Sample records for learning time monitoring
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Nuclear power plant monitoring using real-time learning neural network
International Nuclear Information System (INIS)
Nabeshima, Kunihiko; Tuerkcan, E.; Ciftcioglu, O.
1994-01-01
In the present research, artificial neural network (ANN) with real-time adaptive learning is developed for the plant wide monitoring of Borssele Nuclear Power Plant (NPP). Adaptive ANN learning capability is integrated to the monitoring system so that robust and sensitive on-line monitoring is achieved in real-time environment. The major advantages provided by ANN are that system modelling is formed by means of measurement information obtained from a multi-output process system, explicit modelling is not required and the modelling is not restricted to linear systems. Also ANN can respond very fast to anomalous operational conditions. The real-time ANN learning methodology with adaptive real-time monitoring capability is described below for the wide-range and plant-wide data from an operating nuclear power plant. The layered neural network with error backpropagation algorithm for learning has three layers. The network type is auto-associative, inputs and outputs are exactly the same, using 12 plant signals. (author)
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Agustin, Eny Widhia; Hangga, Arimaz; Fahrian, Muhammad Iqbal; Azhari, Anis Fikri
2018-03-01
The implementation of monitoring system in the facial acupressure learning media could increase the students' proficiency. However the common learning media still has not implemented a monitoring system in their learning process. This research was conducted to implement monitoring system in the mannequin head prototype as a learning media of facial acupressure using Bluetooth, wireless and Ethernet. The results of the implementation of monitoring system in the prototype showed that there were differences in the delay time between Bluetooth and wireless or Ethernet. The results data showed no difference in the average delay time between the use of Bluetooth with wireless and the use of Bluetooth with Ethernet in monitoring system of facial acupressure learning media. From all the facial acupressure points, the forehead facial acupressure point has the longest delay time of 11.93 seconds. The average delay time in all 3 class rooms was 1.96 seconds therefore the use of Bluetooth, wireless and Ethernet is highly recommended in the monitoring system of facial acupressure.
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Li, Xuejian; Wang, Youqing
2016-12-01
Offline general-type models are widely used for patients' monitoring in intensive care units (ICUs), which are developed by using past collected datasets consisting of thousands of patients. However, these models may fail to adapt to the changing states of ICU patients. Thus, to be more robust and effective, the monitoring models should be adaptable to individual patients. A novel combination of just-in-time learning (JITL) and principal component analysis (PCA), referred to learning-type PCA (L-PCA), was proposed for adaptive online monitoring of patients in ICUs. JITL was used to gather the most relevant data samples for adaptive modeling of complex physiological processes. PCA was used to build an online individual-type model and calculate monitoring statistics, and then to judge whether the patient's status is normal or not. The adaptability of L-PCA lies in the usage of individual data and the continuous updating of the training dataset. Twelve subjects were selected from the Physiobank's Multi-parameter Intelligent Monitoring for Intensive Care II (MIMIC II) database, and five vital signs of each subject were chosen. The proposed method was compared with the traditional PCA and fast moving-window PCA (Fast MWPCA). The experimental results demonstrated that the fault detection rates respectively increased by 20 % and 47 % compared with PCA and Fast MWPCA. L-PCA is first introduced into ICU patients monitoring and achieves the best monitoring performance in terms of adaptability to changes in patient status and sensitivity for abnormality detection.
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Time will tell: The role of mobile learning analytics in self-regulated learning
Tabuenca, Bernardo; Kalz, Marco; Drachsler, Hendrik; Specht, Marcus
2015-01-01
This longitudinal study explores the effects of tracking and monitoring time devoted to learn with a mobile tool, on self-regulated learning. Graduate students (n = 36) from three different online courses used their own mobile devices to track how much time they devoted to learn over a period of
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Thomsen, Jakob Louis Demant; Mathiesen, Ole; Hägi-Pedersen, Daniel; Skovgaard, Lene Theil; Østergaard, Doris; Engbaek, Jens; Gätke, Mona Ring
2017-10-06
Muscle relaxants facilitate endotracheal intubation under general anesthesia and improve surgical conditions. Residual neuromuscular blockade occurs when the patient is still partially paralyzed when awakened after surgery. The condition is associated with subjective discomfort and an increased risk of respiratory complications. Use of an objective neuromuscular monitoring device may prevent residual block. Despite this, many anesthetists refrain from using the device. Efforts to increase the use of objective monitoring are time consuming and require the presence of expert personnel. A neuromuscular monitoring e-learning module might support consistent use of neuromuscular monitoring devices. The aim of the study is to assess the effect of a neuromuscular monitoring e-learning module on anesthesia staff's use of objective neuromuscular monitoring and the incidence of residual neuromuscular blockade in surgical patients at 6 Danish teaching hospitals. In this interrupted time series study, we are collecting data repeatedly, in consecutive 3-week periods, before and after the intervention, and we will analyze the effect using segmented regression analysis. Anesthesia departments in the Zealand Region of Denmark are included, and data from all patients receiving a muscle relaxant are collected from the anesthesia information management system MetaVision. We will assess the effect of the module on all levels of potential effect: staff's knowledge and skills, patient care practice, and patient outcomes. The primary outcome is use of neuromuscular monitoring in patients according to the type of muscle relaxant received. Secondary outcomes include last recorded train-of-four value, administration of reversal agents, and time to discharge from the postanesthesia care unit as well as a multiple-choice test to assess knowledge. The e-learning module was developed based on a needs assessment process, including focus group interviews, surveys, and expert opinions. The e-learning
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Monitoring REDD+: From Social Safeguards to Social Learning
Ravikumar, A.; Andersson, K.
2010-12-01
Krister Andersson 1 and Ashwin Ravikumar 1 The UNFCCC requires countries that participate in the REDD+ (Reducing Emissions from Deforestation and Forest Degradation in Developing Countries) program to monitor both forest carbon inventories as well as the governance of REDD+ activities and their social consequences. Exactly how this should be done, however, remains an open question. This paper addresses this question by drawing on existing research on social-ecological systems and new institutional economics. We make the case for a monitoring system that goes beyond a narrow focus of qualitative indicators of REDD+ governance that seek to provide social safeguards for international investors to create a more comprehensive monitoring system that is useful for social learning about how policies affect a variety of forest outcomes. We describe the defining characteristics of five existing approaches to monitoring REDD+ governance. Applying evaluative criteria of affordability, comprehensiveness, transparency, uncertainty specification, and explanatory potential, we analyze the extent to which each of the programs contribute to broader social learning processes in participating countries. Our analysis finds that it makes sense to move from the current narrow focus of monitoring for control to monitoring for social learning. Particularly valuable to participating REDD+ actors would be the creation of learning systems that can help policy makers to identify opportunities for policy improvements, with the ultimate goal of making REDD+ more effective, efficient, and equitable. Such learning is not possible, however, without timely and systematic collection of data on the relationships between forests and forest users. 1University of Colorado at Boulder, Environmental Studies Program, Boulder, CO 80309-0397
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Online neural monitoring of statistical learning.
Batterink, Laura J; Paller, Ken A
2017-05-01
The extraction of patterns in the environment plays a critical role in many types of human learning, from motor skills to language acquisition. This process is known as statistical learning. Here we propose that statistical learning has two dissociable components: (1) perceptual binding of individual stimulus units into integrated composites and (2) storing those integrated representations for later use. Statistical learning is typically assessed using post-learning tasks, such that the two components are conflated. Our goal was to characterize the online perceptual component of statistical learning. Participants were exposed to a structured stream of repeating trisyllabic nonsense words and a random syllable stream. Online learning was indexed by an EEG-based measure that quantified neural entrainment at the frequency of the repeating words relative to that of individual syllables. Statistical learning was subsequently assessed using conventional measures in an explicit rating task and a reaction-time task. In the structured stream, neural entrainment to trisyllabic words was higher than in the random stream, increased as a function of exposure to track the progression of learning, and predicted performance on the reaction time (RT) task. These results demonstrate that monitoring this critical component of learning via rhythmic EEG entrainment reveals a gradual acquisition of knowledge whereby novel stimulus sequences are transformed into familiar composites. This online perceptual transformation is a critical component of learning. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Stiers, Peter; Falbo, Luciana; Goulas, Alexandros; van Gog, Tamara; de Bruin, Anique
2016-01-01
Monitoring of learning is only accurate at some time after learning. It is thought that immediate monitoring is based on working memory, whereas later monitoring requires re-activation of stored items, yielding accurate judgements. Such interpretations are difficult to test because they require
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Application of Machine Learning to Rotorcraft Health Monitoring
Cody, Tyler; Dempsey, Paula J.
2017-01-01
Machine learning is a powerful tool for data exploration and model building with large data sets. This project aimed to use machine learning techniques to explore the inherent structure of data from rotorcraft gear tests, relationships between features and damage states, and to build a system for predicting gear health for future rotorcraft transmission applications. Classical machine learning techniques are difficult, if not irresponsible to apply to time series data because many make the assumption of independence between samples. To overcome this, Hidden Markov Models were used to create a binary classifier for identifying scuffing transitions and Recurrent Neural Networks were used to leverage long distance relationships in predicting discrete damage states. When combined in a workflow, where the binary classifier acted as a filter for the fatigue monitor, the system was able to demonstrate accuracy in damage state prediction and scuffing identification. The time dependent nature of the data restricted data exploration to collecting and analyzing data from the model selection process. The limited amount of available data was unable to give useful information, and the division of training and testing sets tended to heavily influence the scores of the models across combinations of features and hyper-parameters. This work built a framework for tracking scuffing and fatigue on streaming data and demonstrates that machine learning has much to offer rotorcraft health monitoring by using Bayesian learning and deep learning methods to capture the time dependent nature of the data. Suggested future work is to implement the framework developed in this project using a larger variety of data sets to test the generalization capabilities of the models and allow for data exploration.
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A Just-in-Time Learning based Monitoring and Classification Method for Hyper/Hypocalcemia Diagnosis.
Peng, Xin; Tang, Yang; He, Wangli; Du, Wenli; Qian, Feng
2017-01-20
This study focuses on the classification and pathological status monitoring of hyper/hypo-calcemia in the calcium regulatory system. By utilizing the Independent Component Analysis (ICA) mixture model, samples from healthy patients are collected, diagnosed, and subsequently classified according to their underlying behaviors, characteristics, and mechanisms. Then, a Just-in-Time Learning (JITL) has been employed in order to estimate the diseased status dynamically. In terms of JITL, for the purpose of the construction of an appropriate similarity index to identify relevant datasets, a novel similarity index based on the ICA mixture model is proposed in this paper to improve online model quality. The validity and effectiveness of the proposed approach have been demonstrated by applying it to the calcium regulatory system under various hypocalcemic and hypercalcemic diseased conditions.
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Stiers, Peter; Falbo, Luciana; Goulas, Alexandros; van Gog, Tamara; de Bruin, Anique
2016-05-15
Monitoring of learning is only accurate at some time after learning. It is thought that immediate monitoring is based on working memory, whereas later monitoring requires re-activation of stored items, yielding accurate judgements. Such interpretations are difficult to test because they require reverse inference, which presupposes specificity of brain activity for the hidden cognitive processes. We investigated whether multivariate pattern classification can provide this specificity. We used a word recall task to create single trial examples of immediate and long term retrieval and trained a learning algorithm to discriminate them. Next, participants performed a similar task involving monitoring instead of recall. The recall-trained classifier recognized the retrieval patterns underlying immediate and long term monitoring and classified delayed monitoring examples as long-term retrieval. This result demonstrates the feasibility of decoding cognitive processes, instead of their content. Copyright © 2016 Elsevier Inc. All rights reserved.
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Monitoring and regulation of learning in medical education: the need for predictive cues.
de Bruin, Anique B H; Dunlosky, John; Cavalcanti, Rodrigo B
2017-06-01
Being able to accurately monitor learning activities is a key element in self-regulated learning in all settings, including medical schools. Yet students' ability to monitor their progress is often limited, leading to inefficient use of study time. Interventions that improve the accuracy of students' monitoring can optimise self-regulated learning, leading to higher achievement. This paper reviews findings from cognitive psychology and explores potential applications in medical education, as well as areas for future research. Effective monitoring depends on students' ability to generate information ('cues') that accurately reflects their knowledge and skills. The ability of these 'cues' to predict achievement is referred to as 'cue diagnosticity'. Interventions that improve the ability of students to elicit predictive cues typically fall into two categories: (i) self-generation of cues and (ii) generation of cues that is delayed after self-study. Providing feedback and support is useful when cues are predictive but may be too complex to be readily used. Limited evidence exists about interventions to improve the accuracy of self-monitoring among medical students or trainees. Developing interventions that foster use of predictive cues can enhance the accuracy of self-monitoring, thereby improving self-study and clinical reasoning. First, insight should be gained into the characteristics of predictive cues used by medical students and trainees. Next, predictive cue prompts should be designed and tested to improve monitoring and regulation of learning. Finally, the use of predictive cues should be explored in relation to teaching and learning clinical reasoning. Improving self-regulated learning is important to help medical students and trainees efficiently acquire knowledge and skills necessary for clinical practice. Interventions that help students generate and use predictive cues hold the promise of improved self-regulated learning and achievement. This framework is
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Real-time Data Access Monitoring in Distributed, Multi-petabyte Systems
Energy Technology Data Exchange (ETDEWEB)
Azemoon, Tofigh; Becla, Jacek, a=Hanushevsky, Andrew; Turri, Massimiliano; /SLAC
2008-04-22
Petascale systems are in existence today and will become common in the next few years. Such systems are inevitably very complex, highly distributed and heterogeneous. Monitoring a petascale system in real-time and understanding its status at any given moment without impacting its performance is a highly intricate task. Common approaches and off-the-shelf tools are either unusable, do not scale, or severely impact the performance of the monitored servers. This paper describes unobtrusive monitoring software developed at Stanford Linear Accelerator Center (SLAC) for a highly distributed petascale production data set. The paper describes the employed solutions, the lessons learned, the problems still to be addressed, and explains how the system can be reused elsewhere.
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Self-learning health monitoring algorithm in composite structures
Grassia, Luigi; Iannone, Michele; Califano, America; D'Amore, Alberto
2018-02-01
The paper describes a system that it is able of monitoring the health state of a composite structure in real time. The hardware of the system consists of a wire of strain sensors connected to a control unit. The software of the system elaborates the strain data and in real time is able to detect the presence of an eventual damage of the structures monitored with the strain sensors. The algorithm requires as input only the strains of the monitored structured measured on real time, i.e. those strains coming from the deformations of the composite structure due to the working loads. The health monitoring system does not require any additional device to interrogate the structure as often used in the literature, instead it is based on a self-learning procedure. The strain data acquired when the structure is healthy are used to set up the correlations between the strain in different positions of structure by means of neural network. Once the correlations between the strains in different position have been set up, these correlations act as a fingerprint of the healthy structure. In case of damage the correlation between the strains in the position of the structure near the damage will change due to the change of the stiffness of the structure caused by the damage. The developed software is able to recognize the change of the transfer function between the strains and consequently is able to detect the damage.
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NEW MATERIALS FOR PEDAGOGICAL TEACHING-LEARNING IN BIOCHEMISTRY: MONITORING PARTICIPATION
Directory of Open Access Journals (Sweden)
R. S. Campos
2015-08-01
Full Text Available This summary consists of an experience report about actions taken by biochemical monitors with pharmacy students. The reason of our work was the intention to both improve the process of teaching and also learning and invalidate the labels owned by biochemistry of hard and high-level-failure subject. The three actors: teachers, students and monitor could act on an integrated basis for the construction of an articulated pedagogical process between theory/practice and learning signification. Our main objective was to initiate the monitors in teaching practice effected through educational projects aimed at improving the teaching and learning of undergraduate courses and encouraging teacher training, involving teachers and students the guiding condition and monitors, respectively. The methodology was applied in three stages: 1 preparation of teaching materials; 2nd application in class and 3rd students rating of the methodology applied by monitors. The teaching materials presented discussed several biochemistry's topics and students had the opportunity to scaffold their own knowledge actively. Almost 90% considered the tool applied as highly related to classes and 82% considered this way of learning more significant than dialogical lectures. The performance of the monitors, focused on students and their learning, was considered great by students who were more motivated, resulting in the excellent evaluation of the work (100% of acceptance. The failure rate of the subject reduced in the four groups wherein the pedagogical materials were applied. It can demonstrate that both the mastery of scientific content and the pedagogical process involved during the teaching and learning moments are important.
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Wang, Bo; Bukuan, Sun
2015-05-01
Two experiments examined the time-dependent effects of negative emotion on consolidation of item and internal-monitoring source memory. In Experiment 1, participants (n=121) learned a list of words. They were asked to read aloud half of the words and to think about the remaining half. They were instructed to memorize each word and its associative cognitive operation ("reading" versus "thinking"). Immediately following learning they conducted free recall and then watched a 3-min either neutral or negative video clip when 5 min, 30 min or 45 min had elapsed after learning. Twenty-four hours later they returned to take surprise tests for item and source memory. Experiment 2 was similar to Experiment 1 except that participants, without conducting an immediate test of free recall, took tests of source memory for all encoded words both immediately and 24 h after learning. Experiment 1 showed that negative emotion enhanced consolidation of item memory (as measured by retention ratio of free recall) regardless of delay of emotion elicitation and that negative emotion enhanced consolidation of source memory when it was elicited at a 5 min delay but reduced consolidation of source memory when it was elicited at a 30 min delay; when elicited at a 45 min delay, negative emotion had little effect. Furthermore, Experiment 2 replicated the enhancement effect on source memory in the 5 min delay even when participants were tested on all the encoded words. The current study partially replicated prior studies on item memory and extends the literature by providing evidence for a time-dependent effect of negative emotion on consolidation of source memory based on internal monitoring. Copyright © 2015 Elsevier B.V. All rights reserved.
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Forecasting air quality time series using deep learning.
Freeman, Brian S; Taylor, Graham; Gharabaghi, Bahram; Thé, Jesse
2018-04-13
This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O 3 ) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O 3 exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours. Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution
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Real-time bioacoustics monitoring and automated species identification
Directory of Open Access Journals (Sweden)
T. Mitchell Aide
2013-07-01
Full Text Available Traditionally, animal species diversity and abundance is assessed using a variety of methods that are generally costly, limited in space and time, and most importantly, they rarely include a permanent record. Given the urgency of climate change and the loss of habitat, it is vital that we use new technologies to improve and expand global biodiversity monitoring to thousands of sites around the world. In this article, we describe the acoustical component of the Automated Remote Biodiversity Monitoring Network (ARBIMON, a novel combination of hardware and software for automating data acquisition, data management, and species identification based on audio recordings. The major components of the cyberinfrastructure include: a solar powered remote monitoring station that sends 1-min recordings every 10 min to a base station, which relays the recordings in real-time to the project server, where the recordings are processed and uploaded to the project website (arbimon.net. Along with a module for viewing, listening, and annotating recordings, the website includes a species identification interface to help users create machine learning algorithms to automate species identification. To demonstrate the system we present data on the vocal activity patterns of birds, frogs, insects, and mammals from Puerto Rico and Costa Rica.
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Learning Time and Educational Effectiveness.
Anderson, Lorin W.
1980-01-01
To explore the relationship between time and school learning, this paper defines the three kinds of learning time identified by researchers--allocated time, time-on-task, and academic learning time--and relates them to curriculum development. The author cites evidence that time-on-task is related to student achievement and describes two…
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Novel Use of a Noninvasive Hemodynamic Monitor in a Personalized, Active Learning Simulation
Zoller, Jonathan K.; He, Jianghua; Ballew, Angela T.; Orr, Walter N.; Flynn, Brigid C.
2017-01-01
The present study furthered the concept of simulation-based medical education by applying a personalized active learning component. We tested this novel approach utilizing a noninvasive hemodynamic monitor with the capability to measure and display in real time numerous hemodynamic parameters in the exercising participant. Changes in medical…
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Real-Time and Seamless Monitoring of Ground-Level PM2.5 Using Satellite Remote Sensing
Li, Tongwen; Zhang, Chengyue; Shen, Huanfeng; Yuan, Qiangqiang; Zhang, Liangpei
2018-04-01
Satellite remote sensing has been reported to be a promising approach for the monitoring of atmospheric PM2.5. However, the satellite-based monitoring of ground-level PM2.5 is still challenging. First, the previously used polar-orbiting satellite observations, which can be usually acquired only once per day, are hard to monitor PM2.5 in real time. Second, many data gaps exist in satellitederived PM2.5 due to the cloud contamination. In this paper, the hourly geostationary satellite (i.e., Harawari-8) observations were adopted for the real-time monitoring of PM2.5 in a deep learning architecture. On this basis, the satellite-derived PM2.5 in conjunction with ground PM2.5 measurements are incorporated into a spatio-temporal fusion model to fill the data gaps. Using Wuhan Urban Agglomeration as an example, we have successfully derived the real-time and seamless PM2.5 distributions. The results demonstrate that Harawari-8 satellite-based deep learning model achieves a satisfactory performance (out-of-sample cross-validation R2 = 0.80, RMSE = 17.49 μg/m3) for the estimation of PM2.5. The missing data in satellite-derive PM2.5 are accurately recovered, with R2 between recoveries and ground measurements of 0.75. Overall, this study has inherently provided an effective strategy for the realtime and seamless monitoring of ground-level PM2.5.
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Designing monitoring arrangements for collaborative learning about adaptation pathways
Hermans, L.M.; Haasnoot, M.; ter Maat, Judith; Kwakkel, J.H.
2017-01-01
Adaptation pathways approaches support long-term planning under uncertainty. The use of adaptation pathways implies a systematic monitoring effort to inform future adaptation decisions. Such monitoring should feed into a long-term collaborative learning process between multiple actors at various
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Monitoring risk-adjusted medical outcomes allowing for changes over time.
Steiner, Stefan H; Mackay, R Jock
2014-10-01
We consider the problem of monitoring and comparing medical outcomes, such as surgical performance, over time. Performance is subject to change due to a variety of reasons including patient heterogeneity, learning, deteriorating skills due to aging, etc. For instance, we expect inexperienced surgeons to improve their skills with practice. We propose a graphical method to monitor surgical performance that incorporates risk adjustment to account for patient heterogeneity. The procedure gives more weight to recent outcomes and down-weights the influence of outcomes further in the past. The chart is clinically interpretable as it plots an estimate of the failure rate for a "standard" patient. The chart also includes a measure of uncertainty in this estimate. We can implement the method using historical data or start from scratch. As the monitoring proceeds, we can base the estimated failure rate on a known risk model or use the observed outcomes to update the risk model as time passes. We illustrate the proposed method with an example from cardiac surgery. © The Author 2013. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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Parihar, Vijay; Yadav, Y R; Kher, Yatin; Ratre, Shailendra; Sethi, Ashish; Sharma, Dhananjaya
2016-01-01
Steep learning curve is found initially in pure endoscopic procedures. Video telescopic operating monitor (VITOM) is an advance in rigid-lens telescope systems provides an alternative method for learning basics of neuroendoscopy with the help of the familiar principle of microneurosurgery. The aim was to evaluate the clinical utility of VITOM as a learning tool for neuroendoscopy. Video telescopic operating monitor was used 39 cranial and spinal procedures and its utility as a tool for minimally invasive neurosurgery and neuroendoscopy for initial learning curve was studied. Video telescopic operating monitor was used in 25 cranial and 14 spinal procedures. Image quality is comparable to endoscope and microscope. Surgeons comfort improved with VITOM. Frequent repositioning of scope holder and lack of stereopsis is initial limiting factor was compensated for with repeated procedures. Video telescopic operating monitor is found useful to reduce initial learning curve of neuroendoscopy.
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A Deep Learning Approach to Drone Monitoring
Chen, Yueru; Aggarwal, Pranav; Choi, Jongmoo; Kuo, C. -C. Jay
2017-01-01
A drone monitoring system that integrates deep-learning-based detection and tracking modules is proposed in this work. The biggest challenge in adopting deep learning methods for drone detection is the limited amount of training drone images. To address this issue, we develop a model-based drone augmentation technique that automatically generates drone images with a bounding box label on drone's location. To track a small flying drone, we utilize the residual information between consecutive i...
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NEW MATERIALS FOR PEDAGOGICAL TEACHING-LEARNING IN BIOCHEMISTRY: MONITORING PARTICIPATION
Campos, R. S.; Fernandes, I. L.; Andrade, G. P.V.; Matta, L. D.M.; Filgueira, L. G.A.
2015-01-01
This summary consists of an experience report about actions taken by biochemical monitors with pharmacy students. The reason of our work was the intention to both improve the process of teaching and also learning and invalidate the labels owned by biochemistry of hard and high-level-failure subject. The three actors: teachers, students and monitor could act on an integrated basis for the construction of an articulated pedagogical process between theory/practice and learning signification. Ou...
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2016-03-01
actionable information. With many lessons learned , the first implementation of real time physiological monitoring (RT-PSM) uses thermal-work strain... Bidirectional Inductive On-Body Network (BIONET) for WPSM Develop sensor links and processing nodes on-Soldier and non-RF links off-Soldier Elintrix...recent sleep watches (e.g., BASIS Peak, Intel Corp.) are attempting to parse sleep quality beyond duration and interruptions into deep and REM sleep
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Lin, Hsueh-Chun; Hong, Yao-Ming; Kan, Yao-Chiang
2012-01-01
The groundwater level represents a critical factor to evaluate hillside landslides. A monitoring system upon the real-time prediction platform with online analytical functions is important to forecast the groundwater level due to instantaneously monitored data when the heavy precipitation raises the groundwater level under the hillslope and causes instability. This study is to design the backend of an environmental monitoring system with efficient algorithms for machine learning and knowledge bank for the groundwater level fluctuation prediction. A Web-based platform upon the model-view controller-based architecture is established with technology of Web services and engineering data warehouse to support online analytical process and feedback risk assessment parameters for real-time prediction. The proposed system incorporates models of hydrological computation, machine learning, Web services, and online prediction to satisfy varieties of risk assessment requirements and approaches of hazard prevention. The rainfall data monitored from the potential landslide area at Lu-Shan, Nantou and Li-Shan, Taichung, in Taiwan, are applied to examine the system design.
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Experiments with Online Reinforcement Learning in Real-Time Strategy Games
DEFF Research Database (Denmark)
Toftgaard Andersen, Kresten; Zeng, Yifeng; Dahl Christensen, Dennis
2009-01-01
Real-time strategy (RTS) games provide a challenging platform to implement online reinforcement learning (RL) techniques in a real application. Computer, as one game player, monitors opponents' (human or other computers) strategies and then updates its own policy using RL methods. In this article......, we first examine the suitability of applying the online RL in various computer games. Reinforcement learning application depends on both RL complexity and the game features. We then propose a multi-layer framework for implementing online RL in an RTS game. The framework significantly reduces RL...... the effectiveness of our proposed framework and shed light on relevant issues in using online RL in RTS games....
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Task 1. Monitoring real time materials degradation. NRC extended In-situ and real-time Monitoring
Energy Technology Data Exchange (ETDEWEB)
Bakhtiari, Sasan [Argonne National Lab. (ANL), Argonne, IL (United States)
2012-03-01
The overall objective of this project was to perform a scoping study to identify, in concert with the nuclear industry, those sensors and techniques that have the most promising commercial viability and fill a critical inspection or monitoring need. Candidates to be considered include sensors to monitor real-time material degradation, characterize residual stress, monitor and inspect component fabrication, assess radionuclide and associated chemical species concentrations in ground water and soil, characterize fuel properties, and monitor severe accident conditions. Under Task 1—Monitoring Real-Time Materials Degradation—scoping studies were conducted to assess the feasibility of potential inspection and monitoring technologies (i.e., a combination of sensors, advanced signal processing techniques, and data analysis methods) that could be utilized in LWR and/or advanced reactor applications for continuous monitoring of degradation in-situ. The goal was to identify those techniques that appear to be the most promising, i.e., those that are closest to being both technically and commercially viable and that the nuclear industry is most likely to pursue. Current limitations and associated issues that must be overcome before commercial application of certain techniques have also been addressed.
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EXPERT-ANALITICAL MONITORING OF LEARNING PROCESS QUALITY IN HIGH SCHOOL
Directory of Open Access Journals (Sweden)
T. M. Korotun
2010-10-01
Full Text Available The technological model is proposed for monitoring process of learning process quality in high school compliant with current European and home standards. The mathematical methods are elaborated for diverse activities as to learning process objects quality determination unified support. They self-consistently combine: automatic expert evaluation with Bayesian net and Value tree models; Delphi technique enhancement; best practices for education quality assessment. Quality estimates’ consistency index is introduced for their choice and acceptability analysis. Its permanent increasing over monitoring stages is guaranteed. The tools for these stages’ automatic support are described.
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MONITORING OF UNIVERSITY ALUMNI: PERFORMANCE EVALUATION OF LEARNING
Directory of Open Access Journals (Sweden)
Mosienko N. L.
2014-03-01
Full Text Available The paper presents the methodology and results of graduates’ monitoring that solves the problem of the evaluation of higher education institutions. The goal of the present study is to develop methodology of the effectiveness of training estimation in high school and to test it on a sample of Department of Sociology’s graduates. The wide interpretation of learning outcomes, including objective and subjective indicators of employment of graduates of formation and professional, analytical and communication skills has been proposed. The result of monitoring information is the basis of informed decisions in the management of educational processes at the university. In scientific terms, the monitoring data allows us to estimate the impact of the various components of the learning outcomes (formed skills, acquired social capital, etc. at professional tracks. Information base of monitoring made by online alumni Sociology Department EF NSU survey, that’s materials revealed what analytical skills formed during study at the university, allow them to adapt to the diversified requirements of the labor market. Graduates sociologists are divided into two streams: a smaller consisting of working in the specialty, which is formed mainly through personalized contacts, and the bigger distributed to other segments of the labor market through a formal selection process.
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Active Learning Framework for Non-Intrusive Load Monitoring: Preprint
Energy Technology Data Exchange (ETDEWEB)
Jin, Xin
2016-05-16
Non-Intrusive Load Monitoring (NILM) is a set of techniques that estimate the electricity usage of individual appliances from power measurements taken at a limited number of locations in a building. One of the key challenges in NILM is having too much data without class labels yet being unable to label the data manually for cost or time constraints. This paper presents an active learning framework that helps existing NILM techniques to overcome this challenge. Active learning is an advanced machine learning method that interactively queries a user for the class label information. Unlike most existing NILM systems that heuristically request user inputs, the proposed method only needs minimally sufficient information from a user to build a compact and yet highly representative load signature library. Initial results indicate the proposed method can reduce the user inputs by up to 90% while still achieving similar disaggregation performance compared to a heuristic method. Thus, the proposed method can substantially reduce the burden on the user, improve the performance of a NILM system with limited user inputs, and overcome the key market barriers to the wide adoption of NILM technologies.
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Staged Inference using Conditional Deep Learning for energy efficient real-time smart diagnosis.
