Wyniki wyszukiwania - BazTech

1

Badanie procesu spalania osadów ściekowych z wykorzystaniem uczenia głębokiego

Hu Weijie

Przemysł Chemiczny

|

2024

|

T. 103, nr 1

104--111

PL

Model oceny wykonalności procesu spalania osadów ściekowych został opracowany przy użyciu sieci neuronowej wstecznej propagacji (BPNN), aby zapewnić istotne wsparcie dla oczyszczalni ścieków, pomagając im w ocenie warunków ekologicznych i jakości środowiska miejskiego za pomocą zaawansowanej technologii sztucznej inteligencji. Dane z węzłów sieci zostały przeanalizowane w celu ujawnienia ukrytych struktur klasowych w danych dotyczących zanieczyszczeń ekologicznych i wpływu spalania osadów ściekowych na wyniki oczyszczania ścieków. Znaczącą przewagę nad innymi metodami osiągnięto przy wskaźniku dokładności przekraczającym 95%. Ten model może pomóc miejskim oczyszczalniom ścieków w ocenie procesów spalania osadów ściekowych i zwiększeniu wydajności operacyjnej, zmniejszeniu ich śladu ekologicznego i skutecznym sprostaniu współczesnym wyzwaniom.

EN

A feasibility assessment model of sludge incineration treatment was developed by using back-propagation neural network (BPNN) to provide a vital support to sewage treatment plants, aiding them in assessing urban ecol. conditions and environ. qual. with advanced artificial intelligence technol. The data from network nodes were analyzed to reveal hidden class structures in ecol. pollution data and the impact of sludge incineration on sewage treatment outcomes. A significant advantage over other methods was achieved at the accuracy rate over 95%. This model can help urban sewage plants to evaluate sludge incineration treatments and enhance operational efficiency, reduce their ecol. footprint, and address contemporary challenges effectively.

2

Decoupling control for permanent magnet in-wheel motor using internal model control based on back-propagation neural network inverse system

Li Y., Zhang B., Xu X.

Bulletin of the Polish Academy of Sciences. Technical Sciences

|

2018

|

Vol. 66, nr 6

961--972

EN

The permanent magnet in-wheel motor (PMIWM) is a nonlinear, multivariable, strongly coupled and highly complex system. The key to the development and application of the PMIWM consists in the improvement of its control accuracy and dynamic performance. In order to effectively decouple the PMIWM, this paper presents a novel internal model control (IMC) approach based on the back-propagation neural network inverse (BPNNI) control method. First, theoretical analysis is conducted to show the existence of the PMIWM inverse system, to be modeled mathematically. The inverse system approximated and identified by the back-propagation neural network (BPNN) constitutes the back-propagation neural network inverse (BPNNI) system. Then, by cascading the BPNNI system on the left side of the original PMIWM system, a new decoupling, pseudo-linear system is established. Moreover, the 2-DOF internal model control (IMC) method is employed to design the extra closed-loop controller that further improves disturbance rejection and robustness of the whole system. Consequently, the proposed decoupling control approach incorporates the advantages of both the BPNNI and the IMC. Effectiveness of thus proposed control approach is verified by means of simulation and real-time hardware-in-the-loop (HIL) experiments.

3

Using Artificial Neural Networks to Establish a Customer-cancellation Prediction Model

Huang H.-C., Chang A. Y., Ho C.-C.

Przegląd Elektrotechniczny

|

2013

|

R. 89, nr 1b

178--180

EN

In the past, judgments concerning customer cancellations relied primarily on managers’ experience. Prediction errors can cause surpluses or insufficient service capacity. Data mining technology can improve prediction and judgment accuracy. This study applies back propagation neural networks and general regression neural networks to establish a customer-cancellation prediction model. The empirical results showed that both prediction models possessed good predictive abilities and can aid in service capacity scheduling.

PL

W artykule opisano zastosowanie sieci neuronowych o propagacji wstecznej (ang. BPNN) oraz regresji generalnej (ang. GRNN) w budowie modelu anulowania klientów. Działanie to zwykle opiera się na doświadczeniu manager’a, co może doprowadzić do błędnych decyzji. Rezultaty badań empirycznych dowodzą dobrych własności przewidywania i możliwej użyteczności w określaniu potencjalnych działań z klientem opracowanych modeli.

4

Artificial neural networks in civil and structural engineering: Ten years of research in Poland

Waszczyszyn Z.

Computer Assisted Mechanics and Engineering Sciences

|

2006

|

Vol. 13, No. 4

489-512

EN

This state-of-the-art paper reports the last ten year results, obtained by an informal research group completed of participants of some Polish universities at the Institute of Computer Methods in Civil Engineering (now Institute of Computational Civil Engineering) of the Cracow University of Technology, and supervised by the author of the paper. After a short introduction and brief discussion of ANNs basie ideas, the activities in five areas are described: i) ANNs as a new independent computational tool for the analysis of C&SE problems, ii) neural networks in FEM/ANN hybrid systems developed for the C&SE problems analysis, iii) various problems analyzed by ANNs, iv) modifications of BPNNs (Back-Propagation Neural Networks) and new learning methods, as well as other ANNs than those applied in problems mentioned above, v) promotion of ANNs. The representative six selected study cases are discussed: 1) conerete fatigue failure, 2) buckling of cylindrical shells with geometrical imperfections, 3) acceleration response spectra, 4) reliability of a piane frame, 5) hybrid updating of a thin-walled beam FE model, 6) hybrid identification of equivalent materiał in a perforated strip. Some generał conclusions on prospects of ANNs applications in C&SE are given at the end of the paper.