Engineering Commons^™

Cohort Comfort Models — Using Occupant’S Similarity To Predict Personal Thermal Preference With Less Data, Matias Quintana, Stefano Schiavon, Federico Tartarini, Joyce Kim, Clayton Miller

Research Collection College of Integrative Studies

Cohort Comfort Models (CCM) are introduced as a technique for creating a personalized thermal prediction for a new building occupant without the need to collect large amounts of individual comfort-related data. This approach leverages historical data collected from a sample population, who have some underlying preference similarity to the new occupant. The method uses background information such as physical and demographic characteristics and one-time onboarding surveys (satisfaction with life scale, highly sensitive person scale, personality traits) from the new occupant, as well as physiological and environmental sensor measurements paired with a few thermal preference responses. The framework was implemented using …

Data-Integrity Aware Stochastic Model For Cascading Failures In Power Grids, Rezoan Ahmed Shuvro, Pankaz Das, Jamir Shariar Jyoti, Joana M. Abreu, Majeed M. Hayat

Electrical and Computer Engineering Faculty Research and Publications

The reliable operation of power grids during cascading failures is heavily dependent on the interdependencies between the power grid components and the supporting communications and control networks. Moreover, the system operators' expertise in dealing with cascading failures can play a pivotal role during contingencies. In this paper, a dynamical probabilistic model is developed based on Markov-chains, which captures the dynamics of cascading failures in the power grid. Specifically, a previously developed Markov-chain based model is extended to capture the trade-off between the benefits of having a robust communication infrastructure and its vulnerability from data integrity (e.g., cyber-attacks). State-space reduction of …

The Suitability Of Demand-Controlled Sensor Based Ventilation Systems In Retrofit Dwellings - A Longitudinal Study., Seamus Harrington, Mark Mulville

Conference papers

A fabric-first approach to dwelling retrofit results in increased airtightness, therefore there is an obligation to ensure that the upgrades do not lead to poor indoor air quality (IAQ) resulting from inadequate ventilation. The sensor-based demand-controlled ventilation (SBDCV) under review seeks to provide fresh air for breathing and to dilute and exhaust pollutants and odours. This system modulates the ventilation rate over time based on relative humidity levels and/or presence detection and considers that the level of ventilation provided is sufficient to control the concentration of all other indoor air pollutants, including those that are not a result of human …

Towards A Prototype Paleo-Detector For Supernova Neutrino And Dark Matter Detection, Emilie Marie Lavoie-Ingram

UNF Graduate Theses and Dissertations

Using ancient minerals as paleo-detectors is a proposed experimental technique expected to transform supernova neutrino and dark matter detection. In this technique, minerals are processed and closely analyzed for nanometer scale damage track remnants from nuclear recoils caused by supernova neutrinos and possibly dark matter. These damage tracks present the opportunity to directly detect and characterize the core-collapse supernova rate of the Milky Way Galaxy as well as the presence of dark matter. Current literature presents theoretical estimates for these potential tracks, however, there is little research investigating the experimental feasibility of this technique. At the University of North Florida, …

Spontaneous Mirror Symmetry Breaking And Chiral Segregation In The Achiral Ferronematic Compound Dio, Neelam Yadav, Yuri Panarin, Wanhe Jiang, Georg H. Mehl, Jagdish K. Vij

Articles

An achiral compound, DIO, known to exhibit three nematic phases namely N, NX and NF, is studied by polarizing microscopy and electro-optics for different surface conditions in confinement. The high temperature N phase assigned initially as a conventional nematic phase, shows two additional unusual features: the optical activity and the linear electro-optic response related to the polar nature of this phase. An appearance of chiral domains is explained by the spontaneous symmetry breaking arising from the saddle-splay elasticity and followed by the formation of helical domains of the opposite chirality. This is the first example of helical segregation observed in …