Parsa, Maryam; Panda, Priyadarshini; Sen, Shreyas; Roy, Kaushik
2017-07-01
Recent progress in biosensor technology and wearable devices has created a formidable opportunity for remote healthcare monitoring systems as well as real-time diagnosis and disease prevention. The use of data mining techniques is indispensable for analysis of the large pool of data generated by the wearable devices. Deep learning is among the promising methods for analyzing such data for healthcare applications and disease diagnosis. However, the conventional deep neural networks are computationally intensive and it is impractical to use them in real-time diagnosis with low-powered on-body devices. We propose Staged Inference using Conditional Deep Learning (SICDL), as an energy efficient approach for creating healthcare monitoring systems. For smart diagnostics, we observe that all diagnoses are not equally challenging. The proposed approach thus decomposes the diagnoses into preliminary analysis (such as healthy vs unhealthy) and detailed analysis (such as identifying the specific type of cardio disease). The preliminary diagnosis is conducted real-time with a low complexity neural network realized on the resource-constrained on-body device. The detailed diagnosis requires a larger network that is implemented remotely in cloud and is conditionally activated only for detailed diagnosis (unhealthy individuals). We evaluated the proposed approach using available physiological sensor data from Physionet databases, and achieved 38% energy reduction in comparison to the conventional deep learning approach.
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Establishing Time for Professional Learning
Journal of Staff Development, 2013
2013-01-01
Time for collaborative learning is an essential resource for educators working to implement college- and career-ready standards. The pages in this article include tools from the workbook "Establishing Time for Professional Learning." The tools support a complete process to help educators effectively find and use time. The following…
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Real time monitoring of electron processors
International Nuclear Information System (INIS)
Nablo, S.V.; Kneeland, D.R.; McLaughlin, W.L.
1995-01-01
A real time radiation monitor (RTRM) has been developed for monitoring the dose rate (current density) of electron beam processors. The system provides continuous monitoring of processor output, electron beam uniformity, and an independent measure of operating voltage or electron energy. In view of the device's ability to replace labor-intensive dosimetry in verification of machine performance on a real-time basis, its application to providing archival performance data for in-line processing is discussed. (author)
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Real-time performance monitoring and management system
Budhraja, Vikram S [Los Angeles, CA; Dyer, James D [La Mirada, CA; Martinez Morales, Carlos A [Upland, CA
2007-06-19
A real-time performance monitoring system for monitoring an electric power grid. The electric power grid has a plurality of grid portions, each grid portion corresponding to one of a plurality of control areas. The real-time performance monitoring system includes a monitor computer for monitoring at least one of reliability metrics, generation metrics, transmission metrics, suppliers metrics, grid infrastructure security metrics, and markets metrics for the electric power grid. The data for metrics being monitored by the monitor computer are stored in a data base, and a visualization of the metrics is displayed on at least one display computer having a monitor. The at least one display computer in one said control area enables an operator to monitor the grid portion corresponding to a different said control area.
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"Internet of Things" Real-Time Free Flap Monitoring.
Kim, Sang Hun; Shin, Ho Seong; Lee, Sang Hwan
2018-01-01
Free flaps are a common treatment option for head and neck reconstruction in plastic reconstructive surgery, and monitoring of the free flap is the most important factor for flap survival. In this study, the authors performed real-time free flap monitoring based on an implanted Doppler system and "internet of things" (IoT)/wireless Wi-Fi, which is a convenient, accurate, and efficient approach for surgeons to monitor a free flap. Implanted Doppler signals were checked continuously until the patient was discharged by the surgeon and residents using their own cellular phone or personal computer. If the surgeon decided that a revision procedure or exploration was required, the authors checked the consumed time (positive signal-to-operating room time) from the first notification when the flap's status was questioned to the determination for revision surgery according to a chart review. To compare the efficacy of real-time monitoring, the authors paired the same number of free flaps performed by the same surgeon and monitored the flaps using conventional methods such as a physical examination. The total survival rate was greater in the real-time monitoring group (94.7% versus 89.5%). The average time for the real-time monitoring group was shorter than that for the conventional group (65 minutes versus 86 minutes). Based on this study, real-time free flap monitoring using IoT technology is a method that surgeon and reconstruction team can monitor simultaneously at any time in any situation.
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Learning to drive: developing a workable awareness plan for monitoring new technology.
Berryman, Donna R
2010-04-01
Technology is constantly driving forward, and information professionals need to be informed about developments in order to work more effectively, provide new services, understand what users need and want, and to develop professionally. Learning how to monitor these developments in technology is a skill, just like learning to drive. This article provides information about developing a workable awareness plan and provides some suggested sites to monitor and tools to use.
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Le, Long N; Jones, Douglas L
2018-03-01
Audio classification techniques often depend on the availability of a large labeled training dataset for successful performance. However, in many application domains of audio classification (e.g., wildlife monitoring), obtaining labeled data is still a costly and laborious process. Motivated by this observation, a technique is proposed to efficiently learn a clean template from a few labeled, but likely corrupted (by noise and interferences), data samples. This learning can be done efficiently via tensorial dynamic time warping on the articulation index-based time-frequency representations of audio data. The learned template can then be used in audio classification following the standard template-based approach. Experimental results show that the proposed approach outperforms both (1) the recurrent neural network approach and (2) the state-of-the-art in the template-based approach on a wildlife detection application with few training samples.
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Equipment abnormality monitoring method and device therefor
International Nuclear Information System (INIS)
Yamada, Izumi; Asakura, Yamato; Uemura, Hiroshi; Uchida, Shunsuke; Oyamada, Osamu; Oyobe, Koji.
1994-01-01
In the present invention, it is judged whether the operation state of equipments used in a plant are normal or not by using learning performances. That is, a plurality of monitoring parameters are measured for an equipment. Previously determined monitoring parameters are extracted. A leaning mode or a monitoring mode is selected. In the leaning mode, based on the values of previously determined monitoring parameters, values of other monitoring parameters during normal states are learned. In the monitoring mode, based on the values of the previously determined monitoring parameters, typical values of other leaned monitoring parameters are outputted. The typical values of other normal monitoring parameters after learning and the values of other parameters at the present time are compared. If the values of the other parameters at the present time are out of normal range, it is judged as abnormal, and the result is alarmed and displayed. (I.S.)
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Machine learning-based patient specific prompt-gamma dose monitoring in proton therapy
Gueth, P.; Dauvergne, D.; Freud, N.; Létang, J. M.; Ray, C.; Testa, E.; Sarrut, D.
2013-07-01
Online dose monitoring in proton therapy is currently being investigated with prompt-gamma (PG) devices. PG emission was shown to be correlated with dose deposition. This relationship is mostly unknown under real conditions. We propose a machine learning approach based on simulations to create optimized treatment-specific classifiers that detect discrepancies between planned and delivered dose. Simulations were performed with the Monte-Carlo platform Gate/Geant4 for a spot-scanning proton therapy treatment and a PG camera prototype currently under investigation. The method first builds a learning set of perturbed situations corresponding to a range of patient translation. This set is then used to train a combined classifier using distal falloff and registered correlation measures. Classifier performances were evaluated using receiver operating characteristic curves and maximum associated specificity and sensitivity. A leave-one-out study showed that it is possible to detect discrepancies of 5 mm with specificity and sensitivity of 85% whereas using only distal falloff decreases the sensitivity down to 77% on the same data set. The proposed method could help to evaluate performance and to optimize the design of PG monitoring devices. It is generic: other learning sets of deviations, other measures and other types of classifiers could be studied to potentially reach better performance. At the moment, the main limitation lies in the computation time needed to perform the simulations.
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Machine learning-based patient specific prompt-gamma dose monitoring in proton therapy
International Nuclear Information System (INIS)
Gueth, P; Freud, N; Létang, J M; Sarrut, D; Dauvergne, D; Ray, C; Testa, E
2013-01-01
Online dose monitoring in proton therapy is currently being investigated with prompt-gamma (PG) devices. PG emission was shown to be correlated with dose deposition. This relationship is mostly unknown under real conditions. We propose a machine learning approach based on simulations to create optimized treatment-specific classifiers that detect discrepancies between planned and delivered dose. Simulations were performed with the Monte-Carlo platform Gate/Geant4 for a spot-scanning proton therapy treatment and a PG camera prototype currently under investigation. The method first builds a learning set of perturbed situations corresponding to a range of patient translation. This set is then used to train a combined classifier using distal falloff and registered correlation measures. Classifier performances were evaluated using receiver operating characteristic curves and maximum associated specificity and sensitivity. A leave-one-out study showed that it is possible to detect discrepancies of 5 mm with specificity and sensitivity of 85% whereas using only distal falloff decreases the sensitivity down to 77% on the same data set. The proposed method could help to evaluate performance and to optimize the design of PG monitoring devices. It is generic: other learning sets of deviations, other measures and other types of classifiers could be studied to potentially reach better performance. At the moment, the main limitation lies in the computation time needed to perform the simulations. (paper)
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Robot-assisted motor activation monitored by time-domain optical brain imaging
Steinkellner, O.; Wabnitz, H.; Schmid, S.; Steingräber, R.; Schmidt, H.; Krüger, J.; Macdonald, R.
2011-07-01
Robot-assisted motor rehabilitation proved to be an effective supplement to conventional hand-to-hand therapy in stroke patients. In order to analyze and understand motor learning and performance during rehabilitation it is desirable to develop a monitor to provide objective measures of the corresponding brain activity at the rehabilitation progress. We used a portable time-domain near-infrared reflectometer to monitor the hemodynamic brain response to distal upper extremity activities. Four healthy volunteers performed two different robot-assisted wrist/forearm movements, flexion-extension and pronation-supination in comparison with an unassisted squeeze ball exercise. A special headgear with four optical measurement positions to include parts of the pre- and postcentral gyrus provided a good overlap with the expected activation areas. Data analysis based on variance of time-of-flight distributions of photons through tissue was chosen to provide a suitable representation of intracerebral signals. In all subjects several of the four detection channels showed a response. In some cases indications were found of differences in localization of the activated areas for the various tasks.
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Quality of E-Learners’ Time and Learning Performance Beyond Quantitative Time-on-Task
Directory of Open Access Journals (Sweden)
Margarida Romero
2011-06-01
Full Text Available AbstractAlong with the amount of time spent learning (or time-on-task, the quality of learning time has a real influence on learning performance. Quality of time in online learning depends on students’ time availability and their willingness to devote quality cognitive time to learning activities. However, the quantity and quality of the time spent by adult e-learners on learning activities can be reduced by professional, family, and social commitments. Considering that the main time pattern followed by most adult e-learners is a professional one, it may be beneficial for online education programs to offer a certain degree of flexibility in instructional time that might allow adult learners to adjust their learning times to their professional constraints. However, using the time left over once professional and family requirements have been fulfilled could lead to a reduction in quality time for learning. This paper starts by introducing the concept of quality of learning time from an online student-centred perspective. The impact of students’ time-related variables (working hours, time-on-task engagement, time flexibility, time of day, day of week is then analyzed according to individual and collaborative grades achieved during an online master’s degree program. The data show that both students’ time flexibility (r = .98 and especially their availability to learn in the morning are related to better grades in individual (r = .93 and collaborative activities (r = .46.
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Real-time video quality monitoring
Liu, Tao; Narvekar, Niranjan; Wang, Beibei; Ding, Ran; Zou, Dekun; Cash, Glenn; Bhagavathy, Sitaram; Bloom, Jeffrey
2011-12-01
The ITU-T Recommendation G.1070 is a standardized opinion model for video telephony applications that uses video bitrate, frame rate, and packet-loss rate to measure the video quality. However, this model was original designed as an offline quality planning tool. It cannot be directly used for quality monitoring since the above three input parameters are not readily available within a network or at the decoder. And there is a great room for the performance improvement of this quality metric. In this article, we present a real-time video quality monitoring solution based on this Recommendation. We first propose a scheme to efficiently estimate the three parameters from video bitstreams, so that it can be used as a real-time video quality monitoring tool. Furthermore, an enhanced algorithm based on the G.1070 model that provides more accurate quality prediction is proposed. Finally, to use this metric in real-world applications, we present an example emerging application of real-time quality measurement to the management of transmitted videos, especially those delivered to mobile devices.
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Timepiece: Extending and Enhancing Learning Time.
Anderson, Lorin W., Ed.; Walberg, Herbert J., Ed.
This publication offers suggestions for making more productive use of time, a scarce and valued educational resource. The chapter authors, authorities on the use of educational time, write about how to extend and enhance learning time within and outside schools. In "Productive Use of Time," Herbert Walberg describes how learning time can be…
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Tulsyan, Aditya; Garvin, Christopher; Ündey, Cenk
2018-04-06
Biopharmaceutical manufacturing comprises of multiple distinct processing steps that require effective and efficient monitoring of many variables simultaneously in real-time. The state-of-the-art real-time multivariate statistical batch process monitoring (BPM) platforms have been in use in recent years to ensure comprehensive monitoring is in place as a complementary tool for continued process verification to detect weak signals. This article addresses a longstanding, industry-wide problem in BPM, referred to as the "Low-N" problem, wherein a product has a limited production history. The current best industrial practice to address the Low-N problem is to switch from a multivariate to a univariate BPM, until sufficient product history is available to build and deploy a multivariate BPM platform. Every batch run without a robust multivariate BPM platform poses risk of not detecting potential weak signals developing in the process that might have an impact on process and product performance. In this article, we propose an approach to solve the Low-N problem by generating an arbitrarily large number of in silico batches through a combination of hardware exploitation and machine-learning methods. To the best of authors' knowledge, this is the first article to provide a solution to the Low-N problem in biopharmaceutical manufacturing using machine-learning methods. Several industrial case studies from bulk drug substance manufacturing are presented to demonstrate the efficacy of the proposed approach for BPM under various Low-N scenarios. © 2018 Wiley Periodicals, Inc.
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Time factor in e-learning and assessment
Romero Velasco, Margarida
2010-01-01
Peer-reviewed Peer reviewed Time is probably one of the most polysemous words in education. In e-learning, characterization of the time factor is particularly relevant because of the high level of flexibility in the teaching and learning times, and the resulting responsibility of the e-learners in regulating their learning times.
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Sabourin, Jennifer L.; Shores, Lucy R.; Mott, Bradford W.; Lester, James C.
2013-01-01
Self-regulated learning behaviors such as goal setting and monitoring have been found to be crucial to students' success in computer-based learning environments. Consequently, understanding students' self-regulated learning behavior has been the subject of increasing attention. Unfortunately, monitoring these behaviors in real-time has…
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Unsupervised process monitoring and fault diagnosis with machine learning methods
Aldrich, Chris
2013-01-01
This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data
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Guo, Junqi; Zhou, Xi; Sun, Yunchuan; Ping, Gong; Zhao, Guoxing; Li, Zhuorong
2016-06-01
Smartphone based activity recognition has recently received remarkable attention in various applications of mobile health such as safety monitoring, fitness tracking, and disease prediction. To achieve more accurate and simplified medical monitoring, this paper proposes a self-learning scheme for patients' activity recognition, in which a patient only needs to carry an ordinary smartphone that contains common motion sensors. After the real-time data collection though this smartphone, we preprocess the data using coordinate system transformation to eliminate phone orientation influence. A set of robust and effective features are then extracted from the preprocessed data. Because a patient may inevitably perform various unpredictable activities that have no apriori knowledge in the training dataset, we propose a self-learning activity recognition scheme. The scheme determines whether there are apriori training samples and labeled categories in training pools that well match with unpredictable activity data. If not, it automatically assembles these unpredictable samples into different clusters and gives them new category labels. These clustered samples combined with the acquired new category labels are then merged into the training dataset to reinforce recognition ability of the self-learning model. In experiments, we evaluate our scheme using the data collected from two postoperative patient volunteers, including six labeled daily activities as the initial apriori categories in the training pool. Experimental results demonstrate that the proposed self-learning scheme for activity recognition works very well for most cases. When there exist several types of unseen activities without any apriori information, the accuracy reaches above 80 % after the self-learning process converges.
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Wide-area, real-time monitoring and visualization system
Budhraja, Vikram S.; Dyer, James D.; Martinez Morales, Carlos A.
2013-03-19
A real-time performance monitoring system for monitoring an electric power grid. The electric power grid has a plurality of grid portions, each grid portion corresponding to one of a plurality of control areas. The real-time performance monitoring system includes a monitor computer for monitoring at least one of reliability metrics, generation metrics, transmission metrics, suppliers metrics, grid infrastructure security metrics, and markets metrics for the electric power grid. The data for metrics being monitored by the monitor computer are stored in a data base, and a visualization of the metrics is displayed on at least one display computer having a monitor. The at least one display computer in one said control area enables an operator to monitor the grid portion corresponding to a different said control area.
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Ammenwerth, Elske; Hackl, Werner O
2017-01-01
Learning as a constructive process works best in interaction with other learners. Support of social interaction processes is a particular challenge within online learning settings due to the spatial and temporal distribution of participants. It should thus be carefully monitored. We present structural network analysis and related indicators to analyse and visualize interaction patterns of participants in online learning settings. We validate this approach in two online courses and show how the visualization helps to monitor interaction and to identify activity profiles of learners. Structural network analysis is a feasible approach for an analysis of the intensity and direction of interaction in online learning settings.
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Warhurst, Russell
2013-01-01
Purpose: This article aims to show how in times of austerity when formal HRD activity is curtailed and yet the need for learning is greatest, non-formal learning methods such as workplace involvement and participation initiated by line managers can compensate by enabling the required learning and change. Design/methodology/approach: A qualitative…
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Seeking surprise : rethinking monitoring for collective learning in rural resource management
Guijt, I.M.
2008-01-01
Commonsense says that monitoring systems should be able to provide feedback that can help correct ineffective actions. But practice shows that when dealing with complex rural development issues that involve collaborative action by a changing configuration of stakeholders, monitoring practice often falls short of its potential. In this thesis, I describe my search to understand why practice is so limited and what might be needed to design monitoring processes that foster learning in concerted ...
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Novel use of a noninvasive hemodynamic monitor in a personalized, active learning simulation.
Zoller, Jonathan K; He, Jianghua; Ballew, Angela T; Orr, Walter N; Flynn, Brigid C
2017-06-01
The present study furthered the concept of simulation-based medical education by applying a personalized active learning component. We tested this novel approach utilizing a noninvasive hemodynamic monitor with the capability to measure and display in real time numerous hemodynamic parameters in the exercising participant. Changes in medical knowledge concerning physiology were examined with a pre-and posttest. Simply by observation of one's own hemodynamic variables, the understanding of complex physiological concepts was significantly enhanced. Copyright © 2017 the American Physiological Society.
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Air quality monitoring using mobile microscopy and machine learning
Wu, Yi-Chen; Shiledar, Ashutosh; Li, Yi-Cheng; Wong, Jeffrey; Feng, Steve; Chen, Xuan; Chen, Christine; Jin, Kevin; Janamian, Saba; Yang, Zhe; Ballard, Zachary Scott; Gö rö cs, Zoltá n; Feizi, Alborz; Ozcan, Aydogan
2017-01-01
Rapid, accurate and high-throughput sizing and quantification of particulate matter (PM) in air is crucial for monitoring and improving air quality. In fact, particles in air with a diameter of ≤2.5 μm have been classified as carcinogenic by the World Health Organization. Here we present a field-portable cost-effective platform for high-throughput quantification of particulate matter using computational lens-free microscopy and machine-learning. This platform, termed c-Air, is also integrated with a smartphone application for device control and display of results. This mobile device rapidly screens 6.5 L of air in 30 s and generates microscopic images of the aerosols in air. It provides statistics of the particle size and density distribution with a sizing accuracy of ~93%. We tested this mobile platform by measuring the air quality at different indoor and outdoor environments and measurement times, and compared our results to those of an Environmental Protection Agency–approved device based on beta-attenuation monitoring, which showed strong correlation to c-Air measurements. Furthermore, we used c-Air to map the air quality around Los Angeles International Airport (LAX) over 24 h to confirm that the impact of LAX on increased PM concentration was present even at >7 km away from the airport, especially along the direction of landing flights. With its machine-learning-based computational microscopy interface, c-Air can be adaptively tailored to detect specific particles in air, for example, various types of pollen and mold and provide a cost-effective mobile solution for highly accurate and distributed sensing of air quality.
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Air quality monitoring using mobile microscopy and machine learning
Wu, Yi-Chen
2017-09-08
Rapid, accurate and high-throughput sizing and quantification of particulate matter (PM) in air is crucial for monitoring and improving air quality. In fact, particles in air with a diameter of ≤2.5 μm have been classified as carcinogenic by the World Health Organization. Here we present a field-portable cost-effective platform for high-throughput quantification of particulate matter using computational lens-free microscopy and machine-learning. This platform, termed c-Air, is also integrated with a smartphone application for device control and display of results. This mobile device rapidly screens 6.5 L of air in 30 s and generates microscopic images of the aerosols in air. It provides statistics of the particle size and density distribution with a sizing accuracy of ~93%. We tested this mobile platform by measuring the air quality at different indoor and outdoor environments and measurement times, and compared our results to those of an Environmental Protection Agency–approved device based on beta-attenuation monitoring, which showed strong correlation to c-Air measurements. Furthermore, we used c-Air to map the air quality around Los Angeles International Airport (LAX) over 24 h to confirm that the impact of LAX on increased PM concentration was present even at >7 km away from the airport, especially along the direction of landing flights. With its machine-learning-based computational microscopy interface, c-Air can be adaptively tailored to detect specific particles in air, for example, various types of pollen and mold and provide a cost-effective mobile solution for highly accurate and distributed sensing of air quality.
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Marti, Enric; Gil, Debora; Gurguí, Antoni; Hernández-Sabaté, Aura; Rocarías, Jaume; Poveda, Ferran
2015-01-01
This report presents the organisation of PBL (Project Based Learning) for a subject included in the IT engineering degree course. It is the result of 10 years of experience of the implantation and continuous improvement of the PBL class structure. The latest innovations include the experience of part-time monitoring with PBL groups using the Open…
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Real-time optoacoustic monitoring of temperature in tissues
International Nuclear Information System (INIS)
Larina, Irina V; Larin, Kirill V; Esenaliev, Rinat O
2005-01-01
To improve the safety and efficacy of thermal therapy, it is necessary to map tissue temperature in real time with submillimetre spatial resolution. Accurate temperature maps may provide the necessary control of the boundaries of the heated regions and minimize thermal damage to surrounding normal tissues. Current imaging modalities fail to monitor tissue temperature in real time with high resolution and accuracy. We investigated a non-invasive optoacoustic method for accurate, real-time monitoring of tissue temperature during thermotherapy. In this study, we induced temperature gradients in tissue and tissue-like samples and monitored the temperature distribution using the optoacoustic technique. The fundamental harmonic of a Q-switched Nd : YAG laser (λ = 1064 nm) was used for optoacoustic wave generation and probing of tissue temperature. The tissue temperature was also monitored with a multi-sensor temperature probe inserted in the samples. Good agreement between optoacoustically measured and actual tissue temperatures was obtained. The accuracy of temperature monitoring was better than 1 0 C, while the spatial resolution was about 1 mm. These data suggest that the optoacoustic technique has the potential to be used for non-invasive, real-time temperature monitoring during thermotherapy
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Radiologists' preferences for just-in-time learning.
Kahn, Charles E; Ehlers, Kevin C; Wood, Beverly P
2006-09-01
Effective learning can occur at the point of care, when opportunities arise to acquire information and apply it to a clinical problem. To assess interest in point-of-care learning, we conducted a survey to explore radiologists' attitudes and preferences regarding the use of just-in-time learning (JITL) in radiology. Following Institutional Review Board approval, we invited 104 current radiology residents and 86 radiologists in practice to participate in a 12-item Internet-based survey to assess their attitudes toward just-in-time learning. Voluntary participation in the survey was solicited by e-mail; respondents completed the survey on a web-based form. Seventy-nine physicians completed the questionnaire, including 47 radiology residents and 32 radiologists in practice; the overall response rate was 42%. Respondents generally expressed a strong interest for JITL: 96% indicated a willingness to try such a system, and 38% indicated that they definitely would use a JITL system. They expressed a preference for learning interventions of 5-10 min in length. Current and recent radiology trainees have expressed a strong interest in just-in-time learning. The information from this survey should be useful in pursuing the design of learning interventions and systems for delivering just-in-time learning to radiologists.
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Learning During Stressful Times
Shors, Tracey J.
2012-01-01
Stressful life events can have profound effects on our cognitive and motor abilities, from those that could be construed as adaptive to those not so. In this review, I discuss the general notion that acute stressful experience necessarily impairs our abilities to learn and remember. The effects of stress on operant conditioning, that is, learned helplessness, as well as those on classical conditioning procedures are discussed in the context of performance and adaptation. Studies indicating sex differences in learning during stressful times are discussed, as are those attributing different responses to the existence of multiple memory systems and nonlinear relationships. The intent of this review is to highlight the apparent plasticity of the stress response, how it might have evolved to affect both performance and learning processes, and the potential problems with interpreting stress effects on learning as either good or bad. An appreciation for its plasticity may provide new avenues for investigating its underlying neuronal mechanisms. PMID:15054128
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An algorithm for learning real-time automata
Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.
2007-01-01
We describe an algorithm for learning simple timed automata, known as real-time automata. The transitions of real-time automata can have a temporal constraint on the time of occurrence of the current symbol relative to the previous symbol. The learning algorithm is similar to the redblue fringe
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Bultena, Sybrine; Danielmeier, Claudia; Bekkering, Harold; Lemhöfer, Kristin
2017-01-01
Humans monitor their behavior to optimize performance, which presumably relies on stable representations of correct responses. During second language (L2) learning, however, stable representations have yet to be formed while knowledge of the first language (L1) can interfere with learning, which in some cases results in persistent errors. In order to examine how correct L2 representations are stabilized, this study examined performance monitoring in the learning process of second language learners for a feature that conflicts with their first language. Using EEG, we investigated if L2 learners in a feedback-guided word gender assignment task showed signs of error detection in the form of an error-related negativity (ERN) before and after receiving feedback, and how feedback is processed. The results indicated that initially, response-locked negativities for correct (CRN) and incorrect (ERN) responses were of similar size, showing a lack of internal error detection when L2 representations are unstable. As behavioral performance improved following feedback, the ERN became larger than the CRN, pointing to the first signs of successful error detection. Additionally, we observed a second negativity following the ERN/CRN components, the amplitude of which followed a similar pattern as the previous negativities. Feedback-locked data indicated robust FRN and P300 effects in response to negative feedback across different rounds, demonstrating that feedback remained important in order to update memory representations during learning. We thus show that initially, L2 representations may often not be stable enough to warrant successful error monitoring, but can be stabilized through repeated feedback, which means that the brain is able to overcome L1 interference, and can learn to detect errors internally after a short training session. The results contribute a different perspective to the discussion on changes in ERN and FRN components in relation to learning, by extending the
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Sood, Akshay; Ghani, Khurshid R; Ahlawat, Rajesh; Modi, Pranjal; Abaza, Ronney; Jeong, Wooju; Sammon, Jesse D; Diaz, Mireya; Kher, Vijay; Menon, Mani; Bhandari, Mahendra
2014-08-01
Traditional evaluation of the learning curve (LC) of an operation has been retrospective. Furthermore, LC analysis does not permit patient safety monitoring. To prospectively monitor patient safety during the learning phase of robotic kidney transplantation (RKT) and determine when it could be considered learned using the techniques of statistical process control (SPC). From January through May 2013, 41 patients with end-stage renal disease underwent RKT with regional hypothermia at one of two tertiary referral centers adopting RKT. Transplant recipients were classified into three groups based on the robotic training and kidney transplant experience of the surgeons: group 1, robot trained with limited kidney transplant experience (n=7); group 2, robot trained and kidney transplant experienced (n=20); and group 3, kidney transplant experienced with limited robot training (n=14). We employed prospective monitoring using SPC techniques, including cumulative summation (CUSUM) and Shewhart control charts, to perform LC analysis and patient safety monitoring, respectively. Outcomes assessed included post-transplant graft function and measures of surgical process (anastomotic and ischemic times). CUSUM and Shewhart control charts are time trend analytic techniques that allow comparative assessment of outcomes following a new intervention (RKT) relative to those achieved with established techniques (open kidney transplant; target value) in a prospective fashion. CUSUM analysis revealed an initial learning phase for group 3, whereas groups 1 and 2 had no to minimal learning time. The learning phase for group 3 varied depending on the parameter assessed. Shewhart control charts demonstrated no compromise in functional outcomes for groups 1 and 2. Graft function was compromised in one patient in group 3 (pcontrol chart analytic techniques. These methods allow determination of the duration of mentorship and identification of adverse events in a timely manner. A new operation
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Near-Real-Time Monitoring of Insect Defoliation Using Landsat Time Series
Directory of Open Access Journals (Sweden)
Valerie J. Pasquarella
2017-07-01
Full Text Available Introduced insects and pathogens impact millions of acres of forested land in the United States each year, and large-scale monitoring efforts are essential for tracking the spread of outbreaks and quantifying the extent of damage. However, monitoring the impacts of defoliating insects presents a significant challenge due to the ephemeral nature of defoliation events. Using the 2016 gypsy moth (Lymantria dispar outbreak in Southern New England as a case study, we present a new approach for near-real-time defoliation monitoring using synthetic images produced from Landsat time series. By comparing predicted and observed images, we assessed changes in vegetation condition multiple times over the course of an outbreak. Initial measures can be made as imagery becomes available, and season-integrated products provide a wall-to-wall assessment of potential defoliation at 30 m resolution. Qualitative and quantitative comparisons suggest our Landsat Time Series (LTS products improve identification of defoliation events relative to existing products and provide a repeatable metric of change in condition. Our synthetic-image approach is an important step toward using the full temporal potential of the Landsat archive for operational monitoring of forest health over large extents, and provides an important new tool for understanding spatial and temporal dynamics of insect defoliators.
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Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model
Directory of Open Access Journals (Sweden)
Guofeng Wang
2014-11-01
Full Text Available Tool condition monitoring (TCM plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM, hidden Markov model (HMM and radius basis function (RBF are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability.
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Feasibility of a real-time hand hygiene notification machine learning system in outpatient clinics.
Geilleit, R; Hen, Z Q; Chong, C Y; Loh, A P; Pang, N L; Peterson, G M; Ng, K C; Huis, A; de Korne, D F
2018-04-09
Various technologies have been developed to improve hand hygiene (HH) compliance in inpatient settings; however, little is known about the feasibility of machine learning technology for this purpose in outpatient clinics. To assess the effectiveness, user experiences, and costs of implementing a real-time HH notification machine learning system in outpatient clinics. In our mixed methods study, a multi-disciplinary team co-created an infrared guided sensor system to automatically notify clinicians to perform HH just before first patient contact. Notification technology effects were measured by comparing HH compliance at baseline (without notifications) with real-time auditory notifications that continued till HH was performed (intervention I) or notifications lasting 15 s (intervention II). User experiences were collected during daily briefings and semi-structured interviews. Costs of implementation of the system were calculated and compared to the current observational auditing programme. Average baseline HH performance before first patient contact was 53.8%. With real-time auditory notifications that continued till HH was performed, overall HH performance increased to 100% (P machine learning system were estimated to be 46% lower than the observational auditing programme. Machine learning technology that enables real-time HH notification provides a promising cost-effective approach to both improving and monitoring HH, and deserves further development in outpatient settings. Copyright © 2018 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.
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Development of a real time activity monitoring Android application utilizing SmartStep.
