Dynamic behavior of Sandwich Beam with Piezoelectric layers

Sandwich beams with composite faces sheets and foam core are widely used as lightweight components in many of the industries such as automotive, marine and aerospace applications due to its high bending stiffness and strength combined with low weight. Thus, it is important to gain knowledge of their flexural behavior under static as well as dynamic loads. Although extensive research has been devoted to the flexural behavior of composite laminates in general, the flexural behavior of sandwich structures is quite and obviously different. Several works treating the dynamic flexural behavior of sandwich beams have also confirmed the marked susceptibility of sandwich structures to damage caused by the low velocity impact of foreign objects. Impacts can damage the face sheets, the core material, and the core face interface. The type of damage usually found in the faces is similar to that observed after impacts on monolithic composites. However, the damage initiation thresholds and damage area depend on the properties of the core material and the relationship between the properties of the core and those of the face sheets.The modelling is done for sandwich beam with create volume option with dimensions known in the software

Initial Relative-Orbit Determination Using Second-Order Dynamics and Line-of-Sight Measurements

This thesis addresses the problem of determining the initial relative-orbit state between a chief and a deputy satellite using line-of-sight unit vectors. An analytical solution is investigated for estimating the deputy satellite’s initial states relative to the chief satellite, assuming circular chief orbit. The line-of-sight measurements and the relative motion of the deputy satellite, captured with a closed-form second-order solution of relative motion, leads to the nonlinear measurements equation. The measurement equations are transformed using the new proposed formulation which solves directly for the unknown ranges. The new formulation is applied to two solution procedures to solve the relative-orbit determination problem. Within the first solution method, the new formulation is computationally faster and requires fewer measurements, than the previous formulation. The second solution method requires the minimal number of measurements, but the new formulation provides reduced algebraic complexity in comparison to the previously published formulation

Design and Validation of a Course Controller for a Wave Powered Vehicle Using NMPC

Fremveksten av en ny klasse kjøretøyer med grønn energi har introdusert muligheten til å gjennomføre storskala undersøkelser av rom og tid uten behov for energikrevende motordrevet fremdrift. Disse kjøretøyene kan ha utholdenhet som varer i måneder, takket være deres avhengighet av miljøkrefter for fremdrift. Imidlertid gjør denne avhengigheten av eksterne krefter dem utsatt for tap av kontrollerbarhet, spesielt under ugunstige værforhold. Som en konsekvens er det behov for økt robusthet i autonomisystemet ombord for å sikre sikker og effektiv drift av disse kjøretøyene til sjøs. Vi foreslår bruk av et Nonlinear Model Predictive Control basert kurskontrollsystem for å sikre stabil kurs over hele kjøretøyets driftsområde. Vi utledet tre systemmodeller med varierende grad av troskap sammen med to objektive funksjoner chid og dotv for å beregne den optimale kontrollinngangen for kjøretøyet. De utviklede kontrollerene ble testet i simulering ved bruk av både Model-In-the-Loop og Hardware-In-the-Loop metoden. Funnene fra studien demonstrerte ferdigheter i å opprettholde kursnøyaktighet. Kontrolleren oppnådde null steady state-feil under ideelle forhold og var robust mot både støy og forstyrrelser. Under ugunstige forhold, når Speed Over Ground nærmet seg null, bestemte kontrolleren den optimale banen som tillot stabil kurs og minimumssvingninger. Sanntidsytelsen ble validert ved å implementere kontrolleren på en innebygd plattform ved å bruke IPOPT-løseren. Løserberegningstiden varierte, men holdt seg konsekvent innenfor den spesifiserte terskelen, noe som demonstrerer muligheten for online operasjoner.The emergence of a new class of green-energy-powered vehicles has introduced the capability to conduct large-scale spatiotemporal surveys without the need for energy-intensive motored propulsion. These vehicles can have endurance lasting months, thanks to their reliance on environmental forces for propulsion. However, this reliance on external forces makes them susceptible to loss of controllability, particularly in adverse weather conditions. As a consequence, there is a need for increased robustness in the onboard autonomy system to ensure the safe and effective operation of these vehicles at sea. We propose using an Nonlinear Model Predictive Control based course control system to ensure a stable course over the entire operating region of the vehicle. We derived three system models with varying levels of fidelity along with two objective functions chid and dotv to compute the optimal control input for the vehicle. The developed controllers were tested in simulation using both Model-In-the-Loop and Hardware-In-the-Loop methods. The findings of the study demonstrated proficiency in maintaining course accuracy. The controller achieved a zero steady-state error under ideal conditions and was robust to noise and disturbances. In extreme situations, as the Speed Over Ground approached zero, the controller determined the optimal trajectory allowing for a stable course and minimum oscillations. The real-time performance was validated by integrating the controller with the onboard autonomy stack on an embedded platform using the IPOPT solver. The solver computation time varied, but consistently remained within the specified threshold, demonstrating the feasibility of online operations

