Margie and Bill Klesse College of Engineering and Integrated Design
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Browsing Margie and Bill Klesse College of Engineering and Integrated Design by Department "Electrical and Computer Engineering"
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Item A Fault Diagnosis Design Based on Deep Learning Approach for Electric Vehicle Applications(2021-10-13) Kaplan, Halid; Tehrani, Kambiz; Jamshidi, MoDiagnosing faults in electric vehicles (EVs) is a great challenge. The purpose of this paper is to demonstrate the detection of faults in an electromechanical conversion chain for conventional or autonomous EVs. The information and data coming from different sensors make it possible for EVs to recover a series of information including currents, voltages, speeds, and so on. This information is processed to detect any faults in the electromechanical conversion chain. The novelty of this study is to develop an architecture for a fault diagnosis model by means of the feature extraction technique. In this regard, the long short-term memory (LSTM) approach for the fault diagnosis is proposed. This approach has been tested for an EV prototype in practice, is superior in accuracy over other fault diagnosis techniques, and is based on machine learning. An EV in an urban context is modeled, and then the fault diagnosis approach is applied based on deep learning architectures. The EV and the fault diagnosis model is simulated in Matlab software. It is also revealed how deep learning contributes to the fault diagnosis of EVs. The simulation and practical results confirm that higher accuracy in the fault diagnosis is obtained by applying the LSTM.Item A Low Cost, Edge Computing, All-Sky Imager for Cloud Tracking and Intra-Hour Irradiance Forecasting(2017-03-23) Richardson, Walter; Krishnaswami, Hariharan; Vega, Rolando; Cervantes, MichaelWith increasing use of photovoltaic (PV) power generation by utilities and their residential customers, the need for accurate intra-hour and day-ahead solar irradiance forecasting has become critical. This paper details the development of a low cost all-sky imaging system and an intra-hour cloud motion prediction methodology that produces minutes-ahead irradiance forecasts. The SkyImager is designed around a Raspberry Pi single board computer (SBC) with a fully programmable, high resolution Pi Camera, housed in a durable all-weather enclosure. Our software is written in Python 2.7 and utilizes the open source computer vision package OpenCV. The SkyImager can be configured for different operational environments and network designs, from a standalone edge computing model to a fully integrated node in a distributed, cloud-computing based micro-grid. Preliminary results are presented using the imager on site at the National Renewable Energy Laboratory (NREL) in Golden, CO, USA during the fall of 2015 under a variety of cloud conditions.Item A Modular Multilevel Converter with Power Mismatch Control for Grid-Connected Photovoltaic Systems(2017-05-17) Duman, Turgay; Marti, Shilpa; Moonem, M. A.; Abdul Kader, Azas Ahmed Rifath; Krishnaswami, HariharanA modular multilevel power converter configuration for grid connected photovoltaic (PV) systems is proposed. The converter configuration replaces the conventional bulky line frequency transformer with several high frequency transformers, potentially reducing the balance of systems cost of PV systems. The front-end converter for each port is a neutral-point diode clamped (NPC) multi-level dc-dc dual-active bridge (ML-DAB) which allows maximum power point tracking (MPPT). The integrated high frequency transformer provides the galvanic isolation between the PV and grid side and also steps up the low dc voltage from PV source. Following the ML-DAB stage, in each port, is a NPC inverter. N number of NPC inverters’ outputs are cascaded to attain the per-phase line-to-neutral voltage to connect directly to the distribution grid (i.e., 13.8 kV). The cascaded NPC (CNPC) inverters have the inherent advantage of using lower rated devices, smaller filters and low total harmonic distortion required for PV grid interconnection. The proposed converter system is modular, scalable, and serviceable with zero downtime with lower foot print and lower overall cost. A novel voltage balance control at each module based on power mismatch among N-ports, have been presented and verified in simulation. Analysis and simulation results are presented for the N-port converter. The converter performance has also been verified on a hardware prototype.Item A Modular Multilevel Converter with Power Mismatch Control for Grid-Connected Photovoltaic Systems(2017-05-17) Duman, Turgay; Marti, Shilpa; Moonem, M. A.; Abdul Kader, Azas Ahmed