22 results on '"Kerkez B"'
Search Results
2. SOL: An end-to-end solution for real-world remote monitoring systems
- Author
-
Brun-Laguna, K, Watteyne, T, Malek, S, Zhang, Z, Oroza, C, Glaser, SD, and Kerkez, B
- Abstract
This paper introduces SOL, which is both an efficient data representation for sensor measurements and network statistics, and a complete low-power wireless sensor management system that builds around it. A SOL system consists of multiple low-power wireless mesh networks in which motes connected to sensors and actuators send data to a single server. It offers multi-tier data replication and the associated data recovery. SOL is used in 3 pilot deployments for micro-climate monitoring, building automation and agriculture applications. In conjunction with Metronome Systems' NeoMote and Manager devices, SOL is a turn-key ready-to-deploy end-to-end system.
- Published
- 2016
3. Sensor placement strategies for snow water equivalent (SWE) estimation in the American River basin
- Author
-
Welch, SC, Kerkez, B, Bales, RC, Glaser, SD, Rittger, K, and Rice, RR
- Subjects
Environmental Engineering ,Civil Engineering ,Applied Economics ,Physical Geography and Environmental Geoscience - Abstract
A 11 year data set of spatially distributed snow water equivalent (SWE) was used to inform a quantitative, near-optimal sensor placement methodology for real-time SWE estimation in the American River basin of California. Rank-based clustering was compared with geographically based clustering (subbasin delineation) to determine the existence of stationary covariance structures within the overall SWE data set. The historical SWE data, at 500 × 500 m resolution, were split into 8 years of training and 3 years of validation data. Within each cluster, a quantitative sensor-placement algorithm, based on maximizing the metric of mutual information, was implemented and compared with random placement. Gaussian process models were then built from validation data points selected by the algorithm to evaluate the efficacy of each placement approach. Rank-based clusters remained stable interannually, suggesting that rankings of pixel-by-pixel SWE exhibit stationary features that can be exploited by a sensor-placement algorithm, yielding a 200 mm average root-mean-square error (RMSE) for 20 randomly selected sensing locations. This outperformed geographic and basin-wide placement approaches, which generated 460 and 290 mm RMSE, respectively. Mutual information-based sampling provided the best placement strategy, improving RMSE between 0 and 100 mm compared with random placements. Increasing the number of rank-based clusters consistently lowered average RMSE from 400 mm for 1 cluster to 175 mm for 8 clusters, for 20 total sensors placed. To optimize sensor placement, we recommend a strategy that couples rank-based clustering with mutual information-based sampling design. © 2013. American Geophysical Union. All Rights Reserved.
- Published
- 2013
4. Design and performance of a wireless sensor network for catchment-scale snow and soil moisture measurements
- Author
-
Kerkez, B, Glaser, SD, Bales, RC, and Meadows, MW
- Subjects
Environmental Engineering ,Civil Engineering ,Applied Economics ,Physical Geography and Environmental Geoscience - Abstract
[1] A wireless sensor network (WSN) was deployed as part of a water balance instrument cluster across a forested 1 km2 headwater catchment in the southern Sierra Nevada of California. The network, which integrates readings from over 300 sensors, provides spatially representative measurements of snow depth, solar radiation, relative humidity, soil moisture, and matric potential. The ability of this densely instrumented watershed to capture catchment-scale snow depth and soil moisture distributions is investigated through comparison with three comprehensive gridded surveys and 1 day of detailed lidar snow data. Statistical analysis shows that the network effectively characterized catchment-wide distributions of snow depth, while offering a cost-effective, reliable, and energy-efficient means for collecting distributed data in real time. A temporal analysis of snow depth variability reveals that canopy cover is the major explanatory variable of snow depth and that under-canopy measurements persistently show higher variability compared to those in open terrain. An analysis of soil moisture shows lower variability at deeper soil depth and a correlation between mean soil moisture and variability for shallow soils. A three-phase design procedure was used to optimize the WSN deployment. First, as off-the-shelf performance of current WSN platforms for large-scale, long-term deployments cannot be guaranteed, statistics from a prototype deployment were analyzed. Two indicators of network performance, the packet delivery ratio and received signal strength indicator, showed that for our site conditions, a conservative 50 m node-to-node spacing would ensure low-power, reliable, and robust network communications. Second, results from the prototype were used to refine hardware specifications and to guide the layout of the full 57-node wireless network. Of these nodes, 23 were used actively for sensing, while the remaining 34 nodes were used as signal repeaters to ensure proper spatial radio coverage and robust network operations. Further analysis of network statistics is conducted during the third, operational, phase to validate system performance. © 2012. American Geophysical Union.
