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Wyświetlanie 1-15 z 26
Tytuł:
Fast and smooth trajectory planning for a class of linear systems based on parameter and constraint reduction
Autorzy:
Liu, Guangyu
Wu, Shangliang
Zhu, Ling
Wang, Jiajun
Lv, Qiang
Powiązania:
https://bibliotekanauki.pl/articles/2055148.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
constraint reduction
parameter reduction
fast calculation
trajectory planning
redukcja ograniczeń
redukcja parametrów
szybka kalkulacja
planowanie trajektorii
Opis:
Fast and smooth trajectory planning is crucial for modern control systems, e.g., missiles, aircraft, robots and AGVs. However, classical spline based trajectory planning tools introduce redundant constraints and parameters, leading to high costs of computation and complicating fast and smooth execution of trajectory planning tasks. A new tool is proposed that employs truncated power functions to annihilate some constraints and reduce the number of parameters in the optimal model. It enables solving a simplified optimal problem in a shorter time while keeping the trajectory sufficiently smooth. With an engineering background, our case studies show that the proposed method has advantages over other solutions. It is promising in regard to the demanding tasks of trajectory planning.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2022, 32, 1; 11--21
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An algorithm for reducing the dimension and size of a sample for data exploration procedures
Autorzy:
Kulczycki, P.
Łukasik, S.
Powiązania:
https://bibliotekanauki.pl/articles/330110.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
dimension reduction
sample size reduction
linear transformation
simulated annealing
data mining
redukcja wymiaru
transformacja liniowa
wyżarzanie symulowane
eksploracja danych
Opis:
The paper deals with the issue of reducing the dimension and size of a data set (random sample) for exploratory data analysis procedures. The concept of the algorithm investigated here is based on linear transformation to a space of a smaller dimension, while retaining as much as possible the same distances between particular elements. Elements of the transformation matrix are computed using the metaheuristics of parallel fast simulated annealing. Moreover, elimination of or a decrease in importance is performed on those data set elements which have undergone a significant change in location in relation to the others. The presented method can have universal application in a wide range of data exploration problems, offering flexible customization, possibility of use in a dynamic data environment, and comparable or better performance with regards to the principal component analysis. Its positive features were verified in detail for the domain’s fundamental tasks of clustering, classification and detection of atypical elements (outliers).
Źródło:
International Journal of Applied Mathematics and Computer Science; 2014, 24, 1; 133-149
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Approximation of Large-Scale Dynamical Systems: an Overview
Autorzy:
Antoulas, A. C.
Sorensen, D. C.
Powiązania:
https://bibliotekanauki.pl/articles/908058.pdf
Data publikacji:
2001
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
aproksymacja
system dynamiczny
model reduction
SVD
Hankel
balancing
Krylov
Opis:
In this paper we review the state of affairs in the area of approximation of large-scale systems. We distinguish three basic categories, namely the {SVD}-based, the {Krylov}-based and the {SVD-Krylov}-based approximation methods. The first two were developed independently of each other and have distinct sets of attributes and drawbacks. The third approach seeks to combine the best attributes of the first two.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2001, 11, 5; 1093-1121
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Dimension reduction for objects composed of vector sets
Autorzy:
Szemenyei, M.
Vajda, F.
Powiązania:
https://bibliotekanauki.pl/articles/330024.pdf
Data publikacji:
2017
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
dimension reduction
discriminant analysis
object recognition
registration
redukcja wymiaru
analiza dyskryminacyjna
rozpoznawanie obiektu
Opis:
Dimension reduction and feature selection are fundamental tools for machine learning and data mining. Most existing methods, however, assume that objects are represented by a single vectorial descriptor. In reality, some description methods assign unordered sets or graphs of vectors to a single object, where each vector is assumed to have the same number of dimensions, but is drawn from a different probability distribution. Moreover, some applications (such as pose estimation) may require the recognition of individual vectors (nodes) of an object. In such cases it is essential that the nodes within a single object remain distinguishable after dimension reduction. In this paper we propose new discriminant analysis methods that are able to satisfy two criteria at the same time: separating between classes and between the nodes of an object instance. We analyze and evaluate our methods on several different synthetic and real-world datasets.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2017, 27, 1; 169-180
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Reduction of Large Circuit Models Via Low Rank Approximate Gramians
Autorzy:
Li, J. R.
