Papers by Mohsen Ahmadian
Zenodo (CERN European Organization for Nuclear Research), Nov 1, 2017
Hammerstein-Wiener model is a block-oriented model where a linear dynamic system is surrounded by... more Hammerstein-Wiener model is a block-oriented model where a linear dynamic system is surrounded by two static nonlinearities at its input and output and could be used to model various processes. This paper contains an optimization approach method for analysing the problem of Hammerstein-Wiener systems identification. The method relies on reformulate the identification problem; solve it as constraint quadratic problem and analysing its solutions. During the formulation of the problem, effects of adding noise to both input and output signals of nonlinear blocks and disturbance to linear block, in the emerged equations are discussed. Additionally, the possible parametric form of matrix operations to reduce the equation size is presented. To analyse the possible solutions to the mentioned system of equations, a method to reduce the difference between the number of equations and number of unknown variables by formulate and importing existing knowledge about nonlinear functions is presented. Obtained equations are applied to an instance H-W system to validate the results and illustrate the proposed method.
Role of interaction quality and trust in use of AI-based voice-assistant systems
Journal of Systems and Information Technology, Aug 17, 2021
PurposeThe rapid growth of artificial intelligence (AI)-based voice-assistant systems (VASs) has ... more PurposeThe rapid growth of artificial intelligence (AI)-based voice-assistant systems (VASs) has created many opportunities for individuals to use VASs for various purposes in their daily lives. However, traditional quality success factors, such as information quality and system quality, may not be sufficient in explaining the adoption and use of AI-based VASs. This study aims to propose interaction quality as an additional, yet more important quality measure that leads to trust in an AI-based VAS and its adoption. Design/methodology/approachThe authors propose a research model that highlights the importance of interaction quality and trust as underlying mechanisms in the adoption of AI-based VASs. Based on survey methodology and data from 221 respondents, the proposed research model is tested with a partial least squares approach. FindingsThe results suggest that interaction quality and trust are critical factors influencing the adoption of AI-based VASs. The findings also indicate that the impacts of traditional quality factors (i.e. information quality and system quality) occur through interaction quality in the context of AI-based VASs. Originality/valueThis research adds interaction quality as a new quality factor to the traditional quality factors in the information systems success model. Further, given the interactive nature of VASs, the authors use social response theory to explain the importance of the trust mechanism when individuals interact with AI-based VASs. Contribution to Impact
This research reviews the prior behavioral economics studies in simultaneous games and behavioral... more This research reviews the prior behavioral economics studies in simultaneous games and behavioral operations management literature to propose some new research avenues in the field of behavioral operations management with a focus on simultaneous competitions. Findings of this study show that although many behavioral studies have been done, behavioral research on simultaneous competitions in operations management is rare. Review of the literature indicates that some contemporary trends are emerging in behavioral studies, so there are many opportunities for future research in this area. Moreover, this research highlights the importance of decision science as an interdisciplinary field of study, which in turn emphasizes exploring other disciplines to enrich the behavioral decision-making literature and developing more comprehensive and meticulous behavioral theories.
Hammerstein–Wiener model is a block-oriented model<br> where a linear dynamic system is sur... more Hammerstein–Wiener model is a block-oriented model<br> where a linear dynamic system is surrounded by two static<br> nonlinearities at its input and output and could be used to model<br> various processes. This paper contains an optimization approach<br> method for analysing the problem of Hammerstein–Wiener systems<br> identification. The method relies on reformulate the identification<br> problem; solve it as constraint quadratic problem and analysing its<br> solutions. During the formulation of the problem, effects of adding<br> noise to both input and output signals of nonlinear blocks and<br> disturbance to linear block, in the emerged equations are discussed.<br> Additionally, the possible parametric form of matrix operations<br> to reduce the equation size is presented. To analyse the possible<br> solutions to the mentioned system of equations, a method to reduce<br> the difference between the numbe...
AI-Based Voice Assistant Systems: Evaluating from the Interaction and Trust Perspectives
Artificial Intelligence (AI) technologies are one of the new technologies with new complicated fe... more Artificial Intelligence (AI) technologies are one of the new technologies with new complicated features, that are emerging in a fast pace. Although these technologies seem to be extensively adopted, people do not intend to use them in some cases. Technology adoption has been studied for many years, and there are many general models in the literature describing it. However, having more customized models for emerging technologies upon their features seems necessary. In this study, we developed a conceptual model involving a new system quality construct, i.e., interaction quality, which we believe can better describe adoption of AI-based technologies. In order to check our model, we used a voice assistant system (VAS) technology as an example of this technology, and tested a theory-based model using a data set achieved from a field survey. Our results confirm that interaction quality significantly affects individual’s trust and leads to adoption of this technology.
