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Articles 41821 - 41850 of 196358

Full-Text Articles in Engineering

A Risk-Averse Mechanism For Suicidality Assessment On Social Media, Ramit Sawhney, Atula Tejaswi Neerkaje, Manas Gaur Jan 2022

A Risk-Averse Mechanism For Suicidality Assessment On Social Media, Ramit Sawhney, Atula Tejaswi Neerkaje, Manas Gaur

Publications

Recent studies have shown that social media has increasingly become a platform for users to express suicidal thoughts outside traditional clinical settings. With advances in Natural Language Processing strategies, it is now possible to design automated systems to assess suicide risk. However, such systems may generate uncertain predictions, leading to severe consequences. We hence reformulate suicide risk assessment as a selective prioritized prediction problem over the Columbia Suicide Severity Risk Scale (C-SSRS). We propose SASI, a risk-averse and self-aware transformer-based hierarchical attention classifier, augmented to refrain from making uncertain predictions. We show that SASI is able to refrain from 83% …

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Process Knowledge-Infused Learning For Suicidality Assessment On Social Media, Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth Jan 2022

Process Knowledge-Infused Learning For Suicidality Assessment On Social Media, Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth

Publications

Improving the performance and natural language explanations of deep learning algorithms is a priority for adoption by humans in the real world. In several domains, such as healthcare, such technology has significant potential to reduce the burden on humans by providing quality assistance at scale. However, current methods rely on the traditional pipeline of predicting labels from data, thus completely ignoring the process and guidelines used to obtain the labels. Furthermore, post hoc explanations on the data to label prediction using explainable AI (XAI) models, while satisfactory to computer scientists, leave much to be desired to the end users due …

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Wise Causal Models: Wisdom Infused Semantics Enhanced Causal Models - A Study In Suicidality Diagnosis, Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Sanjay Chandrasekar, Amit Sheth Jan 2022

Wise Causal Models: Wisdom Infused Semantics Enhanced Causal Models - A Study In Suicidality Diagnosis, Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Sanjay Chandrasekar, Amit Sheth

Publications

The COVID-19 Pandemic has highlighted the gap between the number of mental health care seekers and care providers. Netizens have taken to internet-based platforms such as Reddit to express their experiences. Mental illness diagnosis processes have clinically accepted causal interpretations and semantics. Curiously, mental illness diagnosis accuracy is low relative to similar well-studied illnesses. Motivated by this discrepancy, we propose Wisdom Infused Semantics Enhanced (WISE) causal models, inspired by the wisdom of the crowd idea that learns from a collective agreement among causal models and their semantics for mental illness diagnoses. We use suicidality diagnosis task descriptions, datasets, and baseline …

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Knowledge-Infused Reinforcement Learning, Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth Jan 2022

Knowledge-Infused Reinforcement Learning, Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth

Publications

Virtual health agents (VHAs) have received considerable attention, but the early focus has been on collecting data, helping patients follow generic health guidelines, and providing reminders for clinical appointments. While presenting the collected data and frequency of visits to the clinician is useful, further context and personalization are needed for a VHA to interpret and understand what the data means in clinical terms. This has made their use in managing health limited. Such understanding enables patient empowerment and self-appraisal – i.e., aiding the patient in interpreting the data to understand the changes in the patient’s health conditions, and self-management – …

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Deeppose: Detecting Gps Spoofing Attack Via Deep Recurrent Neural Network, Peng Jiang, Hongyi Wu, Chunsheng Xin Jan 2022

Deeppose: Detecting Gps Spoofing Attack Via Deep Recurrent Neural Network, Peng Jiang, Hongyi Wu, Chunsheng Xin

Electrical & Computer Engineering Faculty Publications

The Global Positioning System (GPS) has become a foundation for most location-based services and navigation systems, such as autonomous vehicles, drones, ships, and wearable devices. However, it is a challenge to verify if the reported geographic locations are valid due to various GPS spoofing tools. Pervasive tools, such as Fake GPS, Lockito, and software-defined radio, enable ordinary users to hijack and report fake GPS coordinates and cheat the monitoring server without being detected. Furthermore, it is also a challenge to get accurate sensor readings on mobile devices because of the high noise level introduced by commercial motion sensors. To this …

