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Wyszukujesz frazę "support vector machine" wg kryterium: Temat


Tytuł:
Zastosowanie wybranych modeli nieliniowych do prognozy ilości osadu nadmiernego
Application of Selected Nonlinear Methods to Forecast the Amount of Excess Sludge
Autorzy:
Gawdzik, J.
Szeląg, B.
Bezak-Mazur, E.
Stoińska, R.
Powiązania:
https://bibliotekanauki.pl/articles/1818016.pdf
Data publikacji:
2016
Wydawca:
Politechnika Koszalińska. Wydawnictwo Uczelniane
Tematy:
osady nadmierne
oczyszczanie ścieków
metoda wektorów nośnych
k–najbliższego sąsiada
drzewa wzmacniane
excess sludge
wastewater treatment
support vector machine (SVM)
k–nearest neighbour
boosted trees
Opis:
Operation of a sewage treatment plant is a complex task because it requires maintaining the parameters of its activities at the appropriate level in order to achieve the desired effect of reducing pollution and reduce the flow of sediment discharged from the biological reactor. The basis for predicting the amount of excess sludge and operational parameters WWTP can provide physical models describing the biochemical changes occurring in the reactor, in which the input parameters, ie. Indicators of effluent quality and quantity of wastewater are modeled in advance. However, due to numerous interactions and uncertainty of the data in the physical models and forecast errors parameters of the inlet to the treatment plant Simulation results may be affected by significant errors. Therefore, to minimize the prediction error parameters of operation of the technological objects deliberate use of a black box model. In these models at the stage of learning is generated model structure underlying the projections analyzed the operating parameters of the plant. This publication presents the possibility of the use of methods: support vector, k – nearest neighbour and trees reinforced to predict the amount of the resulting excess sludge during wastewater treatment in the WWTP located in Sitkówka – News with a capacity of 72,000 3/d with a load of 275,000 PE . Due to the fact that did not have the quality parameters of wastewater at the inlet to the activated sludge chambers it was not possible to verify the empirical relationships commonly used in engineering practice to determine the size of the daily flow of excess sludge. Due to the significant differences in the amount of excess sludge generated in the period (t = 1-7 days) the simulation of the amount of sludge into the time were performed. To assessment the compatibility of measurement results and simulations quantities of sludge the mean absolute error and relative error of prediction for the considered parameter of technology was used. The analyzes carried out revealed that the amount of generated excess sludge can be predicted on the basis of parameters describing the quantity and quality of influent waste water (slurry concentration of total nitrogen and total phosphorus, BOD5) and the operating parameters of the biological reactor (recirculation rate, concentration and temperature of the sludge, the dosed amount of methanol and PIX). On the basis of computations, it can be concluded that the most accurate forecasting results amounts of sediment were obtained by using a reinforced trees (t = 2 to 5 days) and Support Vector Machines methods (t = 1, 6, 7 days). While the highest values of forecast errors sediments was obtained using a k – nearest neighbor (t = 2 to 5 days) and reinforced trees (t = 1, 6, 7 days).
Źródło:
Rocznik Ochrona Środowiska; 2016, Tom 18, cz. 2; 695-708
1506-218X
Pojawia się w:
Rocznik Ochrona Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Zastosowanie maszyny wektorów nośnych w sterowaniu sygnalizacją świetlną
Application of support vector machine in a traffic lights control
Autorzy:
Całuch, Artur
Cieślikowski, Adam
Plechawska-Wójcik, Małgorzata
Powiązania:
https://bibliotekanauki.pl/articles/98085.pdf
Data publikacji:
2020
Wydawca:
Politechnika Lubelska. Instytut Informatyki
Tematy:
uczenie maszynowe
symulator ruchu ulicznego
maszyna wektorów nośnych
machine learning
traffic simulator
support vector machine
Opis:
Niniejszy artykuł przedstawia proces dostosowania parametrów modelu maszyny wektorów nośnych, który posłuży do zbadania wpływu wartości parametru długości cyklu sygnalizacji świetlnej na jakość ruchu. Badania przeprowadzono z użyciem danych pozyskanych w trakcie przeprowadzonych symulacji w autorskim symulatorze ruchu ulicznego. W artykule przedstawiono i omówiono wyniki poszukiwania optymalnej wartości parametru długości cyklu sygnalizacji świetlnej.