Rifath; Krishnaswami, HariharanA modular multilevel power converter configuration for grid connected photovoltaic (PV) systems is proposed. The converter configuration replaces the conventional bulky line frequency transformer with several high frequency transformers, potentially reducing the balance of systems cost of PV systems. The front-end converter for each port is a neutral-point diode clamped (NPC) multi-level dc-dc dual-active bridge (ML-DAB) which allows maximum power point tracking (MPPT). The integrated high frequency transformer provides the galvanic isolation between the PV and grid side and also steps up the low dc voltage from PV source. Following the ML-DAB stage, in each port, is a NPC inverter. N number of NPC inverters’ outputs are cascaded to attain the per-phase line-to-neutral voltage to connect directly to the distribution grid (i.e., 13.8 kV). The cascaded NPC (CNPC) inverters have the inherent advantage of using lower rated devices, smaller filters and low total harmonic distortion required for PV grid interconnection. The proposed converter system is modular, scalable, and serviceable with zero downtime with lower foot print and lower overall cost. A novel voltage balance control at each module based on power mismatch among N-ports, have been presented and verified in simulation. Analysis and simulation results are presented for the N-port converter. The converter performance has also been verified on a hardware prototype.Item A New Compton Camera Imaging Model to Mitigate the Finite Spatial Resolution of Detectors and New Camera Designs for Implementation(2015-10-27) Smith, BruceAn intrinsic limitation of the accuracy that can be achieved with Compton cameras results from the inevitable fact that the detectors, which comprise the camera, cannot have infinitely-accurate spatial resolution. To mitigate this loss of accuracy, a new imaging model is proposed. The implementation of the new imaging model, however, requires new camera designs. The results of a computer simulation indicate that the new imaging model can produce reasonable images, at least when noiseless simulated data are used. In the future, more work is needed to determine if the use of the new imaging model will improve the imaging capabilities of Compton cameras despite the loss of sensitivity caused by the use of the new camera designs. Regardless of the outcome of this work, the results presented here illustrate that new models for imaging from Compton scatters are possible and motivate the development of further models that could be more advantageous than the ones already developed.Item An Intelligent Approach for Performing Energy-Driven Classification of Buildings Utilizing Joint Electricity–Gas Patterns(2021-11-09) Nichiforov, Cristina; Martinez-Molina, Antonio; Alamaniotis, MiltiadisBuilding type identification is an important task that may be used in confirming and verifying its legitimate operation. One of the main sources of information over the operation of a building is its energy consumption, with the analysis of electricity patterns being at the spotlight of a non-intrusive identification approach. However, electricity patterns are the only source of information, and therefore, their analysis imposes several restrictions. In this work, we introduce a new approach in energy-driven identification by adding one more source of information beyond the electricity pattern that may be utilized, namely the gas consumption pattern. In particular, we propose a new intelligent approach that jointly analyzes the electricity–gas patterns to provide the type of building at hand. Our approach exploits the synergism of the matrix profile data analysis technique with a feed-forward artificial neural network. This approach has applicability in the energy waste elimination through the implementation of different energy efficiency solutions, as well as the optimization of the demand-side process management, safer and reliable operation through fault detection, and the identification and validation of the real operation of the building. The obtained results demonstrate the improvement in identifying the type of the building by employing the proposed approach for joint electricity–gas patterns as compared to only using the electricity patterns.Item An On-Device Learning System for Estimating Liquid Consumption from Consumer-Grade Water Bottles and Its Evaluation(2022-03-25) Roy, Avirup; Dutta, Hrishikesh; Griffith, Henry; Biswas, SubirA lightweight on-device liquid consumption estimation system involving an energy-aware machine learning algorithm is developed in this work. This system consists of two separate on-device neural network models that carry out liquid consumption estimation with the result of two tasks: the detection