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Tytuł:
Forecasting the equity premium: Do deep neural network models work?
Autorzy:
Zhou, Xianzheng
Zhou, Hui
Long, Huaigang
Powiązania:
https://bibliotekanauki.pl/articles/23943440.pdf
Data publikacji:
2023
Wydawca:
Fundacja Naukowa Instytut Współczesnych Finansów
Tematy:
equity premium
return predictability
deep neural network
asset allocation
forecasting performance
Opis:
This paper constructs deep neural network (DNN) models for equity-premium forecasting. We compare the forecasting performance of DNN models with that of ordinary least squares (OLS) and historical average (HA) models. The DNN models robustly work best and significantly outperform both OLS and HA models in both in- and out-of-sample tests and asset allocation exercises. Specifically, DNN models generate monthly out-of-sample R2 of 3.42% and an annual utility gain of 2.99% for a mean-variance investor from 2011:1 to 2016:12. Moreover, the forecasting performance of DNN models is enhanced by adding additional 14 variables selected from finance literature.
Źródło:
Modern Finance; 2023, 1, 1; 1-11
2956-7742
Pojawia się w:
Modern Finance
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Intelligent fault diagnosis of rolling bearings based on continuous wavelet transform-multiscale feature fusion and improved channel attention mechanism
Autorzy:
Zhang, Jiqiang
Kong, Xiangwei
Cheng, Liu
Qi, Haochen
Yu, Mingzhu
Powiązania:
https://bibliotekanauki.pl/articles/24200817.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
deep learning
continuous wavelet transform
improved channel attention mechanism
multi-conditions
convolutional neural network
Opis:
Accurate fault diagnosis is critical to operating rotating machinery safely and efficiently. Traditional fault information description methods rely on experts to extract statistical features, which inevitably leads to the problem of information loss. As a result, this paper proposes an intelligent fault diagnosis of rolling bearings based on a continuous wavelet transform(CWT)-multiscale feature fusion and an improved channel attention mechanism. Different from traditional CNNs, CWT can convert the 1-D signals into 2-D images, and extract the wavelet power spectrum, which is conducive to model recognition. In this case, the multiscale feature fusion was implemented by the parallel 2-D convolutional neural networks to accomplish deeper feature fusion. Meanwhile, the channel attention mechanism is improved by converting from compressed to extended ways in the excitation block to better obtain the evaluation score of the channel. The proposed model has been validated using two bearing datasets, and the results show that it has excellent accuracy compared to existing methods.
Źródło:
Eksploatacja i Niezawodność; 2023, 25, 1; art. no. 16
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Convolutional neural networks in the SSI analysis for mine-induced vibrations
Autorzy:
Zając, Maciej
Kuźniar, Krystyna
Powiązania:
https://bibliotekanauki.pl/articles/38707462.pdf
Data publikacji:
2024
Wydawca:
Instytut Podstawowych Problemów Techniki PAN
Tematy:
deep learning
convolutional neural network
shallow neural network
small data set
soil-structure interaction
mine-induced vibrations
głęboka nauka
splotowa sieć neuronowa
płytka sieć neuronowa
mały zestaw danych
interakcja gleba-struktura
wibracje wywołane minami
Opis:
Deep neural networks (DNNs) have recently become one of the most often used softcomputational tools for numerical analysis. The huge success of DNNs in the field of imageprocessing is associated with the use of convolutional neural networks (CNNs). CNNs,thanks to their characteristic structure, allow for the effective extraction of multi-layerfeatures. In this paper, the application of CNNs to one of the important soil-structureinteraction (SSI) problems, i.e., the analysis of vibrations transmission from the free-field next to a building to the building foundation, is presented in the case of mine-induced vibrations. To achieve this, the dataset from in-situ experimental measurements,containing 1D ground acceleration records, was converted into 2D spectrogram imagesusing either Fourier transform or continuous wavelet transform. Next, these images wereused as input for a pre-trained CNN. The output is a ratio of maximal vibration valuesrecorded simultaneously on the building foundation and on the ground. Therefore, the lastlayer of the CNN had to be changed from a classification to a regression one. The obtainedresults indicate the suitability of CNN for the analyzed problem.