This article presents the process of adapting support vector machine model’s parameters used for studying the effect of traffic light cycle length parameter’s value on traffic quality. The survey is carried out using data collected during running simulations in author’s traffic simulator. The article shows results of searching for optimum traffic light cycle length parameter’s value.
Źródło:
Journal of Computer Sciences Institute; 2020, 14; 37-42
2544-0764
Pojawia się w:
Journal of Computer Sciences Institute
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Wykorzystanie maszyny wektorów wspierających (SVM) do klasyfikacji sygnału EEG na użytek interfejsu mózg-komputer
Implementation of support vector machine for classification of EEG signal for brain-computer interface
Autorzy:
Kołodziej, M.
Majkowski, A.
Rak, R. J.
Powiązania:
https://bibliotekanauki.pl/articles/155968.pdf
Data publikacji:
2011
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
BCI
interfejs mózg-komputer
EEG
maszyna wektorów wspierających
SVM
brain-computer interface
support vector machine
Opis:
W artykule przedstawiono wykorzystanie maszyny wektorów wspierających (SVM) na użytek interfejsów mózg-komputer (BCI). W opracowanych algorytmach jako cechy sygnału EEG wykorzystano jego wariancję. Przedstawiono wyniki badań związanych z wykorzystaniem sieci SVM jako klasyfikatora. Eksperymenty przeprowadzono przy użyciu różnego rodzaju funkcji jądra.
Implementing communication between man and machine by use of EEG signals is one of the biggest challenges in the signal theory. Such communication could improve the standard of living of people with severe motor disabilities. Some disable persons cannot move, however they can think about moving their arms, legs and this way produce stable motor-related EEG signals. These signals can be used to construct BCI systems. However, the proper interpretation of the EEG signals is a very difficult task. There are three main stages in EEG signal analysis: feature extraction, feature selection and classification. The main aim of the paper is to implement a support vector machine as a classifier for the brain-computer interface. The proposed algorithm uses the EEG signal variance in the frequency range 8-30Hz. Experiments were conducted with use of different kernel functions for the SVM classifier. The best results were achieved for the quadratic polynomial kernel function. The classification error for testing data was 0.13.
Źródło:
Pomiary Automatyka Kontrola; 2011, R. 57, nr 12, 12; 1546-1548
0032-4140
Pojawia się w:
Pomiary Automatyka Kontrola
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Wykorzystanie maszyny wektorów nośnych oraz liniowej analizy dyskryminacyjnej jako klasyfikatorów cech w interfejsach mózg-komputer
Using support vector machine and linear discriminant analysis for features classification in brain-computer interfaces
Autorzy:
Jukiewicz, M.
Powiązania:
https://bibliotekanauki.pl/articles/376916.pdf
Data publikacji:
2014
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
interfejs mózg-komputer
Maszyna Wektorów Nośnych
Liniowa Analiza Dyskryminacyjna
brain-computer interface
support vector machine (SVM)
linear discriminant analysis
Opis:
Głównym celem artykułu jest porównanie skuteczności klasyfikacji cech dwóch algorytmów klasyfikujących wykorzystywanych w interfejsach mózg-komputer: SVM (ang. Support Vector Machine, Maszyna Wektorów Nośnych) oraz LDA (ang. Linear Discriminant Analysis, Liniowa Analiza Dyskryminacyjna). W artykule przedstawiono interfejs, w którym użytkownikowi prezentowane są dwa bodźce migające z różną częstotliwością (10 i 15 Hz), a następnie za pomocą elektrod elektroencefalografu mierzona jest odpowiedź elektryczna mózgu. W takich interfejsach sygnał zbierany jest zwykle w okolicach potylicznych (nad korą wzrokową). W prezentowanym rozwiązaniu sygnał mierzony jest z okolic czołowych. W przetwarzaniu i analizie sygnału zastosowano algorytmy statystycznego uczenia maszynowego. Do ekstrakcji cech sygnału wykorzystano Szybką Transformatę Fouriera, do selekcji cech: test t-Welcha, a do klasyfikacji cech: SVM oraz DLA. Na podstawie odpowiedzi uzyskanej z klasyfikatora możliwe jest np. wysterowanie kierunku skrętu robota mobilnego lub włączenie czy wyłączenie oświetlenia.
