scholarly journals Revealing the Challenges of Smart Rainwater Harvesting for Integrated and Digital Resilience of Urban Water Infrastructure

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1902
Author(s):  
Martin Oberascher ◽  
Aun Dastgir ◽  
Jiada Li ◽  
Sina Hesarkazzazi ◽  
Mohsen Hajibabaei ◽  
...  
Keyword(s):  
Large Scale ◽  
Rainwater Harvesting ◽  
Data Communication ◽  
Integrated System ◽  
Coupled Systems ◽  
Urban Resilience ◽  
System Failures ◽  
Household Level ◽  
Performance Improvements ◽  
Smart Water

Smart rainwater harvesting (RWH) systems can automatically release stormwater prior to rainfall events to increase detention capacity on a household level. However, impacts and benefits of a widespread implementation of these systems are often unknown. This works aims to investigate the effect of a large-scale implementation of smart RWH systems on urban resilience by hypothetically retrofitting an Alpine municipality with smart rain barrels. Smart RWH systems represent dynamic systems, and therefore, the interaction between the coupled systems RWH units, an urban drainage network (UDN) and digital infrastructure is critical for evaluating resilience against system failures. In particular, digital parameters (e.g., accuracy of weather forecasts, or reliability of data communication) can differ from an ideal performance. Therefore, different digital parameters are varied to determine the range of uncertainties associated with smart RWH systems. As the results demonstrate, smart RWH systems can further increase integrated system resilience but require a coordinated integration into the overall system. Additionally, sufficient consideration of digital uncertainties is of great importance for smart water systems, as uncertainties can reduce/eliminate gained performance improvements. Moreover, a long-term simulation should be applied to investigate resilience with digital applications to reduce dependence on boundary conditions and rainfall patterns.

scholarly journals Neural methods for effective, efficient, and exposure-aware information retrieval

2021 ◽  
Vol 55 (1) ◽  
pp. 1-2
Author(s):  
Bhaskar Mitra
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Large Scale ◽  
Web Search ◽  
Real Life ◽  
Inverted Index ◽  
Information Need ◽  
Product Model ◽  
Performance Improvements ◽  
Deep Model

Neural networks with deep architectures have demonstrated significant performance improvements in computer vision, speech recognition, and natural language processing. The challenges in information retrieval (IR), however, are different from these other application areas. A common form of IR involves ranking of documents---or short passages---in response to keyword-based queries. Effective IR systems must deal with query-document vocabulary mismatch problem, by modeling relationships between different query and document terms and how they indicate relevance. Models should also consider lexical matches when the query contains rare terms---such as a person's name or a product model number---not seen during training, and to avoid retrieving semantically related but irrelevant results. In many real-life IR tasks, the retrieval involves extremely large collections---such as the document index of a commercial Web search engine---containing billions of documents. Efficient IR methods should take advantage of specialized IR data structures, such as inverted index, to efficiently retrieve from large collections. Given an information need, the IR system also mediates how much exposure an information artifact receives by deciding whether it should be displayed, and where it should be positioned, among other results. Exposure-aware IR systems may optimize for additional objectives, besides relevance, such as parity of exposure for retrieved items and content publishers. In this thesis, we present novel neural architectures and methods motivated by the specific needs and challenges of IR tasks. We ground our contributions with a detailed survey of the growing body of neural IR literature [Mitra and Craswell, 2018]. Our key contribution towards improving the effectiveness of deep ranking models is developing the Duet principle [Mitra et al., 2017] which emphasizes the importance of incorporating evidence based on both patterns of exact term matches and similarities between learned latent representations of query and document. To efficiently retrieve from large collections, we develop a framework to incorporate query term independence [Mitra et al., 2019] into any arbitrary deep model that enables large-scale precomputation and the use of inverted index for fast retrieval. In the context of stochastic ranking, we further develop optimization strategies for exposure-based objectives [Diaz et al., 2020]. Finally, this dissertation also summarizes our contributions towards benchmarking neural IR models in the presence of large training datasets [Craswell et al., 2019] and explores the application of neural methods to other IR tasks, such as query auto-completion.

