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Optimizing Parameters For Efficient Computation With Fully Homomorphic Encryption Schemes, Cavi̇dan Yakupoğlu Karaağaç, Kurt Rohloff
Optimizing Parameters For Efficient Computation With Fully Homomorphic Encryption Schemes, Cavi̇dan Yakupoğlu Karaağaç, Kurt Rohloff
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, we aim to provide a parameter selection approach for the BFVrns scheme, one of the prominent fully homomorphic encryption (FHE) schemes. Selecting parameters for lattice-based FHE schemes poses a practical challenge for both experts and nonexperts. To solve this problem, we introduce a hybrid approach that combines theoretical approach with experimental analysis. First, we employ regression analysis to examine the impact of parameters on both performance and security. The varying behavior of FHE parameters in terms of performance, security, and ciphertext expansion factor (CEF) makes parameter selection more challenging. To address this issue, we employ a multi-objective …
Decomposition Lstm With Dual Multi-Head Self-Attention For Wind Turbine Drivetrain State Forecasting, Haikun Jia, Huini Sun, Shuang Bai
Decomposition Lstm With Dual Multi-Head Self-Attention For Wind Turbine Drivetrain State Forecasting, Haikun Jia, Huini Sun, Shuang Bai
Turkish Journal of Electrical Engineering and Computer Sciences
Due to the clean and renewable nature of wind energy, accurate prediction of rotor loads and operating states for wind turbine units has become of paramount importance. Currently, traditional methods relying on expert analysis combined with instrument testing for qualitative reasoning are both time-consuming and labor-intensive, and their accuracy guarantees are limited. In response to wind farm data entailing the interweaving of data from multiple sources and the diverse interrelations across various features and time steps, this study introduces a method for predicting rotor loads and operating states. Initially, we employ an iterative multi-scale seasonal-trend decomposition block to capture latent …
Fuzzy-Virtual Inertia Control To Improve The Frequency Response Of Multi-Area Power Systems, Nourelhouda Djaraf, Yacine Daili, Abderrahim Zemmit, Abdelghani Harrag
Fuzzy-Virtual Inertia Control To Improve The Frequency Response Of Multi-Area Power Systems, Nourelhouda Djaraf, Yacine Daili, Abderrahim Zemmit, Abdelghani Harrag
Turkish Journal of Electrical Engineering and Computer Sciences
Virtual inertia control (VIC) is essential for power systems dominated by electronic devices to compensate for the lack of inertia and ensure frequency regulation. However, most existing VICs often focus solely on optimizing the virtual inertia parameter to adapt to the high penetration of renewable energy sources (RESs) without considering the damping factor. This oversight can lead to significant fluctuations and power mismatches, especially in interconnected systems where the coordination between MGs is sensitive and essential, and there is a risk of propagation of deviations between MGs, which makes the control more complex. To address these issues, this paper presents …
Enhancing Spatial-Temporal Video Prediction With Ts-Vq-Vae: A Novel Encoder-Processor-Decoder Approach, Mei Feng, Fan Li
Enhancing Spatial-Temporal Video Prediction With Ts-Vq-Vae: A Novel Encoder-Processor-Decoder Approach, Mei Feng, Fan Li
Turkish Journal of Electrical Engineering and Computer Sciences
Video prediction is a significant and actively researched area within the data science community. Its primary objective is to generate future video frames based on historical frames, finding applications in diverse domains such as human motion prediction, climate change analysis, and traffic flow forecasting. Traditional methods combine Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to capture complex correlations in spatial-temporal signals. Recent methods improve video prediction accuracy by introducing external information such as optical flow, semantic maps, and human pose data. However, these methods have limitations, such as not fully exploring the intermediate states of learning representations, overlooking …
Conversational Open-Domain Question Answering For Resource-Constrained Languages, Emrah Budur, Tunga Güngör
Conversational Open-Domain Question Answering For Resource-Constrained Languages, Emrah Budur, Tunga Güngör
