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    Wyświetlanie 1-26 z 26
    Tytuł:
    Investment risk assessment based on the long-term memory parameter
    Autorzy:
    Zeug-Żebro, Katarzyna
    Powiązania:
    https://bibliotekanauki.pl/articles/1878517.pdf
    Data publikacji:
    2020
    Wydawca:
    Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
    Tematy:
    long-term memory
    financial time series
    investment risk
    local Whittle estimator
    pamięć długotrwała
    finansowe szeregi czasowe
    ryzyko inwestycyjne
    Opis:
    Purpose: The presence of a long-term memory component in a time series means that even very distant observations exert a certain influence on subsequent implementations of the process. Generally, this relationship is not particularly strong, but it does exist. Interpreting this phenomenon in the context of financial time series, one can come to the conclusion that information that has affected the market some time ago may still be important for the current quotation. The article is devoted to checking the existence of a long-term memory in the financial time series and assessing the investment risk of these series based on the long-term memory parameter. Design/methodology/approach: In order to study the phenomenon of long-term memory in financial time series, the local Whittle estimator was used, while the investment risk assessment was carried out using the fractal dimension, β-coefficient and standard deviation of rates of return. Findings: In the first part of the study the author indicated time series which were characterized by the phenomenon of long-term memory. Then, on the basis of selected measures, the risk of investment was estimated and shares with the least risk were indicated. Research limitations/implications: The results obtained for selected measures showed discrepancies between the shares with the highest and the lowest level of investment risk. Although the results obtained do not give a definite answer which risk measure is more effective, they encourage the use of other measures related to the phenomenon of long-term memory. Practical implications: Application in portfolio analysis. Originality/value: The use of the long-term memory parameter to assess the investment risk of shares.
    Źródło:
    Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska; 2020, 144; 671-680
    1641-3466
    Pojawia się w:
    Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Long-range dependence in DataCenter networks transmission
    Autorzy:
    Paszkiewicz, A.
    Bolanowski, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/114121.pdf
    Data publikacji:
    2017
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    Hurst exponen
    long-term memory
    network convergence
    data center protocols
    Opis:
    The paper presents the mechanisms of long-range dependence measurement in the context of data transmission in Data Center networks. The research involved mainly analyzing network traffic generated by protocols such as CIFS and iSCSI, which are commonly used in such infrastructures. The purpose of the paper was to determine whether the network traffic of above mentioned protocols encapsulated in TCP/IP protocol will have persistent, anti-persist, or random walk character. By indicating long-range dependencies for this type of network traffic, it will be possible to develop effective mechanisms for detecting anomaly in its transmission as well as flow control, including QoS mechanisms, load balancing, etc.
    Źródło:
    Measurement Automation Monitoring; 2017, 63, 8; 275-277
    2450-2855
    Pojawia się w:
    Measurement Automation Monitoring
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Rola wskazówek i przekonań w długotrwałej pamięci cen
    The role of cues and beliefs in long-term memory for prices
    Autorzy:
    Barzykowski, Krystian
    Leśniak, Agnieszka
    Niedźwieńska, Agnieszka
    Powiązania:
    https://bibliotekanauki.pl/articles/2128534.pdf
    Data publikacji:
    2010
    Wydawca:
    Katolicki Uniwersytet Lubelski Jana Pawła II. Towarzystwo Naukowe KUL
    Tematy:
    prices
    memory
    temporal cues
    theory of change
    ceny
    pamięć
    wskazówki czasowe
    teoria zmiany
    Opis:
    W badaniach pamięci cen sprzed lat osoby mają skłonność do przypominania sobie cen jako niższych niż były w istocie. Celem dwóch przeprowadzonych eksperymentów była analiza czynników wpływających na tę tendencyjność. Założono, że zaniżanie wynika z nietrafnej lokalizacji okresu, z którego cena ma być przywołana, oraz z przekonania o ciągłym i znaczącym wzroście cen. Zgodnie z przewidywaniami, tendencja do zaniżania osłabła, gdy badanym podano dodatkowe wskazówki czasowe, i nie wystąpiła w odniesieniu do produktu uważanego za taniejący. Uzyskane wyniki są spójne z asocjacyjną teorią pamięci cen Kempa (1999), zasadą specyficzności kodowania (Tulving, Thompson, 1973) oraz ujęciami pamięci podkreślającymi rolę prywatnych teorii zmiany w przypominaniu (Ross, 1989).
    Several studies have indicated that people tend to underestimate prices from recent years. Two experiments were conducted to analyze factors that are responsible for the systematic bias. It has been assumed that the tendency results from misremembering the time of the price as well as general beliefs about stable and significant price increases. As expected, the tendency was diminished when participants were provided with temporal cues and did not emerge for the product that was considered to become cheaper. The results accord well with the associative theory of memory for prices (Kemp, 1999), the encoding specificity principle (Tulving & Thompson, 1973), as well as retrieval conceptualizations in which implicit theories of change are stressed (Ross, 1989).