Barrett’s Esophagus: An update

Barrett’s esophagus is premalignant condition in which the stratified squamous epithelium is replaced by metaplastic intestinal epithelium. The cause is usually long-standing gastro-esophageal reflux. Infection with Helicobacter pylori is also believed to play a role in this. The most significant complication is development of dysplasia with an increase in relative risk for development of adenocarcinoma 40–120 times

Reimagining education

Gramin Shiksha Kendra works in villages on the periphery of the Ranthambhore National Park in Sawai Madhopur and the Khandar blocks of the Sawai Madhopur district. The total population of the district is around 14.5 lakhs, having a sex ratio of 897* females per 1000 males. Around 80 percent of the district’s population lives in rural areas. The female and male literacy rates (7+ years) in rural Sawai Madhopur are 42.40 percent and 79.40 percent, respectively. In 2006, the district was declared backward by the Ministry of Panchayati Raj. Sawai Madhopur is largely an agriculture-based economy. The Gurjars (traditionally pastoralists) and the Meenas (a Scheduled Tribe but now mainly involved in agriculture) are the two majority communities here. There is a small but significant population of other caste groups - Malis, Bairwas, Harijans, Bhopas, Jaggas, and some de-notified tribal groups - Gadiya Lauhars, Moghiyas, Bawariyas, Kanjars, to name a few. Tourism is another sector in which the rural population is engaged in, as cleaners, cooks, or tourist guides. Some of them are also running their own dhabas (roadside food-stalls)

Code-Switched Text Synthesis in Unseen Language Pairs

Existing efforts on text synthesis for code-switching mostly require training on code-switched texts in the target language pairs, limiting the deployment of the models to cases lacking code-switched data. In this work, we study the problem of synthesizing code-switched texts for language pairs absent from the training data. We introduce GLOSS, a model built on top of a pre-trained multilingual machine translation model (PMMTM) with an additional code-switching module. This module, either an adapter or extra prefixes, learns code-switching patterns from code-switched data during training, while the primary component of GLOSS, i.e., the PMMTM, is frozen. The design of only adjusting the code-switching module prevents our model from overfitting to the constrained training data for code-switching. Hence, GLOSS exhibits the ability to generalize and synthesize code-switched texts across a broader spectrum of language pairs. Additionally, we develop a self-training algorithm on target language pairs further to enhance the reliability of GLOSS. Automatic evaluations on four language pairs show that GLOSS achieves at least 55% relative BLEU and METEOR scores improvements compared to strong baselines. Human evaluations on two language pairs further validate the success of GLOSS.Comment: Paper accepted by ACL2023 as a Finding pape

Emotion Recognition in Speech by Multimodal Analysis of Audio and Text

Emotion recognition remains a very challenging task in research because of its sensitive and multifaceted nature. Recently, emotion recognition has garnered a lot of attention owing to its significance in psychology, human-computer interaction, and healthcare, where people's facial expressions, voice qualities, and spoken words are used to better understand it. While emotion recognition holds the power to facilitate various health problems, the main challenge emotion recognition systems face is to accurately identify hidden nuances in expressions and thus, the underlying emotions conveyed by them. The true emotions of a person may remain concealed or not properly identified when only one mode of input is analyzed, therefore, multimodal streams of inputs are used to provide a more holistic view of a person's emotions. In this paper, a novel framework that fuses the results of two uni-modal methods of emotion recognition, audio, and text, to develop a robust and versatile emotion recognition system is proposed. The results show that signal processing and language processing can be utilized to reliably detect emotion from audio and text, with an accuracy of 96% and 94.1% respectively. Further, the approach presented in this paper can be used as a depression detection and monitoring tool to further enable mental healthcare professionals accurately detect symptoms of depression