scholarly journals A Survey on Edge Performance Benchmarking

2021 ◽  
Vol 54 (3) ◽  
pp. 1-33
Author(s):  
Blesson Varghese ◽  
Nan Wang ◽  
David Bermbach ◽  
Cheol-Ho Hong ◽  
Eyal De Lara ◽  
...  
Keyword(s):  
Large Scale ◽  
Future Internet ◽  
Coupled Systems ◽  
Operational Conditions ◽  
Loosely Coupled ◽  
Performance Benchmarking ◽  
The Past ◽  
Geographically Dispersed ◽  
Tightly Coupled ◽  
History Of

Edge computing is the next Internet frontier that will leverage computing resources located near users, sensors, and data stores to provide more responsive services. Therefore, it is envisioned that a large-scale, geographically dispersed, and resource-rich distributed system will emerge and play a key role in the future Internet. However, given the loosely coupled nature of such complex systems, their operational conditions are expected to change significantly over time. In this context, the performance characteristics of such systems will need to be captured rapidly, which is referred to as performance benchmarking, for application deployment, resource orchestration, and adaptive decision-making. Edge performance benchmarking is a nascent research avenue that has started gaining momentum over the past five years. This article first reviews articles published over the past three decades to trace the history of performance benchmarking from tightly coupled to loosely coupled systems. It then systematically classifies previous research to identify the system under test, techniques analyzed, and benchmark runtime in edge performance benchmarking.

Remote Thermal Performance Monitoring and Diagnostics: Turning Data Into Knowledge

2013 ◽  
Author(s):  
Xiaomo Jiang ◽  
Craig Foster
Keyword(s):  
Anomaly Detection ◽  
Gas Turbines ◽  
Performance Monitoring ◽  
Combined Cycle ◽  
Integrated System ◽  
Site Specific ◽  
Performance Improvements ◽  
Maintenance Schedule ◽  
Effectiveness Monitoring ◽  
And Performance

Gas turbine simple or combined cycle plants are built and operated with higher availability, reliability, and performance in order to provide the customer with sufficient operating revenues and reduced fuel costs meanwhile enhancing customer dispatch competitiveness. A tremendous amount of operational data is usually collected from the everyday operation of a power plant. It has become an increasingly important but challenging issue about how to turn this data into knowledge and further solutions via developing advanced state-of-the-art analytics. This paper presents an integrated system and methodology to pursue this purpose by automating multi-level, multi-paradigm, multi-facet performance monitoring and anomaly detection for heavy duty gas turbines. The system provides an intelligent platform to drive site-specific performance improvements, mitigate outage risk, rationalize operational pattern, and enhance maintenance schedule and service offerings via taking appropriate proactive actions. In addition, the paper also presents the components in the system, including data sensing, hardware, and operational anomaly detection, expertise proactive act of company, site specific degradation assessment, and water wash effectiveness monitoring and analytics. As demonstrated in two examples, this remote performance monitoring aims to improve equipment efficiency by converting data into knowledge and solutions in order to drive value for customers including lowering operating fuel cost and increasing customer power sales and life cycle value.

scholarly journals Performance Improvements for a Large-scale Geological Simulation

2014 ◽  
Vol 29 ◽  
pp. 256-269
Author(s):  
David Apostal ◽  
Kyle Foerster ◽  
Travis Desell ◽  
William Gosnold
Keyword(s):  
Large Scale ◽  
Performance Improvements

scholarly journals From lithium to sodium: cell chemistry of room temperature sodium–air and sodium–sulfur batteries

2015 ◽  
Vol 6 ◽  
pp. 1016-1055 ◽  
Author(s):  
Philipp Adelhelm ◽  
Pascal Hartmann ◽  
Conrad L Bender ◽  
Martin Busche ◽  
Christine Eufinger ◽  
...  
Keyword(s):  
Energy Density ◽  
Large Scale ◽  
Room Temperature ◽  
Low Cost ◽  
Strong Impact ◽  
Performance Improvements ◽  
Key Issues ◽  
Lithium Oxygen Batteries ◽  
Open Question ◽  
Lithium Sulfur

