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Tytuł:
Cyclic flow shop scheduling problem with two-machine cells
Autorzy:
Bożejko, W.
Gnatowski, A.
Idzikowski, R.
Wodecki, M.
Powiązania:
https://bibliotekanauki.pl/articles/229393.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
job shop
cyclic scheduling
multi-machine
assignment
Opis:
In the paper a variant of cyclic production with setups and two-machine cell is considered. One of the stages of the problem solving consists of assigning each operation to the machine on which it will be carried out. The total number of such assignments is exponential. We propose a polynomial time algorithm finding the optimal operations to machines assignment.
Źródło:
Archives of Control Sciences; 2017, 27, 2; 151-167
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The efficiency of detecting the failures and troubleshooting while applying technical diagnostics for multi-computer systems
Autorzy:
Ye, K. Z.
Kyaw, H. A.
Portnov, E. M.
Bain, A. M.
Vasant, P.
Powiązania:
https://bibliotekanauki.pl/articles/229469.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
technical diagnostics
fault
identification
‘AND-OR’ graph
efficiency
control devices
multi-machine power system
troubleshooting
majority principle
Opis:
This paper presents techniques which base on the concept of flows thinning together with the identification techniques. These methods are proposed to determine the expected number of failures to assess the efficiency of technical diagnostics of instruments. Additionally, this research focuses on the improvement of multi-machine troubleshooting systems, based on the ‘AND-OR’ graphs. Respective algorithms are presented. The majority principle uses the input information to check the correctness of the decision regarding identification of faulty machines. In this paper we base on the complete testing algorithm for elements of multi-computer complexes searching by criteria of failed element.
Źródło:
Archives of Control Sciences; 2015, 25, 1; 87-107
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fast evaluation of the volumetric motion accuracy of multi-axis machine tools using a Double-Ballbar
Autorzy:
Kauschinger, Bernd
Friedrich, Christian
Zhou, Ruiqing
Ihlenfeldt, Steffen
Powiązania:
https://bibliotekanauki.pl/articles/99999.pdf
Data publikacji:
2020
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
Double-Ballbar
fast performance test
volumetric motion accuracy
multi-axis machine tools
Opis:
The proof of manufacturing accuracy requires continuous verification and crosscheck of the motion accuracy of machine tools. Machining in 5 to 6 axes intensifies the problem of measurement and evaluation of volumetric motion accuracy in up to 6 degrees of freedom (DOF) in the whole workspace. Although, there are many known, even standardized, measuring methods, they are either expensive, time-consuming, not applicable in an operational state of the machine under shop floor conditions, or their significance is limited to only 1 or 2 feed-axes. Appropriate methods to be run regularly, fast and cost-efficient by the machine operator as a performance test are still desired. The article presents a new approach that meets these requirements. It is based on a Double-Ballbar (DBB) with enlarged measuring range and volumetric measuring paths of up to 6 DOF with all feed-axes moving simultaneously during continuous measurement, instead of plane circular paths according to ISO 230-4. After an explanation of the proposed method, the developed DBB device is introduced, including its mechanical and sensor design, the data interface, and results of experimental investigations on the measuring accuracy. Furthermore, relevant problems regarding the design, optimization, and programming of appropriate 6 DOF measuring paths are discussed and experimental results are presented that show the advantage compared to other measuring paths.
Źródło:
Journal of Machine Engineering; 2020, 20, 3; 44-62
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Development and implementation of IEC 61131-3 virtual machine
Projektowanie i implementacja maszyny wirtualnej normy IEC 61131-3
Autorzy:
Trybus, B.
Powiązania:
https://bibliotekanauki.pl/articles/375625.pdf
Data publikacji:
2011
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
multi-platform virtual machine
assembler
IEC 61131-3
programmable controllers
Opis:
Virtual machine described in the paper is a runtime program for controllers in small distributed systems. The machine executes intermediate universal code similar to an assembler, compiled in CPDev engineering environment from source programs written in control languages of IEC 61131-3 standard. The machine is implemented as a C program, so it can run on different target platforms. Data formats and commands of the machine code are presented, together with the machine's Petri-net model, C implementation involving universal and platform-dependent modules, target hardware interface, input/output programming mechanisms, and practical applications.
