Ograniczanie wyników
Czasopisma help
Autorzy help
Lata help
Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników

Znaleziono wyników: 624

Liczba wyników na stronie
first rewind previous Strona / 32 next fast forward last
Wyniki wyszukiwania
Wyszukiwano:
w słowach kluczowych:  neural network
help Sortuj według:

help Ogranicz wyniki do:
first rewind previous Strona / 32 next fast forward last
EN
The process of garment production has always been a black box. The production time of different clothing is different and has great changes, thus managers cannot make a production plan accurately. With the world entering the era of industry 4.0 and the accumulation of big data, machine learning can provide services for the garment manufacturing industry. The production cycle time is the key to control the production process. In order to predict the production cycle time more accurately and master the production process in the garment manufacturing process, a neural network model of production cycle time prediction is established in this paper. Using a trained neural network to predict the production cycle time, the overall error of 6 groups is within 5%, and that of 3 groups is between 5% and 10%. Therefore, this neural network can be used to predict the future production cycle time and predict the overall production time of clothing.
PL
Czas produkcji różnych ubrań jest inny i podlega dużym zmianom, dlatego menedżerowie nie mogą dokładnie zaplanować produkcji. Wraz z wkroczeniem świata w erę przemysłu 4.0 i gromadzeniem dużych zbiorów danych dobrym rozwiązaniem dla przemysłu odzieżowego jest zastosowanie maszyn uczących się. Czas cyklu produkcyjnego jest kluczem do kontroli procesu produkcyjnego. W celu dokładniejszego przewidywania czasu cyklu produkcyjnego i opanowania procesu produkcyjnego w procesie produkcji odzieży, w artykule opracowano model sieci neuronowej do przewidywania czasu cyklu produkcyjnego. Do przewidywania czasu cyklu produkcyjnego użyto sieci neuronowej, ogólny błąd 6 grup mieścił się w granicach 5%, a 3 grup – między 5% a 10%. W związku z tym zaprezentowana sieć neuronowa może znaleźć zastosowanie w przewidywaniu czasu cyklu produkcyjnego i całkowitego czasu produkcji odzieży.
EN
Objectives: The focus of this paper is to introduce an automated early Diabetic Retinopathy (DR) detection scheme from colour fundus images through enhanced segmentation and classification strategies by analyzing blood vessels. Methods: The occurrence of DR is increasing from the past years, impacting the eyes due to a sudden rise in the glucose level of blood. All over the world, half of the people who are under age 70 are severely suffered from diabetes. The patients who are affected by DR will lose their vision during the absence of early recognition of DR and appropriate treatment. To decrease the growth and occurrence of loss of vision, the early detection and timely treatment of DR are desirable. At present, deep learning models have presented better performance using retinal images for DR detection. In this work, the input retinal fundus images are initially subjected to pre-processing that undergoes contrast enhancement by Contrast Limited Adaptive Histogram Equalization (CLAHE) and average filtering. Further, the optimized binary thresholding-based segmentation is done for blood vessel segmentation. For the segmented image, Tri-level Discrete Level Decomposition (Tri-DWT) is performed to decompose it. In the feature extraction phase, Local Binary Pattern (LBP), and Gray-Level Co-occurrence Matrices (GLCMs) are extracted. Next, the classification of images is done through the combination of two algorithms, one is Neural Network (NN), and the other Convolutional Neural Network (CNN). The extracted features are subjected to NN, and the triDWT-based segmented image is subjected to CNN. Both the segmentation and classification phases are enhanced by the improved meta-heuristic algorithm called Fitness Ratebased Crow Search Algorithm (FR-CSA), in which few parameters are optimized for attaining maximum detection accuracy. Results: The proposed DR detection model was implemented in MATLAB 2018a, and the analysis was done using three datasets, HRF, Messidor, and DIARETDB. Conclusions: The developed FR-CSA algorithm has the best detection accuracy in diagnosing DR.