The main aim of this article is to compare the effectiveness of the classification of the two classifiers used in brain-computer interfaces: SVM (Support Vector Machine) and LDA (Linear Discriminant Analysis). The article presents an interface in which the subject is presented the two stimuli flashing at different frequencies (10 and 15 Hz) and then by using EEG electrodes electrical response of the brain is measured. In these interfaces, the signal is typically collected in the occipital area (on the visual cortex). In the presented solution the signal is measured form the prefrontal cortex. For signal processing and analysis statistical machine learning algorithms were used. For features’ extraction Fast Fourier Transform was used. For features’ selection Welch’s t test was used. For features’ classification was used SVM and DLA. Based on the responses obtained from the classifier it is possible to control the direction of a mobile robot’s movement or turning the lights on and off.
Źródło:
Poznan University of Technology Academic Journals. Electrical Engineering; 2014, 79; 25-30
1897-0737
Pojawia się w:
Poznan University of Technology Academic Journals. Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Water Quality Classification by Integration of Attribute-Realization and Support Vector Machine for the Chao Phraya River
Autorzy:
Sillberg, Chalisa Veesommai
Kullavanijaya, Pratin
Chavalparit, Orathai
Powiązania:
https://bibliotekanauki.pl/articles/1955579.pdf
Data publikacji:
2021
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
environmental data analysis
machine learning
SVM
support vector machine
water quality index
WQI
Opis:
The water quality index (WQI) is an essential indicator to manage water usage properly. This study aimed at applying a machine learning-based approach integrating attribute-realization (AR) and support vector machine (SVM) algorithm to classify the Chao Phraya River’s water quality. The historical monitoring dataset during 2008-2019 including biological oxygen demand (BOD), conductivity (Cond), dissolved oxygen (DO), faecal coliform bacteria (FCB), total coliform bacteria (TCB), ammonia (NH3-N), nitrate (NO3-N), salinity (Sal), suspended solids (SS), total nitrogen (TN), total dissolved solids (TDS), and turbidity (Turb), were processed via four studied steps: data pre-processing by means substituting method, contributing parameter evaluation by recognition pattern study, examination of the mathematic functions for quality classification, and validation of obtained approach. The results showed that NH3-N, TCB, FCB, BOD, DO, and Sal were the main attributes contributing orderly to water quality classification with confidence values of 0.80, 0.79, 0.78, 0.76, 0.69, and 0.64, respectively. Linear regression was the most suitable function to river water data classification than Sigmoid, Radial basis and Polynomial. The different number of attributes and mathematic functions promoted the different classification performance and accuracy. The validation confirmed that AR-SVM was a potent approach application to classify river water’s quality with 0.86-0.95 accuracy when applied three to six attributes.
Źródło:
Journal of Ecological Engineering; 2021, 22, 9; 70-86
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Using the one-versus-rest strategy with samples balancing to improve pairwise coupling classification
Autorzy:
Chmielnicki, W.
Stąpor, K.
Powiązania:
https://bibliotekanauki.pl/articles/330749.pdf
Data publikacji:
2016
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
multiclass classification
pairwise coupling
problem decomposition
support vector machine (SVM)
klasyfikacja wieloklasowa
rozkład problemu
maszyna wektorów wspierających
Opis:
The simplest classification task is to divide a set of objects into two classes, but most of the problems we find in real life applications are multi-class. There are many methods of decomposing such a task into a set of smaller classification problems involving two classes only. Among the methods, pairwise coupling proposed by Hastie and Tibshirani (1998) is one of the best known. Its principle is to separate each pair of classes ignoring the remaining ones. Then all objects are tested against these classifiers and a voting scheme is applied using pairwise class probability estimates in a joint probability estimate for all classes. A closer look at the pairwise strategy shows the problem which impacts the final result. Each binary classifier votes for each object even if it does not belong to one of the two classes which it is trained on. This problem is addressed in our strategy. We propose to use additional classifiers to select the objects which will be considered by the pairwise classifiers. A similar solution was proposed by Moreira and Mayoraz (1998), but they use classifiers which are biased according to imbalance in the number of samples representing classes.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2016, 26, 1; 191-201
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Use of machine learning algorithm for the better prediction of SR peculiarities of WEDM of Nimonic-90 superalloy
Autorzy:
Singh Nain, S.