White, J.
Powiązania:
https://bibliotekanauki.pl/articles/908056.pdf
Data publikacji:
2001
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
algorytmy
równanie Lyapunova
model reduction
Lyapunov equations
Cholesky-Factor ADI
moment matching
passivity
Opis:
We describe a model reduction algorithm which is well-suited for the reduction of large linear interconnect models. It is an orthogonal projection method which takes as the projection space the sum of the approximate dominant controllable subspace and the approximate dominant observable subspace. These approximate dominant subspaces are obtained using the Cholesky Factor ADI (CF-ADI) algorithm. We describe an improvement upon the existing implementation of CF-ADI which can result in significant savings in computational cost. We show that the new model reduction method matches moments at the negative of the CF-ADI parameters, and that it can be easily adapted to allow for DC matching, as well as for passivity preservation for multi-port RLC circuit models which come from modified nodal analysis.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2001, 11, 5; 1151-1171
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Efficient Numerical Algorithms for Balanced Stochastic Truncation
Autorzy:
Benner, P.
Quintana-Orti, E. S.
Quintana-Orti, G.
Powiązania:
https://bibliotekanauki.pl/articles/908055.pdf
Data publikacji:
2001
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
algorytmy
metoda Newtona
model reduction
stochastic realization
balanced truncation
sign function method
Newton's method
Opis:
We propose an efficient numerical algorithm for relative error model reduction based on balanced stochastic truncation. The method uses full-rank factors of the Gramians to be balanced versus each other and exploits the fact that for large-scale systems these Gramians are often of low numerical rank. We use the easy-to-parallelize sign function method as the major computational tool in determining these full-rank factors and demonstrate the numerical performance of the suggested implementation of balanced stochastic truncation model reduction.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2001, 11, 5; 1123-1150
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Rough Set-Based Dimensionality Reduction for Supervised and Unsupervised Learning
Autorzy:
Shen, Q.
Chouchoulas, A.
Powiązania:
https://bibliotekanauki.pl/articles/908369.pdf
Data publikacji:
2001
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
baza wiedzy
gromadzenie wiedzy
knowledge-based systems
fuzzy rule induction
rough dimensionality reduction
knowledge acquisition
Opis:
The curse of dimensionality is a damning factor for numerous potentially powerful machine learning techniques. Widely approved and otherwise elegant methodologies used for a number of different tasks ranging from classification to function approximation exhibit relatively high computational complexity with respect to dimensionality. This limits severely the applicability of such techniques to real world problems. Rough set theory is a formal methodology that can be employed to reduce the dimensionality of datasets as a preprocessing step to training a learning system on the data. This paper investigates the utility of the Rough Set Attribute Reduction (RSAR) technique to both supervised and unsupervised learning in an effort to probe RSAR's generality. FuREAP, a Fuzzy-Rough Estimator of Algae Populations, which is an existing integration of RSAR and a fuzzy Rule Induction Algorithm (RIA), is used as an example of a supervised learning system with dimensionality reduction capabilities. A similar framework integrating the Multivariate Adaptive Regression Splines (MARS) approach and RSAR is taken to represent unsupervised learning systems. The paper describes the three techniques in question, discusses how RSAR can be employed with a supervised or an unsupervised system, and uses experimental results to draw conclusions on the relative success of the two integration efforts.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2001, 11, 3; 583-601
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
On a stable solution of the problem of disturbance reduction
Autorzy:
Maksimov, Vyacheslav I.
Powiązania:
https://bibliotekanauki.pl/articles/1838215.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
disturbance reduction
dynamical controlled system
guaranteed control theory
redukcja zakłóceń
system sterowany dynamicznie
teoria sterowania
Opis:
We study the problem of active reduction of the influence of a disturbance on the output of a linear control system. We consider a system of linear differential equations under the action of an unknown disturbance and a control to be formed. Our goal is to design an algorithm for reducing the disturbance by means of an appropriate control on the basis of inaccurate measurements of the system phase coordinates. This algorithm should form a feedback control that would guarantee that the trajectory of a given system tracks the trajectory of the reference system, i.e., the system described by the same differential equations but with zero control and disturbance. We present an algorithm for solving this problem. The algorithm, based on the constructions of guaranteed control theory, is stable with respect to informational noises and computational errors.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2021, 31, 2; 187-194
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
One-Dimensional Kohonens Lvq Nets for Multidimensional Patterns Recognition
Autorzy:
Skubalska-Rafajłowicz, E.