EN
The article shows examples of simulation of the chemical composition effect on austenite transformation during continuous cooling. The calculations used own neural model of CCT (Continuous Cooling Transformation) diagrams describing austenite transformations that occur during continuous cooling. The model allows to calculate a CCT diagrams of structural steels and engineering steels based on chemical composition of steel and austenitizing temperature. Examples of simulation shown herein are related to the effect of selected elements on the temperatures of phase transformations, hardness and volume fraction of ferrite, pearlite, bainite and martensite in steel.
4
Content available remote AI-based method of vortex core tracking as an alternative for Lambda2
EN
The paper presents a new method of vortex core detection developed for use in CFD simulation result analysis. Apart from the conventional approach involving vector algebra, mainly the Lambda2 method, it focuses on the identification of certain features in a graphic representation of the velocity field. It is done by generating a series of slices of the said field in the postprocessing software and training a Convolutional Neural Network (AI) to recognize vortex cores. The neural network can be integrated into a simple python program and used to quickly identify vortex cores on a large number of images and translate their locations to coordinates of a CFD model for visualisation.
EN
In this paper, we compare the following machine learning methods as classifiers for sentiment analysis: k – nearest neighbours (kNN), artificial neural network (ANN), support vector machine (SVM), random forest. We used a dataset containing 5,000 movie reviews in which 2,500 were marked as positive and 2,500 as negative. We chose 5,189 words which have an influence on sentence sentiment. The dataset was prepared using a term document matrix (TDM) and classical multidimensional scaling (MDS). This is the first time that TDM and MDS have been used to choose the characteristics of text in sentiment analysis. In this case, we decided to examine different indicators of the specific classifier, such as kernel type for SVM and neighbour count in kNN. All calculations were performed in the R language, in the program R Studio v 3.5.2. Our work can be reproduced because all of our data sets and source code are public.
PL
Na podstawie badań opisanych w artykule opracowano zespół wielowarstwowych jednokierunkowych sztucznych sieci neuronowych, odwzorowujących zależności zachodzące między parametrami wejściowymi procesu azotowania niskociśnieniowego a własnościami końcowymi obrabianej stali, ze szczególnym uwzględnieniem twardości i charakterystyki wytworzonej warstwy. Na tej bazie zbudowano model fizyczny wielosegmentowego azotowania niskociśnieniowego dla stali narzędziowych. Końcowym efektem prac jest aplikacja wspierająca projektowanie, symulację oraz optymalizację tych procesów w rzeczywistości przemysłowej.
EN
Based on the research presented in the paper, a set of multilayer unidirectional artificial neural networks was developed, mapping the relationships between the input parameters of the low-pressure nitriding process and the final properties of the processed steel, with particular emphasis on the hardness and characteristics of the obtained layer. On this basis, a physical model of multi-segment low-pressure nitriding for tool steels was developed. The final effect of the presented work is an application supporting the design, simulation and optimization of these processes in industrial reality.
7
Content available remote Electromechanical positioning system with a neuro-fuzzy corrector
EN
Triple-loop elecromechanical positioning system with neuro-fuzzy corrector of position controller was developed. The structure of the neuro-fuzzy corrector has been grounded and the corrector itself has been designed. A computer Simulink model of a triple-loop two-mass positioning system has been developed. Statics and dynamics of the positioning process in a full range of reference signals and disturbances has been examined. The results of computer simulations demonstrate that the developed positioning system allows implementing optimal laws of actuator’s motion, and required positioning accuracy in a full range of reference signals and disturbances.
PL
Przedstawiono elektromechaniczny system pozycjonowania ze sterowanie wykorzystującym logikę rozmytą. Przeprowadzono symulację układu i analizę właściwości statycznych i dynamicznych. Analizowano treż wpływ zakłóceń.
