Modeling and fault tolerant control of an electro-hydraulic actuator

In the modern industry, electro-hydraulic actuators (EHAs) have been applied to various applications for precise position pressure/ force control tasks. However, operating EHAs under sensor faults is one of the critical challenges for the control engineers. For its enormous nonlinear characteristics, sensor fault could lead the catastrophic failure to the overall system or even put human life in danger. Thus in this paper, a study on mathematical modeling and fault tolerant control (FTC) of a typical EHA for tracking control under sensor-fault conditions has been carried out. In the proposed FTC system, the extended Kalman-Bucy unknown input observer (EKBUIO) -based robust sensor fault detection and identification (FDI) module estimates the system states and the time domain fault information. Once a fault is detected, the controller feedback is switched from the faulty sensor to the estimated output from the EKBUIO owing to mask the sensor fault swiftly and retains the system stability. Additionally, considering the tracking accuracy of the EHA system, an efficient brain emotional learning based intelligent controller (BELBIC) is suggested as the main control unit. Effectiveness of the proposed FTC architecture has been investigated by experimenting on a test bed using an EHA in sensor failure conditions

Translucency and the perception of shape

Previous studies have shown that the perceived threedimensional (3D) shape of objects depends on their material composition. The majority of this work has focused on glossy, flat-matte, or velvety materials. Here, we studied perceived 3D shape of translucent materials. We manipulated the spatial frequency of surface relief perturbations of translucent and opaque objects. Observers indicated which of two surfaces appeared to have more bumps. They also judged local surface orientation using gauge probe figures. We found that translucent surfaces appeared to have fewer bumps than opaque surfaces with the same 3D shape (Experiment 1), particularly when self-occluding contours were hidden from view (Experiment 2). We also found that perceived local curvature was underestimated for translucent objects relative to opaque objects, and that estimates of perceived local surface orientation were similarly correlated with luminance for images of both opaque and translucent objects (Experiment 3). These findings suggest that the perceived mesoscopic shape of completely matte translucent objects can be underestimated due to a decline in the steepness of luminance gradients relative to those of opaque objects

MasonTigers@LT-EDI-2024: An Ensemble Approach Towards Detecting Homophobia and Transphobia in Social Media Comments

In this paper, we describe our approaches and results for Task 2 of the LT-EDI 2024 Workshop, aimed at detecting homophobia and/or transphobia across ten languages. Our methodologies include monolingual transformers and ensemble methods, capitalizing on the strengths of each to enhance the performance of the models. The ensemble models worked well, placing our team, MasonTigers, in the top five for eight of the ten languages, as measured by the macro F1 score. Our work emphasizes the efficacy of ensemble methods in multilingual scenarios, addressing the complexities of language-specific tasks

MasonTigers at SemEval-2024 Task 9: Solving Puzzles with an Ensemble of Chain-of-Thoughts

Our paper presents team MasonTigers submission to the SemEval-2024 Task 9 - which provides a dataset of puzzles for testing natural language understanding. We employ large language models (LLMs) to solve this task through several prompting techniques. Zero-shot and few-shot prompting generate reasonably good results when tested with proprietary LLMs, compared to the open-source models. We obtain further improved results with chain-of-thought prompting, an iterative prompting method that breaks down the reasoning process step-by-step. We obtain our best results by utilizing an ensemble of chain-of-thought prompts, placing 2nd in the word puzzle subtask and 13th in the sentence puzzle subtask. The strong performance of prompted LLMs demonstrates their capability for complex reasoning when provided with a decomposition of the thought process. Our work sheds light on how step-wise explanatory prompts can unlock more of the knowledge encoded in the parameters of large models

Predictive Health Analysis in Industry 5.0: A Scientometric and Systematic Review of Motion Capture in Construction

In an era of rapid technological advancement, the rise of Industry 4.0 has prompted industries to pursue innovative improvements in their processes. As we advance towards Industry 5.0, which focuses more on collaboration between humans and intelligent systems, there is a growing requirement for better sensing technologies for healthcare and safety purposes. Consequently, Motion Capture (MoCap) systems have emerged as critical enablers in this technological evolution by providing unmatched precision and versatility in various workplaces, including construction. As the construction workplace requires physically demanding tasks, leading to work-related musculoskeletal disorders (WMSDs) and health issues, the study explores the increasing relevance of MoCap systems within the concept of Industry 4.0 and 5.0. Despite the growing significance, there needs to be more comprehensive research, a scientometric review that quantitatively assesses the role of MoCap systems in construction. Our study combines bibliometric, scientometric, and systematic review approaches to address this gap, analyzing articles sourced from the Scopus database. A total of 52 papers were carefully selected from a pool of 962 papers for a quantitative study using a scientometric approach and a qualitative, indepth examination. Results showed that MoCap systems are employed to improve worker health and safety and reduce occupational hazards.The in-depth study also finds the most tested construction tasks are masonry, lifting, training, and climbing, with a clear preference for markerless systems

MasonPerplexity at ClimateActivism 2024: Integrating Advanced Ensemble Techniques and Data Augmentation for Climate Activism Stance and Hate Event Identification