Turkish Journal of Electrical Engineering and Computer Sciences
The growing interest in Conversational AI has led to the development of Conversational OpenQA systems as a crucial step for meeting users' information needs in real world scenarios. Conversational OpenQA systems enhance standard OpenQA performance by leveraging conversation history of the users. However, building effective Conversational OpenQA systems requires large-scale Conversational OpenQA datasets, often limited to the English language, hindering progress in low-resource languages. We present a robust Conversational OpenQA system enhanced by conversational context, designed for languages with limited resources and exemplified in our case study for Turkish. To address data limitations in a cost-effective way, we repurpose existing …
A Case Study Of Gray-Box Fuzzing With Byte- And Tree-Level Mutation Strategies In Xml-Based Applications For Exposing Security Vulnerabilities, Şerafetti̇n Şentürk, Vahi̇d Garousi, Nejat Yumuşak
A Case Study Of Gray-Box Fuzzing With Byte- And Tree-Level Mutation Strategies In Xml-Based Applications For Exposing Security Vulnerabilities, Şerafetti̇n Şentürk, Vahi̇d Garousi, Nejat Yumuşak
Turkish Journal of Electrical Engineering and Computer Sciences
Fuzzing is an automated process for detecting crashes and vulnerabilities in software system and it is classified as grammar- or mutation-based in terms of input generation. While the grammar-based fuzzing generates inputs from a specification and takes highly-structured inputs, mutation-based fuzzing generates inputs by modifying input files and abstract syntax trees randomly. There are not many case studies comparing the crash detection capabilities in the scope of mutation-based fuzzing. To add to the body of empirical evidence in this area, this case study compares fuzzing with different mutation strategies to evaluate their effectiveness in three aspects: fault detection effectiveness, fault …
Multikernel Embedded Fusion Unet (Mkef-Unet): A Robust Deep Learning Approach For Accurate Segmentation Of Chagas Parasites, Preet Kumar, Carlos Brito-Loeza, Lavdie Rada
Multikernel Embedded Fusion Unet (Mkef-Unet): A Robust Deep Learning Approach For Accurate Segmentation Of Chagas Parasites, Preet Kumar, Carlos Brito-Loeza, Lavdie Rada
Turkish Journal of Electrical Engineering and Computer Sciences
This paper introduces a novel approach for segmenting Chagas parasites on stained blood smear samples from mice during the acute phase of infection with Trypanosoma cruzi utilizing a U-Net-based deep learning model named multikernel embedded fusion UNet (MKEF-UNet). Our proposed model incorporates DenseNet-121 for feature extraction, a classifier module for predicting parasite information, and a segmentation decoder with multiscale feature fusion to generate precise segmentation results. Notably, the integration of the embedded vector module, multikernel convolutions with dilations, and advanced data augmentation techniques significantly enhance the model’s robustness and generalization capabilities. In extensive experiments on the Chagas dataset, MKEF-UNet achieves …
Increasing Electric Vehicle Charger Availability With A Mobile, Self-Contained Charging Station, Robert Serrano, Arifa Sultana, Declan Kavanaugh, Hongjie Wang
Increasing Electric Vehicle Charger Availability With A Mobile, Self-Contained Charging Station, Robert Serrano, Arifa Sultana, Declan Kavanaugh, Hongjie Wang
Electrical and Computer Engineering Student Research
As the transition to sustainable transportation has accelerated with the rise of electric vehicles (EVs), ensuring drivers have access to charging to maximize the electric miles driven is critical to lowering carbon emissions in the transportation sector. Limited charging station capacity and poor reliability, especially during peak travel times, long distance travels, holidays, and events, have hindered the adoption of EVs and threaten the progress toward reducing greenhouse gas emissions. Adaptive, flexible deployment strategies combined with innovative approaches integrating mobility and renewable energy are essential to address these systemic challenges and bridge the current infrastructure gap. To address these challenges, …
Distributed Repeaters For Extending The Range Of Harmonic Rf Tags, Trevor Saunders