EN
The work presented in this paper is a contribution in the theme of monitoring and diagnosing of faults in the three-phase squirrel cage induction machine. The proposed approach is based on the pattern recognition methods and the artificial intelligence techniques. For so doing, measurements of the stator currents are carried out on a machine subject to various faults such as: short-circuit in the stator windings, bar breakage, bearing failure and eccentricity fault. These acquisitions are classified in databases in order to process them and calculate their Power Spectral Density (PSD). Then, another database is formed of the digital data of the PSD images of the currents associated with the type of fault. After that, a process of learning and classification by artificial neural networks was developed. The test results show the efficiency, robustness and correctness of the proposed approach for the discrimination of faults of electrical or mechanical origin affecting the machine.
PL
Praca przedstawiona w tym artykule stanowi wkład w temat monitorowania i diagnozowania uszkodzeń w trójfazowej maszynie indukcyjnej klatkowej. Proponowane podejście opiera się na metodach rozpoznawania wzorców i technikach sztucznej inteligencji. W tym celu pomiary prądów stojana są przeprowadzane na maszynie podlegającej różnym usterkom, takim jak: zwarcie w uzwojeniach stojana, pęknięcie pręta, uszkodzenie łożyska i błąd mimośrodowości. Przejęcia te są klasyfikowane w bazach danych w celu ich przetworzenia i obliczenia ich gęstości widmowej mocy (PSD). Następnie tworzona jest kolejna baza danych z cyfrowymi danymi obrazów PSD prądów związanych z rodzajem uszkodzenia. Następnie opracowano proces uczenia się i klasyfikacji przez sztuczne sieci neuronowe. Wyniki testu pokazują skuteczność, niezawodność i poprawność proponowanego podejścia do rozróżnienia wad pochodzenia elektrycznego lub mechanicznego mających wpływ na maszynę.
EN
In order to solve the problem for temperature electrical resistance furnace. Characterized by their large inertia, nonlinear, long time delay and time-varying property it is rather difficult to obtain satisfactory control results with Performances of conventional PI control cannot achieve good control effect. In this paper a neural network-based adaptive control approach (ACNN) for electrical furnace is developed .using RBF NN to estimate the unknown functions by neural networks and from good choice of the law of adaptation. Based on the resolution of the lyapunov equation. Taking account of all possible parameter variations the adaptive control is designed so that it has the ability to improve the performance of the closed loop system, producing the control signal by using the information from the system. In this case we use a coping mechanism that observes the signal to control and adjust the synaptic weights of neural networks when system parameters change over time. Result shows that the proposed algorithm (ACNN) performs very well when furnace parameter varies the latter allow the neural model to be identified online and, if necessary its parameters to be stabilized and it is very easy to program it online.
PL
Piec elektryczny charakteryzuje się nieliniowością, dużym czasem opóźnienia co utrudnia sterowanie nime. W pracy zaproponowano system sterowania piecem z wykorzystaniem sieci neuronowej System jest zaprojektowany tak, że uwzględnia zmiany parametrów.
EN
All-electric ships (AES) are considered an effective solution for reducing greenhouse gas emissions as they are a platform to use clean energy sources such as lithium-ion batteries, fuel cells and solar cells instead of fossil fuel. Even though these batteries are a promising alternative, the accuracy of the battery state of charge (SOC) estimation is a critical factor for their safe and reliable operation. The SOC is a key indicator of battery residual capacity. Its estimation can effectively prevent battery over-discharge and over-charge. Next, this enables reliable estimation of the operation time of fully electric ferries, where little time is spent at the harbour, with limited time available for charging. Thus, battery management systems are essential. This paper presents a neural network model of battery SOC estimation, using a long short-term memory (LSTM) recurrent neural network (RNN) as a method for accurate estimation of the SOC in lithium-ion batteries. The current, voltage and surface temperature of the batteries are used as the inputs of the neural network. The influence of different numbers of neurons in the neural network’s hidden layer on the estimation error is analysed, and the estimation error of the neural network under different training times is compared. In addition, the hidden layer is varied from 1 to 3 layers of the LSTM nucleus and the SOC estimation error is analysed. The results show that the maximum absolute SOC estimation error of the LSTM RNN is 1.96% and the root mean square error is 0.986%, which validates the feasibility of the method.
