329 research outputs found

    Remote robot manipulator coupled with remote-controlled guide vehicle for soil sampling in hazardous waste sites

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    The important initial step for remediation of hazardous waste is contaminant analysis since the cleanup operation can not begin until the contaminants in hazardous waste sites have been clearly identified. Ames Laboratory, one of the U.S. Department of Energy sites, has developed a robotic sampling system for automation of real-time contaminant analysis in situ which will provide the advantage of lowering the cost per sample, eliminating personnel exposure to hazardous environments, and allowing quicker results. Successful accomplishment of real-time contaminant analysis will require a remote manipulator to perform the sampling tasks in remote and unstructured surroundings, and a remote-controlled guide vehicle to move a remote manipulator into the desired sampling location;This thesis focuses on the design and construction of a remote-controlled guide vehicle to move the robotic sampling system into the contaminated field to obtain soil samples at the desired locations, the development of an integrated dynamic model of a remote manipulator, the identification of dynamic parameters in the integrated dynamic model, and the design of a mobile robotic sampling system. A four-wheeled vehicle prototype has been constructed and its performance tested manually in the field to verify the design requirements. To remotely control the vehicle, mechanical requirements to activate the brake, throttle, transmission, and steering linkages were determined based on experimental results. A teleoperated control utilizing hundred feet long umbilical cords was first employed to remotely control the vehicle. Next, the vehicle was modified to remotely operate in the field by radio control without the aid of long umbilical cords, satisfying all the design specifications;To reduce modeling error in the robotic system, the integrated dynamic system comprised of a remote manipulator (located on a trailer pulled by the remote-controlled guide vehicle) and its drive system has been modeled. The friction model as a function of velocity is included. The dynamic parameters such as velocity-dependent friction and gravity torque in the integrated dynamic model have been determined based on experimental results;Finally, a robotic arm, a sampling tool, and a soil recovery fixture for a mobile robotic sampling system to be mounted on the remote-controlled guide vehicle have been designed and analyzed. The integrated dynamic model for the robotic arm (mounted on the remote-controlled guide vehicle) and its drive system has also been developed

    Faculty Perceptions of the Spring 2020 Transition from Face-to-Face to Online Instruction: A Case Study of American University with Takeaways and Lessons Learned

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    In response to the COVID-19 pandemic, American University (AU) transitioned all of its instruction online. Here, we report on the efforts undertaken to facilitate the transition and faculty perceptions of those actions and online teaching. In preparation for the transition, American University formed the Instructional Continuity (IC) team, comprised of the Center for Teaching, Research & Learning and the Academic Technology office. The IC team was charged with developing and implementing a responsive and comprehensive training and support schedule that began on March 16, 2020. A survey of faculty toward the end of the semester revealed general satisfaction with the support they received in transitioning to online instruction and with student learning outcome attainment. Faculty who had taught online before were more likely to show self-efficacy in online instruction compared to those who had not taught online before, despite similar, high satisfaction with student learning outcomes. We offer insights on key aspects of our efforts and the institutional structure that undergirded the largely successful transition of AU’s faculty to online instruction

    Fresnel-type Solid Immersion Lens for efficient light collection from quantum defects in diamond

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    Quantum defects in diamonds have been studied as a promising resource for quantum science. The subtractive fabrication process for improving photon collection efficiency often require excessive milling time that can adversely affect the fabrication accuracy. We designed and fabricated a Fresnel-type solid immersion lens using the focused ion beam. For a 5.8 um-deep Nitrogen-vacancy (NV-) center, the milling time was highly reduced (1/3 compared to a hemispherical structure), while retaining high photon collection efficiency (> 2.24 compared to a flat surface). In numerical simulation, this benefit of the proposed structure is expected for a wide range of milling depths.Comment: 16 pages, 9 figure

    Neutron XS Library Generation of ENDF/B-VIII.0 for MOC code STREAM and Monte Carlo code MCS