- Published
- 2012
5. Real time control schemes for improving water quality from bioretention cells
- Author
-
Persaud, P. P., primary, Akin, A. A., additional, Kerkez, B., additional, McCarthy, D. T., additional, and Hathaway, J. M., additional
- Published
- 2019
- Full Text
- View/download PDF
6. Real‐Time Control of Urban Headwater Catchments Through Linear Feedback: Performance, Analysis, and Site Selection
- Author
-
Wong, B. P., primary and Kerkez, B., additional
- Published
- 2018
- Full Text
- View/download PDF
7. The infant mortality rates and its causes in Hatay province during the years 2009 and 2010
- Author
-
Çelik, Tanju, A B. Altun, Kerkez, B., Tatlı, S., Özer, C., and Hatay Mustafa Kemal Üniversitesi
- Subjects
Cerrahi - Abstract
Amaç: Gelişmekte olan ülkelerde sağlık sisteminin ve Ana-Çocuk Sağlığı hizmetlerinin kalitesini gösteren çağdaş parametrelerden en önemlisi “Bebek Ölüm Hızı”dır. Bu bağlamda bebek ölüm hızının değerlendirilmesi ve aşağıya çekilmesi toplum için çok önemlidir. Ülkemize bakıldığında bebek ölüm hızında son yıllarda ciddi iyileşme göze çarpmakla beraber hala il ve bölgeler bazında farklılıklar söz konusudur. Hatay ili de değişik sosyoekonomik yapısıyla bu farklılığı sunan bölgelerden birisi olup bu çalışmada ilimiz verilerini tartışmayı amaçladık. Gereç ve Yöntem: Hatay İl Sağlık Müdürlüğü Bebek Ölümlerini Takip ve Önleme Komisyonu tarafından saptanan Ocak 2009 ile Aralık 2010 tarihleri arasındaki sağlık ocağı ve hastane verileri incelendi. Bunun sonucunda bebek ölüm hızı, prematüre doğum hızı, diare, pnömoni, menenjit, sepsis, konjenital anomaliler ve bunlara etki eden maternal ve çevresel faktörler değerlendirildi. Bulgular: İlimizde 2009 yılında 29006 ve 2010 yılında ise 28352 canlı doğum gerçekleşmiş olup, 2009 yılında 617 (‰15.1) ve 2010 yılında 585 (‰13.4) bebek ölümü gerçekleşti. En sık karşılaşılan ölüm nedeni prematürite (n;185) iken bunu konjenital anomaliler, konjenital kalp hastalıkları, aspirasyon sendromu, respiratuar distres sendromu, sepsis ve alt solunum yolu enfeksiyonları izledi. Sonuç: İlimizde Bebek Ölüm Hızında anlamlı azalma olmuş, ülke ortalamasının altına inmiştir. Ölümlerin en önemli kısmını prematüre doğumlar ve konjenital anomaliler oluşturmaktadır., Aim: The infant mortality rate is a very important indicator of a mother’s and child’s health and health management in developing countries. For this reason, evaluation of the infant mortality rate and precautions for the reduction of it are particularly important. In our country, there is a substantial improvement in the infant mortality rates; however there still remains a disparity between different provinces and regions. In this study we aimed to examine the infant mortality rates and its causes in the province of Hatay, where there is a heterogeneous socioeconomically structure. Materials and Methods: The board established within the Directorate of Health of Hatay reviewed retrospectively the clinical charts and hospital records of babies born between January 2009 and December 2010. As a result, infant mortality and premature birth rates, diarrhea, pneumonia, meningitis, sepsis, congenital anomalies, and contributing maternal and environmental factors were evaluated. Results: In the province, there were 29,006 live births in 2009 and 28,352 in 2010. Of these, 617 (15.1‰) infant deaths occurred in 2009 and 585 (13.4‰) in 2010. Most of the deaths were related to prematurity (n=185), which was followed by congenital anomalies, especially congenital heart defects, aspiration syndrome, respiratory distress syndrome, sepsis and lower respiratory tract infections. Conclusion: In the last two years, the infantile mortality rate decreased in Hatay province, and also declined below the infant mortality rate for the nation. Premature births and congenital abnormalities comprise most of the causes of the deaths.