EN
The possibilities of using cognitive technologies in the organization of systematic industrial enterprise management are described in the article. Strategic links are defined in the development of a system of stochastic models of enterprise management based on artificial intelligence. The possibility of introduction of the Perceptron model in the industrial enterprise management with the purpose of identification of "bottlenecks" in the functionality of business activity and improvement of procedures of decision-making in the framework of creation of the program of development and technical re-equipment of the enterprise is proven. The authors offered an organizational and economic mechanism of operation of an industrial enterprise, which includes new means of implementation of managerial actions through the use of a matrix of assessment of the level of implementation of cognitive technologies. The method of determining priority directions for the implementation of cognitive technologies at an enterprise was developed based on the results of the assessment of the depth of penetration of cognitive technologies and the result obtained from their implementation, which additionally takes into account the resource ratio of the implemented technologies defined as the ratio of estimates of the actual level of competencies to what is needed to work with new cognitive technologies, which allows to obtain the planned economic and organizational effect.
EN
Time standards belong to the key indicators of production process effectiveness. The paper discusses time standard setting in the production process. One of the important stages of the production process is assembly, which is a crucial stage in case of manufacturing customized products. The aim of the article is to show the methods of time standard setting which facilitate assembly planning. Specific goals of the article are focused on finding common attributes useful in assembly tasks characteristics and changeover, as well as finding value intervals helpful in assembly description. Shortening the product lifecycle, new product development and product customization bring about the development of a modular reconfigurable assembly line. The development of flexible assembly lines requires standards related to typical assembly tasks and tools. Reconfiguration and balancing assembly lines require a knowledge base related to time standards. This article presents examples of typical tasks, tools and time standards for planning product assembly and changeover which use the assembly and disassembly processes.
EN
Hereby there is given the speaker identification basic system. There is discussed application and usage of the voice interfaces, in particular, speaker voice identification upon robot and human being communication. There is given description of the information system for speaker automatic identification according to the voice to apply to robotic-verbal systems. There is carried out review of algorithms and computer-aided learning libraries and selected the most appropriate, according to the necessary criteria, ALGLIB. There is conducted the research of identification model operation performance assessment at different set of the fundamental voice tone. As the criterion of accuracy there has been used the percentage of improperly classified cases of a speaker identification.
EN
The linear programming (LP) approach to solve the Bellman equation in dynamic programming is a well-known option for finite state and input spaces to obtain an exact solution. However, with function approximation or continuous state spaces, refinements are necessary. This paper presents a methodology to make approximate dynamic programming via LP work in practical control applications with continuous state and input spaces. There are some guidelines on data and regressor choices needed to obtain meaningful and well-conditioned value function estimates. The work discusses the introduction of terminal ingredients and computation of lower and upper bounds of the value function. An experimental inverted-pendulum application will be used to illustrate the proposal and carry out a suitable comparative analysis with alternative options in the literature.
EN
The article contains a review of selected classification methods of dermatoscopic images with human skin lesions, taking into account various stages of dermatological disease. The described algorithms are widely used in the diagnosis of skin lesions, such as artificial neural networks (CNN, DCNN), random forests, SVM, kNN classifier, AdaBoost MC and their modifications. The effectiveness, specificity and accuracy of classifications based on the same data sets were also compared and analyzed.
PL
Artykuł zawiera przegląd wybranych metod klasyfikacji obrazów dermatoskopowych zmian skórnych człowieka z uwzględnieniem różnych etapów choroby dermatologicznej. Opisane algorytmy są szeroko wykorzystywane w diagnostyce zmian skórnych, takie jak sztuczne sieci neuronowe (CNN, DCNN), random forests, SVM, klasyfikator kNN, AdaBoost MC i ich modyfikacje. Porównana i przeanalizowana została również skuteczność, specyficznośc i dokładność klasyfikatów w oparciu o te same zestawy danych.
