Electrical and Computer Engineering

Permanent URI for this collection

https://hdl.handle.net/10315/35603

Browse

Now showing 1 - 20 of 52

Open Access
Question Generation Using Sequence-to-Sequence Model with Semantic Role Labels
(2023-03-28) Naeiji, Alireza; An, Aijun
Automatic generation of questions from text has gained increasing attention due to its useful applications. We propose a novel question generation method that combines the benefits of rule-based and neural sequence-to-sequence (Seq2Seq) models. The proposed method can automatically generate multiple questions from an input sentence covering different views of the sentence as in rule-based methods, while more complicated "rules" can be learned via the Seq2Seq model. The method utilizes semantic role labeling (SRL) used in rule-based methods to convert training examples into their semantic representations, and then trains a sequence-to-sequence model over the semantic representations. Our extensive experiments on three real-world data sets show that the proposed method significantly improves the state-of-the-art neural question generation approaches in terms of both automatic and human evaluation measures. Moreover, we extend our proposed approach to a paragraph-level SRL-based method and evaluate it on two data sets. Through both automatic and human evaluations, we show that our proposed framework remarkably improves its Seq2Seq counterparts.
Open Access
Step-Up Converter Interfaces for Magnetron Power Supply
(2023-03-28) Bakalian, Matthew Corey; Lam, John Chi Wo
Fine particulate matter like carbon soot harms the respiratory system. One approach to reducing soot pollution is microwave-assisted soot oxidation. The magnetron, which generates microwave energy requires a high-voltage gain converter. In this thesis, a DC/DC converter is proposed to supply the two voltages required by a magnetron by utilizing dual resonant circuit modules. By combining the switches of the step-up resonant stage with a bridgeless power factor correction (PFC) stage, an AC/DC topology is proposed. The proposed AC/DC topology allows for a high power factor (PF) and reduced input conduction losses. The converter utilizes a parallel CL resonant circuit with voltage doubler output to achieve a high-voltage gain, and an LLC resonant circuit to provide the step-down. The circuit is then verified through PSIM with a peak of 1.8kW. A proof-of-concept hardware test with AC input testing step-up 822V and step-down 1.3V outputs simultaneously with a 0.96PF is performed.
Open Access
Deep Unsupervised Learning for Network Resource Allocation Problems with Convex and Non-Convex Constraints
(2023-03-28) Alizadeh, Mehrazin; Tabassum, Hina
Deep neural networks (DNNs) are currently emerging as a potential solution to solve NP-hard wireless resource allocation problems. However, in the presence of intricate constraints, e.g., users' quality-of-service (QoS) constraints or base station quota, guaranteeing constraint satisfaction becomes a fundamental challenge. In this thesis, I propose a novel unsupervised learning framework to solve the classical power control and user assignment problem in a multi-user interference channel, where the objective is to maximize the network sum-rate with QoS, power budget, and base station quota constraints. The proposed method utilizes a differentiable projection function, defined both implicitly and explicitly, to project the output of the DNN to the feasible set of the problem. Extensive simulations depict that the proposed DNN solutions not only improve the achievable data rate, but also achieve zero constraint violation probability, compared to the existing DNNs, and also outperform the optimization-based benchmarks in computation time.
Open Access
Unified Control Strategy for Microgrid Solid-State Transformers
(2022-12-14) Elias, Samy Louis; Rezaei-Zare, Afshin
Solid-state transformers (SST) are particularly useful components in distributed generation systems (DG). This research approaches the control of the SST in a more comprehensive and an organized way. It proposes a compact, versatile and an efficient unified control strategy. This proposal gives rise to three more proposals. i) A method to mitigate the current harmonic distortion which is uniquely software-based and ii) An efficient low-voltage ride-through (LVRT) scheme. Both of those functions come at no extra cost using the proposed unified control scheme. These proposals further demonstrate the proposed strategy’s ability to accommodate further features and modifications. A further contribution to this research addresses the unbalanced load conditions. It proposes a simple, cost-free modification to a resonant filter - making it suitable for the proposed control strategy thus maintaining its simplicity without compromising its practicality. All the proposals of this research have been validated through simulation in Simulink.