Hegde, Nagaraj; Melanson, Edward; Sazonov, Edward
2016-08-01
Footwear based activity monitoring systems are becoming popular in academic research as well as consumer industry segments. In our previous work, we had presented developmental aspects of an insole based activity and gait monitoring system-SmartStep, which is a socially acceptable, fully wireless and versatile insole. The present work describes the development of an Android application that captures the SmartStep data wirelessly over Bluetooth Low energy (BLE), computes features on the received data, runs activity classification algorithms and provides real time feedback. The development of activity classification methods was based on the the data from a human study involving 4 participants. Participants were asked to perform activities of sitting, standing, walking, and cycling while they wore SmartStep insole system. Multinomial Logistic Discrimination (MLD) was utilized in the development of machine learning model for activity prediction. The resulting classification model was implemented in an Android Smartphone. The Android application was benchmarked for power consumption and CPU loading. Leave one out cross validation resulted in average accuracy of 96.9% during model training phase. The Android application for real time activity classification was tested on a human subject wearing SmartStep resulting in testing accuracy of 95.4%.
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Learning from case studies and monitoring of Dutch tunnel projects
Korff, M.
2017-01-01
Individuals and project-based organisations in the construction industry can learn in a (more) systematic way from case studies and the monitoring of underground construction works. Underground construction projects such as tunnels and excavations suffer as much or more from failure costs
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Directory of Open Access Journals (Sweden)
Eva Durall
2017-04-01
Full Text Available The increasing availability of self-monitoring technologies has created opportunities for gaining awareness about one’s own behavior and reflecting on it. In teaching and learning, there is interest in using self-monitoring technologies, but very few studies have explored the possibilities. In this paper, we present a design study that investigates a technology (called Feeler that guides students to follow a specific learning script, monitors changes in their electroencephalogram (EEG while studying, and later provides visualization of the EEG data. The results are two-fold: (1 the hardware/software prototype and (2 the conclusions from the proof-of-concept research conducted with the prototype and six participants. In the research, we collected qualitative data from interviews to identify whether the prototype supported students to develop their reflective skills. The thematic analysis of the interviews showed that the Feeler’s learning script and visualization of the EEG data supported greater levels of reflection by fostering students’ curiosity, puzzlement, and personal inquiry. The proof-of-concept research also provided insights into several factors, such as the value of personal experience, the challenge of assumptions, and the contextualization of the data that trigger reflective thinking. The results validate the design concept and the role of the prototype in supporting awareness of and reflection about students’ mental states when they perform academic tasks.
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E-Learning, Time and Unconscious Thinking
Mathew, David
2014-01-01
This article views the temporal dimensions of e-learning through a psychoanalytic lens, and asks the reader to consider links between online learning and psychoanalysis. It argues that time and its associated philosophical puzzles impinge on both psychoanalytic theory and on e-learning at two specific points. The first is in the distinction…
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Real-time well condition monitoring in extended reach wells
Energy Technology Data Exchange (ETDEWEB)
Kucs, R.; Spoerker, H.F. [OMV Austria Exploration and Production GmbH, Gaenserndorf (Austria); Thonhauser, G. [Montanuniversitaet Leoben (Austria)
2008-10-23
Ever rising daily operating cost for offshore operations make the risk of running into drilling problems due to torque and drag developments in extended reach applications a growing concern. One option to reduce cost related to torque and drag problems can be to monitor torque and drag trends in real time without additional workload on the platform drilling team. To evaluate observed torque or drag trends it is necessary to automatically recognize operations and to have a 'standard value' to compare the measurements to. The presented systematic approach features both options - fully automated operations recognition and real time analysis. Trends can be discussed between rig- and shore-based teams, and decisions can be based on up to date information. Since the system is focused on visualization of real-time torque and drag trends, instead of highly complex and repeated simulations, calculation time is reduced by comparing the real-time rig data against predictions imported from a commercial drilling engineering application. The system allows reacting to emerging stuck pipe situations or developing cuttings beds long before the situations become severe enough to result in substantial lost time. The ability to compare real-time data with historical data from the same or other wells makes the system a valuable tool in supporting a learning organization. The system has been developed in a joint research initiative for field application on the development of an offshore heavy oil field in New Zealand. (orig.)
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Expert systems for real-time monitoring and fault diagnosis
Edwards, S. J.; Caglayan, A. K.
1989-01-01
Methods for building real-time onboard expert systems were investigated, and the use of expert systems technology was demonstrated in improving the performance of current real-time onboard monitoring and fault diagnosis applications. The potential applications of the proposed research include an expert system environment allowing the integration of expert systems into conventional time-critical application solutions, a grammar for describing the discrete event behavior of monitoring and fault diagnosis systems, and their applications to new real-time hardware fault diagnosis and monitoring systems for aircraft.
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Overcoming Learning Time And Space Constraints Through Technological Tool
Directory of Open Access Journals (Sweden)
Nafiseh Zarei
2015-08-01
Full Text Available Today the use of technological tools has become an evolution in language learning and language acquisition. Many instructors and lecturers believe that integrating Web-based learning tools into language courses allows pupils to become active learners during learning process. This study investigate how the Learning Management Blog (LMB overcomes the learning time and space constraints that contribute to students’ language learning and language acquisition processes. The participants were 30 ESL students at National University of Malaysia. A qualitative approach comprising an open-ended questionnaire and a semi-structured interview was used to collect data. The results of the study revealed that the students’ language learning and acquisition processes were enhanced. The students did not face any learning time and space limitations while being engaged in the learning process via the LMB. They learned and acquired knowledge using the language learning materials and forum at anytime and anywhere. Keywords: learning time, learning space, learning management blog
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Monitoring Distributed Real-Time Systems: A Survey and Future Directions
Goodloe, Alwyn E.; Pike, Lee
2010-01-01
Runtime monitors have been proposed as a means to increase the reliability of safety-critical systems. In particular, this report addresses runtime monitors for distributed hard real-time systems. This class of systems has had little attention from the monitoring community. The need for monitors is shown by discussing examples of avionic systems failure. We survey related work in the field of runtime monitoring. Several potential monitoring architectures for distributed real-time systems are presented along with a discussion of how they might be used to monitor properties of interest.
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Leisure time activities, parental monitoring and drunkenness in adolescents.
Tomcikova, Zuzana; Veselska, Zuzana; Madarasova Geckova, Andrea; van Dijk, Jitse P; Reijneveld, Sijmen A
2013-01-01
The aim of this cross-sectional study was to explore the association between adolescent drunkenness and participation in risky leisure time activities and parental monitoring. A sample of 3,694 Slovak elementary school students (mean age 14.5 years; 49.0% males) was assessed for drunkenness in the previous month, participation in risky leisure activities and parental monitoring. Participation in risky leisure time activities increased the probability of drunkenness among adolescents, while parental monitoring decreased it. The effect did not change after adding the mother's and father's monitoring into the models. Our results imply that adolescents involved in going out with friends, having parties with friends and/or visiting sporting events every day or several times a week are at a higher risk of drunkenness, as are those less monitored by their parents. These less monitored adolescents and their parents should become a target group in prevention. Copyright © 2012 S. Karger AG, Basel.
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Cheng, Jie; Qian, Zhaogang; Irani, Keki B.; Etemad, Hossein; Elta, Michael E.
1991-03-01
To meet the ever-increasing demand of the rapidly-growing semiconductor manufacturing industry it is critical to have a comprehensive methodology integrating techniques for process optimization real-time monitoring and adaptive process control. To this end we have accomplished an integrated knowledge-based approach combining latest expert system technology machine learning method and traditional statistical process control (SPC) techniques. This knowledge-based approach is advantageous in that it makes it possible for the task of process optimization and adaptive control to be performed consistently and predictably. Furthermore this approach can be used to construct high-level and qualitative description of processes and thus make the process behavior easy to monitor predict and control. Two software packages RIST (Rule Induction and Statistical Testing) and KARSM (Knowledge Acquisition from Response Surface Methodology) have been developed and incorporated with two commercially available packages G2 (real-time expert system) and ULTRAMAX (a tool for sequential process optimization).
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Time Analyzer for Time Synchronization and Monitor of the Deep Space Network
Cole, Steven; Gonzalez, Jorge, Jr.; Calhoun, Malcolm; Tjoelker, Robert
2003-01-01
A software package has been developed to measure, monitor, and archive the performance of timing signals distributed in the NASA Deep Space Network. Timing signals are generated from a central master clock and distributed to over 100 users at distances up to 30 kilometers. The time offset due to internal distribution delays and time jitter with respect to the central master clock are critical for successful spacecraft navigation, radio science, and very long baseline interferometry (VLBI) applications. The instrument controller and operator interface software is written in LabView and runs on the Linux operating system. The software controls a commercial multiplexer to switch 120 separate timing signals to measure offset and jitter with a time-interval counter referenced to the master clock. The offset of each channel is displayed in histogram form, and "out of specification" alarms are sent to a central complex monitor and control system. At any time, the measurement cycle of 120 signals can be interrupted for diagnostic tests on an individual channel. The instrument also routinely monitors and archives the long-term stability of all frequency standards or any other 1-pps source compared against the master clock. All data is stored and made available for
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[Design and implementation of real-time continuous glucose monitoring instrument].
Huang, Yonghong; Liu, Hongying; Tian, Senfu; Jia, Ziru; Wang, Zi; Pi, Xitian
2017-12-01
Real-time continuous glucose monitoring can help diabetics to control blood sugar levels within the normal range. However, in the process of practical monitoring, the output of real-time continuous glucose monitoring system is susceptible to glucose sensor and environment noise, which will influence the measurement accuracy of the system. Aiming at this problem, a dual-calibration algorithm for the moving-window double-layer filtering algorithm combined with real-time self-compensation calibration algorithm is proposed in this paper, which can realize the signal drift compensation for current data. And a real-time continuous glucose monitoring instrument based on this study was designed. This real-time continuous glucose monitoring instrument consisted of an adjustable excitation voltage module, a current-voltage converter module, a microprocessor and a wireless transceiver module. For portability, the size of the device was only 40 mm × 30 mm × 5 mm and its weight was only 30 g. In addition, a communication command code algorithm was designed to ensure the security and integrity of data transmission in this study. Results of experiments in vitro showed that current detection of the device worked effectively. A 5-hour monitoring of blood glucose level in vivo showed that the device could continuously monitor blood glucose in real time. The relative error of monitoring results of the designed device ranged from 2.22% to 7.17% when comparing to a portable blood meter.
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Chen, Chih-Ming; Wang, Jung-Ying
2018-01-01
Many studies have shown that learners' sustained attention strongly affects e-learning performance, particularly during online synchronous instruction. This work thus develops a novel attention monitoring and alarm mechanism (AMAM) based on brainwave signals to improve learning performance via monitoring the attention state of individual learners…
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Explosion Monitoring with Machine Learning: A LSTM Approach to Seismic Event Discrimination
Magana-Zook, S. A.; Ruppert, S. D.
2017-12-01
The streams of seismic data that analysts look at to discriminate natural from man- made events will soon grow from gigabytes of data per day to exponentially larger rates. This is an interesting problem as the requirement for real-time answers to questions of non-proliferation will remain the same, and the analyst pool cannot grow as fast as the data volume and velocity will. Machine learning is a tool that can solve the problem of seismic explosion monitoring at scale. Using machine learning, and Long Short-term Memory (LSTM) models in particular, analysts can become more efficient by focusing their attention on signals of interest. From a global dataset of earthquake and explosion events, a model was trained to recognize the different classes of events, given their spectrograms. Optimal recurrent node count and training iterations were found, and cross validation was performed to evaluate model performance. A 10-fold mean accuracy of 96.92% was achieved on a balanced dataset of 30,002 instances. Given that the model is 446.52 MB it can be used to simultaneously characterize all incoming signals by researchers looking at events in isolation on desktop machines, as well as at scale on all of the nodes of a real-time streaming platform. LLNL-ABS-735911
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Learned Compact Local Feature Descriptor for Tls-Based Geodetic Monitoring of Natural Outdoor Scenes
Gojcic, Z.; Zhou, C.; Wieser, A.
2018-05-01
The advantages of terrestrial laser scanning (TLS) for geodetic monitoring of man-made and natural objects are not yet fully exploited. Herein we address one of the open challenges by proposing feature-based methods for identification of corresponding points in point clouds of two or more epochs. We propose a learned compact feature descriptor tailored for point clouds of natural outdoor scenes obtained using TLS. We evaluate our method both on a benchmark data set and on a specially acquired outdoor dataset resembling a simplified monitoring scenario where we successfully estimate 3D displacement vectors of a rock that has been displaced between the scans. We show that the proposed descriptor has the capacity to generalize to unseen data and achieves state-of-the-art performance while being time efficient at the matching step due the low dimension.
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Space Weather and Real-Time Monitoring
Directory of Open Access Journals (Sweden)
S Watari
2009-04-01
Full Text Available Recent advance of information and communications technology enables to collect a large amount of ground-based and space-based observation data in real-time. The real-time data realize nowcast of space weather. This paper reports a history of space weather by the International Space Environment Service (ISES in association with the International Geophysical Year (IGY and importance of real-time monitoring in space weather.
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Alkasem, Ameen; Liu, Hongwei; Zuo, Decheng; Algarash, Basheer
2018-01-01
The volume of data being collected, analyzed, and stored has exploded in recent years, in particular in relation to the activity on the cloud computing. While large-scale data processing, analysis, storage, and platform model such as cloud computing were previously and currently are increasingly. Today, the major challenge is it address how to monitor and control these massive amounts of data and perform analysis in real-time at scale. The traditional methods and model systems are unable to cope with these quantities of data in real-time. Here we present a new methodology for constructing a model for optimizing the performance of real-time monitoring of big datasets, which includes a machine learning algorithms and Apache Spark Streaming to accomplish fine-grained fault diagnosis and repair of big dataset. As a case study, we use the failure of Virtual Machines (VMs) to start-up. The methodology proposition ensures that the most sensible action is carried out during the procedure of fine-grained monitoring and generates the highest efficacy and cost-saving fault repair through three construction control steps: (I) data collection; (II) analysis engine and (III) decision engine. We found that running this novel methodology can save a considerate amount of time compared to the Hadoop model, without sacrificing the classification accuracy or optimization of performance. The accuracy of the proposed method (92.13%) is an improvement on traditional approaches.
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Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring
Energy Technology Data Exchange (ETDEWEB)
Kanemoto, Shigeru; Watanabe, Masaya [The University of Aizu, Aizuwakamatsu (Japan); Yusa, Noritaka [Tohoku University, Sendai (Japan)
2014-08-15
The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology.
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Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring
International Nuclear Information System (INIS)
Kanemoto, Shigeru; Watanabe, Masaya; Yusa, Noritaka
2014-01-01
The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology
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Gavrishchaka, Valeriy; Senyukova, Olga; Davis, Kristina
2015-01-01
Previously, we have proposed to use complementary complexity measures discovered by boosting-like ensemble learning for the enhancement of quantitative indicators dealing with necessarily short physiological time series. We have confirmed robustness of such multi-complexity measures for heart rate variability analysis with the emphasis on detection of emerging and intermittent cardiac abnormalities. Recently, we presented preliminary results suggesting that such ensemble-based approach could be also effective in discovering universal meta-indicators for early detection and convenient monitoring of neurological abnormalities using gait time series. Here, we argue and demonstrate that these multi-complexity ensemble measures for gait time series analysis could have significantly wider application scope ranging from diagnostics and early detection of physiological regime change to gait-based biometrics applications.
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Active learning reduces annotation time for clinical concept extraction.
Kholghi, Mahnoosh; Sitbon, Laurianne; Zuccon, Guido; Nguyen, Anthony
2017-10-01
To investigate: (1) the annotation time savings by various active learning query strategies compared to supervised learning and a random sampling baseline, and (2) the benefits of active learning-assisted pre-annotations in accelerating the manual annotation process compared to de novo annotation. There are 73 and 120 discharge summary reports provided by Beth Israel institute in the train and test sets of the concept extraction task in the i2b2/VA 2010 challenge, respectively. The 73 reports were used in user study experiments for manual annotation. First, all sequences within the 73 reports were manually annotated from scratch. Next, active learning models were built to generate pre-annotations for the sequences selected by a query strategy. The annotation/reviewing time per sequence was recorded. The 120 test reports were used to measure the effectiveness of the active learning models. When annotating from scratch, active learning reduced the annotation time up to 35% and 28% compared to a fully supervised approach and a random sampling baseline, respectively. Reviewing active learning-assisted pre-annotations resulted in 20% further reduction of the annotation time when compared to de novo annotation. The number of concepts that require manual annotation is a good indicator of the annotation time for various active learning approaches as demonstrated by high correlation between time rate and concept annotation rate. Active learning has a key role in reducing the time required to manually annotate domain concepts from clinical free text, either when annotating from scratch or reviewing active learning-assisted pre-annotations. Copyright © 2017 Elsevier B.V. All rights reserved.
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Real-time flood monitoring and warning system
Directory of Open Access Journals (Sweden)
Jirapon Sunkpho
2011-04-01
Full Text Available Flooding is one of the major disasters occurring in various parts of the world. The system for real-time monitoring ofwater conditions: water level; flow; and precipitation level, was developed to be employed in monitoring flood in Nakhon SiThammarat, a southern province in Thailand. The two main objectives of the developed system is to serve 1 as informationchannel for flooding between the involved authorities and experts to enhance their responsibilities and collaboration and2 as a web based information source for the public, responding to their need for information on water condition and flooding.The developed system is composed of three major components: sensor network, processing/transmission unit, and database/application server. These real-time data of water condition can be monitored remotely by utilizing wireless sensors networkthat utilizes the mobile General Packet Radio Service (GPRS communication in order to transmit measured data to theapplication server. We implemented a so-called VirtualCOM, a middleware that enables application server to communicatewith the remote sensors connected to a GPRS data unit (GDU. With VirtualCOM, a GDU behaves as if it is a cable directlyconnected the remote sensors to the application server. The application server is a web-based system implemented usingPHP and JAVA as the web application and MySQL as its relational database. Users can view real-time water conditionas well as the forecasting of the water condition directly from the web via web browser or via WAP. The developed systemhas demonstrated the applicability of today’s sensors in wirelessly monitor real-time water conditions.
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The effects of emotion on younger and older adults' monitoring of learning.
Tauber, Sarah K; Dunlosky, John; Urry, Heather L; Opitz, Philipp C
2017-09-01
Age-related differences in memory monitoring appear when people learn emotional words. Namely, younger adults' judgments of learning (JOLs) are higher for positive than neutral words, whereas older adults' JOLs do not discriminate between positive versus neutral words. In two experiments, we evaluated whether this age-related difference extends to learning positive versus neutral pictures. We also evaluated the contribution of two dimensions of emotion that may impact younger and older adults' JOLs: valence and arousal. Younger and older adults studied pictures that were positive or neutral and either high or low in arousal. Participants made immediate JOLs and completed memory tests. In both experiments, the magnitude of older adults' JOLs was influenced by emotion, and both younger and older adults demonstrated an emotional salience effect on JOLs. As important, the magnitude of participants' JOLs was influenced by valence, and not arousal. Emotional salience effects were also evident on participants' free recall, and older adults recalled as many pictures as did younger adults. Taken together, these data suggest that older adults do not have a monitoring deficit when learning positive (vs. neutral) pictures and that emotional salience effects on younger and older adults' JOLs are produced more by valence than by arousal.
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Monitor of dynamic parameters in real time; Monitor de parametros dinamicos en tiempo real
Energy Technology Data Exchange (ETDEWEB)
Rojas S, A.S.; Ruiz E, J.A. [ININ, 52750 La Marquesa, Estado de Mexico (Mexico)
2008-07-01
In the complex physical systems exist parameters that are necessary for monitoring in real time. In the nuclear industry, particularly in a reactor this surveillance is important, where the times of the reactions are almost instantaneous. Although many of these parameters are monitored, given the advance of the computer systems the monitoring could either be enlarged direct or indirect of other parameters. The analysis of the neutron noise in the nuclear reactors, plays an important role, the noise signal it contains information about the operation conditions of a system, when analyzing it with analysis methodologies of analogical signals to provide important information for the early detection of possible flaws and to indicate the permissible operation levels. To show the characteristics of the operation of the system of Monitoring of Dynamic Parameters in Real Time, oscillations of neutron noise of the TRIGA Mark III of the ININ were analyzed, these were caused with the control bar to a power of 10 Watts, the oscillations were carried out to a frequency of 1Hz, signal of low frequency. In this work a virtual instrument that allows by means of the spectral analysis method in frequency point by point is presented, to indicate in real time periodic variations that could be presented in the neutron noise signal, visualizing in advance the dynamic behavior of the system or nuclear plant. Another of the tests of the monitoring system presented is that of the oscillatory event happened in the reactor of Laguna Verde Nucleo electric Central, would be convenient to have an instrument of surveillance for monitoring through the neutron noise signal the behavior of some important parameter to predict and to indicate in an immediate way an abnormal condition in the reactor operation or in the plant system. These parameters can be the power, the recirculation water flow, etc. The monitor is based on a personal computer (PC), a data acquisition card (ADC) and a computer program
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Clean Air Markets - Monitoring Surface Water Chemistry
Learn about how EPA uses Long Term Monitoring (LTM) and Temporily Integrated Monitoring of Ecosystems (TIME) to track the effect of the Clean Air Act Amendments on acidity of surface waters in the eastern U.S.
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Real Time Fire Reconnaissance Satellite Monitoring System Failure Model
Nino Prieto, Omar Ariosto; Colmenares Guillen, Luis Enrique
2013-09-01
In this paper the Real Time Fire Reconnaissance Satellite Monitoring System is presented. This architecture is a legacy of the Detection System for Real-Time Physical Variables which is undergoing a patent process in Mexico. The methodologies for this design are the Structured Analysis for Real Time (SA- RT) [8], and the software is carried out by LACATRE (Langage d'aide à la Conception d'Application multitâche Temps Réel) [9,10] Real Time formal language. The system failures model is analyzed and the proposal is based on the formal language for the design of critical systems and Risk Assessment; AltaRica. This formal architecture uses satellites as input sensors and it was adapted from the original model which is a design pattern for physical variation detection in Real Time. The original design, whose task is to monitor events such as natural disasters and health related applications, or actual sickness monitoring and prevention, as the Real Time Diabetes Monitoring System, among others. Some related work has been presented on the Mexican Space Agency (AEM) Creation and Consultation Forums (2010-2011), and throughout the International Mexican Aerospace Science and Technology Society (SOMECYTA) international congress held in San Luis Potosí, México (2012). This Architecture will allow a Real Time Fire Satellite Monitoring, which will reduce the damage and danger caused by fires which consumes the forests and tropical forests of Mexico. This new proposal, permits having a new system that impacts on disaster prevention, by combining national and international technologies and cooperation for the benefit of humankind.
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Monitoring Quarry Area with Landsat Long Time-Series for Socioeconomic Study
Directory of Open Access Journals (Sweden)
Haoteng Zhao
2018-03-01
Full Text Available Quarry sites result from human activity, which includes the removal of original vegetation and the overlying soil to dig out stones for building use. Therefore, the dynamics of the quarry area provide a unique view of human mining activities. Actually, the topographic changes caused by mining activities are also a result of the development of the local economy. Thus, monitoring the quarry area can provide information about the policies of the economy and environmental protection. In this paper, we developed a combined method of machine learning classification and quarry region analysis to estimate the quarry area in a quarry region near Beijing. A temporal smoothing based on the classification results of all years was applied in post-processing to remove outliers and obtain gently changing sequences along the monitoring term. The method was applied to Landsat images to derive a quarry distribution map and quarry area time series from 1984 to 2017, revealing significant inter-annual variability. The time series revealed a five-stage development of the quarry area with different growth patterns. As the study region lies on two jurisdictions—Tianjin and Hebei—a comparison of the quarry area changes in the two jurisdictions was applied, which revealed that the different policies in the two regions could impose different impacts on the development of a quarry area. An analysis concerning the relationship between quarry area and gross regional product (GRP was performed to explore the potential application on socioeconomic studies, and we found a strong positive correlation between quarry area and GRP in Langfang City, Hebei Province. These results demonstrate the potential benefit of annual monitoring over the long-term for socioeconomic studies, which can be used for mining decision making.
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Indirect Tire Monitoring System - Machine Learning Approach
Svensson, O.; Thelin, S.; Byttner, S.; Fan, Y.
2017-10-01
The heavy vehicle industry has today no requirement to provide a tire pressure monitoring system by law. This has created issues surrounding unknown tire pressure and thread depth during active service. There is also no standardization for these kind of systems which means that different manufacturers and third party solutions work after their own principles and it can be hard to know what works for a given vehicle type. The objective is to create an indirect tire monitoring system that can generalize a method that detect both incorrect tire pressure and thread depth for different type of vehicles within a fleet without the need for additional physical sensors or vehicle specific parameters. The existing sensors that are connected communicate through CAN and are interpreted by the Drivec Bridge hardware that exist in the fleet. By using supervised machine learning a classifier was created for each axle where the main focus was the front axle which had the most issues. The classifier will classify the vehicles tires condition and will be implemented in Drivecs cloud service where it will receive its data. The resulting classifier is a random forest implemented in Python. The result from the front axle with a data set consisting of 9767 samples of buses with correct tire condition and 1909 samples of buses with incorrect tire condition it has an accuracy of 90.54% (0.96%). The data sets are created from 34 unique measurements from buses between January and May 2017. This classifier has been exported and is used inside a Node.js module created for Drivecs cloud service which is the result of the whole implementation. The developed solution is called Indirect Tire Monitoring System (ITMS) and is seen as a process. This process will predict bad classes in the cloud which will lead to warnings. The warnings are defined as incidents. They contain only the information needed and the bandwidth of the incidents are also controlled so incidents are created within an
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Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature
Directory of Open Access Journals (Sweden)
Jihyun Kim
2017-01-01
Full Text Available Monitoring electricity consumption in the home is an important way to help reduce energy usage. Nonintrusive Load Monitoring (NILM is existing technique which helps us monitor electricity consumption effectively and costly. NILM is a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. Among the previous studies, Hidden Markov Model (HMM based models have been studied very much. However, increasing appliances, multistate of appliances, and similar power consumption of appliances are three big issues in NILM recently. In this paper, we address these problems through providing our contributions as follows. First, we proposed state-of-the-art energy disaggregation based on Long Short-Term Memory Recurrent Neural Network (LSTM-RNN model and additional advanced deep learning. Second, we proposed a novel signature to improve classification performance of the proposed model in multistate appliance case. We applied the proposed model on two datasets such as UK-DALE and REDD. Via our experimental results, we have confirmed that our model outperforms the advanced model. Thus, we show that our combination between advanced deep learning and novel signature can be a robust solution to overcome NILM’s issues and improve the performance of load identification.
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Real-time water quality monitoring and providing water quality ...
EPA and the U.S. Geological Survey (USGS) have initiated the “Village Blue” research project to provide real-time water quality monitoring data to the Baltimore community and increase public awareness about local water quality in Baltimore Harbor and the Chesapeake Bay. The Village Blue demonstration project complements work that a number of state and local organizations are doing to make Baltimore Harbor “swimmable and fishable” 2 by 2020. Village Blue is designed to build upon EPA’s “Village Green” project which provides real-time air quality information to communities in six locations across the country. The presentation, “Real-time water quality monitoring and providing water quality information to the Baltimore Community”, summarizes the Village Blue real-time water quality monitoring project being developed for the Baltimore Harbor.
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Tool Wear Monitoring Using Time Series Analysis
Song, Dong Yeul; Ohara, Yasuhiro; Tamaki, Haruo; Suga, Masanobu
A tool wear monitoring approach considering the nonlinear behavior of cutting mechanism caused by tool wear and/or localized chipping is proposed, and its effectiveness is verified through the cutting experiment and actual turning machining. Moreover, the variation in the surface roughness of the machined workpiece is also discussed using this approach. In this approach, the residual error between the actually measured vibration signal and the estimated signal obtained from the time series model corresponding to dynamic model of cutting is introduced as the feature of diagnosis. Consequently, it is found that the early tool wear state (i.e. flank wear under 40µm) can be monitored, and also the optimal tool exchange time and the tool wear state for actual turning machining can be judged by this change in the residual error. Moreover, the variation of surface roughness Pz in the range of 3 to 8µm can be estimated by the monitoring of the residual error.
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Radiation environmental real-time monitoring and dispersion modelling
International Nuclear Information System (INIS)
Kovacik, Andrej; Bartokova, Ivana; Melicherova, Terezia; Omelka, Jozef
2015-01-01
The MicroStep-MIS system of real-time radiation monitoring, which provides a turn-key solution for measurement, acquisition, processing, reporting, archiving and displaying of various radiation data, is described and discussed in detail. The qualities, long-term stability of measurement and sensitivity of the RPSG-05 probe are illustrated on its use within the radiation monitoring network of the Slovak Hydrometeorological Institute and within the monitoring network in the United Arab Emirates. (orig.)
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Real-Time Spatial Monitoring of Vehicle Vibration Data as a Model for TeleGeoMonitoring Systems
Robidoux, Jeff
2005-01-01
This research presents the development and proof of concept of a TeleGeoMonitoring (TGM) system for spatially monitoring and analyzing, in real-time, data derived from vehicle-mounted sensors. In response to the concern for vibration related injuries experienced by equipment operators in surface mining and construction operations, the prototype TGM system focuses on spatially monitoring vehicle vibration in real-time. The TGM vibration system consists of 3 components: (1) Data Acquisition ...
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Monitoring and Depth of Strategy Use in Computer-Based Learning Environments for Science and History
Deekens, Victor M.; Greene, Jeffrey A.; Lobczowski, Nikki G.
2018-01-01
Background: Self-regulated learning (SRL) models position metacognitive monitoring as central to SRL processing and predictive of student learning outcomes (Winne & Hadwin, 2008; Zimmerman, 2000). A body of research evidence also indicates that depth of strategy use, ranging from surface to deep processing, is predictive of learning…
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Real-time electron-beam dose monitoring
International Nuclear Information System (INIS)
McKeown, J.
1995-01-01
A new technique to monitor the integrated dose that a product receives in an irradiation facility is determined by collecting the charge that passes through the product. The technique allows the absorbed dose to be monitored as the irradiation is taking place, i.e. on-line and in real time. The procedure will also provide a means of directly measuring the electron energy, independent of the accelerator control system. The irradiation plant operator can immediately detect a problem of inadequate electron energy and take appropriate action. Examples taken on the IMPELA trademark accelerator at the Iotron Irradiation Facility in Vancouver are presented
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Ullsperger, M.
2010-01-01
Performance monitoring is essential for optimization of action outcomes. Research consistently implicates the posterior medial frontal cortex, particularly the rostral cingulate zone, in monitoring for unfavorable action outcomes, signaling the need for adjustments and learning from feedback.
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Information extraction from dynamic PS-InSAR time series using machine learning
van de Kerkhof, B.; Pankratius, V.; Chang, L.; van Swol, R.; Hanssen, R. F.