- Published
- 2012
8. CASE-BASED PLAN RECOGNITION WITH NOVEL INPUT
- Author
-
Cox, M.T., primary and Kerkez, B., additional
- Published
- 2006
- Full Text
- View/download PDF
9. Tunable linear feedback control of urban drainage systems using models defined purely from data.
- Author
-
Adrian Dantzer T and Kerkez B
- Subjects
- Cities, Feedback, Models, Theoretical, Drainage, Sanitary
- Abstract
Real-time and model-predictive control promises to make urban drainage systems (UDS) adaptive, coordinated, and dynamically optimal. Though early implementations are promising, existing control algorithms have drawbacks in computational expense, trust, system-level coordination, and labor cost. Linear feedback control has distinct advantages in computational expense, interpretation, and coordination. However, current methods for building linear feedback controllers require calibrated software models. Here we present an automated method for generating tunable linear feedback controllers that require only system response data. The controller design consists of three main steps: (1) estimating the network connectivity using tools for causal inference, (2) identifying a linear, time-invariant (LTI) dynamical system which approximates the network, and (3) designing and tuning a feedback controller based on the LTI urban drainage system approximation. The flooding safety, erosion prevention, and water treatment performance of the method are evaluated across 190 design storms on a separated sewer model. Strong results suggest that the system knowledge required for generating effective, safe, and tunable controllers for UDS is surprisingly basic. This method allows near-turnkey synthesis of controllers solely from sensor data or reduction of process-based models., Competing Interests: The authors declare no conflicts of interest., (© 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).)
- Published
- 2024
- Full Text
- View/download PDF
10. Toward the autonomous flushing of building plumbing: Characterizing oxidation-reduction potential and temperature sensor dynamics.
- Author
-
Martinez Paz EF, Raskin L, Wigginton KR, and Kerkez B
- Subjects
- Water Supply, Chloramines, Chlorine, Temperature, Oxidation-Reduction, Sanitary Engineering, Drinking Water
- Abstract
Manual flushing of building plumbing is commonly used to address water quality issues that arise from water stagnation. Autonomous flushing informed by sensors has the potential to aid in the management of building plumbing, but a number of knowledge gaps hinder its application. This study evaluates autonomous flushing of building plumbing with online sensor and actuator nodes deployed under kitchen sinks in five residential houses. Online oxidation-reduction potential (ORP) and temperature data were collected for nine weeks during the winter and summer in houses with both free chlorine and chloramine. ORP levels in houses with free chlorine residuals decreased after overnight stagnation. The overnight decrease in ORP was not observed when tap water was automatically flushed for five minutes at 6:00 h every morning. ORP levels in houses with chloramine residuals did not decrease consistently after overnight stagnation, and daily automated flushes did not have an observable effect on the ORP signals. Additional laboratory experiments were carried out to evaluate ORP signals during chlorine decay and after incremental changes in chlorine, as would be expected in building plumbing conditions. Results from the lab and field deployments suggest on-line ORP sensors may be used to detect free chlorine decay due to stagnating water, but are not as effective in detecting chloramine decay. However, field results also suggest ORP may not respond as expected on a timely manner after free chlorine or chloramine have been restored, hindering their applicability in developing control algorithms. In this paper we tested twice-daily five-minute automatic flushing and found that it counteracts water quality degradation associated with overnight stagnation in free chlorine systems. An automatic sensor-based flushing is proposed using online temperature sensor data to determine when flushing has reached water from the main. The results suggest that flushing informed by temperature sensors can reduce the flushing time by 46 % compared to the preset five-minute static flush., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)