Źródło:
Computer Assisted Methods in Engineering and Science; 2024, 31, 1; 3-28
2299-3649
Pojawia się w:
Computer Assisted Methods in Engineering and Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An Analysis of Novel Money Laundering Data Using Heterogeneous Graph Isomorphism Networks. FinCEN Files Case Study
Wykorzystanie heterogenicznych grafowych sieci izomorficznych w analizie danych związanych z praniem brudnych pieniędzy. Studium przypadku FinCEN
Autorzy:
Wójcik, Filip
Powiązania:
https://bibliotekanauki.pl/articles/38890419.pdf
Data publikacji:
2024
Wydawca:
Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Tematy:
money laundering
deep learning
machine learning
network analysis
graphs
pranie brudnych pieniędzy
uczenie głębokie
analiza sieci
grafy
Opis:
Aim: This study aimed to develop and apply the novel HexGIN (Heterogeneous extension for Graph Isomorphism Network) model to the FinCEN Files case data and compare its performance with existing solutions, such as the SAGE-based graph neural network and Multi-Layer Perceptron (MLP), to demonstrate its potential advantages in the field of anti-money laundering systems (AML). Methodology: The research employed the FinCEN Files case data to develop and apply the HexGIN model in a beneficiary prediction task for a suspicious transactions graph. The model's performance was compared with the existing solutions in a series of cross-validation experiments. Results: The experimental results on the cross-validation data and test dataset indicate the potential advantages of HexGIN over the existing solutions, such as MLP and Graph SAGE. The proposed model outperformed other algorithms in terms of F1 score, precision, and ROC AUC in both training and testing phases. Implications and recommendations: The findings demonstrate the potential of heterogeneous graph neural networks and their highly expressive architectures, such as GIN, in AML. Further research is needed, in particular to combine the proposed model with other existing algorithms and test the solution on various money-laundering datasets. Originality/value: Unlike many AML studies that rely on synthetic or undisclosed data sources, this research was based on a publicly available, real, heterogeneous transaction dataset, being part of a larger investigation. The results indicate a promising direction for the development of modern hybrid AML tools for analysing suspicious transactions; based on heterogeneous graph networks capable of handling various types of entities and their connections.
Cel: Celem niniejszej analizy jest opracowanie i zastosowanie nowego modelu HexGIN (heterogeniczne rozszerzenie dla izomorfizmu sieci grafowych) do danych z dochodzenia dziennikarskiego FinCEN oraz porównanie jego jakości predykcji z istniejącymi rozwiązaniami, takimi jak sieć SAGE i wielowarstwowa sieć neuronowa (MLP). Metodyka: W badaniach wykorzystano dane ze śledztwa FinCEN do opracowania i zastosowania modelu HexGIN w zadaniu przewidywania beneficjenta sieci powiązanych transakcji finansowych. Skuteczność modelu porównano z istniejącymi rozwiązaniami wykorzystującymi sieci neuronowe grafu w serii eksperymentów z walidacją krzyżową. Wyniki: Eksperymentalne wyniki na danych walidacji krzyżowej i zestawie testowym potwierdzają potencjalne zalety HexGIN w porównaniu z istniejącymi rozwiązaniami, takimi jak MLP i SAGE. Proponowany model przewyższa inne algorytmy pod względem wyniku miary F1, precyzji i ROC AUC, w fazie zarówno treningowej, jak i testowej. Implikacje i rekomendacje: Wyniki pokazują potencjał heterogenicznych grafowych sieci i ich wysoce ekspresyjnych implementacji, takich jak GIN, w analizie transakcji finansowych. Potrzebne są dalsze badania, zwłaszcza w celu połączenia proponowanego modelu z innymi istniejącymi algorytmami i przetestowania rozwiązania na różnych zestawach danych dotyczących problemu prania brudnych pieniędzy. Oryginalność/wartość: W przeciwieństwie do wielu badań, które opierają się na syntetycznych lub nieujawnionych źródłach danych związanych z praniem brudnych pieniędzy, to studium przypadku opiera się na publicznie dostępnych, rzeczywistych, heterogenicznych danych transakcyjnych, będących częścią większego śledztwa dziennikarskiego. Wyniki wskazują obiecujący kierunek dla rozwoju nowoczesnych hybrydowych narzędzi do analizy podejrzanych transakcji, opartych na heterogenicznych sieciach grafowych.
