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    Wyświetlanie 1-12 z 12
    Tytuł:
    Classification of high resolution satellite images using improved U-Net
    Autorzy:
    Wang, Yong
    Zhang, Dongfang
    Dai, Guangming
    Powiązania:
    https://bibliotekanauki.pl/articles/331235.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    satellite image classification
    deep learning
    U-net
    spatial pyramid pooling
    zdjęcia satelitarne
    uczenie głębokie
    Opis:
    Satellite image classification is essential for many socio-economic and environmental applications of geographic information systems, including urban and regional planning, conservation and management of natural resources, etc. In this paper, we propose a deep learning architecture to perform the pixel-level understanding of high spatial resolution satellite images and apply it to image classification tasks. Specifically, we augment the spatial pyramid pooling module with image-level features encoding the global context, and integrate it into the U-Net structure. The proposed model solves the problem consisting in the fact that U-Net tends to lose object boundaries after multiple pooling operations. In our experiments, two public datasets are used to assess the performance of the proposed model. Comparison with the results from the published algorithms demonstrates the effectiveness of our approach.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2020, 30, 3; 399-413
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Performance analysis of a dual stage deep rain streak removal convolution neural network module with a modified deep residual dense network
    Autorzy:
    Jayaraman, Thiyagarajan
    Chinnusamy, Shankar
    Powiązania:
    https://bibliotekanauki.pl/articles/2055158.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    single image deraining
    deep learning
    modified residual dense network
    PyTorch
    obraz pojedynczy
    uczenie głębokie
    sieć gęsta
    Opis:
    The visual appearance of outdoor captured images is affected by various weather conditions, such as rain patterns, haze, fog and snow. The rain pattern creates more degradation in the visual quality of the image due to its physical structure compared with other weather conditions. Also, the rain pattern affects both foreground and background image information. The removal of rain patterns from a single image is a critical process, and more attention is given to remove the structural rain pattern from real-time rain images. In this paper, we analyze the single image deraining problem and present a solution using the dual stage deep rain streak removal convolutional neural network. The proposed single image deraining framework primarily consists of three main blocks: a derain streaks removal CNN (derain SRCNN), a modified residual dense block (MRDB), and a six-stage scale feature aggregation module (3SFAM). The ablation study is conducted to evaluate the performance of various modules available in the proposed deraining network. The robustness of the proposed deraining network is evaluated over the popular synthetic and real-time data sets using four performance metrics such as the peak signal-to-noise ratio (PSNR), the feature similarity index (FSIM), the structural similarity index measure (SSIM), and the universal image quality index (UIQI). The experimental results show that the proposed framework outperforms both synthetic and real-time images compared with other state-of-the-art single image deraining approaches. In addition, the proposed network takes less running and training time.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2022, 32, 1; 111--123
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A nested autoencoder approach to automated defect inspection on textured surfaces
    Autorzy:
    Oz, Muhammed Ali Nur
    Kaymakci, Ozgur Turay
    Mercimek, Muharrem
    Powiązania:
    https://bibliotekanauki.pl/articles/2055170.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    autoencoder
    defect detection
    automatic visual inspection
    deep learning
    autoenkoder
    wykrywanie defektów
    inspekcja wizyjna
    inspekcja automatyczna
    uczenie głębokie
    Opis:
    In recent years, there has been a highly competitive pressure on industrial production. To keep ahead of the competition, emerging technologies must be developed and incorporated. Automated visual inspection systems, which improve the overall mass production quantity and quality in lines, are crucial. The modifications of the inspection system involve excessive time and money costs. Therefore, these systems should be flexible in terms of fulfilling the changing requirements of high capacity production support. A coherent defect detection model as a primary application to be used in a real-time intelligent visual surface inspection system is proposed in this paper. The method utilizes a new approach consisting of nested autoencoders trained with defect-free and defect injected samples to detect defects. Making use of two nested autoencoders, the proposed approach shows great performance in eliminating defects. The first autoencoder is used essentially for feature extraction and reconstructing the image from these features. The second one is employed to identify and fix defects in the feature code. Defects are detected by thresholding the difference between decoded feature code outputs of the first and the second autoencoder. The proposed model has a 96% detection rate and a relatively good segmentation performance while being able to inspect fabrics driven at high speeds.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 3; 515--523
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An automated driving strategy generating method based on WGAIL–DDPG