Wind Energy Harvesting And Conversion Systems: A Technical Review, Sinhara M. H. D. Perera, Ghanim Putrus, Michael Conlon, Mahinsasa Narayana, Keith Sunderland

Articles

Wind energy harvesting for electricity generation has a significant role in overcoming the challenges involved with climate change and the energy resource implications involved with population growth and political unrest. Indeed, there has been significant growth in wind energy capacity worldwide with turbine capacity growing significantly over the last two decades. This confidence is echoed in the wind power market and global wind energy statistics. However, wind energy capture and utilisation has always been challenging. Appreciation of the wind as a resource makes for difficulties in modelling and the sensitivities of how the wind resource maps to energy production results …

Design And Implementation Of A New Adaptive Mppt Controller For Solar Pv Systems, Saibal Manna, Deepak Kumar Singh, Ashok Kumar Akella, Hossam Kotb, Kareem M. Aboras, Hossam Zawbaa, Salah Kamel

Articles

This research provides an adaptive control design in a photovoltaic system (PV) for maximum power point tracking (MPPT). In the PV system, MPPT strategies are used to deliver the maximum available power to the load under solar radiation and atmospheric temperature changes. This article presents a new adaptive control framework to enhance the performance of MPPT, which will minimize the complexity in system control and efficiently manage uncertainties and disruptions in the environment and PV system. Here, the MPPT algorithm is decoupled with model reference adaptive control (MRAC) techniques, and the system gains MPPT with overall system stability. The simulation …

Design Of Event-Triggered Asynchronous H∞ Filter For Switched Systems Using The Sampled-Data Approach, Sheraz Shafique, Ghulam Mustafa, Nouman Ashraf, Owais Khan

Articles

The design of networked switched systems with event-based communication is attractive due to its potential to save bandwidth and energy. However, ensuring the stability and performance of networked systems with event-triggered communication and asynchronous switching is challenging due to their time-varying nature. This paper presents a novel sampled-data approach to design event-triggered asynchronous H∞ filters for networked switched systems. Unlike most existing event-based filtering results, which either design the event-triggering scheme only or co-design the event-triggering condition and the filter, we consider that the event-triggering policy is predefined and synthesize the filter. We model the estimation error system as an …

Embedded Ai For Wheat Yellow Rust Infection Type Classification, Uferah Shafi, Rafia Mumtaz, Muhammad Deedahwar Mazhar Qureshi, Zahid Mahmood, Sikander Khan Tanveer, Ihsan Ul Haq, Syed Mohammad Hassan Zaidi

Articles

Wheat is the most important and dominating crop in Pakistan in terms of production and acreage, which is grown on 37% of the cultivated area, accounting for 70% of the total production. However, wheat yield is highly affected by stripe rust, which is considered the most devastating fungal disease, causing 5.5 million tonnes of loss per year globally. In order to minimize this loss, the accurate and timely detection of rust disease is crucial instead of manual inspection. Towards this end, we propose a system to detect wheat rust disease and classify its infection types into four classes, including healthy, …

Control And Protection Of Mmc-Based Hvdc Systems: A Review, Rashid Hussain Chandio, Faheem Akhtar Chachar, Jahangeer Badar Soomro, Jamshed Ahmed Ansari, Mudassir Munir Mudassir Munir, Hossam Zawbaa, Salah Kamel Salah Kamel

Articles

The voltage source converter (VSC) based HVDC (high voltage direct current system) offers the possibility to integrate other renewable energy sources (RES) into the electrical grid, and allows power flow reversal capability. These appealing features of VSC technology led to the further development of multi-terminal direct current (MTDC) systems. MTDC grids provide the possibility of interconnection between conventional power systems and other large-scale offshore sources like wind and solar systems. The modular multilevel converter (MMC) has become a popular technology in the development of the VSC-MTDC system due to its salient features such as modularity and scalability. Although, the employment …