- Published
- 2024
- Full Text
- View/download PDF
11. Machine Learning-Assisted, Process-Based Quality Control for Detecting Compromised Environmental Sensors.
- Author
-
Schmidt JQ and Kerkez B
- Subjects
- Machine Learning, Algorithms, Michigan, Drinking Water
- Abstract
Machine learning (ML) techniques promise to revolutionize environmental research and management, but collecting the necessary volumes of high-quality data remains challenging. Environmental sensors are often deployed under harsh conditions, requiring labor-intensive quality assurance and control (QAQC) processes. The need for manual QAQC is a major impediment to the scalability of these sensor networks. Existing techniques for automated QAQC make strong assumptions about noise profiles in the data they filter that do not necessarily hold for broadly deployed environmental sensors, however. Toward the goal of increasing the volume of high-quality environmental data, we introduce an ML-assisted QAQC methodology that is robust to low signal-to-noise ratio data. Our approach embeds sensor measurements into a dynamical feature space and trains a binary classification algorithm (Support Vector Machine) to detect deviation from expected process dynamics, indicating whether a sensor has become compromised and requires maintenance. This strategy enables the automated detection of a wide variety of nonphysical signals. We apply the methodology to three novel data sets produced by 136 low-cost environmental sensors (stream level, drinking water pH, and drinking water electroconductivity), deployed by our group across 250,000 km
2 in Michigan, USA. The proposed methodology achieved accuracy scores of up to 0.97 and consistently outperformed state-of-the-art anomaly detection techniques.- Published
- 2023
- Full Text
- View/download PDF
12. An Automated Toolchain for Camera-Enabled Sensing of Drinking Water Chlorine Residual.
- Author
-
Schubert A, Pifer L, Cheng J, McElmurry SP, Kerkez B, and Love NG
- Abstract
Chlorine residual concentration is an important parameter to prevent pathogen growth in drinking water. Disposable color changing test strips that measure chlorine in tap water are commercially available to the public; however, the color changes are difficult to read by eye, and the data are not captured for water service providers. Here we present an automated toolchain designed to process digital images of free chlorine residual test strips taken with mobile phone cameras. The toolchain crops the image using image processing algorithms that isolate the areas relevant for analysis and automatically white balances the image to allow for use with different phones and lighting conditions. The average red, green, and blue (RGB) color values of the image are used to predict a free chlorine concentration that is classified into three concentration tiers (<0.2 mg/L, 0.2-0.5 mg/L, or >0.5 mg/L), which can be reported to water users and recorded for utility use. The proposed approach was applied to three different phone types under three different lighting conditions using a standard background. This approach can discriminate between concentrations above and below 0.5 mg/L with an accuracy of 90% and 94% for training and testing data sets, respectively. Furthermore, it can discriminate between concentrations of <0.2 mg/L, 0.2-0.5 mg/L, or >0.5 mg/L with weighted-averaged F1 scores of 79% and 88% for training and testing data sets, respectively. This tool sets the stage for tap water consumers and water utilities to gather frequent measurements and high-resolution temporal and spatial data on drinking water quality., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)
- Published
- 2022
- Full Text
- View/download PDF
13. Stationary and portable multipollutant monitors for high-spatiotemporal-resolution air quality studies including online calibration.