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    Department of Nuclear EngineeringIn this paper, the verification and validation of ENDF/B-VIII.0 XS data uses STREAM code and MCS code. ENDF/B-VIII.0 XS data compare with ENDF/B-VII.1 XS data. In the case of a library of unique nuclear data for STREAM, the neutron transport analysis code, ENDF files for each nuclide were produced in Group-wise XS using NJOY code system, and in the second step, STREAM XS Library was produced using STREAM library production system, NTOS. Using this multi-group nuclear data production system, STREAM XS library was produced for all nuclides based on ENDF/B-VII.1 nuclear data library and ENDF/B-VIII.0 nuclear data library. To assess the accuracy of the library, STREAM code and MCS code were simultaneously compared for each ENDF version. For the validation, each of benchmarks are used, NCA benchmark, VERA benchmark and ICSBEP benchmark. NCA benchmark analysis, which is the critical experiments performed at the Toshiba Nuclear Critical Assembly (NCA) critical facility. Each benchmark was compared in STREAM and MCS, and NCA benchmark analysis include tungsten gray rods demonstrates the accuracy of STREAM code???s pin power distribution. STREAM code nuclear source used the both of ENDF/B-VIII.0 and ENDF/B-VII.1, and their results compared with each of MCS code results. When STREAM's results were compared for each version of ENDF, NCA benchmark found that the difference of the effective multiplication factor was within 100 pcm for the problem, confirming that ENDF/B-VIII.0 STREAM XS Library was properly produced. Also in case of VERA benchmark and ICSBEP benchmark, the difference of the effective multiplication factor was within 300 pcm for the problem with ENDF/B-VII.1 XS Library results and ENDF/B-VIII.0 XS Library results. The accuracy of STREAM results was verified by comparing them with the MCS results of the same problem for each result.ope

    Smooth Model Predictive Path Integral Control without Smoothing

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    We present a sampling-based control approach that can generate smooth actions for general nonlinear systems without external smoothing algorithms. Model Predictive Path Integral (MPPI) control has been utilized in numerous robotic applications due to its appealing characteristics to solve non-convex optimization problems. However, the stochastic nature of sampling-based methods can cause significant chattering in the resulting commands. Chattering becomes more prominent in cases where the environment changes rapidly, possibly even causing the MPPI to diverge. To address this issue, we propose a method that seamlessly combines MPPI with an input-lifting strategy. In addition, we introduce a new action cost to smooth control sequence during trajectory rollouts while preserving the information theoretic interpretation of MPPI, which was derived from non-affine dynamics. We validate our method in two nonlinear control tasks with neural network dynamics: a pendulum swing-up task and a challenging autonomous driving task. The experimental results demonstrate that our method outperforms the MPPI baselines with additionally applied smoothing algorithms.Comment: Accepted to IEEE Robotics and Automation Letters (and IROS 2022). Our video can be found at https://youtu.be/ibIks6ExGw

    Evaluating the Effectiveness and Robustness of Visual Similarity-based Phishing Detection Models

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    Phishing attacks pose a significant threat to Internet users, with cybercriminals elaborately replicating the visual appearance of legitimate websites to deceive victims. Visual similarity-based detection systems have emerged as an effective countermeasure, but their effectiveness and robustness in real-world scenarios have been unexplored. In this paper, we comprehensively scrutinize and evaluate state-of-the-art visual similarity-based anti-phishing models using a large-scale dataset of 450K real-world phishing websites. Our analysis reveals that while certain models maintain high accuracy, others exhibit notably lower performance than results on curated datasets, highlighting the importance of real-world evaluation. In addition, we observe the real-world tactic of manipulating visual components that phishing attackers employ to circumvent the detection systems. To assess the resilience of existing models against adversarial attacks and robustness, we apply visible and perturbation-based manipulations to website logos, which adversaries typically target. We then evaluate the models' robustness in handling these adversarial samples. Our findings reveal vulnerabilities in several models, emphasizing the need for more robust visual similarity techniques capable of withstanding sophisticated evasion attempts. We provide actionable insights for enhancing the security of phishing defense systems, encouraging proactive actions. To the best of our knowledge, this work represents the first large-scale, systematic evaluation of visual similarity-based models for phishing detection in real-world settings, necessitating the development of more effective and robust defenses.Comment: 12 page

    Attentional Avoidance for Guilty Knowledge Among Deceptive Individuals

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    The purpose of the present study is to differentiate between innocent suspects who have knowledge of crime information and guilty suspects. The study investigated eye-movement differences among three groups: a guilty group who took part in a mock crime, an innocent-aware group who did not commit a mock crime but were exposed to the crime stimuli, and an innocent-unaware group who neither committed a mock crime nor had crime-relevant information. Each group's eye movements were tracked while all participants viewed stimuli (crime-relevant, crime-irrelevant, and neutral). The results revealed that the guilty group not only viewed all stimuli later than the other groups, they also viewed crime-relevant and crime-irrelevant stimuli for a shorter time period than the innocent-aware group; the innocent-aware group focused their attention on crime-relevant and crime-irrelevant stimuli longer than neutral stimuli, and the innocent-unaware group showed no differences in their attention focus among all types of stimuli. This present study suggests that guilty individuals show attentional avoidance from all stimuli in a lie detection situation, whereas innocent-aware and innocent-unaware individuals did not show avoidance responses
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