Outage-Watch: Early Prediction of Outages using Extreme Event Regularizer

Cloud services are omnipresent and critical cloud service failure is a fact of life. In order to retain customers and prevent revenue loss, it is important to provide high reliability guarantees for these services. One way to do this is by predicting outages in advance, which can help in reducing the severity as well as time to recovery. It is difficult to forecast critical failures due to the rarity of these events. Moreover, critical failures are ill-defined in terms of observable data. Our proposed method, Outage-Watch, defines critical service outages as deteriorations in the Quality of Service (QoS) captured by a set of metrics. Outage-Watch detects such outages in advance by using current system state to predict whether the QoS metrics will cross a threshold and initiate an extreme event. A mixture of Gaussian is used to model the distribution of the QoS metrics for flexibility and an extreme event regularizer helps in improving learning in tail of the distribution. An outage is predicted if the probability of any one of the QoS metrics crossing threshold changes significantly. Our evaluation on a real-world SaaS company dataset shows that Outage-Watch significantly outperforms traditional methods with an average AUC of 0.98. Additionally, Outage-Watch detects all the outages exhibiting a change in service metrics and reduces the Mean Time To Detection (MTTD) of outages by up to 88% when deployed in an enterprise cloud-service system, demonstrating efficacy of our proposed method.Comment: Accepted to ESEC/FSE 202

ESRO: Experience Assisted Service Reliability against Outages

Modern cloud services are prone to failures due to their complex architecture, making diagnosis a critical process. Site Reliability Engineers (SREs) spend hours leveraging multiple sources of data, including the alerts, error logs, and domain expertise through past experiences to locate the root cause(s). These experiences are documented as natural language text in outage reports for previous outages. However, utilizing the raw yet rich semi-structured information in the reports systematically is time-consuming. Structured information, on the other hand, such as alerts that are often used during fault diagnosis, is voluminous and requires expert knowledge to discern. Several strategies have been proposed to use each source of data separately for root cause analysis. In this work, we build a diagnostic service called ESRO that recommends root causes and remediation for failures by utilizing structured as well as semi-structured sources of data systematically. ESRO constructs a causal graph using alerts and a knowledge graph using outage reports, and merges them in a novel way to form a unified graph during training. A retrieval-based mechanism is then used to search the unified graph and rank the likely root causes and remediation techniques based on the alerts fired during an outage at inference time. Not only the individual alerts, but their respective importance in predicting an outage group is taken into account during recommendation. We evaluated our model on several cloud service outages of a large SaaS enterprise over the course of ~2 years, and obtained an average improvement of 27% in rouge scores after comparing the likely root causes against the ground truth over state-of-the-art baselines. We further establish the effectiveness of ESRO through qualitative analysis on multiple real outage examples.Comment: Accepted to 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023

RoboEXP: Action-Conditioned Scene Graph via Interactive Exploration for Robotic Manipulation

Robots need to explore their surroundings to adapt to and tackle tasks in unknown environments. Prior work has proposed building scene graphs of the environment but typically assumes that the environment is static, omitting regions that require active interactions. This severely limits their ability to handle more complex tasks in household and office environments: before setting up a table, robots must explore drawers and cabinets to locate all utensils and condiments. In this work, we introduce the novel task of interactive scene exploration, wherein robots autonomously explore environments and produce an action-conditioned scene graph (ACSG) that captures the structure of the underlying environment. The ACSG accounts for both low-level information, such as geometry and semantics, and high-level information, such as the action-conditioned relationships between different entities in the scene. To this end, we present the Robotic Exploration (RoboEXP) system, which incorporates the Large Multimodal Model (LMM) and an explicit memory design to enhance our system's capabilities. The robot reasons about what and how to explore an object, accumulating new information through the interaction process and incrementally constructing the ACSG. We apply our system across various real-world settings in a zero-shot manner, demonstrating its effectiveness in exploring and modeling environments it has never seen before. Leveraging the constructed ACSG, we illustrate the effectiveness and efficiency of our RoboEXP system in facilitating a wide range of real-world manipulation tasks involving rigid, articulated objects, nested objects like Matryoshka dolls, and deformable objects like cloth.Comment: Project Page: https://jianghanxiao.github.io/roboexp-web