New techniques for selecting test frequencies for linear analog circuits

International audienceIn this paper we show that the problem of minimizing the number of test frequencies necessary to detect all possible faults in a multi-frequency test approach for linear analog circuits can be modeled as a set covering problem. We will show in particular, that under some conditions on the considered faults, the coefficient matrix of the problem has the strong consecutive-ones property and hence the corresponding set covering problem can be solved in polynomial time. For an efficient solution of the problem, an interval graph formulation is also used and a polynomial algorithm using the interval graph structure is suggested. The optimization of test frequencies for a case-study biquadratic filter is presented for illustration purposes. Numerical simulations with a set of randomly generated problem instances demonstrate two different implementation approaches to solve the optimization problem very fast, with a good time complexity

A Method for Solving Linear Programming Problems with Unknown Parameters

A method is proposed for solving a problem of linear programming with unknown constraints. The form of the unknown constraints needs to be identified by a proper choice of the observation data. The present method is based upon a bicriterion formulation to the joint identification and optimization problem. A parametric approach is used to obtain an efficient solution to the bicriterion problem. Further, a decomposition into subproblems easily solvable is introduced. The interaction between subproblems is coordinated by an adjustment of a scalar parameter varying over the unit interval

Robust design of inspection schedules by means of probability boxes for structural systems prone to damage accumulation

The design of inspection schedules is a complex optimization problem that requires the reliability to be assessed. The solution to this problem can be found balancing the costs associated to inspection/repair activities against the benefits related to the faultless operation of the infrastructure. The optimization aims at minimizing the total cost, obtained as the combination of maintenance and failure costs, by tuning some design parameters, such as the number, time and quality of inspections. The reliability is assessed making use of probability boxes, i.e. by accounting for both variability and imprecision. The use of probability boxes relaxes the assumption of exact input probability distributions, which is always too strong given that these distributions are very often estimated within a degree of confidence, or elicited from a finite set of experimental data. The optimization problem is formulated as a time-dependent reliability-based optimization problem, where both objective and constraint functions require the evaluation of upper and lower reliability bounds. The solution to this problem represents a real technological challenge, as the reliability assessment by means of p-boxes is a computationally intensive task, which may take up to few days to be completed on last generation processors. In this paper, an efficient and generally applicable numerical technique, which is capable of producing a solution in a very short amount of time (≤1 hour), is proposed. The technique combines a forced Monte Carlo simulation method with an optimization strategy, which makes the interval reliability assessment particularly efficient. The efficiency and accuracy of the proposed technique is shown by means of a literature example involving a fatigue-prone weld in a bridge girder

Reconstructing cancer genomes from paired-end sequencing data

<p>Abstract</p> <p>Background</p> <p>A cancer genome is derived from the germline genome through a series of somatic mutations. Somatic structural variants - including duplications, deletions, inversions, translocations, and other rearrangements - result in a cancer genome that is a scrambling of intervals, or "blocks" of the germline genome sequence. We present an efficient algorithm for reconstructing the block organization of a cancer genome from paired-end DNA sequencing data.</p> <p>Results</p> <p>By aligning paired reads from a cancer genome - and a matched germline genome, if available - to the human reference genome, we derive: (i) a partition of the reference genome into intervals; (ii) adjacencies between these intervals in the cancer genome; (iii) an estimated copy number for each interval. We formulate the Copy Number and Adjacency Genome Reconstruction Problem of determining the cancer genome as a sequence of the derived intervals that is consistent with the measured adjacencies and copy numbers. We design an efficient algorithm, called Paired-end Reconstruction of Genome Organization (PREGO), to solve this problem by reducing it to an optimization problem on an interval-adjacency graph constructed from the data. The solution to the optimization problem results in an Eulerian graph, containing an alternating Eulerian tour that corresponds to a cancer genome that is consistent with the sequencing data. We apply our algorithm to five ovarian cancer genomes that were sequenced as part of The Cancer Genome Atlas. We identify numerous rearrangements, or structural variants, in these genomes, analyze reciprocal vs. non-reciprocal rearrangements, and identify rearrangements consistent with known mechanisms of duplication such as tandem duplications and breakage/fusion/bridge (B/F/B) cycles.</p> <p>Conclusions</p> <p>We demonstrate that PREGO efficiently identifies complex and biologically relevant rearrangements in cancer genome sequencing data. An implementation of the PREGO algorithm is available at <url>http://compbio.cs.brown.edu/software/</url>.</p

An efficient ant colony system based on receding horizon control for the aircraft arrival sequencing and scheduling problem

The aircraft arrival sequencing and scheduling (ASS) problem is a salient problem in air traffic control (ATC), which proves to be nondeterministic polynomial (NP) hard. This paper formulates the ASS problem in the form of a permutation problem and proposes a new solution framework that makes the first attempt at using an ant colony system (ACS) algorithm based on the receding horizon control (RHC) to solve it. The resultant RHC-improved ACS algorithm for the ASS problem (termed the RHC-ACS-ASS algorithm) is robust, effective, and efficient, not only due to that the ACS algorithm has a strong global search ability and has been proven to be suitable for these kinds of NP-hard problems but also due to that the RHC technique can divide the problem with receding time windows to reduce the computational burden and enhance the solution's quality. The RHC-ACS-ASS algorithm is extensively tested on the cases from the literatures and the cases randomly generated. Comprehensive investigations are also made for the evaluation of the influences of ACS and RHC parameters on the performance of the algorithm. Moreover, the proposed algorithm is further enhanced by using a two-opt exchange heuristic local search. Experimental results verify that the proposed RHC-ACS-ASS algorithm generally outperforms ordinary ACS without using the RHC technique and genetic algorithms (GAs) in solving the ASS problems and offers high robustness, effectiveness, and efficienc