Structural performance evaluation and optimization through cyber-physical systems using substructure real-time hybrid simulation

Natural hazards continue to demonstrate the vulnerability of civil infrastructure worldwide. Engineers are dedicated to improving structural performance against natural hazards with improved design codes and computational tools. These improvements are often driven by experiments. Experimental testing not only enables the prediction of structural responses under those dynamic loads but also provide a reliable way to investigate new solutions for hazard mitigation. Common experimental techniques in structural engineering include quasi-static testing, shake table testing, and hybrid simulation. In recent years, real-time hybrid simulation (RTHS) has emerged as a powerful alternative to drive improvements in civil infrastructure as the entire structure’s dynamic performance is captured with reduced experimental requirements. In addition, RTHS provides an attractive opportunity to investigate the optimal performance of complex structures or components against multi-hazards by embedding it in an optimization framework. RTHS stands to accelerate advancements in civil engineering, in particular for designing new structural systems or devices in a performance-based design environment. This dissertation focuses on the use of cyber-physical systems (CPS) to evaluate structural performance and achieve optimal designs for seismic protection. This dissertation presents systematic studies on the development and validation of the dynamic substructuring RTHS technique using shake tables, novel techniques in increasing RTHS stability by introducing artificial damping to an under-actuated physical specimen, and the optimal design of the structure or supplemental control devices for seismic protection through a cyber-physical substructure optimization (CPSO) framework using substructure RTHS

MULTIPLE ENERGY DISSIPATION STRATEGIES OF BASE ISOLATED STRUCTURES UNDER BLAST AND EARTHQUAKE

Terrorist attacks have become a growing threat worldwide in recent years. Explosive devices, the weapon of choice for a majority of terrorist attacks, significantly threaten civilian and military personnel. Accordingly it is very important to protect critical buildings against blast loads with the main goal of preventing loss of life of the occupants. The research detailed within this dissertation will investigate innovative smart structures, including the mitigation of damage and loss of life under blast loading through base isolated structures combined with supplemental passive control devices without compromising the innate seismic protection that base isolation provides. The focus of this dissertation is the development and simulation of multiple control strategies for multi-story structures subjected to surface blasts and seismic excitations. The goal is to study and improve the response of base isolated structures under blast loadings and simultaneously keep the same level or better performance under earthquakes through alternative energy dissipation systems

Experimental design-aided systematic pathway optimization of glucose uptake and deoxyxylulose phosphate pathway for improved amorphadiene production

Artemisinin is a potent antimalarial drug; however, it suffers from unstable and insufficient supply from plant source. Here, we established a novel multivariate-modular approach based on experimental design for systematic pathway optimization that succeeded in improving the production of amorphadiene (AD), the precursor of artemisinin, in Escherichia coli. It was initially found that the AD production was limited by the imbalance of glyceraldehyde 3-phosphate (GAP) and pyruvate (PYR), the two precursors of the 1-deoxy-d-xylulose-5-phosphate (DXP) pathway. Furthermore, it was identified that GAP and PYR could be balanced by replacing the phosphoenolpyruvate (PEP)-dependent phosphotransferase system (PTS) with the ATP-dependent galactose permease and glucose kinase system (GGS) and this resulted in fivefold increase in AD titer (11 to 60 mg/L). Subsequently, the experimental design-aided systematic pathway optimization (EDASPO) method was applied to systematically optimize the transcriptional expressions of eight critical genes in the glucose uptake and the DXP and AD synthesis pathways. These genes were classified into four modules and simultaneously controlled by T7 promoter or its variants. A regression model was generated using the four-module experimental data and predicted the optimal expression ratios among these modules, resulting in another threefold increase in AD titer (60 to 201 mg/L). This EDASPO method may be useful for the optimization of other pathways and products beyond the scope of this study.Singapore-MIT Alliance for Research and Technology (SMART

FarSense: pushing the range limit of WiFi-based respiration sensing with CSI ratio of two antennas