EN
The article considers the main criteria for the selection and formation of the wardrobe, which is one of the areas of application of methods and means for image classification. Typical software solutions for the task are analyzed, and the Analytic Hierarchy Process was used to analyze such applications. To improve the wardrobe selection process, the concept of an intelligent information system based on the use of convolutional neural networks was proposed.
17
Content available Bitmap Image Recognition with Neural Networks
EN
Logistics, finance, science, and trade are just some of the areas that require computer vision technology, which includes number recognition. The need to recognize numbers in images or photographs is found in tasks such as recognizing car numbers, reading values from paper bills, recognizing object identification numbers, and reading credit card numbers. The development of an online application for recognition numbers in bitmap images using machine training technologies, namely an artificial neural network based on the class of neural networks perceptron, is an actual task.
EN
The way brain networks maintain high transmission efficiency is believed to be fundamental in understanding brain activity. Brains consisting of more cells render information transmission more reliable and robust to noise. On the other hand, processing information in larger networks requires additional energy. Recent studies suggest that it is complexity, connectivity, and function diversity, rather than just size and the number of neurons, that could favour the evolution of memory, learning, and higher cognition. In this paper, we use Shannon information theory to address transmission efficiency quantitatively. We describe neural networks as communication channels, and then we measure information as mutual information between stimuli and network responses. We employ a probabilistic neuron model based on the approach proposed by Levy and Baxter, which comprises essential qualitative information transfer mechanisms. In this paper, we overview and discuss our previous quantitative results regarding brain-inspired networks, addressing their qualitative consequences in the context of broader literature. It is shown that mutual information is often maximized in a very noisy environment e.g., where only one-third of all input spikes are allowed to pass through noisy synapses and farther into the network. Moreover, we show that inhibitory connections as well as properly displaced long-range connections often significantly improve transmission efficiency. A deep understanding of brain processes in terms of advanced mathematical science plays an important role in the explanation of the nature of brain efficiency. Our results confirm that basic brain components that appear during the evolution process arise to optimise transmission performance.
EN
In this paper, feature weighting is used to develop an effective computer-aided diagnosis system for breast cancer. Feature weighting is employed because it boosts the classification performance more as compared to feature subset selection. Specifically, a wrapper method utilizing the Ant Lion Optimization algorithm is presented that searches for best feature weights and parametric values of Multilayer Neural Network simultaneously. The selection of hidden neurons and backpropagation training algorithms are used as parameters of neural networks. The performance of the proposed approach is evaluated on three breast cancer datasets. The data is initially normalized using tanh method to remove the effects of dominant features and outliers. The results show that the proposed wrapper method has a better ability to attain higher accuracy as compared to the existing techniques. The obtained high classification performance validates the work which has the potential for becoming an alternative to the other well-known techniques.
EN
Load distribution analysis on foot surface allows knowing human mechanical behavior and aids the doctor in the detection of gait disorders like, the risk of foot ulcerations, leg discrepancy, and footprint alterations. Plantar pressure data combined with techniques that use integral reasoning produce easy understanding medical tools for assisting in treatment, early detection, and the development of preventive strategies. The present research compares the classification of human plantar foot alterations using Fuzzy Cognitive Maps (FCM) trained by Genetic Algorithm (GA) against a Multi-Layer Perceptron Neural Network (MLPNN). One hundred and fifty-one subject volunteers (aged 7–77) were classified previously with the flat foot (n = 70) and cavus foot (n = 81) by specialized physicians of the Piédica diagnostic center. The trial walking was conducted using plantar pressure platforms FreeMed®. The foot surface was divided into 14 areas that included toe 1 st to 5th, metatarsal joint 1st to 5th, lateral midfoot, medial midfoot, lateral heel, and medial heel. Pressure data were normalized for each area. Better performance in the classification using small amounts of data were found by using Fuzzy rather than non-Fuzzy approach.
first rewind previous Strona / 32 next fast forward last
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.