A Pole-Assignment Controller for an Electrohydraulic Cylinder Drive

1985 ◽  
Vol 107 (2) ◽  
pp. 145-150 ◽  
Author(s):  
J. M. Finney ◽  
A. de Pennington ◽  
M. S. Bloor ◽  
G. S. Gill
Keyword(s):  
Sampling Rate ◽  
Pole Assignment ◽  
Controller Synthesis ◽  
Model Parameters ◽  
Practical Application ◽  
Assignment Algorithm ◽  
Self Tuning ◽  
Novel Method ◽  
Experimental Rig ◽  
Square Root Filter

This paper is concerned with the practical application of self-tuning control to an electrohydraulic cylinder drive. The experimental drive studied had a dominant natural frequency of 19 Hz and consequently to achieve the fast sampling rate a novel method of controller implementation is required. Estimates of the model parameters are obtained by a square root filter and controller synthesis is achieved by a pole-assignment algorithm. The results presented were generated by an experimental rig under the control of a DEC LSI 11/23 microprocessor.

scholarly journals Efficient detection of repeating sites to accelerate phylogenetic likelihood calculations

2016 ◽  
Author(s):  
Kassian Kobert ◽  
Alexandros Stamatakis ◽  
Tomáš Flouri
Keyword(s):  
Evolutionary Biology ◽  
Likelihood Function ◽  
Simulated Data ◽  
Evolutionary Model ◽  
Identical Result ◽  
Model Parameters ◽  
Data Sets ◽  
Efficient Detection ◽  
Novel Method ◽  
Computational Bottleneck

The phylogenetic likelihood function is the major computational bottleneck in several applications of evolutionary biology such as phylogenetic inference, species delimitation, model selection and divergence times estimation. Given the alignment, a tree and the evolutionary model parameters, the likelihood function computes the conditional likelihood vectors for every node of the tree. Vector entries for which all input data are identical result in redundant likelihood operations which, in turn, yield identical conditional values. Such operations can be omitted for improving run-time and, using appropriate data structures, reducing memory usage. We present a fast, novel method for identifying and omitting such redundant operations in phylogenetic likelihood calculations, and assess the performance improvement and memory saving attained by our method. Using empirical and simulated data sets, we show that a prototype implementation of our method yields up to 10-fold speedups and uses up to 78% less memory than one of the fastest and most highly tuned implementations of the phylogenetic likelihood function currently available. Our method is generic and can seamlessly be integrated into any phylogenetic likelihood implementation.

scholarly journals Cost-effective surveillance of invasive species using info-gap theory

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yang Liu ◽  
Penghao Wang ◽  
Melissa L. Thomas ◽  
Dan Zheng ◽  
Simon J. McKirdy
Keyword(s):  
Invasive Species ◽  
Native Species ◽  
Cost Effective ◽  
Expert Assessment ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Surveillance Systems ◽  
Effective Manner ◽  
Novel Method ◽  
Population Threshold

AbstractInvasive species can lead to community-level damage to the invaded ecosystem and extinction of native species. Most surveillance systems for the detection of invasive species are developed based on expert assessment, inherently coming with a level of uncertainty. In this research, info-gap decision theory (IGDT) is applied to model and manage such uncertainty. Surveillance of the Asian House Gecko, Hemidactylus frenatus Duméril and Bibron, 1836 on Barrow Island, is used as a case study. Our research provides a novel method for applying IGDT to determine the population threshold ($$K$$ K ) so that the decision can be robust to the deep uncertainty present in model parameters. We further robust-optimize surveillance costs rather than minimize surveillance costs. We demonstrate that increasing the population threshold for detection increases both robustness to the errors in the model parameter estimates, and opportuneness to lower surveillance costs than the accepted maximum budget. This paper provides guidance for decision makers to balance robustness and required surveillance expenditure. IGDT offers a novel method to model and manage the uncertainty prevalent in biodiversity conservation practices and modelling. The method outlined here can be used to design robust surveillance systems for invasive species in a wider context, and to better tackle uncertainty in protection of biodiversity and native species in a cost-effective manner.