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Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification At Jefferson Laboratory, Lasitha Vidyaratne, Adam Carpenter, Tom Powers, Chris Tennant, Khan M. Iftekharuddin, Md. Monibor Rahman, Anna S. Shabalina Jan 2022

Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification At Jefferson Laboratory, Lasitha Vidyaratne, Adam Carpenter, Tom Powers, Chris Tennant, Khan M. Iftekharuddin, Md. Monibor Rahman, Anna S. Shabalina

Electrical & Computer Engineering Faculty Publications

This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a large, high-power continuous wave recirculating linac that utilizes 418 SRF cavities to accelerate electrons up to 12 GeV. Recent upgrades to CEBAF include installation of 11 new cryomodules (88 cavities) equipped with a low-level RF system that records RF time-series data from each cavity at the onset of an RF failure. Typically, subject matter experts (SME) analyze this data to determine the fault type and identify the cavity of …

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Bitcoin Selfish Mining Modeling And Dependability Analysis, Chencheng Zhou, Liudong Xing, Jun Guo, Qisi Liu Jan 2022

Bitcoin Selfish Mining Modeling And Dependability Analysis, Chencheng Zhou, Liudong Xing, Jun Guo, Qisi Liu

Electrical & Computer Engineering Faculty Publications

Blockchain technology has gained prominence over the last decade. Numerous achievements have been made regarding how this technology can be utilized in different aspects of the industry, market, and governmental departments. Due to the safety-critical and security-critical nature of their uses, it is pivotal to model the dependability of blockchain-based systems. In this study, we focus on Bitcoin, a blockchain-based peer-to-peer cryptocurrency system. A continuous-time Markov chain-based analytical method is put forward to model and quantify the dependability of the Bitcoin system under selfish mining attacks. Numerical results are provided to examine the influences of several key parameters related to …

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Using Skeleton Correction To Improve Flash Lidar-Based Gait Recognition, Nasrin Sadeghzadehyazdi, Tamal Batabyal, Alexander Glandon, Nibir Dhar, Babajide Familoni, Khan Iftekharuddin, Scott T. Acton Jan 2022

Using Skeleton Correction To Improve Flash Lidar-Based Gait Recognition, Nasrin Sadeghzadehyazdi, Tamal Batabyal, Alexander Glandon, Nibir Dhar, Babajide Familoni, Khan Iftekharuddin, Scott T. Acton

Electrical & Computer Engineering Faculty Publications

This paper presents GlidarPoly, an efficacious pipeline of 3D gait recognition for flash lidar data based on pose estimation and robust correction of erroneous and missing joint measurements. A flash lidar can provide new opportunities for gait recognition through a fast acquisition of depth and intensity data over an extended range of distance. However, the flash lidar data are plagued by artifacts, outliers, noise, and sometimes missing measurements, which negatively affects the performance of existing analytics solutions. We present a filtering mechanism that corrects noisy and missing skeleton joint measurements to improve gait recognition. Furthermore, robust statistics are integrated with …

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"Mystify": A Proactive Moving-Target Defense For A Resilient Sdn Controller In Software Defined Cps, Mohamed Azab, Mohamed Samir, Effat Samir Jan 2022

"Mystify": A Proactive Moving-Target Defense For A Resilient Sdn Controller In Software Defined Cps, Mohamed Azab, Mohamed Samir, Effat Samir

Electrical & Computer Engineering Faculty Publications

The recent devastating mission Cyber–Physical System (CPS) attacks, failures, and the desperate need to scale and to dynamically adapt to changes, revolutionized traditional CPS to what we name as Software Defined CPS (SD-CPS). SD-CPS embraces the concept of Software Defined (SD) everything where CPS infrastructure is more elastic, dynamically adaptable and online-programmable. However, in SD-CPS, the threat became more immanent, as the long-been physically-protected assets are now programmatically accessible to cyber attackers. In SD-CPSs, a network failure hinders the entire functionality of the system. In this paper, we present MystifY, a spatiotemporal runtime diversification for Moving-Target Defense (MTD) to secure …