W artykule przedstawiono projekt i implementację maszyny wirtualnej będącą elementem środowiska wykonawczego dla sterowników. Przeznaczona jest przede wszystkim do małych, rozproszonych systemów sterowania. Maszyna współpracuje z pakietem CPDev, opracowanym na Politechnice Rzeszowskiej, który służy do programowania w językach normy IEC 61131-3 (PN-EN 61131-3) (Rys. 1). Programy w ST, IL lub FBD są kompilowane do kodu pośredniego VMASM, który w postaci binarnej może być wykonywany przez maszynę na platformie docelowej (Rys. 2 i Tab. 2). Zestaw instrukcji maszyny wirtualnej oraz obsługiwane przez nią typy danych zostały dostosowane do normy IEC (Tab. 1). Działanie maszyny zostało zamodelowane za pomocą hierarchicznej czasowej kolorowanej sieci Petriego. Elementami tego modelu jest strona przedstawiająca cykl zadania (nadrzędna, Rys. 3) oraz podrzędna, reprezentująca moduł wykonawczy (Rys. 4). Symulacja modelu pozwoliła zweryfikować przyjęte założenia projektowe. Maszyna wirtualna została zaimplementowana jako program w języku C. Jej strukturę wewnętrzną przedstawiono na Rys. 5. Część modułów jest uniwersalna, pozostałe zależą od platformy docelowej sterownika. Dzięki takiemu układowi, maszyna może być przystosowana do różnego sprzętu. Dostosowanie maszyny polega na przygotowaniu funkcji wchodzących w skład interfejsu sprzętowego, określających m.in. sposób ładowania programu, obsługę cyklu zadania i zegara czasu rzeczywistego. Współpraca ze sprzętem obejmuje także odczyt wejść i zapis wyjść procesowych. Konfigurator zasobów sprzętowych pozwala przypisać zmienne programu do określonych wejść/wyjść. Mechanizm bloków sprzętowych pozwala natomiast bezpośrednio korzystać z mechanizmów niskopoziomowych w kodzie programu. W ten sposób zrealizowano m.in. obsługę protokołu NMEA (Rys. 4). Dwa pierwsze zastosowania maszyny wirtualnej ze środowiskiem CPDev to sterownik SMC polskiej firmy Lumel, będący centralnym węzłem małego rozproszonego systemu sterowania (mini-DCS, Rys. 6a, b) oraz system Mini-Guard z Praxis Automation (Holandia) stosowany do monitorowania systemów na statku i jego pozycjonowania (Rys. 6c). Dzięki maszynie wirtualnej programy tworzone w środowisku CPDev w językach normy IEC 61131-3 (ST, IL, FBD) mogą być uruchamiane na różnych sterownikach, wyposażonych w procesory AVR, ARM, x86 i inne. Przedmiotem dalszych prac będzie możliwość jednoczesnego wykonywania przez maszynę kilku zadań sterujących (wielozadaniowość).
Źródło:
Theoretical and Applied Informatics; 2011, 23, 1; 21-35
1896-5334
Pojawia się w:
Theoretical and Applied Informatics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analysis, simulation and experimental strategies of 5-phase permanent magnet motor control
Autorzy:
Mekri, Fatima
Elghali, Seifeddine Ben
Charpentier, Jean-Frédéric
Powiązania:
https://bibliotekanauki.pl/articles/140704.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
control strategy
constant torque
current references
minimum copper losses
modeling
multi-phase machine
Opis:
This paper presents a study of control strategies for 5-phase permanent magnet synchronous motors (PMSMs) supplied by a five-leg voltage source inverter. Based on the vectorial decomposition of the multi-phase machine, fictitious machines, magnetically decoupled, allow a more adequate control. In this paper, our study focuses on the vector control of a multi-phase machine using a linear proportional-integral-derivative (PID) current regulator in the cases of sinusoidal and trapezoidal back-electromotive force (EMF) waveforms. In order to determine currents’ references, two strategies are adopted. First one aims to minimize copper losses under constant torque, while the second one targets to increase torque for a given copper losses. These techniques are tested under a variable speed control strategy based on a proportional-integral (PI) regulator and experimentally validated.
Źródło:
Archives of Electrical Engineering; 2019, 68, 3; 629-641
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The relationship between the mass of the harvester head and its maximum cutting diameter
Masa głowicy harwesterowej a jej maksymalna średnica cięcia
Autorzy:
Leszczyński, N.
Tomczak, A.
Kowalczuk, J.
Zarajczyk, J.
Węgrzyn, A.
Kocira, S.
Depo, K.