    Źródło:
    Roczniki Psychologiczne; 2010, 13, 2; 125-144
    1507-7888
    Pojawia się w:
    Roczniki Psychologiczne
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    COMPARING THE SHORT AND LONG-TERM EFFECTS OF ACUTE MODERATE-INTENSITY EXERCISE ON MNEMONIC SIMILARITY AND RMOTIONAL MEMORY TASKS
    Autorzy:
    Acevedo-Triana, Cesar
    Cordoba-Patiño, Diana
    Muñoz, Juan Francisco
    Cifuentes, Julian
    Melgarejo Pinto, Victor
    Rodriguez W, Oscar
    Hurtado-Parrado, Camilo
    Powiązania:
    https://bibliotekanauki.pl/articles/2137978.pdf
    Data publikacji:
    2021-01-28
    Wydawca:
    Fundacja Edukacji Medycznej, Promocji Zdrowia, Sztuki i Kultury Ars Medica
    Tematy:
    exercise moderate-intensity
    pattern separation
    emotional memory
    long term memory
    young
    Opis:
    Mounting research has linked acute moderate-intensity exercise with changes indiscrimination of similar events – i.e., mnemonic memory. Conversely, few studies have compared performance in tasks associated to each type of memory(mnemonic similarity and emotional) and less have evaluated performance several days after exercise sessions. Thirty-five undergraduate students were randomly distributed in three groups that differed in the assigned duration of the moderate-intensity ex- ercise session. We established first the moderate-intensity exercise program by calculating the VO2max 50%. Two-to-five days later, participants engaged in the exercise condition to which they were assigned, followed by a five-minute rest period. Immediately after, all participants were ex posed to the training phase of both memory tasks. The first retrieval phase was tested 45 minutes after encoding phase was completed. Subsequent retrieval phases were conducted 24, 48, and 168 hours post-training. Exercise of long duration increased discrimination performance in images of low similarity. Comparison of the effects of exercise on discrimination of the three types of images that the emotional-memory task entails showed improved performance only for aversive and neutral images. Exercise improves discrimination of low similarity images, with better overall perform- ance after a longer exercise session. This finding adds to previous reports that have found analogous effects using other memory tasks. It also supports the notion that acute effects due to exercise are specifically related to hippocampal functionality and its ability to separate patterns. Finally, maintenance of emotional informa- tion across time suggest a different mechanism, independent of pattern- separation processing. ----------------------------------------------------------------------------------------------------------------------------
    Źródło:
    Acta Neuropsychologica; 2021, 19(1); 33-61
    1730-7503
    2084-4298
    Pojawia się w:
    Acta Neuropsychologica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Concept Maps and Obliteration in Bilinguals
    Autorzy:
    Gómez Ramos, José Luis
    Bravo Palomares, Silvia María
    Powiązania:
    https://bibliotekanauki.pl/articles/43467352.pdf
    Data publikacji:
    2024
    Wydawca:
    Uniwersytet Dolnośląski DSW. Wydawnictwo Naukowe DSW
    Tematy:
    concept maps
    long-term memory
    CLIL
    bilingual education
    instructional design
    Opis:
    This study examines the effectiveness of concept maps in promoting long-term memory among Content and Language Integrated Learning (CLIL) students. It focuses on the accuracy of content transmission and the acquisition of meaningful learning in bilingual education by connecting new and carefully organized information to students' prior knowledge. Thus, the research assesses the use of concept maps as instructional tools in foreign language (L2, or 'second language') settings, addressing a lack of evidence regarding their effectiveness. It also considers how concept mapping affects long-term memory through factors such as perception, processing, cognition, and transfer. The study examines how bilingualism, bilingual education, and curricular content influence instructional design when using concept maps. The study involved 60 Spanish primary education students attending a semi-public bilingual school. The research results aim to contribute to the development of effective teaching strategies and instructional design in CLIL classrooms, ultimately enhancing students' long-term memory and learning outcomes.
    Źródło:
    Teraźniejszość – Człowiek – Edukacja; 2024, 26, 1(95); 9-32
    1505-8808
    2450-3428
    Pojawia się w:
    Teraźniejszość – Człowiek – Edukacja
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Adaptive changes in human memory: a literature review
    Autorzy:
    Sabiniewicz, Agnieszka Laura
    Sorokowski, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/954177.pdf
    Data publikacji:
    2017
    Wydawca:
    Fundacja Pro Scientia Publica
    Tematy:
    adaptation
    environmental changes
    long-term memory
    short-term memory
    working memory
    sensory memory
    Opis:
    The paper contains a review of the literature concerning memory abilities and human senses performance under different environmental circumstances. A number of studies indicated that environment has a significant impact on human senses functioning. It can affect it in a mechanical way, by a chronic exposure to potentially harmful substances or processes in different work environments. Also, some cognitive abilities that have evolved to perform evolutionary essential functions lost their importance because of the change of environment impact. Moreover, training can be a source of improvement of both human senses and cognitive abilities, as well. That might suggest that, while using, under different environmental circumstances different cognitive abilities develop. We take into a particular consideration human memory and its role, show current studies in this field and suggest new research directions.
    Źródło:
    Journal of Education Culture and Society; 2017, 8, 1; 79-87
    2081-1640
    Pojawia się w:
    Journal of Education Culture and Society
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Missing Precipitation Data Estimation Using Long Short-Term Memory Deep Neural Networks
    Autorzy:
    Djerbouai, Salim
    Powiązania:
    https://bibliotekanauki.pl/articles/2086428.pdf
    Data publikacji:
    2022
    Wydawca:
    Polskie Towarzystwo Inżynierii Ekologicznej
    Tematy:
    Hodna
    K'sob basin
    missing precipitation data
    long short-term memory
    CCWM
    coefficient of correlation weighting method
    IDWM
    inverse distance weighting method
    Opis:
    Due to the spatiotemporal variability of precipitation and the complexity of physical processes involved, missing precipitation data estimation remains as a significant problem. Algeria, like other countries in the world, is affected by this problem. In the present paper, Long Short-Term Memory (LSTM) deep neural Networks model was tested to estimate missing monthly precipitation data. The application was presented for the K'sob basin, Algeria. In the present paper, the optimal architecture of LSTM model was adjusted by trial-and-error-procedure. The LSTM model was compared with the most widely used classical methods including inverse distance weighting method (IDWM) and the coefficient of correlation weighting method (CCWM). Finally, it was concluded that the LSTM model performed better than the other methods.
    Źródło:
    Journal of Ecological Engineering; 2022, 23, 5; 216--225
    2299-8993
    Pojawia się w:
    Journal of Ecological Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Pamięć autobiograficzna
    Autobiographical memory
    Autorzy:
    Walczak, Aleksandra
    Wiśniewska, Barbara
    Powiązania:
    https://bibliotekanauki.pl/articles/945132.pdf
    Data publikacji:
    2011
    Wydawca:
    Medical Communications
    Tematy:
    autobiographical memory
    declarative memory
    episodic memory
    long-term memory
    semantic memory
    pamięć autobiograficzna
    pamięć deklaratywna
    pamięć długotrwała
    pamięć epizodyczna
    pamięć semantyczna
    Opis:
    The term of autobiographical memory (AM) started to occur in scientific papers in the eighties of the 20th century. It has been defined by Tulving as a memory of individual past and mostly described as long-term, declarative memory. It comprises episodic and semantic elements, some researchers even claim that AM is a special case of episodic memory. The aim of this study is to introduce the issue of AM along with its chosen concepts, functions, neurobiological basis and assessment methods. The most popular concepts of AM have been described in this paper. In the first one a distinct influence of Tulving’s division of declarative memory into episodic and semantic memory is shown. The author of the second concept claims that AM comprises autobiographical memories and autobiographical database. This study describes also the functions of AM with the classification presented by Maruszewski, choosing such functions as informative, communicative, interpersonal, motivating-emotional and organizational one. The parts of brain structures responsible for its correct functioning, i.a. hippocampus or thalamus have also been characterised along with the results of its malfunctioning as well as chosen assessment methods of AM, which are i.a. the oldest – Galton technique, Crovitz and Schiffman technique, Autobiographical Memory Interview (AMI) and the Autobiographical Memory Test (AMT). Despite the fact that the term of AM is relatively new, it is the subject of interest of many researchers, which shows its great role in human life, if only because it enables the establishment and maintenance of relations with other people.