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A Channel State Information Based Virtual Mac Spoofing Detector, Peng Jiang, Hongyi Wu, Chunsheng Xin Jan 2022

A Channel State Information Based Virtual Mac Spoofing Detector, Peng Jiang, Hongyi Wu, Chunsheng Xin

Electrical & Computer Engineering Faculty Publications

Physical layer security has attracted lots of attention with the expansion of wireless devices to the edge networks in recent years. Due to limited authentication mechanisms, MAC spoofing attack, also known as the identity attack, threatens wireless systems. In this paper, we study a new type of MAC spoofing attack, the virtual MAC spoofing attack, in a tight environment with strong spatial similarities, which can create multiple counterfeits entities powered by the virtualization technologies to interrupt regular services. We develop a system to effectively detect such virtual MAC spoofing attacks via the deep learning method as a countermeasure. …

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Uncertainty Estimation In Classification Of Mgnt Using Radiogenomics For Glioblastoma Patients, W. Farzana, Z. A. Shboul, A. Temtam, K. M. Iftekharuddin Jan 2022

Uncertainty Estimation In Classification Of Mgnt Using Radiogenomics For Glioblastoma Patients, W. Farzana, Z. A. Shboul, A. Temtam, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Glioblastoma Multiforme (GBM) is one of the most malignant brain tumors among all high-grade brain cancers. Temozolomide (TMZ) is the first-line chemotherapeutic regimen for glioblastoma patients. The methylation status of the O6-methylguanine-DNA-methyltransferase (MGMT) gene is a prognostic biomarker for tumor sensitivity to TMZ chemotherapy. However, the standardized procedure for assessing the methylation status of MGMT is an invasive surgical biopsy, and accuracy is susceptible to resection sample and heterogeneity of the tumor. Recently, radio-genomics which associates radiological image phenotype with genetic or molecular mutations has shown promise in the non-invasive assessment of radiotherapeutic treatment. This study proposes a machine-learning framework …

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Broadband Dielectric Spectroscopic Detection Of Aliphatic Alcohol Vapors With Surface-Mounted Hkust-1 Mofs As Sensing Media, Papa K. Amoah, Zeinab Mohammed Hassan, Rhonda R. Franklin, Helmut Baumgart, Engelbert Redel, Yaw S. Obeng Jan 2022

Broadband Dielectric Spectroscopic Detection Of Aliphatic Alcohol Vapors With Surface-Mounted Hkust-1 Mofs As Sensing Media, Papa K. Amoah, Zeinab Mohammed Hassan, Rhonda R. Franklin, Helmut Baumgart, Engelbert Redel, Yaw S. Obeng

Electrical & Computer Engineering Faculty Publications

We leveraged chemical-induced changes to microwave signal propagation characteristics (i.e., S-parameters) to characterize the detection of aliphatic alcohol (methanol, ethanol, and 2-propanol) vapors using TCNQ-doped HKUST-1 metal-organic-framework films as the sensing material, at temperatures under 100 °C. We show that the sensitivity of aliphatic alcohol detection depends on the oxidation potential of the analyte, and the impedance of the detection setup depends on the analyte-loading of the sensing medium. The microwaves-based detection technique can also afford new mechanistic insights into VOC detection, with surface-anchored metal-organic frameworks (SURMOFs), which is inaccessible with the traditional coulometric (i.e., resistance-based) measurements.