Distributed Repeaters For Extending The Range Of Harmonic Rf Tags, Trevor Saunders
USF Tampa Graduate Theses and Dissertations
Passive harmonic Radio Frequency (RF) tags are highly advantageous compared to theiractive counterparts due to their ability to operate effectively in environments that are cluttered or where access to the tag for battery maintenance is challenging, dangerous and/or time consuming. They are also resistant to the local oscillator (LO) phase noise in radar applications where the doppler frequency from slow moving targets is buried beneath the noise. One of the major constraints of passive harmonic RF tags, however, lies in their operating range and the significant amount of power required to overcome the conversion loss and activate the nonlinear device …
Emg-Based Intraoperative Neuromonitoring Using Advanced Machine Learning Approaches, Abdalla Nabil Elsharkawy, Nourhan Zayed
Emg-Based Intraoperative Neuromonitoring Using Advanced Machine Learning Approaches, Abdalla Nabil Elsharkawy, Nourhan Zayed
Mechanical Engineering
Intraoperative neuromonitoring (IONM) plays a critical role in minimizing nerve damage during surgeries by providing real-time feedback on neural integrity. This study evaluated models associated with deep learning and machine learning models for electromyography classification of signal during intraoperative neuromonitoring (IONM). The CNNLSTM model achieved the highest accuracy (85.2%), outperforming traditional models like KNN (53%), RF (62%), and CNN (76%). This demonstrates the degree to which the CNN-LSTM model can gain insight into temporal and spatial dependencies throughout the EMG signals, which makes it optimal for real-time classification in IONM applications. This implies that deep learning techniques can improve surgical …
Novel Approach To Traveling-Wave-Based Fault Location In Nonhomogeneous Transmission Lines, John Parker Burrell
Novel Approach To Traveling-Wave-Based Fault Location In Nonhomogeneous Transmission Lines, John Parker Burrell
Master's Theses
Accurate fault location is a critical aspect of power system protection, ensuring grid reliability and minimizing downtime. Traditional traveling-wave-based fault location methods face limitations when applied to nonhomogeneous transmission lines due to the reliance on precise segment velocities and propagation time data. This thesis addresses these challenges by proposing a novel algorithm that leverages historical fault data to estimate segment velocities and refine these estimates as more faults occur. The algorithm was rigorously tested using the digital model of an 11-segment, 65.694 km real overhead transmission line and validated using both simulations and hardware tests using commercially available time-domain protective …
Real-Time Prediction Of Dynamical Systems Using A Hybrid Analog Computer: Network Traffic Modeling, Majd Zuhair Tahat
Real-Time Prediction Of Dynamical Systems Using A Hybrid Analog Computer: Network Traffic Modeling, Majd Zuhair Tahat
Doctoral Dissertations
As the number of online users grows exponentially, the number and severity of cyber threats escalate, urgently requiring advancements in real-time network modeling and response. Swiftly predicting and analyzing network traffic is crucial for effective network monitoring and control, preventing cyber breaches, and maintaining healthy network functionality. This research presents a novel approach to real-time modeling based on analyzing evolving properties and patterns in a dynamical network system using a hybrid analog-digital computer. An analog computer was utilized as a co-processor to compute differential equations that model the Transmission Control Protocol (TCP) window size. A comparative analysis was conducted between …
Applying Image Processing And Computer Vision For Damage Detection In Photovoltaic Panels, Montaser Abdelsattar, Ahmed Abdelmoety, Ahmed Emad-Eldeen
Applying Image Processing And Computer Vision For Damage Detection In Photovoltaic Panels, Montaser Abdelsattar, Ahmed Abdelmoety, Ahmed Emad-Eldeen
Mansoura Engineering Journal