Open Access
A Highly-Scalable DC-Coupled Direct-ADC Neural Recording Channel Architecture with Input-Adaptive Resolution
(2022-12-14) Sayedi, Sayedeh Mina; Kassiri, Hossein
This thesis presents the design, development, and characterization of a novel neural recording channel architecture with (a) quantization resolution that is adaptive to the input signal's level of activity, (b) fully-dynamic power consumption that is linearly proportional to the recording resolution, and (c) immunity to DC offset and drifts at the input. Our results demonstrate the proposed design's capability in conducting neural recording with near lossless input-adaptive data compression, leading to a significant reduction in the energy required for both recording and data transmission, hence allowing for a potential high scaling of the number of recording channels integrated on a single implanted microchip without the need to increase the power budget. The proposed channel with the implemented compression technique is implemented in a standard 130nm CMOS technology with overall power consumption of 7.6uW and active area of 92×92µm for the implemented digital-backend.
Open Access
Sparse matrix based power flow solver for real-time simulation of power system
(2022-12-14) Shawlin, Sk Subrina; Rezaei-Zare, Afshin
Analyzing a massive number of Power Flow (PF) equations even on almost identical or similar network topology is a highly time-consuming process for large-scale power systems. The major computation time is hoarded by the iterative linear solving process to solve nonlinear equations until convergence is achieved. This is a paramount concern for any PF analysis methods. This thesis presents a sparse matrix-based power flow solver that is fast enough to be implemented in the real-time analysis of largescale power systems. It uses KLU, a sparse matrix solver, for PF analysis. It also implements parallel processing of CPU and GPU which enables the simultaneous computation of multiple blocks in the algorithm leading to faster execution. It runs 1000 times and 200 times faster than newton raphson method for DC and AC power system respectively. On average, it is around 10 times faster than MATPOWER for both AC and DC power system.
Open Access
Optimal Mitigation of Geomagnetically Induced Current Effects in Power Systems Considering Transformer Thermal Limits
(2022-12-14) Lamichhane, Anusha; Rezaei-Zare, Afshin
An efficient energy transfer from the solar wind into the earth’s space environment causes temporary disturbance to the earth’s magnetosphere. Solar flares and coronal mass ejections (CME) of charged and magnetized particles can disturb the earth’s magnetic field and cause geomagnetic disturbance (GMD). GMDs are of particular concern as they give rise to geomagnetically induced currents (GIC) which have adverse effects on the national power grid and potentially damage transformers on the grid. GIC flowing along transmission lines and through the transformers in power systems can be attributed to problems ranging from overheating of power transformers, harmonic generation, and voltage collapse due to the half-cycle saturation of power transformers. To prevent the power system and its equipment from the adverse effects of GMD, blocking device (BD) can be placed to block the GIC flow in the transformers. However, BD placement is a complex problem, and the cost of BD is very high, so optimization techniques should be employed for BD placement to minimize the number and costs of BDs. Although there has been research on placing blocking devices and their optimal placement, none of them considers the hotspot temperature rise in transformers during GIC. Therefore, Voltage violation and rise in hotspot temperature of transformers are the main concerns in this thesis. This work presents two approaches for the optimal placement of blocking devices on the neutral of high voltage transformers to prevent the power system from the impacts of GIC caused by geomagnetic disturbance. The thesis focuses on the optimization problem based on overheating of power transformers due to GIC and maintaining the hotspot temperature of transformers within the limit, as well as maintaining the voltage profile of the power system. The problem is formulated by first calculating the GIC and increased reactive power demand of each transformer during the GIC flow, performing power flow analysis, checking if system voltage has been violated, calculating the transformers’ windings and metallic hotspot temperatures, checking if the limits are reached, and optimally placing BDs on selective transformers such that the hotspot temperature of transformers is within maximum limits, and the system voltage is recovered above minimum permissible voltage. The optimization is done using the Surrogate optimization and Genetic algorithm of the MATLAB optimization toolbox and made sure that the number of BDs is minimized. A comparative analysis is done from the results obtained from both of the methods. The findings of the thesis highlight the optimization approach for the placement of blocking devices that takes into account the hotspot temperature rise of transformer tie-plates and windings and a realistic criterion that includes the cost of the repair or replacement of transformers based on the hotspot temperature rise of transformers into the optimization approach. The thesis presents the selection criteria for the two optimization solvers, surrogate optimization and genetic algorithm, after researching and reviewing different solvers from the MATLAB optimization toolbox. The total cost of BD placement is reduced where the total load is reduced to some extent based on different levels of geoelectric field ($E$) to maintain the bus voltages above minimum permissible voltage, and the cost can be calculated based on the loss of load, and extra number of BDs can be avoided. The results obtained from surrogate optimization are proved to be effective and efficient as the total number of BDs resulting from surrogate optimization is less than the total number of BDs resulting from genetic algorithm. The nature of genetic algorithm is stochastic in nature, the result not converging to the global minimum, and the time taken by genetic algorithm for the program execution were major drawbacks. In contrast, the characteristics of surrogate algorithm, such as a result, proved to be converging, non-stochastic in nature, unlike genetic algorithm, and comparatively less time consuming than genetic algorithm proving surrogate optimization to be more reliable and efficient.