The task of identifying public opinions on social media, particularly regarding climate activism and the detection of hate events, has emerged as a critical area of research in our rapidly changing world. With a growing number of people voicing either to support or oppose to climate-related issues - understanding these diverse viewpoints has become increasingly vital. Our team, MasonPerplexity, participates in a significant research initiative focused on this subject. We extensively test various models and methods, discovering that our most effective results are achieved through ensemble modeling, enhanced by data augmentation techniques like back-translation. In the specific components of this research task, our team achieved notable positions, ranking 5th, 1st, and 6th in the respective sub-tasks, thereby illustrating the effectiveness of our approach in this important field of study

MasonTigers at SemEval-2024 Task 8: Performance Analysis of Transformer-based Models on Machine-Generated Text Detection

This paper presents the MasonTigers entry to the SemEval-2024 Task 8 - Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection. The task encompasses Binary Human-Written vs. Machine-Generated Text Classification (Track A), Multi-Way Machine-Generated Text Classification (Track B), and Human-Machine Mixed Text Detection (Track C). Our best performing approaches utilize mainly the ensemble of discriminator transformer models along with sentence transformer and statistical machine learning approaches in specific cases. Moreover, zero-shot prompting and fine-tuning of FLAN-T5 are used for Track A and B

MasonTigers at SemEval-2024 Task 1: An Ensemble Approach for Semantic Textual Relatedness

This paper presents the MasonTigers entry to the SemEval-2024 Task 1 - Semantic Textual Relatedness. The task encompasses supervised (Track A), unsupervised (Track B), and cross-lingual (Track C) approaches across 14 different languages. MasonTigers stands out as one of the two teams who participated in all languages across the three tracks. Our approaches achieved rankings ranging from 11th to 21st in Track A, from 1st to 8th in Track B, and from 5th to 12th in Track C. Adhering to the task-specific constraints, our best performing approaches utilize ensemble of statistical machine learning approaches combined with language-specific BERT based models and sentence transformers

Feasibility and safety of combining repetitive transcranial magnetic stimulation and quadriceps strengthening exercise for chronic pain in knee osteoarthritis: A study protocol for a pilot randomised controlled trial

Introduction Knee osteoarthritis is a leading cause of disability, resulting in pain and reduced quality of life. Exercise is the cornerstone of conservative management but effects are, at best, moderate. Early evidence suggests that repetitive transcranial magnetic stimulation (rTMS) applied over the primary motor cortex (M1) may improve the effect of exercise in knee osteoarthritis. This pilot study aims to (1) determine the feasibility, safety and participant-rated response to an intervention adding M1 rTMS to exercise in knee osteoarthritis; (2) elucidate physiological mechanisms in response to the intervention; (3) provide data to conduct a sample size calculation for a fully powered trial. Methods and analysis This is a pilot randomised, assessor-blind, therapist-blind and participant-blind, sham-controlled trial. Thirty individuals with painful knee osteoarthritis will be recruited and randomly allocated to receive either: (1) active rTMS+exercise or (2) sham rTMS+exercise intervention. Participants will receive 15 min of either active or sham rTMS immediately prior to 30 min of supervised muscle strengthening exercise (2×/week, 6 weeks) and complete unsupervised home exercises. Outcome measures of feasibility, safety, pain, function and physiological mechanisms will be assessed before and/or after the intervention. Feasibility and safety will be analysed using descriptive analysis. Within-group and between-group comparisons of pain and function will be conducted to examine trends of efficacy. Ethics and dissemination This study has been approved by the University of New South Wales Human Research Ethics Committee (HC210954). All participants will provide written informed consent. The study results will be submitted for peer-reviewed publication. Trial registration number ACTRN12621001712897p

A novel cortical biomarker signature for predicting pain sensitivity : protocol for the PREDICT longitudinal analytical validation study

Introduction: Temporomandibular disorder is a common musculoskeletal pain condition with development of chronic symptoms in 49% of patients. Although a number of biological factors have shown an association with chronic temporomandibular disorder in cross-sectional and case control studies, there are currently no biomarkers that can predict the development of chronic symptoms. The PREDICT study aims to undertake analytical validation of a novel peak alpha frequency (PAF) and corticomotor excitability (CME) biomarker signature using a human model of the transition to sustained myofascial temporomandibular pain (masseter intramuscular injection of nerve growth factor [NGF]). This article describes, a priori, the methods and analysis plan. Methods: This study uses a multisite longitudinal, experimental study to follow individuals for a period of 30 days as they progressively develop and experience complete resolution of NGF-induced muscle pain. One hundred fifty healthy participants will be recruited. Participants will complete twice daily electronic pain diaries from day 0 to day 30 and undergo assessment of pressure pain thresholds, and recording of PAF and CME on days 0, 2, and 5. Intramuscular injection of NGF will be given into the right masseter muscle on days 0 and 2. The primary outcome is pain sensitivity. Perspective: PREDICT is the first study to undertake analytical validation of a PAF and CME biomarker signature. The study will determine the sensitivity, specificity, and accuracy of the biomarker signature to predict an individual's sensitivity to pain