scholarly journals Compressed sensing with continuous parametric reconstruction

2021 ◽  
Vol 11 (1) ◽  
pp. 851
Author(s):  
Imrich Andras ◽  
Linus Michaeli ◽  
Jan Saliga
Keyword(s):  
Signal Reconstruction ◽  
Sampling Rate ◽  
Quality Monitoring ◽  
Wireless Sensor ◽  
Model Parameters ◽  
Sampling Process ◽  
Continuous Approach ◽  
Continuous Models ◽  
Stable Performance ◽  
Unconventional Method

This work presents a novel unconventional method of signal reconstruction after compressive sensing. Instead of usual matrices, continuous models are used to describe both the sampling process and acquired signal. Reconstruction is performed by finding suitable values of model parameters in order to obtain the most probable fit. A continuous approach allows more precise modelling of physical sampling circuitry and signal reconstruction at arbitrary sampling rate. Application of this method is demonstrated using a wireless sensor network used for freshwater quality monitoring. Results show that the proposed method is more robust and offers stable performance when the samples are noisy or otherwise distorted.

scholarly journals Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT

2020 ◽  
Vol 34 (05) ◽  
pp. 8815-8821 ◽  
Author(s):  
Sheng Shen ◽  
Zhen Dong ◽  
Jiayu Ye ◽  
Linjian Ma ◽  
Zhewei Yao ◽  
...  
Keyword(s):  
Language Processing ◽  
Fine Tuning ◽  
Model Parameters ◽  
Quantization Scheme ◽  
Performance Loss ◽  
Extensive Analysis ◽  
Constrained Environments ◽  
Comparable Performance ◽  
Novel Method ◽  
Tuning Strategy

Transformer based architectures have become de-facto models used for a range of Natural Language Processing tasks. In particular, the BERT based models achieved significant accuracy gain for GLUE tasks, CoNLL-03 and SQuAD. However, BERT based models have a prohibitive memory footprint and latency. As a result, deploying BERT based models in resource constrained environments has become a challenging task. In this work, we perform an extensive analysis of fine-tuned BERT models using second order Hessian information, and we use our results to propose a novel method for quantizing BERT models to ultra low precision. In particular, we propose a new group-wise quantization scheme, and we use Hessian-based mix-precision method to compress the model further. We extensively test our proposed method on BERT downstream tasks of SST-2, MNLI, CoNLL-03, and SQuAD. We can achieve comparable performance to baseline with at most 2.3% performance degradation, even with ultra-low precision quantization down to 2 bits, corresponding up to 13× compression of the model parameters, and up to 4× compression of the embedding table as well as activations. Among all tasks, we observed the highest performance loss for BERT fine-tuned on SQuAD. By probing into the Hessian based analysis as well as visualization, we show that this is related to the fact that current training/fine-tuning strategy of BERT does not converge for SQuAD.

scholarly journals Sequential Diffusion Spectra as a Tool for Studying Time-Dependent Translational Molecular Dynamics: A Cement Hydration Study

Molecules ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 68
Author(s):  
Igor Serša
Keyword(s):  
Molecular Dynamics ◽  
Porous Structure ◽  
Time Scales ◽  
Cement Hydration ◽  
Pulse Sequence ◽  
Spin Echo ◽  
Model Parameters ◽  
Signal Attenuation ◽  
Wide Range ◽  
Novel Method

The translational molecular dynamics in porous materials are affected by the presence of the porous structure that presents an obstacle for diffusing molecules in longer time scales, but not as much in shorter time scales. The characteristic time scales have equivalent frequency ranges of molecular dynamics, where longer time scales correspond to lower frequencies while the shorter time scales correspond to higher frequencies of molecular dynamics. In this study, a novel method for direct measurement of diffusion at a given frequency of translational molecular dynamics is exploited to measure the diffusion spectra, i.e., distribution of diffusion in a wide range of frequencies. This method utilizes NMR modulated gradient spin-echo (MGSE) pulse sequence to measure the signal attenuation during the train of spin-echoes formed in the presence of a constant gradient. From attenuation, the diffusion coefficient at the frequency equal to the inverse double inter-echo time is calculated. The method was employed to study the white cement hydration process by the sequential acquisition of the diffusion spectra. The measured spectra were also analyzed by the diffusion spectra model to obtain the time-dependence of the best-fit model parameters. The presented method can also be applied to study other similar systems with the time evolution of porous structure.

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