    Pojęcie pamięć autobiograficzna (PA) pojawiło się w dysertacjach naukowych w latach 80. XX wieku. Jest ono definiowane jako pamięć indywidualnej przeszłości, którą zalicza się głównie do pamięci trwałej, deklaratywnej. Składa się z elementów epizodycznych i semantycznych, a niektórzy badacze wręcz określają PA jako specjalny przypadek pamięci epizodycznej. Celem niniejszej pracy jest przybliżenie pojęcia PA wraz z jej wybranymi koncepcjami, funkcjami, neurobiologicznymi podstawami oraz metodami badania. W pracy zostały przedstawione najpopularniejsze sposoby rozumienia PA. W pierwszej opisanej koncepcji widać wyraźny wpływ dokonanego przez Tulvinga podziału pamięci deklaratywnej na epizodyczną i semantyczną, w drugiej zaś następuje podział składowych PA na wspomnienia autobiograficzne oraz bazę danych autobiograficznych. W niniejszej pracy przedstawiono także funkcje PA, w tym między innymi ich podział zaprezentowany przez Maruszewskiego na takie jak: informacyjna, komunikacyjna, interpersonalna, motywacyjno-emocjonalna oraz organizacyjna. Opisano również struktury odpowiedzialne za właściwe funkcjonowanie tejże pamięci, między innymi hipokamp oraz wzgórze, a także skutki ich uszkodzenia. Scharakteryzowano wybrane metody badania pamięci autobiograficznej, między innymi najstarszą metodę swobodnych skojarzeń Galtona, metodę kierowanych skojarzeń Crovitza i Schiffmana, wywiad do badania pamięci autobiograficznej (AMI) oraz test do badania pamięci autobiograficznej (AMT). Choć pojęcie PA jest relatywnie nowe, stanowi przedmiot zainteresowania wielu badaczy, co świadczy o jego doniosłej roli w życiu człowieka, choćby ze względu na umożliwienie nawiązywania i podtrzymywania kontaktów z innymi.
    Źródło:
    Psychiatria i Psychologia Kliniczna; 2011, 11, 1; 51-54
    1644-6313
    2451-0645
    Pojawia się w:
    Psychiatria i Psychologia Kliniczna
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep Belief Neural Networks and Bidirectional Long-Short Term Memory Hybrid for Speech Recognition
    Autorzy:
    Brocki, Ł.
    Marasek, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/177625.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    deep belief neural networks
    long-short term memory
    bidirectional recurrent neural networks
    speech recognition
    large vocabulary continuous speech recognition
    Opis:
    This paper describes a Deep Belief Neural Network (DBNN) and Bidirectional Long-Short Term Memory (LSTM) hybrid used as an acoustic model for Speech Recognition. It was demonstrated by many independent researchers that DBNNs exhibit superior performance to other known machine learning frameworks in terms of speech recognition accuracy. Their superiority comes from the fact that these are deep learning networks. However, a trained DBNN is simply a feed-forward network with no internal memory, unlike Recurrent Neural Networks (RNNs) which are Turing complete and do posses internal memory, thus allowing them to make use of longer context. In this paper, an experiment is performed to make a hybrid of a DBNN with an advanced bidirectional RNN used to process its output. Results show that the use of the new DBNN-BLSTM hybrid as the acoustic model for the Large Vocabulary Continuous Speech Recognition (LVCSR) increases word recognition accuracy. However, the new model has many parameters and in some cases it may suffer performance issues in real-time applications.
    Źródło:
    Archives of Acoustics; 2015, 40, 2; 191-195
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analysis and comparison of long short-term memory networks short-term traffic prediction performance
    Autorzy:
    Dogan, Erdem
    Powiązania:
    https://bibliotekanauki.pl/articles/2091136.pdf
    Data publikacji:
    2020
    Wydawca:
    Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
    Tematy:
    deep learning
    traffic flow
    short-term
    prediction
    LSTM
    nonlinear autoregressive
    training set size
    uczenie głębokie
    ruch uliczny
    krótki termin
    prognoza
    autoregresja nieliniowa
    Opis:
    Long short-term memory networks (LSTM) produces promising results in the prediction of traffic flows. However, LSTM needs large numbers of data to produce satisfactory results. Therefore, the effect of LSTM training set size on performance and optimum training set size for short-term traffic flow prediction problems were investigated in this study. To achieve this, the numbers of data in the training set was set between 480 and 2800, and the prediction performance of the LSTMs trained using these adjusted training sets was measured. In addition, LSTM prediction results were compared with nonlinear autoregressive neural networks (NAR) trained using the same training sets. Consequently, it was seen that the increase in LSTM's training cluster size increased performance to a certain point. However, after this point, the performance decreased. Three main results emerged in this study: First, the optimum training set size for LSTM significantly improves the prediction performance of the model. Second, LSTM makes short-term traffic forecasting better than NAR. Third, LSTM predictions fluctuate less than the NAR model following instant traffic flow changes.
    Źródło:
    Zeszyty Naukowe. Transport / Politechnika Śląska; 2020, 107; 19--32
    0209-3324
    2450-1549
    Pojawia się w:
    Zeszyty Naukowe. Transport / Politechnika Śląska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of long short term memory neural networks for GPS satellite clock bias prediction
    Autorzy:
    Gnyś, Piotr
    Przestrzelski, Paweł
    Powiązania:
    https://bibliotekanauki.pl/articles/1987078.pdf
    Data publikacji:
    2021-12-30
    Wydawca:
    Politechnika Gdańska
    Tematy:
    neural networks
    LSTM
    time series prediction
    clock bias
    GNSS
    machine learning
    Opis:
    Satellite-based localization systems like GPS or Galileo are one of the most commonly used tools in outdoor navigation. While for most applications, like car navigation or hiking, the level of precision provided by commercial solutions is satisfactory it is not always the case for mobile robots. In the case of long-time autonomy and robots that operate in remote areas battery usage and access to synchronization data becomes a problem. In this paper, a solution providing a real-time onboard clock synchronization is presented. Results achieved are better than the current state-of-the-art solution in real-time clock bias prediction for most satellites.