Open Access
An Enhanced Method for Full-Inversion-Based Ultrasound Elastography of The Liver
(2022-12-14) Mohamed Atia Aboutaleb; Ali Sadeghi-Naini
Similar to many other types of cancer, liver cancer is associated with biological changes that lead to tissue stiffening. An effective imaging technique that can be used for liver cancer detection through visualizing tissue stiffness is ultrasound elastography. In this thesis, we show the effectiveness of an enhanced method of tissue motion tracking used in quasi-static ultrasound elastography for liver cancer assessment compared to other state of the art methods. The method utilizes initial estimates of axial and lateral displacement fields obtained using conventional time delay estimation (TDE) methods in conjunction with a recently proposed strain refinement algorithm to generate enhanced versions of the axial and lateral strain images. Another primary objective of this work is to investigate the sensitivity of the proposed method to the quality of these initial displacement estimates. The proposed algorithm is founded on the tissue mechanics principles of incompressibility and strain compatibility. Tissue strain images can serve as input for full-inversion-based elasticity image reconstruction algorithm. In this work, we applied strain images generated by the proposed method in conjunction with an iterative elasticity reconstruction algorithm for full-inversion-based liver elastography. Moreover, a set of in-silico experiments were conducted to validate the assumptions used in the reconstruction technique to improve the realism of the method. Ultrasound RF data collected from a tissue-mimicking phantom and from four liver cancer patients who underwent open surgical RF thermal ablation therapy were used to evaluate the proposed method. The results showed that the proposed method produces superior results to other state of the art methods. Moreover, while there is some sensitivity to the displacement field initial estimates, overall, the proposed method is robust to the quality of the initial estimates.
Open Access
Noise2NoiseFlow: Realistic Camera Noise Modeling without Clean Images
(2022-12-14) Maleky, Ali; Brown, Michael S.
Image noise modeling is a long-standing problem with many applications in computer vision. Early attempts that propose simple models, such as signal-independent additive white Gaussian noise or the heteroscedastic Gaussian noise model (a.k.a., camera noise level function) are not sufficient to learn the complex behavior of the camera sensor noise. Recently, more complex learning-based models have been proposed that yield better results in noise synthesis and downstream tasks, such as denoising. However, their dependence on supervised data (i.e., paired clean images) is a limiting factor given the challenges in producing ground-truth images. This paper proposes a framework for training a noise model and a denoiser simultaneously while relying only on pairs of noisy images rather than noisy/clean paired image data. We apply this framework to the training of the Noise Flow architecture. The noise synthesis and density estimation results show that our framework outperforms previous signal-processing-based noise models and is on par with its supervised counterpart. The trained denoiser is also shown to significantly improve upon both supervised and weakly supervised baseline denoising approaches. The results indicate that the joint training of a denoiser and a noise model yields significant improvements in the denoiser.
Open Access
API Knowledge Guided Test Generation for Machine Learning Libraries
(2022-12-14) Narayanan, Arunkaleeshwaran; Wang, Song
This thesis proposes MUTester to generate test cases for APIs of machine learning libraries by leveraging the API constraints mined from the corresponding API documentation and the API usage patterns mined from code fragments in Stack Overflow (SO). First, we propose a set of 18 linguistic rules for mining API constraints from the API documents. Then, we use the frequent itemset mining technique to mine the API usage patterns from a large corpus of machine learning API related code fragments collected from SO. Finally, we use the above two types of API knowledge to guide the test generation of existing test generators, for machine learning libraries. To evaluate the performance of MUTester, we first collected 2,889 APIs from five widely used machine learning libraries (i.e., Scikit-learn, Pandas, Numpy, Scipy, and PyTorch),then for each API, we further extract their API knowledge, i.e., API constraints and API usage patterns. Given an API, MUTester combines its API knowledge with existing test generators (e.g., search-based test generator PyEvosuite and random test generator PyRandoop) to generate test cases to test the API. Results of our experiment show that MUTester can significantly improve the corresponding test generation methods. And the improvement in code coverage ranges from 18.0% to 41.9% on average.In addition, it also reduced 21% of invalid tests generated by the existing test generators.
