A simple fatigue life prediction algorithm using the modified NASGRO equation is proposed in this paper. The NASGRO equation is modified by introducing the concept of intrinsic effective threshold stress intensity fac...
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A simple fatigue life prediction algorithm using the modified NASGRO equation is proposed in this paper. The NASGRO equation is modified by introducing the concept of intrinsic effective threshold stress intensity factor (SIF) range (Delta K-eff)(th). One advantage of the proposed method is that the complex growth behavior analysis of small cracks can be avoided, and then the fatigue life can be calculated by directly integrating the crack growth model from the initial defect size to the critical crack size. The fatigue limit and the intrinsic effective threshold SIF range (Delta K-eff)(th) are used to calculate the initial defect size or initial flaw size. The value of (Delta K-eff)(th) is determined by extrapolating the crack propagation rate curves. Instead of using the fatigue limit determined by the fatigue strength at the specific fatigue life, the fatigue limit is selected based on the horizontal tendency of the S-N curve. The calculated fatigue lives are compared to the experimental data of two different alloys. The predicted S-N curves agree with the test data well. Besides, the prediction results are compared with that calculated using the FASTRAN code. Results indicate that the proposed life prediction algorithm is simple and efficient.
The purpose of this paper is the design of guidance and control algorithms for orbital space maneuvers. A 6-dof orbital simulator, based on Clohessy-Wiltshire-Hill equations, is developed in C language, considering co...
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The purpose of this paper is the design of guidance and control algorithms for orbital space maneuvers. A 6-dof orbital simulator, based on Clohessy-Wiltshire-Hill equations, is developed in C language, considering cold gas reaction thrusters and reaction wheels as actuation system. The computational limitations of on-board computers are also included. A combination of guidance and control algorithms for an orbital maneuver is proposed: (i) a suitably designed Zero-Effort-Miss/Zero-Effort-Velocity (ZEM/ZEV) algorithm is adopted for the guidance and (ii) a linear quadratic regulator (LQR) is used for the attitude control. The proposed approach is verified for different cases, including external environment disturbances and errors on the actuation system.
This study presents a simple but high performance vector quantisation (VQ) codebook search algorithm for line spectral frequency quantisation in ITU-T G.729 speech codec, designated as the binary search space-structur...
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This study presents a simple but high performance vector quantisation (VQ) codebook search algorithm for line spectral frequency quantisation in ITU-T G.729 speech codec, designated as the binary search space-structured VQ (BSS-VQ) algorithm. This is done through a combined use of a fast locating technique and lookup tables, such that an input vector, ahead of VQ encoding, is assigned efficiently to a subspace where merely a small number of codeword searches are required to be performed. As a consequence, the computational load is reduced remarkably. A trade-off can be made easy to meet a user's requirement when performing VQ encoding. It is experimentally validated that a search accuracy is well maintained at 99% approximately for a threshold of quantisation accuracy of 0.99. More importantly, with a full search algorithm as a benchmark for search load comparison, this proposal provides a 85% search load reduction, a figure far beyond 43% in multiple triangular inequality elimination approach, 47% in TIE with dynamic and intersection rules and 59% in quasi-binary search VQ algorithm.
Evaluating the reliability of Multistate Flow Network (MFN) is an NP-hard problem. Ordered binary decision diagram (OBDD) or variants thereof, such as multivalued decision diagram (MDD), are compact and efficient data...
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Evaluating the reliability of Multistate Flow Network (MFN) is an NP-hard problem. Ordered binary decision diagram (OBDD) or variants thereof, such as multivalued decision diagram (MDD), are compact and efficient data structures suitable for dealing with large-scale problems. Two symbolic algorithms for evaluating the reliability of MFN, MFN OBDD and MFN MDD, are proposed in this paper. In the algorithms, several operating functions are defined to prune the generated decision diagrams. Thereby the state space of capacity combinations is further compressed and the operational complexity of the decision diagrams is further reduced. Meanwhile, the related theoretical proofs and complexity analysis are carried out. Experimental results show the following: (1) compared to the existing decomposition algorithm, the proposed algorithms take less memory space and fewer loops. (2) The number of nodes and the number of variables of MDD generated in MFN MDD algorithm are much smaller than those of OBDD built in the MFN OBDD algorithm. (3) In two cases with the same number of arcs, the proposed algorithms are more suitable for calculating the reliability of sparse networks.
Purpose: To compare the treatment plans for accelerated partial breast irradiation calculated by the new commercially available collapsed cone convolution (CCC) and current standard TG-43-based algorithms for 50 patie...
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Purpose: To compare the treatment plans for accelerated partial breast irradiation calculated by the new commercially available collapsed cone convolution (CCC) and current standard TG-43-based algorithms for 50 patients treated at our institution with either a Strut-Adjusted Volume Implant (SAVI) or Contura device. Methods and Materials: We recalculated target coverage, volume of highly dosed normal tissue, and dose to organs at risk (ribs, skin, and lung) with each algorithm. For 1 case an artificial air pocket was added to simulate 10% nonconformance. We performed a Wilcoxon signed rank test to determine the median differences in the clinical indices V90, V95, V100, V150, V200, and highest-dosed 0.1 cm(3) and 1.0 cm(3) of rib, skin, and lung between the two algorithms. Results: The CCC algorithm calculated lower values on average for all dose-volume histogram parameters. Across the entire patient cohort, the median difference in the clinical indices calculated by the 2 algorithms was < 10% for dose to organs at risk, < 5% for target volume coverage (V90, V95, and V100), and < 4 cm(3) for dose to normal breast tissue (V150 and V200). No discernable difference was seen in the nonconformance case. Conclusions: We found that on average over our patient population CCC calculated (<10%) lower doses than TG-43. These results should inform clinicians as they prepare for the transition to heterogeneous dose calculation algorithms and determine whether clinical tolerance limits warrant modification. (C) 2016 Elsevier Inc. All rights reserved.