Powiązania:
https://bibliotekanauki.pl/articles/911149.pdf
Data publikacji:
2000
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
krzywa przestrzenna
rozpoznawanie obrazów
space-filling curve
pattern recognition
learning vector quantization
reduction of dimension
Opis:
A new neural network based pattern recognition algorithm is proposed. The method consists in preprocessing the multidimensional data, using a space-filling curve based transformation into the unit interval, and employing Kohonen's vector quantization algorithms (of SOM and LVQ types) in one dimension. The space-filling based transformation preserves the theoretical Bayes risk. Experiments show that such an approach can produce good or even better error rates than the classical LVQ performed in a multidimensional space.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2000, 10, 4; 767-778
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The limit of inconsistency reduction in pairwise comparisons
Autorzy:
Koczkodaj, W. W.
Szybowski, J.
Powiązania:
https://bibliotekanauki.pl/articles/330335.pdf
Data publikacji:
2016
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
pairwise comparison
inconsistency reduction
convergence limit
decision making
porównanie parami
redukcja niezgodności
granica konwergencji
podejmowanie decyzji
Opis:
This study provides a proof that the limit of a distance-based inconsistency reduction process is a matrix induced by the vector of geometric means of rows when a distance-based inconsistent pairwise comparisons matrix is transformed into a consistent PC matrix by stepwise inconsistency reduction in triads. The distance-based inconsistency indicator was defined by Koczkodaj (1993) for pairwise comparisons. Its convergence was analyzed in 1996 (regretfully, with an incomplete proof) and finally completed in 2010. However, there was no interpretation provided for the limit of convergence despite its considerable importance. This study also demonstrates that the vector of geometric means and the right principal eigenvector are linearly independent for the pairwise comparisons matrix size greater than three, although both vectors are identical (when normalized) for a consistent PC matrix of any size.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2016, 26, 3; 721-729
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
IoT sensing networks for gait velocity measurement
Autorzy:
Chou, Jyun-Jhe
Shih, Chi-Sheng
Wang, Wei-Dean
Huang, Kuo-Chin
Powiązania:
https://bibliotekanauki.pl/articles/330707.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
internet of things
IoT middleware
data fusion
data reduction
internet rzeczy
oprogramowanie pośredniczące
fuzja danych
redukcja danych
Opis:
Gait velocity has been considered the sixth vital sign. It can be used not only to estimate the survival rate of the elderly, but also to predict the tendency of falling. Unfortunately, gait velocity is usually measured on a specially designed walk path, which has to be done at clinics or health institutes. Wearable tracking services using an accelerometer or an inertial measurement unit can measure the velocity for a certain time interval, but not all the time, due to the lack of a sustainable energy source. To tackle the shortcomings of wearable sensors, this work develops a framework to measure gait velocity using distributed tracking services deployed indoors. Two major challenges are tackled in this paper. The first is to minimize the sensing errors caused by thermal noise and overlapping sensing regions. The second is to minimize the data volume to be stored or transmitted. Given numerous errors caused by remote sensing, the framework takes into account the temporal and spatial relationship among tracking services to calibrate the services systematically. Consequently, gait velocity can be measured without wearable sensors and with higher accuracy. The developed method is built on top of WuKong, which is an intelligent IoT middleware, to enable location and temporal-aware data collection. In this work, we present an iterative method to reduce the data volume collected by thermal sensors. The evaluation results show that the file size is up to 25% of that of the JPEG format when the RMSE is limited to 0.5º.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2019, 29, 2; 245-259
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A contemporary multi-objective feature selection model for depression detection using a hybrid pBGSK optimization algorithm
Autorzy:
Kavi Priya, Santhosam
Pon Karthika, Kasirajan
Powiązania:
https://bibliotekanauki.pl/articles/2201021.pdf
Data publikacji:
2023
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
depression detection
text classification
dimensionality reduction
hybrid feature selection
wykrywanie depresji
klasyfikacja tekstu
redukcja wymiarowości
wybór funkcji
Opis:
Depression is one of the primary causes of global mental illnesses and an underlying reason for suicide. The user generated text content available in social media forums offers an opportunity to build automatic and reliable depression detection models. The core objective of this work is to select an optimal set of features that may help in classifying depressive contents posted on social media. To this end, a novel multi-objective feature selection technique (EFS-pBGSK) and machine learning algorithms are employed to train the proposed model. The novel feature selection technique incorporates a binary gaining-sharing knowledge-based optimization algorithm with population reduction (pBGSK) to obtain the optimized features from the original feature space. The extensive feature selector (EFS) is used to filter out the excessive features based on their ranking. Two text depression datasets collected from Twitter and Reddit forums are used for the evaluation of the proposed feature selection model. The experimentation is carried out using naive Bayes (NB) and support vector machine (SVM) classifiers for five different feature subset sizes (10, 50, 100, 300 and 500). The experimental outcome indicates that the proposed model can achieve superior performance scores. The top results are obtained using the SVM classifier for the SDD dataset with 0.962 accuracy, 0.929 F1 score, 0.0809 log-loss and 0.0717 mean absolute error (MAE). As a result, the optimal combination of features selected by the proposed hybrid model significantly improves the performance of the depression detection system.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2023, 33, 1; 117--131
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of agent-based simulated annealing and tabu search procedures to solving the data reduction problem
Autorzy:
Czarnowski, I.