    Źródło:
    TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2021, 25, 4; 381-395
    1428-6394
    Pojawia się w:
    TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Coherence Model of Instruction
    Autorzy:
    Kapoun, Pavel
    Powiązania:
    https://bibliotekanauki.pl/articles/448436.pdf
    Data publikacji:
    2017-04-11
    Wydawca:
    Wydawnictwo Uniwersytetu Śląskiego
    Tematy:
    retention of the curriculum in long-term memory
    excursion
    coherence model of instruction
    cooperative learning
    mobile learning
    museum pedagogy and didactics
    understanding in context
    spatial learning strategies
    Opis:
    The article deals with three main issues: the understanding of curriculum in context, the ability of contextualisation, and retention of knowledge in long-term memory. The paper first suggests principles based on the coherence model of instruction, which aims to achieve coherence of knowledge of isolated facts through a network of semantic relationships. Then, the theoretical basis of the model is described, including spatial learning strategies, cooperative learning, and excursions in an authentic environment supported by mobile devices. A methodology of teaching was designed according to the principles of the coherence model, and a virtual guide through educational exhibitions was developed. The virtual guide was tested with students of a primary school during an experimental lecture in the Ostrava Zoo. An evaluation of the coherence model and the virtual guide was carried out using three methods: an observation of students’ behaviour and learning during the experimental lecture, a pedagogical experiment, and an evaluation of questionnaires. The results of the evaluation proved that the coherence model of instruction has a positive impact on understanding in context, ability of contextualisation, and retention of the curriculum in long-term memory.
    Źródło:
    International Journal of Research in E-learning IJREL; 2016, 2, 2; 81-91
    2451-2583
    2543-6155
    Pojawia się w:
    International Journal of Research in E-learning IJREL
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Interval Prediction of Remaining Useful Life based on Convolutional Auto-Encode and Lower Upper Bound Estimation
    Autorzy:
    Lyu, Yi
    Zhang, Qichen
    Chen, Aiguo
    Wen, Zhenfei
    Powiązania:
    https://bibliotekanauki.pl/articles/24200839.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    remaining useful life
    lower upper bound estimation
    Long Short-Term Memory
    prediction interval
    Opis:
    Deep learning is widely used in remaining useful life (RUL) prediction because it does not require prior knowledge and has strong nonlinear fitting ability. However, most of the existing prediction methods are point prediction. In practical engineering applications, confidence interval of RUL prediction is more important for maintenance strategies. This paper proposes an interval prediction model based on Long Short-Term Memory (LSTM) and lower upper bound estimation (LUBE) for RUL prediction. First, convolutional auto-encode network is used to encode the multi-dimensional sensor data into one-dimensional features, which can well represent the main degradation trend. Then, the features are input into the prediction framework composed of LSTM and LUBE for RUL interval prediction, which effectively solves the defect that the traditional LUBE network cannot analyze the internal time dependence of time series. In the experiment section, a case study is conducted using the turbofan engine data set CMAPSS, and the advantage is validated by carrying out a comparison with other methods.
    Źródło:
    Eksploatacja i Niezawodność; 2023, 25, 2; art. no. 165811
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A novel method of health indicator construction and remaining useful life prediction based on deep learning
    Autorzy:
    Zhan, Xianbiao
    Liu, Zixuan
    Yan, Hao
    Wu, Zhenghao
    Guo, Chiming
    Jia, Xisheng
    Powiązania:
    https://bibliotekanauki.pl/articles/27312791.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    stacked sparse autoencoder
    health indicator
    long short-term memory network
    remaining useful life prediction
    Opis:
    The construction of health indicators (HI) for traditional deep learning requires human training labels and poor interpretability. This paper proposes an HI construction method based on Stacked Sparse Autoencoder (SSAE) and combines SSAE with Long short-term memory (LSTM) network to predict the remaining useful life (RUL). Extracting features from a single domain may result in insufficient feature extraction and cannot comprehensively reflect the degradation status information of mechanical equipment. In order to solve the problem, this article extracts features from time domain, frequency domain, and time-frequency domain to construct a comprehensive original feature set. Based on monotonicity, trendiness, and robustness, the most sensitive features from the original feature set are selected and put into the SSAE network to construct HI for state partitioning, and then LSTM is used for RUL prediction. By comparing with the existing methods, it is proved that the prediction effect of the proposed method in this paper is satisfied.
    Źródło:
    Eksploatacja i Niezawodność; 2023, 25, 4; art. no. 171374
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Tool wear condition monitoring in milling process based on data fusion enhanced long short-term memory network under different cutting conditions
    Autorzy:
    Zheng, Guoxiao
    Sun, Weifang
    Zhang, Hao
    Zhou, Yuqing
    Gao, Chen
    Powiązania:
    https://bibliotekanauki.pl/articles/2038054.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    tool wear condition monitoring
    empirical mode decomposition
    variational mode decomposition
    fourier synchro squeezed transform
    neighborhood component analysis
    long short-term memory network
    Opis:
    Tool wear condition monitoring (TCM) is essential for milling process to ensure the machining quality, and the long short-term memory network (LSTM) is a good choice for predicting tool wear value. However, the robustness of LSTM- based method is poor when cutting condition changes. A novel method based on data fusion enhanced LSTM is proposed to estimate tool wear value under different cutting conditions. Firstly, vibration time series signal collected from milling process are transformed to feature space through empirical mode decomposition, variational mode decomposition and fourier synchro squeezed transform. And then few feature series are selected by neighborhood component analysis to reduce dimension of the signal features. Finally, these selected feature series are input to train the bidirectional LSTM network and estimate tool wear value. Applications of the proposed method to milling TCM experiments demonstrate it outperforms significantly SVR- based and RNN- based methods under different cutting conditions.
    Źródło:
    Eksploatacja i Niezawodność; 2021, 23, 4; 612-618
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Detection and Localization of Audio Event for Home Surveillance Using CRNN
    Autorzy:
    Suruthhi, V. S.
    Smita, V.
    Rolant Gini, J.
    Ramachandran, K. I.
    Powiązania:
    https://bibliotekanauki.pl/articles/2055274.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    convolutional recurrent neural network
    CRNN
    gated recurrent unit
    GRU
    long short-term memory
    LSTM
    sound event localization and detection
    SELD
    Opis:
    Safety and security have been a prime priority in people’s lives, and having a surveillance system at home keeps people and their property more secured. In this paper, an audio surveillance system has been proposed that does both the detection and localization of the audio or sound events. The combined task of detecting and localizing the audio events is known as Sound Event Localization and Detection (SELD). The SELD in this work is executed through Convolutional Recurrent Neural Network (CRNN) architecture. CRNN is a stacked layer of convolutional neural network (CNN), recurrent neural network (RNN) and fully connected neural network (FNN). The CRNN takes multichannel audio as input, extracts features and does the detection and localization of the input audio events in parallel. The SELD results obtained by CRNN with the gated recurrent unit (GRU) and with long short-term memory (LSTM) unit are compared and discussed in this paper. The SELD results of CRNN with LSTM unit gives 75% F1 score and 82.8% frame recall for one overlapping sound. Therefore, the proposed audio surveillance system that uses LSTM unit produces better detection and overall performance for one overlapping sound.