Źródło:
Econometrics. Ekonometria. Advances in Applied Data Analytics; 2024, 28, 2; 32-49
1507-3866
Pojawia się w:
Econometrics. Ekonometria. Advances in Applied Data Analytics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Single target tracking algorithm for lightweight Siamese networks based on global attention
Autorzy:
Wang, Zhentao
He, Xiaowei
Cheng, Rao
Powiązania:
https://bibliotekanauki.pl/articles/2173664.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
target tracking
Siamese network
semantic information
training strategy
feature fusion
deep learning
śledzenie celu
sieć syjamska
informacja semantyczna
strategia szkolenia
fuzja funkcji
głęboka nauka
Opis:
Object tracking based on Siamese networks has achieved great success in recent years, but increasingly advanced trackers are also becoming cumbersome, which will severely limit deployment on resource-constrained devices. To solve the above problems, we designed a network with the same or higher tracking performance as other lightweight models based on the SiamFC lightweight tracking model. At the same time, for the problems that the SiamFC tracking network is poor in processing similar semantic information, deformation, illumination change, and scale change, we propose a global attention module and different scale training and testing strategies to solve them. To verify the effectiveness of the proposed algorithm, this paper has done comparative experiments on the ILSVRC, OTB100, VOT2018 datasets. The experimental results show that the method proposed in this paper can significantly improve the performance of the benchmark algorithm.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 3; art. no. e139961
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Position Estimation in Mixed Indoor-Outdoor Environment Using Signals of Opportunity and Deep Learning Approach
Autorzy:
Urwan, Sebastian
Wysocka, Dominika R.
Pietrzak, Alicja
Cwalina, Krzysztof K.
Powiązania:
https://bibliotekanauki.pl/articles/2134050.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
radiolocalization
deep neural network
hybrid localization
Opis:
To improve the user’s localization estimation in indoor and outdoor environment a novel radiolocalization system using deep learning dedicated to work both in indoor and outdoor environment is proposed. It is based on the radio signatures using radio signals of opportunity from LTE an WiFi networks. The measurements of channel state estimators from LTE network and from WiFi network are taken by using the developed application. The user’s position is calculated with a trained neural network system’s models. Additionally the influence of various number of measurements from LTE and WiFi networks in the input vector on the positioning accuracy was examined. From the results it can be seen that using hybrid deep learning algorithm with a radio signatures method can result in localization error 24.3 m and 1.9 m lower comparing respectively to the GPS system and standalone deep learning algorithm with a radio signatures method in indoor environment. What is more, the combination of LTE and WiFi signals measurement in an input vector results in better indoor and outdoor as well as floor classification accuracy and less positioning error comparing to the input vector consisting measurements from only LTE network or from only WiFi network.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 3; 594-607
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Vehicle classification using the convolution neural network approach
Autorzy:
Trivedi, Janak
Devi, Mandalapu Sarada
Dhara, Dave
Powiązania:
https://bibliotekanauki.pl/articles/2091225.pdf
Data publikacji:
2021
Wydawca:
Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:
convolution neural network
vehicle classification
deep learning
intelligent transportation system
konwolucyjna sieć neuronowa
klasyfikacja pojazdów
uczenie głębokie
inteligentny system transportowy
Opis:
We present vehicle detection classification using the Convolution Neural Network (CNN) of the deep learning approach. The automatic vehicle classification for traffic surveillance video systems is challenging for the Intelligent Transportation System (ITS) to build a smart city. In this article, three different vehicles: bike, car and truck classification are considered for around 3,000 bikes, 6,000 cars, and 2,000 images of trucks. CNN can automatically absorb and extract different vehicle dataset’s different features without a manual selection of features. The accuracy of CNN is measured in terms of the confidence values of the detected object. The highest confidence value is about 0.99 in the case of the bike category vehicle classification. The automatic vehicle classification supports building an electronic toll collection system and identifying emergency vehicles in the traffic.