of sip from gestures with which the bottle is handled by its user and the detection of first sips after a bottle refill. This predictive volume estimation framework incorporates a self-correction mechanism that can minimize the error after each bottle fill-up cycle, which makes the system robust to errors from the sip classification module. In this paper, a detailed characterization of sip detection is performed to understand the accuracy-complexity tradeoffs by developing and implementing a variety of different ML models with varying complexities. The maximum energy consumed by the entire framework is around 119 mJ during a maximum computation time of 300 µs. The energy consumption and computation times of the proposed framework is suitable for implementation in low-power embedded hardware that can be incorporated in consumer grade water bottles.Item Analysis of a Multilevel Dual Active Bridge (ML-DAB) DC-DC Converter Using Symmetric Modulation(2015-04-20) Moonem, M. A.; Pechacek, C. L.; Hernandez, R.; Krishnaswami, HariharanDual active bridge (DAB) converters have been popular in high voltage, low and medium power DC-DC applications, as well as an intermediate high frequency link in solid state transformers. In this paper, a multilevel DAB (ML-DAB) has been proposed in which two active bridges produce two-level (2L)-5L, 5L-2L and 3L-5L voltage waveforms across the high frequency transformer. The proposed ML-DAB has the advantage of being used in high step-up/down converters, which deal with higher voltages, as compared to conventional two-level DABs. A three-level neutral point diode clamped (NPC) topology has been used in the high voltage bridge, which enables the semiconductor switches to be operated within a higher voltage range without the need for cascaded bridges or multiple two-level DAB converters. A symmetric modulation scheme, based on the least number of angular parameters rather than the duty-ratio, has been proposed for a different combination of bridge voltages. This ML-DAB is also suitable for maximum power point tracking (MPPT) control in photovoltaic applications. Steady-state analysis of the converter with symmetric phase-shift modulation is presented and verified using simulation and hardware experiments.Item Bernstein Polynomial-Based Method for Solving Optimal Trajectory Generation Problems(2022-02-27) Kielas-Jensen, Calvin; Cichella, Venanzio; Berry, Thomas; Kaminer, Isaac; Walton, Claire; Pascoal, AntonioThis paper presents a method for the generation of trajectories for autonomous system operations. The proposed method is based on the use of Bernstein polynomial approximations to transcribe infinite dimensional optimization problems into nonlinear programming problems. These, in turn, can be solved using off-the-shelf optimization solvers. The main motivation for this approach is that Bernstein polynomials possess favorable geometric properties and yield computationally efficient algorithms that enable a trajectory planner to efficiently evaluate and enforce constraints along the vehicles' trajectories, including maximum speed and angular rates as well as minimum distance between trajectories and between the vehicles and obstacles. By virtue of these properties and algorithms, feasibility and safety constraints typically imposed on autonomous vehicle operations can be enforced and guaranteed independently of the order of the polynomials. To support the use of the proposed method we introduce BeBOT (Bernstein/Bézier Optimal Trajectories), an open-source toolbox that implements the operations and algorithms for Bernstein polynomials. We show that BeBOT can be used to efficiently generate feasible and collision-free trajectories for single and multiple vehicles, and can be deployed for real-time safety critical applications in complex environments.Item Can Hierarchical Transformers Learn Facial Geometry?(2023-01-13) Young, Paul; Ebadi, Nima; Das, Arun; Bethany, Mazal; Desai, Kevin; Najafirad, PeymanHuman faces are a core part of our identity and expression, and thus, understanding facial geometry is key to capturing this information. Automated systems that seek to make use of this information must have a way of modeling facial features in a way that makes them accessible. Hierarchical, multi-level architectures have the capability of capturing the different resolutions of representation involved. In this work, we propose using a hierarchical transformer architecture as a means of capturing a robust representation of facial geometry. We further demonstrate the versatility of our approach by using this transformer as a backbone to support three facial representation problems: face anti-spoofing, facial expression representation, and deepfake detection. The combination of effective