    Źródło:
    International Journal of Electronics and Telecommunications; 2021, 67, 4; 735--741
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Forecasting future values of time series using the lstm network on the example of currencies and WIG20 companies
    Prognozowanie przyszłych wartości szeregów czasowych z wykorzystaniem sieci lstm na przykładzie kursów walut i spółek WIG20
    Autorzy:
    Mróz, Bartosz
    Nowicki, Filip
    Powiązania:
    https://bibliotekanauki.pl/articles/2016294.pdf
    Data publikacji:
    2020
    Wydawca:
    Politechnika Bydgoska im. Jana i Jędrzeja Śniadeckich. Wydawnictwo PB
    Tematy:
    recurrent neural network
    RNN
    gated recurrent unit
    GRU
    long short-term memory
    LSTM
    rekurencyjna sieć neuronowa
    blok rekurencyjny
    pamięć długookresowa
    Opis:
    The article presents a comparison of the RNN, GRU and LSTM networks in predicting future values of time series on the example of currencies and listed companies. The stages of creating an application which is a implementation of the analyzed issue were also shown – the selection of networks, technologies, selection of optimal network parameters. Additionally, two conducted experiments were discussed. The first was to predict the next values of WIG20 companies, exchange rates and cryptocurrencies. The second was based on investments in cryptocurrencies guided solely by the predictions of artificial intelligence. This was to check whether the investments guided by the predictions of such a program have a chance of effective earnings. The discussion of the results of the experiment includes an analysis of various interesting phenomena that occurred during its duration and a comprehensive presentation of the relatively high efficiency of the proposed solution, along with all kinds of graphs and comparisons with real data. The difficulties that occurred during the experiments, such as coronavirus or socio-economic events, such as riots in the USA, were also analyzed. Finally, elements were proposed that should be improved or included in future versions of the solution – taking into account world events, market anomalies and the use of supervised learning.
    W artykule przedstawiono porównanie sieci RNN, GRU i LSTM w przewidywaniu przyszłych wartości szeregów czasowych na przykładzie walut i spółek giełdowych. Przedstawiono również etapy tworzenia aplikacji będącej realizacją analizowanego zagadnienia – dobór sieci, technologii, dobór optymalnych parametrów sieci. Dodatkowo omówiono dwa przeprowadzone eksperymenty. Pierwszym było przewidywanie kolejnych wartości spółek z WIG20, kursów walut i kryptowalut. Drugi opierał się na inwestycjach w kryptowaluty, kierując się wyłącznie przewidywaniami sztucznej inteligencji. Miało to na celu sprawdzenie, czy inwestowanie na podstawie przewidywania takiego programu pozwala na efektywne zarobki. Omówienie wyników eksperymentu obejmuje analizę różnych ciekawych zjawisk, które wystąpiły w czasie jego trwania oraz kompleksowe przedstawienie relatywnie wysokiej skuteczności proponowanego rozwiązania wraz z wszelkiego rodzaju wykresami i porównaniami z rzeczywistymi danymi. Analizowano również trudności, które wystąpiły podczas eksperymentów, takie jak koronawirus, wydarzenia społeczno-gospodarcze czy zamieszki w USA. Na koniec zaproponowano elementy, które należałoby ulepszyć lub uwzględnić w przyszłych wersjach rozwiązania, uwzględniając wydarzenia na świecie, anomalie rynkowe oraz wykorzystanie uczenia się nadzorowanego.
    Źródło:
    Zeszyty Naukowe. Telekomunikacja i Elektronika / Uniwersytet Technologiczno-Przyrodniczy w Bydgoszczy; 2020, 24; 13-30
    1899-0088
    Pojawia się w:
    Zeszyty Naukowe. Telekomunikacja i Elektronika / Uniwersytet Technologiczno-Przyrodniczy w Bydgoszczy
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An optimized parallel implementation of non-iteratively trained recurrent neural networks
    Autorzy:
    El Zini, Julia
    Rizk, Yara
    Awad, Mariette
    Powiązania:
    https://bibliotekanauki.pl/articles/2031147.pdf
    Data publikacji:
    2021
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    GPU implementation
    parallelization
    Recurrent Neural Network
    RNN
    Long-short Term Memory
    LSTM
    Gated Recurrent Unit
    GRU
    Extreme Learning Machines
    ELM
    non-iterative training
    Opis:
    Recurrent neural networks (RNN) have been successfully applied to various sequential decision-making tasks, natural language processing applications, and time-series predictions. Such networks are usually trained through back-propagation through time (BPTT) which is prohibitively expensive, especially when the length of the time dependencies and the number of hidden neurons increase. To reduce the training time, extreme learning machines (ELMs) have been recently applied to RNN training, reaching a 99% speedup on some applications. Due to its non-iterative nature, ELM training, when parallelized, has the potential to reach higher speedups than BPTT. In this work, we present Opt-PR-ELM, an optimized parallel RNN training algorithm based on ELM that takes advantage of the GPU shared memory and of parallel QR factorization algorithms to efficiently reach optimal solutions. The theoretical analysis of the proposed algorithm is presented on six RNN architectures, including LSTM and GRU, and its performance is empirically tested on ten time-series prediction applications. Opt- PR-ELM is shown to reach up to 461 times speedup over its sequential counterpart and to require up to 20x less time to train than parallel BPTT. Such high speedups over new generation CPUs are extremely crucial in real-time applications and IoT environments.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2021, 11, 1; 33-50
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analysis and Forecasting of the Primary Energy Consumption in Poland Using Deep Learning
    Analiza i prognozowanie zużycia energii pierwotnej w Polsce z wykorzystaniem technik głębokiego uczenia
    Autorzy:
    Manowska, Anna
    Powiązania:
    https://bibliotekanauki.pl/articles/318083.pdf
    Data publikacji:
    2020
    Wydawca:
    Polskie Towarzystwo Przeróbki Kopalin
    Tematy:
    primary energy consumption
    deep learning methods
    long short-term memory
    deep neural network
    konsumpcja energii pierwotnej
    metody głębokiego uczenia
    sztuczne sieci neuronowe
    LSTM
    Opis:
    Consumption of fossil energy resources were increased dramatically, due to the economic and population growth. In turn, the consumption of fossil resources causes depletion of resources and contributes to environmental pollution. The European Union's "climate neutrality" initiative requires effective energy management from the member states. By this is meant a resource-efficient and competitive economy in which there is no greenhouse gas emission and where economic growth is decoupled from resource consumption. The article analyzes the level of primary energy consumption in Poland. It was examined whether a 23% drop in energy consumption could be achieved in 2030 compared to the base year and according with energy efficiency assumptions. A methodology for forecasting primary energy consumption based on deep neural networks, in particular on Long Short Term Memory (LSTM) algorithms was also presented.