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Runtime Power Allocation Based On Multi-Gpu Utilization In Gamess, Masha Sosonkina, Vaibhav Sundriyal, Jorge Luis Galvez Vallejo Jan 2022

Runtime Power Allocation Based On Multi-Gpu Utilization In Gamess, Masha Sosonkina, Vaibhav Sundriyal, Jorge Luis Galvez Vallejo

Electrical & Computer Engineering Faculty Publications

To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize performance under a given power budget by distributing the available power according to the relative GPU utilization. Time series forecasting methods were used to develop workload prediction models that provide accurate prediction of GPU utilization during application execution. Experiments were performed on a multi-GPU computing platform DGX-1 equipped with eight NVIDIA V100 GPUs used for quantum chemistry calculations in the GAMESS package. For a limited power budget, the proposed strategy …

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Grand Challenges In Low Temperature Plasmas, Xinpei Lu, Peter J. Bruggeman, Stephan Reuter, George Naidis, Annemie Bogaerts, Mounir Laroussi, Michael Keidar, Eric Robert, Jean-Michel Pouvesle, Dawei Liu, Kostya (Ken) Ostrikov Jan 2022

Grand Challenges In Low Temperature Plasmas, Xinpei Lu, Peter J. Bruggeman, Stephan Reuter, George Naidis, Annemie Bogaerts, Mounir Laroussi, Michael Keidar, Eric Robert, Jean-Michel Pouvesle, Dawei Liu, Kostya (Ken) Ostrikov

Electrical & Computer Engineering Faculty Publications

Low temperature plasmas (LTPs) enable to create a highly reactive environment at near ambient temperatures due to the energetic electrons with typical kinetic energies in the range of 1 to 10 eV (1 eV = 11600K), which are being used in applications ranging from plasma etching of electronic chips and additive manufacturing to plasma-assisted combustion. LTPs are at the core of many advanced technologies. Without LTPs, many of the conveniences of modern society would simply not exist. New applications of LTPs are continuously being proposed. Researchers are facing many grand challenges before these new applications can be translated to practice. …

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Entropy Of Generating Series For Nonlinear Input-Output Systems And Their Interconnections, W. Steven Gray Jan 2022

Entropy Of Generating Series For Nonlinear Input-Output Systems And Their Interconnections, W. Steven Gray

Electrical & Computer Engineering Faculty Publications

This paper has two main objectives. The first is to introduce a notion of entropy that is well suited for the analysis of nonlinear input-output systems that have a Chen-Fliess series representation. The latter is defined in terms of its generating series over a noncommutative alphabet. The idea is to assign an entropy to a generating series as an element of a graded vector space. The second objective is to describe the entropy of generating series originating from interconnected systems of Chen-Fliess series that arise in the context of control theory. It is shown that one set of interconnections can …

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Arithfusion: An Arithmetic Deep Model For Temporal Remote Sensing Image Fusion, Md Reshad Ul Hoque, Jian Wu, Chiman Kwan, Krzysztof Koperski, Jiang Li Jan 2022

Arithfusion: An Arithmetic Deep Model For Temporal Remote Sensing Image Fusion, Md Reshad Ul Hoque, Jian Wu, Chiman Kwan, Krzysztof Koperski, Jiang Li

Electrical & Computer Engineering Faculty Publications

Different satellite images may consist of variable numbers of channels which have different resolutions, and each satellite has a unique revisit period. For example, the Landsat-8 satellite images have 30 m resolution in their multispectral channels, the Sentinel-2 satellite images have 10 m resolution in the pan-sharp channel, and the National Agriculture Imagery Program (NAIP) aerial images have 1 m resolution. In this study, we propose a simple yet effective arithmetic deep model for multimodal temporal remote sensing image fusion. The proposed model takes both low- and high-resolution remote sensing images at t1 together with low-resolution images at a …

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A Primer On Software Defined Radios, Dimitrie C. Popescu, Rolland Vida Jan 2022

A Primer On Software Defined Radios, Dimitrie C. Popescu, Rolland Vida

Electrical & Computer Engineering Faculty Publications

The commercial success of cellular phone systems during the late 1980s and early 1990 years heralded the wireless revolution that became apparent at the turn of the 21st century and has led the modern society to a highly interconnected world where ubiquitous connectivity and mobility are enabled by powerful wireless terminals. Software defined radio (SDR) technology has played a major role in accelerating the pace at which wireless capabilities have advanced, in particular over the past 15 years, and SDRs are now at the core of modern wireless communication systems. In this paper we give an overview of SDRs that …