Open Access
Joint Demosaicking / Rectification of Fisheye Camera Images using Multi-color Graph Laplacian Regulation
(2022-12-14) Lan, Fengbo; Cheung, Gene
To compose one 360 degrees image from multiple viewpoint images taken from different fisheye cameras on a rig for viewing on a head-mounted display (HMD), a conventional processing pipeline first performs demosaicking on each fisheye camera's Bayer-patterned grid, then translates demosaicked pixels from the camera grid to a rectified image grid. By performing two image interpolation steps in sequence, interpolation errors can accumulate, and acquisition noise in each captured pixel can pollute its neighbors, resulting in correlated noise. In this paper, a joint processing framework is proposed that performs demosaicking and grid-to-grid mapping simultaneously, thus limiting noise pollution to one interpolation. Specifically, a reverse mapping function is first obtained from a regular on-grid location in the rectified image to an irregular off-grid location in the camera's Bayer-patterned image. For each pair of adjacent pixels in the rectified grid, its gradient is estimated using the pair's neighboring pixel gradients in three colors in the Bayer-patterned grid. A similarity graph is constructed based on the estimated gradients, and pixels are interpolated in the rectified grid directly via graph Laplacian regularization (GLR). To establish ground truth for objective testing, a large dataset containing pairs of simulated images both in the fisheye camera grid and the rectified image grid is built. Experiments show that the proposed joint demosaicking / rectification method outperforms competing schemes that execute demosaicking and rectification in sequence in both objective and subjective measures.
Open Access
Learning-Based Data-Driven and Vision Methodology for Optimized Printed Electronics
(2022-12-14) Brishty, Fahmida Pervin; Grau, Gerd
Inkjet printing is an active domain of additive manufacturing and printed electronics due to its promising features, starting from low-cost, scalability, non-contact printing, and microscale on-demand pattern customization. Up until now, mainstream research has been making headway in the development of ink material and printing process optimization through traditional methods, with almost no work concentrated on machine learning and vision-based drop behavior prediction, pattern generation, and enhancement. In this work, we first carry out a systematic piezoelectric drop on demand inkjet drop generation and characterization study to structure our dataset, which is later used to develop a drop formulation prediction module for diverse materials. Machine learning enables us to predict the drop speed and radius for particular material and printer electrical signal configuration. We verify our prediction results with untested graphene oxide ink. Thereafter, we study automated pattern generation and evaluation algorithms for inkjet printing via computer vision schema for several shapes, scales and finalize the best sequencing method in terms of comparative pattern quality, along with the underlying causes. In a nutshell, we develop and validate an automated vision methodology to optimize any given two-dimensional patterns. We show that traditional raster printing is inferior to other promising methods such as contour printing, segmented matrix printing, depending on the shape and dimension of the designed pattern. Our proposed vision-based printing algorithm eliminates manual printing configuration workload and is intelligent enough to decide on which segment of the pattern should be printed in which order and sequence. Besides, process defect monitoring and tracking has shown promising results equivalent to manual short circuit, open circuit, and sheet resistance testing for deciding over pattern acceptance or rejection with reduced device testing time. Drop behavior forecast, automatic pattern optimization, and defect quantization compared with the designed image allow dynamic adaptation of any materials properties with regards to any substrate and sophisticated design as established here with varying material properties; complex design features such as corners, edges, and miniature scale can be achieved.