    Zużycie kopalnych surowców energetycznych wzrasta, a wzrost ten jest skorelowany ze wzrostem ludności i rozwojem gospodarczym. Z kolei zużycie kopalnych surowców energetycznych powoduje wyczerpywanie się zasobów i przyczynia się do zanieczyszczenia środowiska. Inicjatywa Unii Europejskiej "neutralność klimatyczna" wymaga od państw członkowskich efektywnego zarządzania energią. Przez co rozumie się zasobooszczędną i konkurencyjną gospodarką, w której nie ma emisji netto gazów cieplarnianych i gdzie wzrost gospodarczy jest oddzielony od zużycia zasobów. W artykule przeanalizowano poziom zużycia energii pierwotnej w Polsce. Zbadano, czy w roku 2030 uda się osiągnąć 23% spadek konsumpcji energii w odniesieniu do roku bazowego, zgodnie z przyjętymi założeniami o efektywności energetycznej. Przedstawiono również metodologię prognozowania zużycia energii pierwotnej opartą na głębokich sieciach neuronowych, w szczególności na algorytmach Long Short Term Memory (LSTM).
    Źródło:
    Inżynieria Mineralna; 2020, 1, 1; 217-222
    1640-4920
    Pojawia się w:
    Inżynieria Mineralna
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A deep hybrid model for human-computer interaction using dynamic hand gesture recognition
    Autorzy:
    Ramalingam, Brindha
    Angappan, Geetha
    Powiązania:
    https://bibliotekanauki.pl/articles/38702766.pdf
    Data publikacji:
    2023
    Wydawca:
    Instytut Podstawowych Problemów Techniki PAN
    Tematy:
    dynamic hand gesture
    human-computer interaction
    long short-term memory
    convolutional neural network
    dynamiczny gest ręki
    interakcja człowiek-komputer
    pamięć krótkotrwała
    konwolucyjna sieć neuronowa
    Opis:
    Dynamic hand gestures attract great interest and are utilized in different fields. Amongthese, man-machine interaction is an interesting area that makes use of the hand to providea natural way of interaction between them. A dynamic hand gesture recognition system isproposed in this paper, which helps to perform control operations in applications such asmusic players, video games, etc. The key motivation of this research is to provide a simple, touch-free system for effortless and faster human-computer interaction (HCI). As thisproposed model employs dynamic hand gestures, HCI is achieved by building a modelwith a convolutional neural network (CNN) and long short-term memory (LSTM) net-works. CNN helps in extracting important features from the images and LSTM helpsto extract the motion information between the frames. Various models are constructedby differing the LSTM and CNN layers. The proposed system is tested on an existing EgoGesture dataset that has several classes of gestures from which the dynamic gesturesare utilized. This dataset is used as it has more data with a complex background, actionsperformed with varying speeds, lighting conditions, etc. This proposed hand gesture recognition system attained an accuracy of 93%, which is better than other existing systemssubject to certain limitations.
    Źródło:
    Computer Assisted Methods in Engineering and Science; 2023, 30, 3; 263-276
    2299-3649
    Pojawia się w:
    Computer Assisted Methods in Engineering and Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep learning based Tamil Parts of Speech (POS) tagger
    Autorzy:
    Anbukkarasi, S.
    Varadhaganapathy, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/2086879.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    POS tagging
    part of speech
    deep learning
    natural language processing
    BiLSTM
    Bi-directional long short term memory
    tagowanie POS
    części mowy
    uczenie głębokie
    przetwarzanie języka naturalnego
    Opis:
    This paper addresses the problem of part of speech (POS) tagging for the Tamil language, which is low resourced and agglutinative. POS tagging is the process of assigning syntactic categories for the words in a sentence. This is the preliminary step for many of the Natural Language Processing (NLP) tasks. For this work, various sequential deep learning models such as recurrent neural network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Bi-directional Long Short-Term Memory (Bi-LSTM) were used at the word level. For evaluating the model, the performance metrics such as precision, recall, F1-score and accuracy were used. Further, a tag set of 32 tags and 225 000 tagged Tamil words was utilized for training. To find the appropriate hidden state, the hidden states were varied as 4, 16, 32 and 64, and the models were trained. The experiments indicated that the increase in hidden state improves the performance of the model. Among all the combinations, Bi-LSTM with 64 hidden states displayed the best accuracy (94%). For Tamil POS tagging, this is the initial attempt to be carried out using a deep learning model.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 6; e138820, 1--6
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Urban traffic crash analysis using deep learning techniques
    Analiza kolizji w ruchu miejskim z wykorzystaniem technik głębokiego uczenia
    Autorzy:
    Sobhana, Mummaneni
    Vemulapalli, Nihitha
    Mendu, Gnana Siva Sai Venkatesh
    Ginjupalli, Naga Deepika
    Dodda, Pragathi
    Subramanyam, Rayanoothala Bala Venkata
    Powiązania:
    https://bibliotekanauki.pl/articles/27315440.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej
    Tematy:
    classification
    gated recurrent unit
    long-short term memory
    multilayer perceptron
    recurrent neural network
    road accidents
    klasyfikacja
    pamięć długotrwała
    pamięć krótkotrwała
    perceptron wielowarstwowy
    rekurencyjna sieć neuronowa
    wypadki drogowe
    Opis:
    Road accidents are concerningly increasing in Andhra Pradesh. In 2021, Andhra Pradesh experienced a 20 percent upsurge in road accidents. The state's unfortunate position of being ranked eighth in terms of fatalities, with 8,946 lives lost in 22,311 traffic accidents, underscores the urgent nature of the problem. The significant financial impact on the victims and their families stresses the necessity for effective actions to reduce road accidents.This study proposes a framework that collects accident data from regions, namely Patamata, Penamaluru, Mylavaram, Krishnalanka, Ibrahimpatnam,and Gandhinagar in Vijayawada(India)from 2019 to 2021. The dataset comprises over 12,000 records of accident data. Deep learning techniquesare applied to classify the severity of road accidents into Fatal, Grievous, and Severe Injuries. The classification procedure leverages advanced neural network models, including the Multilayer Perceptron, Long-Short Term Memory, Recurrent Neural Network, and Gated Recurrent Unit. These modelsare trained on the collected data to accurately predict the severity of road accidents. The project study to make important contributions for suggesting proactive measures and policies to reduce the severity and frequency of road accidents in Andhra Pradesh.