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Facial Landmark Feature Fusion In Transfer Learning Of Child Facial Expressions, Megan A. Witherow, Manar D. Samad, Norou Diawara, Khan M. Iftekharuddin Jan 2022

Facial Landmark Feature Fusion In Transfer Learning Of Child Facial Expressions, Megan A. Witherow, Manar D. Samad, Norou Diawara, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Automatic classification of child facial expressions is challenging due to the scarcity of image samples with annotations. Transfer learning of deep convolutional neural networks (CNNs), pretrained on adult facial expressions, can be effectively finetuned for child facial expression classification using limited facial images of children. Recent work inspired by facial age estimation and age-invariant face recognition proposes a fusion of facial landmark features with deep representation learning to augment facial expression classification performance. We hypothesize that deep transfer learning of child facial expressions may also benefit from fusing facial landmark features. Our proposed model architecture integrates two input branches: a …

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Nb₃Sn Coating Of A 2.6 Ghz Srf Cavity By Sputter Deposition Technique, M. S. Shakel, Wei Cao, H. Elsayed-Ali, G. V. Eremeev, U. Pudasaini, A. M. Valente-Feliciano Jan 2022

Nb₃Sn Coating Of A 2.6 Ghz Srf Cavity By Sputter Deposition Technique, M. S. Shakel, Wei Cao, H. Elsayed-Ali, G. V. Eremeev, U. Pudasaini, A. M. Valente-Feliciano

Electrical & Computer Engineering Faculty Publications

Nb₃Sn is of interest as a coating for SRF cavities due to its higher transition temperature Tc ~18.3 K and superheating field Hsh ~400 mT, both are twice that of Nb. Nb₃Sn coated cavities can achieve high-quality factors at 4 K and can replace the bulk Nb cavities operated at 2 K. A cylindrical magnetron sputtering system was built, commissioned, and used to deposit Nb₃Sn on the inner surface of a 2.6 GHz single-cell Nb cavity. With two identical cylindrical magnetrons, this system can coat a cavity with high symmetry and uniform thickness. Using Nb-Sn multilayer sequential sputtering followed by …

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Load Sharing Assessment Of Osseoligamentous Structures Within A Thoracic Spine Segment During Surgical Release, Michael Polanco, James Bennett, Austin Tapp, Michel Audette, Stacie Ringleb, Sebastian Bawab Jan 2022

Load Sharing Assessment Of Osseoligamentous Structures Within A Thoracic Spine Segment During Surgical Release, Michael Polanco, James Bennett, Austin Tapp, Michel Audette, Stacie Ringleb, Sebastian Bawab

Electrical & Computer Engineering Faculty Publications

Spinal surgical procedures often require release of intervertebral discs and ligaments to optimally achieve postural correction on a patient-specific basis. In this paper, a T7-T8 Finite Element (FE) model is utilized to examine internal load sharing during resection steps performed in a Ponte osteotomy. The FE model was rotated bidirectionally along three anatomical planes using an externally applied moment. In each step, the Ranges of Motion (RoM), Instantaneous Centers of Rotation (ICR), and forces from ligaments, discs, facet, and costovertebral joints were calculated. The product of each component’s force and the distance between the ICR and their position were used …

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Particle Identification And Tracking In Real Time Using Machine Learning On Fpga, F. Barbosa, L. Belfore, C. Dickover, C. Fanelli, S. Furletov, Y. Furletova, L. Jokhovets, D. Lawrence, D. Romanov Jan 2022

Particle Identification And Tracking In Real Time Using Machine Learning On Fpga, F. Barbosa, L. Belfore, C. Dickover, C. Fanelli, S. Furletov, Y. Furletova, L. Jokhovets, D. Lawrence, D. Romanov