2017-12-01
Due to the increasing number of SAR satellites, with shorter repeat intervals and higher resolutions, SAR data volumes are exploding. Time series analyses of SAR data, i.e. Persistent Scatterer (PS) InSAR, enable the deformation monitoring of the built environment at an unprecedented scale, with hundreds of scatterers per km2, updated weekly. Potential hazards, e.g. due to failure of aging infrastructure, can be detected at an early stage. Yet, this requires the operational data processing of billions of measurement points, over hundreds of epochs, updating this data set dynamically as new data come in, and testing whether points (start to) behave in an anomalous way. Moreover, the quality of PS-InSAR measurements is ambiguous and heterogeneous, which will yield false positives and false negatives. Such analyses are numerically challenging. Here we extract relevant information from PS-InSAR time series using machine learning algorithms. We cluster (group together) time series with similar behaviour, even though they may not be spatially close, such that the results can be used for further analysis. First we reduce the dimensionality of the dataset in order to be able to cluster the data, since applying clustering techniques on high dimensional datasets often result in unsatisfying results. Our approach is to apply t-distributed Stochastic Neighbor Embedding (t-SNE), a machine learning algorithm for dimensionality reduction of high-dimensional data to a 2D or 3D map, and cluster this result using Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The results show that we are able to detect and cluster time series with similar behaviour, which is the starting point for more extensive analysis into the underlying driving mechanisms. The results of the methods are compared to conventional hypothesis testing as well as a Self-Organising Map (SOM) approach. Hypothesis testing is robust and takes the stochastic nature of the observations into account
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Bishop, Crystal D.; Snyder, Patricia A.; Crow, Robert E.
2015-01-01
We used a multi-component single-subject experimental design across three preschool teachers to examine the effects of video self-monitoring with graduated training and feedback on the accuracy with which teachers monitored their implementation of embedded instructional learning trials. We also examined changes in teachers' implementation of…
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A Model for Learning Over Time: The Big Picture
Amato, Herbert K.; Konin, Jeff G.; Brader, Holly
2002-01-01
Objective: To present a method of describing the concept of “learning over time” with respect to its implementation into an athletic training education program curriculum. Background: The formal process of learning over time has recently been introduced as a required way for athletic training educational competencies and clinical proficiencies to be delivered and mastered. Learning over time incorporates the documented cognitive, psychomotor, and affective skills associated with the acquisition, progression, and reflection of information. This method of academic preparation represents a move away from a quantitative-based learning module toward a proficiency-based mastery of learning. Little research or documentation can be found demonstrating either the specificity of this concept or suggestions for its application. Description: We present a model for learning over time that encompasses multiple indicators for assessment in a successive format. Based on a continuum approach, cognitive, psychomotor, and affective characteristics are assessed at different levels in classroom and clinical environments. Clinical proficiencies are a common set of entry-level skills that need to be integrated into the athletic training educational domains. Objective documentation is presented, including the skill breakdown of a task and a matrix to identify a timeline of competency and proficiency delivery. Clinical Advantages: The advantages of learning over time pertain to the integration of cognitive knowledge into clinical skill acquisition. Given the fact that learning over time has been implemented as a required concept for athletic training education programs, this model may serve to assist those program faculty who have not yet developed, or are in the process of developing, a method of administering this approach to learning. PMID:12937551
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Developments in real-time monitoring for geologic hazard warnings (Invited)
Leith, W. S.; Mandeville, C. W.; Earle, P. S.
2013-12-01
Real-time data from global, national and local sensor networks enable prompt alerts and warnings of earthquakes, tsunami, volcanic eruptions, geomagnetic storms , broad-scale crustal deformation and landslides. State-of-the-art seismic systems can locate and evaluate earthquake sources in seconds, enabling 'earthquake early warnings' to be broadcast ahead of the damaging surface waves so that protective actions can be taken. Strong motion monitoring systems in buildings now support near-real-time structural damage detection systems, and in quiet times can be used for state-of-health monitoring. High-rate GPS data are being integrated with seismic strong motion data, allowing accurate determination of earthquake displacements in near-real time. GPS data, combined with rainfall, groundwater and geophone data, are now used for near-real-time landslide monitoring and warnings. Real-time sea-floor water pressure data are key for assessing tsunami generation by large earthquakes. For monitoring remote volcanoes that lack local ground-based instrumentation, the USGS uses new technologies such as infrasound arrays and the worldwide lightning detection array to detect eruptions in progress. A new real-time UV-camera system for measuring the two dimensional SO2 flux from volcanic plumes will allow correlations with other volcano monitoring data streams to yield fundamental data on changes in gas flux as an eruption precursor, and how magmas de-gas prior to and during eruptions. Precision magnetic field data support the generation of real-time indices of geomagnetic disturbances (Dst, K and others), and can be used to model electrical currents in the crust and bulk power system. Ground-induced electrical current monitors are used to track those currents so that power grids can be effectively managed during geomagnetic storms. Beyond geophysical sensor data, USGS is using social media to rapidly detect possible earthquakes and to collect firsthand accounts of the impacts of
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Ambrus, Géza Gergely; Chaieb, Leila; Stilling, Roman; Rothkegel, Holger; Antal, Andrea; Paulus, Walter
2016-03-11
The measurement of the motor evoked potential (MEP) amplitudes using single pulse transcranial magnetic stimulation (TMS) is a common method to observe changes in motor cortical excitability. The level of cortical excitability has been shown to change during motor learning. Conversely, motor learning can be improved by using anodal transcranial direct current stimulation (tDCS). In the present study, we aimed to monitor cortical excitability changes during an implicit motor learning paradigm, a version of the serial reaction time task (SRTT). Responses from the first dorsal interosseous (FDI) and forearm flexor (FLEX) muscles were recorded before, during and after the performance of the SRTT. Online measurements were combined with anodal, cathodal or sham tDCS for the duration of the SRTT. Negative correlations between the amplitude of online FDI MEPs and SRTT reaction times (RTs) were observed across the learning blocks in the cathodal condition (higher average MEP amplitudes associated with lower RTs) but no significant differences in the anodal and sham conditions. tDCS did not have an impact on SRTT performance, as would be predicted based on previous studies. The offline before-after SRTT MEP amplitudes showed an increase after anodal and a tendency to decrease after cathodal stimulation, but these changes were not significant. The combination of different interventions during tDCS might result in reduced efficacy of the stimulation that in future studies need further attention. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
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Real-time personal exposure and health condition monitoring system
Energy Technology Data Exchange (ETDEWEB)
Saitou, Isamu; Kanda, Hiroaki; Asai, Akio; Takeishi, Naoki; Ota, Yoshito [Hitachi Aloka Medical, Ltd., Measuring Systems Engineering Dept., Tokyo (Japan); Hanawa, Nobuhiro; Ueda, Hisao; Kusunoki, Tsuyoshi; Ishitsuka, Etsuo; Kawamura, Hiroshi [Japan Atomic Energy Agency, Oarai Research and Development Center, Oarai, Ibaraki (Japan)
2012-03-15
JAEA (Japan Atomic Energy Agency) and HAM (Hitachi Aloka Medical, Ltd) have proposed novel monitoring system for workers of nuclear facility. In these facilities, exposure management for workers is mainly used access control and personal exposure recordings. This system is currently only for reports management but is not confirmative for surveillance when work in progress. Therefore, JAEA and HAM integrate access control and personal exposure recordings and two real-time monitoring systems which are position sensing and vital sign monitor. Furthermore change personal exposure management to real-time management, this system integration prevents workers from risk of accidents, and makes possible take appropriate action quickly. This novel system is going to start for tentative operation, using position sensing and real-time personal dosimeter with database in Apr. 2012. (author)
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Saqr, Mohammed; Fors, Uno; Tedre, Matti; Nouri, Jalal
2018-01-01
To ensure online collaborative learning meets the intended pedagogical goals (is actually collaborative and stimulates learning), mechanisms are needed for monitoring the efficiency of online collaboration. Various studies have indicated that social network analysis can be particularly effective in studying students' interactions in online collaboration. However, research in education has only focused on the theoretical potential of using SNA, not on the actual benefits they achieved. This study investigated how social network analysis can be used to monitor online collaborative learning, find aspects in need of improvement, guide an informed intervention, and assess the efficacy of intervention using an experimental, observational repeated-measurement design in three courses over a full-term duration. Using a combination of SNA-based visual and quantitative analysis, we monitored three SNA constructs for each participant: the level of interactivity, the role, and position in information exchange, and the role played by each participant in the collaboration. On the group level, we monitored interactivity and group cohesion indicators. Our monitoring uncovered a non-collaborative teacher-centered pattern of interactions in the three studied courses as well as very few interactions among students, limited information exchange or negotiation, and very limited student networks dominated by the teacher. An intervention based on SNA-generated insights was designed. The intervention was structured into five actions: increasing awareness, promoting collaboration, improving the content, preparing teachers, and finally practicing with feedback. Evaluation of the intervention revealed that it has significantly enhanced student-student interactions and teacher-student interactions, as well as produced a collaborative pattern of interactions among most students and teachers. Since efficient and communicative activities are essential prerequisites for successful content
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E-learning for Part-Time Medical Studies
Directory of Open Access Journals (Sweden)
Półjanowicz Wiesław
2016-12-01
Full Text Available Distance education undoubtedly has many advantages, such as individualization of the learning process, unified transmission of teaching materials, the opportunity to study at any place and any time, reduction of financial costs for commuting to classes or accommodation of participants, etc. Adequate working conditions on the e-learning portal must also be present, eg. well-prepared, substantive courses and good communication between the participants. Therefore, an important element in the process of conducting e-learning courses is to measure the increase of knowledge and satisfaction of participants with distance learning. It allows for fine-tuning the content of the course and for classes to be properly organized. This paper presents the results of teaching and assessment of satisfaction with e-learning courses in “Problems of multiculturalism in medicine”, “Selected issues of visual rehabilitation” and “Ophthalmology and Ophthalmic Nursing”, which were carried out experimentally at the Faculty of Health Sciences at the Medical University of Bialystok for nursing students for the 2010/2011 academic year. The study group consisted of 72 part-time students who learnt in e-learning mode and the control group of 87 students who learnt in the traditional way. The students’ opinions about the teaching process and final exam scores were analyzed based on a specially prepared survey questionnaire. Organization of e-learning classes was rated positively by 90% of students. The average result on the final exams for all distance learning subjects was at the level of 82%, while for classes taught in the traditional form it was 81%. Based on these results, we conclude that distance learning is as effective as learning according to the traditional form in medical education studies.
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Professional Learning in Part-time University Study
DEFF Research Database (Denmark)
Rasmussen, Palle
2007-01-01
The theme of this article is adult students' learning in part-time studies at university level in Denmark. One issue discussed is the interplay of research and teaching in this kind of study programme. Examples are presented from the Master of Learning Processes study programme at Aalborg...
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Monitoring activities of satellite data processing services in real-time with SDDS Live Monitor
Duc Nguyen, Minh
2017-10-01
This work describes Live Monitor, the monitoring subsystem of SDDS - an automated system for space experiment data processing, storage, and distribution created at SINP MSU. Live Monitor allows operators and developers of satellite data centers to identify errors occurred in data processing quickly and to prevent further consequences caused by the errors. All activities of the whole data processing cycle are illustrated via a web interface in real-time. Notification messages are delivered to responsible people via emails and Telegram messenger service. The flexible monitoring mechanism implemented in Live Monitor allows us to dynamically change and control events being shown on the web interface on our demands. Physicists, whose space weather analysis models are functioning upon satellite data provided by SDDS, can use the developed RESTful API to monitor their own events and deliver customized notification messages by their needs.
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Liening, Andreas; Strunk, Guido; Mittelstadt, Ewald
2013-10-01
Much has been written about the differences between single- and double-loop learning, or more general between lower level and higher level learning. Especially in times of a fundamental crisis, a transition between lower and higher level learning would be an appropriate reaction to a challenge coming entirely out of the dark. However, so far there is no quantitative method to monitor such a transition. Therefore we introduce theory and methods of synergetics and present results from an experimental study based on the simulation of a crisis within a business simulation game. Hypothesized critical fluctuations - as a marker for so-called phase transitions - have been assessed with permutation entropy. Results show evidence for a phase transition during the crisis, which can be interpreted as a transition between lower and higher level learning.
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An LCD Monitor with Sufficiently Precise Timing for Research in Vision
Wang, Peng; Nikolić, Danko
2011-01-01
Until now, liquid crystal display (LCD) monitors have not been used widely for research in vision. Despite their main advantages of continuous illumination and low electromagnetic emission, these monitors had problems with timing and reliability. Here we report that there is at least one new inexpensive 120 Hz model, whose timing and stability is on a par with a benchmark cathode-ray tube monitor, or even better. The onset time was stable across repetitions, 95% confidence interval (the erro...
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Timing and control monitor system upgrade design document. Version 4
International Nuclear Information System (INIS)
Brandt, J.J.
1984-01-01
This is a design document for the Timing and Control Monitor System Upgrade Project. This project is intended to provide a replacement system for the existing user Encoder Monitor Systems and Varian 72 Control Room computer systems. All of these systems reside at the Nevada Test Site. The function of the T and C Monitor System is to gather real-time statistics and data on user defined key variables from control, communication, data acquistion systems, and from the monitoring system itself. The control, communication, and data acquisition systems each operate separately from the monitor system. The T and C Monitor System gathers this data in order to verify the readiness of an event to begin countdown. This includes setup, verification, calibration, and peripheral services, report any failures that may occur during the countdown, verify detonation and containment, and assist reentry activities after the event
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Spaced learning and innovative teaching: school time, pedagogy of attention and learning awareness
Directory of Open Access Journals (Sweden)
Garzia Maeca
2016-06-01
Full Text Available Currently, the ‘time’ variable has taken on the function of instructional and pedagogical innovation catalyst, after representing-over the years-a symbol of democratisation, learning opportunity and instruction quality, able to incorporate themes such as school dropout, personalisation and vocation into learning. Spaced Learning is a teaching methodology useful to quickly seize information in long-term memory based on a particular arrangement of the lesson time that comprises three input sessions and two intervals. Herein we refer to a teachers’ training initiative on Spaced Learning within the programme ‘DocentiInFormAzione’ in the EDOC@WORK3.0 Project in Apulia region in 2015. The training experience aimed at increasing teachers’ competencies in the Spaced Learning method implemented in a context of collaborative reflection and reciprocal enrichment. The intent of the article is to show how a process of rooting of the same culture of innovation, which opens to the discovery (or rediscovery of effective teaching practices sustained by scientific evidences, can be successfully implemented and to understand how or whether this innovation- based on the particular organisation of instructional time-links learning awareness to learning outcomes.
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Progress and lessons learned from water-quality monitoring networks
Myers, Donna N.; Ludtke, Amy S.
2017-01-01
Stream-quality monitoring networks in the United States were initiated and expanded after passage of successive federal water-pollution control laws from 1948 to 1972. The first networks addressed information gaps on the extent and severity of stream pollution and served as early warning systems for spills. From 1965 to 1972, monitoring networks expanded to evaluate compliance with stream standards, track emerging issues, and assess water-quality status and trends. After 1972, concerns arose regarding the ability of monitoring networks to determine if water quality was getting better or worse and why. As a result, monitoring networks adopted a hydrologic systems approach targeted to key water-quality issues, accounted for human and natural factors affecting water quality, innovated new statistical methods, and introduced geographic information systems and models that predict water quality at unmeasured locations. Despite improvements, national-scale monitoring networks have declined over time. Only about 1%, or 217, of more than 36,000 US Geological Survey monitoring sites sampled from 1975 to 2014 have been operated throughout the four decades since passage of the 1972 Clean Water Act. Efforts to sustain monitoring networks are important because these networks have collected information crucial to the description of water-quality trends over time and are providing information against which to evaluate future trends.
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Gravitational Lens Time Delays Using Polarization Monitoring
Directory of Open Access Journals (Sweden)
Andrew Biggs
2017-11-01
Full Text Available Gravitational lens time delays provide a means of measuring the expansion of the Universe at high redshift (and therefore in the ‘Hubble flow’ that is independent of local calibrations. It was hoped that many of the radio lenses found in the JVAS/CLASS survey would yield time delays as these were selected to have flat spectra and are dominated by multiple compact components. However, despite extensive monitoring with the Very Large Array (VLA, time delays have only been measured for three of these systems (out of 22. We have begun a programme to reanalyse the existing VLA monitoring data with the goal of producing light curves in polarized flux and polarization position angle, either to improve delay measurements or to find delays for new sources. Here, we present preliminary results on the lens system B1600+434 which demonstrate the presence of correlated and substantial polarization variability in each image.
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Real-time earthquake monitoring: Early warning and rapid response
1991-01-01
A panel was established to investigate the subject of real-time earthquake monitoring (RTEM) and suggest recommendations on the feasibility of using a real-time earthquake warning system to mitigate earthquake damage in regions of the United States. The findings of the investigation and the related recommendations are described in this report. A brief review of existing real-time seismic systems is presented with particular emphasis given to the current California seismic networks. Specific applications of a real-time monitoring system are discussed along with issues related to system deployment and technical feasibility. In addition, several non-technical considerations are addressed including cost-benefit analysis, public perceptions, safety, and liability.
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A tool for monitoring lecturers’ interactions with Learning Management Systems
Directory of Open Access Journals (Sweden)
Magdalena Cantabella
2016-12-01
Full Text Available Learning Management Systems’ (LMS interaction mechanisms are mainly focused on the improvement of students’ experiences and academic results. However, special attention should also be given to the interaction between these LMS and other actors involved in the educational process. This paper specifically targets the interaction of degree coordinators with LMS when monitoring lecturers’ performance, especially in an online mode. The methodology is guided by the following three objectives: (1 analysis of the limitations of monitoring lecturers in current LMS; (2 development of software program to overcome such limitations; and (3 empirical evaluation of the proposed program. The results show that this type of tool helps coordinators to intuitively and efficiently analyze the status of the subjects taught in their degree programs.
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Increasing instruction time in school does increase learning
DEFF Research Database (Denmark)
Andersen, Simon Calmar; Humlum, Maria; Nandrup, Anne Brink
2016-01-01
Increasing instruction time in school is a central element in the attempts of many governments to improve student learning, but prior research—mainly based on observational data—disputes the effect of this approach and points out the potential negative effects on student behavior. Based on a large......-scale, cluster-randomized trial, we find that increasing instruction time increases student learning and that a general increase in instruction time is at least as efficient as an expert-developed, detailed teaching program that increases instruction with the same amount of time. These findings support the value...... of increased instruction time....
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Monitoring activities of satellite data processing services in real-time with SDDS Live Monitor
Directory of Open Access Journals (Sweden)
Duc Nguyen Minh
2017-01-01
Full Text Available This work describes Live Monitor, the monitoring subsystem of SDDS – an automated system for space experiment data processing, storage, and distribution created at SINP MSU. Live Monitor allows operators and developers of satellite data centers to identify errors occurred in data processing quickly and to prevent further consequences caused by the errors. All activities of the whole data processing cycle are illustrated via a web interface in real-time. Notification messages are delivered to responsible people via emails and Telegram messenger service. The flexible monitoring mechanism implemented in Live Monitor allows us to dynamically change and control events being shown on the web interface on our demands. Physicists, whose space weather analysis models are functioning upon satellite data provided by SDDS, can use the developed RESTful API to monitor their own events and deliver customized notification messages by their needs.
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Real-time Color Codes for Assessing Learning Process
Dzelzkalēja, L; Kapenieks, J
2016-01-01
Effective assessment is an important way for improving the learning process. There are existing guidelines for assessing the learning process, but they lack holistic digital knowledge society considerations. In this paper the authors propose a method for real-time evaluation of students’ learning process and, consequently, for quality evaluation of teaching materials both in the classroom and in the distance learning environment. The main idea of the proposed Color code method (CCM) is to use...
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Timed Runtime Monitoring for Multiparty Conversations
Directory of Open Access Journals (Sweden)
Rumyana Neykova
2014-08-01
Full Text Available We propose a dynamic verification framework for protocols in real-time distributed systems. The framework is based on Scribble, a tool-chain for design and verification of choreographies based on multiparty session types, developed with our industrial partners. Drawing from recent work on multiparty session types for real-time interactions, we extend Scribble with clocks, resets, and clock predicates constraining the times in which interactions should occur. We present a timed API for Python to program distributed implementations of Scribble specifications. A dynamic verification framework ensures the safe execution of applications written with our timed API: we have implemented dedicated runtime monitors that check that each interaction occurs at a correct timing with respect to the corresponding Scribble specification. The performance of our implementation and its practicability are analysed via benchmarking.
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Lessons Learned In Aerosol Monitoring With The RASA
International Nuclear Information System (INIS)
Forrester, Joel B.; Bowyer, Ted W.; Carty, Fitz; Comes, Laura; Eslinger, Paul W.; Greenwood, Lawrence R.; Haas, Derek A.; Hayes, James C.; Kirkham, Randy R.; Lepel, Elwood A.; Litke, Kevin E.; Miley, Harry S.; Morris, Scott J.; Schrom, Brian T.; Van Davelaar, Peter; Woods, Vincent T.
2011-01-01
The Radionuclide Aerosol Sampler/Analyzer (RASA) is an automated aerosol collection and analysis system designed by Pacific Northwest National Laboratory (PNNL) in the 1990's and is deployed in several locations around the world as part of the International Monitoring System (IMS) required under the Comprehensive Nuclear-Test-Ban Treaty (CTBT). The RASA operates unattended, save for regularly scheduled maintenance, iterating samples through a three-step process on a 24-hour interval. In its 15-year history, much has been learned from the operation and maintenance of the RASA that can benefit engineering updates or future aerosol systems. On 11 March 2011, a 9.0 magnitude earthquake and tsunami rocked the eastern coast of Japan, resulting in power loss and cooling failures at the Daiichi nuclear power plants in Fukushima Prefecture. Aerosol collections were conducted with the RASA in Richland, WA. We present a summary of the lessons learned over the history of the RASA, including lessons taken from the Fukushima incident, regarding the RASA IMS stations operated by the United States.
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Hawker, Morgan J.; Dysleski, Lisa; Rickey, Dawn
2016-01-01
Metacognitive monitoring of one's own understanding plays a key role in learning. An aspect of metacognitive monitoring can be measured by comparing a student's prediction or postdiction of performance (a judgment made before or after completing the relevant task) with the student's actual performance. In this study, we investigated students'…
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Ranalli, Jim
2018-01-01
Accurate metacognitive monitoring of one's own knowledge or performance is a precondition for self-regulated learning; monitoring informs metacognitive control, which in turn affects task outcomes. Studies of monitoring accuracy and its connection to knowledge and performance are common in psychology and educational research but rare in instructed…
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Optimizing the De-Noise Neural Network Model for GPS Time-Series Monitoring of Structures
Directory of Open Access Journals (Sweden)
Mosbeh R. Kaloop
2015-09-01
Full Text Available The Global Positioning System (GPS is recently used widely in structures and other applications. Notwithstanding, the GPS accuracy still suffers from the errors afflicting the measurements, particularly the short-period displacement of structural components. Previously, the multi filter method is utilized to remove the displacement errors. This paper aims at using a novel application for the neural network prediction models to improve the GPS monitoring time series data. Four prediction models for the learning algorithms are applied and used with neural network solutions: back-propagation, Cascade-forward back-propagation, adaptive filter and extended Kalman filter, to estimate which model can be recommended. The noise simulation and bridge’s short-period GPS of the monitoring displacement component of one Hz sampling frequency are used to validate the four models and the previous method. The results show that the Adaptive neural networks filter is suggested for de-noising the observations, specifically for the GPS displacement components of structures. Also, this model is expected to have significant influence on the design of structures in the low frequency responses and measurements’ contents.
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Linear time relational prototype based learning.
Gisbrecht, Andrej; Mokbel, Bassam; Schleif, Frank-Michael; Zhu, Xibin; Hammer, Barbara
2012-10-01
Prototype based learning offers an intuitive interface to inspect large quantities of electronic data in supervised or unsupervised settings. Recently, many techniques have been extended to data described by general dissimilarities rather than Euclidean vectors, so-called relational data settings. Unlike the Euclidean counterparts, the techniques have quadratic time complexity due to the underlying quadratic dissimilarity matrix. Thus, they are infeasible already for medium sized data sets. The contribution of this article is twofold: On the one hand we propose a novel supervised prototype based classification technique for dissimilarity data based on popular learning vector quantization (LVQ), on the other hand we transfer a linear time approximation technique, the Nyström approximation, to this algorithm and an unsupervised counterpart, the relational generative topographic mapping (GTM). This way, linear time and space methods result. We evaluate the techniques on three examples from the biomedical domain.
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Field Demonstration of Real-Time Wind Turbine Foundation Strain Monitoring.
Rubert, Tim; Perry, Marcus; Fusiek, Grzegorz; McAlorum, Jack; Niewczas, Pawel; Brotherston, Amanda; McCallum, David
2017-12-31
Onshore wind turbine foundations are generally over-engineered as their internal stress states are challenging to directly monitor during operation. While there are industry drivers to shift towards more economical foundation designs, making this transition safely will require new monitoring techniques, so that the uncertainties around structural health can be reduced. This paper presents the initial results of a real-time strain monitoring campaign for an operating wind turbine foundation. Selected reinforcement bars were instrumented with metal packaged optical fibre strain sensors prior to concrete casting. In this paper, we outline the sensors' design, characterisation and installation, and present 67 days of operational data. During this time, measured foundation strains did not exceed 95 μ ϵ , and showed a strong correlation with both measured tower displacements and the results of a foundation finite element model. The work demonstrates that real-time foundation monitoring is not only achievable, but that it has the potential to help operators and policymakers quantify the conservatism of their existing design codes.
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Establishing monitoring programs for travel time reliability.
2014-01-01
Within the second Strategic Highway Research Program (SHRP 2), Project L02 focused on creating a suite of methods by which transportation agencies could monitor and evaluate travel time reliability. Creation of the methods also produced an improved u...
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A GPS-based Real-time Road Traffic Monitoring System
Tanti, Kamal Kumar
In recent years, monitoring systems are astonishingly inclined towards ever more automatic; reliably interconnected, distributed and autonomous operation. Specifically, the measurement, logging, data processing and interpretation activities may be carried out by separate units at different locations in near real-time. The recent evolution of mobile communication devices and communication technologies has fostered a growing interest in the GIS & GPS-based location-aware systems and services. This paper describes a real-time road traffic monitoring system based on integrated mobile field devices (GPS/GSM/IOs) working in tandem with advanced GIS-based application software providing on-the-fly authentications for real-time monitoring and security enhancement. The described system is developed as a fully automated, continuous, real-time monitoring system that employs GPS sensors and Ethernet and/or serial port communication techniques are used to transfer data between GPS receivers at target points and a central processing computer. The data can be processed locally or remotely based on the requirements of client’s satisfaction. Due to the modular architecture of the system, other sensor types may be supported with minimal effort. Data on the distributed network & measurements are transmitted via cellular SIM cards to a Control Unit, which provides for post-processing and network management. The Control Unit may be remotely accessed via an Internet connection. The new system will not only provide more consistent data about the road traffic conditions but also will provide methods for integrating with other Intelligent Transportation Systems (ITS). For communication between the mobile device and central monitoring service GSM technology is used. The resulting system is characterized by autonomy, reliability and a high degree of automation.
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Ecological monitoring in a discrete-time prey-predator model.
Gámez, M; López, I; Rodríguez, C; Varga, Z; Garay, J
2017-09-21
The paper is aimed at the methodological development of ecological monitoring in discrete-time dynamic models. In earlier papers, in the framework of continuous-time models, we have shown how a systems-theoretical methodology can be applied to the monitoring of the state process of a system of interacting populations, also estimating certain abiotic environmental changes such as pollution, climatic or seasonal changes. In practice, however, there may be good reasons to use discrete-time models. (For instance, there may be discrete cycles in the development of the populations, or observations can be made only at discrete time steps.) Therefore the present paper is devoted to the development of the monitoring methodology in the framework of discrete-time models of population ecology. By monitoring we mean that, observing only certain component(s) of the system, we reconstruct the whole state process. This may be necessary, e.g., when in a complex ecosystem the observation of the densities of certain species is impossible, or too expensive. For the first presentation of the offered methodology, we have chosen a discrete-time version of the classical Lotka-Volterra prey-predator model. This is a minimal but not trivial system where the methodology can still be presented. We also show how this methodology can be applied to estimate the effect of an abiotic environmental change, using a component of the population system as an environmental indicator. Although this approach is illustrated in a simplest possible case, it can be easily extended to larger ecosystems with several interacting populations and different types of abiotic environmental effects. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Learned Interval Time Facilitates Associate Memory Retrieval
van de Ven, Vincent; Kochs, Sarah; Smulders, Fren; De Weerd, Peter
2017-01-01
The extent to which time is represented in memory remains underinvestigated. We designed a time paired associate task (TPAT) in which participants implicitly learned cue-time-target associations between cue-target pairs and specific cue-target intervals. During subsequent memory testing, participants showed increased accuracy of identifying…
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A Lecture Supporting System Based on Real-Time Learning Analytics
Shimada, Atsushi; Konomi, Shin'ichi
2017-01-01
A new lecture supporting system based on real-time learning analytics is proposed. Our target is on-site classrooms where teachers give their lectures, and a lot of students listen to teachers' explanation, conduct exercises etc. We utilize not only an e-Learning system, but also an e-Book system to collect real-time learning activities during the…
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Biosensor-based real-time monitoring of paracetamol photocatalytic degradation.
Calas-Blanchard, Carole; Istamboulié, Georges; Bontoux, Margot; Plantard, Gaël; Goetz, Vincent; Noguer, Thierry
2015-07-01
This paper presents for the first time the integration of a biosensor for the on-line, real-time monitoring of a photocatalytic degradation process. Paracetamol was used as a model molecule due to its wide use and occurrence in environmental waters. The biosensor was developed based on tyrosinase immobilization in a polyvinylalcohol photocrosslinkable polymer. It was inserted in a computer-controlled flow system installed besides a photocatalytic reactor including titanium dioxide (TiO2) as photocatalyst. It was shown that the biosensor was able to accurately monitor the paracetamol degradation with time. Compared with conventional HPLC analysis, the described device provides a real-time information on the reaction advancement, allowing a better control of the photodegradation process. Copyright © 2015 Elsevier Ltd. All rights reserved.
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From feedback- to response-based performance monitoring in active and observational learning.
Bellebaum, Christian; Colosio, Marco
2014-09-01
Humans can adapt their behavior by learning from the consequences of their own actions or by observing others. Gradual active learning of action-outcome contingencies is accompanied by a shift from feedback- to response-based performance monitoring. This shift is reflected by complementary learning-related changes of two ACC-driven ERP components, the feedback-related negativity (FRN) and the error-related negativity (ERN), which have both been suggested to signal events "worse than expected," that is, a negative prediction error. Although recent research has identified comparable components for observed behavior and outcomes (observational ERN and FRN), it is as yet unknown, whether these components are similarly modulated by prediction errors and thus also reflect behavioral adaptation. In this study, two groups of 15 participants learned action-outcome contingencies either actively or by observation. In active learners, FRN amplitude for negative feedback decreased and ERN amplitude in response to erroneous actions increased with learning, whereas observational ERN and FRN in observational learners did not exhibit learning-related changes. Learning performance, assessed in test trials without feedback, was comparable between groups, as was the ERN following actively performed errors during test trials. In summary, the results show that action-outcome associations can be learned similarly well actively and by observation. The mechanisms involved appear to differ, with the FRN in active learning reflecting the integration of information about own actions and the accompanying outcomes.