International Journal of Global Business and Competitiveness, 2021
This research investigates how competition intensity and differences in cost structures affect de... more This research investigates how competition intensity and differences in cost structures affect decisions made by competing suppliers and the role that behavioral factors play as influences. We use controlled laboratory experiments to study the scenario of suppliers competing for a share of demand being outsourced by a single buyer. The buyer seeks to maximize the service level provided by suppliers by allocating based on different performance measures which create varying levels of competition intensity. The experimental treatments include those performance measures as well as differences in supplier cost structures. Our experimental results show that in the majority of cases suppliers' decisions do not confirm theoretical predictions from the Nash equilibrium, and we find patterns in those deviations. To explain them, we first evaluate behavioral factors found in the literature including bounded rationality, learning, and other-regarding behavior. We then introduce a new behavioral factor, rival-chasing. Rival-chasing builds on other-regarding behavior by considering competitors' actions in addition to their outcomes. We find that rival-chasing can explain patterns in suppliers' behavior that cannot be explained by other behavioral factors.
Role of interaction quality and trust in use of AI-based voice-assistant systems
Journal of Systems and Information Technology, 2021
PurposeThe rapid growth of artificial intelligence (AI)-based voice-assistant systems (VASs) has ... more PurposeThe rapid growth of artificial intelligence (AI)-based voice-assistant systems (VASs) has created many opportunities for individuals to use VASs for various purposes in their daily lives. However, traditional quality success factors, such as information quality and system quality, may not be sufficient in explaining the adoption and use of AI-based VASs. This study aims to propose interaction quality as an additional, yet more important quality measure that leads to trust in an AI-based VAS and its adoption. Design/methodology/approachThe authors propose a research model that highlights the importance of interaction quality and trust as underlying mechanisms in the adoption of AI-based VASs. Based on survey methodology and data from 221 respondents, the proposed research model is tested with a partial least squares approach. FindingsThe results suggest that interaction quality and trust are critical factors influencing the adoption of AI-based VASs. The findings also indicate...
Inspection of integrated circuit database through reticle and wafer simulation: the lithography process window performance monitoring
SPIE Proceedings, 2005
Approaches to verify post-OPC designs for manufacturing have evolved from a number of separate in... more Approaches to verify post-OPC designs for manufacturing have evolved from a number of separate inspection strategies. OPC decorations are verified by design rule or optical rule checkers, the reticle is verified by a reticle inspection system, and the patterned wafers are verified by ...
Pressure Transient Analyses and Poroelastic Modeling of Hydraulic Fracture Dilation for Multiple Injections at the Devine Fracture Pilot Site
Our team has conducted electromagnetic (EM) surveys for the past six years to monitor hydraulic-f... more Our team has conducted electromagnetic (EM) surveys for the past six years to monitor hydraulic-fracture behavior at the Devine Fracture Pilot Site (DFPS). The sub-horizontal orientation of a shallow hydraulic fracture at the DFPS provides uniform access to the fracture area for interrogation and data collection. Ahmadian et al. (2023) suggested a possible correlation between spatiotemporal changes in the flow rate, bottomhole pressure (BHP), and the observed surface recorded electric field at the DFPS. In this paper, we present the development of poroelastic forward models and pressure transient analyses (PTAs) to support the development of a multiphysics inverse model for these EM surveys.First, we conducted PTAs of the shut-in periods after six injections out of 10 to determine the fracture closure pressure (FCP) or the overburden pressure used in a poroelastic fracture reopening model. Second, we developed a finite-element poroelastic model throughout five injection cycles to include the effect of the cumulative injected volumes due to the previous injections on current fracture dilation in the presence of highly permeable unpropped and propped zones adjacent to the cohesive layer that models fracture reopening. Fracture reopening in this poroelastic model is based on a calibrated traction-separation response using the bottomhole pressure collected in two injection campaigns in 2020 and 2022. We used the outcomes of a previous simulation study of the primary hydraulic-fracturing stimulation to define the dimension of an unpropped fracture zone ahead of the propped fracture area.The PTAs led to FCPs consistent with those obtained using the injection data collected at the DFPS in 2020. Further, these analyses showed that at later injections, the fracture closure occurred at a later time with respect to the shut-in time, inferring the effect of cumulative injected volumes in previous injections. The simulation results show that considering the propped and unpropped fracture zones improves our poroelastic model in predicting the injection-well BHP. The numerical simulation results demonstrate a significant excess pore pressure near the fracture because of the preceding formation loadings by the previous injections.The obtained fracture dilation area and fluid pressure distribution provide a basis to improve the development of a multiphysics inverse model. Furthermore, in an iteratively coupled scheme, this pressure distribution can be introduced into EM models to render a holistic view of the causative mechanisms for the surface signal anomalies.