Electrical & Computer Engineering Faculty Publications

This project is a multi-disciplinary endeavour between Physics, Electrical Engineering, and Computer Engineering. The purpose is to develop and implement an FPGA(*) based Machine Learning algorithm for real-time particle identification, filtering, and data reduction. This is important research that can be applied to streaming readout systems being developed now at JLab and other facilities. Real-time data processing is a frontier field in experimental physics, especially in HEP. The application of FPGAs at the trigger level is used by many current and planned experiments (CMS, LHCb, Belle2, PANDA). Usually they use conventional processing algorithms. LHCb has implemented ML elements for real-time …

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Srf Cavity Fault Classification And Prediction At Jefferson Lab, Chris Tennant, Adam Carpenter, Lasitha Vidyaratne, Md. Monibor Rahman, Khan Iftekharuddin Jan 2022

Srf Cavity Fault Classification And Prediction At Jefferson Lab, Chris Tennant, Adam Carpenter, Lasitha Vidyaratne, Md. Monibor Rahman, Khan Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Over the last few years several machine learning projects at Jefferson Lab have had a common focus to optimize operation of superconducting RF (SRF) cavities in the Continuous Electron Beam Accelerator Facility (CEBAF). In this talk we highlight work to identify and classify types of faults from C100-type cavities and then to extend those capabilities to provide real-time fault prediction. Early prediction may enable mitigation strategies to prevent some types of faults. In our approach we apply a two-step fault prediction pipeline. In the first step, a model distinguishes between faulty and normal signals. In the second step, signals flagged …

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Time-Resolved Method For Spectral Analysis Based On Linear Predictive Coding, With Application To Eeg Analysis, Jin Xu Jan 2022

Time-Resolved Method For Spectral Analysis Based On Linear Predictive Coding, With Application To Eeg Analysis, Jin Xu

Doctoral

EEG (Electroencephalogram) signal is a biological signal in BCI (Brain-Computer Interface) systems to realise the information exchange between the brain and the external environment. It is characterised by a poor signal-to-noise ratio, is time-varying, is intermittent and contains multiple frequency components. This research work has developed a new parameterised time-frequency method called the Linear Predictive Coding Pole Processing (LPCPP) method which can be used for identifying and tracking the dominant frequency components of an EEG signal. The LPCPP method further processes LPC (Linear Predictive Coding) poles to produce a series of reduced-order filter transfer functions to estimate the dominant frequencies. …

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A Socio-Ecological, Multi-Stakeholder Investigation Of Apprenticeship Training Engagement In The Irish Construction Industry, Eoghan Ó Murchadha Jan 2022

A Socio-Ecological, Multi-Stakeholder Investigation Of Apprenticeship Training Engagement In The Irish Construction Industry, Eoghan Ó Murchadha

Doctoral

The construction industry in Ireland is crucial to the national economy, not just in terms of economic output, but as a major provider of employment. The construction labour market is a complex concatenation of professions relying upon adequate skills supply. Apprenticeships play a critical role in future skills provision and are thus vital to the successful implementation of strategic government targets such as the National Development Plan 2021-2030. However, the extant employer-led model of skills engagement is prone to labour market failure given the cyclical elasticity of the industry. Nonetheless, there is a paucity of research into apprenticeship engagement …

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Effect Of Connection State & Transport/Application Protocol On The Machine Learning Outlier Detection Of Network Intrusions, George Yuchi, Torrey J. Wagner, Paul Auclair, Brent T. Langhals Jan 2022

Effect Of Connection State & Transport/Application Protocol On The Machine Learning Outlier Detection Of Network Intrusions, George Yuchi, Torrey J. Wagner, Paul Auclair, Brent T. Langhals