A Molecular Dynamics Study Of Water Confined In Between Two Graphene Sheets Under Compression, Ming-Lang Tseng, Ayomide Adesiyan, Abdelaziz Gassoumi, Nima E. Gorji

Articles

Several studies have demonstrated interest in creating surfaces with improved water interaction and adaptive properties because the behavior of water confined at the nanoscale plays a significant role in the synthesis of materials for technological applications. Remarkably, confinement at the nanoscale significantly modifies the characteristics of water. We determine the phase diagram of water contained by graphene stack sheets in slab form, at T=300 K, and for a constant pressure using molecular dynamics simulations. We discover that, as shown in the simulation, water can exist in both the liquid and vapor phases depending on the confining geometry and compressibility ratio. …

Performance Improvement Of Hybrid System Based Dfig-Wind/Pv/Batteries Connected To Dc And Ac Grid By Applying Intelligent Control, Younes Sahri, Salah Tamalouzt, Sofia Lalouni Belaid, Mohit Bajaj, Sherif S.M. Ghoneim, Hossam Zawbaa, Salah Kamel

Articles

One of the main causes of CO2 emissions is the production of electrical energy. Therefore, many researchers goal’s is to develop renewable power systems. This paper proposes a new intelligent control development of hybrid PV–Wind-Batteries. Neuro-Fuzzy Direct Power Control (NF-DPC) is invested in order to enhance system performance and generated currents quality. An improved MPPT algorithm based on Fuzzy Controller (FC) is invested for PV power optimization. In addition, a new Modified Fuzzy Direct Power Control (MF-DPC) is developed and applied to the grid side converter to control the active and reactive power by monitoring the involved active power flow …

Distributed Intermittent Fault Diagnosis In Wireless Sensor Network Using Likelihood Ratio Test, Bhabani Sankar Gouda, Meenakshi Panda, Trilochan Panigrahi, Sudhakar Das, Bhargav Appasani, Omprakash Acharya, Hossam Zawbaa, Salah Kamel

Articles

In current days, sensor nodes are deployed in hostile environments for various military and commercial applications. Sensor nodes are becoming faulty and having adverse effects in the network if they are not diagnosed and inform the fault status to other nodes. Fault diagnosis is difficult when the nodes behave faulty some times and provide good data at other times. The intermittent disturbances may be random or kind of spikes either in regular or irregular intervals. In literature, the fault diagnosis algorithms are based on statistical methods using repeated testing or machine learning. To avoid more complex and time consuming repeated …

Metaheuristic Optimization Techniques Used In Controlling Of An Active Magnetic Bearing System For High-Speed Machining Application, Suraj Suraj, Pabitra Kumar Biswas Pabitra Kumar Biswas, Sukanta Debnath, Anumoy Ghosh, Thanikanti Sudhakar Babu, Hossam Zawbaa, Salah Kemal

Articles

Smart control tactics, wider stability region, rapid reaction time, and high-speed performance are essential requirements for any controller to provide a smooth, vibrationless, and efficient performance of an in-house fabricated active magnetic bearing (AMB) system. In this manuscript, three pre-eminent population-based metaheuristic optimization techniques: Genetic algorithm (GA), Particle swarm optimization (PSO), and Cuckoo search algorithm (CSA) are implemented one by one, to calculate optimized gain parameters of PID controller for the proposed closed-loop active magnetic bearing (AMB) system. Performance indices or, objective functions on which these optimization techniques are executed are integral absolute error (IAE), integral square error (ISE), integral …

Local Electricity Market Operation In Presence Of Residential Energy Storage In Low Voltage Distribution Network: Role Of Retail Market Pricing, Aziz Saif, Shafi K. Khadem Shafi K. Khadem, Michael Conlon, Brian Norton