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Attention focussing and anomaly detection in real-time systems monitoring
Doyle, Richard J.; Chien, Steve A.; Fayyad, Usama M.; Porta, Harry J.
1993-01-01
In real-time monitoring situations, more information is not necessarily better. When faced with complex emergency situations, operators can experience information overload and a compromising of their ability to react quickly and correctly. We describe an approach to focusing operator attention in real-time systems monitoring based on a set of empirical and model-based measures for determining the relative importance of sensor data.
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Real-Time Monitoring of Psychotherapeutic Processes: Concept and Compliance
Directory of Open Access Journals (Sweden)
Guenter Karl Schiepek
2016-05-01
Full Text Available AbstractObjective. The feasibility of a high-frequency real-time monitoring approach to psychotherapy is outlined and tested for patients’ compliance to evaluate its integration to everyday practice. Criteria concern the ecological momentary assessment, the assessment of therapy-related cognitions and emotions, equidistant time sampling, real-time nonlinear time series analysis, continuous participative process control by client and therapist, and the application of idiographic (person-specific surveys. Methods. The process-outcome monitoring is technically realized by an internet-based device for data collection and data analysis, the Synergetic Navigation System. Its feasibility is documented by a compliance study on 151 clients treated in an inpatient and a day-treatment clinic. Results. We found high compliance rates (mean: 78.3%, median: 89.4% amongst the respondents, independent of the severity of symptoms or the degree of impairment. Compared to other diagnoses, the compliance rate was lower in the group diagnosed with personality disorders. Conclusion. The results support the feasibility of high-frequency monitoring in routine psychotherapy settings. Daily collection of psychological surveys allows for assessment of highly resolved, equidistant time series data which gives insight into the nonlinear qualities of therapeutic change processes (e.g., pattern transitions, critical instabilities.
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Real-Time Monitoring of Psychotherapeutic Processes: Concept and Compliance
Schiepek, Günter; Aichhorn, Wolfgang; Gruber, Martin; Strunk, Guido; Bachler, Egon; Aas, Benjamin
2016-01-01
Objective: The feasibility of a high-frequency real-time monitoring approach to psychotherapy is outlined and tested for patients' compliance to evaluate its integration to everyday practice. Criteria concern the ecological momentary assessment, the assessment of therapy-related cognitions and emotions, equidistant time sampling, real-time nonlinear time series analysis, continuous participative process control by client and therapist, and the application of idiographic (person-specific) surveys. Methods: The process-outcome monitoring is technically realized by an internet-based device for data collection and data analysis, the Synergetic Navigation System. Its feasibility is documented by a compliance study on 151 clients treated in an inpatient and a day-treatment clinic. Results: We found high compliance rates (mean: 78.3%, median: 89.4%) amongst the respondents, independent of the severity of symptoms or the degree of impairment. Compared to other diagnoses, the compliance rate was lower in the group diagnosed with personality disorders. Conclusion: The results support the feasibility of high-frequency monitoring in routine psychotherapy settings. Daily collection of psychological surveys allows for the assessment of highly resolved, equidistant time series data which gives insight into the nonlinear qualities of therapeutic change processes (e.g., pattern transitions, critical instabilities). PMID:27199837
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An OpenMP Parallelisation of Real-time Processing of CERN LHC Beam Position Monitor Data
Renshall, H
2012-01-01
SUSSIX is a FORTRAN program for the post processing of turn-by-turn Beam Position Monitor (BPM) data, which computes the frequency, amplitude, and phase of tunes and resonant lines to a high degree of precision. For analysis of LHC BPM data a specific version run through a C steering code has been implemented in the CERN Control Centre to run on a server under the Linux operating system but became a real time computational bottleneck preventing truly online study of the BPM data. Timing studies showed that the independent processing of each BPMs data was a candidate for parallelization and the Open Multiprocessing (OpenMP) package with its simple insertion of compiler directives was tried. It proved to be easy to learn and use, problem free and efficient in this case reaching a factor of ten reductions in real-time over twelve cores on a dedicated server. This paper reviews the problem, shows the critical code fragments with their OpenMP directives and the results obtained.
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Directory of Open Access Journals (Sweden)
Oleksandr M. Korniiets
2012-12-01
Full Text Available The article deals with the application of social services WEB 2.0 for personal learning environment creation that is used for professional orientation work of social educator. The feedback is must be in personal learning environment for the effective professional orientation work. This feedback can be organized through statistical monitoring. The typical solution for organizing personal learning environment with built-in statistical surveys and statistical data processing is considered in the article. The possibilities of the statistical data collection and processing services on the example of Google Analytics are investigated.
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Human learning: Power laws or multiple characteristic time scales?
Directory of Open Access Journals (Sweden)
Gottfried Mayer-Kress
2006-09-01
Full Text Available The central proposal of A. Newell and Rosenbloom (1981 was that the power law is the ubiquitous law of learning. This proposition is discussed in the context of the key factors that led to the acceptance of the power law as the function of learning. We then outline the principles of an epigenetic landscape framework for considering the role of the characteristic time scales of learning and an approach to system identification of the processes of performance dynamics. In this view, the change of performance over time is the product of a superposition of characteristic exponential time scales that reflect the influence of different processes. This theoretical approach can reproduce the traditional power law of practice within the experimental resolution of performance data sets - but we hypothesize that this function may prove to be a special and perhaps idealized case of learning.
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Analysis of Land Subsidence Monitoring in Mining Area with Time-Series Insar Technology
Sun, N.; Wang, Y. J.
2018-04-01
Time-series InSAR technology has become a popular land subsidence monitoring method in recent years, because of its advantages such as high accuracy, wide area, low expenditure, intensive monitoring points and free from accessibility restrictions. In this paper, we applied two kinds of satellite data, ALOS PALSAR and RADARSAT-2, to get the subsidence monitoring results of the study area in two time periods by time-series InSAR technology. By analyzing the deformation range, rate and amount, the time-series analysis of land subsidence in mining area was realized. The results show that InSAR technology could be used to monitor land subsidence in large area and meet the demand of subsidence monitoring in mining area.
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Ghergulescu, Ioana; Muntean, Cristina Hava
2016-01-01
Engagement influences participation, progression and retention in game-based e-learning (GBeL). Therefore, GBeL systems should engage the players in order to support them to maximize their learning outcomes, and provide the players with adequate feedback to maintain their motivation. Innovative engagement monitoring solutions based on players'…
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RadNet Real-Time Monitoring Spectrometry Data Inventory
U.S. Environmental Protection Agency — The RadNet Real-Time Monitoring Spectrometry Data Inventory contains measured data used to identify and measure specific radioactive materials in the atmosphere at...
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An Empirical Investigation of Individual Differences in Time to Learn
Anderson, Lorin W.
1976-01-01
Results show that student differences in time-on-task to learn to criterion are alterable and can be minimized over a sequence of learning units given appropriate adaptive learning strategies. (Author/DEP)
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Directory of Open Access Journals (Sweden)
David Afolabi
2017-11-01
Full Text Available The importance of an interference-less machine learning scheme in time series prediction is crucial, as an oversight can have a negative cumulative effect, especially when predicting many steps ahead of the currently available data. The on-going research on noise elimination in time series forecasting has led to a successful approach of decomposing the data sequence into component trends to identify noise-inducing information. The empirical mode decomposition method separates the time series/signal into a set of intrinsic mode functions ranging from high to low frequencies, which can be summed up to reconstruct the original data. The usual assumption that random noises are only contained in the high-frequency component has been shown not to be the case, as observed in our previous findings. The results from that experiment reveal that noise can be present in a low frequency component, and this motivates the newly-proposed algorithm. Additionally, to prevent the erosion of periodic trends and patterns within the series, we perform the learning of local and global trends separately in a hierarchical manner which succeeds in detecting and eliminating short/long term noise. The algorithm is tested on four datasets from financial market data and physical science data. The simulation results are compared with the conventional and state-of-the-art approaches for time series machine learning, such as the non-linear autoregressive neural network and the long short-term memory recurrent neural network, respectively. Statistically significant performance gains are recorded when the meta-learning algorithm for noise reduction is used in combination with these artificial neural networks. For time series data which cannot be decomposed into meaningful trends, applying the moving average method to create meta-information for guiding the learning process is still better than the traditional approach. Therefore, this new approach is applicable to the forecasting
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Real time water chemistry monitoring and diagnostics
International Nuclear Information System (INIS)
Gaudreau, T.M.; Choi, S.S.
2002-01-01
EPRI has produced a real time water chemistry monitoring and diagnostic system. This system is called SMART ChemWorks and is based on the EPRI ChemWorks codes. System models, chemistry parameter relationships and diagnostic approaches from these codes are integrated with real time data collection, an intelligence engine and Internet technologies to allow for automated analysis of system chemistry. Significant data management capabilities are also included which allow the user to evaluate data and create automated reporting. Additional features have been added to the system in recent years including tracking and evaluation of primary chemistry as well as the calculation and tracking of primary to secondary leakage in PWRs. This system performs virtual sensing, identifies normal and upset conditions, and evaluates the consistency of on-line monitor and grab sample readings. The system also makes use of virtual fingerprinting to identify the cause of any chemistry upsets. This technology employs plant-specific data and models to determine the chemical state of the steam cycle. (authors)
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Activity Learning as a Foundation for Security Monitoring in Smart Homes.
Dahmen, Jessamyn; Thomas, Brian L; Cook, Diane J; Wang, Xiaobo
2017-03-31
Smart environment technology has matured to the point where it is regularly used in everyday homes as well as research labs. With this maturation of the technology, we can consider using smart homes as a practical mechanism for improving home security. In this paper, we introduce an activity-aware approach to security monitoring and threat detection in smart homes. We describe our approach using the CASAS smart home framework and activity learning algorithms. By monitoring for activity-based anomalies we can detect possible threats and take appropriate action. We evaluate our proposed method using data collected in CASAS smart homes and demonstrate the partnership between activity-aware smart homes and biometric devices in the context of the CASAS on-campus smart apartment testbed.
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Activity Learning as a Foundation for Security Monitoring in Smart Homes
Directory of Open Access Journals (Sweden)
Jessamyn Dahmen
2017-03-01
Full Text Available Smart environment technology has matured to the point where it is regularly used in everyday homes as well as research labs. With this maturation of the technology, we can consider using smart homes as a practical mechanism for improving home security. In this paper, we introduce an activity-aware approach to security monitoring and threat detection in smart homes. We describe our approach using the CASAS smart home framework and activity learning algorithms. By monitoring for activity-based anomalies we can detect possible threats and take appropriate action. We evaluate our proposed method using data collected in CASAS smart homes and demonstrate the partnership between activity-aware smart homes and biometric devices in the context of the CASAS on-campus smart apartment testbed.
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Real-time beam monitoring in scanned proton therapy
Klimpki, G.; Eichin, M.; Bula, C.; Rechsteiner, U.; Psoroulas, S.; Weber, D. C.; Lomax, A.; Meer, D.
2018-05-01
When treating cancerous tissues with protons beams, many centers make use of a step-and-shoot irradiation technique, in which the beam is steered to discrete grid points in the tumor volume. For safety reasons, the irradiation is supervised by an independent monitoring system validating cyclically that the correct amount of protons has been delivered to the correct position in the patient. Whenever unacceptable inaccuracies are detected, the irradiation can be interrupted to reinforce a high degree of radiation protection. At the Paul Scherrer Institute, we plan to irradiate tumors continuously. By giving up the idea of discrete grid points, we aim to be faster and more flexible in the irradiation. But the increase in speed and dynamics necessitates a highly responsive monitoring system to guarantee the same level of patient safety as for conventional step-and-shoot irradiations. Hence, we developed and implemented real-time monitoring of the proton beam current and position. As such, we read out diagnostic devices with 100 kHz and compare their signals against safety tolerances in an FPGA. In this paper, we report on necessary software and firmware enhancements of our control system and test their functionality based on three exemplary error scenarios. We demonstrate successful implementation of real-time beam monitoring and, consequently, compliance with international patient safety regulations.
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Real-time individualized training vectors for experiential learning.
Energy Technology Data Exchange (ETDEWEB)
Willis, Matt; Tucker, Eilish Marie; Raybourn, Elaine Marie; Glickman, Matthew R.; Fabian, Nathan
2011-01-01
Military training utilizing serious games or virtual worlds potentially generate data that can be mined to better understand how trainees learn in experiential exercises. Few data mining approaches for deployed military training games exist. Opportunities exist to collect and analyze these data, as well as to construct a full-history learner model. Outcomes discussed in the present document include results from a quasi-experimental research study on military game-based experiential learning, the deployment of an online game for training evidence collection, and results from a proof-of-concept pilot study on the development of individualized training vectors. This Lab Directed Research & Development (LDRD) project leveraged products within projects, such as Titan (Network Grand Challenge), Real-Time Feedback and Evaluation System, (America's Army Adaptive Thinking and Leadership, DARWARS Ambush! NK), and Dynamic Bayesian Networks to investigate whether machine learning capabilities could perform real-time, in-game similarity vectors of learner performance, toward adaptation of content delivery, and quantitative measurement of experiential learning.
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Wang, Ji-Bo; Wang, Ming-Zheng; Ji, Ping
2012-05-01
In this article, we consider a single machine scheduling problem with a time-dependent learning effect and deteriorating jobs. By the effects of time-dependent learning and deterioration, we mean that the job processing time is defined by a function of its starting time and total normal processing time of jobs in front of it in the sequence. The objective is to determine an optimal schedule so as to minimize the total completion time. This problem remains open for the case of -1 < a < 0, where a denotes the learning index; we show that an optimal schedule of the problem is V-shaped with respect to job normal processing times. Three heuristic algorithms utilising the V-shaped property are proposed, and computational experiments show that the last heuristic algorithm performs effectively and efficiently in obtaining near-optimal solutions.
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Real time n/γ discrimination for the JET neutron profile monitor
Energy Technology Data Exchange (ETDEWEB)
Riva, M., E-mail: marco.riva@enea.it [Associazione EURATOM-ENEA sulla Fusione, C.P. 65, Frascati I-00044, Roma (Italy); Esposito, B.; Marocco, D.; Belli, F. [Associazione EURATOM-ENEA sulla Fusione, C.P. 65, Frascati I-00044, Roma (Italy); Syme, B. [EURATOM/CCFE Fusion Association, OX14 3DB Abingdon (United Kingdom); Giacomelli, L. [Dipartimento di Fisica, Università degli Studi di Milano-Bicocca (Italy); Istituto di Fisica del Plasma, Associazione EURATOM-ENEA-CNR, 20100 Milano (Italy); JET-EFDA, Culham Science Centre, OX14 3DB Abingdon (United Kingdom)
2013-10-15
Highlights: ► Development of a pulse oriented acquisition system able for the JET neutron profile monitor to separate neutron and gamma pulses. ► Description of the FPGA hardware architecture. ► Comparison between the off-line and real time neutron count rates from the last JET experimental campaign. ► Estimate of the maximum sustainable count rate of the system. ► Statistical analysis of neutron measurements from JET neutron profile monitor and neutron monitors. -- Abstract: The JET neutron profile monitor provides the measurement of the neutron flux along 19 collimated lines of sight from which the neutron emissivity profile can be obtained through reconstruction based on inversion methods. The neutron detectors are liquid organic scintillators featuring n/γ pulse shape discrimination. A recent digital upgrade of the neutron profile monitor acquisition system (200 MSamples/s sampling rate per channel, 14 bit resolution) offers new real-time capabilities. An algorithm performing real-time n/γ discrimination by means of the charge comparison method is implemented in the acquisition system FPGA. The algorithm produces two distinct count rates (n and γ) that are sent to the JET real time network ready for control applications and are simultaneously stored into the JET archive together with all the samples of each pulse. The paper describes the architecture of the FPGA implementation and reports the analysis of data collected during the 2011–2012 JET campaigns. The comparison between the real-time and post-processed (off-line) neutron count rates shows an agreement within 5% for all 19 detectors. Moreover, it is shown that the maximum count rate sustainable by the acquisition system when storing raw data (∼900 kHz as evaluated in laboratory tests) can be extended up to 5 MHz when using the real-time implementation with no local data storage. Finally, a statistical analysis of the ratio between the line-integrated measurements from the neutron profile
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SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS MONITORING USING GAUSSIAN PROCESS
National Aeronautics and Space Administration — SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS MONITORING USING GAUSSIAN PROCESS VARUN CHANDOLA AND RANGA RAJU VATSAVAI Abstract. Biomass monitoring,...
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The Real-Time Monitoring Service Platform for Land Supervision Based on Cloud Integration
Sun, J.; Mao, M.; Xiang, H.; Wang, G.; Liang, Y.
2018-04-01
Remote sensing monitoring has become the important means for land and resources departments to strengthen supervision. Aiming at the problems of low monitoring frequency and poor data currency in current remote sensing monitoring, this paper researched and developed the cloud-integrated real-time monitoring service platform for land supervision which enhanced the monitoring frequency by acquiring the domestic satellite image data overall and accelerated the remote sensing image data processing efficiency by exploiting the intelligent dynamic processing technology of multi-source images. Through the pilot application in Jinan Bureau of State Land Supervision, it has been proved that the real-time monitoring technical method for land supervision is feasible. In addition, the functions of real-time monitoring and early warning are carried out on illegal land use, permanent basic farmland protection and boundary breakthrough in urban development. The application has achieved remarkable results.
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Directory of Open Access Journals (Sweden)
Bang Liu
2018-01-01
Full Text Available In mHealth field, accurate breathing rate monitoring technique has benefited a broad array of healthcare-related applications. Many approaches try to use smartphone or wearable device with fine-grained monitoring algorithm to accomplish the task, which can only be done by professional medical equipment before. However, such schemes usually result in bad performance in comparison to professional medical equipment. In this paper, we propose DeepFilter, a deep learning-based fine-grained breathing rate monitoring algorithm that works on smartphone and achieves professional-level accuracy. DeepFilter is a bidirectional recurrent neural network (RNN stacked with convolutional layers and speeded up by batch normalization. Moreover, we collect 16.17 GB breathing sound recording data of 248 hours from 109 and another 10 volunteers to train and test our model, respectively. The results show a reasonably good accuracy of breathing rate monitoring.
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Data-driven strategies for robust forecast of continuous glucose monitoring time-series.
Fiorini, Samuele; Martini, Chiara; Malpassi, Davide; Cordera, Renzo; Maggi, Davide; Verri, Alessandro; Barla, Annalisa
2017-07-01
Over the past decade, continuous glucose monitoring (CGM) has proven to be a very resourceful tool for diabetes management. To date, CGM devices are employed for both retrospective and online applications. Their use allows to better describe the patients' pathology as well as to achieve a better control of patients' level of glycemia. The analysis of CGM sensor data makes possible to observe a wide range of metrics, such as the glycemic variability during the day or the amount of time spent below or above certain glycemic thresholds. However, due to the high variability of the glycemic signals among sensors and individuals, CGM data analysis is a non-trivial task. Standard signal filtering solutions fall short when an appropriate model personalization is not applied. State-of-the-art data-driven strategies for online CGM forecasting rely upon the use of recursive filters. Each time a new sample is collected, such models need to adjust their parameters in order to predict the next glycemic level. In this paper we aim at demonstrating that the problem of online CGM forecasting can be successfully tackled by personalized machine learning models, that do not need to recursively update their parameters.
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Air-Flow-Driven Triboelectric Nanogenerators for Self-Powered Real-Time Respiratory Monitoring.
Wang, Meng; Zhang, Jiahao; Tang, Yingjie; Li, Jun; Zhang, Baosen; Liang, Erjun; Mao, Yanchao; Wang, Xudong
2018-06-04
Respiration is one of the most important vital signs of humans, and respiratory monitoring plays an important role in physical health management. A low-cost and convenient real-time respiratory monitoring system is extremely desirable. In this work, we demonstrated an air-flow-driven triboelectric nanogenerator (TENG) for self-powered real-time respiratory monitoring by converting mechanical energy of human respiration into electric output signals. The operation of the TENG was based on the air-flow-driven vibration of a flexible nanostructured polytetrafluoroethylene (n-PTFE) thin film in an acrylic tube. This TENG can generate distinct real-time electric signals when exposed to the air flow from different breath behaviors. It was also found that the accumulative charge transferred in breath sensing corresponds well to the total volume of air exchanged during the respiration process. Based on this TENG device, an intelligent wireless respiratory monitoring and alert system was further developed, which used the TENG signal to directly trigger a wireless alarm or dial a cell phone to provide timely alerts in response to breath behavior changes. This research offers a promising solution for developing self-powered real-time respiratory monitoring devices.
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Time to rethink the neural mechanisms of learning and memory.
Gallistel, Charles R; Balsam, Peter D
2014-02-01
Most studies in the neurobiology of learning assume that the underlying learning process is a pairing - dependent change in synaptic strength that requires repeated experience of events presented in close temporal contiguity. However, much learning is rapid and does not depend on temporal contiguity, which has never been precisely defined. These points are well illustrated by studies showing that the temporal relations between events are rapidly learned- even over long delays- and that this knowledge governs the form and timing of behavior. The speed with which anticipatory responses emerge in conditioning paradigms is determined by the information that cues provide about the timing of rewards. The challenge for understanding the neurobiology of learning is to understand the mechanisms in the nervous system that encode information from even a single experience, the nature of the memory mechanisms that can encode quantities such as time, and how the brain can flexibly perform computations based on this information. Copyright © 2013 Elsevier Inc. All rights reserved.
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On learning science and pseudoscience from prime-time television programming
Whittle, Christopher Henry
The purpose of the present dissertation is to determine whether the viewing of two particular prime-time television programs, ER and The X-Files, increases viewer knowledge of science and to identify factors that may influence learning from entertainment television programming. Viewer knowledge of scientific dialogue from two science-based prime-time television programs, ER, a serial drama in a hospital emergency room and The X-Files, a drama about two Federal Bureau of Investigation agents who pursue alleged extraterrestrial life and paranormal activity, is studied. Level of viewing, education level, science education level, experiential factors, level of parasocial interaction, and demographic characteristics are assessed as independent variables affecting learning from entertainment television viewing. The present research involved a nine-month long content analysis of target television program dialogue and data collection from an Internet-based survey questionnaire posted to target program-specific on-line "chat" groups. The present study demonstrated that entertainment television program viewers incidentally learn science from entertainment television program dialogue. The more they watch, the more they learn. Viewing a pseudoscientific fictional television program does necessarily influence viewer beliefs in pseudoscience. Higher levels of formal science study are reflected in more science learning and less learning of pseudoscience from entertainment television program viewing. Pseudoscience learning from entertainment television programming is significantly related to experience with paranormal phenomena, higher levels of viewer parasocial interaction, and specifically, higher levels of cognitive parasocial interaction. In summary, the greater a viewer's understanding of science the more they learn when they watch their favorite science-based prime-time television programs. Viewers of pseudoscience-based prime-time television programming with higher levels
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Leal, Yenny; Gonzalez-Abril, Luis; Lorencio, Carol; Bondia, Jorge; Vehi, Josep
2013-07-01
Support vector machines (SVMs) are an attractive option for detecting correct and incorrect measurements in real-time continuous glucose monitoring systems (RTCGMSs), because their learning mechanism can introduce a postprocessing strategy for imbalanced datasets. The proposed SVM considers the geometric mean to obtain a more balanced performance between sensitivity and specificity. To test this approach, 23 critically ill patients receiving insulin therapy were monitored over 72 h using an RTCGMS, and a dataset of 537 samples, classified according to International Standards Organization (ISO) criteria (372 correct and 165 incorrect measurements), was obtained. The results obtained were promising for patients with septic shock or with sepsis, for which the proposed system can be considered as reliable. However, this approach cannot be considered suitable for patients without sepsis.
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A real-time, wearable elemental carbon monitor for use in underground mines
International Nuclear Information System (INIS)
Takiff, L.; Aiken, G.
2010-01-01
A real-time, wearable elemental carbon monitor has been developed to determines the exposure of workers in underground mines to diesel particulate material (DPM). ICx Technologies designed the device in an effort to address the health hazards associated with DPM exposure. Occupational exposure to DPM in underground metal and nonmetal mines is regulated by the Mine Safety and Health Administration. The most common method of measuring exposure to elemental or total carbon nanoparticles involves capturing the particles on a filter followed by a thermo-optical laboratory analysis, which integrates the exposure spatially and in time. The ICx monitor is based on a design developed and tested by the National Institute of Occupational Safety and Health (NIOSH). The ICx monitor uses a real-time particle capture and light transmission method to yield elemental carbon values that are displayed for the wearer and are stored internally in a compact device. The ICx monitoring results were found to be in good agreement with the established laboratory method (NIOSH Method 5040) for elemental carbon emissions from a diesel engine. The monitors are compact and powered by a rechargeable lithium-ion battery. Examples of DPM monitoring in mines demonstrated how the real-time data can be more useful that time-averaged results. The information can be used to determine ventilation rates needed at any given location to lower the DPM concentrations.15 refs., 6 figs.
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A real-time, wearable elemental carbon monitor for use in underground mines
Energy Technology Data Exchange (ETDEWEB)
Takiff, L. [ICx Technologies, Cambridge, MA (United States); Aiken, G. [ICx Technologies, Albuquerque, NM (United States)
2010-07-01
A real-time, wearable elemental carbon monitor has been developed to determines the exposure of workers in underground mines to diesel particulate material (DPM). ICx Technologies designed the device in an effort to address the health hazards associated with DPM exposure. Occupational exposure to DPM in underground metal and nonmetal mines is regulated by the Mine Safety and Health Administration. The most common method of measuring exposure to elemental or total carbon nanoparticles involves capturing the particles on a filter followed by a thermo-optical laboratory analysis, which integrates the exposure spatially and in time. The ICx monitor is based on a design developed and tested by the National Institute of Occupational Safety and Health (NIOSH). The ICx monitor uses a real-time particle capture and light transmission method to yield elemental carbon values that are displayed for the wearer and are stored internally in a compact device. The ICx monitoring results were found to be in good agreement with the established laboratory method (NIOSH Method 5040) for elemental carbon emissions from a diesel engine. The monitors are compact and powered by a rechargeable lithium-ion battery. Examples of DPM monitoring in mines demonstrated how the real-time data can be more useful that time-averaged results. The information can be used to determine ventilation rates needed at any given location to lower the DPM concentrations.15 refs., 6 figs.
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Real-time power plant monitoring and verification and validation issues
International Nuclear Information System (INIS)
Ciftcioglu, Oe.; Tuerkcan, E.
1993-03-01
By means of the advances in the computer technology, the implementation of a real-time power plant monitoring and dynamic signal analysis system is described. As hardware and software, the system has several essential components to perform the task. Among these, mention may be made of a remote-controlled data acquisition system, a fast data processing system and a dynamic signal analysis system. For a complex system like an NPP, the system verification and validation is an important issue as the plant operation involves many engineering disciplines and also the 'soft sciences'. Additionally, the real-time requirements impose substantial time limitation for the implementation of tasks. The system V and V is accomplished partly by means of V and V of the system components which are monitored by the help of sensory signals. Therefore, an essential part of the V and V task involves the real-time analyses of the data provided by these signals. In this respect the NPP real-time monitoring system described possesses the required design features to carry out this task which provides enhanced reliability and availability in plant operation. (orig./HP)
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A presentation system for just-in-time learning in radiology.
Kahn, Charles E; Santos, Amadeu; Thao, Cheng; Rock, Jayson J; Nagy, Paul G; Ehlers, Kevin C
2007-03-01
There is growing interest in bringing medical educational materials to the point of care. We sought to develop a system for just-in-time learning in radiology. A database of 34 learning modules was derived from previously published journal articles. Learning objectives were specified for each module, and multiple-choice test items were created. A web-based system-called TEMPO-was developed to allow radiologists to select and view the learning modules. Web services were used to exchange clinical context information between TEMPO and the simulated radiology work station. Preliminary evaluation was conducted using the System Usability Scale (SUS) questionnaire. TEMPO identified learning modules that were relevant to the age, sex, imaging modality, and body part or organ system of the patient being viewed by the radiologist on the simulated clinical work station. Users expressed a high degree of satisfaction with the system's design and user interface. TEMPO enables just-in-time learning in radiology, and can be extended to create a fully functional learning management system for point-of-care learning in radiology.
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A real-time intercepting beam-profile monitor for a medical cyclotron
Energy Technology Data Exchange (ETDEWEB)
Hendriks, C.; Uittenbosch, T.; Cameron, D.; Kellogg, S.; Gray, D.; Buckley, K.; Schaffer, P.; Verzilov, V.; Hoehr, C. [TRIUMF, 4004 Wesbrook Mall, Vancouver, British Columbia V6T 2A3 (Canada)
2013-11-15
There is a lack of real-time continuous beam-diagnostic tools for medical cyclotrons due to high power deposition during proton irradiation. To overcome this limitation, we have developed a profile monitor that is capable of providing continuous feedback about beam shape and current in real time while it is inserted in the beam path. This enables users to optimize the beam profile and observe fluctuations in the beam over time with periodic insertion of the monitor.
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Ratnaningsih, N.; El Akbar, R. R.; Hidayat, E.
2018-05-01
One of ways to improve students' learning ability is conduct a research, with purpose to obtain a method to improve students' ability. Research often carried out on the modification of teaching methods, uses of teaching media, motivation, interests and talents of students. Research related to the internal condition of students becomes very interesting to studied, including research on circadian rhythms. Every person in circadian rhythms has its own Chronotype, which divided into two types namely early type and night late type. Chronotype affects the comfort in activity, for example a person with Chronotype category of early type tends to be more comfort in daytime activities. The purpose of this study is to examine the conditions of students, related Chronotype suitable or appropriate for student learning time. This suitability then studied in relation to the ability of learning mathematics with self- regulated learning approach. This study consists of three stages; (i) student Chronotype measurement, (ii) data retrieval, and (iii) analysis of research results. The results show the relationship between the students' learning ability in mathematics to learning time corresponding to Chronotype.
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Application of real time spectrum measurement to radiation monitors
International Nuclear Information System (INIS)
Matsuno, K.; Watanabe, M.; Sakamaki, T.
1996-01-01
A multichannel analyzer (MCA) and two realtime spectrum monitoring methods have been developed for use in radiation monitors. The new MCA was designed to be installed at a local site as a component of a radiation monitor. The MCA repeats spectrum measurement at short intervals (Δt) and, after each measurement, transmits a spectrum datum to the operation console. The authors applied two methods to process Δt spectrum counts for each channel for longer time interval. One method of processing counts is the 'running average (RA) method'. The other method is the 'exponential smoothing (ES) method', which simulates RC rate meters by subtracting a fraction corresponding to the accumulated counts. Relative standard deviations for each channel can be made the same by selecting an appropriate value. The response with the 'ES' method is initially faster than that with the 'RA' method, but the 'RA' method allows a full response to be reached at a predictable time. (author)
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Integrated active sensor system for real time vibration monitoring.
