Events Recognition System for Water Treatment Works

The supply of drinking water in sufficient quantity and required quality is a challenging task for water companies. Tackling this task successfully depends largely on ensuring a continuous high quality level of water treatment at Water Treatment Works (WTW). Therefore, processes at WTWs are highly automated and controlled. A reliable and rapid detection of faulty sensor data and failure events at WTWs processes is of prime importance for its efficient and effective operation. Therefore, the vast majority of WTWs operated in the UK make use of event detection systems that automatically generate alarms after the detection of abnormal behaviour on observed signals to ensure an early detection of WTW’s process failures. Event detection systems usually deployed at WTWs apply thresholds to the monitored signals for the recognition of WTW’s faulty processes. The research work described in this thesis investigates new methods for near real-time event detection at WTWs by the implementation of statistical process control and machine learning techniques applied for an automated near real-time recognition of failure events at WTWs processes. The resulting novel Hybrid CUSUM Event Recognition System (HC-ERS) makes use of new online sensor data validation and pre-processing techniques and utilises two distinct detection methodologies: first for fault detection on individual signals and second for the recognition of faulty processes and events at WTWs. The fault detection methodology automatically detects abnormal behaviour of observed water quality parameters in near real-time using the data of the corresponding sensors that is online validated and pre-processed. The methodology utilises CUSUM control charts to predict the presence of faults by tracking the variation of each signal individually to identify abnormal shifts in its mean. The basic CUSUM methodology was refined by investigating optimised interdependent parameters for each signal individually. The combined predictions of CUSUM fault detection on individual signals serves the basis for application of the second event detection methodology. The second event detection methodology automatically identifies faults at WTW’s processes respectively failure events at WTWs in near real-time, utilising the faults detected by CUSUM fault detection on individual signals beforehand. The method applies Random Forest classifiers to predict the presence of an event at WTW’s processes. All methods have been developed to be generic and generalising well across different drinking water treatment processes at WTWs. HC-ERS has proved to be effective in the detection of failure events at WTWs demonstrated by the application on real data of water quality signals with historical events from a UK’s WTWs. The methodology achieved a peak F1 value of 0.84 and generates 0.3 false alarms per week. These results demonstrate the ability of method to automatically and reliably detect failure events at WTW’s processes in near real-time and also show promise for practical application of the HC-ERS in industry. The combination of both methodologies presents a unique contribution to the field of near real-time event detection at WTW

Improving Detection of Events at Water Treatment Works: A UK Case Study

This is the author accepted manuscriptThis study presents improvements to the event detection capabilities of the existing, thresholdbased detection system used by United Utilities in one of their Water Treatment Works. These improvements were achieved by using new threshold and persistence values identified by performing a sensitivity type analysis. The findings from this study show that, although an overall increase in the true detection rate and decrease in the number of false alarms were achieved, the high number of false alarms remains an issue

Active Learning Metamodels for ATM Simulation Modeling

Transportation systems are particularly prone to exhibiting overwhelming complexity on account of the numerous involved variables and their interrelationships, unknown stochastic phenomena, and ultimately human behavior. Simulation approaches are commonly used tools to describe and study such intricate real-world systems. Despite their obvious advantages,simulation models can still end up being quite complex themselves. The field of Air Traffic Management (ATM) modeling is no stranger to such concerns, as it traditionally involves laborious and systematic analyses built upon computationally heavy simulation models. This rather frequent shortcoming can be addressed by employing simulation metamodels combined with active learning strategies to approximate the input-output mappings inherently defined by the simulation models in an efficient way. In this work, we propose an exploration framework that integrates active learning and simulation metamodeling in a single unified approach to address recurrent computational bottlenecks typically associated with intense performance impact assessments within the field of ATM. Our methodology is designed to systematically explore the simulation input space in an efficient and self-guided manner, ultimately providing ATM practitioners with meaningful insights concerning the simulation models under study. Using a fully developed state-of-the-art ATM simulator and employing a Gaussian Process as a metamodel, we show that active learning is indeed capable of enhancing both the modeling and performances of simulation metamodeling by strategically avoiding redundant computer experiments and predicting simulation outputs values

Active Learning for Air Traffic Management Simulation Metamodeling

Transportation systems are particularly prone to exhibiting overwhelming complexity on account of the numerous involved variables, corresponding interrelationships, and the unpredictability of human behavior. Simulation approaches are commonly used tools to describe and study such intricate real-world systems. Despite their clear advantages, these models can too suffer from high complexity and computational hindrances, especially when designed along with fine detail. The field of Air Traffic Management (ATM) modeling is no stranger to such concerns, as it traditionally involves exhausting and manual-driven intense analyses built upon computationally heavy simulation models. This rather frequent shortcoming can be addressed by employing simulation metamodels combined with active learning strategies to approximate, via fast functions, the input-output mappings inherently defined by the simulation models in an efficient way. In this work, we propose an exploration framework that integrates active learning and simulation metamodeling in a single unified approach to address recurrent computational bottlenecks typically associated with intense performance impact assessments within the field of ATM. Our methodology is designed to systematically explore the simulation input space in an efficient and self-guided manner, ultimately providing ATM practitioners with meaningful insights concerning the simulation models under study. Using a fully developed state-of-the-art ATM simulator and employing a Gaussian Process as a metamodel, we show that active learning is indeed capable of enhancing both the modeling and performances of simulation metamodeling by strategically avoiding redundant computer experiments and predicting simulation outputs values given a pre-specified input region