Powiązania:
https://bibliotekanauki.pl/articles/337522.pdf
Data publikacji:
2016
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Maszyn Rolniczych
Tematy:
timber harvesting
multi-operational logging machine
European market
pine harvesting
pozyskiwanie drewna
maszyna wielooperacyjna
rynek europejski
sosna
Opis:
The aim of the study was to demonstrate a relationship between the type/mass of harvester heads and their maximum cutting diameter. The study evaluated heads for felling, delimbing, and bucking trees, mounted on an extensible boom, with a chain saw and feed rollers, designed principally for coniferous stands. The heads included in the study were mostly products of European manufacturers: AFM, CTL, Kesla, Keto, Komatsu, Konrad, Logmax, Logset, Moipu, Ponsse, Rottne, Silvatec, SP Maskiner, and Viking. The analysis was based on technical specifications provided by the producers. The results were analyzed statistically by ANOVA and T-Tukey's multiple confidence intervals at the level of significance α=0.05. The trend lines were fitted by the method of least squares. The slopes of linear regression lines representing the mass of harvester heads by particular manufacturers as a function of their maximum cutting diameter differ considerably from one producer to another: 22.5 for Logset, 22.5 for Ponsse, 30.4 for Keto, 33.9 for Kesla, 40.7 for SP Maskiner, 57.1 for Logmax, 66.5 for Komatsu, and 78.2 for AFM. Keto Forst Ecolit weighing 297 kg, designed for felling the thinnest trees (of up to 30 cm in diameter) is the lightest European harvester head with feed rollers and a chain saw. By contrast, Keto 825TS, weighing 2450 kg is the lightest head capable of felling, delimbing, and bucking the thickest trees (of up to 102 cm in diameter). Heads for final felling and thinning with a maximum cutting diameter of 60-70 cm have the largest percentage (31.5%) in European harvester heads offered on the market. The smallest percentage (5.6%) is noted for thinning heads with a maximum cutting diameter of up to 40 cm. There is a positive correlation between the mass of harvester heads and their maximum cutting diameter. Harvester heads designed for felling or for felling and thinning are statistically heavier than smaller types of heads. There is, however, no statistically significant difference in mass between heads for thinning and heads for thinning and felling. A head by the same manufacturer, with a maximum cutting diameter greater by 10 cm, is heavier by 225-782 kg, depending on the manufacturer.
Celem pracy było wykazanie zależności między typem/masą głowicy harwesterowej a jej przydatnością do pozyskiwania drewna w różnych grupach wymiarowych drzew. Ocenie poddano głowice ścinkowo-okrzesująco-przerzynające, przeznaczone do mocowania wysięgnikowego, wyposażone w piłę łańcuchową oraz rolki napędowe, przeznaczone głównie do pozyskiwania drewna w drzewostanach iglastych. Badaniami objęto głowice producentów europejskich: AFM, CTL, Kesla, Keto, Komatsu, Konrad, Logmax, Logset, Moipu, Ponsse, Rottne, Silvatec, SP Maskiner i Viking. Analizę przeprowadzono na podstawie danych technicznych przedstawionych przez oferentów. Uzyskane wyniki poddano analizie statystycznej przy wykorzystaniu metody analizy wariancji i wielokrotnych przedziałów ufności T-Tukeya, przy założonym poziomie istotności α=0,05. Linie trendu dopasowano metodą najmniejszych kwadratów. Współczynnik kątowy prostych regresji, wyznaczonych dla ich masy w funkcji ich maksymalnej średnicy cięcia, dla głowic każdego producenta jest inny i wynosi: Logset - 22,5, Ponsse - 22,5, Keto - 30,4, Kesla - 33,9, SP Maskiner - 40,7, Logmax - 57,1 i Komatsu - 66,5, AFM - 78,2. Najlżejszą produkowaną w Europie głowicą rolkową wyposażoną w piłę łańcuchową jest Keto Forst Ecolit o masie 297 kg, która przeznaczona jest do ścinania najcieńszych drzew (o średnicy do 30 cm). Z kolei najlżejszą głowicą umożliwiającą ścinkę, okrzesanie i przerzynkę najgrubszych drzew (o średnicy do 102 cm) jest Keto 825TS o masie 2450 kg. Głowice zrębowotrzebieżowe (o maksymalnej średnicy ścinki z przedziału 60-70 cm) stanowią największy udział (31,5%) wśród europejskich głowic harwesterowych. Najmniejszy udział w rynku (5,6%) obejmują głowice trzebieżowe (o maksymalnej średnicy ścinki do 40 cm). Istnieje dodatnia korelacja między masą głowicy harwesterowej a jej średnicą cięcia, a także głowice zrębowe i zrębowo-trzebieżowe swoją masą odbiegają statystyczne od mniejszych typów głowic. Natomiast brak statystycznej różnicy w masie pomiędzy głowicami trzebieżowymi i trzebieżowo-zrębowymi. Zamiana głowicy na głowicę tej samej firmy, ale o większej o 10 cm maksymalnej średnicy ścinki skutkuje zwiększeniem jej masy, zależnie od producenta, o 225-782 kg.