    Autorzy:
    Zhang, Mingheng
    Wan, Xing
    Gang, Longhui
    Lv, Xinfei
    Wu, Zengwen
    Liu, Zhaoyang
    Powiązania:
    https://bibliotekanauki.pl/articles/2055167.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    automated driving system
    deep learning
    deep reinforcement learning
    imitation learning
    deep deterministic policy gradient
    system jezdny
    uczenie głębokie
    uczenie przez naśladowanie
    Opis:
    Reliability, efficiency and generalization are basic evaluation criteria for a vehicle automated driving system. This paper proposes an automated driving decision-making method based on the Wasserstein generative adversarial imitation learning–deep deterministic policy gradient (WGAIL–DDPG(λ)). Here the exact reward function is designed based on the requirements of a vehicle’s driving performance, i.e., safety, dynamic and ride comfort performance. The model’s training efficiency is improved through the proposed imitation learning strategy, and a gain regulator is designed to smooth the transition from imitation to reinforcement phases. Test results show that the proposed decision-making model can generate actions quickly and accurately according to the surrounding environment. Meanwhile, the imitation learning strategy based on expert experience and the gain regulator can effectively improve the training efficiency for the reinforcement learning model. Additionally, an extended test also proves its good adaptability for different driving conditions.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 3; 461--470
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A hybrid two-stage SqueezeNet and support vector machine system for Parkinson’s disease detection based on handwritten spiral patterns
    Autorzy:
    Bernardo, Lucas Salvador
    Damaševičius, Robertas
    de Albuquerque, Victor Hugo C.
    Maskeliūnas, Rytis
    Powiązania:
    https://bibliotekanauki.pl/articles/2055162.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    Parkinson’s disease
    spirography
    convolutional neural network
    deep learning
    choroba Parkinsona
    spirografia
    sieć neuronowa konwolucyjna
    uczenie głębokie
    Opis:
    Parkinson’s disease (PD) is the second most common neurological disorder in the world. Nowadays, it is estimated that it affects from 2% to 3% of the global population over 65 years old. In clinical environments, a spiral drawing task is performed to help to obtain the disease’s diagnosis. The spiral trajectory differs between people with PD and healthy ones. This paper aims to analyze differences between handmade drawings of PD patients and healthy subjects by applying the SqueezeNet convolutional neural network (CNN) model as a feature extractor, and a support vector machine (SVM) as a classifier. The dataset used for training and testing consists of 514 handwritten draws of Archimedes’ spiral images derived from heterogeneous sources (digital and paper-based), from which 296 correspond to PD patients and 218 to healthy subjects. To extract features using the proposed CNN, a model is trained and 20% of its data is used for testing. Feature extraction results in 512 features, which are used for SVM training and testing, while the performance is compared with that of other machine learning classifiers such as a Gaussian naive Bayes (GNB) classifier (82.61%) and a random forest (RF) (87.38%). The proposed method displays an accuracy of 91.26%, which represents an improvement when compared to pure CNN-based models such as SqueezeNet (85.29%), VGG11 (87.25%), and ResNet (89.22%).
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 4; 549--561
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A genetic algorithm based optimized convolutional neural network for face recognition
    Autorzy:
    Karlupia, Namrata
    Mahajan, Palak
    Abrol, Pawanesh
    Lehana, Parveen K.
    Powiązania:
    https://bibliotekanauki.pl/articles/2201023.pdf
    Data publikacji:
    2023
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    convolutional neural network
    genetic algorithm
    deep learning
    evolutionary technique
    sieć neuronowa konwolucyjna
    algorytm genetyczny
    uczenie głębokie
    technika ewolucyjna
    Opis:
    Face recognition (FR) is one of the most active research areas in the field of computer vision. Convolutional neural networks (CNNs) have been extensively used in this field due to their good efficiency. Thus, it is important to find the best CNN parameters for its best performance. Hyperparameter optimization is one of the various techniques for increasing the performance of CNN models. Since manual tuning of hyperparameters is a tedious and time-consuming task, population based metaheuristic techniques can be used for the automatic hyperparameter optimization of CNNs. Automatic tuning of parameters reduces manual efforts and improves the efficiency of the CNN model. In the proposed work, genetic algorithm (GA) based hyperparameter optimization of CNNs is applied for face recognition. GAs are used for the optimization of various hyperparameters like filter size as well as the number of filters and of hidden layers. For analysis, a benchmark dataset for FR with ninety subjects is used. The experimental results indicate that the proposed GA-CNN model generates an improved model accuracy in comparison with existing CNN models. In each iteration, the GA minimizes the objective function by selecting the best combination set of CNN hyperparameters. An improved accuracy of 94.5% is obtained for FR.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2023, 33, 1; 21--31
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Applications of rough sets in big data analysis: An overview
    Autorzy:
    Pięta, Piotr
    Szmuc, Tomasz