- Author
-
Buehler C, Xiong F, Zamora ML, Skog KM, Kohrman-Glaser J, Colton S, McNamara M, Ryan K, Redlich C, Bartos M, Wong B, Kerkez B, Koehler K, and Gentner DR
- Abstract
The distribution and dynamics of atmospheric pollutants are spatiotemporally heterogeneous due to variability in emissions, transport, chemistry, and deposition. To understand these processes at high spatiotemporal resolution and their implications for air quality and personal exposure, we present custom, low-cost air quality monitors that measure concentrations of contaminants relevant to human health and climate, including gases (e.g., O
3 , NO, NO2 , CO, CO2 , CH4 , and SO2 ) and size-resolved (0.3-10 μm) particulate matter. The devices transmit sensor data and location via cellular communications and are capable of providing concentration data down to second-level temporal resolution. We produce two models: one designed for stationary (or mobile platform) operation and a wearable, portable model for directly measuring personal exposure in the breathing zone. To address persistent problems with sensor drift and environmental sensitivities (e.g., relative humidity and temperature), we present the first online calibration system designed specifically for low-cost air quality sensors to calibrate zero and span concentrations at hourly to weekly intervals. Monitors are tested and validated in a number of environments across multiple outdoor and indoor sites in New Haven, CT; Baltimore, MD; and New York City. The evaluated pollutants (O3 , NO2 , NO, CO, CO2 , and PM2.5 ) performed well against reference instrumentation (e.g., r = 0.66-0.98) in urban field evaluations with fast e -folding response times (≤1 min), making them suitable for both large-scale network deployments and smaller-scale targeted experiments at a wide range of temporal resolutions. We also provide a discussion of best practices on monitor design, construction, systematic testing, and deployment., Competing Interests: Competing interests. Drew R. Gentner has externally funded projects on low-cost air quality monitoring technology (EPA, HKF Technology), where the developed technology has been licensed by Yale to HKF Technology.- Published
- 2021
- Full Text
- View/download PDF
14. Extracting useful signals from flawed sensor data: Developing hybrid data-driven approaches with physical factors.
- Author
-
Yang C, Daigger GT, Belia E, and Kerkez B
- Subjects
- Wastewater, Water Quality
- Abstract
Increased availability and affordability of sensors, especially water quality sensors, is poised to improve process control and modelling in water and wastewater systems. Sensor measurements are often flawed by unavoidable influent complexity and sensor instability, making extraction of useful signals problematic. Although a natural reaction is to put extra effort into sensor maintenance to achieve more reliable measurements, useful signals can be extracted from those unqualified signals by appropriate usage of available data-driven tools instructed by physical factors (e.g. prior process knowledge, physical constraints, phenomenal observations). Such methodology is herein defined as hybrid approaches. While the concept of coupling physical factors into data-driven tools is not new in downstream applications such as process modelling and control, little literature has explicitly applied it in the first and equally important step - signal processing. With flawed influent five-day biochemical oxygen demand (BOD
5 ) sensor measurements as an example, this paper provides a comprehensive case study demonstrating how physical factors were incorporated throughout the procedures of processing a flawed signal for its maximum value. Results showed that useful signals were extracted and validated via an assembly of well-established machine learning tools, whose performance was improved with physical factors. An Improved Standard Signal Processing Architecture (ISSPA) is also proposed based on the results of this research., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier Ltd.)- Published