Sai, R.
Sihag, P.
Vambol, S.
Vambol, V.
Powiązania:
https://bibliotekanauki.pl/articles/378951.pdf
Data publikacji:
2019
Wydawca:
Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
Tematy:
support vector machine
Gaussian process
artificial neural network
WEDM
maszyna wektorów nośnych
proces gaussowski
sztuczna sieć neuronowa
Opis:
Purpose: With the end goal to fulfil stringent structural shape of the component in aeronautics industry, machining of Nimonic-90 super alloy turns out to be exceptionally troublesome and costly by customary procedures, for example, milling, grinding, turning, etc. For that reason, the manufacture and design engineer worked on contactless machining process like EDM and WEDM. Based on previous studies, it has been observed that rare research work has been published pertaining to the use of machine learning in manufacturing. Therefore the current research work proposed the use of SVM, GP and ANN methods to evaluate the WEDM of Nimonic-90. Design/methodology/approach: The experiments have been performed on the WEDM considering five process variables. The Taguchi L 18 mixed type array is used to formulate the experimental plan. The surface roughness is checked by using surface contact profilometre. The evolutionary algorithms like SVM, GP and ANN approaches have been used to evaluate the SR of WEDM of Nimonic-90 super alloy. Findings: The entire models present the significant results for the better prediction of SR peculiarities of WEDM of Nimonic-90 superalloy. The GP PUK kernel model is dominating the entire model. Research limitations/implications: The investigation was carried for the Nimonic-90 super alloy is selected as a work material. Practical implications: The results of this study provide an opportunity to conduct contactless processing superalloy Nimonic-90. At the same time, this contactless process is much cheaper, faster and more accurate. Originality/value: An experimental work has been reported on the WEDM of Udimet-L605 and use of advance machine learning algorithm and optimization approaches like SVM, and GRA is recommended. A study on WEDM of Inconel 625 has been explored and optimized the process using Taguchi coupled with grey relational approach. The applicability of some evolutionary algorithm like random forest, M5P, and SVM also tested to evaluate the WEDM of Udimet-L605.The fuzzy- inference and BP-ANN approached is used to evaluate the WEDM process. The multi-objective optimization using ratio analysis approach has been utilized to evaluate the WEDM of high carbon & chromium steel. But this current research work proposed the use of SVM, GP and ANN methods to evaluate the WEDM of Nimonic-90.
Źródło:
Archives of Materials Science and Engineering; 2019, 95, 1; 12-19
1897-2764
Pojawia się w:
Archives of Materials Science and Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Two-stage classification approach for human detection in camera video in bulk ports
Autorzy:
Mi, C.
Zhang, Z.
He, X.
Huang, Y.
Mi, W.
Powiązania:
https://bibliotekanauki.pl/articles/259499.pdf
Data publikacji:
2015
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
Human Detection
Histograms of Oriented Gradients
Support Vector Machine
classification
Opis:
With the development of automation in ports, the video surveillance systems with automated human detection begun to be applied in open-air handling operation areas for safety and security. The accuracy of traditional human detection based on the video camera is not high enough to meet the requirements of operation surveillance. One of the key reasons is that Histograms of Oriented Gradients (HOG) features of the human body will show great different between front & back standing (F&B) and side standing (Side) human body. Therefore, the final training for classifier will only gain a few useful specific features which have contribution to classification and are insufficient to support effective classification, while using the HOG features directly extracted by the samples from different human postures. This paper proposes a two-stage classification method to improve the accuracy of human detection. In the first stage, during preprocessing classification, images is mainly divided into possible F&B human body and not F&B human body, and then they were put into the second-stage classification among side human and non-human recognition. The experimental results in Tianjin port show that the two-stage classifier can improve the classification accuracy of human detection obviously.