Faculty Publications

The majority of cyber infiltration & exfiltration intrusions leave a network footprint, and due to the multi-faceted nature of detecting network intrusions, it is often difficult to detect. In this work a Zeek-processed PCAP dataset containing the metadata of 36,667 network packets was modeled with several machine learning algorithms to classify normal vs. anomalous network activity. Principal component analysis with a 10% contamination factor was used to identify anomalous behavior. Models were created using recursive feature elimination on logistic regression and XGBClassifier algorithms, and also using Bayesian and bandit optimization of neural network hyperparameters. These models were trained on a …

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Moisture Dependence Of Electrical Resistivity In Under-Percolated Cement-Based Composites With Multi-Walled Carbon Nanotubes, Geuntae Hong, Seongcheol Choi, Doo-Yeol Yoo, Taekgeun Oh, Yooseob Song, Jung Heum Yeon Jan 2022

Moisture Dependence Of Electrical Resistivity In Under-Percolated Cement-Based Composites With Multi-Walled Carbon Nanotubes, Geuntae Hong, Seongcheol Choi, Doo-Yeol Yoo, Taekgeun Oh, Yooseob Song, Jung Heum Yeon

Civil Engineering Faculty Publications

Cement-based piezoresistive composites have attracted significant attention as smart construction materials for embedding self-sensing capability in concrete infrastructure. Although a number of studies have been conducted using multi-walled carbon nanotubes (MWCNTs) as a functional filler for self-sensing cement-based composites, studies addressing the influence of the internal moisture state on the electrical properties are relatively scant. In this study, we aim to experimentally investigate the effect of internal moisture state on the electrical resistivity of cement-based composites containing MWCNTs as an electrically conductive medium to raise a need for calibration of self-sensing data considering the internal moisture state. To this end, …

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Concentration Of Fecal Coliforms In Marine Waters Using Satellite Images In The Vicinity Of Pucusana. Bay, Peru., Y A. Palma-Gongora, F V. Zuta-Medina, Luis Angel Gomez-Cunya Jan 2022

Concentration Of Fecal Coliforms In Marine Waters Using Satellite Images In The Vicinity Of Pucusana. Bay, Peru., Y A. Palma-Gongora, F V. Zuta-Medina, Luis Angel Gomez-Cunya

Civil Engineering Faculty Publications

Water quality monitoring in coastal areas is challenging due to cost and time constraints. Identifying and selecting sampling sites accurately and effectively is crucial for efficient monitoring. The need for efficient monitoring of marine waters has led to exploring the use of remote sensing as one helpful alternative. Remote sensing is practical in several applications based on pattern recognition and information processing of large terrestrial and aquatic surface areas. Collected information is processed with various image processing techniques to identify objects such as microorganisms. Fecal coliforms are microorganisms that are indicators of sanitary quality and are present in human and …

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Sediment Transport On The River Bandon, Co. Cork, Ireland, Laurence Lomasney Jan 2022

Sediment Transport On The River Bandon, Co. Cork, Ireland, Laurence Lomasney

Theses

This thesis analyses sediment transport on the River Bandon, Co. Cork, Ireland. Bedload transport and suspended sediment transport were monitored on the River Bandon over an extended period to determine rates of transport over varying flow conditions. A literature review of sediment properties (individual and bulk sediment), sediment transport, and numerical modelling was undertaken. A manual field sampling programme was undertaken at different locations on the River Bandon to collect bed sediment and suspended sediment for laboratory testing. This data, and data collected using an automatic water sampler and bed sediment traps also, was used to determine baseline conditions for …

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Robust Control Strategies For Optimal Operation Of Distributed Generation In Smart Microgrids, Chi-Thang Phan-Tan Jan 2022

Robust Control Strategies For Optimal Operation Of Distributed Generation In Smart Microgrids, Chi-Thang Phan-Tan

Theses

The high penetration of PV systems and fast communications networks increase the potential for PV inverters to support the stability and performance of smart grids and microgrids. PV inverters in the distribution network can work cooperatively and follow centralized and decentralized control commands to optimize energy production while meeting grid code requirements. However, there are older autonomous inverters that have already been installed and will operate in the same network as smart controllable ones. This research proposes a decentralized optimal control (DOC) that performs multi-objective optimization for a group of PV inverters in a network of existing residential loads and …