Liang, Qijie; Yan, Xiaoqin; Liao, Xinqin; Cao, Shiyao; Lu, Shengnan; Zheng, Xin; Zhang, Yue
2015-11-05
We report a self-powered, lightweight and cost-effective active sensor system for vibration monitoring with multiplexed operation based on contact electrification between sensor and detected objects. The as-fabricated sensor matrix is capable of monitoring and mapping the vibration state of large amounts of units. The monitoring contents include: on-off state, vibration frequency and vibration amplitude of each unit. The active sensor system delivers a detection range of 0-60 Hz, high accuracy (relative error below 0.42%), long-term stability (10000 cycles). On the time dimension, the sensor can provide the vibration process memory by recording the outputs of the sensor system in an extend period of time. Besides, the developed sensor system can realize detection under contact mode and non-contact mode. Its high performance is not sensitive to the shape or the conductivity of the detected object. With these features, the active sensor system has great potential in automatic control, remote operation, surveillance and security systems.
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In real time: exploring nursing students' learning during an international experience.
Afriyie Asenso, Barbara; Reimer-Kirkham, Sheryl; Astle, Barbara
2013-10-11
Abstract Nursing education has increasingly turned to international learning experiences to educate students who are globally minded and aware of social injustices in local and global communities. To date, research with international learning experiences has focused on the benefits for the students participating, after they have completed the international experience. The purpose of this qualitative study was to explore how nursing students learn during the international experience. The sample consisted of eight nursing students who enrolled in an international learning experience, and data were collected in "real time" in Zambia. The students were observed during learning activities and were interviewed three times. Three major themes emerged from the thematic analysis: expectations shaped students' learning, engagement facilitated learning, and critical reflection enhanced learning. Implications are discussed, related to disrupting media representations of Africa that shape students' expectations, and educational strategies for transformative learning and global citizenship.
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Improving Neuromuscular Monitoring and Reducing Residual Neuromuscular Blockade With E-Learning
DEFF Research Database (Denmark)
Thomsen, Jakob Louis Demant; Mathiesen, Ole; Hägi-Pedersen, Daniel
2017-01-01
BACKGROUND: Muscle relaxants facilitate endotracheal intubation under general anesthesia and improve surgical conditions. Residual neuromuscular blockade occurs when the patient is still partially paralyzed when awakened after surgery. The condition is associated with subjective discomfort and an......-learning module can increase anesthetists' use of neuromuscular monitoring. TRIAL REGISTRATION: Clinicaltrials.gov NCT02925143; https://clinicaltrials.gov/ct2/show/NCT02925143 (Archived by WebCite® at http://www.webcitation.org/6s50iTV2x)....
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Towards Real-Time Speech Emotion Recognition for Affective E-Learning
Bahreini, Kiavash; Nadolski, Rob; Westera, Wim
2016-01-01
This paper presents the voice emotion recognition part of the FILTWAM framework for real-time emotion recognition in affective e-learning settings. FILTWAM (Framework for Improving Learning Through Webcams And Microphones) intends to offer timely and appropriate online feedback based upon learner's vocal intonations and facial expressions in order…
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Real-time long term measurement using integrated framework for ubiquitous smart monitoring
Heo, Gwanghee; Lee, Giu; Lee, Woosang; Jeon, Joonryong; Kim, Pil-Joong
2007-04-01
Ubiquitous monitoring combining internet technologies and wireless communication is one of the most promising technologies of infrastructure health monitoring against the natural of man-made hazards. In this paper, an integrated framework of the ubiquitous monitoring is developed for real-time long term measurement in internet environment. This framework develops a wireless sensor system based on Bluetooth technology and sends measured acceleration data to the host computer through TCP/IP protocol. And it is also designed to respond to the request of web user on real time basis. In order to verify this system, real time monitoring tests are carried out on a prototype self-anchored suspension bridge. Also, wireless measurement system is analyzed to estimate its sensing capacity and evaluate its performance for monitoring purpose. Based on the evaluation, this paper proposes the effective strategies for integrated framework in order to detect structural deficiencies and to design an early warning system.
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Näykki, Piia; Isohätälä, Jaana; Järvelä, Sanna; Pöysä-Tarhonen, Johanna; Häkkinen, Päivi
2017-01-01
This study examines student teachers' collaborative learning by focusing on socio-cognitive and socio-emotional monitoring processes during more and less active script discussions as well as the near transfer of monitoring activities in the subsequent task work. The participants of this study were teacher education students whose collaborative…
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Real Time Radiation Monitoring Using Nanotechnology
Li, Jing (Inventor); Hanratty, James J. (Inventor); Wilkins, Richard T. (Inventor); Lu, Yijiang (Inventor)
2016-01-01
System and method for monitoring receipt and estimating flux value, in real time, of incident radiation, using two or more nanostructures (NSs) and associated terminals to provide closed electrical paths and to measure one or more electrical property change values .DELTA.EPV, associated with irradiated NSs, during a sequence of irradiation time intervals. Effects of irradiation, without healing and with healing, of the NSs, are separately modeled for first order and second order healing. Change values.DELTA.EPV are related to flux, to cumulative dose received by NSs, and to radiation and healing effectivity parameters and/or.mu., associated with the NS material and to the flux. Flux and/or dose are estimated in real time, based on EPV change values, using measured .DELTA.EPV values. Threshold dose for specified changes of biological origin (usually undesired) can be estimated. Effects of time-dependent radiation flux are analyzed in pre-healing and healing regimes.
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Simultaneous real-time monitoring of multiple cortical systems.
Gupta, Disha; Jeremy Hill, N; Brunner, Peter; Gunduz, Aysegul; Ritaccio, Anthony L; Schalk, Gerwin
2014-10-01
Real-time monitoring of the brain is potentially valuable for performance monitoring, communication, training or rehabilitation. In natural situations, the brain performs a complex mix of various sensory, motor or cognitive functions. Thus, real-time brain monitoring would be most valuable if (a) it could decode information from multiple brain systems simultaneously, and (b) this decoding of each brain system were robust to variations in the activity of other (unrelated) brain systems. Previous studies showed that it is possible to decode some information from different brain systems in retrospect and/or in isolation. In our study, we set out to determine whether it is possible to simultaneously decode important information about a user from different brain systems in real time, and to evaluate the impact of concurrent activity in different brain systems on decoding performance. We study these questions using electrocorticographic signals recorded in humans. We first document procedures for generating stable decoding models given little training data, and then report their use for offline and for real-time decoding from 12 subjects (six for offline parameter optimization, six for online experimentation). The subjects engage in tasks that involve movement intention, movement execution and auditory functions, separately, and then simultaneously. Main Results: Our real-time results demonstrate that our system can identify intention and movement periods in single trials with an accuracy of 80.4% and 86.8%, respectively (where 50% would be expected by chance). Simultaneously, the decoding of the power envelope of an auditory stimulus resulted in an average correlation coefficient of 0.37 between the actual and decoded power envelopes. These decoders were trained separately and executed simultaneously in real time. This study yielded the first demonstration that it is possible to decode simultaneously the functional activity of multiple independent brain systems. Our
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Time representation in reinforcement learning models of the basal ganglia
Directory of Open Access Journals (Sweden)
Samuel Joseph Gershman
2014-01-01
Full Text Available Reinforcement learning models have been influential in understanding many aspects of basal ganglia function, from reward prediction to action selection. Time plays an important role in these models, but there is still no theoretical consensus about what kind of time representation is used by the basal ganglia. We review several theoretical accounts and their supporting evidence. We then discuss the relationship between reinforcement learning models and the timing mechanisms that have been attributed to the basal ganglia. We hypothesize that a single computational system may underlie both reinforcement learning and interval timing—the perception of duration in the range of seconds to hours. This hypothesis, which extends earlier models by incorporating a time-sensitive action selection mechanism, may have important implications for understanding disorders like Parkinson's disease in which both decision making and timing are impaired.
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Hommes, J; Van den Bossche, P; de Grave, W; Bos, G; Schuwirth, L; Scherpbier, A
2014-10-01
Little is known how time influences collaborative learning groups in medical education. Therefore a thorough exploration of the development of learning processes over time was undertaken in an undergraduate PBL curriculum over 18 months. A mixed-methods triangulation design was used. First, the quantitative study measured how various learning processes developed within and over three periods in the first 1,5 study years of an undergraduate curriculum. Next, a qualitative study using semi-structured individual interviews focused on detailed development of group processes driving collaborative learning during one period in seven tutorial groups. The hierarchic multilevel analyses of the quantitative data showed that a varying combination of group processes developed within and over the three observed periods. The qualitative study illustrated development in psychological safety, interdependence, potency, group learning behaviour, social and task cohesion. Two new processes emerged: 'transactive memory' and 'convergence in mental models'. The results indicate that groups are dynamic social systems with numerous contextual influences. Future research should thus include time as an important influence on collaborative learning. Practical implications are discussed.
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Adaptation and learning: characteristic time scales of performance dynamics.
Newell, Karl M; Mayer-Kress, Gottfried; Hong, S Lee; Liu, Yeou-Teh
2009-12-01
A multiple time scales landscape model is presented that reveals structures of performance dynamics that were not resolved in the traditional power law analysis of motor learning. It shows the co-existence of separate processes during and between practice sessions that evolve in two independent dimensions characterized by time scales that differ by about an order of magnitude. Performance along the slow persistent dimension of learning improves often as much and sometimes more during rest (memory consolidation and/or insight generation processes) than during a practice session itself. In contrast, the process characterized by the fast, transient dimension of adaptation reverses direction between practice sessions, thereby significantly degrading performance at the beginning of the next practice session (warm-up decrement). The theoretical model fits qualitatively and quantitatively the data from Snoddy's [Snoddy, G. S. (1926). Learning and stability. Journal of Applied Psychology, 10, 1-36] classic learning study of mirror tracing and other averaged and individual data sets, and provides a new account of the processes of change in adaptation and learning. 2009 Elsevier B.V. All rights reserved.
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Bunch-length and beam-timing monitors in the SLC final focus
International Nuclear Information System (INIS)
Zimmermann, F.; Yocky, G.; Whittum, D.H.; Seidel, M.; Ng, C.K.; McCormick, D.; Bane, K.L.F.
1998-07-01
During the 1997/98 luminosity run of the Stanford Linear Collider (SLC), two novel RF-based detectors were brought into operation, in order to monitor the interaction-point (IP) bunch lengths and fluctuations in the relative arrival time of the two colliding beams. Both bunch length and timing can strongly affect the SLC luminosity and had not been monitored in previous years. The two new detectors utilize a broad-band microwave signal, which is excited by the beam through a ceramic gap in the final-focus beam pipe and transported outside of the beam line vault by a 160-ft long X-Band waveguide. The authors describe the estimated luminosity reduction due to bunch-length drift and IP timing fluctuation, the monitor layout, the expected responses and signal levels, calibration measurements, and beam observations
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The learning in a science museum and the role of the monitor
Directory of Open Access Journals (Sweden)
Tassiana Fernanda Genzini de Carvalho
2015-03-01
Full Text Available The importance attributed to Science Museums regarding to their educational role increased in the past few decades. However there is a considerable distance between the intentions of an exhibition and the perception and possible interpretation of visitors to what is shown to them. This work investigates the part played by monitors at the Estação Ciência da USP in Brazil (Science Station when they follow visiting students groups. The process developed in the interaction with visitors aims to communicate the scientific knowledge involved in the exhibition object when the situation is permeated by social interactions that in a Vigotskian view about learning should favor the building up of scientific concepts. The focus, however, is to realize characteristics of the discourses of monitors with a view to scientific communication; Results have led us to conclude that monitors speeches are far from their intentions showing inconsistencies, and being simplistic, poor in analogies, without scientific rigor.
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Time-lapse electrical geophysical monitoring of amendment-based biostimulation
Johnson, Timothy C.; Versteeg, Roelof J.; Day-Lewis, Frederick D.; Major, William; Lane, John W.
2015-01-01
Biostimulation is increasingly used to accelerate microbial remediation of recalcitrant groundwater contaminants. Effective application of biostimulation requires successful emplacement of amendment in the contaminant target zone. Verification of remediation performance requires postemplacement assessment and contaminant monitoring. Sampling-based approaches are expensive and provide low-density spatial and temporal information. Time-lapse electrical resistivity tomography (ERT) is an effective geophysical method for determining temporal changes in subsurface electrical conductivity. Because remedial amendments and biostimulation-related biogeochemical processes often change subsurface electrical conductivity, ERT can complement and enhance sampling-based approaches for assessing emplacement and monitoring biostimulation-based remediation.Field studies demonstrating the ability of time-lapse ERT to monitor amendment emplacement and behavior were performed during a biostimulation remediation effort conducted at the Department of Defense Reutilization and Marketing Office (DRMO) Yard, in Brandywine, Maryland, United States. Geochemical fluid sampling was used to calibrate a petrophysical relation in order to predict groundwater indicators of amendment distribution. The petrophysical relations were field validated by comparing predictions to sequestered fluid sample results, thus demonstrating the potential of electrical geophysics for quantitative assessment of amendment-related geochemical properties. Crosshole radar zero-offset profile and borehole geophysical logging were also performed to augment the data set and validate interpretation.In addition to delineating amendment transport in the first 10 months after emplacement, the time-lapse ERT results show later changes in bulk electrical properties interpreted as mineral precipitation. Results support the use of more cost-effective surface-based ERT in conjunction with limited field sampling to improve spatial
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CONCEPT AND STRUCTURE OF AUTOMATED SYSTEM FOR MONITORING STUDENT LEARNING QUALITY
Directory of Open Access Journals (Sweden)
M. Yu. Kataev
2017-01-01
organization and management of the learning process in a higher educational institution. The factors that affect the level of student knowledge obtained during training are shown. On this basis, the determining factors in assessing the level of knowledge are highlighted. It is offered to build the managing of individual training at any time interval on the basis of a calculation of the generalized criterion which consists of students’ current progress, their activity and time spent for training.The block structure of the automated program system of continuous monitoring of achievements of each student is described. All functional blocks of system are interconnected with educational process. The main advantage of this system is that students have continuous access to materials about own individual achievements and mistakes; from passive consumers of information they turn into active members of the education, and thus, they can achieve bigger effectiveness of personal vocational training. It is pointed out that information base of such system has to be available not only to students and teachers, but also future employers of university graduates.Practical significance. The concept of automated system for education results monitoring and technique of processing of collected material presented in the article are based on a simple and obvious circumstance: a student with high progress spends more time on training and leads active lifestyle in comparison with fellow students; therefore, that student with high probability will be more successful in the chosen profession. Thus, for ease of use, complete, fully detailed and digitized information on individual educational achievements of future expert is necessary not only for effective management of educational process in higher education institutions, but also for employers interested in well-prepared, qualified and hard-working staff intended to take responsibility for labour duties.
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Dissociable effects of practice variability on learning motor and timing skills.
Caramiaux, Baptiste; Bevilacqua, Frédéric; Wanderley, Marcelo M; Palmer, Caroline
2018-01-01
Motor skill acquisition inherently depends on the way one practices the motor task. The amount of motor task variability during practice has been shown to foster transfer of the learned skill to other similar motor tasks. In addition, variability in a learning schedule, in which a task and its variations are interweaved during practice, has been shown to help the transfer of learning in motor skill acquisition. However, there is little evidence on how motor task variations and variability schedules during practice act on the acquisition of complex motor skills such as music performance, in which a performer learns both the right movements (motor skill) and the right time to perform them (timing skill). This study investigated the impact of rate (tempo) variability and the schedule of tempo change during practice on timing and motor skill acquisition. Complete novices, with no musical training, practiced a simple musical sequence on a piano keyboard at different rates. Each novice was assigned to one of four learning conditions designed to manipulate the amount of tempo variability across trials (large or small tempo set) and the schedule of tempo change (randomized or non-randomized order) during practice. At test, the novices performed the same musical sequence at a familiar tempo and at novel tempi (testing tempo transfer), as well as two novel (but related) sequences at a familiar tempo (testing spatial transfer). We found that practice conditions had little effect on learning and transfer performance of timing skill. Interestingly, practice conditions influenced motor skill learning (reduction of movement variability): lower temporal variability during practice facilitated transfer to new tempi and new sequences; non-randomized learning schedule improved transfer to new tempi and new sequences. Tempo (rate) and the sequence difficulty (spatial manipulation) affected performance variability in both timing and movement. These findings suggest that there is a
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Real time neutron flux monitoring using Rh self powered neutron detector
Energy Technology Data Exchange (ETDEWEB)
Juna, Byung Jin; Lee, Byung Chul; Park, Sang Jun; Jung, Hoan Sung [KAERI, Daejeon (Korea, Republic of)
2012-10-15
Rhodium (Rh) self powered neutron detectors (SPNDs) are widely used for on line monitoring of local neutron flux. Its signal is slower than the actual variation of neutron flux owing to a delayed {beta} decay of the Rh activation product, but real time monitoring is possible by solving equations between the neutron reaction rate in the detector and its signal. While the measuring system is highly reliable, the accuracy depends on the method solving the equations and accuracy of the parameters in the equations. The uncertain parameters are the contribution of gamma rays to the signal, and the branching ratios of Rh 104 and Rh 104m after the neutron absorption of Rh 103. Real time neutron flux monitoring using Rh SPNDs has been quite successful for neutron transmutation doping (NTD) at HANARO. We revisited the initial data used for the verification of a real time monitoring system, to refine algorithm for a better solution and to check the parameters for correctness. As a result, we suggest an effective way to determine the prompt parameter.
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Real time neutron flux monitoring using Rh self powered neutron detector
International Nuclear Information System (INIS)
Juna, Byung Jin; Lee, Byung Chul; Park, Sang Jun; Jung, Hoan Sung
2012-01-01
Rhodium (Rh) self powered neutron detectors (SPNDs) are widely used for on line monitoring of local neutron flux. Its signal is slower than the actual variation of neutron flux owing to a delayed β decay of the Rh activation product, but real time monitoring is possible by solving equations between the neutron reaction rate in the detector and its signal. While the measuring system is highly reliable, the accuracy depends on the method solving the equations and accuracy of the parameters in the equations. The uncertain parameters are the contribution of gamma rays to the signal, and the branching ratios of Rh 104 and Rh 104m after the neutron absorption of Rh 103. Real time neutron flux monitoring using Rh SPNDs has been quite successful for neutron transmutation doping (NTD) at HANARO. We revisited the initial data used for the verification of a real time monitoring system, to refine algorithm for a better solution and to check the parameters for correctness. As a result, we suggest an effective way to determine the prompt parameter
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Real-time monitoring, prognosis, and resilient control for wind turbine systems
Energy Technology Data Exchange (ETDEWEB)
Gao, Zhiwei; Sheng, Shuangwen
2018-02-01
This special issue aims to provide a platform for academic and industrial communities to report recent results and emerging research in real-time monitoring, fault diagnosis, prognosis, and resilient control and design of wind turbine systems. After a strict peer-review process, 20 papers were selected, which represent the most recent progress of the real-time monitoring, diagnosis, prognosis, and resilient control methods/techniques in wind turbine systems.
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Improving Neuromuscular Monitoring and Reducing Residual Neuromuscular Blockade With E-Learning
DEFF Research Database (Denmark)
Thomsen, Jakob Louis Demant; Mathiesen, Ole; Hägi-Pedersen, Daniel
2017-01-01
neuromuscular blockade in surgical patients at 6 Danish teaching hospitals. METHODS: In this interrupted time series study, we are collecting data repeatedly, in consecutive 3-week periods, before and after the intervention, and we will analyze the effect using segmented regression analysis. Anesthesia...... and an increased risk of respiratory complications. Use of an objective neuromuscular monitoring device may prevent residual block. Despite this, many anesthetists refrain from using the device. Efforts to increase the use of objective monitoring are time consuming and require the presence of expert personnel...... practice, and patient outcomes. The primary outcome is use of neuromuscular monitoring in patients according to the type of muscle relaxant received. Secondary outcomes include last recorded train-of-four value, administration of reversal agents, and time to discharge from the postanesthesia care unit...
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A novel time series link prediction method: Learning automata approach
Moradabadi, Behnaz; Meybodi, Mohammad Reza
2017-09-01
Link prediction is a main social network challenge that uses the network structure to predict future links. The common link prediction approaches to predict hidden links use a static graph representation where a snapshot of the network is analyzed to find hidden or future links. For example, similarity metric based link predictions are a common traditional approach that calculates the similarity metric for each non-connected link and sort the links based on their similarity metrics and label the links with higher similarity scores as the future links. Because people activities in social networks are dynamic and uncertainty, and the structure of the networks changes over time, using deterministic graphs for modeling and analysis of the social network may not be appropriate. In the time-series link prediction problem, the time series link occurrences are used to predict the future links In this paper, we propose a new time series link prediction based on learning automata. In the proposed algorithm for each link that must be predicted there is one learning automaton and each learning automaton tries to predict the existence or non-existence of the corresponding link. To predict the link occurrence in time T, there is a chain consists of stages 1 through T - 1 and the learning automaton passes from these stages to learn the existence or non-existence of the corresponding link. Our preliminary link prediction experiments with co-authorship and email networks have provided satisfactory results when time series link occurrences are considered.
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Real-time monitoring/emergency response modeling workstation for a tritium facility
International Nuclear Information System (INIS)
Lawver, B.S.; Sims, J.M.; Baskett, R.L.
1993-01-01
At Lawrence Livermore National Laboratory (LLNL) we have developed a real-time system to monitor two stacks on our tritium handling facility. The monitors transmit the stack data to a workstation, which computes a three-dimensional numerical model of atmospheric dispersion. The workstation also collects surface and upper air data from meteorological towers and a sodar. The complex meteorological and terrain setting in the Livermore Valley demands more sophisticated resolution of the three-dimensional structure of the atmosphere to reliably calculate plume dispersion than afforded by Gaussian models. We experience both mountain valley and sea breeze flows. To address these complexities, we have implemented the three-dimensional diagnostic MATHEW mass-adjusted wind field and ADPIC particle-in-cell dispersion models on the workstation for use in real-time emergency response modeling. Both MATHEW and ADPIC have shown their utility in a variety of complex settings over the last 15 yr within the U.S. Department of Energy's Atmospheric Release Advisory Capability (ARAC) project. Faster workstations and real-time instruments allow utilization of more complex three-dimensional models, which provides a foundation for building a real-time monitoring and emergency response workstation for a tritium facility. The stack monitors are two ion chambers per stack
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Novel Real-Time Flight Envelope Monitoring System, Phase II
National Aeronautics and Space Administration — The proposed innovation is an aircraft flight envelope monitoring system that will provide real-time in-cockpit estimations of aircraft flight envelope boundaries....
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Real Time Corrosion Monitoring in Lead and Lead-Bismuth Systems
Energy Technology Data Exchange (ETDEWEB)
James F. Stubbins; Alan Bolind; Ziang Chen
2010-02-25
The objective of this research program is to develop a real-time, in situ corrosion monitoring technique for flowing liquid Pb and eutectic PbBi (LBE) systems in a temperature range of 400 to 650 C. These conditions are relevant to future liquid metal cooled fast reactor operating parameters. THis program was aligned with the Gen IV Reactor initiative to develp technologies to support the design and opertion of a Pb or LBE-cooled fast reactor. The ability to monitor corrosion for protection of structural components is a high priority issue for the safe and prolonged operation of advanced liquid metal fast reactor systems. In those systems, protective oxide layers are intentionally formed and maintained to limit corrosion rates during operation. This program developed a real time, in situ corrosion monitoring tecnique using impedance spectroscopy (IS) technology.
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Real time monitoring of moment magnitude by waveform inversion
Lee, J.; Friederich, W.; Meier, T.
2012-01-01
An instantaneous measure of the moment magnitude (Mw) of an ongoing earthquake is estimated from the moment rate function (MRF) determined in real-time from available seismic data using waveform inversion. Integration of the MRF gives the moment function from which an instantaneous Mw is derived. By repeating the inversion procedure at regular intervals while seismic data are coming in we can monitor the evolution of seismic moment and Mw with time. The final size and duration of a strong earthquake can be obtained within 12 to 15 minutes after the origin time. We show examples of Mw monitoring for three large earthquakes at regional distances. The estimated Mw is only weakly sensitive to changes in the assumed source parameters. Depending on the availability of seismic stations close to the epicenter, a rapid estimation of the Mw as a prerequisite for the assessment of earthquake damage potential appears to be feasible.
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Learning Styles of Medical Students Change in Relation to Time
Gurpinar, Erol; Bati, Hilal; Tetik, Cihat
2011-01-01
The aim of the present study was to investigate if any changes exist in the learning styles of medical students over time and in relation to different curriculum models with these learning styles. This prospective cohort study was conducted in three different medical faculties, which implement problem-based learning (PBL), hybrid, and integrated…
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Generalization bounds of ERM-based learning processes for continuous-time Markov chains.
Zhang, Chao; Tao, Dacheng
2012-12-01
Many existing results on statistical learning theory are based on the assumption that samples are independently and identically distributed (i.i.d.). However, the assumption of i.i.d. samples is not suitable for practical application to problems in which samples are time dependent. In this paper, we are mainly concerned with the empirical risk minimization (ERM) based learning process for time-dependent samples drawn from a continuous-time Markov chain. This learning process covers many kinds of practical applications, e.g., the prediction for a time series and the estimation of channel state information. Thus, it is significant to study its theoretical properties including the generalization bound, the asymptotic convergence, and the rate of convergence. It is noteworthy that, since samples are time dependent in this learning process, the concerns of this paper cannot (at least straightforwardly) be addressed by existing methods developed under the sample i.i.d. assumption. We first develop a deviation inequality for a sequence of time-dependent samples drawn from a continuous-time Markov chain and present a symmetrization inequality for such a sequence. By using the resultant deviation inequality and symmetrization inequality, we then obtain the generalization bounds of the ERM-based learning process for time-dependent samples drawn from a continuous-time Markov chain. Finally, based on the resultant generalization bounds, we analyze the asymptotic convergence and the rate of convergence of the learning process.
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4g-Based Specialty Vehicles Real-Time Monitoring System Design and Implementation
Directory of Open Access Journals (Sweden)
Zhuang Yu-Feng
2017-01-01
Full Text Available In the future development of natural gas transportation industry, emerging ITS technology will be applied more and more, aiming at integrating precise positioning technology, geographic information system technology, database technology, multimedia technology and modern communication technology, sensor network technology and video capture technology, so as to achieve the transport steam (oil vehicles in real time monitoring and management. The main research content of this paper is to design and research the monitoring and locating system of luck (oil vehicle based on 4G on Android System. Real-time monitoring and alarming by sensor module, real-time video recording and uploading through camera module, real-time position recording and uploading through GPS module, vehicle navigation module and quick alarm module, which is composed of five parts. The system is the application of new intelligent transport technology in the field of special vehicle transport. It apply electronic information technology and internet of things technology to the vehicle system, so we can monitor natural gas and other special dangerous goods anytime, anywhere.
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Real-time remote diagnostic monitoring test-bed in JET
International Nuclear Information System (INIS)
Castro, R.; Kneupner, K.; Vega, J.; De Arcas, G.; Lopez, J.M.; Purahoo, K.; Murari, A.; Fonseca, A.; Pereira, A.; Portas, A.
2010-01-01
Based on the remote experimentation concept oriented to long pulse shots, a test-bed system has been implemented in JET. Its main functionality is the real-time monitoring, on remote, of a reflectometer diagnostic, to visualize different data outputs and status information. The architecture of the system is formed by: the data generator components, the data distribution system, an access control service, and the client applications. In the test-bed there is one data generator, which is the acquisition equipment associated with the reflectometer diagnostic that generates data and status information. The data distribution system has been implemented using a publishing-subscribing technology that receives data from data generators and redistributes them to client applications. And finally, for monitoring, a client application based on JAVA Web Start technology has been used. There are three interesting results from this project. The first one is the analysis of different aspects (data formats, data frame rate, data resolution, etc) related with remote real-time diagnostic monitoring oriented to long pulse experiments. The second one is the definition and implementation of an architecture, flexible enough to be applied to different types of data generated from other diagnostics, and that fits with remote access requirements. Finally, the third result is a secure system, taking into account internal networks and firewalls aspects of JET, and securing the access from remote users. For this last issue, PAPI technology has been used, enabling access control based on user attributes, enabling mobile users to monitor diagnostics in real-time, and enabling the integration of this service into the EFDA Federation (Castro et al., 2008 ).
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Real-time remote diagnostic monitoring test-bed in JET
Energy Technology Data Exchange (ETDEWEB)
Castro, R., E-mail: rodrigo.castro@ciemat.e [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Kneupner, K. [EURATOM/UKAEA Fusion Association, Culham Science Centre, Abingdon, OX14 3DB (United Kingdom); Vega, J. [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); De Arcas, G.; Lopez, J.M. [Universidad Politecnica de Madrid, Grupo I2A2, Madrid (Spain); Purahoo, K. [EURATOM/UKAEA Fusion Association, Culham Science Centre, Abingdon, OX14 3DB (United Kingdom); Murari, A. [Associazione EURATOM-ENEA per la Fusione, Consorzio RFX, 4-35127 Padova (Italy); Fonseca, A. [Associacao EURATOM/IST, Lisbon (Portugal); Pereira, A.; Portas, A. [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain)
2010-07-15
Based on the remote experimentation concept oriented to long pulse shots, a test-bed system has been implemented in JET. Its main functionality is the real-time monitoring, on remote, of a reflectometer diagnostic, to visualize different data outputs and status information. The architecture of the system is formed by: the data generator components, the data distribution system, an access control service, and the client applications. In the test-bed there is one data generator, which is the acquisition equipment associated with the reflectometer diagnostic that generates data and status information. The data distribution system has been implemented using a publishing-subscribing technology that receives data from data generators and redistributes them to client applications. And finally, for monitoring, a client application based on JAVA Web Start technology has been used. There are three interesting results from this project. The first one is the analysis of different aspects (data formats, data frame rate, data resolution, etc) related with remote real-time diagnostic monitoring oriented to long pulse experiments. The second one is the definition and implementation of an architecture, flexible enough to be applied to different types of data generated from other diagnostics, and that fits with remote access requirements. Finally, the third result is a secure system, taking into account internal networks and firewalls aspects of JET, and securing the access from remote users. For this last issue, PAPI technology has been used, enabling access control based on user attributes, enabling mobile users to monitor diagnostics in real-time, and enabling the integration of this service into the EFDA Federation (Castro et al., 2008 ).
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DeepRT: deep learning for peptide retention time prediction in proteomics
Ma, Chunwei; Zhu, Zhiyong; Ye, Jun; Yang, Jiarui; Pei, Jianguo; Xu, Shaohang; Zhou, Ruo; Yu, Chang; Mo, Fan; Wen, Bo; Liu, Siqi
2017-01-01
Accurate predictions of peptide retention times (RT) in liquid chromatography have many applications in mass spectrometry-based proteomics. Herein, we present DeepRT, a deep learning based software for peptide retention time prediction. DeepRT automatically learns features directly from the peptide sequences using the deep convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model, which eliminates the need to use hand-crafted features or rules. After the feature learning, pr...