Źródło:
Polish Maritime Research; 2015, S 1; 163-170
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Traffic fatalities prediction based on support vector machine
Autorzy:
Li, T.
Yang, Y.
Wang, Y.
Chen, C.
Yao, J.
Powiązania:
https://bibliotekanauki.pl/articles/223743.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
traffic accident
support vector machine
SVM
particle swarm optimization (PSO)
PSO
prediction model
optimal parameters
wypadek drogowy
Particle Swarm Optimization
model prognostyczny
optymalne parametry
Opis:
To effectively predict traffic fatalities and promote the friendly development of transportation, a prediction model of traffic fatalities is established based on support vector machine (SVM). As the prediction accuracy of SVM largely depends on the selection of parameters, Particle Swarm Optimization (PSO) is introduced to find the optimal parameters. In this paper, small sample and nonlinear data are used to predict fatalities of traffic accident. Traffic accident statistics data of China from 1981 to 2012 are chosen as experimental data. The input variables for predicting accident are highway mileage, vehicle number and population size while the output variables are traffic fatality. To verify the validity of the proposed prediction method, the back-propagation neural network (BPNN) prediction model and SVM prediction model are also used to predict the traffic fatalities. The results show that compared with BPNN prediction model and SVM model, the prediction model of traffic fatalities based on PSO-SVM has higher prediction precision and smaller errors. The model can be more effective to forecast the traffic fatalities. And the method using particle swarm optimization algorithm for parameter optimization of SVM is feasible and effective. In addition, this method avoids overcomes the problem of “over learning” in neural network training progress.
Źródło:
Archives of Transport; 2016, 39, 3; 21-30
0866-9546
2300-8830
Pojawia się w:
Archives of Transport
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The Learning System by the Least Squares Support Vector Machine Method and its Application in Medicine
Autorzy:
Szewczyk, P.
Baszun, M.
Powiązania:
https://bibliotekanauki.pl/articles/307897.pdf
Data publikacji:
2011
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
classification
Grid-Search
particle swarm optimization (PSO)
patients diagnosis
support vector machine (SVM)
Opis:
In the paper it has been presented the possibility of using the least squares support vector machine to the initial diagnosis of patients. In order to find some optimal parameters making the work of the algorithm more detailed, the following techniques have been used: K-fold Cross Validation, Grid-Search, Particle Swarm Optimization. The result of the classification has been checked by some labels assigned by an expert. The created system has been tested on the artificially made data and the data taken from the real database. The results of the computer simulations have been presented in two forms: numerical and graphic. All the algorithms have been implemented in the C# language.
Źródło:
Journal of Telecommunications and Information Technology; 2011, 3; 109-113
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
System automatycznego wsparcia triażu wykorzystujący algorytm drzewa decyzyjnego i funkcję szans przeżycia
Automated triage supporting system with a decision tree algorithm and survival function
Autorzy:
Dobrowolski, Andrzej P.
