10,126 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    An innovative EEG-based emotion recognition using a single channel-specific feature from the brain rhythm code method

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    IntroductionEfficiently recognizing emotions is a critical pursuit in brain–computer interface (BCI), as it has many applications for intelligent healthcare services. In this work, an innovative approach inspired by the genetic code in bioinformatics, which utilizes brain rhythm code features consisting of δ, θ, α, β, or γ, is proposed for electroencephalography (EEG)-based emotion recognition.MethodsThese features are first extracted from the sequencing technique. After evaluating them using four conventional machine learning classifiers, an optimal channel-specific feature that produces the highest accuracy in each emotional case is identified, so emotion recognition through minimal data is realized. By doing so, the complexity of emotion recognition can be significantly reduced, making it more achievable for practical hardware setups.ResultsThe best classification accuracies achieved for the DEAP and MAHNOB datasets range from 83–92%, and for the SEED dataset, it is 78%. The experimental results are impressive, considering the minimal data employed. Further investigation of the optimal features shows that their representative channels are primarily on the frontal region, and associated rhythmic characteristics are typical of multiple kinds. Additionally, individual differences are found, as the optimal feature varies with subjects.DiscussionCompared to previous studies, this work provides insights into designing portable devices, as only one electrode is appropriate to generate satisfactory performances. Consequently, it would advance the understanding of brain rhythms, which offers an innovative solution for classifying EEG signals in diverse BCI applications, including emotion recognition

    Technology for Low Resolution Space Based RSO Detection and Characterisation

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    Space Situational Awareness (SSA) refers to all activities to detect, identify and track objects in Earth orbit. SSA is critical to all current and future space activities and protect space assets by providing access control, conjunction warnings, and monitoring status of active satellites. Currently SSA methods and infrastructure are not sufficient to account for the proliferations of space debris. In response to the need for better SSA there has been many different areas of research looking to improve SSA most of the requiring dedicated ground or space-based infrastructure. In this thesis, a novel approach for the characterisation of RSO’s (Resident Space Objects) from passive low-resolution space-based sensors is presented with all the background work performed to enable this novel method. Low resolution space-based sensors are common on current satellites, with many of these sensors being in space using them passively to detect RSO’s can greatly augment SSA with out expensive infrastructure or long lead times. One of the largest hurtles to overcome with research in the area has to do with the lack of publicly available labelled data to test and confirm results with. To overcome this hurtle a simulation software, ORBITALS, was created. To verify and validate the ORBITALS simulator it was compared with the Fast Auroral Imager images, which is one of the only publicly available low-resolution space-based images found with auxiliary data. During the development of the ORBITALS simulator it was found that the generation of these simulated images are computationally intensive when propagating the entire space catalog. To overcome this an upgrade of the currently used propagation method, Specialised General Perturbation Method 4th order (SGP4), was performed to allow the algorithm to run in parallel reducing the computational time required to propagate entire catalogs of RSO’s. From the results it was found that the standard facet model with a particle swarm optimisation performed the best estimating an RSO’s attitude with a 0.66 degree RMSE accuracy across a sequence, and ~1% MAPE accuracy for the optical properties. This accomplished this thesis goal of demonstrating the feasibility of low-resolution passive RSO characterisation from space-based platforms in a simulated environment

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Reinforcement learning in large state action spaces

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    Reinforcement learning (RL) is a promising framework for training intelligent agents which learn to optimize long term utility by directly interacting with the environment. Creating RL methods which scale to large state-action spaces is a critical problem towards ensuring real world deployment of RL systems. However, several challenges limit the applicability of RL to large scale settings. These include difficulties with exploration, low sample efficiency, computational intractability, task constraints like decentralization and lack of guarantees about important properties like performance, generalization and robustness in potentially unseen scenarios. This thesis is motivated towards bridging the aforementioned gap. We propose several principled algorithms and frameworks for studying and addressing the above challenges RL. The proposed methods cover a wide range of RL settings (single and multi-agent systems (MAS) with all the variations in the latter, prediction and control, model-based and model-free methods, value-based and policy-based methods). In this work we propose the first results on several different problems: e.g. tensorization of the Bellman equation which allows exponential sample efficiency gains (Chapter 4), provable suboptimality arising from structural constraints in MAS(Chapter 3), combinatorial generalization results in cooperative MAS(Chapter 5), generalization results on observation shifts(Chapter 7), learning deterministic policies in a probabilistic RL framework(Chapter 6). Our algorithms exhibit provably enhanced performance and sample efficiency along with better scalability. Additionally, we also shed light on generalization aspects of the agents under different frameworks. These properties have been been driven by the use of several advanced tools (e.g. statistical machine learning, state abstraction, variational inference, tensor theory). In summary, the contributions in this thesis significantly advance progress towards making RL agents ready for large scale, real world applications