Pseudo-time Schroedinger equation with absorbing potential for quantum scattering calculations

The Schroedinger equation with an energy-dependent complex absorbing potential, associated with a scattering system, can be reduced for a special choice of the energy-dependence to a harmonic inversion problem of a discrete pseudo-time correlation function. An efficient formula for Green's function matrix elements is also derived. Since the exact propagation up to time 2t can be done with only t real matrix-vector products, this gives an unprecedently efficient scheme for accurate calculations of quantum spectra for possibly very large systems.Comment: 9 page

Detection of water quality failure events at treatment works using a hybrid two-stage method with CUSUM and random forest algorithms

This is the final version. Available from IWA Publishing via the DOI in this record. Data cannot be made publicly available; readers should contact the corresponding author for details.Near-real-time event detection is crucial for water utilities to be able to detect failure events in their water treatment works (WTW) quickly and efficiently. This paper presents a new method for an automated, near-real-time recognition of failure events at WTWs by the application of combined statistical process control and machine-learning techniques. The resulting novel hybrid CUSUM event recognition system (HC-ERS) uses two distinct detection methodologies: one for fault detection at the level of individual water quality signals and the second for the recognition of faulty processes at the WTW level. HC-ERS was tested and validated on historical failure events at a real-life UK WTW. The new methodology proved to be effective in the detection of failure events, achieving a high true-detection rate of 82% combined with a low false-alarm rate (average 0.3 false alarms per week), reaching a peak F1 score of 84% as a measure of accuracy. The new method also demonstrated higher accuracy compared with the CANARY detection methodology. When applied to real-world data, the HC-ERS method showed the capability to detect faulty processes at WTW automatically and reliably, and hence potential for practical application in the water industry.Engineering and Physical Sciences Research Council (EPSRC

Dominant B-cell epitopes from cancer/stem cell antigen SOC2 recognized by serum samples from cancer patients

Cataloged from PDF version of article.Human sex determining region Y-box 2 (SOX2) is an important transcriptional factor involved in the pluripotency and stemness of human embryonic stem cells. SOX2 plays important roles in maintaining cancer stem cell activities of melanoma and cancers of the brain, prostate, breast, and lung. SOX2 is also a lineage survival oncogene for squamous cell carcinoma of the lung and esophagus. Spontaneous cellular and humoral immune responses against SOX2 present in cancer patients classify it as a tumor-associated antigen (TAA) shared by lung cancer, glioblastoma, and prostate cancer among others. In this study, B-cell epitopes were predicted using computer-assisted algorithms. Synthetic peptides based on the prediction were screened for recognition by serum samples from cancer patients using ELISA. Two dominant B-cell epitopes, SOX2:52-87 and SOX2:98-124 were identified. Prostate cancer, glioblastoma and lung cancer serum samples that recognized the above SOX2 epitopes also recognized the full-length protein based on Western blot. These B-cell epitopes may be used in assessing humoral immune responses against SOX2 in cancer immunotherapy and stem cell-related transplantation

Partial pharmacologic blockade shows sympathetic connection between blood pressure and cerebral blood flow velocity fluctuations

Cerebral autoregulation (CA) dampens transfer of blood pressure (BP)-fluctuations onto cerebral blood flow velocity (CBFV). Thus, CBFV-oscillations precede BP-oscillations. The phase angle (PA) between sympathetically mediated low-frequency (LF: 0.03–0.15 Hz) BP- and CBFV-oscillations is a measure of CA quality. To evaluate whether PA depends on sympathetic modulation, we assessed PA-changes upon sympathetic stimulation with and without pharmacologic sympathetic blockade. In 10 healthy, young men, we monitored mean BP and CBFV before and during 120-second cold pressor stimulation (CPS) of one foot (0 °C ice-water). We calculated mean values, standard deviations and sympathetic LF-powers of all signals, and PAs between LF-BP- and LF–CBFV-oscillations. We repeated measurements after ingestion of the adrenoceptor-blocker carvedilol (25 mg). We compared parameters before and during CPS, without and after carvedilol (analysis of variance, post-hoc t-tests, significance: p < 0.05). Without carvedilol, CPS increased BP, CBFV, BP-LF- and CBFV-LF-powers, and shortened PA. Carvedilol decreased resting BP, CBFV, BP-LF- and CBFV-LF-powers, while PAs remained unchanged. During CPS, BPs, CBFVs, BP-LF- and CBFV-LF-powers were lower, while PAs were longer with than without carvedilol. With carvedilol, CPS no longer shortened resting PA. Sympathetic activation shortens PA. Partial adrenoceptor blockade abolishes this PA-shortening. Thus, PA-measurements provide a subtle marker of sympathetic influences on CA and might refine CA evaluation

Conceptual Foundations for a Service-oriented Knowledge and Learning Architecture: Supporting Content, Process and Ontology Maturing

Abstract: The knowledge maturing model views learning activities as embedded into, interwoven with, and even indistinguishable from everyday work processes. Learning is understood as an inherently social and collaborative activity. The Knowledge Maturing Process Model structures this process into five phases: expressing ideas, distributing in communities, formalizing, ad-hoc learning and standardization. It is applicable not only for content but also to process knowledge and semantics. In the MATURE IP two toolsets will be develop that support the maturing process: a personal learning environment and an organisation learning environment integrating the levels of individuals, communities and organisation. The development is guided by the SER theory of seeding, evolutionary growth and reseeding and is based on generally applicable maturing services