A study of defect measurement techniques and corresponding effects on the lithographic process window for a 193-nm EPSM photomask
23rd Annual BACUS Symposium on Photomask Technology, Dec 15, 2003
Photomasks with small dense features and high mask error enhancement factor (MEEF) lithography pr... more Photomasks with small dense features and high mask error enhancement factor (MEEF) lithography processes require stringent reticle quality control. The ability to quickly and accurately measure reticle defects on a high-resolution inspection system and to simulate their impact on wafer printing are key components in ensuring photomask quality. This paper discusses the correlation of measurements made with UV and DUV-based
Accelerating Hydraulic Fracture Imaging by Deep Transfer Learning
IEEE Transactions on Antennas and Propagation, Jul 1, 2022
Power Density Distribution in Subsurface Fractures Due to an Energized Steel Well-casing Source
Journal of Environmental and Engineering Geophysics, Jun 1, 2019
Robust in situ power harvesting underlies the realization of embedded wireless sensors for monito... more Robust in situ power harvesting underlies the realization of embedded wireless sensors for monitoring the physicochemical state of subsurface engineered structures and environments. The use of electromagnetic (EM) contrast agents in hydraulically fractured reservoirs, in coordination with completion design of wells, offers a way to transmit energy to remotely charge distributed sensors and interrogate fracture width, extent, and fracture-stage cross-communication. The quantification of available power in fracture networks due to energized steel-cased wells is crucial for such sensor designs; however, this has not been clarified via numerical modeling in the limit of Direct Current (DC). This paper presents a numerical modeling study to determine the EM characteristics of a subsurface system that is based on a highly instrumented field observatory. We use those realistic field scenarios incorporating geometry and material properties of contrast agents, the wellbore, and the surrounding geologic environment to estimate volumetric power density near the wellbore and within hydraulic fractures. The numerical modeling results indicate that the highest power densities are mainly focused around the wellbore excited by a point current source and the fracture boundary. Using DC excitation, the highest power density in the fracture is at the fracture tip. The relatively high-power density on the order of tens of mW/m3 at the vicinity of the wellbore and at fracture tips suggests that remote charging of sensor devices may be readily possible. Simulation results also show that the region of the highest power density can be significantly increased when the EM source is located inside a conductive fracture, which may lead to a promising deployment strategy for embedded micro-sensors in geologic formations.
Validation of the Utility of the Contrast-Agent-Assisted Electromagnetic Tomography Method for Precise Imaging of a Hydraulically Induced Fracture Network
Characterizing hydraulically induced fractures—height, length, orientation, and shape—is key to u... more Characterizing hydraulically induced fractures—height, length, orientation, and shape—is key to understanding reservoir performance. Our previous work has focused on the comparison of the state-of-the-art geophysical techniques currently used in hydraulic fracture imaging (microseismicity, tracer, tiltmeter, and distributed acoustic and temperature sensors) to perform a comprehensive set of electromagnetically active proppant (EAP)–assisted tomography methods (LaBrecque et al., 2016; Ahmadian et al., 2018). In our latest study, we conducted a field pilot at The University of Texas at Austin Bureau of Economic Geology's Devine Test Site, located approximately 50 miles southwest of San Antonio, Texas. Following hydraulic fracturing with EAP, we detected a measurable electromagnetic (EM) fracture anomaly at a depth of 175 ft (~53 m) by use of a set of four PVC-cased wells equipped with electrode arrays for single hole, hole-to-surface, and cross-hole electrical resistivity tomography. Because of relatively low overburden pressure, and as designed, fractures grew horizontally and appear nonaxisymmetric about the center injection well (fracture image looks like a human foot). This design allowed us to verify our results with drilling and logging of eight vertical wells. In addition, we cored two wells, and these samples further corroborated the presence of EAP proppants at the predicted depth. Together, these results conclusively corroborate the accuracy of our EM inversion models to within 5 ft of the physical edge of the EAP-filled fracture anomaly. We are currently using results from our ongoing geophysical surveys to refine and verify the efficiency of forward and inverse EM modeling codes for open-borehole and through steel casing scenarios. This paper describes the ground-truth validation of our model predictions, as well as the future direction of our research.