Open Access
Wireless Implantable ICs for Energy-Efficient Long-Term Ambulatory EEG Monitoring
(2022-08-08) Freeman, Al; Kassiri, Hossein
This thesis presents the design, development, and experimental characterization of wireless subcutaneous implantable integrated circuits and systems for long-term ambulatory EEG monitoring. Application-, system- and circuit-level requirements for such a device are discussed and a critical review of the state-of-the-art academic and currently available commercial solutions are provided. Two prototypes are presented: The first prototype presented in Chapter 2 is an 8-channel wireless implantable device with a 2.5×1.5 mm2 custom-designed integrated circuit implemented using CMOS 180nm technology at its core. The microchip is fabricated and the measurement results showing its efficacy in EEG signal recording in terms of input-referred noise, voltage gain, signal-to-noise ratio, and power consumption are presented. The chip is implemented together with a BLE 5.0 module on the same platform. Our vision and discussions on biocompatible encapsulation of this system, as well as its integration with a microelectrode array as also provided. The second prototype, also implemented in CMOS 180nm technology and presented in Chapter 3, employs a novel EEG recording channel architecture that enables long-term implantation of EEG monitoring devices through significant improvement of their energy efficiency. The channel leverages the inherent sparsity of the EEG signals and conducts recording in an activity-dependent adaptive manner. Thanks to the proposed fully dynamic spectral-compressing architecture, the recording channels power consumption is drastically reduced. More importantly, the proposed architecture reduces the required wireless transmission throughput by more than an order of magnitude. Our test results on 10 different patients’ pre-recorded human EEG data shows an average of 12.6× improvement in the device’s energy efficiency.
Open Access
Data Rate Enhancement in Ultrasonic Data Telemetry Links
(2022-08-08) Abbasi Shakooh, Ali; Sodagar, Amir
This research aims at introducing a novel technique for the enhancement of data rate in wireless data transfer through ultrasonic telemetry links. Referred to as INtervened-Timing, Enhanced-Rate Interval Modulation (INTERIM), the proposed technique is an isochronous pulse-based modulation technique, which embeds the telemetered symbol in the time interval between two consecutive ultrasonic tone bursts of different frequencies. To realize this idea, two pairs of ultrasonic transducers with different central frequencies and non-overlapping frequency responses are used. Each transducer pair oversees the transmission and receipt of one of the tone bursts. This is while using the same transducers in the same operational conditions (i.e., with the same excitatory-inhibitory pulse (EIP) excitations, same link medium, and at same distance), the INTERIM approach offered a maximum data rate of 500 kbps and 800 kbps at bit error rate (BER) of 1.4% and 6% respectively.
Open Access
Adaptive Reconfiguration of Protection Relays to Accommodate Distributed
(2022-08-08) Ouda, Gehad; Srikantha,Pirathayini
The rise in the installation of distributed generation systems (DGs) requires the reconfiguration of the protection system of the distribution network (DN) in place. Increased integration of DGs in the DN can cause incorrect tripping and false non-tripping of the overcurrent (OC) protection relays in the DN as well as increased possibilities of faults due to the aging infrastructure of DN. Incorrect tripping is also known as sympathetic tripping and false non-tripping is also known as blinding operation of an OC relay. As such, a reconfiguration of the adaptive protective OC relays is desired to mitigate the issues with increased DG penetration in the DN. A new algorithm is proposed in this thesis for adaptive, distributed protection relay reconfiguration for the DN with DGs. Through theoretical studies based on potential games and practical simulations, the OC relays' settings are reconfigured to adapt to the DN's changes in the system as a result of DG integration.
Open Access
On Performance Tuning of Serverless IoT Applications
(2022-08-08) Dantas, Jaime Cristalino Jales; Khazaei, Hamzeh
Cloud computing has become a predominant IT operation platform in the past decade. Small and large companies have been migrating their workloads to the cloud, and serverless architectures, such as container and Function as a Service (FaaS), are among the popular choices for cluster software deployments. Within this context, autoscaling, the ability to dynamically adapt the cluster capacity based on the current demand is pivotal for maintaining Quality of Service (QoS) and optimizing the cost in the presence of workload fluctuations. The first contribution of this thesis is a novel autoscaling solution that uses burstable instances along with regular instances to handle the queueing arising in traffic and flash crowds. In a second contribution, we evaluate different types of deployments for FaaS, and present three recommendations that developers can consider when deploying their workloads on the public cloud. Finally, we present a resource-aware dynamic load balancer component for edge computing platforms using one of the most fast-growing IoT services in the industry. The contributions are tested and validated on public clouds.
