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1

Urban traffic crash analysis using deep learning techniques

Sobhana Mummaneni, Vemulapalli Nihitha, Mendu Gnana Siva Sai Venkatesh, Ginjupalli Naga Deepika, Dodda Pragathi, Subramanyam Rayanoothala Bala Venkata

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2023

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T. 13, nr 3

56--63

EN

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.

PL

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.

2

Influence of modelling phase transformations with the use of LSTM network on the accuracy of computations of residual stresses for the hardening process

Wróbel Joanna, Kulawik Adam

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2023

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Vol. 71, nr 4

art. no. e145681

EN

Replacing mathematical models with artificial intelligence tools can play an important role in numerical models. This paper analyses the modeling of the hardening process in terms of temperature, phase transformations in the solid state and stresses in the elastic-plastic range. Currently, the use of artificial intelligence tools is increasing, both to make greater generalizations and to reduce possible errors in the numerical simulation process. It is possible to replace the mathematical model of phase transformations in the solid state with an artificial neural network (ANN). Such a substitution requires an ANN network that converts time series (temperature curves) into shares of phase transformations with a small training error. With an insufficient training level of the network, significant differences in stress values will occur due to the existing couplings. Long-Short-Term Memory (LSTM) networks were chosen for the analysis. The paper compares the differences in stress levels with two coupled models using a macroscopic model based on CCT diagram analysis and using the Johnson-Mehl-Avrami-Kolmogorov (JMAK) and Koistinen-Marburger (KM) equations, against the model memorized by the LSTM network. In addition, two levels of network training accuracy were also compared. Considering the results obtained from the model based on LSTM networks, it can be concluded that it is possible to effectively replace the classical model in modeling the phenomena of the heat treatment process.

3

Robust zeroing neural networks with two novel power-versatile activation functions for solving dynamic Sylvester equation

Zhou Peng, Tan Mingtao

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2022

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Vol. 70, nr 3

art. no. e141307

EN

In this work, two robust zeroing neural network (RZNN) models are presented for online fast solving of the dynamic Sylvester equation (DSE), by introducing two novel power-versatile activation functions (PVAF), respectively. Differing from most of the zeroing neural network (ZNN) models activated by recently reported activation functions (AF), both of the presented PVAF-based RZNN models can achieve predefined time convergence in noise and disturbance polluted environment. Compared with the exponential and finite-time convergent ZNN models, the most important improvement of the proposed RZNN models is their fixed-time convergence. Their effectiveness and stability are analyzed in theory and demonstrated through numerical and experimental examples.

4

Forecasting future values of time series using the lstm network on the example of currencies and WIG20 companies

Mróz Bartosz, Nowicki Filip

Zeszyty Naukowe. Telekomunikacja i Elektronika / Uniwersytet Technologiczno-Przyrodniczy w Bydgoszczy

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2020

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nr 24

13--30

EN

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.

PL

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.

5

Predykacja zajętości kanału LTE za pomocą rekurencyjnej sieci neuronowej

Wasilewska Małgorzata, Bogucka Hanna

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

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2020

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nr 7-8

269--273, CD

PL

W artykule przedstawiono metody detekcji i przewidywania obecności transmisji sygnału LTE. Zastosowano algorytmy uczenia maszynowego, takie jak algorytm k najbliższych sąsiadów, drzewo decyzyjne, sieć neuronową oraz rekurencyjną sieć neuronową. Za pomocą eksperymentów wykazano, że wymienione algorytmy, a w szczególności sieć rekurencyjna osiągają wysokie wartości prawdopodobieństwa poprawnej detekcji zarówno dla detekcji w czasie rzeczywistym, jak i dla przewidywania obecności sygnałów w przyszłości.

EN

In the paper, the methods of LTE spectrum detection and future state predictions have been presented. Machine learning algorithms have been implemented for spectrum sensing, namely k-nearest neighbors, decision tree, neural network and recurrent neural network. Conducted experiment has shown that these algorithms reach high values of probability of correct detection for current moment as well as for future prediction.