Jędrzejowicz, P.
Powiązania:
https://bibliotekanauki.pl/articles/907819.pdf
Data publikacji:
2011
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
redukcja danych
komputerowe uczenie się
optymalizacja
system wieloagentowy
data reduction
machine learning
A-Team
optimization
multi-agent system
Opis:
The problem considered concerns data reduction for machine learning. Data reduction aims at deciding which features and instances from the training set should be retained for further use during the learning process. Data reduction results in increased capabilities and generalization properties of the learning model and a shorter time of the learning process. It can also help in scaling up to large data sources. The paper proposes an agent-based data reduction approach with the learning process executed by a team of agents (A-Team). Several A-Team architectures with agents executing the simulated annealing and tabu search procedures are proposed and investigated. The paper includes a detailed description of the proposed approach and discusses the results of a validating experiment.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2011, 21, 1; 57-68
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Random projections and Hotelling’s T2 statistics for change detection in high-dimensional data streams
Autorzy:
Skubalska-Rafajłowicz, E.
Powiązania:
https://bibliotekanauki.pl/articles/331099.pdf
Data publikacji:
2013
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
change detection
multidimensional control charts
dimensionality reduction
random projections
process monitoring
wykrywanie zmian
karta kontrolna
redukcja wymiarowości
monitorowanie procesu
Opis:
The method of change (or anomaly) detection in high-dimensional discrete-time processes using a multivariate Hotelling chart is presented. We use normal random projections as a method of dimensionality reduction. We indicate diagnostic properties of the Hotelling control chart applied to data projected onto a random subspace of Rn. We examine the random projection method using artificial noisy image sequences as examples.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2013, 23, 2; 447-461
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analysis of correlation based dimension reduction methods
Autorzy:
Shin, Y. J.
Park, C. H.
Powiązania:
https://bibliotekanauki.pl/articles/907508.pdf
Data publikacji:
2011
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
analiza korelacyjna
redukcja wymiaru
liniowa analiza dyskryminacji
canonical correlation analysis
dimension reduction
discriminative canonical correlation analysis
linear discriminant analysis
Opis:
Dimension reduction is an important topic in data mining and machine learning. Especially dimension reduction combined with feature fusion is an effective preprocessing step when the data are described by multiple feature sets. Canonical Correlation Analysis (CCA) and Discriminative Canonical Correlation Analysis (DCCA) are feature fusion methods based on correlation. However, they are different in that DCCA is a supervised method utilizing class label information, while CCA is an unsupervised method. It has been shown that the classification performance of DCCA is superior to that of CCA due to the discriminative power using class label information. On the other hand, Linear Discriminant Analysis (LDA) is a supervised dimension reduction method and it is known as a special case of CCA. In this paper, we analyze the relationship between DCCA and LDA, showing that the projective directions by DCCA are equal to the ones obtained from LDA with respect to an orthogonal transformation. Using the relation with LDA, we propose a new method that can enhance the performance of DCCA. The experimental results show that the proposed method exhibits better classification performance than the original DCCA.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2011, 21, 3; 549-558
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 26

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