Computational Engineering Commons^™

Mathematical Modelling Of Nitrification And Denitrification Processes Based On Neuro-Fuzzy Bioreactor Models, Mirkhalil Agzamovich Ismailov, Boburbek Zokirjon Ugli Mannobjonov

Technical science and innovation

This paper presents the development and investigation of hybrid neural network and fuzzy models for the mathematical modelling of nitrification and denitrification processes in a biological wastewater treatment bioreactor. A comprehensive approach is proposed, integrating a mechanistic model of the ASM1/ASM2d type with neural networks (LSTM and Gaussian Process Regression), as well as a fuzzy control system based on an extended set of expert rules. A digital twin of the bioreactor was developed to allow for the prediction of the dynamic behavior of key parameters such as NH₄⁺, NO₃⁻, dissolved oxygen, etc. within a prediction range of 1 to 12 …

Smart System For Wheat Diseases Early Detection, Rustam Baratov, Himola Sunnatillayeva, Almardon Mamatovich Mustafoqulov

Chemical Technology, Control and Management

This paper presents a smart system for early detection of wheat plant diseases in the vegetation period. The proposed smart system allows detecting three types of wheat diseases, particularly yellow rust, powdery mildew and septoria at early stage and significantly improves the soil and ecology by locally spraying harmful chemicals just to sickness plants. The proposed diagnostic program is created in the C++ programming language. The basic structure of the smart system consists of Raspberry PI 4 MODULE, Logitech HD Pro Webcam C920, buzzer, HC-SR04 distance sensor, DC motor driver, AC motor, power supply, relay and some digital devices.

Bitcoin Price Prediction Using Neural Networks, Vladislav Killiakov

Electrical Engineering

In this project, I will investigate the performance of several major neural network architectures for the task of Bitcoin price prediction. Bitcoin is a cryptocurrency that is recently becoming increasingly more popular, and more widely adopted as a financial instrument. As a result, more efforts have been made in the past several years to model and predict its price. However, to this moment a large portion of work on Bitcoin price modeling was done using statistical or classical machine learning techniques. At the same time, other artificial intelligence based prediction techniques, and specifically neural networks, have not been explored to …

Receptive Fields Optimization In Deep Learning For Enhanced Interpretability, Diversity, And Resource Efficiency., Babajide Odunitan Ayinde

Electronic Theses and Dissertations

In both supervised and unsupervised learning settings, deep neural networks (DNNs) are known to perform hierarchical and discriminative representation of data. They are capable of automatically extracting excellent hierarchy of features from raw data without the need for manual feature engineering. Over the past few years, the general trend has been that DNNs have grown deeper and larger, amounting to huge number of final parameters and highly nonlinear cascade of features, thus improving the flexibility and accuracy of resulting models. In order to account for the scale, diversity and the difficulty of data DNNs learn from, the architectural complexity and …

Why Latent Representations In Convolutional Neural Networks Fall Outside Visual Space, Katerina Malakhova

MODVIS Workshop

It is common to compare properties of visual information processing by artificial neural networks and the primate visual system.

Some remarkable similarities were observed in the responses of neurons in IT cortex and units in higher layers of CNNs. Here I show that latent representations formed by weights in convolutional layers do not necessarily reflect visual domain. Instead they are strongly dependent on a choice of training set and cost function.

The most striking example is when an individual unit, which is highly selective to some members of a category is, nevertheless, inhibited by visually similar objects of the same …

Solving The Vehicle Re-Identification Problem By Using Neural Networks, Tanweer Rashid

Computational Modeling & Simulation Engineering Theses & Dissertations

Vehicle re-identification is the process by which vehicle attributes measured at one point on a road network are compared to vehicle attributes measured at another point in an effort to match vehicles without using any unique identifiers such as license plate numbers. A match is made if the two measurements are estimated to belong to the same vehicle. Vehicle attributes can be sensor readings such as loop induction signatures, or they can also be actual vehicle characteristics such as length, weight, number of axles, etc. This research makes use of vehicle length, travel time, axle spacing and axle weights for …

Electroencephalogram Artifact Removal Using A Wavelet Neural Network, Hoang-Anh T. Nguyen

Electrical & Computer Engineering Theses & Dissertations

A wavelet neural network (WNN) technique rs developed for electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural networks and the time/frequency property of wavelet, where the neural network was trained on a simulated dataset with known ground truths. The contribution of this thesis is two-fold. First, many EEG artifact removal algorithms, including regression based methods, require reference EOG signals, which are not always available. To remove EEG ai1ifacts, a WNN tries to learn the characteristics of the artifacts first and does not need reference EOG signals once trained. Second, WNNs are …

Generalization Metrics For Neural Modeling Applications In System Identification, Denise M. Reeves

Electrical & Computer Engineering Theses & Dissertations

In this thesis a procedure to design multilayer feedforward networks for system identification with good prediction properties is presented. Central to the design procedure is a means to characterize the prediction capabilities of various trained neural networks. Such knowledge will allow for the identification of the best network design. For system identification purposes, a "good" model is one that is good at predicting, In particular, a good model is one that produces small prediction errors when applied to a set of cross-validation data. We formulate and implement a criterion function designed to measure the size of a trained neural network's …