Oskwarek, Paweł
Rokicki, Szymon
Wiktorzak, Paweł
Łubkowski, Piotr
Murawski, Piotr
Powiązania:
https://bibliotekanauki.pl/articles/24065020.pdf
Data publikacji:
2022
Wydawca:
Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
Tematy:
parametry życiowe
triaż
sieć wektorów wspierających
vital signs
triage
support vector machine
Opis:
Zdarzenia z dużą liczbą poszkodowanych są elementem nieodłącznie związanym z działaniami na polu walki. różnica między triażem stosowanym na polu walki i tym dotyczącym cywilnych wypadków o charakterze masowym wynika bezpośrednio ze specyfiki zdarzenia i założonych celów. Podczas konfliktów zbrojnych priorytetem jest zrealizowanie postawionych zadań i celów. z punktu widzenia dowodzenia misja ratowania poszkodowanych odbywa się w dużej mierze po to, by mogli oni jak najszybciej wrócić do dalszych działań - priorytetem na polu walki jest wykonanie misji, a nie ratowanie wszystkich rannych. w trakcie konfliktów zbrojnych siły i środki zawsze będą ograniczone, a ewakuacja poszkodowanych będzie musiała się odbywać wieloetapowo lub będzie wydłużona w czasie. ratownicy często mają do czynienia z przedłużającą się opieką na polu walki i są zmuszeni zajmować się rannymi dużo dłużej niż podczas cywilnych zdarzeń o charakterze masowym. implementacja nowych rozwiązań technologicznych minimalizujących potencjalny błąd ludzki, gromadzących i automatycznie analizujących dane medyczne w czasie rzeczywistym, umożliwi - szczególnie w teatrze działań wojennych - szybszą identyfikację stanu poszkodowanych i wyznaczenie priorytetów. obecnie, gdy wojna przybiera zupełnie inną formę, należy szukać rozwiązań, które dadzą szansę przeżycia rannym. Priorytetem w przypadku zdarzeń o charakterze masowym staje się jak najszybsza ocena parametrów życiowych. Pozwala to na celowane udzielenie pomocy i ma zmniejszyć śmiertelność poszkodowanych oraz dać szansę dotarcia specjalistycznej pomocy. wykorzystanie sztucznej inteligencji umożliwi zoptymalizowanie działań ratowników już na etapie docierania na miejsce zdarzenia. w artykule przedstawiono nowatorski algorytm segregacji uwzględniający wartość tzw. funkcji szans przeżycia, który jest elementem systemu wspomagania decyzji ewakuacji medycznej opartego na integracji monitoringu i analizy parametrów życiowych żołnierza z systemem zabezpieczenia medycznego.
Events with a large number of casualties are an element inherent in activities on the battlefield. The difference in the triage used on the battlefield in relation to the triage used in the case of mass civil accidents results directly from the specificity of the event and the assumed goals. during armed conflicts, the priority is to achieve the tasks and goals set. From the point of view of the command, the mission to rescue the casualties takes place largely, so that they can be restored to further operations as soon as possible - the priority on the battlefield is to complete the mission, not to rescue all the wounded. during armed conflicts, forces and resources will always be limited, and the evacuation of the victims will have to be carried out in several stages or will be extended in time. rescuers often have to deal with prolonged care on the battlefield and they are forced to deal with the wounded much longer than during mass civilian incidents. The implementation of new technological solutions that minimise potential human error, collect and automatically analyse medical data in real time, will enable us - especially in the theater of war - faster identification of the condition of the injured and setting priorities. nowadays, when war takes a completely different form, solutions should be sought that will give the wounded a chance to survive. The priority in the case of MASCAL events is the quickest possible assessment of vital parameters that allow for targeted assistance, which are intended to reduce the mortality rate of the victims, give a chance to get specialist help. The use of artificial intelligence will make it possible to optimise the activities of rescuers already at the stage of reaching the scene of the event. The article presents an innovative segregation algorithm that takes into account the value of the so-called function of survival chances, which is an element of the medical evacuation decision support system, based on the integration of monitoring and analysis of soldier’s vital signs with the medical security system.
Źródło:
Biuletyn Wojskowej Akademii Technicznej; 2022, 71, 3; 31--67
1234-5865
Pojawia się w:
Biuletyn Wojskowej Akademii Technicznej
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Support Vector Machine based Decoding Algorithm for BCH Codes
Autorzy:
Sudharsan, V.
Yamuna, B.
Powiązania:
https://bibliotekanauki.pl/articles/958048.pdf
Data publikacji:
2016
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
BCH codes
Chase-2 algorithm
coding gain
kernel
multi-class classification
Soft Decision Decoding
Support Vector Machine
Opis:
Modern communication systems require robust, adaptable and high performance decoders for efficient data transmission. Support Vector Machine (SVM) is a margin based classification and regression technique. In this paper, decoding of Bose Chaudhuri Hocquenghem codes has been approached as a multi-class classification problem using SVM. In conventional decoding algorithms, the procedure for decoding is usually fixed irrespective of the SNR environment in which the transmission takes place, but SVM being a machinelearning algorithm is adaptable to the communication environment. Since the construction of SVM decoder model uses the training data set, application specific decoders can be designed by choosing the training size efficiently. With the soft margin width in SVM being controlled by an equation, which has been formulated as a quadratic programming problem, there are no local minima issues in SVM and is robust to outliers.