fine-grained details alongside global attention representations makes this architecture an excellent candidate for these facial representation problems. We conduct numerous experiments first showcasing the ability of our approach to address common issues in facial modeling (pose, occlusions, and background variation) and capture facial symmetry, then demonstrating its effectiveness on three supplemental tasks.Item Characterizing Macrophages Diversity in COVID-19 Patients Using Deep Learning(2022-12-01) Flores, Mario A.; Paniagua, Karla; Huang, Wenjian; Ramirez, Ricardo; Falcon, Leonardo; Liu, Andy; Chen, Yidong; Huang, Yufei; Jin, YufangThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent responsible for coronavirus disease 2019 (COVID-19), has affected the lives of billions and killed millions of infected people. This virus has been demonstrated to have different outcomes among individuals, with some of them presenting a mild infection, while others present severe symptoms or even death. The identification of the molecular states related to the severity of a COVID-19 infection has become of the utmost importance to understanding the differences in critical immune response. In this study, we computationally processed a set of publicly available single-cell RNA-Seq (scRNA-Seq) data of 12 Bronchoalveolar Lavage Fluid (BALF) samples diagnosed as having a mild, severe, or no infection, and generated a high-quality dataset that consists of 63,734 cells, each with 23,916 genes. We extended the cell-type and sub-type composition identification and our analysis showed significant differences in cell-type composition in mild and severe groups compared to the normal. Importantly, inflammatory responses were dramatically elevated in the severe group, which was evidenced by the significant increase in macrophages, from 10.56% in the normal group to 20.97% in the mild group and 34.15% in the severe group. As an indicator of immune defense, populations of T cells accounted for 24.76% in the mild group and decreased to 7.35% in the severe group. To verify these findings, we developed several artificial neural networks (ANNs) and graph convolutional neural network (GCNN) models. We showed that the GCNN models reach a prediction accuracy of the infection of 91.16% using data from subtypes of macrophages. Overall, our study indicates significant differences in the gene expression profiles of inflammatory response and immune cells of severely infected patients.Item Combined Emission Economic Dispatch using Quantum-inspired Particle Swarm Optimization and its Variants(SAGE Publications, 2024-03-19) Asif, Muhammad; Amin, Adil; Jamil, Umar; Mahmood, Anzar; Ahmed, Ubaid; Razzaq, Sohail; Mahdi, Fahad ParvezThe ever-increasing electricity demand, its dependency on fossil fuels, and the consequent environmental degradation are major concerns of this era. The worldwide domination of fossil fuels in bulk electricity generation is rapidly increasing the emissions of CO2 and other environmentally dangerous gases that are contributing to climate change. The economic and emission dispatch are two important problems in thermal power generation whose combination produces a complex highly constrained nonlinear optimization problem known as combined economic and emission dispatch. The optimization of combined economic and emission dispatch aims to allocate the generation of committed units to minimize fuel cost and emissions, simultaneously while honoring all equality and inequality constraints. Therefore, in this article, we investigate a solution of the combined economic and emission dispatch problem using quantum particle swarm optimization and its two modified versions, that is, enhanced quantum particle swarm optimization and quantum particle swarm optimization integrated with weighted mean personal best and adaptive local attractor. The enhanced quantum particle swarm optimization algorithm achieves particles’ diversification at early stages and shows good performance in local search at later stages. The quantum particle swarm optimization integrated with weighted mean personal best and adaptive local attractor boosts search performance of quantum particle swarm optimization and attains better global optimality. The suggested methods are employed to achieve solution for the combined economic and emission dispatch in four distinct systems, encompassing two scenarios with 6 units each, one with a 10-unit configuration, and another with an 11-unit setup. A comparative analysis with methodologies documented in existing literature reveals that the proposed approach outperforms others, demonstrating superior computational performance and robust efficiency.Item Consistency of Approximation of Bernstein Polynomial-Based Direct Methods