Bayesian framework and message passing for joint support and signal recovery of approximately sparse signals

In this paper, we develop a low-complexity message passing algorithm for joint support and signal recovery of approximately sparse signals. The problem of recovery of strictly sparse signals from noisy measurements can be viewed as a problem of recovery of approximately sparse signals from noiseless measurements, making the approach applicable to strictly sparse signal recovery from noisy measurements. The support recovery embedded in the approach makes it suitable for recovery of signals with same sparsity profiles, as in the problem of multiple measurement vectors (MMV). Simulation results show that the proposed algorithm, termed as JSSR-MP (joint support and signal recovery via message passing) algorithm, achieves performance comparable to that of sparse Bayesian learning (M-SBL) algorithm in the literature, at one order less complexity compared to the M-SBL algorithm.

Optimal maintenance policy of a machine subject to failure

Optimal maintenance policies for a machine with degradation in performance with age and subject to failure are derived using optimal control theory. The optimal policies are shown to be, normally, of bang-coast nature, except in the case when probability of machine failure is a function of maintenance. It is also shown, in the deterministic case that a higher depreciation rate tends to reverse this policy to coast-bang. When the probability of failure is a function of maintenance, considerable computational effort is needed to obtain an optimal policy and the resulting policy is not easily implementable. For this case also, an optimal policy in the class of bang-coast policies is derived, using a semi-Markov decision model. A simple procedure for modifying the probability of machine failure with maintenance is employed. The results obtained extend and unify the recent results for this problem along both theoretical and practical lines. Numerical examples are presented to illustrate the results obtained.

Analysis and design of machine foundations at resonance

The design of machine foundations are done on the basis of two principal criteria viz., vibration amplitude should be within the permissible limits and natural frequency of machine-foundation-soil system should be away from the operating frequency (i.e. avoidance of resonance condition). In this paper the nondimensional amplitude factor M-m or M-r m and the nondimensional frequency factor a(o m) at resonance are related using elastic half space theory and is used as a new approach for a simplified design procedure for the design of machine foundations for all the modes of vibration fiz. vertical, horizontal, rocking and torsional for rigid base pressure distribution and weighted average displacement condition. The analysis show that one need not know the value of Poisson's ratio for rotating mass system for all the modes of vibration.

Intelligent Approach for Fault Diagnosis in Power Transmission Systems Using Support Vector Machines

This paper presents an approach for identifying the faulted line section and fault location on transmission systems using support vector machines (SVMs) for diagnosis/post-fault analysis purpose. Power system disturbances are often caused by faults on transmission lines. When fault occurs on a transmission system, the protective relay detects the fault and initiates the tripping operation, which isolates the affected part from the rest of the power system. Based on the fault section identified, rapid and corrective restoration procedures can thus be taken to minimize the power interruption and limit the impact of outage on the system. The approach is particularly important for post-fault diagnosis of any mal-operation of relays following a disturbance in the neighboring line connected to the same substation. This may help in improving the fault monitoring/diagnosis process, thus assuring secure operation of the power systems. In this paper we compare SVMs with radial basis function neural networks (RBFNN) in data sets corresponding to different faults on a transmission system. Classification and regression accuracy is reported for both strategies. Studies on a practical 24-Bus equivalent EHV transmission system of the Indian Southern region is presented for indicating the improved generalization with the large margin classifiers in enhancing the efficacy of the chosen model.

Daily relative humidity projections in an Indian river basin for IPCC SRES scenarios

A two-stage methodology is developed to obtain future projections of daily relative humidity in a river basin for climate change scenarios. In the first stage, Support Vector Machine (SVM) models are developed to downscale nine sets of predictor variables (large-scale atmospheric variables) for Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES) (A1B, A2, B1, and COMMIT) to R (H) in a river basin at monthly scale. Uncertainty in the future projections of R (H) is studied for combinations of SRES scenarios, and predictors selected. Subsequently, in the second stage, the monthly sequences of R (H) are disaggregated to daily scale using k-nearest neighbor method. The effectiveness of the developed methodology is demonstrated through application to the catchment of Malaprabha reservoir in India. For downscaling, the probable predictor variables are extracted from the (1) National Centers for Environmental Prediction reanalysis data set for the period 1978-2000 and (2) simulations of the third-generation Canadian Coupled Global Climate Model for the period 1978-2100. The performance of the downscaling and disaggregation models is evaluated by split sample validation. Results show that among the SVM models, the model developed using predictors pertaining to only land location performed better. The R (H) is projected to increase in the future for A1B and A2 scenarios, while no trend is discerned for B1 and COMMIT.

Scheduling identical parallel machines with machine eligibility restrictions to minimize total weighted flowtime in automobile gear manufacturing

In this paper, we address a scheduling problem for minimizing total weighted flowtime, observed in automobile gear manufacturing. Specifically, the bottleneck operation of the pre-heat treatment stage of gear manufacturing process has been dealt with in scheduling. Many real-life scenarios like unequal release times, sequence dependent setup times, and machine eligibility restrictions have been considered. A mathematical model taking into account dynamic starting conditions has been proposed. The problem is derived to be NP-hard. To approach the problem, a few heuristic algorithms have been proposed. Based on planned computational experiments, the performance of the proposed heuristic algorithms is evaluated: (a) in comparison with optimal solution for small-size problem instances and (b) in comparison with the estimated optimal solution for large-size problem instances. Extensive computational analyses reveal that the proposed heuristic algorithms are capable of consistently yielding near-statistically estimated optimal solutions in a reasonable computational time.