Źródło:
Journal of Telecommunications and Information Technology; 2016, 2; 108-112
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Specific emitter identification using geometric features of frequency drift curve
Autorzy:
Zhao, Y.
Wui, L.
Zhang, J.
Li, Y.
Powiązania:
https://bibliotekanauki.pl/articles/200575.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
specific emitter identification
geometric features
frequency drift
adaptive fractional spectrogram
support vector machine
emiter
cechy geometryczne
dryf częstotliwości
spektrogram
Opis:
Specific emitter identification (SEI) is a technique for recognizing different emitters of the same type which have the same modulation parameters. Using only the classic modulation parameters for recognition, one cannot distinguish different emitters of a same type. To solve the problem, new features urgently need to be developed for recognition. This paper focuses on the common phenomenon of frequency drift, defines geometric features of frequency drift curve and, finally, proposes a practical algorithm of specific emitter identification using the geometric features. The proposed algorithm consists of three processes: instantaneous frequency estimation based on the adaptive fractional spectrogram, feature extraction of frequency drift curve based on geometric methods for describing a curve and recognition process based on support vector machine. Simulation results show that the identification rate is generally more than 98% above –5 dB of signal to noise ratio (SNR), and real data experiment verifies the practical performance of the proposed algorithm.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 1; 99-108
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Soft Sensing Method Of LS-SVM Using Temperature Time Series For Gas Flow Measurements
Autorzy:
Xu, W.
Fan, Z.
Cai, M.
Shi, Y.
Tong, X.
Sun, J.
Powiązania:
https://bibliotekanauki.pl/articles/221824.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
gas flow
soft sensor
support vector machine (SVM)
temperature time series
Opis:
This paper proposes a soft sensing method of least squares support vector machine (LS-SVM) using temperature time series for gas flow measurements. A heater unit has been installed on the external wall of a pipeline to generate heat pulses. Dynamic temperature signals have been collected upstream of the heater unit. The temperature time series are the main secondary variables of soft sensing technique for estimating the flow rate. A LS-SVM model is proposed to construct a non-linear relation between the flow rate and temperature time series. To select its inputs, parameters of the measurement system are divided into three categories: blind, invalid and secondary variables. Then the kernel function parameters are optimized to improve estimation accuracy. The experiments have been conducted both in the single-pulse and multiple-pulse heating modes. The results show that estimations are acceptable.
Źródło:
Metrology and Measurement Systems; 2015, 22, 3; 383-392
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Smart Substation Network Fault Classification Based on a Hybrid Optimization Algorithm
Autorzy:
Xia, Xin
Liu, Xiaofeng
Lou, Jichao
Powiązania:
https://bibliotekanauki.pl/articles/227220.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
smart substation
network fault classification
improved separation interval method (ISIM)
support vector
machine (SVM)
Anti-noise processing (ANP)
Opis:
Accurate network fault diagnosis in smart substations is key to strengthening grid security. To solve fault classification problems and enhance classification accuracy, we propose a hybrid optimization algorithm consisting of three parts: anti-noise processing (ANP), an improved separation interval method (ISIM), and a genetic algorithm-particle swarm optimization (GA-PSO) method. ANP cleans out the outliers and noise in the dataset. ISIM uses a support vector machine (SVM) architecture to optimize SVM kernel parameters. Finally, we propose the GA-PSO algorithm, which combines the advantages of both genetic and particle swarm optimization algorithms to optimize the penalty parameter. The experimental results show that our proposed hybrid optimization algorithm enhances the classification accuracy of smart substation network faults and shows stronger performance compared with existing methods.
Źródło:
International Journal of Electronics and Telecommunications; 2019, 65, 4; 657-663
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł

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