for Optimal Control(2022-11-28) Cichella, Venanzio; Kaminer, Isaac; Walton, Claire; Hovakimyan, Naira; Pascoal, AntónioBernstein polynomial approximation of continuous function has a slower rate of convergence compared to other approximation methods. "The fact seems to have precluded any numerical application of Bernstein polynomials from having been made. Perhaps they will find application when the properties of the approximant in the large are of more importance than the closeness of the approximation" — remarked P.J. Davis in his 1963 book, Interpolation and Approximation. This paper presents a direct approximation method for nonlinear optimal control problems with mixed input and state constraints based on Bernstein polynomial approximation. We provide a rigorous analysis showing that the proposed method yields consistent approximations of time-continuous optimal control problems and can be used for costate estimation of the optimal control problems. This result leads to the formulation of the Covector Mapping Theorem for Bernstein polynomial approximation. Finally, we explore the numerical and geometric properties of Bernstein polynomials, and illustrate the advantages of the proposed approximation method through several numerical examples.Item Context Modulation Enables Multi-tasking and Resource Efficiency in Liquid State Machines(Association for Computing Machinery, 2023-08-28) Helfer, Peter; Teeter, Corinne; Hill, Aaron; Vineyard, Craig M.; Aimone, James B.; Kudithipudi, DhireeshaMemory storage and retrieval are context-sensitive in both humans and animals; memories are more accurately retrieved in the context where they were acquired, and similar stimuli can elicit different responses in different contexts. Researchers have suggested that such effects may be underpinned by mechanisms that modulate the dynamics of neural circuits in a context-dependent fashion. Based on this idea, we design a mechanism for context-dependent modulation of a liquid state machine, a recurrent spiking artificial neural network. We find that context modulation enables a single network to multitask and requires fewer neurons than when several smaller networks are used to perform the tasks individually.Item Damping control for a target oscillation mode using battery energy storage(Springer, 2018-07) Zhu, Yongli; Liu, Chengxi; Wang, Bin; Sun, KaiIn this paper, a battery energy storage system (BESS) based control method is proposed to improve the damping ratio of a target oscillation mode to a desired level by charging or discharging the installed BESS using local measurements. The expected damping improvement by BESS is derived analytically for both a single-machine-infinite-bus system and a multi-machine system. This BESS-based approach is tested on a four-generator, two-area power system. Effects of the power converter limit, response time delay, power system stabilizers and battery state-of-charge on the control performance are also investigated. Simulation results validate the effectiveness of the proposed approach.Item Defense against Adversarial Swarms with Parameter Uncertainty(2022-06-24) Walton, Claire; Kaminer, Isaac; Gong, Qi; Clark, Abram H.; Tsatsanifos, TheodorosThis paper addresses the problem of optimal defense of a high-value unit (HVU) against a large-scale swarm attack. We discuss multiple models for intra-swarm cooperation strategies and provide a framework for combining these cooperative models with HVU tracking and adversarial interaction forces. We show that the problem of defending against a swarm attack can be cast in the framework of uncertain parameter optimal control. We discuss numerical solution methods, then derive a consistency result for the dual problem of this framework, providing a tool for verifying computational results. We also show that the dual conditions can be computed numerically, providing further computational utility. Finally, we apply these numerical results to derive optimal defender strategies against a 100-agent swarm attack.Item Energy Harvesting Using a Stacked PZT Transducer for Self-Sustainable Remote Multi-Sensing and Data Logging System(2022-02-06) Dipon, Wasim; Gamboa, Bryan; Guo, Ruyan; Bhalla, AmarThe work discussed is developing a self-sustainable low-power remote multi-sensing and data logging system for traffic sensing. The system is powered by the energy harvested using a stacked PZT (Lead zirconate titanate) transducer from the mechanical vibration from the vehicles passing over roads. The system is capable of multi-sensing functionality, logging the sensor data, and wirelessly transferring sensory data to an end-user device. Various power management techniques and engineering applications were made to achieve low power operation of the system while maintaining