Articles

Local Electricity Market (LEM) appears as a promising consumer-centric market-based approach that extends the self-consumption method, widely implemented in residential households, to collective self-consumption in the local energy communities, enabled through peer-to-peer (P2P) transactions. To facilitate the integration of LEM in the wholesale electricity market (WEM), it is paramount to comprehend the synergy of retail electricity pricing on the LEM operation hosted in the low-voltage distribution network (LVDN). The paper presents a co-simulation framework consisting of a local electricity market model coupled with a three-phase distribution network simulator to perform a holistic case study for a smart energy community in …

Nipuna: A Novel Optimizer Activation Function For Deep Neural Networks, Golla Madhu, Sandeep Kautish, Khalid Abdulaziz Alnowibet, Hossam Zawbaa, Ali Wagdy Mohamed

Articles

In recent years, various deep neural networks with different learning paradigms have been widely employed in various applications, including medical diagnosis, image analysis, self-driving vehicles and others. The activation functions employed in deep neural networks have a huge impact on the training model and the reliability of the model. The Rectified Linear Unit (ReLU) has recently emerged as the most popular and extensively utilized activation function. ReLU has some flaws, such as the fact that it is only active when the units are positive during back-propagation and zero otherwise. This causes neurons to die (dying ReLU) and a shift in …

Interpreting Energy Utilisation With Shapley Additive Explanations By Defining A Synthetic Data Generator For Plausible Charging Sessions Of Electric Vehicles, Prasant Kumar Mohanty, Gayadhar Panda, Malabika Basu, Diptendu Sinha Roy

Articles

Electric vehicles (EVs) are an effective solution for reducing reliance on non-renewable energy sources. However, the lack of charging infrastructure and concerns over their range are some of the biggest hurdles to adopting EVs. Charging infrastructure for EVs is, however, on the rise. Proper planning of charging stations vis-`a-vis road networks and related points of interest such as transportation hubs, schools, shopping centres, etc., alongside such roads become vital to laying out a plan for such infrastructure, particularly for developing countries like India where EV adoption is relatively in a nascent stage. Synthetic datasets can help overcome these hurdles and …

Lightnet: A Novel Lightweight Convolutional Network For Brain Tumor Segmentation In Healthcare, Dongyuan Wu, Junyi Tao, Zhen Qin, Rao Asad Mumtaz, Jin Qin, Linfang Yu, Jane Courtney

Articles

Diagnosis, treatment planning, surveillance, and the monitoring of clinical trials for brain diseases all benefit greatly from neuroimaging-based tumor segmentation. Recently, Convolutional Neural Networks (CNNs) have demonstrated promising results in enhancing the efficiency of image-based brain tumor segmentation. Most current work on CNNs, however, is devoted to creating increasingly complicated convolution modules to improve performance, which in turn raises the computing cost of the model. This work proposes a simple and effective feed-forward CNN, LightNet (Light Network). Based on multi-path and multi-level, it replaces traditional convolutional methods with light operations, which reduces network parameters and redundant feature maps. In the …

Optical Properties Of Periodically And Aperiodically Nanostructured P-N Junctions, Z. Taliashvili, E Łusakowska E Łusakowska, S. Chusnutdinow, A. Tavkhelidze, L. Jangidze, S. Sikharulidze, Nima Ghadiri, Z. Chubinidze, R. Melkadze

Articles

Recently, semiconductor nanograting layers have been introduced and their optical properties have been studied. Spectroscopic ellipsometry has shown that nanograting significantly modifies the dielectric function of c-Si layers. Photoluminescence spectroscopy reveals the emergence of an emission band with a remarkable peak structure. It has been observed that nanograting also alters the electronic and magnetic properties. In this study, we investigate the quantum efficiency and spectral response of Si p-n junctions fabricated using subwavelength grating layers and aperiodically nanostructured layers.