    An American Knightmare: Joker, Fandom, and Malicious Movie Meaning-Making

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    This monograph concerns the long-standing communication problem of how individuals can identify and resist the influence of unethical public speakers. Scholarship on the issue of what Socrates & Plato called the “Evil Lover” – i.e., the ill-intended rhetor – began with the Greek philosophers, but has carried into [post]Modern anxieties. For instance, the study of Nazi propaganda machines, and the rhetoric of Hitler himself, rejuvenated interest in the study of speech and communication in the U.S. and Europe. Whereas unscrupulous sophists used lectures and legal forums, and Hitler used a microphone, contemporary Evil Lovers primarily draw on new, internet-related tools to share their malicious influence. These new tools of influence are both more far-reaching and more subtle than the traditional practices of listening to a designated speaker appearing at an overtly political event. Rhetorician Ashley Hinck has recently noted the ways that popular culture – communication about texts which are commonly accessible and shared – are now significant sites through which citizens learn moral and political values. Accordingly, the talk of internet influencers who interpret popular texts for other fans has the potential to constitute strong persuasive power regarding ethics and civic responsibility. The present work identifies and responds to a particular case example of popular culture text that has been recently, and frequently, leveraged in moral and civic discourses: Todd Phillips’ Joker. Specifically, this study takes a hermeneutic approach to understanding responses, especially those explicitly invoking political ideology, to Joker as a method of examining civic meaning-making. A special emphasis is placed on the online film criticisms of Joker from white nationalist movie fans, who clearly exemplify ways that media responses can be leveraged by unethical speakers (i.e., Evil Lovers) and subtly diffused. The study conveys that these racist movie fans can embed values related to “trolling,” incelism, and xenophobia into otherwise seemingly innocuous talk about film. While the sharing of such speech does not immediately mean its positive reception, this kind of communication yet constitutes a new and understudied attack on democratic values such as justice and equity. The case of white nationalist movie fan film criticism therefore reflects a particular brand of communicative strategy for contemporary Evil Lovers in communicating unethical messages under the covert guise of mundane movie talk

    The Effects of Salt Precipitation During CO2 Injection into Deep Saline Aquifer and Remediation Techniques