    Powiązania:
    https://bibliotekanauki.pl/articles/2055175.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    rough sets theory
    big data analysis
    deep learning
    data mining
    teoria zbiorów przybliżonych
    duży zbiór danych
    uczenie głębokie
    eksploracja danych
    Opis:
    Big data, artificial intelligence and the Internet of things (IoT) are still very popular areas in current research and industrial applications. Processing massive amounts of data generated by the IoT and stored in distributed space is not a straightforward task and may cause many problems. During the last few decades, scientists have proposed many interesting approaches to extract information and discover knowledge from data collected in database systems or other sources. We observe a permanent development of machine learning algorithms that support each phase of the data mining process, ensuring achievement of better results than before. Rough set theory (RST) delivers a formal insight into information, knowledge, data reduction, uncertainty, and missing values. This formalism, formulated in the 1980s and developed by several researches, can serve as a theoretical basis and practical background for dealing with ambiguities, data reduction, building ontologies, etc. Moreover, as a mature theory, it has evolved into numerous extensions and has been transformed through various incarnations, which have enriched expressiveness and applicability of the related tools. The main aim of this article is to present an overview of selected applications of RST in big data analysis and processing. Thousands of publications on rough sets have been contributed; therefore, we focus on papers published in the last few years. The applications of RST are considered from two main perspectives: direct use of the RST concepts and tools, and jointly with other approaches, i.e., fuzzy sets, probabilistic concepts, and deep learning. The latter hybrid idea seems to be very promising for developing new methods and related tools as well as extensions of the application area.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 4; 659--683
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A hybrid control strategy for a dynamic scheduling problem in transit networks
    Autorzy:
    Liu, Zhongshan
    Yu, Bin
    Zhang, Li
    Wang, Wensi
    Powiązania:
    https://bibliotekanauki.pl/articles/2172126.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    service reliability
    transit network
    proactive control method
    deep reinforcement learning
    hybrid control strategy
    niezawodność usług
    sieć tranzytowa
    uczenie głębokie
    kontrola hybrydowa
    Opis:
    Public transportation is often disrupted by disturbances, such as the uncertain travel time caused by road congestion. Therefore, the operators need to take real-time measures to guarantee the service reliability of transit networks. In this paper, we investigate a dynamic scheduling problem in a transit network, which takes account of the impact of disturbances on bus services. The objective is to minimize the total travel time of passengers in the transit network. A two-layer control method is developed to solve the proposed problem based on a hybrid control strategy. Specifically, relying on conventional strategies (e.g., holding, stop-skipping), the hybrid control strategy makes full use of the idle standby buses at the depot. Standby buses can be dispatched to bus fleets to provide temporary or regular services. Besides, deep reinforcement learning (DRL) is adopted to solve the problem of continuous decision-making. A long short-term memory (LSTM) method is added to the DRL framework to predict the passenger demand in the future, which enables the current decision to adapt to disturbances. The numerical results indicate that the hybrid control strategy can reduce the average headway of the bus fleet and improve the reliability of bus service.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2022, 32, 4; 553--567
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Vision-based positioning of electric buses for assisted docking to charging stations
    Autorzy:
    Nowak, Tomasz
    Nowicki, Michał R.
    Skrzypczyński, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/2172128.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    AI transport
    monocular vision
    deep learning
    keypoint
    advanced driver assistance system
    wizja jednookularowa
    uczenie głębokie
    punkt charakterystyczny
    zaawansowany system wspomagania kierowcy
    Opis:
    We present a novel approach to vision-based localization of electric city buses for assisted docking to a charging station. The method assumes that the charging station is a known object, and employs a monocular camera system for positioning upon carefully selected point features detected on the charging station. While the pose is estimated using a geometric method and taking advantage of the known structure of the feature points, the detection of keypoints themselves and the initial recognition of the charging station are accomplished using neural network models. We propose two novel neural network architectures for the estimation of keypoints. Extensive experiments presented in the paper made it possible to select the MRHKN architecture as the one that outperforms state-of-the-art keypoint detectors in the task considered, and offers the best performance with respect to the estimated translation and rotation of the bus with a low-cost hardware setup and minimal passive markers on the charging station.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2022, 32, 4; 583--599
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Exploring complex and big data
    Autorzy:
    Stefanowski, J.
    Krawiec, K.