As the use of Photovoltaic (PV) panels as a sustainable energy source grows, there is a need for effective and precise techniques to monitor and manage these systems. Conventional techniques for identifying faults in PV panels, such as manual inspections, require a significant amount of effort and are susceptible to mistakes made by humans. This study introduces an innovative automated method that utilizes image processing techniques implemented using the OpenCV library to identify panel faults, namely hotspots, which are important indications of possible failures. The study approach combines grayscale conversion, histogram analysis, and adaptive thresholding to accurately detect and evaluate …
Quantum Finite Automaton Using Ternary Rotation Quantum Gates And Chrestenson Family Quantum Gates, Yuchen Huang, Marek Perkowski, Xiaoyu Song, John M. Acken
Quantum Finite Automaton Using Ternary Rotation Quantum Gates And Chrestenson Family Quantum Gates, Yuchen Huang, Marek Perkowski, Xiaoyu Song, John M. Acken
Electrical and Computer Engineering Faculty Publications and Presentations
Quantum automata can solve certain problems with a smaller state space than classical automata. We developed a quantum finite automaton using ternary rotation quantum gates and the Chrestenson family of ternary quantum gates. The main idea of this paper is to show how to combine rotation ternary quantum circuit-based QuantumFinite Automaton and quantum reversible circuit-based Deterministic Finite Automaton to build a more powerful machine. The combined machine can enable more complex language and pattern recognition. The developed quantum finite automaton and resulting combined machine can be used for robotics applications such as language, gesture, and motion recognition.
Calibration And Demonstration Of A Dropped Channel Polarimetric Compressive Sensing Radar System, Cameron G. Goodbar
Calibration And Demonstration Of A Dropped Channel Polarimetric Compressive Sensing Radar System, Cameron G. Goodbar
Theses and Dissertations
The Air Force Institute of Technology (AFIT) Dropped Channel Polarimetric Compressive Sensing (DCPCS) Radar System is a polarimetric radar utilizing four horn antennae with a unique cross-coupling architecture that enables direct control of system parameters to embed signals into adjacent channels. This thesis characterizes the nature of the system, develops system calibration, and illustrates the performance of the DCPCS technique under multiple system configurations. As shown in the results, DCPCS can successfully reconstruct full-polarization data from a subset of polarization measurements. In many cases, the target estimation and signal reconstruction is precise despite less-than-ideal conditioning of the canonical target dictionary …
Island Nation Duress: Simulating Passive Peer-To-Peer Bluetooth Communication During Disaster Relief, Jason K. Medeiros
Island Nation Duress: Simulating Passive Peer-To-Peer Bluetooth Communication During Disaster Relief, Jason K. Medeiros
Theses and Dissertations
Pacific Islands under U.S. jurisdiction are highly vulnerable to natural disasters, yet many lack the infrastructure to effectively respond and recover. Clear communication during and after such events is critical for evacuation, hazard awareness, and first responders’ coordination. This research explores a simulation-based approach using Bluetooth communication to relay messages across Guam, assessing its efficiency through statistical analysis. By examining regional differences and geographic impacts on Bluetooth messaging, the study aims to identify key factors that enhance peer-to-peer communication for timely and effective disaster response.
Statistical Analysis Of Spreading Code Authentication (Sca) Performance Under Varying Signal Conditions And Marker Quantization Schemes, Joseph Quinones-Ocasio
Statistical Analysis Of Spreading Code Authentication (Sca) Performance Under Varying Signal Conditions And Marker Quantization Schemes, Joseph Quinones-Ocasio
Theses and Dissertations
This thesis analyzes Spreading Code Authentication (SCA) in the GPS L1C signal using the PyChips software-defined receiver (SDR)framework. Monte Carlo simulations evaluate authentication performance under varying signal conditions, assessing the impact of double-precision and quantized data on signal integrity. Results demonstrate that authentication remains achievable at low signal-to-noise ratio(SNR) conditions but introduces trade-offs in memory usage and authentication time. These findings provide insights into optimizing SCAfor resource-constrained environments, contributing to secure GPS operations in critical applications such as aviation, autonomous systems,and national defense.