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    The by-products of combustion from the utilisation of fossil fuels for energy generation are a source of greenhouse gas emissions, mainly Carbon dioxide (CO2). This has been attributed to climate change because of global warming. Carbon capture and storage (CCS) technology has the potential to reduce anthropogenic greenhouse gas emissions by capturing CO2 from emissions sources and stored in underground formations such as depleted oil and gas reservoirs or deep saline formations. Deep saline aquifers for disposal of greenhouse gases are attracting much attention as a result of their large storage capacity. The problem encountered during CO2 trapping in the saline aquifer is the vaporisation of water along with the dissolution of CO2. This vaporisation cause salt precipitation which eventually reduces porosity and impairs the permeability of the reservoir thereby impeding the storage capacity and efficiency of the technology. Salt precipitation during CO2 storage in deep saline aquifers can have severe consequences during carbon capture and storage operations in terms of CO2 injectivity.This work investigates and assesses, experimentally, the effects of the presence of salt precipitation on the CO2 injectivity, the factors that influence them on selected core samples by core flooding experiments, and remediation of salt precipitation during CO2 injection. The investigation also covered the determination of optimum range of deep saline aquifers for CO2 storage, and the effects of different brine-saturated sandstones during CO2 sequestration in deep saline aquifers. In this investigation, three (3) different sandstone core samples (Bentheimer, Salt Wash North, and Grey Berea) with different petrophysical properties were used for the study. This is carried out in three different phases for a good presentation.• Phase I of this study involved brine preparation and measurement of brine properties such as brine salinity, viscosity, and density. The brine solutions were prepared from different salts (NaCl, CaCl2, KCl, MgCl2), which represent the salt composition of a typical deep saline aquifer. The core samples were saturated with different brine salinities (5, 10, 15, 20, 25, wt.% Salt) and testing was conducted using the three selected core samples.• Phase II entailed the cleaning and characterisation of the core samples by experimental core analyses to determine the petrophysical properties: porosity and permeability. Helium Porosimetry and saturation methods were used for porosity determination. Core flooding was used to determine the permeability of the core samples. The core flooding process was conducted at a simulated reservoir pressure of 1500 psig, the temperature of 45 °C, with injection rates of 3.0 ml/min respectively. Interfacial tension (IFT) measurements between the CO2 and various brine salinities as used in the core flooding were also conducted in this phase. Remediation scenarios of opening the pore spaces of the core samples were carried out using the same core flooding rig and the precipitated core samples were flooded with remediation fluids (low salinity brine and seawater) under the same reservoir conditions. The petrophysical properties (Porosity, Permeability) of the core samples were measured before core flooding, after core flooding and remediation test respectively.• In phase III of the study, SEM Image analyses were conducted on the core samples before core flooding, after core flooding, and remediation test respectively. This was achieved by using the FEI Quanta FEG 250 FEG high-resolution Scanning Electron Microscope (SEM) interfaced to EDAX Energy Dispersive X-ray Analysis (EDX).xivResults from Bentheimer, Salt Wash North, and Grey Berea core samples indicated a reduction in porosity, permeability impairment, as well as salt precipitation. It was also found that, at 10 to 20 wt.% brine concentrations in both monovalent and divalent brine, a substantial volume of CO2 is sequestered, which indicates the optimum concentration ranges for storage purposes. The salting-out effect was greater in divalent salt, MgCl2 and CaCl2 as compared to monovalent salt (NaCl and KCl). Porosity decreased by 0.5% to 7% while permeability was decreased by up to 50% in all the tested scenarios. CO2 solubility was evaluated in a pressure decay test, which in turn affects injectivity. Hence, the magnitude of CO2 injectivity impairment depends on both the concentration and type of salt species. The findings from this study are directly relevant to CO2 sequestration in deep saline aquifers as well as screening criteria for carbon storage with enhanced gas and oil recovery processes. Injection of remediation fluids during remediation tests effectively opened the pore spaces and pore throats of the core samples and thereby increasing the core sample's porosity in the range of 14.0% to 28.5% and 2.2% to 12.9% after using low salinity brine and seawater remediation fluids respectively. Permeability also increases in the range of 40.6% to 68.4% and 7.4% to 17.2% after using low salinity brine and seawater remediation fluids respectively. These findings provide remediation strategies useful in dissolving precipitated salt as well as decreasing the salinity of the near-well brine which causes precipitation.The SEM images of the core samples after the flooding showed that salt precipitation not only plugged the pore spaces of the core matrix but also showed significant precipitation around the rock grains thereby showing an aggregation of the salts. This clearly proved that the reduction in the capacity of the rock is associated with salt precipitation in the pore spaces as well as the pore throats. Thus, insight gained in this study could be useful in designing a better mitigation technique, CO2 injectivity scenarios, as well as an operating condition for CO2 sequestration in deep saline aquifers

    Key technologies for safe and autonomous drones

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    Drones/UAVs are able to perform air operations that are very difficult to be performed by manned aircrafts. In addition, drones' usage brings significant economic savings and environmental benefits, while reducing risks to human life. In this paper, we present key technologies that enable development of drone systems. The technologies are identified based on the usages of drones (driven by COMP4DRONES project use cases). These technologies are grouped into four categories: U-space capabilities, system functions, payloads, and tools. Also, we present the contributions of the COMP4DRONES project to improve existing technologies. These contributions aim to ease drones’ customization, and enable their safe operation.This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 826610. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Austria, Belgium, Czech Republic, France, Italy, Latvia, Netherlands. The total project budget is 28,590,748.75 EUR (excluding ESIF partners), while the requested grant is 7,983,731.61 EUR to ECSEL JU, and 8,874,523.84 EUR of National and ESIF Funding. The project has been started on 1st October 2019

    Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions

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    In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request

    A fuzzy set theory-based fast fault diagnosis approach for rotators of induction motors

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    Induction motors have been widely used in industry, agriculture, transportation, national defense engineering, etc. Defects of the motors will not only cause the abnormal operation of production equipment but also cause the motor to run in a state of low energy efficiency before evolving into a fault shutdown. The former may lead to the suspension of the production process, while the latter may lead to additional energy loss. This paper studies a fuzzy rule-based expert system for this purpose and focuses on the analysis of many knowledge representation methods and reasoning techniques. The rotator fault of induction motors is analyzed and diagnosed by using this knowledge, and the diagnosis result is displayed. The simulation model can effectively simulate the broken rotator fault by changing the resistance value of the equivalent rotor winding. And the influence of the broken rotor bar fault on the motors is described, which provides a basis for the fault characteristics analysis. The simulation results show that the proposed method can realize fast fault diagnosis for rotators of induction motors
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