Analysis Of Degradation Of Sb2se3 Thin Film Solar Cells Deploying A Time-Dependent Approach Linked With 1d-Amps Simulation, Ming-Lang Tseng, Malek Gassoumi, Nima Ghadiri

Articles

In this paper, we have developed a time-dependent model to study defect growth in the absorber layer of Sb2Se3 thin film solar cells. This model has been integrated with the AMPS-1D simulation platform to investigate the impact of increasing defect density at different positions within the Sb2Se3 layer on the electrical parameters of the solar cell. We adopted the Gloeckler standard model for thin films in AMPS to represent Sb2Se3 materials. The study focuses on tracking the degradation of device performance parameters as donor-like mid-gap states accumulate in the Sb2Se3 layer over time. We monitored the variation of key electrical …

Experimental Investigation Of Heat Transfer To A Dual Jet Flow With Varying Velocity Ratio, Paula J. Murphy, Sajad Alimohammadi, Seamus M. O'Shaughnessy

Conference Papers

Dual jet flow is a topical area of research due to their wide range of current and potential uses in industry. Despite this, there is still a lack of published studies which focus on the characterization of dual jet flow, particularly regarding their heat transfer capabilities. The objective of this investigation is to therefore build upon the available dual jet data and conduct an experimental study which focusses on the effect of the jet velocity ratio on heat transfer to a dual jet flow for a constant offset ratio of 3, where air is used as the working fluid. The …

Corporate Governance And Risk-Taking: A Statistical Approach, Steven L. Schwarcz

Faculty Scholarship

Because prudent corporate governance often requires managers to take risks based on statistically expected outcomes, corporate failures that have a small but finite chance of occurring cannot always be prevented. This Article makes three related claims about risk-taking in corporate governance.

This Article’s first claim is that managers should not automatically be presumed to be at fault for corporate failures that result from risk-taking decisions based on statistical methodologies that reasonably justify the decisions ex ante. Conceptually, the business judgment rule should protect corporate managers for engaging in a reasonable decision-making process, including one that is statistically based. Jurisdictionally, however, …

A Partial Discharge Inception Voltage Modeling Approach, Shilpi Mukherjee, Tristan Mark Evans, David Huitink, H. Alan Mantooth

Electrical Engineering Faculty Publications and Presentations

A partial discharge inception voltage modeling approach promoting design-for-reliability considerations in advanced power modules is presented. As power modules are being operated at higher voltages and become more compact to meet power density demands, there is an increased risk of partial discharge, the silent precursor to electrical breakdown that degrades insulation material. The trade-off between voltage class and module compaction must be quantified. This work presents a methodology to model the tradeoff for any substrate and encapsulant material. A surface charge density-based partial discharge inception voltage (PDIV) model was developed to overcome the challenges in electric field-based models. The model …

Indication Of Pedestrian’S Travel Direction Through Bluetooth Low Energy Signals Perceived By A Single Observer Device, Mayank Parmar, Paula Kelly, Damon Berry

Conference papers

This paper presents a study to understand the directional sensitivity of a Bluetooth Low Energy (BLE) monitoring device (Observer) and whether, using a single such Observer, the characteristics of its antenna can be used to identify the direction of a pedestrian’s movement. To comprehend the directional characteristics of the antenna of the Observer employed for this study, the device is subjected to BLE signals emitted from a BLE beacon (Broadcaster) in an anechoic chamber. The results of this study confirmed that in the clean, noiseless environment of the chamber, the antenna we employed is clearly more receptive to signals emerging …

The Role Of Soc In Ensuring The Security Of Iot Devices: A Review Of Current Challenges And Future Directions, Hajar Bennouri, Abdiaziz Abdi, Iqbal Hossain, Alexandre Pujol

Conference papers

The growing popularity and deployment of Internet of Things (IoT) devices has led to serious security concerns. The integration of a security operations center (SOC) becomes increasingly important in this situation to ensure the security of IoT devices. In this article, we will present a summary of IoT device security issues, their vulnerabilities, a review of current challenges to keep these devices secure, and discuss the role that SOC can bring in protecting IoT devices while considering the challenges encountered and the directions to consider when implementing a reliable SOC for IoT monitoring.