Numerical Studies Of Semiclassical Light Storage Using The Coherent Atomic Transfer Function, Zachary T. Johnson
Numerical Studies Of Semiclassical Light Storage Using The Coherent Atomic Transfer Function, Zachary T. Johnson
Theses and Dissertations
Quantum communication through photons relies on photonic storage to preserve quantum states. However, when photons interact with matter, quantum information becomes distorted. A recently developed semi-classical analytical model predicts output light pulses from an electromagnetically induced transparency (EIT) system. Using the predictions, the model known as the Coherent Atomic Transfer (CAT) Function, is capable of predicting the stored pulse or reconstructing the original pulse. Using numerical convolution and deconvolution with the CAT function as an analog of the point spread function of Fourier optics can provide insights on the effects of EIT storage on the retrieved pulse. Blind deconvolution is …
Quantum Ghost Imaging Through An Atmospheric Turbulence Simulator, Mikaela R. Morris
Quantum Ghost Imaging Through An Atmospheric Turbulence Simulator, Mikaela R. Morris
Theses and Dissertations
The use of single photons and photon pairs has become a cornerstone for new developments in the sensing and imaging community for their ability to improve image resolution and signal to noise readings in low light environments and large standoff distances. Quantum imaging techniques have an ability to overcome the fundamental limits of classical imaging, like diffraction. Quantum ghost imaging, a specific type of quantum imaging, uses a bi-photon pair produced during the process of spontaneous parametric down conversion to produce an image. The pair is split using a beam splitter, the signal photon is transmitted through an aperture and …
Misalignment Uncertainty In Near-Field Thz Scattering Experiments, Philip Patterson
Misalignment Uncertainty In Near-Field Thz Scattering Experiments, Philip Patterson
Theses and Dissertations
This research investigates the effect of misalignment on the near-field scattering of cylinders in the 550-700 GHz frequency band. A Type-1 calibration is performed on previously collected data, using a near-field physical optics solution to simulate scattering at various positions and orientations. The alignment of the cylinders at the time of measurement is predicted by comparing the range profiles of the theoretical and calibrated responses. The data with the most similar range profiles had a mean calibration difference metric of -2.78 dB and a standard deviation of -0.57 dB, demonstrating the presence of sources of error that are dominant over …
Analyzing Lf/Vlf Lightning Waveforms To Estimate D-Region Electron Density Profiles: Process, Application, And Comparison, William R. Smith
Analyzing Lf/Vlf Lightning Waveforms To Estimate D-Region Electron Density Profiles: Process, Application, And Comparison, William R. Smith
Theses and Dissertations
Lightning waveforms in the low frequency (LF; 30-300 kHz) and the very low frequency (VLF; 3-30 kHz) can be exploited to produce data-driven ionospheric Dregion electron density profile (EDP) estimates with significantly higher spatial and temporal coverage than previously available. The lightning waveforms used in this paper are signals detected in the LF/VLF of negative cloud-to-ground lightning by the Earth Networks Total Lightning Detection Network. Each waveform contains a ground wave and a time-delayed ionospheric reflection. The time delay between the ground wave and ionospheric reflection has previously been used to estimate a single specular reflection altitude, where LF/VLF emissions …
Metal Nitrides As Cathode Hosts For Lithium-Sulfur Batteries, Hai-Ji Xiong, Cheng-Wei Zhu, Ding-Rong Deng, Qi-Hui Wu
Metal Nitrides As Cathode Hosts For Lithium-Sulfur Batteries, Hai-Ji Xiong, Cheng-Wei Zhu, Ding-Rong Deng, Qi-Hui Wu
Journal of Electrochemistry
