A Modified Q-Filter Model-Inverse Based ILC and Its Application on PMLSM

Community Partnership Program in small home industry of making fish floss brand "Savitri" and "Tiaras" inKupang city in the form of application of electric centrifugation filter model has been implemented. One of the problemsin the production of Savitri and Tiaras fish floss is that there is still a lot of oil in the frying product which is only drainedon a simple sieve and then placed for some time on traditional tools of filter and paper that can cause low quality and lowquality, so it is necessary to reduce the oil yield frying fish floss product. In order to increase the added value and sellingvalue of the products, it is necessary synergy between Universities and Partners in the form of application of the results ofscience and technology to the public. The main components of the composer of the versatile filter are the cylindrical plateas the bucket of the filter, the cylinder filter, the electric dynamo as the centrifuge system rotator and the load-bearingconstruction. The method of this program is the provision of materials, design and manufacture of tools, demonstrationtools and the provision of a versatile filter cylinder system centrifuge model by the team, simulation tools, specialcounseling at the location of partners and suggestions as a follow-up in the endurance of production. The output of thisprogram can provide added value in the form of the use of this tool can improve labor efficiency, increase productivitypartners by producing 40 kg to 50 kg raw material, raw fish marlin, fish meat after cleaning, processed, fried, in andpacked into 80-100 packs of fish floss with the price of Rp. 40,000.00 per package weighing 250 grams. With estimatedpartners will earn a profit of Rp 3,200,000 - 4,000,000.00 per production or 12,800,000.00 - 16,000,000.00 per month incash and consumptions, as well as the abundance of non-greasy, clean and hybrid fish products

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Bootstrapping Nonlinear Least Squares Estimates in the Kalman Filter Model.

10.21236/ada167099 ◽

1986 ◽

Author(s):

David S. Stoffer

Keyword(s):

Kalman Filter ◽

Least Squares ◽

Nonlinear Least Squares ◽

Filter Model ◽

Least Squares Estimates

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A Dual Source-Filter Model of Snore Audio for Snorer Group Classification

10.21437/interspeech.2017-1211 ◽

2017 ◽

Cited By ~ 1

Author(s):

Achuth Rao M.V. ◽

Shivani Yadav ◽

Prasanta Kumar Ghosh

Keyword(s):

Group Classification ◽

Filter Model ◽

Dual Source

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A New Efficient Filtering Model for GPS/SINS Ultratight Integration System

Mathematical Problems in Engineering ◽

10.1155/2020/9158185 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Chaochen Wang ◽

Yuming Bo ◽

Changhui Jiang

Keyword(s):

Navigation System ◽

The State ◽

Integrated System ◽

State Model ◽

Integration System ◽

Filter Model ◽

Integration Mode ◽

Measurement Vector ◽

Computation Load ◽

Vector Dimension

Global Positioning System (GPS) and strap-down inertial navigation system (SINS) are recognized as highly complementary and widely employed in the community. The GPS has the advantage of providing precise navigation solutions without divergence, but the GPS signals might be blocked and attenuated. The SINS is a totally self-contained navigation system which is hardly disturbed. The GPS/SINS integration system could utilize the advantages of both the GPS and SINS and provide more reliable navigation solutions. According to the data fusion strategies, the GPS/SINS integrated system could be divided into three different modes: loose, tight, and ultratight integration (LI, TI, and UTC). In the loose integration mode, position and velocity difference from the GPS and SINS are employed to compose measurement vector, in which the vector dimension has nothing to do with the amount of the available satellites. However, in the tight and ultratight modes, difference of pseudoranges and pseudorange rates from the GPS and SINS are employed to compose the measurement vector, in which the measurement vector dimension increases with the amount of available satellites. In addition, compared with the loose integration mode, clock bias and drift are included in the integration state model. The two characteristics magnify the computation load of the tight and ultratight modes. In this paper, a new efficient filter model was proposed and evaluated. Two schemes were included in this design for reducing the computation load. Firstly, a difference between pseudorange measurements was determined, by which clock bias and drift were excluded from the integration state model. This step reduced the dimension of the state vector. Secondly, the integration filter was divided into two subfilters: pseudorange subfilter and pseudorange rate subfilter. A federated filter was utilized to estimate the state errors optimally. In the second step, the two subfilters could run in parallel and the measurement vector was divided into two subvectors with lower dimension. A simulation implemented in MATLAB software was conducted to evaluate the performance of the new efficient integration method in UTC. The simulation results showed that the method could reduce the computation load with the navigation solutions almost unchanged.

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Source‐filter Model Analysis

Speech Acoustic Analysis ◽

10.1002/9781119808411.ch5 ◽

2021 ◽

pp. 77-84

Keyword(s):

Model Analysis ◽

Filter Model

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Ischemic Stroke Prediction by Exploring Sleep Related Features

Applied Sciences ◽

10.3390/app11052083 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2083

Author(s):

Jia Xie ◽

Zhu Wang ◽

Zhiwen Yu ◽

Bin Guo ◽

Xingshe Zhou

Keyword(s):

Ischemic Stroke ◽

Life Quality ◽

Area Under The Curve ◽

Prediction Method ◽

Neural System ◽

Human Beings ◽

Filter Model ◽

Fine Grained ◽

Proposed Model ◽

Precision Recall Curve

Ischemic stroke is one of the typical chronic diseases caused by the degeneration of the neural system, which usually leads to great damages to human beings and reduces life quality significantly. Thereby, it is crucial to extract useful predictors from physiological signals, and further diagnose or predict ischemic stroke when there are no apparent symptoms. Specifically, in this study, we put forward a novel prediction method by exploring sleep related features. First, to characterize the pattern of ischemic stroke accurately, we extract a set of effective features from several aspects, including clinical features, fine-grained sleep structure-related features and electroencephalogram-related features. Second, a two-step prediction model is designed, which combines commonly used classifiers and a data filter model together to optimize the prediction result. We evaluate the framework using a real polysomnogram dataset that contains 20 stroke patients and 159 healthy individuals. Experimental results demonstrate that the proposed model can predict stroke events effectively, and the Precision, Recall, Precision Recall Curve and Area Under the Curve are 63%, 85%, 0.773 and 0.919, respectively.

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