Cache filtering algorithm for least frequently used data with accurate memory simulation

10.32920/ryerson.14655531 ◽

2021 ◽

Author(s):

Kiu Kwan Leung

Keyword(s):

Simulation Model ◽

Improve Performance ◽

Filtering Algorithm ◽

Processor Performance ◽

Cache System

We propose a cache filtering algorithm to improve processor performance using a small buffer inside the processor and an algorithm to filter least frequently used accesses from Ll and L2 caches. The algorithm uses simple DRAM fast-page accessing mode to identity accesses that are not previously accessed or not frequently used and keep them out of the cache system and store them in small buffer. We have also added a realistic page interleaved DDR3 memory simulation model to the SimpleScalar simulator. This model supports any processor and memory clock speeds, different sets of memory latencies, various configurations of memory banks and channels. Results show that the filtering algorithm could improve· performance of some applications compared to the same system that does not use the filtering algorithm

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Amélioration de méthodes d'estimation des débits journaliers

Canadian Journal of Civil Engineering ◽

10.1139/l93-063 ◽

1993 ◽

Vol 20 (3) ◽

pp. 490-499

Author(s):

Saad Bennis ◽

Pierre Bruneau

Keyword(s):

Kalman Filter ◽

Test Area ◽

Estimation Methods ◽

Improve Performance ◽

Physical Test ◽

Noise Covariance Matrix ◽

Filtering Algorithm ◽

Daily Streamflow ◽

Streamflow Estimation ◽

Noise Covariance

The aim of the research described in this paper was to improve results obtained with conventional daily streamflow estimation methods. The technique requires a robust filter such as the Kalman filter. An explanation of the general filtering algorithm is first given, followed by illustration of how the robust-filter technique can be combined with daily streamflow estimation methods to improve performance. In particular, missing data estimates were more precise with the robust filter, and independent residuals with autocorrelation functions close to zero were obtained. The Saint-François River basin was used as a physical test area. Key words: Kalman filter, missing streamflow record, persistence, extrapolation, noise covariance matrix, residual autocorrelation. [Journal translation]

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Study on WSN Localization Algorithm and Simulation Model Based on Kalman Filtering Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.945-949.2380 ◽

2014 ◽

Vol 945-949 ◽

pp. 2380-2385

Author(s):

Lian Zhou Gao

Keyword(s):

Wireless Sensor Network ◽

Simulation Model ◽

Sensor Network ◽

Kalman Filtering ◽

Intelligent Transportation System ◽

Intelligent Transportation ◽

Wireless Sensor ◽

Localization Algorithm ◽

Filtering Algorithm ◽

Kalman Filtering Algorithm

This paper studies on the algorithm to improve the location of Wireless Sensor Network (WSN) in Intelligent Transportation System (ITS). Considering multi-path effect in the localization, an improved RSSI algorithm is introduced in the localization algorithm. The localization results are analyzed under different density of beacon nodes, and Kalman filtering algorithm is introduced to reduce the influence of signal noise. Finally, to test the algorithm based on Kalman filtering algorithm, a simulation model of ITS is developed, which is used to simulate the localization of real vehicles. The simulation shows the algorithm has effect to improve location accuracy and to application.

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Application of a Penalty Function to Improve Performance of an Automatic Calibration for a Watershed Runoff Event Simulation Model

Journal of Korea Water Resources Association ◽

10.3741/jkwra.2012.45.12.1213 ◽

2012 ◽

Vol 45 (12) ◽

pp. 1213-1226

Author(s):

Taeuk Kang ◽

Sangho Lee

Keyword(s):

Simulation Model ◽

Penalty Function ◽

Automatic Calibration ◽

Improve Performance ◽

Event Simulation ◽

Runoff Event

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Using PRRSV-Resilient Sows Improve Performance in Endemic Infected Farms with Recurrent Outbreaks

Animals ◽

10.3390/ani11030740 ◽

2021 ◽

Vol 11 (3) ◽

pp. 740

Author(s):

Gloria Abella ◽

Adela Pagès-Bernaus ◽

Joan Estany ◽

Ramona Natacha Pena ◽

Lorenzo Fraile ◽

...