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Real-Time Remote Diagnostic Monitoring Test-bed in JET
Energy Technology Data Exchange (ETDEWEB)
Castro, R. [Asociation Euratom/CIEMAT para Fusion, Madrid (Spain); Kneupner, K.; Purahoo, K. [EURATOM/UKAEA Fusion Association, Abingdon (United Kingdom); Vega, J.; Pereira, A.; Portas, A. [Association EuratomCIEMAT para Fusion, Madrid (Spain); De Arcas, G.; Lopez, J.M. [Universidad Politecnica de Madrid (Spain); Murari, A. [Consorzio RFX, Padova (Italy); Fonseca, A. [Associacao URATOM/IST, Lisboa (Portugal); Contributors, J.E. [JET-EFDA, Abingdon (United Kingdom)
2009-07-01
Based on the remote experimentation concept oriented to long pulse shots, a test-bed system has been implemented in JET. It integrates 2 functionalities. The first one is the real-time monitoring, on remote, of a reflectometer diagnostic, to visualize different data outputs and status information. The second one is the integration of dotJET (Diagnostic Overview Tool for JET), which internally provides at JET an overview about the current diagnostic systems state, in order to monitor, on remote, JET diagnostics status. The architecture of the system is formed by: the data generator components, the data distribution system, an access control service, and the client applications. In the test-bed there are two data generators: the acquisition equipment associated with the reflectometer diagnostic that generates data and status information, and dotJET server that centralize the access to the status information of JET diagnostics. The data distribution system has been implemented using a publishing-subscribing technology that receives data from data generators and redistributes them to client applications. And finally, for monitoring, a client application based on Java Web Start technology, and a dotJET client application have been used. There are 3 interesting results from this project. The first one is the analysis of different aspects (data formats, data frame rate, data resolution, etc) related with remote real-time diagnostic monitoring oriented to long pulse experiments. The second one is the definition and implementation of a flexible enough architecture, to be applied to different types of data generated from other diagnostics, and that fits with remote access requirements; and the third one is to have achieved a secure system, taking into account internal networks and firewalls aspects in JET, and securing the access from remote users. For this last issue, PAPI technology has been used, enabling access control based on user attributes, enabling mobile users to
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Supervised Learning Using Spike-Timing-Dependent Plasticity of Memristive Synapses.
Nishitani, Yu; Kaneko, Yukihiro; Ueda, Michihito
2015-12-01
We propose a supervised learning model that enables error backpropagation for spiking neural network hardware. The method is modeled by modifying an existing model to suit the hardware implementation. An example of a network circuit for the model is also presented. In this circuit, a three-terminal ferroelectric memristor (3T-FeMEM), which is a field-effect transistor with a gate insulator composed of ferroelectric materials, is used as an electric synapse device to store the analog synaptic weight. Our model can be implemented by reflecting the network error to the write voltage of the 3T-FeMEMs and introducing a spike-timing-dependent learning function to the device. An XOR problem was successfully demonstrated as a benchmark learning by numerical simulations using the circuit properties to estimate the learning performance. In principle, the learning time per step of this supervised learning model and the circuit is independent of the number of neurons in each layer, promising a high-speed and low-power calculation in large-scale neural networks.
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Real-Time Management of Multimodal Streaming Data for Monitoring of Epileptic Patients.
Triantafyllopoulos, Dimitrios; Korvesis, Panagiotis; Mporas, Iosif; Megalooikonomou, Vasileios
2016-03-01
New generation of healthcare is represented by wearable health monitoring systems, which provide real-time monitoring of patient's physiological parameters. It is expected that continuous ambulatory monitoring of vital signals will improve treatment of patients and enable proactive personal health management. In this paper, we present the implementation of a multimodal real-time system for epilepsy management. The proposed methodology is based on a data streaming architecture and efficient management of a big flow of physiological parameters. The performance of this architecture is examined for varying spatial resolution of the recorded data.
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Sepulveda Carlo, E.; Escobar, V. M.; Delgado Arias, S.; Forgotson, C.
2017-12-01
The NASA Carbon Monitoring System initiated by U.S. Congress in 2010 is developing products that characterize and quantify carbon sources and sinks in the United States and the global tropics. In 2013, an applications effort was selected to engage potential end users and gather feedback about their data needs. For the past four years the CMS applications efforts has expanded and implemented a number of strategies to connect carbon scientists to decision-makers, contributing to the societal benefits of CMS data products. The applications efforts use crowd sourcing to collects feedback from stakeholders on challenges and lessons learned in the use of CMS data products. Some of the most common data needs from engaged organizations include above and below-ground biomass and fluxes in forestlands and wetlands, and greenhouse gas (GHG) emissions across all land use/cover and land use changes. Stakeholder organizations' needs for CMS data products support national GHG inventories following the Paris Agreement, carbon markets, and sub-national natural resources management and policies. The lessons learned report presents stakeholder specific applications, challenges, and successes from using CMS data products. To date, the most common uses of CMS products include: conservation efforts, emissions inventory, forestry and land cover applications, and carbon offset projects. The most common challenges include: the need for familiar and consistent products over time, budget constraints, and concern with uncertainty of modeled results. Recurrent recommendations from stakeholder indicate that CMS should provide high resolution (30m) and frequent data products updates (annually). The applications efforts have also helped identified success stories from different CMS projects, including the development of the GHG emissions inventory from Providence, RI, the improvement of the U.S. GHG Inventory though the use of satellite data, and the use of high resolution canopy cover maps for
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Radiation environmental real-time monitoring and dispersion modeling
International Nuclear Information System (INIS)
Kovacik, A.; Bartokova, I.; Omelka, J.; Melicherova, T.
2014-01-01
The system of real-time radiation monitoring provided by MicroStep-MIS is a turn-key solution for measurement, acquisition, processing, reporting, archiving and displaying of various radiation data. At the level of measurements, the monitoring stations can be equipped with various devices from radiation probes, measuring the actual ambient gamma dose rate, to fully automated aerosol monitors, returning analysis results of natural and manmade radionuclides concentrations in the air. Using data gathered by our radiation probes RPSG-05 integrated into monitoring network of Crisis Management of the Slovak Republic and into monitoring network of Slovak Hydrometeorological Institute, we demonstrate its reliability and long-term stability of measurements. Data from RPSG-05 probes and GammaTracer probes, both of these types are used in the SHI network, are compared. The sensitivity of RPSG-05 is documented on data where changes of dose rate are caused by precipitation. Qualities of RPSG-05 probe are illustrated also on example of its use in radiation monitoring network in the United Arab Emirates. A more detailed information about radioactivity of the atmosphere can be obtained by using spectrometric detectors (e.g. scintillation detectors) which, besides gamma dose rate values, offer also a possibility to identify different radionuclides. However, this possibility is limited by technical parameters of detector like energetic resolution and detection efficiency in given geometry of measurement. A clearer information with less doubts can be obtained from aerosol monitors with a built-in silicon detector of alpha and beta particles and with an electrically cooled HPGe detector dedicated for gamma-ray spectrometry, which is performed during the sampling. Data from a complex radiation monitoring network can be used, together with meteorological data, in radiation dispersion model by MicroStep-MIS. This model serves for simulation of atmospheric propagation of radionuclides
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Zhang, Hong; Li, Na; Zhao, Dandan; Jiang, Jie; You, Hong
2017-09-01
Real-time monitoring of photocatalytic reactions facilitates the elucidation of the mechanisms of the reactions. However, suitable tools for real-time monitoring are lacking. Herein, a novel method based on droplet spray ionization named substrate-coated illumination droplet spray ionization (SCI-DSI) for direct analysis of photocatalytic reaction solution is reported. SCI-DSI addresses many of the analytical limitations of electrospray ionization (ESI) for analysis of photocatalytic-reaction intermediates, and has potential for both in situ analysis and real-time monitoring of photocatalytic reactions. In SCI-DSI-mass spectrometry (MS), a photocatalytic reaction occurs by loading sample solutions onto the substrate-coated cover slip and by applying UV light above the modified slip; one corner of this slip adjacent to the inlet of a mass spectrometer is the high-electric-field location for launching a charged-droplet spray. After both testing and optimizing the performance of SCI-DSI, the value of this method for in situ analysis and real-time monitoring of photocatalytic reactions was demonstrated by the removal of cyclophosphamide (CP) in TiO2/UV. Reaction times ranged from seconds to minutes, and the proposed reaction intermediates were captured and identified by tandem mass spectrometry. Moreover, the free hydroxyl radical (·OH) was identified as the main radicals for CP removal. These results show that SCI-DSI is suitable for in situ analysis and real-time monitoring of CP removal under TiO2-based photocatalytic reactions. SCI-DSI is also a potential tool for in situ analysis and real-time assessment of the roles of radicals during CP removal under TiO2-based photocatalytic reactions. Graphical Abstract[Figure not available: see fulltext.
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Hommes, J.; Van den Bossche, P.; de Grave, W.; Bos, G.; Schuwirth, L.; Scherpbier, A.
2014-01-01
Little is known how time influences collaborative learning groups in medical education. Therefore a thorough exploration of the development of learning processes over time was undertaken in an undergraduate PBL curriculum over 18 months. A mixed-methods triangulation design was used. First, the quantitative study measured how various learning…
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Devices for Self-Monitoring Sedentary Time or Physical Activity: A Scoping Review.
Sanders, James P; Loveday, Adam; Pearson, Natalie; Edwardson, Charlotte; Yates, Thomas; Biddle, Stuart J H; Esliger, Dale W
2016-05-04
It is well documented that meeting the guideline levels (150 minutes per week) of moderate-to-vigorous physical activity (PA) is protective against chronic disease. Conversely, emerging evidence indicates the deleterious effects of prolonged sitting. Therefore, there is a need to change both behaviors. Self-monitoring of behavior is one of the most robust behavior-change techniques available. The growing number of technologies in the consumer electronics sector provides a unique opportunity for individuals to self-monitor their behavior. The aim of this study is to review the characteristics and measurement properties of currently available self-monitoring devices for sedentary time and/or PA. To identify technologies, four scientific databases were systematically searched using key terms related to behavior, measurement, and population. Articles published through October 2015 were identified. To identify technologies from the consumer electronic sector, systematic searches of three Internet search engines were also performed through to October 1, 2015. The initial database searches identified 46 devices and the Internet search engines identified 100 devices yielding a total of 146 technologies. Of these, 64 were further removed because they were currently unavailable for purchase or there was no evidence that they were designed for, had been used in, or could readily be modified for self-monitoring purposes. The remaining 82 technologies were included in this review (73 devices self-monitored PA, 9 devices self-monitored sedentary time). Of the 82 devices included, this review identified no published articles in which these devices were used for the purpose of self-monitoring PA and/or sedentary behavior; however, a number of technologies were found via Internet searches that matched the criteria for self-monitoring and provided immediate feedback on PA (ActiGraph Link, Microsoft Band, and Garmin Vivofit) and sedentary time (activPAL VT, the Lumo Back, and Darma
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Time and Associative Learning.
Balsam, Peter D; Drew, Michael R; Gallistel, C R
2010-01-01
In a basic associative learning paradigm, learning is said to have occurred when the conditioned stimulus evokes an anticipatory response. This learning is widely believed to depend on the contiguous presentation of conditioned and unconditioned stimulus. However, what it means to be contiguous has not been rigorously defined. Here we examine the empirical bases for these beliefs and suggest an alternative view based on the hypothesis that learning about the temporal relationships between events determines the speed of emergence, vigor and form of conditioned behavior. This temporal learning occurs very rapidly and prior to the appearance of the anticipatory response. The temporal relations are learned even when no anticipatory response is evoked. The speed with which an anticipatory response emerges is proportional to the informativeness of the predictive cue (CS) regarding the rate of occurrence of the predicted event (US). This analysis gives an account of what we mean by "temporal pairing" and is in accord with the data on speed of acquisition and basic findings in the cue competition literature. In this account, learning depends on perceiving and encoding temporal regularities rather than stimulus contiguities.
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Distributed computing for real-time petroleum reservoir monitoring
Energy Technology Data Exchange (ETDEWEB)
Ayodele, O. R. [University of Alberta, Edmonton, AB (Canada)
2004-05-01
Computer software architecture is presented to illustrate how the concept of distributed computing can be applied to real-time reservoir monitoring processes, permitting the continuous monitoring of the dynamic behaviour of petroleum reservoirs at much shorter intervals. The paper describes the fundamental technologies driving distributed computing, namely Java 2 Platform Enterprise edition (J2EE) by Sun Microsystems, and the Microsoft Dot-Net (Microsoft.Net) initiative, and explains the challenges involved in distributed computing. These are: (1) availability of permanently placed downhole equipment to acquire and transmit seismic data; (2) availability of high bandwidth to transmit the data; (3) security considerations; (4) adaptation of existing legacy codes to run on networks as downloads on demand; and (5) credibility issues concerning data security over the Internet. Other applications of distributed computing in the petroleum industry are also considered, specifically MWD, LWD and SWD (measurement-while-drilling, logging-while-drilling, and simulation-while-drilling), and drill-string vibration monitoring. 23 refs., 1 fig.
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Eberle, J.; Hüttich, C.; Schmullius, C.
2014-12-01
Spatial time series data are freely available around the globe from earth observation satellites and meteorological stations for many years until now. They provide useful and important information to detect ongoing changes of the environment; but for end-users it is often too complex to extract this information out of the original time series datasets. This issue led to the development of the Earth Observation Monitor (EOM), an operational framework and research project to provide simple access, analysis and monitoring tools for global spatial time series data. A multi-source data processing middleware in the backend is linked to MODIS data from Land Processes Distributed Archive Center (LP DAAC) and Google Earth Engine as well as daily climate station data from NOAA National Climatic Data Center. OGC Web Processing Services are used to integrate datasets from linked data providers or external OGC-compliant interfaces to the EOM. Users can either use the web portal (webEOM) or the mobile application (mobileEOM) to execute these processing services and to retrieve the requested data for a given point or polygon in userfriendly file formats (CSV, GeoTiff). Beside providing just data access tools, users can also do further time series analyses like trend calculations, breakpoint detections or the derivation of phenological parameters from vegetation time series data. Furthermore data from climate stations can be aggregated over a given time interval. Calculated results can be visualized in the client and downloaded for offline usage. Automated monitoring and alerting of the time series data integrated by the user is provided by an OGC Sensor Observation Service with a coupled OGC Web Notification Service. Users can decide which datasets and parameters are monitored with a given filter expression (e.g., precipitation value higher than x millimeter per day, occurrence of a MODIS Fire point, detection of a time series anomaly). Datasets integrated in the SOS service are
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Embedded Triboelectric Active Sensors for Real-Time Pneumatic Monitoring.
Fu, Xian Peng; Bu, Tian Zhao; Xi, Feng Ben; Cheng, Ting Hai; Zhang, Chi; Wang, Zhong Lin
2017-09-20
Pneumatic monitoring sensors have great demands for power supply in cylinder systems. Here, we present an embedded sliding triboelectric nanogenerator (TENG) in air cylinder as active sensors for position and velocity monitoring. The embedded TENG is composed of a circular poly(tetrafluoroethylene) polymer and a triangular copper electrode. The working mechanism as triboelectric active sensors and electric output performance are systematically investigated. By integrating into the pneumatic system, the embedded triboelectric active sensors have been used for real-time air pressure/flow monitoring and energy storage. Air pressures are measured from 0.04 to 0.12 MPa at a step of 0.02 MPa with a sensitivity of 49.235 V/MPa, as well as airflow from 50 to 250 L/min at a step of 50 L/min with a sensitivity of 0.002 μA·min/L. This work has first demonstrated triboelectric active sensors for pneumatic monitoring and may promote the development of TENG in intelligent pneumatic system.
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Crabtree, Tim; Alber-Morgan, Sheila R.; Konrad, Moira
2010-01-01
This study used a multiple baseline across participants design to examine the effects of self-monitoring and active responding on the reading comprehension of three high school seniors with learning disabilities and significant attention problems. The self-monitoring intervention required the participants to read a story and stop reading at three…
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Real-time stability monitoring method for boiling water reactor nuclear power plants
International Nuclear Information System (INIS)
Fukunishi, K.; Suzuki, S.
1987-01-01
A method for real-time stability monitoring is developed for supervising the steady-state operation of a boiling water reactor core. The decay ratio of the reactor power fluctuation is determined by measuring only the output neutron noise. The concept of an inverse system is introduced to identify the dynamic characteristics of the reactor core. The adoption of an adaptive digital filter is useful in real-time identification. A feasibility test that used measured output noise as an indication of reactor power suggests that this method is useful in a real-time stability monitoring system. Using this method, the tedious and difficult work for modeling reactor core dynamics can be reduced. The method employs a simple algorithm that eliminates the need for stochastic computation, thus making the method suitable for real-time computation with a simple microprocessor. In addition, there is no need to disturb the reactor core during operation. Real-time stability monitoring using the proposed algorithm may allow operation under less stable margins
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New and Emerging Technologies for Real-Time Air and Surface Beryllium Monitoring
Energy Technology Data Exchange (ETDEWEB)
Phifer, B.E. Jr.; Churnetski, E.L.; Cooke, L.E.; Reed, J.J.; Howell, M.L.; Smith, V.D.
2001-09-01
In this study, five emerging technologies were identified for real-time monitoring of airborne beryllium: Microwave-Induced Plasma Spectroscopy (MIPS), Aerosol Beam-Focused Laser-Induced Plasma Spectroscopy (ABFLIPS), Laser-Induced Breakdown Spectroscopy (LIBS), Surfaced-Enhanced Raman Scattering (SERS) Spectroscopy, and Micro-Calorimetric Spectroscopy (CalSpec). Desired features of real-time air beryllium monitoring instrumentation were developed from the Y-12 CBDPP. These features were used as guidelines for the identification of potential technologies as well as their unique demonstrated capability to provide real-time monitoring of similar materials. However, best available technologies were considered, regardless of their ability to comply with the desired features. None of the five technologies have the capability to measure the particle size of airborne beryllium. Although reducing the total concentration of airborne beryllium is important, current literature suggests that reducing or eliminating the concentration of respirable beryllium is critical for worker health protection. Eight emerging technologies were identified for surface monitoring of beryllium. CalSpec, MIPS, SERS, LIBS, Laser Ablation, Absorptive Stripping Voltametry (ASV), Modified Inductively Coupled Plasma (ICP) Spectroscopy, and Gamma BeAST. Desired features of real-time surface beryllium monitoring were developed from the Y-12 CBDPP. These features were used as guidelines for the identification of potential technologies. However, the best available technologies were considered regardless of their ability to comply with the desired features.
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TU-EF-210-03: Real-Time Ablation Monitoring and Lesion Quantification Using Harmonic Motion Imaging
Energy Technology Data Exchange (ETDEWEB)
Konofagou, E. [Columbia University (United States)
2015-06-15
The use of therapeutic ultrasound to provide targeted therapy is an active research area that has a broad application scope. The invited talks in this session will address currently implemented strategies and protocols for both hyperthermia and ablation applications using therapeutic ultrasound. The role of both ultrasound and MRI in the monitoring and assessment of these therapies will be explored in both pre-clinical and clinical applications. Katherine Ferrara: High Intensity Focused Ultrasound, Drug Delivery, and Immunotherapy Rajiv Chopra: Translating Localized Doxorubicin Delivery to Pediatric Oncology using MRI-guided HIFU Elisa Konofagou: Real-time Ablation Monitoring and Lesion Quantification using Harmonic Motion Imaging Keyvan Farahani: AAPM Task Groups in Interventional Ultrasound Imaging and Therapy Learning Objectives: Understand the role of ultrasound in localized drug delivery and the effects of immunotherapy when used in conjunction with ultrasound therapy. Understand potential targeted drug delivery clinical applications including pediatric oncology. Understand the technical requirements for performing targeted drug delivery. Understand how radiation-force approaches can be used to both monitor and assess high intensity focused ultrasound ablation therapy. Understand the role of AAPM task groups in ultrasound imaging and therapies. Chopra: Funding from Cancer Prevention and Research Initiative of Texas (CPRIT), Award R1308 Evelyn and M.R. Hudson Foundation; Research Support from Research Contract with Philips Healthcare; COI are Co-founder of FUS Instruments Inc Ferrara: Supported by NIH, UCDavis and California (CIRM and BHCE) Farahani: In-kind research support from Philips Healthcare.
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TU-EF-210-03: Real-Time Ablation Monitoring and Lesion Quantification Using Harmonic Motion Imaging
International Nuclear Information System (INIS)
Konofagou, E.
2015-01-01
The use of therapeutic ultrasound to provide targeted therapy is an active research area that has a broad application scope. The invited talks in this session will address currently implemented strategies and protocols for both hyperthermia and ablation applications using therapeutic ultrasound. The role of both ultrasound and MRI in the monitoring and assessment of these therapies will be explored in both pre-clinical and clinical applications. Katherine Ferrara: High Intensity Focused Ultrasound, Drug Delivery, and Immunotherapy Rajiv Chopra: Translating Localized Doxorubicin Delivery to Pediatric Oncology using MRI-guided HIFU Elisa Konofagou: Real-time Ablation Monitoring and Lesion Quantification using Harmonic Motion Imaging Keyvan Farahani: AAPM Task Groups in Interventional Ultrasound Imaging and Therapy Learning Objectives: Understand the role of ultrasound in localized drug delivery and the effects of immunotherapy when used in conjunction with ultrasound therapy. Understand potential targeted drug delivery clinical applications including pediatric oncology. Understand the technical requirements for performing targeted drug delivery. Understand how radiation-force approaches can be used to both monitor and assess high intensity focused ultrasound ablation therapy. Understand the role of AAPM task groups in ultrasound imaging and therapies. Chopra: Funding from Cancer Prevention and Research Initiative of Texas (CPRIT), Award R1308 Evelyn and M.R. Hudson Foundation; Research Support from Research Contract with Philips Healthcare; COI are Co-founder of FUS Instruments Inc Ferrara: Supported by NIH, UCDavis and California (CIRM and BHCE) Farahani: In-kind research support from Philips Healthcare
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Algorithm Development for a Real-Time Military Noise Monitor
National Research Council Canada - National Science Library
Vipperman, Jeffrey S; Bucci, Brian
2006-01-01
The long-range goal of this 1-year SERDP Exploratory Development (SEED) project was to create an improved real-time, high-energy military impulse noise monitoring system that can detect events with peak levels (Lpk...
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Time-Lapse Electrical Geophysical Monitoring of Amendment-Based Biostimulation.
Johnson, Timothy C; Versteeg, Roelof J; Day-Lewis, Frederick D; Major, William; Lane, John W
2015-01-01
Biostimulation is increasingly used to accelerate microbial remediation of recalcitrant groundwater contaminants. Effective application of biostimulation requires successful emplacement of amendment in the contaminant target zone. Verification of remediation performance requires postemplacement assessment and contaminant monitoring. Sampling-based approaches are expensive and provide low-density spatial and temporal information. Time-lapse electrical resistivity tomography (ERT) is an effective geophysical method for determining temporal changes in subsurface electrical conductivity. Because remedial amendments and biostimulation-related biogeochemical processes often change subsurface electrical conductivity, ERT can complement and enhance sampling-based approaches for assessing emplacement and monitoring biostimulation-based remediation. Field studies demonstrating the ability of time-lapse ERT to monitor amendment emplacement and behavior were performed during a biostimulation remediation effort conducted at the Department of Defense Reutilization and Marketing Office (DRMO) Yard, in Brandywine, Maryland, United States. Geochemical fluid sampling was used to calibrate a petrophysical relation in order to predict groundwater indicators of amendment distribution. The petrophysical relations were field validated by comparing predictions to sequestered fluid sample results, thus demonstrating the potential of electrical geophysics for quantitative assessment of amendment-related geochemical properties. Crosshole radar zero-offset profile and borehole geophysical logging were also performed to augment the data set and validate interpretation. In addition to delineating amendment transport in the first 10 months after emplacement, the time-lapse ERT results show later changes in bulk electrical properties interpreted as mineral precipitation. Results support the use of more cost-effective surface-based ERT in conjunction with limited field sampling to improve spatial
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Cluster analysis of activity-time series in motor learning
DEFF Research Database (Denmark)
Balslev, Daniela; Nielsen, Finn Årup; Frutiger, Sally A.
2002-01-01
Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel...... practice-related activity in a fronto-parieto-cerebellar network, in agreement with previous studies of motor learning. These voxels were separated from a group of voxels showing an unspecific time-effect and another group of voxels, whose activation was an artifact from smoothing. Hum. Brain Mapping 15...
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Hall-Effect Sensors for Real-Time Monitoring Pier Scour
Directory of Open Access Journals (Sweden)
Chen-Chia CHEN
2015-01-01
Full Text Available Scour around bridge pier is a major cause of bridge failure such as collapse resulted in loss of life and property. Most of available sensors and approaches for monitoring bridge pier scour are very expensive, which is a main challenge for mass deployment of numerous bridges. Our proposed scour monitoring system utilized low-cost commercial sensors, hall-effect sensors (unit price< $1 that are capable of real-time measuring bridge pier scour with resolution of ~ 2.5 cm, and overall cost for single sensor node of our proposed work is at least 40 % less expensive than existing work. The hall- effect sensor is evaluated under controlled conditions in two laboratory flumes. After scour event, the typical voltage change of the hall-effect sensor is ~ 300 mV, and the system achieve signal-to-noise ratio performance of ~ 60 dB. Finally, we also provide an equation to predict the time variation of scour depth around pier model. Moreover, the master-slave architecture of bridge pier scour monitoring system has scalability and flexibility for mass deployment. This technique has the potential for further widespread implementation in the field.
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Kauffman, Douglas F.; Zhao, Ruomeng; Yang, Ya-Shu
2011-01-01
This study explored conditions under which note taking methods and self-monitoring prompts are most effective for facilitating information collection and achievement in an online learning environment. In experiment 1 30 students collected notes from a website using an online conventional, outline, or matrix note taking tool. In experiment 2 119…
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Trip Travel Time Forecasting Based on Selective Forgetting Extreme Learning Machine
Directory of Open Access Journals (Sweden)
Zhiming Gui
2014-01-01
Full Text Available Travel time estimation on road networks is a valuable traffic metric. In this paper, we propose a machine learning based method for trip travel time estimation in road networks. The method uses the historical trip information extracted from taxis trace data as the training data. An optimized online sequential extreme machine, selective forgetting extreme learning machine, is adopted to make the prediction. Its selective forgetting learning ability enables the prediction algorithm to adapt to trip conditions changes well. Experimental results using real-life taxis trace data show that the forecasting model provides an effective and practical way for the travel time forecasting.
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Universal SaaS platform of internet of things for real-time monitoring
Liu, Tongke; Wu, Gang
2018-04-01
Real-time monitoring service, as a member of the IoT (Internet of Things) service, has a wide range application scenario. To support rapid construction and deployment of applications and avoid repetitive development works in these processes, this paper designs and develops a universal SaaS platform of IoT for real-time monitoring. Evaluation shows that this platform can provide SaaS service to multiple tenants and achieve high real-time performance under the situation of large amount of device access.
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Keim-Malpass, Jessica; Kitzmiller, Rebecca R; Skeeles-Worley, Angela; Lindberg, Curt; Clark, Matthew T; Tai, Robert; Calland, James Forrest; Sullivan, Kevin; Randall Moorman, J; Anderson, Ruth A
2018-06-01
In the intensive care unit, clinicians monitor a diverse array of data inputs to detect early signs of impending clinical demise or improvement. Continuous predictive analytics monitoring synthesizes data from a variety of inputs into a risk estimate that clinicians can observe in a streaming environment. For this to be useful, clinicians must engage with the data in a way that makes sense for their clinical workflow in the context of a learning health system (LHS). This article describes the processes needed to evoke clinical action after initiation of continuous predictive analytics monitoring in an LHS. Copyright © 2018 Elsevier Inc. All rights reserved.
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Li, Yiming; Qian, Mingli; Li, Long; Li, Bin
2014-07-01
This paper proposed a real-time monitoring system for running status of medical monitors based on the internet of things. In the aspect of hardware, a solution of ZigBee networks plus 470 MHz networks is proposed. In the aspect of software, graphical display of monitoring interface and real-time equipment failure alarm is implemented. The system has the function of remote equipment failure detection and wireless localization, which provides a practical and effective method for medical equipment management.
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Sensor response monitoring in pressurized water reactors using time series modeling
International Nuclear Information System (INIS)
Upadhyaya, B.R.; Kerlin, T.W.
1978-01-01
Random data analysis in nuclear power reactors for purposes of process surveillance, pattern recognition and monitoring of temperature, pressure, flow and neutron sensors has gained increasing attention in view of their potential for helping to ensure safe plant operation. In this study, application of autoregressive moving-average (ARMA) time series modeling for monitoring temperature sensor response characteristrics is presented. The ARMA model is used to estimate the step and ramp response of the sensors and the related time constant and ramp delay time. The ARMA parameters are estimated by a two-stage algorithm in the spectral domain. Results of sensor testing for an operating pressurized water reactor are presented. 16 refs
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Directory of Open Access Journals (Sweden)
Maria E. Fernandez-Gimenez
2008-12-01
Full Text Available Collaborative and community-based monitoring are becoming more frequent, yet few studies have examined the process and outcomes of these monitoring approaches. We studied 18 collaborative or community-based ecological assessment or monitoring projects undertaken by five community-based forestry organizations (CBFs, to investigate the objectives, process, and outcomes of collaborative ecological monitoring by CBF organizations. We found that collaborative monitoring can lead to shared ecological understanding among diverse participants, build trust internally and credibility externally, foster social learning and community-building, and advance adaptive management. The CBFs experienced challenges in recruiting and sustaining community participation in monitoring, building needed technical capacity for monitoring, and communicating monitoring results back to the broader community. Our results suggest that involving diverse and sometimes adversarial interests at key points in the monitoring process can help resolve conflicts and advance social learning, while also strengthening the link between social and ecological systems by improving the information base for management and increasing collective awareness of the interdependence of human and natural forest communities.
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Directory of Open Access Journals (Sweden)
Lilian Regina de Carvalho
Full Text Available ABSTRACT Objective: to assess the usability of a digital learning technology prototype as a new method for minimally invasive monitoring of intracranial pressure. Method: descriptive study using a quantitative approach on assessing the usability of a prototype based on Nielsen's ten heuristics. Four experts in the area of Human-Computer interaction participated in the study. Results: the evaluation delivered eight violated heuristics and 31 usability problems in the 32 screens of the prototype. Conclusion: the suggestions of the evaluators were critical for developing an intuitive, user-friendly interface and will be included in the final version of the digital learning technology.
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Cooperating Expert Systems for the Next Generation of Real-time Monitoring Applications
Schwuttke, U.; Veregge, J.; Quan, A.
1995-01-01
A distributed monitoring and diagnosis system has been developed and successfully applied to real-time monitoring of interplanetary spacecraft at NASA's Jet Propulsion Laboratory. This system uses a combination of conventional processing and artificial intelligence.
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Time course influences transfer of visual perceptual learning across spatial location.