6

Human activity recognition using improved complete ensemble EMD with adaptive noise and long short-term memory neural networks

Altuve Miguel, Lizarazo Paula, Villamizar Javier

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 3

901--909

EN

The recognition of human activities is a topic of great relevance due to its wide range of applications. Different approaches have been proposed to recognize human activities, ranging from the comparison of signals with thresholds to the application of deep and machine learning techniques. In this work, the classification of six human activities (walking, walking downstairs, walking upstairs, standing, sitting, and lying down) is performed using bidirectional LSTM networks that exploit intrinsic mode function (IMF) representation of inertial signals. Records with inertial signals (accelerometer and gyroscope) of 2.56 s, available at the UCI Machine Learning Repository, were collected from 30 subjects using a smartphone. First, inertial signals were standardized to take them to the same scale and were decomposed into IMF using the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). IMF were then segmented (split) into nine segments of 1.28 s with 12.5% overlap and introduced to a first network with four outputs to identify the dynamic activities and the statics as a single class called ‘‘statics’’, giving 98.86% accuracy. Then, the non-segmented IMF of the records assigned to the statics class were introduced to a second network to classify their three activities, giving an accuracy of 88.46%. In total, 92.91% accuracy was obtained to classify the six human activities. This performance is because ICEEMDAN allowed the extraction of information that was embedded in the signal, and the segmentation of the IMF allowed the network to discriminate between static and dynamic activities.

7

A novel deep LSTM network for artifacts detection in microelectrode recordings

Hosny Mohamed, Zhu Minwei, Gao Wenpeng, Fu Yili

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 3

1052--1063

EN

Microelectrode recording (MER) signals are world-widely used for validating the planned trajectories in the procedure of deep brain stimulation (DBS) surgery to obtain accurate position of electrodes inside the brain structure. Besides, MER signals are important source for studying extracellular neuronal activity and DBS biomarkers, such as, spike clustering and sorting. However, MER signals are prone to several artifacts derived from electrical equipment in the operating room, electrode movement and patient activities, etc., which reduce the signal-to-noise ratio of the MER signals. Therefore, in this paper, we propose a novel deep learning architecture based on long short-term memory (LSTM) network for automatic artifact detection in MER signals. Frequency and time-domain features were extracted from the raw MER signals and fed to the deep LSTM network. A manually annotated MER database obtained from 17 Parkinson's disease (PD) patients were used to validate the proposed architecture. The proposed architecture achieved promising results of 97.49% accuracy, 98.21% sensitivity and 96.87% specificity on an unseen test set. To our best knowledge, this is the first study to use LSTM network for artifacts detection in MER signals. The MER data will be available at http://homepage.hit.edu.cn/wpgao.

8

Shallow, Deep, Ensemble models for Network Device Workload Forecasting

Liu Cenru

Annals of Computer Science and Information Systems

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2020

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Vol. 21

101--104

EN

Reliable prediction of workload-related characteristics of monitored devices is important and helpful for management of infrastructure capacity. This paper presents 3 machine learning models (shallow, deep, ensemble) with different complexity for network device workload forecasting. The performance of these models have been compared using the data provided in FedCSIS'20 Challenge. The R2 scores achieved from the cascade Support Vector Regression (SVR) based shallow model, Long short-term memory (LSTM) based deep model, and hierarchical linear weighted ensemble model are 0.2506, 0.2831, and 0.3059, respectively, and was ranked 3rd place in the preliminary stage of the challenges.

9

Comparison of optimization algorithms of connectionist temporal classifier for speech recognition system

Amirgaliyev Yedilkhan, Darkhan Kuanyshbay, Shoiynbek Aisultan

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2019

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T. 9, nr 3

54--57

EN

This paper evaluates and compares the performances of three well-known optimization algorithms (Adagrad, Adam, Momentum) for faster training the neural network of CTC algorithm for speech recognition. For CTC algorithms recurrent neural network has been used, specifically Long- Short-Term memory. LSTM is effective and often used model. Data has been downloaded from VCTK corpus of Edinburgh University. The results of optimization algorithms have been evaluated by the Label error rate and CTC loss.

PL

W artykule dokonano oceny i porównania wydajności trzech znanych algorytmów optymalizacyjnych (Adagrad, Adam, Momentum) w celu przyspieszenia treningu sieci neuronowej algorytmu CTC do rozpoznawania mowy. Dla algorytmów CTC wykorzystano rekurencyjną sieć neuronową, w szczególności LSTM, która jest efektywnym i często używanym modelem. Dane zostały pobrane z wydziału VCTK Uniwersytetu w Edynburgu. Wyniki algorytmów optymalizacyjnych zostały ocenione na podstawie wskaźników Label error i CTC loss.