    Liczba wypadków drogowych w Andhra Pradesh niepokojąco rośnie. W 2021 r. stan Andhra Pradesh odnotował 20% wzrost liczby wypadków drogowych. Niefortunna pozycja stanu, który zajmuje ósme miejsce pod względem liczby ofiar śmiertelnych, z 8946 ofiarami śmiertelnymiw 22311 wypadkach drogowych, podkreśla pilny charakter problemu. Znaczący wymiar finansowy dla ofiari ich rodziny podkreśla konieczność podjęcia skutecznych działań w celu ograniczenia liczby wypadków drogowych. W niniejszym badaniu zaproponowano system gromadzenia danych o wypadkachz regionów Patamata, Penamaluru, Mylavaram, Krishnalanka, Ibrahimpatnam i Gandhinagar w Vijayawada (India) w latach 2019–2021. Zbiór danych obejmuje ponad 12 000 rekordów danych o wypadkach. Techniki głębokiego uczenia są stosowane do klasyfikowania wagi wypadków drogowychna śmiertelne, poważne i ciężkie obrażenia. Procedura klasyfikacji wykorzystuje zaawansowane modele sieci neuronowych, w tymwielowarstwowy perceptron, pamięć długoterminową i krótkoterminową, rekurencyjną sieć neuronową i Gated Recurrent Unit. Modele te są trenowane na zebranych danych w celu dokładnego przewidywania wagi wypadków drogowych. Projekt ma wnieść istotny wkład w sugerowanie proaktywnych środków i polityk mających na celu zmniejszenie dotkliwości i częstotliwości wypadków drogowych w Andhra Pradesh.
    Źródło:
    Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska; 2023, 13, 3; 56--63
    2083-0157
    2391-6761
    Pojawia się w:
    Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    ERPS AS AN INDEX OF IMPAIRED WORKING MEMORY IN AN ISCHEMIC BRAIN STROKE APHASIC PATIENT AWAKENED FROM A LONG-TERM COMA FOLLOWING AN AMPHETAMINE OVERDOSE
    Autorzy:
    Wilczek-Rużyczka, Ewa
    Grzywniak, Celestyna
    Korab, Maciej
    Cielebąk, Ksenia
    Powiązania:
    https://bibliotekanauki.pl/articles/2138031.pdf
    Data publikacji:
    2021-03-14
    Wydawca:
    Fundacja Edukacji Medycznej, Promocji Zdrowia, Sztuki i Kultury Ars Medica
    Tematy:
    consciousness
    Charles-Bonnet syndrome
    working memory
    ERPs
    Opis:
    Nowadays, amphetamines constitute the prescription drugs most commonly abused by adolescents and young adults (Berman, O’Neill, Fears et al. 2008). The prevalence of problematic (mainly illegal) use of amphetamines as a stimulant by college students, and here especially before serious examinations, has also been rising. This fact represents a serious public health concern. The patient, aged 19, was awakened from from a long-term coma that had lasted 21 days following an amphetamine overdose and manifested tetraparesis, cortical blindness and deficits in cognitive and emotional processes. After a year of rehabilitation the majority of symptoms had disappeared, but cortical blindness andworking memory deficits remained. In addition, frontal lobe syndrome symptoms appeared. After two years of therapy as a result of immense tiredness caused by all an night wedding reception she started to manifest Charles-Bonnet syndrome. She experienced strange visual sensations such as visual hallucinations and saw various non-existing shapes (coloured blots, patterns and fireworks of vivid colours). She also saw objects (often terrifying) as well as animals (mainly African) and people with deformed faces and long teeth, and persons in African dress with feathers and coral beads in their hair. Her real identity was not remembered by the patient for longer than 2 hours and even then she insisted on being referred to as Shakira. She was given a qEEG examination (in open and closed eyes conditions) and ERPs with the use of auditory stimuli at the period when the hallucinations (to a small degree) still occurred. Studies conducted into the functional neuroimaging of the brain work in milliseconds in the examined patient can explain her symptoms. A comparison of the subject’s ERPs with the grand average of ERPs in healthy controls shows that the N170 and N 250 components are impaired in the subject: the occipital-temporal area of the subject brain shows a strong positivity instead of negativities. This positivity might reflect an enhanced reactivity of neurons in the corresponding area induced by the removal of lateral inhibition from the neurons as a result of local damage. ------------------------------------------------------------------------------------------------------------------------------------
    Źródło:
    Acta Neuropsychologica; 2021, 19(2); 137-145
    1730-7503
    2084-4298
    Pojawia się w:
    Acta Neuropsychologica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Lies, Damned Lies, and Statistics? Examples From Finance and Economics
    Autorzy:
    Abadir, Karim M.
    Powiązania:
    https://bibliotekanauki.pl/articles/483313.pdf
    Data publikacji:
    2013
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    flexible density specification
    option pricing
    term structure of interest rates
    expectation hypothesis
    nonlinear long-memory
    macroeconomic dynamics
    Opis:
    Reliable data analysis is one of the hardest tasks in sciences and social sciences. Often misleading and sometimes puzzling results arise when the analysis is done without regard for the special features of the data. In this exposition, I will focus on designing new statistical tools to deal with some prominent questions in Finance and Economics. In particular, I will talk about the following. (1) How to characterize the randomness of variables, motivated by a problem in the pricing of financial options. (2) Uncovering the relation between interest rates on different maturities, now and in the future; the "term structure of interest rates". (3) Modelling the unconventional nonlinear long-memory dynamics that arise from a general-equilibrium economic model, and their implications for exchange rates, stock market indexes, and all macroeconomic variables; with recommendations for trading in financial markets, but also for the design of macroeconomic stabilization policies by governments.
    Źródło:
    Central European Journal of Economic Modelling and Econometrics; 2013, 5, 4; 231-248
    2080-0886
    2080-119X
    Pojawia się w:
    Central European Journal of Economic Modelling and Econometrics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Recurrent neural networks for dynamic reliability analysis
    Autorzy:
    Cadini, F.