Źródło:
Zeszyty Naukowe. Transport / Politechnika Śląska; 2021, 112; Bibliogr. 13 poz.
0209-3324
2450-1549
Pojawia się w:
Zeszyty Naukowe. Transport / Politechnika Śląska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural model of human gait and its implementation in MATLAB and Simulink Environment using Deep Learning Toolbox
Autorzy:
Tchórzewski, Jerzy
Wielgo, Arkadiusz
Powiązania:
https://bibliotekanauki.pl/articles/2052427.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
artificial neural network
deep learning toolbox
humanoid robots
MATLAB environment
Simulink environment
modeling of human walking motion
Opis:
The article presents selected results of research on the modeling of humanoid robots, including the results of neural modeling of human gait and its implementation in the environment MATLAB and Simulink with the use of Deep Learning Toolbox. The subject of the research was placed within the scope of the available literature on the subject. Then, appropriate research experiments on human movement along a given trajectory were developed. First, the method of measuring the parameters present in the experiment was established, i.e. input quantities (displacement of the left heel, displacement of the right heel) and output quantities (displacement of the measurement point of the human body in space). Then, research experiments were carried out, as a result of which numerical data were measured in order to use them for teaching and testing the Artificial Neural Network. The Perceptron Artificial Neural Network architecture was used to build a model of a neural human walk along a given trajectory. The obtained results were discussed and interpreted, drawing a number of important conclusions.
Źródło:
Studia Informatica : systems and information technology; 2021, 1-2(25); 39-65
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Anonymous traffic classification based on three-dimensional Markov image and deep learning
Autorzy:
Tang, Xin
Li, Huanzhou
Zhang, Jian
Tang, Zhangguo
Wang, Han
Cai, Cheng
Powiązania:
https://bibliotekanauki.pl/articles/27311448.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
anonymous network
traffic classification
three-dimensional Markov image
output self-attention
deep learning
sieć anonimowa
klasyfikacja ruchu
trójwymiarowy obraz Markowa
samouwaga wyjściowa
uczenie głębokie
Opis:
Illegal elements use the characteristics of an anonymous network hidden service mechanism to build a dark network and conduct various illegal activities, which brings a serious challenge to network security. The existing anonymous traffic classification methods suffer from cumbersome feature selection and difficult feature information extraction, resulting in low accuracy of classification. To solve this problem, a classification method based on three-dimensional Markov images and output self-attention convolutional neural network is proposed. This method first divides and cleans anonymous traffic data packets according to sessions, then converts the cleaned traffic data into three-dimensional Markov images according to the transition probability matrix of bytes, and finally inputs the images to the output self-attention convolution neural network to train the model and perform classification. The experimental results show that the classification accuracy and F1-score of the proposed method for Tor, I2P, Freenet, and ZeroNet can exceed 98.5%, and the average classification accuracy and F1-score for 8 kinds of user behaviors of each type of anonymous traffic can reach 93.7%. The proposed method significantly improves the classification effect of anonymous traffic compared with the existing methods.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 4; art. no. e145676
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Model numeryczny wgłębnej filtracji wody
Numerical model of deep-bed water filtration
Autorzy:
Sztuk, E.
Żywczyk, Ł.
Moskal, A.
Gradoń, L.
Powiązania:
https://bibliotekanauki.pl/articles/2072353.pdf
Data publikacji:
2013
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
filtracja wgłębna
network model
deep-bed filtration
Opis:
Korzystając z 2-wymiarowego modelu network model zaprojektowano strukturę filtra składającego się z 3 warstw o różnych porowatościach. Umożliwia on efektywne usuwanie cząstek o danym rozmiarze z odpowiednio wysoką sprawnością oraz powoduje możliwie niskie opory przepływu płynu. Poszczególne warstwy złączono w trzech różnych konfiguracjach. Zbadano również przypadek włókniny jednorodnej (monowarstwy) o uśrednionych parametrach. Obliczono efektywność depozycji dla cząstek o rozmiarach 1:10 um. Wyniki dowodzą, że korzystne jest tworzenie filtrów o strukturze wielowarstwowej, a szczególnie istotna jest kolejność ułożenia warstw o różnych parametrach.