the full functionality and the accuracy of the sensor data. The energy harvester used is a custom-designed and fabricated stacked piezoelectric transducer optimized for maximum energy harvesting from the mechanical vibration from roadway traffic. A custom-built AC to DC converter is used to convert the harvested energy into useable electrical power. The system was tested under various experimental setups yielding satisfactory data accuracy while operating at low power. The system also successfully transferred sensor data remotely. All these features make the system self-sustainable and suitable for remote sensing applications without a conventional power source.Item Enhancing Historic Building Performance with the Use of Fuzzy Inference System to Control the Electric Cooling System(MDPI, 2020-07-21) Martinez-Molina, Antonio; Alamaniotis, MiltiadisIn recent years, the interest in properly conditioning the indoor environment of historic buildings has increased significantly. However, maintaining a suitable environment for building and artwork preservation while keeping comfortable conditions for occupants is a very challenging and multi-layered job that might require a considerable increase in energy consumption. Most historic structures use traditional on/off heating, ventilation, and air conditioning (HVAC) system controllers with predetermined setpoints. However, these controllers neglect the building sensitivity to occupancy and relative humidity changes. Thus, sophisticated controllers are needed to enhance historic building performance to reduce electric energy consumption and increase sustainability while maintaining the building historic values. This study presents an electric cooling air controller based on a fuzzy inference system (FIS) model to, simultaneously, control air temperature and relative humidity, taking into account building occupancy patterns. The FIS numerically expresses variables via predetermined fuzzy sets and their correlation via 27 fuzzy rules. This intelligent model is compared to the typical thermostat on/off baseline control to evaluate conditions of cooling supply during cooling season. The comparative analysis shows a FIS controller enhancing building performance by improving thermal comfort and optimizing indoor environmental conditions for building and artwork preservation, while reducing the HVAC operation time by 5.7%.Item Estimation of Dynamic Bivariate Correlation Using a Weighted Graph Algorithm(2020-06-02) John, Majnu; Wu, Yihren; Narayan, Manjari; John, Aparna; Ikuta, Toshikazu; Ferbinteanu, JaninaDynamic correlation is the correlation between two time series across time. Two approaches that currently exist in neuroscience literature for dynamic correlation estimation are the sliding window method and dynamic conditional correlation. In this paper, we first show the limitations of these two methods especially in the presence of extreme values. We present an alternate approach for dynamic correlation estimation based on a weighted graph and show using simulations and real data analyses the advantages of the new approach over the existing ones. We also provide some theoretical justifications and present a framework for quantifying uncertainty and testing hypotheses.Item Evolutionary Multi-Objective Cost and Privacy Driven Load Morphing in Smart Electricity Grid Partition(2019-06-26) Alamaniotis, Miltiadis; Gatsis, NikolaosUtilization of digital connectivity tools is the driving force behind the transformation of the power distribution system into a smart grid. This paper places itself in the smart grid domain where consumers exploit digital connectivity to form partitions within the grid. Every partition, which is independent but connected to the grid, has a set of goals associated with the consumption of electric energy. In this work, we consider that each partition aims at morphing the initial anticipated partition consumption in order to concurrently minimize the cost of consumption and ensure the privacy of its consumers. These goals are formulated as two objectives functions, i.e., a single objective for each goal, and subsequently determining a multi-objective problem. The solution to the problem is sought via an evolutionary algorithm, and more specifically, the non-dominated sorting genetic algorithm-II (NSGA-II). NSGA-II is able to locate an optimal solution by utilizing the Pareto optimality theory. The proposed load morphing methodology is tested on a set of real-world smart meter data put together to comprise partitions of various numbers of consumers. Results demonstrate the efficiency of the proposed morphing methodology as a mechanism to attain low cost and privacy for the overall grid partition.
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