Źródło:
Journal of Research and Applications in Agricultural Engineering; 2016, 61, 2; 50-54
1642-686X
2719-423X
Pojawia się w:
Journal of Research and Applications in Agricultural Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A real-valued genetic algorithm to optimize the parameters of support vector machine for classification of multiple faults in NPP
Autorzy:
Amer, F. Z.
El-Garhy, A. M.
Awadalla, M. H.
Rashad, S. M.
Abdien, A. K.
Powiązania:
https://bibliotekanauki.pl/articles/147652.pdf
Data publikacji:
2011
Wydawca:
Instytut Chemii i Techniki Jądrowej
Tematy:
support vector machine (SVM)
fault classification
multi fault classification
genetic algorithm (GA)
machine learning
Opis:
Two parameters, regularization parameter c, which determines the trade off cost between minimizing the training error and minimizing the complexity of the model and parameter sigma (σ) of the kernel function which defines the non-linear mapping from the input space to some high-dimensional feature space, which constructs a non-linear decision hyper surface in an input space, must be carefully predetermined in establishing an efficient support vector machine (SVM) model. Therefore, the purpose of this study is to develop a genetic-based SVM (GASVM) model that can automatically determine the optimal parameters, c and sigma, of SVM with the highest predictive accuracy and generalization ability simultaneously. The GASVM scheme is applied on observed monitored data of a pressurized water reactor nuclear power plant (PWRNPP) to classify its associated faults. Compared to the standard SVM model, simulation of GASVM indicates its superiority when applied on the dataset with unbalanced classes. GASVM scheme can gain higher classification with accurate and faster learning speed.
Źródło:
Nukleonika; 2011, 56, 4; 323-332
0029-5922
1508-5791
Pojawia się w:
Nukleonika
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A hybridization of machine learning and NSGA-II for multi-objective optimization of surface roughness and cutting force in AISI 4340 alloy steel turning
Autorzy:
Nguyen, Anh-Tu
Nguyen, Van-Hai
Le, Tien-Thinh
Nguyen, Nhu-Tung
Powiązania:
https://bibliotekanauki.pl/articles/2200263.pdf
Data publikacji:
2023
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
multi-objective optimisation
machine learning
AISI 4340
NSGA-II
ANN
Opis:
This work focuses on optimizing process parameters in turning AISI 4340 alloy steel. A hybridization of Machine Learning (ML) algorithms and a Non-Dominated Sorting Genetic Algorithm (NSGA-II) is applied to find the Pareto solution. The objective functions are a simultaneous minimum of average surface roughness (Ra) and cutting force under the cutting parameter constraints of cutting speed, feed rate, depth of cut, and tool nose radius in a range of 50–375 m/min, 0.02–0.25 mm/rev, 0.1–1.5 mm, and 0.4–0.8 mm, respectively. The present study uses five ML models – namely SVR, CAT, RFR, GBR, and ANN – to predict Ra and cutting force. Results indicate that ANN offers the best predictive performance in respect of all accuracy metrics: root-mean-squared-error (RMSE), mean-absolute-error (MAE), and coefficient of determination (R2). In addition, a hybridization of NSGA-II and ANN is implemented to find the optimal solutions for machining parameters, which lie on the Pareto front. The results of this multi-objective optimization indicate that Ra lies in a range between 1.032 and 1.048 μm, and cutting force was found to range between 7.981 and 8.277 kgf for the five selected Pareto solutions. In the set of non-dominated keys, none of the individual solutions is superior to any of the others, so it is the manufacturer's decision which dataset to select. Results summarize the value range in the Pareto solutions generated by NSGA-II: cutting speeds between 72.92 and 75.11 m/min, a feed rate of 0.02 mm/rev, a depth of cut between 0.62 and 0.79 mm, and a tool nose radius of 0.4 mm, are recommended. Following that, experimental validations were finally conducted to verify the optimization procedure.