Decentralized linear quadratic power system stabilizers for multi-machine power systems

Linear quadratic stabilizers are well-known for their superior control capabilities when compared to the conventional lead-lag power system stabilizers. However, they have not seen much of practical importance as the state variables are generally not measurable; especially the generator rotor angle measurement is not available in most of the power plants. Full state feedback controllers require feedback of other machine states in a multi-machine power system and necessitate block diagonal structure constraints for decentralized implementation. This paper investigates the design of Linear Quadratic Power System Stabilizers using a recently proposed modified Heffron-Phillip's model. This model is derived by taking the secondary bus voltage of the step-up transformer as reference instead of the infinite bus. The state variables of this model can be obtained by local measurements. This model allows a coordinated linear quadratic control design in multi machine systems. The performance of the proposed controller has been evaluated on two widely used multi-machine power systems, 4 generator 10 bus and 10 generator 39 bus systems. It has been observed that the performance of the proposed controller is superior to that of the conventional Power System Stabilizers (PSS) over a wide range of operating and system conditions.

Probabilistic Shared Cache Management (PriSM)

Effective sharing of the last level cache has a significant influence on the overall performance of a multicore system. We observe that existing solutions control cache occupancy at a coarser granularity, do not scale well to large core counts and in some cases lack the flexibility to support a variety of performance goals. In this paper, we propose Probabilistic Shared Cache Management (PriSM), a framework to manage the cache occupancy of different cores at cache block granularity by controlling their eviction probabilities. The proposed framework requires only simple hardware changes to implement, can scale to larger core count and is flexible enough to support a variety of performance goals. We demonstrate the flexibility of PriSM, by computing the eviction probabilities needed to achieve goals like hit-maximization, fairness and QOS. PriSM-HitMax improves performance by 18.7% over LRU and 11.8% over previously proposed schemes in a sixteen core machine. PriSM-Fairness improves fairness over existing solutions by 23.3% along with a performance improvement of 19.0%. PriSM-QOS successfully achieves the desired QOS targets.

Efficient Algorithms for Linear Summed Error Structural SVMs

Structural Support Vector Machines (SSVMs) have become a popular tool in machine learning for predicting structured objects like parse trees, Part-of-Speech (POS) label sequences and image segments. Various efficient algorithmic techniques have been proposed for training SSVMs for large datasets. The typical SSVM formulation contains a regularizer term and a composite loss term. The loss term is usually composed of the Linear Maximum Error (LME) associated with the training examples. Other alternatives for the loss term are yet to be explored for SSVMs. We formulate a new SSVM with Linear Summed Error (LSE) loss term and propose efficient algorithms to train the new SSVM formulation using primal cutting-plane method and sequential dual coordinate descent method. Numerical experiments on benchmark datasets demonstrate that the sequential dual coordinate descent method is faster than the cutting-plane method and reaches the steady-state generalization performance faster. It is thus a useful alternative for training SSVMs when linear summed error is used.

Prediction of Shallow Landslide prone regions in Undulating Terrains

Genetic Algorithm for Rule-set Prediction (GARP) and Support Vector Machine (SVM) with free and open source software (FOSS) - Open Modeller were used to model the probable landslide occurrence points. Environmental layers such as aspect, digital elevation, flow accumulation, flow direction, slope, land cover, compound topographic index and precipitation have been used in modeling. Simulated output of these techniques is validated with the actual landslide occurrence points, which showed 92% (GARP) and 96% (SVM) accuracy considering precipitation in the wettest month and 91% and 94% accuracy considering precipitation in the wettest quarter of the year.

A Durable Graphitic-Carbon Support for Pt and Pt3Co Cathode Catalysts in Polymer Electrolyte Fuel Cells

The high efficiency of fuel-cell-powered electric vehicles makes them a potentially viable option for future transportation. Polymer Electrolyte Fuel Cells (PEFCs) are most promising among various fuel cells for electric traction due to their quick start-up and low-temperature operation. In recent years, the performance of PEFCs has reached the acceptable level both for automotive and stationary applications and efforts are now being expended in increasing their durability, which remains a major concern in their commercialization. To make PEFCs meet automotive targets an understanding of the factors affecting the stability of carbon support and platinum catalyst is critical. Alloying platinum (Pt) with first-row transition metals such as cobalt (Co) is reported to facilitate both higher degree of crystallinity and enhanced activity in relation to pristine Pt. But a major challenge for the application of Pt-transition metal alloys in PEFCs is to improve the stability of these binary catalysts. Dissolution of the non-precious metal in the acidic environment could alleviate the activity of the catalysts and hence cell performance. The use of graphitic carbon as cathode-catalyst support enhances the long-term stability of Pt and its alloys in relation to non-graphitic carbon as the former exhibits higher resistance to carbon corrosion in relation to the latter in PEFC cathodes during accelerated-stress test (AST). Changes in electrochemical surface area (ESA), cell performance and charge-transfer resistance are monitored during AST through cyclic voltammetry, cell polarization and impedance measurements, respectively. Studies on catalytic electrodes with X-ray diffraction, Raman spectroscopy and transmission electron microscopy reflect that graphitic carbon-support resists carbon corrosion and helps mitigating aggregation of Pt and Pt3Co catalyst particles. (C) 2012 The Electrochemical Society. DOI: 10.1149/2.051301jes] All rights reserved.