed video coding characteristics to achieve a low-complexity video coding. However, how to design an efficient joint reconstruction by leveraging more realistic signal models is still an open challenge. In this paper, we present a novel optimal-correlation-based reconstruction method for compressively sampled videos from multiple measurement vectors. In our method, the sparsity is mainly exploited through inter-signal correlations rather than the traditional frequency transform, wherein the optimization is not only over the signal space to satisfy data consistency but also over all possible linear correlation models to achieve minimum-l₁-norm correlation noise. Additionally, a two-phase Bregman iterative based algorithm is outlined for solving the optimization problem. Simulation results show that our proposal can achieve an improved reconstruction performance in comparison to the conventional approaches, and especially, offer a 0.7–9.9 dB gain in the average PSNR for DCVS.     

On optimizing system energy of voltage–frequency island based 3-D multi-core SoCs under thermal constraints

Three dimensional (3-D) multi-core SoC has been recognized as a promising solution for implementing complex applications with lower system energy. Recently, voltage–frequency island (VFI)-based design paradigm was widely adopted for energy optimization. However, the existing work commonly targeted 2-D platform, which cannot handle the exacerbated thermal issues and the increased solution space from 3-D integration. In this paper, we propose an optimization framework targeting VFI-based 3-D multi-core SoCs to minimize system energy meanwhile still meeting task deadline and thermal constraints. Our framework conducts at an earlier design phase in which designers have the freedom to determine the core stacks and map them into the hardware platform. Besides energy-aware task scheduling, we also conduct core stacking and task adjusting to balance the powers across the chip for thermal optimization. Moreover, by treating each core stack as a unity, the complicated problem of core mapping and VFI partitioning in 3-D platform can be simplified as a 2-D one. Experimental results demonstrate that on average our framework can achieve an energy reduction of 15.8% over the prior thermal balancing algorithm [17] (X. Zhou, J. Yang, Y. Xu, et al. Thermal-aware task scheduling for 3D multicore processors, IEEE Trans. Parallel Distrib. Syst. (TPDS), 21(1) (2010), 60–71.). Moreover, on average a reduction of 4.8 °C in peak temperature is achieved by our framework, compared with the state-of-the-art energy optimization scheme [8] (U.Y. Ogras, R. Marculescu, P. Choudhary, et al. Voltage–frequency island partitioning for GALS-based networks-on-chip, in: ACM/IEEE Design Automation Conference (DAC), 2007, pp. 110–115.).     

Finite-point method for efficient timing characterization of sequential elements

Timing characterization of sequential elements, such as latches and flip-flops, is one of the critical steps for timing closure in the pipelined design. Traditional characterization of setup and hold time constraints is computationally intensive, due to the demand on high accuracy in monitoring the operation failure. To improve the efficiency, this work proposes a finite-point based method for the characterization of setup and hold time constraints. The finite-point method identifies several critical data points in the non-linear curve of timing characteristics, and abstracts the essential setup/hold information from them. Moreover, compact models are derived for each point, further reducing the computation cost. The proposed method is general for all sequential elements in the standard cell library. It is comprehensively validated using benchmark circuits at 45 nm node. Experimental results demonstrate approximately 25× reduction in characterization time, with the prediction error in setup and hold time within 9% of FO₄ nominal delay, as compared to that of SPICE simulation results.     

NBTI alleviation on FinFET-made GPUs by utilizing device heterogeneity

Recent experimental studies reveal that FinFET devices commercialized in recent years tend to suffer from more severe NBTI degradation compared to planar transistors, necessitating effective techniques on processors built with FinFET for endurable operations. We propose to address this problem by exploiting the device heterogeneity and leveraging the slower NBTI aging rate manifested on the planar devices. We focus on modern graphics processing units in this study due to their wide usage in the current community. We validate the effectiveness of the technique by applying it to the warp scheduler and L2 cache, and demonstrate that NBTI degradation is considerably alleviated with slight performance overhead.     

Electrochemical generation of hydrogenated graphene flakes

A cathodic electrochemical method for the exfoliation of graphite to produce hydrogenated graphenic flakes is introduced. The resulting solutions consist of micrometer-sized and predominantly 1–4 layers thick hydrogenated graphenic flakes. In contrast to oxygenation, chemisorption of hydrogen avoids the formation of structural vacancy defects in the exfoliated flakes. Thermal desorption of hydrogen therefore results in graphenic flakes with a low defect density and consequently good electrical conductivity. Cathodic electrochemical exfoliation offers a remarkably simple and effective technique for the production of high quality graphene flakes and their hydrogenated relatives.