    Wrembel, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/330152.pdf
    Data publikacji:
    2017
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    big data
    complex data
    data integration
    data provenance
    data streams
    deep learning
    dane złożone
    integracja danych
    pochodzenie danych
    strumień danych
    uczenie głębokie
    Opis:
    This paper shows how big data analysis opens a range of research and technological problems and calls for new approaches. We start with defining the essential properties of big data and discussing the main types of data involved. We then survey the dedicated solutions for storing and processing big data, including a data lake, virtual integration, and a polystore architecture. Difficulties in managing data quality and provenance are also highlighted. The characteristics of big data imply also specific requirements and challenges for data mining algorithms, which we address as well. The links with related areas, including data streams and deep learning, are discussed. The common theme that naturally emerges from this characterization is complexity. All in all, we consider it to be the truly defining feature of big data (posing particular research and technological challenges), which ultimately seems to be of greater importance than the sheer data volume.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2017, 27, 4; 669-679
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A hybrid approach of a deep learning technique for real-time ECG beat detection
    Autorzy:
    Patro, Kiran Kumar
    Prakash, Allam Jaya
    Samantray, Saunak
    Pławiak, Joanna
    Tadeusiewicz, Ryszard
    Pławiak, Paweł
    Powiązania:
    https://bibliotekanauki.pl/articles/2172118.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    cardiac abnormalities
    CAD
    convolutional neural network
    CNN
    deep learning
    ECG
    electrocardiogram
    supra ventricular ectopic beats
    SVE
    nieprawidłowości kardiologiczne
    sieć neuronowa konwolucyjna
    uczenie głębokie
    EKG
    elektrokardiogram
    Opis:
    This paper presents a new customized hybrid approach for early detection of cardiac abnormalities using an electrocardiogram (ECG). The ECG is a bio-electrical signal that helps monitor the heart’s electrical activity. It can provide health information about the normal and abnormal physiology of the heart. Early diagnosis of cardiac abnormalities is critical for cardiac patients to avoid stroke or sudden cardiac death. The main aim of this paper is to detect crucial beats that can damage the functioning of the heart. Initially, a modified Pan–Tompkins algorithm identifies the characteristic points, followed by heartbeat segmentation. Subsequently, a different hybrid deep convolutional neural network (CNN) is proposed to experiment on standard and real-time long-term ECG databases. This work successfully classifies several cardiac beat abnormalities such as supra-ventricular ectopic beats (SVE), ventricular beats (VE), intra-ventricular conduction disturbances beats (IVCD), and normal beats (N). The obtained classification results show a better accuracy of 99.28% with an F1 score of 99.24% with the MIT–BIH database and a descent accuracy of 99.12% with the real-time acquired database.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2022, 32, 3; 455--465
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Constant Q-transform-based deep learning architecture for detection of obstructive sleep apnea
    Autorzy:
    Kandukuri, Usha Rani
    Prakash, Allam Jaya
    Patro, Kiran Kumar
    Neelapu, Bala Chakravarthy
    Tadeusiewicz, Ryszard
    Pławiak, Paweł
    Powiązania:
    https://bibliotekanauki.pl/articles/24200694.pdf
    Data publikacji:
    2023
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    sleep apnea
    convolutional neural network
    constant Q-transform
    deep learning
    single lead ECG signal
    non apnea
    obstructive sleep apnea
    bezdech senny
    sieć neuronowa konwolucyjna
    uczenie głębokie
    sygnał EKG
    obturacyjny bezdech senny
    Opis:
    Obstructive sleep apnea (OSA) is a long-term sleep disorder that causes temporary disruption in breathing while sleeping. Polysomnography (PSG) is the technique for monitoring different signals during the patient’s sleep cycle, including electroencephalogram (EEG), electromyography (EMG), electrocardiogram (ECG), and oxygen saturation (SpO2). Due to the high cost and inconvenience of polysomnography, the usefulness of ECG signals in detecting OSA is explored in this work, which proposes a two-dimensional convolutional neural network (2D-CNN) model for detecting OSA using ECG signals. A publicly available apnea ECG database from PhysioNet is used for experimentation. Further, a constant Q-transform (CQT) is applied for segmentation, filtering, and conversion of ECG beats into images. The proposed CNN model demonstrates an average accuracy, sensitivity and specificity of 91.34%, 90.68% and 90.70%, respectively. The findings obtained using the proposed approach are comparable to those of many other existing methods for automatic detection of OSA.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2023, 33, 3; 493--506
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-12 z 12

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