The results of 12 well-known and three fault-primitive-based memory test algorithms applied to 0.13 micron technology 512 kB single-port SRAMs are presented. Each test algorithm is used with up to 16 different stress ...
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The results of 12 well-known and three fault-primitive-based memory test algorithms applied to 0.13 micron technology 512 kB single-port SRAMs are presented. Each test algorithm is used with up to 16 different stress combinations (SCs) (i.e. different address sequences, data backgrounds and voltages) resulting in 122 tests. The results show that SCs influence the fault coverage (FC) of the test algorithms, that the highest FC is obtained at a low voltage level and that the highest detected number of unique faults is obtained at a high voltage level. They also show that the tests with the most promising FC, based on the theory, also tend to have the highest FC in practice. Moreover, the test results show that some algorithms detect faults that cannot be explained with the existing fault models, indicating that the existing fault models still leave much to be explained;for example, no theoretical basis exists to model the stresses and the predicted FC for a given test.
Asian options are popular financial derivative securities. Unfortunately, no exact pricing formulas exist for their price under continuous-time models. Asian options can also be priced on the lattice, which is a discr...
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Asian options are popular financial derivative securities. Unfortunately, no exact pricing formulas exist for their price under continuous-time models. Asian options can also be priced on the lattice, which is a discretized version of the continuous- time model. But only exponential-time algorithms exist if the options are priced on the lattice without approximations. Although efficient approximation methods are available, they lack accuracy guarantees in general. This paper proposes a novel lattice structure for pricing Asian options. The resulting pricing algorithm is exact (i.e., without approximations), converges to the value under the continuous-time model, and runs in subexponential time. This is the first exact, convergent lattice algorithm to break the long-standing exponential-time barrier.
This study proposes a method for designing advanced power distribution system (PDS) including distributed generations, using a combination of fundamental loop generator and multi-objective seeker-optimisation algorith...
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This study proposes a method for designing advanced power distribution system (PDS) including distributed generations, using a combination of fundamental loop generator and multi-objective seeker-optimisation algorithm (MOSOA). The proposed approach reduces the searching space using fundamental loop generator technique to obtain initial feasible solutions which is further improved by SOA to generate new set of solutions with improved aptitude. The proposed methodology uses a contingency-load-loss-index for reliability evaluation, which is independent of the estimation of failure rate and fault repair duration of feeder branches. This planning strategy includes distribution automation devices such as automatic reclosers (RAs) to enhance the reliability of PDS. The proposed algorithm generates a set of non-dominated solution by simultaneous optimisation of two conflicting objectives (economic cost and system reliability) using Pareto-optimality-based trade-off analysis including a fuzzy-operation to automatically select the most suitable solution over the Pareto-front. The performance of the proposed approach is assessed and illustrated on 54-bus and 100-bus PDS, considering realtime design practices. Extensive comparisons are made against some well-known and efficient MO algorithms such as fast non-dominated sorting genetic algorithm-II, MO particle-swarm-optimisation and MO immunised-particleswarm-optimisation. Simulation results show that the proposed approach is accurate and efficient, and a potential candidate for large-scale PDS planning.
When thermopile sensor is used for safety monitoring of equipment in industrial environments, particularly for measuring the thermal radiation information of device, the measured result of this kind of sensor is usual...
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When thermopile sensor is used for safety monitoring of equipment in industrial environments, particularly for measuring the thermal radiation information of device, the measured result of this kind of sensor is usually affected by ambient temperature due to its unique structure. An improved PSO-BP algorithm is proposed for temperature compensation of thermopile sensor and correcting the error in the condition of the system accuracy requirements reduced by temperature. The core of improved PSO-BP algorithm is to improve the certainty of initial weights and thresholds that belonged to BP neural network and then train the samples by using BP neural network for enhancing the generalization ability and stability of system. The experimental results show that the proposed PSO-BP network outperforms other similar algorithms with faster convergence speed, lower errors, and higher accuracy.
This paper presents an efficient MPC algorithm for uncertain time-varying systems with input constraints. The main advantage of this algorithm with respect to other published algorithms is to significantly enlarge the...
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This paper presents an efficient MPC algorithm for uncertain time-varying systems with input constraints. The main advantage of this algorithm with respect to other published algorithms is to significantly enlarge the size of the stabilization set without regard to computational burdens. Specially, we introduce an off-line region-dependent MPC scheme to avoid the size limitation of the control horizon caused by huge on-line computational burdens. A numerical example is included to illustrate the validity of the result.
Digital image is always polluted by noise and made data postprocessing difficult. To remove noise and preserve detail of image as much as possible, this paper proposed image filter algorithm which combined the merits ...