Lithium-sulfur batteries are considered as one of the potential solutions as integrating renewable energy systems for large-scale energy storage because of their high theoretical energy density (2600 Wh·kg–1) and specific capacity (1675 mAh·g–1). Currently, various strategies have been proposed to overcome the technical barriers, e.g., “shuttle effect”, capacity decay and volumetric change, which impede the successful commercialization of lithium-sulfur batteries. This paper reviews the applications of metal nitrides as the cathode hosts for high-performance lithium-sulfur batteries, summarizes the design strategies of different host materials, and discusses the relationship between the properties of metal nitrides and their …
Enhancing Cycle Life Of Graphite ‖ Lifepo4 Batteries Via Copper Substituted Li2Ni1-XCuXO2 Cathode Prelithiation Additive, Jian-Ming Zheng, Jing-Wen Zhang, Tian-Peng Jiao
Enhancing Cycle Life Of Graphite ‖ Lifepo4 Batteries Via Copper Substituted Li2Ni1-XCuXO2 Cathode Prelithiation Additive, Jian-Ming Zheng, Jing-Wen Zhang, Tian-Peng Jiao
Journal of Electrochemistry
Lithium nickel oxide (Li2NiO2), as a sacrificial cathode prelithiation additive, has been used to compensate for the lithium loss for improving the lifespan of lithium-ion batteries (LIBs). However, high-cost Li2NiO2 suffers from inferior delithiation kinetics during the first cycle. Herein, we investigate the effects of the cost-effective Cu substitution of Li2Ni1-xCuxO2 (x = 0, 0.2, 0.3, 0.5, 0.7) synthesized by high-temperature solid-phase method on the structure, morphology, electrochemical performance of graphite‖LiFePO4 battery. The X-ray Diffraction (XRD) refinement result demonstrates that Cu substitution strategy is favorable …
Sno2 Particles Embedded Into Carbon Coated Mesoporous SioX Rod As High Volumetric Capacity Anode For Lithium-Ion Batteries, Jia-Lin Guo, Ni-Ni Li, Peng Zheng
Sno2 Particles Embedded Into Carbon Coated Mesoporous SioX Rod As High Volumetric Capacity Anode For Lithium-Ion Batteries, Jia-Lin Guo, Ni-Ni Li, Peng Zheng
Journal of Electrochemistry
Due to the high capacity and moderate volume expansion of silicon protoxide SiOx (160%) compared with that of Si (300%), reducing silicon dioxide SiO2 into SiOx while maintaining its special nano-morphology makes it attractive as an anode of Li-ion batteries. Herein, through a one-pot facile high-temperature annealing route, using SBA15 as the silicon source, and embedding tin dioxide SnO2 particles into carbon coated SiOx, the mesoporous SiOx-SnO2@C rod composite was prepared and tested as the anode material. The results revealed that the SnO2 particles were distributed uniformly in the …
Investigation Of Dynamic Characteristics Of New Magneto-Elastic Transducers Of Mechanical Quantities, Sulton Amirov, Kamila Jurayeva
Investigation Of Dynamic Characteristics Of New Magneto-Elastic Transducers Of Mechanical Quantities, Sulton Amirov, Kamila Jurayeva
Chemical Technology, Control and Management
In the article examines the dynamic characteristics of new magnetically elastic converters of mechanical quantities by the method of parametric structural circuits. It is shown that the developed differential transformer magneto–elastic converters of power parameters in automatic monitoring and control systems act as a real differentiating link without statics, and magneto-elastic converters of motion parameters act as a whole in the form of sequentially connected oscillatory and real differentiating links. It has been established that the duration of the transient process in magnetoelastic converters of power parameters is determined by the parameters (magnetic capacitance and active magnetic resistance) of the …
Modeling Of Analog-To-Digital Converter In Signal Processing, Ravshan Aliev, A.U. Djalilov
Modeling Of Analog-To-Digital Converter In Signal Processing, Ravshan Aliev, A.U. Djalilov
Chemical Technology, Control and Management
This article is devoted to the study of the modeling process of analog-to-digital converters (ADCs) that process signals, one of the main parts of control system elements and devices. As we know, ADCs are an important part of modern control systems. During the research, the main stages of analog signal conversion were analyzed, i.e. discretization, quantization, coding. A classification of analog-to-digital conversion methods was made and the advantages and disadvantages of each were identified. Also, the characteristics and parameters of ADC were studied, their impact on ADCs performance was evaluated, and it was determined that certain characteristics should be taken …