International audienceThe past few years have witnessed the great potential of exploiting channel state information retrieved from commodity WiFi devices for respiration monitoring. However, existing approaches only work when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away. On the other hand, most home environments only have one WiFi access point and it may not be located in the same room as the target. This sensing range constraint greatly limits the application of the proposed approaches in real life. This paper presents FarSense-the first real-time system that can reliably monitor human respiration when the target is far away from the WiFi transceiver pair. FarSense works well even when one of the transceivers is located in another room, moving a big step towards real-life deployment. We propose two novel schemes to achieve this goal: (1) Instead of applying the raw CSI readings of individual antenna for sensing, we employ the ratio of CSI readings from two antennas, whose noise is mostly canceled out by the division operation to significantly increase the sensing range; (2) The division operation further enables us to utilize the phase information which is not usable with one single antenna for sensing. The orthogonal amplitude and phase are elaborately combined to address the "blind spots" issue and further increase the sensing range. Extensive experiments show that FarSense is able to accurately monitor human respiration even when the target is 8 meters away from the transceiver pair, increasing the sensing range by more than 100%. 1 We believe this is the first system to enable through-wall respiration sensing with commodity WiFi devices and the proposed method could also benefit other sensing applications

Towards All-around Knowledge Transferring: Learning From Task-irrelevant Labels

Deep neural models have hitherto achieved significant performances on numerous classification tasks, but meanwhile require sufficient manually annotated data. Since it is extremely time-consuming and expensive to annotate adequate data for each classification task, learning an empirically effective model with generalization on small dataset has received increased attention. Existing efforts mainly focus on transferring task-relevant knowledge from other similar data to tackle the issue. These approaches have yielded remarkable improvements, yet neglecting the fact that the task-irrelevant features could bring out massive negative transfer effects. To date, no large-scale studies have been performed to investigate the impact of task-irrelevant features, let alone the utilization of this kind of features. In this paper, we firstly propose Task-Irrelevant Transfer Learning (TIRTL) to exploit task-irrelevant features, which mainly are extracted from task-irrelevant labels. Particularly, we suppress the expression of task-irrelevant information and facilitate the learning process of classification. We also provide a theoretical explanation of our method. In addition, TIRTL does not conflict with those that have previously exploited task-relevant knowledge and can be well combined to enable the simultaneous utilization of task-relevant and task-irrelevant features for the first time. In order to verify the effectiveness of our theory and method, we conduct extensive experiments on facial expression recognition and digit recognition tasks. Our source code will be also available in the future for reproducibility

When Automated Assessment Meets Automated Content Generation: Examining Text Quality in the Era of GPTs

The use of machine learning (ML) models to assess and score textual data has become increasingly pervasive in an array of contexts including natural language processing, information retrieval, search and recommendation, and credibility assessment of online content. A significant disruption at the intersection of ML and text are text-generating large-language models such as generative pre-trained transformers (GPTs). We empirically assess the differences in how ML-based scoring models trained on human content assess the quality of content generated by humans versus GPTs. To do so, we propose an analysis framework that encompasses essay scoring ML-models, human and ML-generated essays, and a statistical model that parsimoniously considers the impact of type of respondent, prompt genre, and the ML model used for assessment model. A rich testbed is utilized that encompasses 18,460 human-generated and GPT-based essays. Results of our benchmark analysis reveal that transformer pretrained language models (PLMs) more accurately score human essay quality as compared to CNN/RNN and feature-based ML methods. Interestingly, we find that the transformer PLMs tend to score GPT-generated text 10-15\% higher on average, relative to human-authored documents. Conversely, traditional deep learning and feature-based ML models score human text considerably higher. Further analysis reveals that although the transformer PLMs are exclusively fine-tuned on human text, they more prominently attend to certain tokens appearing only in GPT-generated text, possibly due to familiarity/overlap in pre-training. Our framework and results have implications for text classification settings where automated scoring of text is likely to be disrupted by generative AI.Comment: Data available at: https://github.com/nd-hal/automated-ML-scoring-versus-generatio