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การวิเคราะห์การใช้พลังงานของการตัดแผ่นหินอ่อนและหินแกรนิต : กรณีศึกษา, ทิชากร โพธิ์นรินทร์ Jan 2022

การวิเคราะห์การใช้พลังงานของการตัดแผ่นหินอ่อนและหินแกรนิต : กรณีศึกษา, ทิชากร โพธิ์นรินทร์

Chulalongkorn University Theses and Dissertations (Chula ETD)

หินอ่อนและหินแกรนิต เป็นวัสดุที่ได้รับความนิยมอย่างมากในกลุ่มวัสดุก่อสร้าง เพราะมีความสวยงาม คงทนและสะดวกต่อการดูแลรักษา ซึ่งการจะได้มาซึ่งแผ่นหินที่ใช้ตกแต่งได้นั้น จะต้องนำแผ่นหินขนาดใหญ่ (Slabs) มาตัดตามขนาดที่ต้องการเสียก่อน เนื่องจากหินทั้งสองชนิดมีความแข็งที่แตกต่างกัน ทำให้ต้องใช้ความเร็วในการตัด ความเร็วรอบของใบเลื่อย รวมทั้งขนาดของใบเลื่อยที่ต่างกัน สำหรับโรงงานที่ใช้ศึกษาในครั้งนี้ การตัดหินแต่ละชนิดจะใช้พารามิเตอร์ต่างกัน สำหรับหินอ่อน จะใช้ความเร็วรอบของใบเลื่อยอยู่ที่ 2,700 rpm ความเร็วในการตัดอยู่ที่ 1, 2 และ 3 เมตรต่อนาที ใบเลื่อยที่ใช้ตัดจะอยู่ที่ 12 นิ้ว และใช้หินอ่อน Rosso Levanto ในการทดสอบ สำหรับหินแกรนิต ใช้รอบการหมุนของใบเลื่อยอยู่ที่ 1,700 rpm ความเร็วในการตัดอยู่ที่ 1, 2 และ 2.5 เมตรต่อนาที และใช้หินแกรนิตดำอินเดียในการทดสอบ จากการทดลองวัดกระแสไฟฟ้าขณะตัดแผ่นหินเพื่อนำมาคำนวณหาค่ากำลังไฟฟ้า พบว่าใช้กำลังไฟฟ้าขณะที่ตัดอยู่ที่ 21.13, 21.34 และ 24.36 กิโลวัตต์ (kW) ตามลำดับความเร็วที่ใช้ตัด และสามารถคำนวณหน่วยไฟฟ้าที่ใช้ต่อการตัดแผ่นหินอ่อน 1 สแลบ อยู่ที่ 8.25, 4.19 และ 3.30 หน่วย ตามลำดับ และเมื่อทดสอบกับหินแกรนิตดำอินเดีย พบว่าใช้กำลังไฟฟ้าขณะที่ตัด อยู่ที่ 19.48, 20.29 และ 20.95 กิโลวัตต์ (kW) และหน่วยไฟฟ้าที่ใช้ต่อการตัดแผ่นหินแกรนิต 1 สแลบ อยู่ที่ 7.50, 3.95 และ 3.32 ตามลำดับ เมื่อนำค่าไฟฟ้าเฉลี่ยของโรงงานมาคำนวณคาดการณ์ในการตัดแผ่นหินใน 1 ปี พบว่า หากตัดแผ่นหินอ่อนด้วยความเร็ว 3 เมตรต่อนาที จะประหยัดค่าใช้จ่ายมากกว่าการตัดด้วยความเร็ว 1 เมตรต่อนาที ถึง 65,488.74 บาท หรือประมาณ 60.01% และหากตัดแผ่นหินแกรนิตด้วยความเร็ว 2.5 เมตรต่อนาที สามารถประหยัดค่าใช้จ่ายได้มากกว่าการตัดด้วยความเร็ว 1 เมตรต่อนาที …

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