- 2020
- Full Text
- View/download PDF
15. Windshield wipers on connected vehicles produce high-accuracy rainfall maps.
- Author
-
Bartos M, Park H, Zhou T, Kerkez B, and Vasudevan R
- Abstract
Connected vehicles are poised to transform the field of environmental sensing by enabling acquisition of scientific data at unprecedented scales. Drawing on a real-world dataset collected from almost 70 connected vehicles, this study generates improved rainfall estimates by combining weather radar with windshield wiper observations. Existing methods for measuring precipitation are subject to spatial and temporal uncertainties that compromise high-precision applications like flash flood forecasting. Windshield wiper measurements from connected vehicles correct these uncertainties by providing precise information about the timing and location of rainfall. Using co-located vehicle dashboard camera footage, we find that wiper measurements are a stronger predictor of binary rainfall state than traditional stationary gages or radar-based measurements. We introduce a Bayesian filtering framework that generates improved rainfall estimates by updating radar rainfall fields with windshield wiper observations. We find that the resulting rainfall field estimate captures rainfall events that would otherwise be missed by conventional measurements. We discuss how these enhanced rainfall maps can be used to improve flood warnings and facilitate real-time operation of stormwater infrastructure.
- Published
- 2019
- Full Text
- View/download PDF
16. Field and Laboratory Evaluations of the Low-Cost Plantower Particulate Matter Sensor.
- Author
-
Levy Zamora M, Xiong F, Gentner D, Kerkez B, Kohrman-Glaser J, and Koehler K
- Subjects
- Baltimore, Environmental Monitoring, Particle Size, Particulate Matter, Air Pollutants, Air Pollution, Air Pollution, Indoor
- Abstract
Due to the rapid development of low-cost air-quality sensors, a rigorous scientific evaluation has not been conducted for many available sensors. We evaluated three Plantower PMS A003 sensors when exposed to eight particulate matter (PM) sources (i.e., incense, oleic acid, NaCl, talcum powder, cooking emissions, and monodispersed polystyrene latex spheres under controlled laboratory conditions and also residential air and ambient outdoor air in Baltimore, MD). The PM
2.5 sensors exhibited a high degree of precision and R2 values greater than 0.86 for all sources, but the accuracy ranged from 13 to >90% compared with reference instruments. The sensors were most accurate for PM with diameters below 1 μm, and they poorly measured PM in the 2.5-5 μm range. The accuracy of the sensors was dependent on relative humidity (RH), with decreases in accuracy at RH > 50%. The sensors were able to produce meaningful data at low and high temperatures and when in motion, as it would be if utilized for outdoor or personal monitoring applications. It was most accurate in environments with polydispersed particle sources and may not be useful in specialized environments or experiments with narrow distributions of PM or aerosols with a large proportion of coarse PM.- Published
- 2019
- Full Text
- View/download PDF
17. Are all data useful? Inferring causality to predict flows across sewer and drainage systems using directed information and boosted regression trees.
- Author
-
Hu Y, Scavia D, and Kerkez B
- Subjects
- Cities, Michigan, Models, Theoretical, Rain
- Abstract
As more sensor data become available across urban water systems, it is often unclear which of these new measurements are actually useful and how they can be efficiently ingested to improve predictions. We present a data-driven approach for modeling and predicting flows across combined sewer and drainage systems, which fuses sensor measurements with output of a large numerical simulation model. Rather than adjusting the structure and parameters of the numerical model, as is commonly done when new data become available, our approach instead learns causal relationships between the numerically-modeled outputs, distributed rainfall measurements, and measured flows. By treating an existing numerical model - even one that may be outdated - as just another data stream, we illustrate how to automatically select and combine features that best explain flows for any given location. This allows for new sensor measurements to be rapidly fused with existing knowledge of the system without requiring recalibration of the underlying physics. Our approach, based on Directed Information (DI) and Boosted Regression Trees (BRT), is evaluated by fusing measurements across nearly 30 rain gages, 15 flow locations, and the outputs of a numerical sewer model in the city of Detroit, Michigan: one of the largest combined sewer systems in the world. The results illustrate that the Boosted Regression Trees provide skillful predictions of flow, especially when compared to an existing numerical model. The innovation of this paper is the use of the Directed Information step, which selects only those inputs that are causal with measurements at locations of interest. Better predictions are achieved when the Directed Information step is used because it reduces overfitting during the training phase of the predictive algorithm. In the age of "big water data", this finding highlights the importance of screening all available data sources before using them as inputs to data-driven models, since more may not always be better. We discuss the generalizability of the case study and the requirements of transferring the approach to other systems., (Copyright © 2018 Elsevier Ltd. All rights reserved.)