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Digital image is always polluted by noise and made data postprocessing difficult. To remove noise and preserve detail of image as much as possible, this paper proposed image filter algorithm which combined the merits of Shearlet transformation and particle swarmoptimization (PSO) algorithm. Firstly, we use classical Shearlet transformto decompose noised image into many subwavelets under multiscale and multiorientation. Secondly, we gave weighted factor to those subwavelets obtained. Then, using classical Shearlet inverse transform, we obtained a composite image which is composed of those weighted subwavelets. After that, we designed fast and rough evaluation method to evaluate noise level of the new image;by using this method as fitness, we adopted PSO to find the optimal weighted factor we added;after lots of iterations, by the optimal factors and Shearlet inverse transform, we got the best denoised image. Experimental results have shown that proposed algorithmeliminates noise effectively and yields good peak signal noise ratio (PSNR).
We present an image denoising method using the edge map of an image. The denoised image is considered as a linear combination of the observed image and its average value, where the coefficients are controlled by a loc...
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We present an image denoising method using the edge map of an image. The denoised image is considered as a linear combination of the observed image and its average value, where the coefficients are controlled by a local edge detector. The parameters are set on suitable values related to noise energy computed by the curvature norm of the original image. Implementation can be done in a single iteration and the speed of the process is reasonably high. Noise reduction quality of the introduced method is compared with Wiener and Total Variation based filters for some images. The method appears to be easy, fast and useful for very noisy images. The differences between our method and the patent 6229578 "Edge Detection Based Noise Removal Algorithm" are explained. (C) 2013 Elsevier Ltd. All rights reserved.
Improving the energy efficiency of buildings by examining their heating, ventilating, and air-conditioning (HVAC) systems represents an opportunity. To improve energy efficiency, to increase occupant comfort, and to p...
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Improving the energy efficiency of buildings by examining their heating, ventilating, and air-conditioning (HVAC) systems represents an opportunity. To improve energy efficiency, to increase occupant comfort, and to provide better system operation and control algorithms for these systems, online estimation of system parameters, including system thermophysical parameters and thermal loads, is desirable. Several reported studies have presented simulation results and assumed that the thermal loads are known. A difficulty in HVAC system parameter estimation is that most HVAC systems are nonlinear have multiple and time varying parameters, and require an estimate of the thermal loads for a building zone. In this study, building zones and variable-air-volume units are modeled. The system parameters including the thermal loads are estimated using the recursive-least-squares method with a variable forgetting factor. The sensitivity of the estimation results to different factors is examined. The estimated parameters are used to predict the zone and variable-air-volume-discharge-air temperatures. Several experiments are used to validate the prediction results. The comparisons show good agreement between the experiments and the prediction results.
Attainable region (AR) ideas have previously been used to identify candidate attainable regions (AR(C)s) for the oxidative dehydrogenation (ODH) of n-butane to butenes and butadiene and in so doing to identify the max...
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Attainable region (AR) ideas have previously been used to identify candidate attainable regions (AR(C)s) for the oxidative dehydrogenation (ODH) of n-butane to butenes and butadiene and in so doing to identify the maximum possible yields of different hydrocarbon product. Because of the large dimensionality of the problem, it was not possible then to do a complete AR analysis. Among the configurations considered, it was found that the reactor configuration for the respective AR(C)s ill all instances was an inert membrane reactor (IMR) functioning as a differential side-stream reactor in which one of the reactants, oxygen, was introduced along the length of the reactor so as to maintain a very low and constant Value of its partial pressure. Nevertheless, despite producing high yields of product, extremely large and impractical residence times ensued. In this paper, a new tool, the recursive convex control (RCC) policy, is used to identify the AR(C)s in the full dimensional space. These AR(C)s showed excellent agreement with those previously published, and the optimal reactor structures presented in those publications have been confirmed albeit with different oxygen control parameters. The maximum yields are now achieved with very much lower residence times. These results also confirm the benefit from using the AR approach on problems where a full AR analysis is not possible.
We study the order acceptance and scheduling (OAS) problem with time-dependent earliness-tardiness penalties in a single agile earth observation satellite environment where orders are defined by their release dates, a...
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We study the order acceptance and scheduling (OAS) problem with time-dependent earliness-tardiness penalties in a single agile earth observation satellite environment where orders are defined by their release dates, available processing time windows ranging from earliest start date to deadline, processing times, due dates, sequence-dependent setup times, and revenues. The objective is to maximise total revenue, where the revenue from an order is a piecewise linear function of its earliness and tardiness with reference to its due date. We formulate this problem as a mixed integer linear programming model and develop a novel hybrid differential evolution (DE) algorithm under self-adaptation framework to solve this problem. Compared with classical DE, hybrid DE employs two mutation operations, scaling factor adaptation and crossover probability adaptation. Computational tests indicate that the proposed algorithm outperforms classical DE in addition to two other variants of DE.
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