Principles And Models Of Construction Of Linear Motion Actuators With Holonomic Structure For Intelligent Robot Movement, Matyokubov Nurbek Rustamovich, Temurbek Omonboevich Rakhimov, Yusupov Bekmurod Bayotovich
Principles And Models Of Construction Of Linear Motion Actuators With Holonomic Structure For Intelligent Robot Movement, Matyokubov Nurbek Rustamovich, Temurbek Omonboevich Rakhimov, Yusupov Bekmurod Bayotovich
Chemical Technology, Control and Management
This article is devoted to the principles and models of building linear motion actuators with holonomic structure for the movement of intelligent robots. Also, the classification of the elements of the linear movement performance according to their interconnections and technical characteristics, taking into account their physical characteristics, was seen. A morphological matrix of the construction of holonomic structured linear motion performance elements based on the classification according to the considered technical specifications is presented. The given morphological matrix of linear motion actuators serves to develop new actuators for intelligent mechatronic and robotic systems. Based on the morphological matrix of the …
Methodology Of Automated Control Of Situations In Structurally Complex Systems With Recycled Flows, Khusan Zokirovich Igamberdiev Academician, Madina Mirxalilovma Fozilova
Methodology Of Automated Control Of Situations In Structurally Complex Systems With Recycled Flows, Khusan Zokirovich Igamberdiev Academician, Madina Mirxalilovma Fozilova
Chemical Technology, Control and Management
This article discusses the methodology of automated management of situations in structurally complex systems with recycled flows. The need to develop a correct model for effectively controlling such systems based on scientific principles of analysis and decision-making is substantiated. Particular attention is paid to modeling, optimizing, and using digital technologies, including artificial intelligence, to improve the accuracy and efficiency of management decisions. The principles of decomposition, multi-criteria optimization, and linguistic models of fuzzy correspondence are described, which can be used in conditions of uncertainty and complexity of industrial facilities.
Application Of An Adaptive Neuro-Fuzzy Inference System To Control The Wastewater Treatment Process, Jaloliddin Abdurazzakovich Eshbobaev, Bakhodir Tajiddinovich Khamidov, Marcos Torices Fallanza
Application Of An Adaptive Neuro-Fuzzy Inference System To Control The Wastewater Treatment Process, Jaloliddin Abdurazzakovich Eshbobaev, Bakhodir Tajiddinovich Khamidov, Marcos Torices Fallanza
Chemical Technology, Control and Management
This study explores the application of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for controlling wastewater treatment processes using ion-exchange resins. It addresses the critical challenges of water scarcity and pollution by enhancing the regulation of water hardness (H) and Total Dissolved Solids (TDS). Using a pilot laboratory device and experimental data from the mixed wastewater of the Kungrad Soda Plant in Uzbekistan, an ANFIS model was developed in MATLAB to automate process control. The model leverages water hardness and TDS as input parameters to regulate the water flow rate by servo valve opening degree, ensuring precise and efficient treatment. Compared …
Joint Estimation Of The State And Parameters Of Dynamic Control Objects Based On The Maine Estimator, Yulduz Abdurakhmanova
Joint Estimation Of The State And Parameters Of Dynamic Control Objects Based On The Maine Estimator, Yulduz Abdurakhmanova
Chemical Technology, Control and Management
The issues of constructing an adaptive joint estimation of the state and parameters of dynamic control objects using the Maine estimator are considered. There are various variants of the extended filter, and a variant based on iterations between parameter and state estimates was used in the work. In this version of the extended Kalman filter, the problem of joint parameter and state estimation is solved in such a way that parameter estimation is performed before state estimation. Then, the parameter values are used to assess the state. In this case, further iterations between the state vector estimation and the parameter …