A rule-based static dataflow clustering algorithm for efficient embedded software synthesis

2011 ◽  
Author(s):  
J Falk ◽  
C Zebelein ◽  
C Haubelt ◽  
J Teich
Keyword(s):  
Clustering Algorithm ◽  
Embedded Software ◽  
Software Synthesis ◽  
Rule Based

Random Forest Model for Trip End Identification Using Cellular Phone and Points of Interest Data

2021 ◽  
pp. 036119812110315
Author(s):  
Fei Yang ◽  
Yanchen Wang ◽  
Peter J. Jin ◽  
Dingbang Li ◽  
Zhenxing Yao
Keyword(s):  
Random Forest ◽  
Clustering Algorithm ◽  
Subjective Experience ◽  
Average Distance ◽  
Cellular Phone ◽  
Random Forest Model ◽  
Support Vector ◽  
Rule Based ◽  
Forest Model ◽  
Points Of Interest

Cellular phone data has been proven to be valuable in the analysis of residents’ travel patterns. Existing studies mostly identify the trip ends through rule-based or clustering algorithms. These methods largely depend on subjective experience and users’ communication behaviors. Moreover, limited by privacy policy, the accuracy of these methods is difficult to assess. In this paper, points of interest data is applied to supplement cellular phone data’s missing information generated by users’ behaviors. Specifically, a random forest model for trip end identification is proposed using multi-dimensional attributes. A field data acquisition test is designed and conducted with communication operators to implement synchronized cellular phone data and real trip information collection. The proposed identification approach is empirically evaluated with real trip information. Results show that the overall trip end detection precision and recall reach 95.2% and 88.7% with an average distance error of 269 m, and the time errors of the trip ends are less than 10 min. Compared with the rule-based approach, clustering algorithm, naive Bayes method, and support vector machine, the proposed method has better performance in accuracy and consistency.

scholarly journals A New Fuzzy Rule Based Contrast Enhancement Method using The Two-Steps Automatic Clustering Algorithm

2018 ◽  
Vol 2 (4) ◽  
pp. 239
Author(s):  
Ha Che-Ngoc ◽  
Anh-Thy Pham-Chau ◽  
Dibya Jyoti Bora
Keyword(s):  
Contrast Enhancement ◽  
Clustering Algorithm ◽  
Fuzzy Rule ◽  
Pixel Intensity ◽  
Rule Based ◽  
Creative Commons ◽  
Automatic Clustering ◽  
Image Contrast Enhancement ◽  
Enhancement Method ◽  
Human Eyes

The contrast is a major factor influencing the image quality; therefore, image contrast enhancement technique is more and more widely applied in the field of image processing. In this paper, a new fuzzy rule-based contrast enhancement method using the two-steps automatic clustering algorithm is proposed. Specifically, based on the Automatic clustering algorithm, a state-of-art method in cluster analysis and data mining, this paper proposes a two-steps Automatic clustering method to determine the number of fuzzy sets and locate the critical point in membership functions so that they are suitable for the distribution of pixel intensity values. The experiments on the "Lena" image and other natural images demonstrate that the new method can effectively enhance the contrast of the images and meet the demands of human eyes perception at the same time.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

scholarly journals Embedded software synthesis and prototyping

2004 ◽  
Vol 50 (1) ◽  
pp. 386-392 ◽  
Author(s):  
Trong-Yen Lee ◽  
Pao-Ann Hsiung
Keyword(s):  
Embedded Software ◽  
Software Synthesis

Permeability prediction in shaly formations: The fuzzy modeling approach

Geophysics ◽  
2002 ◽  
Vol 67 (3) ◽  
pp. 817-829 ◽  
Author(s):  
Jose Finol ◽  
Xu‐Dong D. Jing
Keyword(s):  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Learning Task ◽  
Well Log ◽  
Rule Based ◽  
Maracaibo Basin ◽  
Core Permeability ◽  
Rule Based Approach

This paper shows how fuzzy rule‐based systems help predict permeability in sedimentary rocks using well‐log responses. The fuzzy rule‐based approach represents a global nonlinear relationship between permeability and a set of input log responses as a smooth concatenation of a finite family of flexible local submodels. The fuzzy inference rules expressing the local input‐output relationships are obtained automatically from a set of observed measurements using a fuzzy clustering algorithm. This approach simplifies the process of constructing fuzzy systems without much computation effort. The benefits of the methodology are demonstrated with a case study in the Lake Maracaibo basin, Venezuela. Special core analyses from three early development wells provide the data for the learning task. Core permeability and well‐log data from a fourth well provide the basis for model validation. Numerical simulation results show that the fuzzy system is an improvement over conventional empirical methods in terms of predictive capability.

Input space-adaptive optimization for embedded-software synthesis

2005 ◽  
Vol 24 (11) ◽  
pp. 1677-1693
Author(s):  
Weidong Wang ◽  
A. Raghunathan ◽  
G. Lakshminarayana ◽  
N.K. Jha
Keyword(s):  
Embedded Software ◽  
Adaptive Optimization ◽  
Software Synthesis ◽  
Input Space

scholarly journals Ensemble Precipitation Estimation Using a Fuzzy Rule-Based Model

2021 ◽  
Vol 5 (1) ◽  
pp. 48
Author(s):  
O. Burak Akgun ◽  
Elcin Kentel
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Climate Models ◽  
Regional Climate ◽  
Fuzzy Rule ◽  
Regional Climate Models ◽  
Subtractive Clustering ◽  
Monthly Precipitation ◽  
Precipitation Time Series ◽  
Rule Based

In this study, a Takagi-Sugeno (TS) fuzzy rule-based (FRB) model is used for ensembling precipitation time series. The TS FRB model takes precipitation predictions of grid-based regional climate models (RCMs) from the EUR11 domain, available from the CORDEX database, as inputs to generate ensembled precipitation time series for two meteorological stations (MSs) in the Mediterranean region of Turkey. For each MS, RCM data that are available at the closest grid to the corresponding MSs are used. To generate the fuzzy rules of the TS FRB model, the subtractive clustering algorithm (SC) is utilized. Together with the TS FRB, the simple ensemble mean approach is also applied, and the performances of these two model results and individual RCM predictions are compared. The results show that ensembled models outperform individual RCMs, for monthly precipitation, for both MSs. On the other hand, although ensemble models capture the general trend in the observations, they underestimate the peak precipitation events.

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