MuNES: Multifloor Navigation Including Elevators and Stairs

We propose a scheme called MuNES for single mapping and trajectory planning including elevators and stairs. Optimized multifloor trajectories are important for optimal interfloor movements of robots. However, given two or more options of moving between floors, it is difficult to select the best trajectory because there are no suitable indoor multifloor maps in the existing methods. To solve this problem, MuNES creates a single multifloor map including elevators and stairs by estimating altitude changes based on pressure data. In addition, the proposed method performs floor-based loop detection for faster and more accurate loop closure. The single multifloor map is then voxelized leaving only the parts needed for trajectory planning. An optimal and realistic multifloor trajectory is generated by exploring the voxels using an A* algorithm based on the proposed cost function, which affects realistic factors. We tested this algorithm using data acquired from around a campus and note that a single accurate multifloor map could be created. Furthermore, optimal and realistic multifloor trajectory could be found by selecting the means of motion between floors between elevators and stairs according to factors such as the starting point, ending point, and elevator waiting time. The code and data used in this work are available at https://github.com/donghwijung/MuNES

Multifunctional-high resolution imaging plate based on hydrophilic graphene for digital pathology

In the present study, we showed that hydrophilic graphene can serve as an ideal imaging plate for biological specimens. Graphene being a single-atom-thick semi-metal with low secondary electron emission, array tomography analysis of serial sections of biological specimens on a graphene substrate showed excellent image quality with improved z-axis resolution, without including any conductive surface coatings. However, the hydrophobic nature of graphene makes the placement of biological specimens difficult; graphene functionalized with polydimethylsiloxane oligomer was fabricated using a simple soft lithography technique and then processed with oxygen plasma to provide hydrophilic graphene with minimal damage to graphene. High-quality scanning electron microscopy images of biological specimens free from charging effects or distortion were obtained, and the optical transparency of graphene enabled fluorescence imaging of the specimen; high-resolution correlated electron and light microscopy analysis of the specimen became possible with the hydrophilic graphene plate

Functional bio-inspired hybrid fliers with separated ring and leading edge vortices

Recent advances in passive flying systems inspired by wind-dispersed seeds contribute to increasing interest in their use for remote sensing applications across large spatial domains in the Lagrangian frame of reference. These concepts create possibilities for developing and studying structures with performance characteristics and operating mechanisms that lie beyond those found in nature. Here, we demonstrate a hybrid flier system, fabricated through a process of controlled buckling, to yield unusual geometries optimized for flight. Specifically, these constructs simultaneously exploit distinct fluid phenomena, including separated vortex rings from features that resemble those of dandelion seeds and the leading-edge vortices derived from behaviors of maple seeds. Advanced experimental measurements and computational simulations of the aerodynamics and induced flow physics of these hybrid fliers establish a concise, scalable analytical framework for understanding their flight mechanisms. Demonstrations with functional payloads in various forms, including bioresorbable, colorimetric, gas-sensing, and light-emitting platforms, illustrate examples with diverse capabilities in sensing and tracking. © The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.TRUEscopu

Robust and Resilient Water Distribution Systems

The purpose of a water distribution system (WDS) is to deliver the required amount of water to the customer under the desired pressure and quality. However, demand change and component failure result in low pressures at customer taps and make it difficult to achieve the goal. To mitigate the impact of the disturbances, system performance measure such as robustness and resilience can be considered in the WDS design and operation. Robustness is generally defined as an ability of the systems to maintain its function under a defined set of disturbance. On the other hand, Resilience is a system's ability to prepare and recover from a failure. The goal of this dissertation is to develop methodologies to enhance WDS robustness and resilience. In robustness-based design, reliability has been considered. Reliability is generally defined as the system's ability to provide an adequate service to customers under uncertain system condition and measured by the probability that stochastic nodal pressures are greater than or equal to a prescribed minimum pressure. However, although improving reliability will improve system robustness, the question is how the reliability index will improve system robustness. Robustness incorporates the variation of system performance; an additional aspect of system performance that reliability does not encompass. Pipe bursts are the most common failure in WDS. Therefore, promptly detecting and locating bursts will decrease the failure duration and increase system resilience. While many burst detection methods are available, identifying the method with the highest detectability is important to system owners/operators. However, to date, no cross comparisons of these methods have been completed for burst detection using a common data set. In addition, most traditional burst detection methods do not have a mechanism to include system operational changes. This dissertation is composed of three journal manuscripts that address these three key issues on WDS robustness and resilience. For WDS robustness improvement, a new robustness index is developed and used for multi-objective robustness-based design. The robustness-based design is compared to conventional reliability-based design. For WDS resilience improvement, the best method among six Statistical Process Control (SPC) methods is identified in terms of detection effectiveness and efficiency. Finally, a burst detection method applicable under system operational condition change is posed

Emerging Issues and Methodologies for Resilient and Robust Water Distribution Systems