In nuclear power plant construction scheduling, a project is generally defined by its dependent preparation time, the time required for construction, and its reactor installation time. The issues of multiple construct...
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In nuclear power plant construction scheduling, a project is generally defined by its dependent preparation time, the time required for construction, and its reactor installation time. The issues of multiple construction teams and multiple reactor installation teams are considered. In this paper, a hierarchical particle swarm optimization algorithm is proposed to solve the nuclear power plant construction scheduling problem and minimize the occurrence of projects failing to achieve deliverables within applicable due times and deadlines.
High-performance heterogeneous computing systems are achieved by the use of efficient application scheduling algorithms. However, most of the current algorithms have low efficiency in scheduling. Aiming at solving thi...
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High-performance heterogeneous computing systems are achieved by the use of efficient application scheduling algorithms. However, most of the current algorithms have low efficiency in scheduling. Aiming at solving this problem, we propose a novel task scheduling algorithm for heterogeneous computing named HSIP (heterogeneous scheduling algorithm with improved task priority) whose functionality relies on three pillars: (1) an improved task priority strategy based on standard deviation with improved magnitude as computation weight and communication cost weight to make scheduling priority more reasonable;(2) an entry task duplication selection policy to make the makespan shorter;and (3) an improved idle time slots (ITS) insertion-based optimizing policy to make the task scheduling more efficient. We evaluate our proposed algorithm on randomly generated DAGs, using some real application DAGs by comparison with some classical scheduling algorithms. According to the experimental results, our proposed algorithm appears to perform better than other algorithms in terms of schedule length ratio, efficiency, and frequency of best results.
We present a fully-distributed self-healing algorithm dex that maintains a constant degree expander network in a dynamic setting. To the best of our knowledge, our algorithm provides the first efficient distributed co...
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We present a fully-distributed self-healing algorithm dex that maintains a constant degree expander network in a dynamic setting. To the best of our knowledge, our algorithm provides the first efficient distributed construction of expanders-whose expansion properties hold deterministically-that works even under an all-powerful adaptive adversary that controls the dynamic changes to the network ( the adversary has unlimited computational power and knowledge of the entire network state, can decide which nodes join and leave and at what time, and knows the past random choices made by the algorithm). Previous distributed expander constructions typically provide only probabilistic guarantees on the network expansion which rapidly degrade in a dynamic setting;in particular, the expansion properties can degrade even more rapidly under adversarial insertions and deletions. Our algorithm provides efficient maintenance and incurs a low overhead per insertion/deletion by an adaptive adversary: only O(log n) rounds and O( log n) messages are needed with high probability ( n is the number of nodes currently in the network). The algorithm requires only a constant number of topology changes. Moreover, our algorithm allows for an efficient implementation and maintenance of a distributed hash table on top of dex with only a constant additional overhead. Our results are a step towards implementing efficient self-healing networks that have guaranteed properties ( constant bounded degree and expansion) despite dynamic changes.
Twelve years have elapsed since the first Quantified Boolean Formulas (QBFs) evaluation was held as an event linked to SAT conferences. During this period, researchers have striven to propose new algorithms and tools ...
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Twelve years have elapsed since the first Quantified Boolean Formulas (QBFs) evaluation was held as an event linked to SAT conferences. During this period, researchers have striven to propose new algorithms and tools to solve challenging formulas, with evaluations periodically trying to assess the current state of the art. In this paper, we present an experimental account of solvers and formulas with the aim to understand the progress in the QBF arena across these years. Unlike typical evaluations, the analysis is not confined to the snapshot of submitted solvers and formulas, but rather we consider several tools that were proposed over the last decade, and we run them on different formulas from previous QBF evaluations. The main contributions of our analysis, which are also the messages we would like to pass along to the research community, are: (i) many formulas that turned out to be difficult to solve in past evaluations, remain still challenging after twelve years, (ii) there is no single solver which can significantly outperform all the others, unless specific categories of formulas are considered, and (iii) effectiveness of preprocessing depends both on the coupled solver and the structure of the formula.
Since the normal matrix is often ill-conditioned when solving Rational Function Model (RFM) for the satellite remote sensing imagery, combining with matrix orthogonal decomposition, Levenberg-Marquardt algorithm and C...
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Since the normal matrix is often ill-conditioned when solving Rational Function Model (RFM) for the satellite remote sensing imagery, combining with matrix orthogonal decomposition, Levenberg-Marquardt algorithm and Compute Unified Device Architecture high-performance computing technique, a fast and robust method directly based on the error equation coefficient matrix is proposed. The method is analysed by different parameters of control point grid and compared with the common methods, namely L-curve based ridge estimation and Iteration by Correcting Characteristic Value. The experimental results show that RFM parameters derived by ourmethod have higher fitting accuracy.
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