Źródło:
Journal of Machine Engineering; 2023, 23, 1; 133--153
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multi-view learning for software defect prediction
Autorzy:
Kiyak, Elife Ozturk
Birant, Derya
Birant, Kokten Ulas
Powiązania:
https://bibliotekanauki.pl/articles/2060905.pdf
Data publikacji:
2021
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
software defect prediction
multi-view learning
machine learning
k-nearest neighbor
Opis:
Background: Traditionally, machine learning algorithms have been simply applied for software defect prediction by considering single-view data, meaning the input data contains a single feature vector. Nevertheless, different software engineering data sources may include multiple and partially independent information, which makes the standard single-view approaches ineffective. Objective: In order to overcome the single-view limitation in the current studies, this article proposes the usage of a multi-view learning method for software defect classification problems. Method: The Multi-View k-Nearest Neighbors (MVKNN) method was used in the software engineering field. In this method, first, base classifiers are constructed to learn from each view, and then classifiers are combined to create a robust multi-view model. Results: In the experimental studies, our algorithm (MVKNN) is compared with the standard k-nearest neighbors (KNN) algorithm on 50 datasets obtained from different software bug repositories. The experimental results demonstrate that the MVKNN method outperformed KNN on most of the datasets in terms of accuracy. The average accuracy values of MVKNN are 86.59%, 88.09%, and 83.10% for the NASA MDP, Softlab, and OSSP datasets, respectively. Conclusion: The results show that using multiple views (MVKNN) can usually improve classification accuracy compared to a single-view strategy (KNN) for software defect prediction.
Źródło:
e-Informatica Software Engineering Journal; 2021, 15, 1; 163--184
1897-7979
Pojawia się w:
e-Informatica Software Engineering Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Developing a data-driven soft sensor to predict silicate impurity in iron ore flotation concentrate
Autorzy:
Pural, Yusuf Enes
Powiązania:
https://bibliotekanauki.pl/articles/24148677.pdf
Data publikacji:
2023
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
soft sensor
machine learning
random forest
multi-layer perceptron
flotation
grade estimation
Opis:
Soft sensors are mathematical models that estimate the value of a process variable that is difficult or expensive to measure directly. They can be based on first principle models, data-based models, or a combination of both. These models are increasingly used in mineral processing to estimate and optimize important performance parameters such as mill load, mineral grades, and particle size. This study investigates the development of a data-driven soft sensor to predict the silicate content in iron ore reverse flotation concentrate, a crucial indicator of plant performance. The proposed soft sensor model employs a dataset obtained from Kaggle, which includes measurements of iron and silicate content in the feed to the plant, reagent dosages, weight and pH of pulp, as well as the amount of air and froth levels in the flotation units. To reduce the dimensionality of the dataset, Principal Component Analysis, an unsupervised machine learning method, was applied. The soft sensor model was developed using three machine learning algorithms, namely, Ridge Regression, Multi-Layer Perceptron, and Random Forest. The Random Forest model, created with non-reduced data, demonstrated superior performance, with an R-squared value of 96.5% and a mean absolute error of 0.089. The results suggest that the proposed soft sensor model can accurately predict the silicate content in the iron ore flotation concentrate using machine learning algorithms. Moreover, the study highlights the importance of selecting appropriate algorithms for soft sensor developments in mineral processing plants.
Źródło:
Physicochemical Problems of Mineral Processing; 2023, 59, 5; art. no. 169823
1643-1049
2084-4735
Pojawia się w:
Physicochemical Problems of Mineral Processing
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Selected issues regarding achievements in component importance analysis for complex technical systems
Autorzy:
Chybowski, L.
Gawdzińska, K.
Powiązania:
https://bibliotekanauki.pl/articles/135196.pdf
Data publikacji:
2017
Wydawca:
Akademia Morska w Szczecinie. Wydawnictwo AMSz
Tematy:
importance analysis
complex system
human machine interfaces
machinery
quality criteria
multi-criteria analysis
Opis:
Selected issues of component importance analysis for complex technical systems have been presented in this paper. A generic example of a complex technical system and selected statistics of operating losses have been described. A description and diagrams of both qualitative and quantitative importance analysis have also been included. The most significant problems facing complex technical system modelling have been pointed out. A multi-criteria system component importance analysis and the basic criteria for a system component quality evaluation have also been introduced. Some factors influencing the importance of the technical system’s components have also been described. Finally, the necessity of further developing importance analysis methods for machinery operation has been highlighted.