Research devoted to room temperature lithium–sulfur (Li/S8) and lithium–oxygen (Li/O2) batteries has significantly increased over the past ten years. The race to develop such cell systems is mainly motivated by the very high theoretical energy density and the abundance of sulfur and oxygen. The cell chemistry, however, is complex, and progress toward practical device development remains hampered by some fundamental key issues, which are currently being tackled by numerous approaches. Quite surprisingly, not much is known about the analogous sodium-based battery systems, although the already commercialized, high-temperature Na/S8 and Na/NiCl2 batteries suggest that a rechargeable battery based on sodium is feasible on a large scale. Moreover, the natural abundance of sodium is an attractive benefit for the development of batteries based on low cost components. This review provides a summary of the state-of-the-art knowledge on lithium–sulfur and lithium–oxygen batteries and a direct comparison with the analogous sodium systems. The general properties, major benefits and challenges, recent strategies for performance improvements and general guidelines for further development are summarized and critically discussed. In general, the substitution of lithium for sodium has a strong impact on the overall properties of the cell reaction and differences in ion transport, phase stability, electrode potential, energy density, etc. can be thus expected. Whether these differences will benefit a more reversible cell chemistry is still an open question, but some of the first reports on room temperature Na/S8 and Na/O2 cells already show some exciting differences as compared to the established Li/S8 and Li/O2 systems.

What Kind of Friction Model Is Needed to Predict Brake Squeal?

2012 ◽  
Author(s):  
Tore Butlin ◽  
Jim Woodhouse
Keyword(s):  
Large Scale ◽  
Dynamic Properties ◽  
Theoretical Work ◽  
Friction Model ◽  
Coupled Systems ◽  
Brake Squeal ◽  
Model Framework ◽  
Wide Range ◽  
Pin On Disc ◽  
Almost All

Predictive models of friction-induced vibration have proved elusive despite decades of research. There are many mechanisms that can cause brake squeal; friction coupled systems can be highly sensitive to small perturbations; and the dynamic properties of friction at the contact zone seem to be poorly understood. This paper describes experimental and theoretical work aimed at identifying the key ingredients of a predictive model. A large-scale experiment was carried out to identify squeal initiations using a pin-on-disc test rig: approximately 30,000 squeal initiations were recorded, covering a very wide range of frequencies. The theoretical model allows for completely general linear systems coupled at a single sliding point by friction: squeal is predicted using a linearised stability analysis. Results will be presented that show that almost all observed squeal events can be predicted within this model framework, but that some subsets require innovative friction modelling: predictions are highly dependent on the particular choice of friction model and its associated parameters.

scholarly journals Linking child travel routes and routine health data

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Amy Mizen ◽  
Sarah Rodgers ◽  
Richard Fry ◽  
Ronan Lyons
Keyword(s):  
Large Scale ◽  
Data Linkage ◽  
Routine Data ◽  
Environmental Exposures ◽  
Health Data ◽  
Walking Distance ◽  
Complete Analysis ◽  
Natural Experiments ◽  
Older Children ◽  
Household Level

ABSTRACTObjectivesLinking routinely collected health and environment data can allow for large scale evaluations of how our environment impacts our health. Our data linkage approach advances previous research where residence-based environmental exposures were anonymously linked in the SAIL databank using Residential Anonymous Linking Fields (RALFs). The dose-response relationship between exposure to food and dietary intake has not been widely investigated. Previous research found conflicting views on whether increased environmental exposure to unhealthy food contributes to higher BMIs. This may have been due to different methodological approaches, including imprecise exposures, small numbers, and the use of self-reported BMIs. ApproachThis investigation calculated food exposure environments for routes from all homes to and from school. A Geographic Information System was used to calculate the environmental exposures along all potential routes up to a maximum age-appropriate walking distance from each school. Once within the SAIL databank we selected relevant routes using linked demographic and pupil datasets. To maintain privacy, the primary (doctoral) researcher generating the environmental exposures, did not have access to the final household-level exposure data in their identifiable form. The researcher automated their method so a second researcher could run the GIS analysis. Accuracy of modelled exposures will be compared with actual routes collected from GPS traces of children walking to school. ResultsRemoving access to the final identifiable household-level route exposures enabled the primary researcher to complete analysis on the combined household and individual-level data within the secure environment. The environmental exposures were linked with routine health data from the SAIL databank; including BMI as an indicator of obesity. BMI data for 4-5 year olds, and a sample of 1300 13-14 year olds were linked to associated environmental exposures. ConclusionDepending on modelled accuracy, a GIS and data linkage approach may allow the investigation of natural experiments and intervention evaluation at the scale of the total population. This is the first step towards anonymously modelling part of the daily exposure environment using routine data. A limitation is the lack of routinely collected BMI data for older children and teenagers an age when they are more likely to have the option to choose to buy food on the school route. This work will have many potential applications, including the delivery and evaluation of multiple school and workplace commuting interventions.