A 2D network model was used to design the optimal structure of fibrous filter. It enables the efficient capture of particles, creates the lowest pressure drop and guarantees a long time usage. The individual layers were joined at three different configurations. A case of non-woven uniform monolayer with average performance was also examined. The deposition efficiency for particles diameter between 1-M0 pm was calculated. Results clearly indicate that combining layers with different porosities and especially their layering sequence has a major effect on deposition ratios inside the filter structure.
Źródło:
Inżynieria i Aparatura Chemiczna; 2013, 5; 481--482
0368-0827
Pojawia się w:
Inżynieria i Aparatura Chemiczna
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Minimization of Energy and Service Latency Computation Offloading using Neural Network in 5G NOMA System
Autorzy:
Suprith, P. G.
Ahmed, Mohammed Riyaz
Powiązania:
https://bibliotekanauki.pl/articles/27311932.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
Mobile edge computing
Deep Q Network Algorithm
latency optimized
computation offloading
5G
Opis:
The future Internet of Things (IoT) era is anticipated to support computation-intensive and time-critical applications using edge computing for mobile (MEC), which is regarded as promising technique. However, the transmitting uplink performance will be highly impacted by the hostile wireless channel, the low bandwidth, and the low transmission power of IoT devices. Using edge computing for mobile (MEC) to offload tasks becomes a crucial technology to reduce service latency for computation-intensive applications and reduce the computational workloads of mobile devices. Under the restrictions of computation latency and cloud computing capacity, our goal is to reduce the overall energy consumption of all users, including transmission energy and local computation energy. In this article, the Deep Q Network Algorithm (DQNA) to deal with the data rates with respect to the user base in different time slots of 5G NOMA network. The DQNA is optimized by considering more number of cell structures like 2, 4, 6 and 8. Therefore, the DQNA provides the optimal distribution of power among all 3 users in the 5G network, which gives the increased data rates. The existing various power distribution algorithms like frequent pattern (FP), weighted least squares mean error weighted least squares mean error (WLSME), and Random Power and Maximal Power allocation are used to justify the proposed DQNA technique. The proposed technique which gives 81.6% more the data rates when increased the cell structure to 8. Thus 25% more in comparison to other algorithms like FP, WLSME Random Power and Maximal Power allocation.
Źródło:
International Journal of Electronics and Telecommunications; 2023, 69, 4; 661--667
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Recognition of font and tamil letter in images using deep learning
Autorzy:
Sridharan, Manikandan
Arulanandam, Delphin Carolina Rani
Chinnasamy, Rajeswari K
Thimmanna, Suma
Dhandapani, Sivabalaselvamani
Powiązania:
https://bibliotekanauki.pl/articles/1837779.pdf
Data publikacji:
2021
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
deep convolution network
Tamil Letter
recognition system
font recognition
filtering
głęboka sieć konwolucyjna
system rozpoznawania
rozpoznawanie czcionek
filtrowanie
Opis:
This paper proposes a deep learning approach to recognize Tamil Letter from images which contains text. This is recognition process, the text in the images are divided to letter or characters. Each recognized letters are sending to recognition system and filter the text using deep learning algorithms. Our proposed algorithm is used to separate letter from the text using convolution neural network approach. The filtering system is used for identifying font based on that letters are found. The Tamil letters are test data and loaded in recognition systems. The trained data are input which contains filtered letter from image. For example, Tamil letters such as are available in test dataset. The trained data are applied into deep convolution neural network process. The two dataset are created which contains test data with Tamil letter and second one for recognized input data or trained data. 15 thousands of letters are taken and 512 X 512 X 3 size deep convolution network is created with font and letters. As the result, 85% Tamil letters are recognized and 82% are tested using font. TensorFlow is used for testing the accuracy and success rate.