Źródło:
Zeszyty Naukowe Akademii Morskiej w Szczecinie; 2017, 52 (124); 137-144
1733-8670
2392-0378
Pojawia się w:
Zeszyty Naukowe Akademii Morskiej w Szczecinie
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A novel method for 3D measurement of RFID multi-tag network using a machine vision system
Autorzy:
Zhuang, X.
Yu, X.
Zhao, Z.
Zhang, W.
Liu, Z.
Lu, D.
Dong, D.
Powiązania:
https://bibliotekanauki.pl/articles/221058.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
3D measurement
RFID multi-tag network
dual-CCD system
neural network
machine vision
Opis:
The three-dimensional (3D) coordinate measurement of radio frequency identification (RFID) multi-tag networks is one of the important issues in the field of RFID, which affects the reading performance of RFID multi-tag networks. In this paper, a novel method for 3D coordinate measurement of RFID multi-tag networks is proposed. A dual-CCD system (vertical and horizontal cameras) is used to obtain images of RFID multi-tag networks from different angles. The iterative threshold segmentation and the morphological filtering method are used to process the images. The template matching method is respectively used to determine the two-dimensional (2D) coordinate and the vertical coordinate of each tag. After that, the 3D coordinate of each tag is obtained. Finally, a back-propagation (BP) neural network is used to model the nonlinear relationship between the RFID multi-tag network and the corresponding reading distance. The BP neural network can predict the reading distances of unknown tag groups and find out the optimal distribution structure of the tag groups corresponding to the maximum reading distance. In the future work, the corresponding in-depth research on the neural network to adjust the distribution of tags will be done.
Źródło:
Metrology and Measurement Systems; 2018, 25, 3; 475-486
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Machine Learning Model for Improving Building Detection in Informal Areas: A Case Study of Greater Cairo
Autorzy:
Taha, Lamyaa Gamal El-deen
Ibrahim, Rania Elsayed
Powiązania:
https://bibliotekanauki.pl/articles/2055780.pdf
Data publikacji:
2022
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
multi-source image fusion
random forest
support vector machine
DEM extraction
unplanned unsafe areas
remote sensing
Opis:
Building detection in Ashwa’iyyat is a fundamental yet challenging problem, mainly because it requires the correct recovery of building footprints from images with high-object density and scene complexity. A classification model was proposed to integrate spectral, height and textural features. It was developed for the automatic detection of the rectangular, irregular structure and quite small size buildings or buildings which are close to each other but not adjoined. It is intended to improve the precision with which buildings are classified using scikit learn Python libraries and QGIS. WorldView-2 and Spot-5 imagery were combined using three image fusion techniques. The Grey-Level Co-occurrence Matrix was applied to determine which attributes are important in detecting and extracting buildings. The Normalized Digital Surface Model was also generated with 0.5-m resolution. The results demonstrated that when textural features of colour images were introduced as classifier input, the overall accuracy was improved in most cases. The results show that the proposed model was more accurate and efficient than the state-of-the-art methods and can be used effectively to extract the boundaries of small size buildings. The use of a classifier ensample is recommended for the extraction of buildings.
Źródło:
Geomatics and Environmental Engineering; 2022, 16, 2; 39--58
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
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:
A rainfall forecasting method using machine learning models and its application to the Fukuoka city case
Autorzy:
Sumi, S. M.
Zaman, M. F.
Hirose, H.
Powiązania:
https://bibliotekanauki.pl/articles/331290.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
maszyna ucząca się
metoda wielomodelowa
przetwarzanie wstępne
rainfall forecasting
machine learning
multi model method
preprocessing
model ranking
Opis:
In the present article, an attempt is made to derive optimal data-driven machine learning methods for forecasting an average daily and monthly rainfall of the Fukuoka city in Japan. This comparative study is conducted concentrating on three aspects: modelling inputs, modelling methods and pre-processing techniques. A comparison between linear correlation analysis and average mutual information is made to find an optimal input technique. For the modelling of the rainfall, a novel hybrid multi-model method is proposed and compared with its constituent models. The models include the artificial neural network, multivariate adaptive regression splines, the k-nearest neighbour, and radial basis support vector regression. Each of these methods is applied to model the daily and monthly rainfall, coupled with a pre-processing technique including moving average and principal component analysis. In the first stage of the hybrid method, sub-models from each of the above methods are constructed with different parameter settings. In the second stage, the sub-models are ranked with a variable selection technique and the higher ranked models are selected based on the leave-one-out cross-validation error. The forecasting of the hybrid model is performed by the weighted combination of the finally selected models.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 841-854
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 25

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