Keyword(s):

Simulation Model ◽

Production Cost ◽

Discrete Event ◽

Reproductive Parameters ◽

Productive Performance ◽

Improve Performance ◽

Gross Margin ◽

Event Based ◽

Improve Disease Control ◽

Selection Of

The selection of porcine reproductive and respiratory syndrome (PRRS) resilient sows has been proposed as a strategy to control this disease. A discrete event-based simulation model was developed to mimic the outcome of farms with resilient or susceptible sows suffering recurrent PRRSV outbreaks. Records of both phenotypes were registered in a PRRSV-positive farm of 1500 sows during three years. The information was split in the whole period of observation to include a PRRSV outbreak that lasted 24 weeks (endemic/epidemic or En/Ep) or only the endemic phase (En). Twenty simulations were modeled for each farm: Resilient/En, Resilient/En_Ep, Susceptible/En, and Susceptible/En_Ep during twelve years and analyzed for the productive performance and economic outcome, using reference values. The reproductive parameters were generally better for resilient than for susceptible sows in the PRRSV En/Ep scenario, and the contrary was observed in the endemic case. The piglet production cost was always lower for resilient than for susceptible sows but showed only significant differences in the PRRSV En/Ep scenario. Finally, the annual gross margin by sow is significantly better for resilient than for susceptible sows for the PRRSV endemic (12%) and endemic/epidemic scenarios (17%). Thus, the selection of PRRSV resilient sows is a profitable approach for producers to improve disease control.

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Study on WSN Localization Algorithm and Simulation Model for Intelligent Transportation System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.548-549.1407 ◽

2014 ◽

Vol 548-549 ◽

pp. 1407-1414

Author(s):

Zheng Feng Li ◽

Lian Zhou Gao

Keyword(s):

Simulation Model ◽

White Noise ◽

Kalman Filtering ◽

Intelligent Transportation System ◽

Gaussian White Noise ◽

Intelligent Transportation ◽

Transportation System ◽

Localization Algorithm ◽

Filtering Algorithm ◽

Kalman Filtering Algorithm

This paper conducts research on the algorithm to improve the location of Wireless Sensor Network (WSN) in Intelligent Transportation System (ITS). The localization algorithm introduced an improved RSSI vehicle localization algorithm based on multi-path effect and Gaussian white noise. The localization results under different values of Gaussian white noise and different density of beacon nodes are analyzes, and Kalman filtering algorithm is introduced to reduce the influence of signal noise. Finally, a simulation model of ITS is developed to test the algorithm based on mixed noise and Kalman filtering algorithm, which is used to simulate the localization of real vehicles. The simulation shows the algorithm has effect to improve location accuracy and to application

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Study on WSN Localization Algorithm and Simulation Model for Intelligent Transportation System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.539.867 ◽

2014 ◽

Vol 539 ◽

pp. 867-873 ◽

Cited By ~ 1

Author(s):

Lian Zhou Gao

Keyword(s):

Simulation Model ◽

White Noise ◽

Kalman Filtering ◽

Intelligent Transportation System ◽

Gaussian White Noise ◽

Intelligent Transportation ◽

Transportation System ◽

Localization Algorithm ◽

Filtering Algorithm ◽

Kalman Filtering Algorithm

This paper conducts research on the algorithm to improve the location of Wireless Sensor Network (WSN) in Intelligent Transportation System (ITS). The localization algorithm introduced an improved RSSI vehicle localization algorithm based on multi-path effect and Gaussian white noise. The localization results under different values of Gaussian white noise and different density of beacon nodes are analyzes, and Kalman filtering algorithm is introduced to reduce the influence of signal noise. Finally, a simulation model of ITS is developed to test the algorithm based on mixed noise and Kalman filtering algorithm, which is used to simulate the localization of real vehicles. The simulation shows the algorithm has effect to improve location accuracy and to application

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Finite Element Modeling of Structural Relaxation During Annealing of a Precision-Molded Glass Lens

Journal of Manufacturing Science and Engineering ◽

10.1115/1.2163362 ◽

2005 ◽

Vol 128 (3) ◽

pp. 683-690 ◽

Cited By ~ 30

Author(s):

Anurag Jain ◽

Allen Y. Yi

Keyword(s):

Finite Element Method ◽

Finite Element ◽

Simulation Model ◽

Structural Relaxation ◽

Improve Performance ◽

Molding Process ◽

Relaxation Theory ◽

Annealing Stage ◽

Process Understanding ◽

Glass Lens

The precision lens molding process is numerically modeled by incorporating the characteristic structural relaxation phenomenon of glass during the annealing stage. A review of the structural relaxation theory as explained by the Narayanaswamy model (Narayanaswamy, 1971, J. Am. Ceram. Soc., 54(10), pp. 491–498) is provided and is then implemented into the simulation model. The commercial finite element method (FEM) program MSC MARC is utilized for the analysis. Glass material is modeled as a linear Newtonian fluid during the molding stage and as a viscoelastic material exhibiting stress and structural relaxation during the annealing stage. A comparison between the final lens shape and volume results obtained by implementing structural relaxation and thermal expansion coefficient is shown. The results demonstrate the need for including structural relaxation in the simulation model for reliable predictions. The results also show that the FEM can be used to predict the process, improve performance, and also provide a deeper process understanding.

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