    Zio, E.
    Pedroni, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/2069583.pdf
    Data publikacji:
    2007
    Wydawca:
    Uniwersytet Morski w Gdyni. Polskie Towarzystwo Bezpieczeństwa i Niezawodności
    Tematy:
    dynamic reliability analysis
    infinite impulse response
    locally recurrent neural network
    long-term non-linear dynamics
    system state memory
    simplified nuclear reactor
    Opis:
    A dynamic approach to the reliability analysis of realistic systems is likely to increase the computational burden, due to the need of integrating the dynamics with the system stochastic evolution. Hence, fast-running models of process evolution are sought. In this respect, empirical modelling is becoming a popular approach to system dynamics simulation since it allows identifying the underlying dynamic model by fitting system operational data through a procedure often referred to as ‘learning’. In this paper, a Locally Recurrent Neural Network (LRNN) trained according to a Recursive Back-Propagation (RBP) algorithm is investigated as an efficient tool for fast dynamic simulation. An application is performed with respect to the simulation of the non-linear dynamics of a nuclear reactor, as described by a simplified model of literature.
    Źródło:
    Journal of Polish Safety and Reliability Association; 2007, 1; 45--53
    2084-5316
    Pojawia się w:
    Journal of Polish Safety and Reliability Association
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Dalekowzroczne cele edukacji komunikacyjnej w tworzeniu bezpieczeństwa ruchu drogowego a proces egzaminowania kierujących pojazdem
    Long-Term Goals of Communication Education in Creating the Foundations of Road Safety Behaviors and the Efficiency of Examining Vehicle Drivers
    Autorzy:
    Grzesiuk, Antoni
    Powiązania:
    https://bibliotekanauki.pl/articles/1810845.pdf
    Data publikacji:
    2020-12-28
    Wydawca:
    Katolicki Uniwersytet Lubelski Jana Pawła II. Towarzystwo Naukowe KUL
    Tematy:
    transport
    przyszłość
    ekonomia
    wyzwanie
    bezpieczeństwo
    edukacja
    psychologia
    motoryzacja
    wyobrażanie
    antycypacja
    obserwowanie
    pamięć
    uwaga
    edukacja w komunikacji drogowej
    cele dalekosiężne edukacji w transporcie
    treść egzaminu kierowców
    bezpieczeństwo ruchu drogowego
    future
    economy
    challenge
    safety
    education
    psychology
    motorization
    concept
    anticipation
    observations
    memory
    attention
    education in road communication
    long-term goals of education in transport
    content of drivers’ exam
    road safety
    Opis:
    Dalekosiężne cele komunikacji i transportu są integracją myślenia o ruchu drogowym, działania operacyjnego, sytuacyjnego i strategicznego tworzącego: percepcję, uwagę, pamięć i myślenie, pozwalające tworzyć całość, przewidywanie, ideacje i konkretyzacje powodujące zmiany znaczeniowe i proceduralne w torach emocjonalnych, intelektualnych i motorycznych. Nasze działanie i myślenie można przygotować przez dostosowanie do nadchodzącej przyszłości. Wolna wola i swoboda jest ograniczona rozumieniem ograniczeń materialnych, intelektualnych i duchowych: jest w naszym władaniu i może wytworzyć dobro lub zło, bezpieczeństwo lub katastrofę ludzkości, świadomość lub niewiedzę. Edukacja jest nadzieją na dalekosiężną przyszłość. Krótkowzroczność, brak wiedzy i przewidywania jest chorobą naszych czasów. Komunikacja i transport jako ruch przenosi nas z niepewności w przewidywanie oraz w celne dopasowywanie wiedzy i umiejętności do zmieniającej się realności naszego otoczenia. Skuteczne plany, kreatywność, prowadzenie nowych technologii transportowych i ruchu o różnej modalności transportu, prowadzą do przemieszczeń tworzących wspólne budowanie i powiązania społeczne. Duma z nowoczesnych technologii transportowych i opracowań tworzy nowoczesność i współpracę społeczną, tworzy też integrację budującą. Przewidywanie i prognozowanie indywidualne i społeczne są szansą na przetrwanie, pokonanie kataklizmów i błędów cywilizacji. Z kolei niedoskonałość nowych celów rozwoju i racjonalizacji gospodarowania ziemią, tworzy brak szans na przetrwanie i na kreślenie wyobrażeń strategicznych. Nieuporządkowanie myślenia i przewidywania odnośnie do transportu zmniejsza szanse na rozwój i przeżycie ludzkości. Rozwój pedagogiki i psychologii transportu tworzy szanse na pomyślne życie, pozwala na tworzenia wartości nowych, umiejętności upraktycznionych, upraktycznionej wiedzy proceduralnej i strategii w realnych warunkach ekologicznych, zdrowotnych i psychospołecznych, w jakich funkcjonują ludzie.
    The long-term goals of communication and transport are the integration of thinking about traffic, operational behavior, situational and strategic actions creating: perceptions, attention, memory and thinking, allowing to create the whole, prediction, ideation and concretization causing changes in meaning and procedures in the emotional and motor tracks of human intellectual activity. Our actions and thinking can be prepared by adapting to the future. Free will and freedom is limited by the understanding of material, intellectual and spiritual limitations: it is in our possession and can produce good or evil, security or catastrophe of humanity, consciousness or ignorance. Education is hope for a far-reaching future. Myopia, lack of knowledge and inability to predict is a disease of our time. Communication and transport, as a move, move us from uncertainty into anticipation and accurate matching of knowledge and skills to the changing reality of our environment. Effective plans, creativity, introduction of new transport technologies and traffic with different transport modalities lead to displacements creating joint building and social connections. Pride in modern transport technologies and studies, creates modernity and social cooperation, and creates fruitful  integration. Individual and social prediction and forecasting are a chance to survive, overcome cataclysms and civilization errors. In turn, the imperfection of new development goals and lack of rationalization of land management, creates a lack of opportunities for survival and for drawing strategic ideas. Disorderly thinking and anticipation about transport reduces the chances of humanity's development and survival. The development of transport pedagogy and transport psychology creates opportunities for a successful life, which allows creating new values, practical skills, practical procedural knowledge and strategies in real ecological, health and psychosocial conditions in which people operate.
    Źródło:
    Roczniki Pedagogiczne; 2020, 12, 3; 25-46
    2080-850X
    Pojawia się w:
    Roczniki Pedagogiczne
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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