- Published
- 2018
- Full Text
- View/download PDF
18. Shaping Streamflow Using a Real-Time Stormwater Control Network.
- Author
-
Mullapudi A, Bartos M, Wong B, and Kerkez B
- Abstract
"Smart" water systems are transforming the field of stormwater management by enabling real-time monitoring and control of previously static infrastructure. While the localized benefits of active control are well-established, the potential for system-scale control of watersheds is poorly understood. This study shows how a real-world smart stormwater system can be leveraged to shape streamflow within an urban watershed. Specifically, we coordinate releases from two internet-controlled stormwater basins to achieve desired control objectives downstream-such as maintaining the flow at a set-point, and generating interleaved waves. In the first part of the study, we describe the construction of the control network using a low-cost, open-source hardware stack and a cloud-based controller scheduling application. Next, we characterize the system's control capabilities by determining the travel times, decay times, and magnitudes of various waves released from the upstream retention basins. With this characterization in hand, we use the system to generate two desired responses at a critical downstream junction. First, we generate a set-point hydrograph, in which flow is maintained at an approximately constant rate. Next, we generate a series of overlapping and interleaved waves using timed releases from both retention basins. We discuss how these control strategies can be used to stabilize flows, thereby mitigating streambed erosion and reducing contaminant loads into downstream waterbodies.
- Published
- 2018
- Full Text
- View/download PDF
19. An automated toolchain for the data-driven and dynamical modeling of combined sewer systems.
- Author
-
Troutman SC, Schambach N, Love NG, and Kerkez B
- Subjects
- Calibration, Rain, Wastewater, Weather, Hydrology methods, Models, Theoretical, Sewage, Waste Management methods
- Abstract
The recent availability and affordability of sensors and wireless communications is poised to transform our understanding and management of water systems. This will enable a new generation of adaptive water models that can ingest large quantities of sensor feeds and provide the best possible estimates of current and future conditions. To that end, this paper presents a novel data-driven identification/learning toolchain for combined sewer and stormwater systems. The toolchain uses Gaussian Processes to model dry-weather flows (domestic wastewater) and dynamical System Identification to represent wet-weather flows (rainfall runoff). By using a large and high-resolution sensor dataset across a real-world combined sewer system, we illustrate that relatively simple models can achieve good forecasting performance, subject to a finely-tuned and continuous re-calibration procedure. The data requirements of the proposed toolchain are evaluated, showing sensitivity to spatial heterogeneity and unique time-scales across which models of individual sites remain representative. We identify a near-optimal time record, or data "age," for which historical measurements must be available to ensure good forecasting performance. We also show that more data do not always lead to a better model due to system uncertainty, such as shifts in climate or seasonal wastewater patterns. Furthermore, the individual components of the model (wet- and dry-weather) often require different volumes of historical observations for optimal forecasting performance, thus highlighting the need for a flexible re-calibration toolchain rather than a one-size-fits-all approach., (Copyright © 2017 Elsevier Ltd. All rights reserved.)
- Published
- 2017
- Full Text
- View/download PDF
20. Smarter Stormwater Systems.
- Author
-
Kerkez B, Gruden C, Lewis M, Montestruque L, Quigley M, Wong B, Bedig A, Kertesz R, Braun T, Cadwalader O, Poresky A, and Pak C
- Subjects
- Rain, Water Quality, Climate Change, Urbanization
- Abstract
Existing stormwater systems require significant investments to meet challenges imposed by climate change, rapid urbanization, and evolving regulations. There is an unprecedented opportunity to improve urban water quality by equipping stormwater systems with low-cost sensors and controllers. This will transform their operation from static to adaptive, permitting them to be instantly "redesigned" to respond to individual storms and evolving land uses.