Open Access
Design and Analysis of Non-Orthogonal Multiple Access Techniques for Terahertz Networks
(2022-08-08) Melhem, Sadeq Bani; Tabassum, Hina
Fueled by the emergence of machine-type communications in a variety of wireless applications, the provisioning of massive connectivity becomes instrumental. On the other hand, accommodating trillions of devices within the extremely congested and limited sub-6GHz spectrum is becoming challenging. In this context, shifting to higher frequency terahertz (THz) communication is under consideration to obtain the data rates in the order of hundreds of gigabits per second (Gbps). Also, non-orthogonal multiple access schemes are becoming popular to support multiple users in the same frequency and time resource block, while leveraging on efficient interference cancellation mechanisms. In this thesis, I develop a comprehensive mathematical framework to analyze the performance of emerging non-orthogonal channel access schemes, such as non-orthogonal multiple access (NOMA) and rate-splitting multiple access (RSMA), in THz networks. In the first part of the thesis, I develop a statistical framework to analyze the performance of NOMA in the downlink of a single-carrier and multi-carrier THz network considering Nakagami-m fading and molecular absorption noise. In this context, I first develop a novel user pairing scheme which ensures the performance gains of NOMA over orthogonal multiple access (OMA) for each individual user in the NOMA pair and adapts according to the THz molecular absorption. Then, I characterize novel outage probability expressions considering a single-carrier and multi-carrier THz-NOMA network in the presence of various user pairing schemes, Nakagami-m channel fading, and molecular absorption noise. Specifically, I propose a moment-generating-function (MGF) based approach to analyze the outage probability of users in a multi-carrier THz network. For negligible thermal noise, I provide simplified single-integral expression to compute the outage in a multi-carrier network. Numerical results demonstrate the efficiency of the proposed user-pairing scheme compared to the existing benchmarks and validate the accuracy of the derived expressions. Finally, in the second part of the thesis, I extend the developed framework to analyze the performance of RSMA in the downlink transmission of Sub-6 GHz and THz networks.
Open Access
Printed Electronics for Multi-functional Carbon Fiber Composites
(2022-08-08) Idris, Mohamad Kannan; Grau, Gerd
Extrusion printing is a contactless nozzle-based digital printing method used to print stretchable and flexible circuit elements. Printing electronics on textiles integrates the enhanced functionality of electrical elements with the physical properties of textiles. Extrusion printing on textiles faces challenges that are overcome in this thesis. Extrusion printing is used to print electrical contacts directly on carbon fiber weaves. This can be integrated into structural carbon fiber composites manufactured with traditional methods. by exploiting the thermal and electrical conductivity of carbon fiber, two carbon fiber-based devices are fabricated, a heater and a damage sensor. Unmanned Aerial Vehicles (UAVs) suffer from ice accumulation on wings, and commercial solutions for de-icing are limited. The proposed heater can be integrated into UAV wings for de-icing. Structural Health Monitoring (SHM) aims to detect damage in structures such as cracks and holes using sensors. The proposed damage sensor detects holes in structures for SHM.
Open Access
Performance Characteristics of Function as a Service Platforms
(2022-03-03) Ngo, Kim Long; Litoiu, Marin; Jiang, Zhen Ming (Jack)
Function as a Service (FaaS) is a new cloud technology where resource management is automatically handled by cloud providers. However, the automated resource management also reduces the transparency needed for software engineering tasks and additional FaaS' characteristics such as cloud function idle timeout, auto-scaling policies, response time to bursting workloads are unknown to software engineers. In this thesis, we propose a methodology to measure the cloud function instance idle timeout. Next, we characterize FaaS' scalability and elasticity using intensive workloads. Finally, we propose a strategy to improve the FaaS' performance under saturation scenario. The results show that cloud function instances are decommissioned if being left idle beyond certain period. Load and performance experiments reveal that different cloud platforms adopt distinct auto-scaling policies and when FaaS has reached the upper concurrency limit, a workload smoother can help to boost the system's success rates from 60 - 80% to 99 - 100%.
Open Access
Modeling and Control of Electrolysis Based Hydrogen Production System
(2022-03-03) Abomazid, Abdulrahman Mohamed; Farag, Hany E. Z.
Hydrogen fuel is essential for mitigating climate change and solving energy sector challenges. There are challenges associated with producing hydrogen through electrolysis as a result of the lack of an electrolyzer model and the high costs of hydrogen production. Therefore, this thesis intends to (1) model the electrolyzer accurately and (2) develop an energy management system (EMS) to minimize the cost of hydrogen (CoH) production. Most literature on electrolyzers assumes a linear model and ignores nonlinear behavior. This may lead to inefficient hydrogen production. Therefore, the first part of this thesis studies the modeling problem of electrolyzer in terms of the parameter estimation. Moreover, most existing EMSs ignore electrolyzer efficiency variations. Therefore, in the second part of this thesis, an EMS is developed for minimizing CoH production by accounting for efficiency variation. Historical electricity prices have also been incorporated into EMS for seasonal storage of hydrogen.