This editorial summarizes the 11 papers published in the Special Issue entitled “Resilient and Robust Water Distribution Systems: State-of-the-Art and Research Challenges” which were classified into five themes related to water distribution systems (WDSs): (1) state-of-the-art review on WDS resilience and robustness (ROB), (2) WDS performance quantification and recovery under earthquakes, (3) criticality analysis and visualization, (4) novel design methodologies, and (5) hydraulic parameter monitoring for WDS rapidity improvement. Following the provision of the number of views and citations of each paper in a brief manner, a paper in category (1) that reviewed recent studies on WDS robustness is summarized. Category (2) covers three papers on improving the WDS capacity to fulfil customers’ demands in the case of an earthquake, a representative catastrophic failure event, while category (3) includes papers on visualization methods to represent the system’s criticality. The studies included in themes (4) and (5) proposed novel design methods and monitoring approaches for improving WDS resilience, respectively. Contributions from each study are described in the context of WDS resilience. We hope that this Special Issue can (1) serve as a reference point from which readers review progress, recent trends, and emerging issues, and (2) shed light on the appropriate future directions of WDS resilience studies

Robustness and Water Distribution System: State-of-the-Art Review

The resilience of a water distribution system (WDS) is defined as its ability to prepare, respond to, and recover from a catastrophic failure event such as an earthquake or intentional contamination. Robustness (ROB), one of the components of resilience, is the ability to maintain functionality to meet customer demands. Recently, the traditional probability-based system performance perspective has begun to shift toward the ROB and system performance variation point of view. This paper provides a state-of-the-art review of WDS ROB-based approaches proposed in three research categories: Design, operation, and management. While few pioneering works have been published in the latter two areas, an ROB indicator was proposed and thoroughly investigated for WDS design. Then, some future works are recommended in each of the three domains to promote developments in WDS ROB. Finally, a brief summary of this paper is presented, from which the final conclusions of the state-of-the-art review and recommendations are drawn. The new paradigm of WDS ROB-based design, operation, and management is in its infant stage and should be carved out in future studies

Multiobjective Automatic Parameter Calibration of a Hydrological Model

This study proposes variable balancing approaches for the exploration (diversification) and exploitation (intensification) of the non-dominated sorting genetic algorithm-II (NSGA-II) with simulated binary crossover (SBX) and polynomial mutation (PM) in the multiobjective automatic parameter calibration of a lumped hydrological model, the HYMOD model. Two objectives—minimizing the percent bias and minimizing three peak flow differences—are considered in the calibration of the six parameters of the model. The proposed balancing approaches, which migrate the focus between exploration and exploitation over generations by varying the crossover and mutation distribution indices of SBX and PM, respectively, are compared with traditional static balancing approaches (the two dices value is fixed during optimization) in a benchmark hydrological calibration problem for the Leaf River (1950 km2) near Collins, Mississippi. Three performance metrics—solution quality, spacing, and convergence—are used to quantify and compare the quality of the Pareto solutions obtained by the two different balancing approaches. The variable balancing approaches that migrate the focus of exploration and exploitation differently for SBX and PM outperformed other methods

Development of Cross-Domain Artificial Neural Network to Predict High-Temporal Resolution Pressure Data

Forecasting hydraulic data such as pressure and demand in water distribution system (WDS) is an important task that helps ensure efficient and accurate operations. Despite high-performance data prediction, missing data can still occur, making it difficult to effectively operate WDS. Though the pressure data are directly related to the rules of operation for pumps or valves, few studies have been conducted on pressure data forecasting. This study proposes a new missing and incomplete data control approach based on real pressure data for reliable and efficient WDS operation and maintenance. The proposed approach is: (1) application of source data from high-resolution, real-world pressure data; (2) development of a cross-domain artificial neural network (CDANN), combining the standard artificial neural networks (ANNs) and the cross-domain training approach for missing data control; and (3) analysis of standard data mining according to external factors to improve prediction accuracy. To verify the proposed approach, a real-world network located in South Korea was used, and the forecasting results were evaluated through performance indicators (i.e., overall, special points, and percentage errors). The performance of the CDANN is compared with that of standard ANNs, and CDANN was found to provide better predictions than traditional ANNs.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Robust Meter Network for Water Distribution Pipe Burst Detection

A meter network is a set of meters installed throughout a water distribution system to measure system variables, such as the pipe flow rate and pressure. In the current hyper-connected world, meter networks are being exposed to meter failure conditions, such as malfunction of the meter’s physical system and communication system failure. Therefore, a meter network’s robustness should be secured for reliable provision of informative meter data. This paper introduces a multi-objective optimal meter placement model that maximizes the detection probability, minimizes false alarms, and maximizes the robustness of a meter network given a predefined number of meters. A meter network’s robustness is defined as its ability to consistently provide quality data in the event of meter failure. Based on a single-meter failure simulation, a robustness indicator for the meter network is introduced and maximized as the third objective of the proposed model. The proposed model was applied to the Austin network to determine the independent placement of pipe flow and pressure meters with three or five available meters. The results showed that the proposed model is a useful tool for determining meter locations to secure high detectability and robustness