- Published
- 2016
- Full Text
- View/download PDF
21. Evaluation of married haemoglobinopathic carrier couples for prevention of haemoglobinopathic births.
- Author
-
Nazlıcan E, Celenk O, Kerkez B, Demirhindi H, Akbaba M, and Kiremitçi M
- Abstract
Background: Abnormal haemoglobins (Hb) and thalassaemias are some of the most frequently observed hereditary disorders in the world, but especially in the Mediterranean region where Turkey is located. Hatay province is one of the largest provinces in the region, suggested as a target area to be selected for preventive programs after studies by three Turkish universities, i.e. Çukurova, Akdeniz and Hacettepe Universities in Turkey., Aims: The aim of this study was to determine demographic and family characteristics of all haemoglobinopathy carrier married couples registered in the Hatay Provincial Health Directorate registry and to educate the target population about pregnancy, births, prenatal diagnosis and genetic counselling with the particularly emphasised scope of eliminating all haemoglobinopathic births., Study Design: Descriptive cross-sectional and intervention study., Methods: 1065 couples both being haemoglobinopathic carriers, registered in the Hatay Provincial Health Directorate registry were investigated for socio-demographic characteristics, obstetrical status and especially for a present pregnancy, the presence of any haemoglobinopathic patients or carrier children in the family., Results: Among women with a history of pregnancy, 47.3% reported that they had never had any prenatal testing, while 33.1% had got received testing in each of their pregnancies. The most frequent reason for not having the test was declared as unawareness of the test (66.0%), followed by economic insufficiencies (17.1%), destiny/religious reasons (9.1%) and family interference (7.8%). After a series of descriptive analyses, the results of the final binary logistic regression model constructed to find out the risk factors significantly affecting the presence of a sick child in the family were grouped as risk increasing factors like age (95%CI between 1.002 and 1.122), marriage before 1994 (95%CI=1.081-4.161), and risk decreasing factors like family willingness for screening (95%CI=0.167-0.854), rate of prenatal testing (95%CI=0.147-0.414), age at first pregnancy (95%CI=0.469-0.882); while the frequency of births was found to have no significant effect (p>0.05)., Conclusion: Besides all legal regulations and applications, time is still needed for real success against such a diffuse and congenitally transferred disease. The education of the target populations appears to be crucial. Official applications should be forced based upon present or future laws.
- Published
- 2013
- Full Text
- View/download PDF
22. TDMA-based dual-mode communication for mobile wireless sensor networks.
- Author
-
Mehta A, Kerkez B, Glaser SD, and Pister KS
- Subjects
- Algorithms, Glycine chemistry, Humans, Robotics methods, Telemetry instrumentation, Vitamin E chemistry, Computer Communication Networks instrumentation, Glycine analogs & derivatives, Robotics instrumentation, Vitamin E analogs & derivatives, Wireless Technology
- Abstract
Small highly mobile robots, and in particular micro air vehicles (MAVs), are well suited to the task of exploring unknown indoor environments such as buildings and caves. Such a task imposes a number of requirements on the underlying communication infrastructure, with differing goals during various stages of the mission. This work addresses those requirements with a hybrid communications infrastructure consisting of a stationary mesh network along with the mobile nodes. The combined network operates in two independent modes, coupling a highly efficient, low duty cycle, low throughput mode for routing and persistent sensing with a burst mode for high data rate communication. By strategically distributing available frequency channels between the mobile agents and the stationary nodes, the overall network provides reliable long-term communication paths while maximizing data throughput when needed.
- Published
- 2012
- Full Text
- View/download PDF
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.