Remote Imaging of Proppants in Hydraulic Fracture Networks Using Electromagnetic Methods: Results of Small-Scale Field Experiments
The goal of this project is to develop techniques for monitoring hydraulic fractures in reservoir... more The goal of this project is to develop techniques for monitoring hydraulic fractures in reservoirs by injecting electrically conductive, dielectric, or magnetically permeable proppants. The contrasts between the properties of the proppants and the subsurface provided the basis for imaging using geophysical methods. The initial experiments focused on a series of small, shallow fractures; however, the goal of the project is to develop methods applicable to oil-field fractures. The project began by screening different proppant types using laboratory and numerical analyses that have been ongoing by researchers at the Advanced Energy Consortium (AEC). This work identified Loresco coke breeze and steel shot as materials that could create significant electrical or magnetic contrasts with most geological formations. These proppants were tested by creating hydraulic fractures in a shallow field setting consisting of highly weathered residual saprolite near Clemson University in western South Carolina. Six hydraulic fractures were created in highly monitored cells by injecting the contrasting proppants at a depth of approximately 1.5 m. This created sub-horizontal fractures filled with proppant approximately 10 mm thick and extending 3 to 5 m in maximum dimension. Each cell had a dense array of electrodes and magnetic sensors on the surface, as well as four shallow vertical electrode arrays that were used to obtain data before and after hydraulic fracturing. Net vertical displacement, cores and trenching were used to characterize the fracture geometries. Hydraulic fracture geometries were estimated by inverting pre- and post-injection geophysical data using various codes. Data from cores and excavation show that the hydraulic fractures formed a saucer-shape with a preferred propagation in the horizontal direction. The geophysical inversions generated images with remarkably similar form, size, and location to the ground truth from direct observation. Displacement and tilt data appear promising as a constraint on fracture geometry.
Real-Time Monitoring of Fracture Dynamics with a Contrast Agent-Assisted Electromagnetic Method
Day 2 Wed, February 01, 2023
In collaboration with the Advanced Energy Consortium, our team has previously demonstrated that t... more In collaboration with the Advanced Energy Consortium, our team has previously demonstrated that the placement of electrically active proppants (EAPs) in a hydraulic fracture surveyed by electromagnetic (EM) methods can enhance the imaging of the stimulated reservoir volumes during hydraulic fracturing. That work culminated in constructing a well-characterized EAP-filled fracture anomaly at the Devine field pilot site (DFPS). In subsequent laboratory studies, we observed that the electrical conductivity of our EAP correlates with changes in pressure, salinity, and flow. Thus, we postulated that the EAP could be used as an in-situ sensor for the remote monitoring of these changes in previously EAP-filled fractures. This paper presents our latest field data from the DFPS to demonstrate such correlations at an intermediate pilot scale.We conducted surface-based EM surveys during freshwater (200 ppm) and saltwater (2,500 ppm) slug injections while running surfaced-based EM surveys. Simul...
Pressure Transient Analyses and Poroelastic Modeling of Hydraulic Fracture Dilation for Multiple Injections at the Devine Fracture Pilot Site
Day 1 Tue, January 31, 2023
Our team has conducted electromagnetic (EM) surveys for the past six years to monitor hydraulic-f... more Our team has conducted electromagnetic (EM) surveys for the past six years to monitor hydraulic-fracture behavior at the Devine Fracture Pilot Site (DFPS). The sub-horizontal orientation of a shallow hydraulic fracture at the DFPS provides uniform access to the fracture area for interrogation and data collection. Ahmadian et al. (2023) suggested a possible correlation between spatiotemporal changes in the flow rate, bottomhole pressure (BHP), and the observed surface recorded electric field at the DFPS. In this paper, we present the development of poroelastic forward models and pressure transient analyses (PTAs) to support the development of a multiphysics inverse model for these EM surveys.First, we conducted PTAs of the shut-in periods after six injections out of 10 to determine the fracture closure pressure (FCP) or the overburden pressure used in a poroelastic fracture reopening model. Second, we developed a finite-element poroelastic model throughout five injection cycles to in...
Fiber Optic Distributed Acoustic Sensing in DFPS East Well
OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Dec 1, 2022
Geomechanical and Hydrogeological Evaluation of a Shallow Hydraulic Fracture at the Devine Fracture Pilot Site, Medina County, Texas
Rock Mechanics and Rock Engineering
Accelerating Hydraulic Fracture Imaging by Deep Transfer Learning
IEEE Transactions on Antennas and Propagation
Advanced Downhole Acoustic Sensing for Wellbore Integrity (Final Report)
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Papers by Mohsen Ahmadian