scholarly journals A Wireless Sensor Network Model for E-Metering and Fault Identification in Smart Water Distribution Systems

2019 ◽  
Vol 8 (10S) ◽  
pp. 227-232
Keyword(s):  
Wireless Sensor Network ◽  
Real Time ◽  
Sensor Network ◽  
Network Model ◽  
Distribution System ◽  
Water Distribution ◽  
Large Scale ◽  
Water Distribution System ◽  
Wireless Sensor ◽  
Smart Water

Water distribution system is a network that supplies water to all the consumers through different means. Proper means of providing water to houses without compromising in quantity and quality is always a challenge. As it is a huge network keeping track of the utilization is difficult for the utility. Hence through this project we come up with a solution to solve this issue. Current technologies like Low Power Wide Area Networks, LoRa and sensor deployment techniques have been in research and were also tested in few rural areas but issues due to hardware deployment and large scale real time implementation was a challenge hence through this system we aim to create and simulate a real time scenario to test a sensor network model that could be implemented in large scale further. This project aims in building a wireless sensor network model for a smart water distribution system. In this system there is bidirectional communication between the consumer and the utility. Each house has a meter through which the amount of water consumed is sent to the utility board. The data has two fields containing the house ID and the data (water consumed); it is being sent to the data collection unit (DCU) which in-turn sends it to the central server so that the consumption is monitored in real time. All this is simulated using NETSIM and MATLAB.

scholarly journals PENGIRIMAN DATA NRF24L01+ DENGAN KONDISI LINE OF SIGHT DAN NON LINE OF SIGHT

2020 ◽  
Vol 3 (2) ◽  
pp. 128-139
Author(s):  
I Gusti Made Ngurah Desnanjaya ◽  
Mohammad Dwi Alfian
Keyword(s):  
Packet Loss ◽  
Data Transmission ◽  
Large Scale ◽  
Extreme Environments ◽  
Data Communication ◽  
Sensor Nodes ◽  
Line Of Sight ◽  
Effective Distance ◽  
Communication Distance ◽  
Non Line Of Sight

Wireless Sensor Network is a wireless network technology that includes sensor nodes and embedded systems. WSN has several advantages: it is cheaper for large-scale applications, can withstand extreme environments, and data transmission is relatively more stable. One of the WSN devices is nRF24L01+. Within the specifications given, the maximum communication distance is 1.1 km. However, the most effective distance for transmitting data in line of sight and non-line of sight is still unknown. Therefore, testing and analysis are needed so that the nRF24L01+ device can be used optimally for communication and data transmission. Through testing analysis on nRF24L01+ line of sight, Kuta beach location in Bali and non-line of sight on the STMIK STIKOM Indonesia campus. The effective communication distance of the nRF24L01+ module in line of sight is between 1 and 1000 meters. The distance of 1000 meters is the limit of the effective distance for sending data, and the packet loss rate is less than 15% which is included in the medium category. Meanwhile, in the non-line of sight, the effective distance of the nRF24L01+ communication module is 20 meters, and the packet loss is close to 15%, which is a moderate level limit. With the analysis module, nRF24L01+ can be a reference in determining the effective distance on WSN nRF24L01+ in determining remote control equipment data communication.

Sign in / Sign up

Export Citation Format

Share Document