Robustness Of Image-Based Malware Classification Models Trained With Generative Adversarial Networks, Ciaran Reilly, Stephen O Shaughnessy, Christina Thorpe

Conference papers

As malware continues to evolve, deep learning models are increasingly used for malware detection and classification, including image based classification. However, adversarial attacks can be used to perturb images so as to evade detection by these models. This study investigates the effectiveness of training deep learning models with Generative Adversarial Network-generated data to improve their robustness against such attacks. Two image conversion methods, byte plot and space-filling curves, were used to represent the malware samples, and a ResNet-50 architecture was used to train models on the image datasets. The models were then tested against a projected gradient descent attack. It …

Bilstm−Bigru: A Fusion Deep Neural Network For Predicting Air Pollutant Concentration, Prasanjit Dey, Soumyabrata Dev, Bianca Schoen-Phelan

Conference papers

Predicting air pollutant concentrations is an efficient way to prevent incidents by providing early warnings of harmful air pollutants. A precise prediction of air pollutant concentrations is an important factor in controlling and preventing air pollution. In this paper, we develop a bidirectional long-short-term memory and a bidirectional gated recurrent unit (BiLSTM−BiGRU) to predict PM 2.5 concentrations in a target city for different lead times. The BiLSTM extracts preliminary features, and the BiGRU further extracts deep features from air pollutant and meteorological data. The fully connected (FC) layer receives the output and makes an accurate prediction of the PM 2.5 …

Combinedeepnet: A Deep Network For Multistep Prediction Of Near-Surface Pm2.5 Concentration, Prasanjit Dey, Soumyabrata Dev, Bianca Schoen-Phelan

Conference papers

PM2.5 is a type of air pollutant that can cause respiratory and cardiovascular problems. Precise PM2.5 ( μg/m3 ) concentration prediction may help reduce health concerns and provide early warnings. To better understand air pollution, a number of approaches have been presented for predicting PM2.5 concentrations. Previous research used deep learning models for hourly predictions of air pollutants due to their success in pattern recognition, however, these models were unsuitable for multisite, long-term predictions, particularly in regard to the correlation between pollutants and meteorological data. This article proposes the combine deep network (CombineDeepNet), which combines multiple deep networks, including a …

Actor-Centric Spatio-Temporal Feature Extraction For Action Recognition, Anil Kunchala, Bianca Schoen-Phelan, Mélanie Bouroche

Conference papers

Action understanding involves the recognition and detection of specific actions within videos. This crucial task in computer vision gained significant attention due to its multitude of applications across various domains. The current action detection models, inspired by 2D object detection methods, employ two-stage architectures. The first stage is to extract actor-centric video sub-clips, i.e. tubelets of individuals, and the second stage is to classify these tubelets using action recognition networks. The majority of these recognition models utilize a frame-level pre-trained 3D Convolutional Neural Networks (3D CNN) to extract spatio-temporal features of a given tubelet. This, however, results in suboptimal spatio-temporal …

Efficient Gpu Implementation Of Automatic Differentiation For Computational Fluid Dynamics, Mohammad Zubair, Desh Ranjan, Aaron Walden, Gabriel Nastac, Eric Nielsen, Boris Diskin, Marc Paterno, Samuel Jung, Joshua Hoke Davis

Computer Science Faculty Publications

Many scientific and engineering applications require repeated calculations of derivatives of output functions with respect to input parameters. Automatic Differentiation (AD) is a method that automates derivative calculations and can significantly speed up code development. In Computational Fluid Dynamics (CFD), derivatives of flux functions with respect to state variables (Jacobian) are needed for efficient solutions of the nonlinear governing equations. AD of flux functions on graphics processing units (GPUs) is challenging as flux computations involve many intermediate variables that create high register pressure and require significant memory traffic because of the need to store the derivatives. This paper presents a …