Źródło:
Applied Computer Science; 2021, 17, 2; 90-99
1895-3735
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Skin Lesion Analysis Toward Melanoma Detection Using Deep Learning Techniques
Autorzy:
Sherif, Fatma
Mohamed, Wael A.
Mohra, A.S.
Powiązania:
https://bibliotekanauki.pl/articles/226719.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
melanoma
skin cancer
convolutional neural network
deep learning
Opis:
In the last few years, a great attention was paid to the deep learning Techniques used for image analysis because of their ability to use machine learning techniques to transform input data into high level presentation. For the sake of accurate diagnosis, the medical field has a steadily growing interest in such technology especially in the diagnosis of melanoma. These deep learning networks work through making coarse segmentation, conventional filters and pooling layers. However, this segmentation of the skin lesions results in image of lower resolution than the original skin image. In this paper, we present deep learning based approaches to solve the problems in skin lesion analysis using a dermoscopic image containing skin tumor. The proposed models are trained and evaluated on standard benchmark datasets from the International Skin Imaging Collaboration (ISIC) 2018 Challenge. The proposed method achieves an accuracy of 96.67% for the validation set. The experimental tests carried out on a clinical dataset show that the classification performance using deep learning-based features performs better than the state-of-the-art techniques.
Źródło:
International Journal of Electronics and Telecommunications; 2019, 65, 4; 597-602
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Deep Q-Learning Network for ship stowage planning problem
Autorzy:
Shen, Y.
Zhao, N.
Xia, M.
Du, X.
Powiązania:
https://bibliotekanauki.pl/articles/260614.pdf
Data publikacji:
2017
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
Deep Q-Leaning Network (DQN)
container terminal
ship stowage plan
markov decision process
value function approximation
generalization
Opis:
Ship stowage plan is the management connection of quae crane scheduling and yard crane scheduling. The quality of ship stowage plan affects the productivity greatly. Previous studies mainly focuses on solving stowage planning problem with online searching algorithm, efficiency of which is significantly affected by case size. In this study, a Deep Q-Learning Network (DQN) is proposed to solve ship stowage planning problem. With DQN, massive calculation and training is done in pre-training stage, while in application stage stowage plan can be made in seconds. To formulate network input, decision factors are analyzed to compose feature vector of stowage plan. States subject to constraints, available action and reward function of Q-value are designed. With these information and design, an 8-layer DQN is formulated with an evaluation function of mean square error is composed to learn stowage planning. At the end of this study, several production cases are solved with proposed DQN to validate the effectiveness and generalization ability. Result shows a good availability of DQN to solve ship stowage planning problem.
Źródło:
Polish Maritime Research; 2017, S 3; 102-109
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Study of Correlation between Fishing Activity and AIS Data by Deep Learning
Autorzy:
Shen, K. Y.
Chu, Y. J.
Chang, S. J.
Chang, S. M.
Powiązania:
https://bibliotekanauki.pl/articles/1841621.pdf
Data publikacji:
2020
Wydawca:
Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:
AIS Data
deep learning framework
learning methods
Recurrent Neural Network
(RNN)
Automatic Identification System
(AIS)
fishing operation
Opis:
Previous researches on the prediction of fishing activities mainly rely on the speed over ground (SOG) as the referential attribute to determine whether the vessel is navigating or in fishing operation. Since more and more fishing vessels install Automatic Identification System (AIS) either voluntarily or under regulatory requirement, data collected from AIS in real time provide more attributes than SOG which may be utilized to improve the prediction. To be specific, the ships' trajectory patterns and the changes in course become available and should be considered. This paper aims to improve the accuracy in the identification of fishing activities. First, we do feature extraction from the AIS data of coastal waters around Taiwan and build a Recurrent Neural Network (RNN) model. Then, the activity data of fishing vessels are divided into fishing and non-fishing. Finally, based on the testing by feeding various fishing activity data, we can identify the fishing status automatically.
Źródło:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2020, 14, 3; 527-531
2083-6473
2083-6481
Pojawia się w:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 75

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