10

Inter-patient ECG classification with convolutional and recurrent neural networks

Guo Li, Sim Gavin, Matuszewski Bogdan

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 3

868--879

EN

The recent advances in ECG sensor devices provide opportunities for user self-managed auto-diagnosis and monitoring services over the internet. This imposes the requirements for generic ECG classification methods that are inter-patient and device independent. In this paper, we present our work on using the densely connected convolutional neural network (DenseNet) and gated recurrent unit network (GRU) for addressing the inter-patient ECG classification problem. A deep learning model architecture is proposed and is evaluated using the MIT-BIH Arrhythmia and Supraventricular Databases. The results obtained show that without applying any complicated data pre-processing or feature engineering methods, both of our models have considerably outperformed the state-of-the-art performance for supraventricular (SVEB) and ventricular (VEB) arrhythmia classifications on the unseen testing dataset (with the F1 score improved from 51.08 to 61.25 for SVEB detection and from 88.59 to 89.75 for VEB detection respectively). As no patient-specific or device-specific information is used at the training stage in this work, it can be considered as a more generic approach for dealing with scenarios in which varieties of ECG signals are collected from different patients using different types of sensor devices.

11

Zastosowanie sterowania z modelem wewnętrznym opartego o sieci neuronowe dla napędu z elastycznym sprzęgłem

Żychlewicz M., Derugo P.

Przegląd Elektrotechniczny

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2018

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R. 94, nr 5

163--168

PL

W artykule opisano sterowanie układem napędowym z połączeniem sprężystym, pętla regulacji prędkości została zaprojektowana w oparciu o dwa modele neuronowe. Jeden z nich stanowi główny regulator, natomiast drugi jest modelem odniesienia wykorzystywanym w trakcie obliczeń. Adaptacja wag sieci neuronowych jest realizowana on-line. Artykuł zawiera opis teoretyczny zaimplementowanej struktury, a także badania symulacyjne oraz eksperymentalne zrealizowane z wykorzystaniem procesora sygnałowego karty dSPACE1103.

EN

Paper presents control system applied for electrical drive with elastic connections. Speed control loop of the whole structure is based on two neural models. One of them is applied as the main controller, the second is the internal model of the plant used for calculations of control signal. Adaptation of weights in neural networks is done in on-line mode. Article contains theoretical description of implemented control structure, simulation tests as well as experimental tests using digital signal processor of dSPACE1103.

12

A segment-wise reconstruction method based on bidirectional long short term memory for Power Line Interference suppression

Qiu Y., Huang K., Xiao F., Shen H.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 2

217--227

EN

The overlap between the signal components of Power Line Interference (PLI) and biomedical signals in the frequency domain makes the filtered results prone to severe distortion. Electrocardiogram (ECG) is a type of biomedical electronic signal used for cardiac diagnosis. The objective of this work is to suppress the PLI components from biomedical signals with minimal distortion, and the object of study is mainly the ECG signals. In this study, we propose a novel segment-wise reconstruction method to suppress the PLI in biomedical signals based on the Bidirectional Recurrent Neural Networks with Long Short Term Memory (Bi-LSTM). Experiments are conducted on both synthetic and real signals, and quantitative comparisons are made with a traditional IIR notch filter and two state-of-the-art methods in the literature. The results show that by our method, the output Signal-to-Noise Ratio (SNR) is improved by more than 7 dB and the settling time for step response is reduced to 0.09 s on average. The results also demonstrate that our method has enough generalization ability for unforeseen signals without retraining.

13

Nonlinear model predictive control of a boiler unit: a fault tolerant control study

Patan K., Korbicz J.

International Journal of Applied Mathematics and Computer Science

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2012

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Vol. 22, no. 1

225-237

EN

This paper deals with a nonlinear model predictive control designed for a boiler unit. The predictive controller is realized by means of a recurrent neural network which acts as a one-step ahead predictor. Then, based on the neural predictor, the control law is derived solving an optimization problem. Fault tolerant properties of the proposed control system are also investigated. A set of eight faulty scenarios is prepared to verify the quality of the fault tolerant control. Based of different faulty situations, a fault compensation problem is also investigated. As the automatic control system can hide faults from being observed, the control system is equipped with a fault detection block. The fault detection module designed using the one-step ahead predictor and constant thresholds informs the user about any abnormal behaviour of the system even in the cases when faults are quickly and reliably compensated by the predictive controller.