Larcombe, S J; Kennard, C; Bridge, H
2017-06-01
Visual perceptual learning describes the improvement of visual perception with repeated practice. Previous research has established that the learning effects of perceptual training may be transferable to untrained stimulus attributes such as spatial location under certain circumstances. However, the mechanisms involved in transfer have not yet been fully elucidated. Here, we investigated the effect of altering training time course on the transferability of learning effects. Participants were trained on a motion direction discrimination task or a sinusoidal grating orientation discrimination task in a single visual hemifield. The 4000 training trials were either condensed into one day, or spread evenly across five training days. When participants were trained over a five-day period, there was transfer of learning to both the untrained visual hemifield and the untrained task. In contrast, when the same amount of training was condensed into a single day, participants did not show any transfer of learning. Thus, learning time course may influence the transferability of perceptual learning effects. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Long-term real-time structural health monitoring using wireless smart sensor
Jang, Shinae; Mensah-Bonsu, Priscilla O.; Li, Jingcheng; Dahal, Sushil
2013-04-01
Improving the safety and security of civil infrastructure has become a critical issue for decades since it plays a central role in the economics and politics of a modern society. Structural health monitoring of civil infrastructure using wireless smart sensor network has emerged as a promising solution recently to increase structural reliability, enhance inspection quality, and reduce maintenance costs. Though hardware and software framework are well prepared for wireless smart sensors, the long-term real-time health monitoring strategy are still not available due to the lack of systematic interface. In this paper, the Imote2 smart sensor platform is employed, and a graphical user interface for the long-term real-time structural health monitoring has been developed based on Matlab for the Imote2 platform. This computer-aided engineering platform enables the control, visualization of measured data as well as safety alarm feature based on modal property fluctuation. A new decision making strategy to check the safety is also developed and integrated in this software. Laboratory validation of the computer aided engineering platform for the Imote2 on a truss bridge and a building structure has shown the potential of the interface for long-term real-time structural health monitoring.
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Directory of Open Access Journals (Sweden)
Lun Zhai
2014-01-01
Full Text Available A parametric learning based robust iterative learning control (ILC scheme is applied to the time varying delay multiple-input and multiple-output (MIMO linear systems. The convergence conditions are derived by using the H∞ and linear matrix inequality (LMI approaches, and the convergence speed is analyzed as well. A practical identification strategy is applied to optimize the learning laws and to improve the robustness and performance of the control system. Numerical simulations are illustrated to validate the above concepts.
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Chen, Hong-Ren
2012-01-01
Recognition of students' facial expressions can be used to understand their level of attention. In a traditional classroom setting, teachers guide the classes and continuously monitor and engage the students to evaluate their understanding and progress. Given the current popularity of e-learning environments, it has become important to assess the…
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Satellite image time series simulation for environmental monitoring
Guo, Tao
2014-11-01
The performance of environmental monitoring heavily depends on the availability of consecutive observation data and it turns out an increasing demand in remote sensing community for satellite image data in the sufficient resolution with respect to both spatial and temporal requirements, which appear to be conflictive and hard to tune tradeoffs. Multiple constellations could be a solution if without concerning cost, and thus it is so far interesting but very challenging to develop a method which can simultaneously improve both spatial and temporal details. There are some research efforts to deal with the problem from various aspects, a type of approaches is to enhance the spatial resolution using techniques of super resolution, pan-sharpen etc. which can produce good visual effects, but mostly cannot preserve spectral signatures and result in losing analytical value. Another type is to fill temporal frequency gaps by adopting time interpolation, which actually doesn't increase informative context at all. In this paper we presented a novel method to generate satellite images in higher spatial and temporal details, which further enables satellite image time series simulation. Our method starts with a pair of high-low resolution data set, and then a spatial registration is done by introducing LDA model to map high and low resolution pixels correspondingly. Afterwards, temporal change information is captured through a comparison of low resolution time series data, and the temporal change is then projected onto high resolution data plane and assigned to each high resolution pixel referring the predefined temporal change patterns of each type of ground objects to generate a simulated high resolution data. A preliminary experiment shows that our method can simulate a high resolution data with a good accuracy. We consider the contribution of our method is to enable timely monitoring of temporal changes through analysis of low resolution images time series only, and usage of
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A Wearable System for Real-Time Continuous Monitoring of Physical Activity
Directory of Open Access Journals (Sweden)
Fabrizio Taffoni
2018-01-01
Full Text Available Over the last decades, wearable systems have gained interest for monitoring of physiological variables, promoting health, and improving exercise adherence in different populations ranging from elite athletes to patients. In this paper, we present a wearable system for the continuous real-time monitoring of respiratory frequency (fR, heart rate (HR, and movement cadence during physical activity. The system has been experimentally tested in the laboratory (by simulating the breathing pattern with a mechanical ventilator and by collecting data from one healthy volunteer. Results show the feasibility of the proposed device for real-time continuous monitoring of fR, HR, and movement cadence both in resting condition and during activity. Finally, different synchronization techniques have been investigated to enable simultaneous data collection from different wearable modules.
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Learning of pitch and time structures in an artificial grammar setting.
Prince, Jon B; Stevens, Catherine J; Jones, Mari Riess; Tillmann, Barbara
2018-04-12
Despite the empirical evidence for the power of the cognitive capacity of implicit learning of structures and regularities in several modalities and materials, it remains controversial whether implicit learning extends to the learning of temporal structures and regularities. We investigated whether (a) an artificial grammar can be learned equally well when expressed in duration sequences as when expressed in pitch sequences, (b) learning of the artificial grammar in either duration or pitch (as the primary dimension) sequences can be influenced by the properties of the secondary dimension (invariant vs. randomized), and (c) learning can be boosted when the artificial grammar is expressed in both pitch and duration. After an exposure phase with grammatical sequences, learning in a subsequent test phase was assessed in a grammaticality judgment task. Participants in both the pitch and duration conditions showed incidental (not fully implicit) learning of the artificial grammar when the secondary dimension was invariant, but randomizing the pitch sequence prevented learning of the artificial grammar in duration sequences. Expressing the artificial grammar in both pitch and duration resulted in disproportionately better performance, suggesting an interaction between the learning of pitch and temporal structure. The findings are relevant to research investigating the learning of temporal structures and the learning of structures presented simultaneously in 2 dimensions (e.g., space and time, space and objects). By investigating learning, the findings provide further insight into the potential specificity of pitch and time processing, and their integrated versus independent processing, as previously debated in music cognition research. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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Romero, Margarida; Usart, Mireia
2013-01-01
The use of games for educational purposes has been considered as a learning methodology that attracts the students' attention and may allow focusing individuals on the learning activity through the [serious games] SG game dynamic. Based on the hypothesis that students' Temporal Perspective has an impact on learning performance and time-on-task,…
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Establishing monitoring programs for travel time reliability. [supporting datasets
2014-01-01
The objective of this project was to develop system designs for programs to monitor travel time reliability and to prepare a guidebook that practitioners and others can use to design, build, operate, and maintain such systems. Generally, such travel ...
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Directory of Open Access Journals (Sweden)
Ari Pujiatmoko
2014-06-01
Full Text Available The purpose of this study were: 1 to determine whether there is a correlation between students' interest in learning and the learning achievement of automotive electrical, 2 to determine whether there is a correlation between the use of time studying the learning achievement of automotive electrical, 3 to determine whether there is a correlation between student interest and use the time to learn and the learning achievement of students of class XII automotive electrical TKR SMK PIRI 1 Yogyakarta academic year 2013/2014. This research was conducted in class XII TKR SMK PIRI 1 Yogyakarta academic year 2013/2014. This study is an ex-post facto. This study used two independent variables and the interest in learning the use of learning time, while the dependent variable is the electrical automotive learning achievement. This study is a population study by the respondent amounted to 100 students. Techniques of data collection using questionnaire techniques and engineering documentation. Research instrument in this study is a questionnaire interest in learning, inquiry learning time management and documentation of student achievement. Trials using the instrument validity and reliability test. The analysis technique used is the prerequisite test for normality, linearity, and multicollinearity. Then test hypotheses using partial correlation analysis techniques and correlation. The results showed that: 1 students' interest to have a strong positive correlation with school performance automotive electrical ρ value of 0.737; 2 the use of learning time have a low positive correlation with school performance automotive electrical ρ value of 0.275; 3 interest student learning and the use of study time has a very strong positive correlation with learning achievement of students of class XII automotive electrical TKR SMK PIRI I Yogyakarta academic year 2013/2014 as evidenced by the value of R = 0.811.
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Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks.
Zhao, Rui; Yan, Ruqiang; Wang, Jinjiang; Mao, Kezhi
2017-01-30
In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redefine representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data. CBLSTM firstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks(LSTMs) are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods.
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Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks
Directory of Open Access Journals (Sweden)
Rui Zhao
2017-01-01
Full Text Available In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redefine representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM has been designed here to address raw sensory data. CBLSTM firstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks(LSTMs are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods.
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The Relationship between Motivation, Learning Approaches, Academic Performance and Time Spent
Everaert, Patricia; Opdecam, Evelien; Maussen, Sophie
2017-01-01
Previous literature calls for further investigation in terms of precedents and consequences of learning approaches (deep learning and surface learning). Motivation as precedent and time spent and academic performance as consequences are addressed in this paper. The study is administered in a first-year undergraduate course. Results show that the…
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Hurk, M.M. van den
2006-01-01
In problem-based learning (PBL) students are encouraged to take responsibility for their own self-regulated learning process. The present study focuses on two self-regulated learning strategies, namely time planning and self-monitoring. Time planning involves time management, scheduling and planning
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Learning of time series through neuron-to-neuron instruction
Energy Technology Data Exchange (ETDEWEB)
Miyazaki, Y [Department of Physics, Kyoto University, Kyoto 606-8502, (Japan); Kinzel, W [Institut fuer Theoretische Physik, Universitaet Wurzburg, 97074 Wurzburg (Germany); Shinomoto, S [Department of Physics, Kyoto University, Kyoto (Japan)
2003-02-07
A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space.
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Learning of time series through neuron-to-neuron instruction
International Nuclear Information System (INIS)
Miyazaki, Y; Kinzel, W; Shinomoto, S
2003-01-01
A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space
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Optimized Scheduling of Smart Meter Data Access for Real-time Voltage Quality Monitoring
DEFF Research Database (Denmark)
Kemal, Mohammed Seifu; Olsen, Rasmus Løvenstein; Schwefel, Hans-Peter
2018-01-01
Abstract—Active low-voltage distribution grids that support high integration of distributed generation such as photovoltaics and wind turbines require real-time voltage monitoring. At the same time, countries in Europe such as Denmark have close to 100% rollout of smart metering infrastructure....... The metering infrastructure has limitations to provide real-time measurements with small-time granularity. This paper presents an algorithm for optimized scheduling of smart meter data access to provide real-time voltage quality monitoring. The algorithm is analyzed using a real distribution grid in Denmark...
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Secure and Time-Aware Communication of Wireless Sensors Monitoring Overhead Transmission Lines.
Mazur, Katarzyna; Wydra, Michal; Ksiezopolski, Bogdan
2017-07-11
Existing transmission power grids suffer from high maintenance costs and scalability issues along with a lack of effective and secure system monitoring. To address these problems, we propose to use Wireless Sensor Networks (WSNs) as a technology to achieve energy efficient, reliable, and low-cost remote monitoring of transmission grids. With WSNs, smart grid enables both utilities and customers to monitor, predict and manage energy usage effectively and react to possible power grid disturbances in a timely manner. However, the increased application of WSNs also introduces new security challenges, especially related to privacy, connectivity, and security management, repeatedly causing unpredicted expenditures. Monitoring the status of the power system, a large amount of sensors generates massive amount of sensitive data. In order to build an effective Wireless Sensor Network (WSN) for a smart grid, we focus on designing a methodology of efficient and secure delivery of the data measured on transmission lines. We perform a set of simulations, in which we examine different routing algorithms, security mechanisms and WSN deployments in order to select the parameters that will not affect the delivery time but fulfill their role and ensure security at the same time. Furthermore, we analyze the optimal placement of direct wireless links, aiming at minimizing time delays, balancing network performance and decreasing deployment costs.
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Sun, Baozhou; Lam, Dao; Yang, Deshan; Grantham, Kevin; Zhang, Tiezhi; Mutic, Sasa; Zhao, Tianyu
2018-05-01
Clinical treatment planning systems for proton therapy currently do not calculate monitor units (MUs) in passive scatter proton therapy due to the complexity of the beam delivery systems. Physical phantom measurements are commonly employed to determine the field-specific output factors (OFs) but are often subject to limited machine time, measurement uncertainties and intensive labor. In this study, a machine learning-based approach was developed to predict output (cGy/MU) and derive MUs, incorporating the dependencies on gantry angle and field size for a single-room proton therapy system. The goal of this study was to develop a secondary check tool for OF measurements and eventually eliminate patient-specific OF measurements. The OFs of 1754 fields previously measured in a water phantom with calibrated ionization chambers and electrometers for patient-specific fields with various range and modulation width combinations for 23 options were included in this study. The training data sets for machine learning models in three different methods (Random Forest, XGBoost and Cubist) included 1431 (~81%) OFs. Ten-fold cross-validation was used to prevent "overfitting" and to validate each model. The remaining 323 (~19%) OFs were used to test the trained models. The difference between the measured and predicted values from machine learning models was analyzed. Model prediction accuracy was also compared with that of the semi-empirical model developed by Kooy (Phys. Med. Biol. 50, 2005). Additionally, gantry angle dependence of OFs was measured for three groups of options categorized on the selection of the second scatters. Field size dependence of OFs was investigated for the measurements with and without patient-specific apertures. All three machine learning methods showed higher accuracy than the semi-empirical model which shows considerably large discrepancy of up to 7.7% for the treatment fields with full range and full modulation width. The Cubist-based solution
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Directory of Open Access Journals (Sweden)
Juan Pardo
2015-04-01
Full Text Available Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.
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Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma
2015-01-01
Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources. PMID:25905698
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Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma
2015-04-21
Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.
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Time for Learning: An Exploratory Analysis of NAEP Data
Ginsburg, Alan; Chudowsky, Naomi
2012-01-01
This report uses NAEP background data to track time and learning since the mid-1990s in three areas: student absenteeism; classroom instructional time in mathematics, reading, music and the visual arts; and homework time expected by teachers. Key report findings are: (1) Students with higher rates of "monthly absenteeism" score…
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Lee, Shiann-Jong; Liang, Wen-Tzong; Cheng, Hui-Wen; Tu, Feng-Shan; Ma, Kuo-Fong; Tsuruoka, Hiroshi; Kawakatsu, Hitoshi; Huang, Bor-Shouh; Liu, Chun-Chi
2014-01-01
We have developed a real-time moment tensor monitoring system (RMT) which takes advantage of a grid-based moment tensor inversion technique and real-time broad-band seismic recordings to automatically monitor earthquake activities in the vicinity of Taiwan. The centroid moment tensor (CMT) inversion technique and a grid search scheme are applied to obtain the information of earthquake source parameters, including the event origin time, hypocentral location, moment magnitude and focal mechanism. All of these source parameters can be determined simultaneously within 117 s after the occurrence of an earthquake. The monitoring area involves the entire Taiwan Island and the offshore region, which covers the area of 119.3°E to 123.0°E and 21.0°N to 26.0°N, with a depth from 6 to 136 km. A 3-D grid system is implemented in the monitoring area with a uniform horizontal interval of 0.1° and a vertical interval of 10 km. The inversion procedure is based on a 1-D Green's function database calculated by the frequency-wavenumber (fk) method. We compare our results with the Central Weather Bureau (CWB) catalogue data for earthquakes occurred between 2010 and 2012. The average differences between event origin time and hypocentral location are less than 2 s and 10 km, respectively. The focal mechanisms determined by RMT are also comparable with the Broadband Array in Taiwan for Seismology (BATS) CMT solutions. These results indicate that the RMT system is realizable and efficient to monitor local seismic activities. In addition, the time needed to obtain all the point source parameters is reduced substantially compared to routine earthquake reports. By connecting RMT with a real-time online earthquake simulation (ROS) system, all the source parameters will be forwarded to the ROS to make the real-time earthquake simulation feasible. The RMT has operated offline (2010-2011) and online (since January 2012 to present) at the Institute of Earth Sciences (IES), Academia Sinica
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RFID and IOT for Attendance Monitoring System
Directory of Open Access Journals (Sweden)
Dedy Irawan Joseph
2018-01-01
Full Text Available In recent years, RFID technology has been widely used in various sectors, such as in-education, transportation, agriculture, animal husbandry, store sales and other sectors. RFID utilization in education is student attendance monitoring system, by using Internet of Things (IoT and Cloud technology, it will produce a real time attendance monitoring system that can be accessed by various parties, such as lecturer, campus administration and parents. With this monitoring system if there are students who are not present can be immediately discovered and can be taken immediate action and the learning process can run smoothly.
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Quasi Real Time Data Analysis for Air Quality Monitoring with an Electronic Nose
Zhou, Hanying; Shevade, Abhijit V.; Pelletier, Christine C.; Homer, Margie L.; Ryan, M. Amy
2006-01-01
Cabin Air Quality Monitoring: A) Functions; 1) Incident monitor for targeted contaminants exceeding targeted concentrations. Identify and quantify. 2) Monitor for presence of compounds associated with fires or overheating electronics. 3) Monitor clean-up process. B) Characteristics; 1) Low mass, low power device. 2) Requires little crew time for maintenance and calibration. 3) Detects, identifies and quantifies selected chemical species at or below 24 hour SMAC.
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Real time monitoring of slope condition for transmission tower safety in Kenyir, Malaysia
Omar, R. C.; Ismail, A.; Khalid, N. H. N.; Din, N. M.; Hussain, H.; Jamaludin, M. Z.; Abdullah, F.; Arazad, A. Z.; Yusop, H.
2013-06-01
The Malaysia national electricity grid traverses throughout the nation over urban and rural areas including mountainous terrain. A major number of the transmission towers have been in existence for over 40 years and some traversed through very remote and high altitude areas like the Titiwangsa range that forms the backbone of the Malay Peninsula. This paper describes the instrumentation and real time monitoring in a transmission tower site in Kenyir, a hilly terrain in the East Coast of Malaysia. The site itself which is between 300-500m above sea level is deep in the rainforest area of Kenyir. The site and surrounding areas has been identified with signs of slope failure. A design concern is the real time slope monitoring sensors reliability and data integrity from the remote area with potential interference to the electronics equipment from the power line. The monitoring system comprised of an automated system for collecting and reporting field monitoring data. The instruments collect readings and transmit real time through GSM to the monitoring office over designated intervals. This initiative is a part of a project on developing an early warning system for monitoring landslide hazards at selected transmission towers. This paper reviews the various instrumentation used and challenges faced and the output received for slope movement warnings.
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Real time monitoring of slope condition for transmission tower safety in Kenyir, Malaysia
International Nuclear Information System (INIS)
Omar, R C; Ismail, A; Khalid, N H N; Din, N M; Hussain, H; Jamaludin, M Z; Abdullah, F; Arazad, A Z; Yusop, H
2013-01-01
The Malaysia national electricity grid traverses throughout the nation over urban and rural areas including mountainous terrain. A major number of the transmission towers have been in existence for over 40 years and some traversed through very remote and high altitude areas like the Titiwangsa range that forms the backbone of the Malay Peninsula. This paper describes the instrumentation and real time monitoring in a transmission tower site in Kenyir, a hilly terrain in the East Coast of Malaysia. The site itself which is between 300–500m above sea level is deep in the rainforest area of Kenyir. The site and surrounding areas has been identified with signs of slope failure. A design concern is the real time slope monitoring sensors reliability and data integrity from the remote area with potential interference to the electronics equipment from the power line. The monitoring system comprised of an automated system for collecting and reporting field monitoring data. The instruments collect readings and transmit real time through GSM to the monitoring office over designated intervals. This initiative is a part of a project on developing an early warning system for monitoring landslide hazards at selected transmission towers. This paper reviews the various instrumentation used and challenges faced and the output received for slope movement warnings.
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Benefits for Voice Learning Caused by Concurrent Faces Develop over Time.
Zäske, Romi; Mühl, Constanze; Schweinberger, Stefan R
2015-01-01
Recognition of personally familiar voices benefits from the concurrent presentation of the corresponding speakers' faces. This effect of audiovisual integration is most pronounced for voices combined with dynamic articulating faces. However, it is unclear if learning unfamiliar voices also benefits from audiovisual face-voice integration or, alternatively, is hampered by attentional capture of faces, i.e., "face-overshadowing". In six study-test cycles we compared the recognition of newly-learned voices following unimodal voice learning vs. bimodal face-voice learning with either static (Exp. 1) or dynamic articulating faces (Exp. 2). Voice recognition accuracies significantly increased for bimodal learning across study-test cycles while remaining stable for unimodal learning, as reflected in numerical costs of bimodal relative to unimodal voice learning in the first two study-test cycles and benefits in the last two cycles. This was independent of whether faces were static images (Exp. 1) or dynamic videos (Exp. 2). In both experiments, slower reaction times to voices previously studied with faces compared to voices only may result from visual search for faces during memory retrieval. A general decrease of reaction times across study-test cycles suggests facilitated recognition with more speaker repetitions. Overall, our data suggest two simultaneous and opposing mechanisms during bimodal face-voice learning: while attentional capture of faces may initially impede voice learning, audiovisual integration may facilitate it thereafter.
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Simulated real-time process monitoring of a molten salt electrorefiner
International Nuclear Information System (INIS)
Rappleye, Devin; Simpson, Michael; Cumberland, Riley; McNelis, David; Yim, Man-Sung
2014-01-01
Highlights: • An alternative approach to safeguarding and monitoring pyroprocessing is proposed. • Possible signals to be used to monitor an electrorefiner are identified. • An inverse model was developed to determine deposition rates at the cathode. • The sensitivity of certain parameters in the inverse model are presented. - Abstract: An alternative approach to monitoring the pyrochemical process (pyroprocessing) for spent nuclear fuel treatment is proposed and examined. This approach relies on modeling and the real-time analysis of process readings. Using an electrorefiner model, named ERAD, cathode potential and cell current were identified as useful process readings. To provide a real-time analysis of these two process readings, an inverse model was developed based on fundamental electrochemical relations. The model was applied to the following operating modes: pure uranium deposition, co-deposition of uranium and plutonium, and co-deposition of uranium and zirconium. Using the cell current and cathode potential, the model predicted which species were depositing and their rates. The deposition rates predicted by the inverse model compared favorably to those calculated by ERAD
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Wen-Jye Shyr; Te-Jen Su; Chia-Ming Lin
2013-01-01
This study develops a novel method for learning mechatronics using remote monitoring and control, based on a programmable logic controller (PLC) and WebAccess. A mechatronics module, a Web‐CAM and a PLC were integrated with WebAccess software to organize a remote laboratory. The proposed system enables users to access the Internet for remote monitoring and control of the mechatronics module via a web browser, thereby enhancing work flexibility by enabling personnel to control mechatronics equ...
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Real-time monitoring and control of the plasma hearth process
International Nuclear Information System (INIS)
Power, M.A.; Carney, K.P.; Peters, G.G.
1996-01-01
A distributed monitoring and control system is proposed for a plasma hearth, which will be used to decompose hazardous organic materials, encapsulate actinide waste in an obsidian-like slag, and reduce storage volume of actinide waste. The plasma hearth will be installed at ANL-West with the assistance of SAIC. Real-time monitoring of the off-gas system is accomplished using a Sun Workstation and embedded PCs. LabWindows/CVI software serves as the graphical user interface
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Chen, B.; Harp, D. R.; Lin, Y.; Keating, E. H.; Pawar, R.
2017-12-01
Monitoring is a crucial aspect of geologic carbon sequestration (GCS) risk management. It has gained importance as a means to ensure CO2 is safely and permanently stored underground throughout the lifecycle of a GCS project. Three issues are often involved in a monitoring project: (i) where is the optimal location to place the monitoring well(s), (ii) what type of data (pressure, rate and/or CO2 concentration) should be measured, and (iii) What is the optimal frequency to collect the data. In order to address these important issues, a filtering-based data assimilation procedure is developed to perform the monitoring optimization. The optimal monitoring strategy is selected based on the uncertainty reduction of the objective of interest (e.g., cumulative CO2 leak) for all potential monitoring strategies. To reduce the computational cost of the filtering-based data assimilation process, two machine-learning algorithms: Support Vector Regression (SVR) and Multivariate Adaptive Regression Splines (MARS) are used to develop the computationally efficient reduced-order-models (ROMs) from full numerical simulations of CO2 and brine flow. The proposed framework for GCS monitoring optimization is demonstrated with two examples: a simple 3D synthetic case and a real field case named Rock Spring Uplift carbon storage site in Southwestern Wyoming.
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Connection with seismic networks and construction of real time earthquake monitoring system
Energy Technology Data Exchange (ETDEWEB)
Chi, Heon Cheol; Lee, H. I.; Shin, I. C.; Lim, I. S.; Park, J. H.; Lee, B. K.; Whee, K. H.; Cho, C. S. [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)
2000-12-15
It is natural to use the nuclear power plant seismic network which have been operated by KEPRI(Korea Electric Power Research Institute) and local seismic network by KIGAM(Korea Institute of Geology, Mining and Material). The real time earthquake monitoring system is composed with monitoring module and data base module. Data base module plays role of seismic data storage and classification and the other, monitoring module represents the status of acceleration in the nuclear power plant area. This research placed the target on the first, networking the KIN's seismic monitoring system with KIGAM and KEPRI seismic network and the second, construction the KIN's Independent earthquake monitoring system.
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Connection with seismic networks and construction of real time earthquake monitoring system
International Nuclear Information System (INIS)
Chi, Heon Cheol; Lee, H. I.; Shin, I. C.; Lim, I. S.; Park, J. H.; Lee, B. K.; Whee, K. H.; Cho, C. S.
2000-12-01
It is natural to use the nuclear power plant seismic network which have been operated by KEPRI(Korea Electric Power Research Institute) and local seismic network by KIGAM(Korea Institute of Geology, Mining and Material). The real time earthquake monitoring system is composed with monitoring module and data base module. Data base module plays role of seismic data storage and classification and the other, monitoring module represents the status of acceleration in the nuclear power plant area. This research placed the target on the first, networking the KIN's seismic monitoring system with KIGAM and KEPRI seismic network and the second, construction the KIN's Independent earthquake monitoring system
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Leow, Li-Ann; Gunn, Reece; Marinovic, Welber; Carroll, Timothy J
2017-08-01
When sensory feedback is perturbed, accurate movement is restored by a combination of implicit processes and deliberate reaiming to strategically compensate for errors. Here, we directly compare two methods used previously to dissociate implicit from explicit learning on a trial-by-trial basis: 1 ) asking participants to report the direction that they aim their movements, and contrasting this with the directions of the target and the movement that they actually produce, and 2 ) manipulating movement preparation time. By instructing participants to reaim without a sensory perturbation, we show that reaiming is possible even with the shortest possible preparation times, particularly when targets are narrowly distributed. Nonetheless, reaiming is effortful and comes at the cost of increased variability, so we tested whether constraining preparation time is sufficient to suppress strategic reaiming during adaptation to visuomotor rotation with a broad target distribution. The rate and extent of error reduction under preparation time constraints were similar to estimates of implicit learning obtained from self-report without time pressure, suggesting that participants chose not to apply a reaiming strategy to correct visual errors under time pressure. Surprisingly, participants who reported aiming directions showed less implicit learning according to an alternative measure, obtained during trials performed without visual feedback. This suggests that the process of reporting can affect the extent or persistence of implicit learning. The data extend existing evidence that restricting preparation time can suppress explicit reaiming and provide an estimate of implicit visuomotor rotation learning that does not require participants to report their aiming directions. NEW & NOTEWORTHY During sensorimotor adaptation, implicit error-driven learning can be isolated from explicit strategy-driven reaiming by subtracting self-reported aiming directions from movement directions, or
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Time Spent, Workload, and Student and Faculty Perceptions in a Blended Learning Environment
Schumacher, Christie; Arif, Sally
2016-01-01
Objective. To evaluate student perception and time spent on asynchronous online lectures in a blended learning environment (BLE) and to assess faculty workload and perception. Methods. Students (n=427) time spent viewing online lectures was measured in three courses. Students and faculty members completed a survey to assess perceptions of a BLE. Faculty members recorded time spent creating BLEs. Results. Total time spent in the BLE was less than the allocated time for two of the three courses by 3-15%. Students preferred online lectures for their flexibility, students’ ability to apply information learned, and congruence with their learning styles. Faculty members reported the BLE facilitated higher levels of learning during class sessions but noted an increase in workload. Conclusion. A BLE increased faculty workload but was well received by students. Time spent viewing online lectures was less than what was allocated in two of the three courses. PMID:27667839
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Real-time trend monitoring of gas compressor stations
Energy Technology Data Exchange (ETDEWEB)
Van Hardeveld, T. (Nova, an Alberta Corp., AB (Canada))
1991-02-01
The authors' company has developed a machinery health monitoring system (MHealth) for short-term and long-term historical trending and analysis of data from its 40 gas compressor stations. The author discusses the benefits of real-time trending in troubleshooting operations, in preventative maintenance scheduling and cites specific applications in the startup operations of several new gas compressor/centrifugal compressor units.
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A knowledge-based system framework for real-time monitoring applications
International Nuclear Information System (INIS)
Heaberlin, J.O.; Robinson, A.H.
1989-01-01
A real-time environment presents a challenge for knowledge-based systems for process monitoring with on-line data acquisition in nuclear power plants. These applications are typically data intensive. This, coupled with the dynamic nature of events on which problematic decisions are based, requires the development of techniques fundamentally different from those generally employed. Traditional approaches involve knowledge management techniques developed for static data, the majority of which is elicited directly from the user in a consultation environment. Inference mechanisms are generally noninterruptible, requiring all appropriate rules to be fired before new data can be accommodated. As a result, traditional knowledge-based applications in real-time environments have inherent problems in dealing with the time dependence of both the data and the solution process. For example, potential problems include obtaining a correct solution too late to be of use or focusing computing resources on problems that no longer exist. A knowledge-based system framework, the real-time framework (RTF), has been developed that can accommodate the time dependencies and resource trade-offs required for real-time process monitoring applications. This framework provides real-time functionality by using generalized problem-solving goals and control strategies that are modifiable during system operation and capable of accommodating feedback for redirection of activities
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Modeling Time Series Data for Supervised Learning
Baydogan, Mustafa Gokce
2012-01-01
Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning…
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Online Quiz Time Limits and Learning Outcomes in Economics
Evans, Brent; Culp, Robert
2015-01-01
In an effort to better understand the impact of timing limits, the authors compare the learning outcomes of students who completed timed quizzes with students who took untimed quizzes in economics principles courses. Students were assigned two online quizzes--one timed and one untimed--and re-tested on the material the following class day. Our…
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Real-time Environmental Monitoring Data on the internet
International Nuclear Information System (INIS)
Nakashima, N.I.; Maruo, Y.; Tobita, K.; Takeyasu, M.
2000-01-01
Japan Nuclear Cycle Development Institute (JNC) places great emphasis on safety, information disclosure and communication with the local community. The Real-time Environmental Monitoring Data (REMD) was made to provide to the public on the JNC web-site (http://www.jnc.go.jp/). It is the first organization having nuclear facilities in Japan to open REMD on the Internet web-site. JNC Tokai Works included Tokai Reprocessing Plant (TRP) started to open REMD in Oct. 1998. O-arai Engineering Center (OEC) included the Experimental Fast Reactor JOYO opened in April 1999. OEC produced this web-site in both Japanese and English (http://www.jnc.go.jp/zooarai/Oantai_e/html/index.html). REMD means airborne gamma radiation dose rate, and Meteorological Observation Data. Tokai Works has 13 Monitoring Posts/Stations and OEC has 8 Monitoring Posts to measure airborne gamma radiation dose. The data from these Monitoring Posts/Stations are shown on the web-site. The Meteorological Observation Data in this web-site are wind direction, wind speed, temperature, humidity, precipitation, and atmospheric stability. Atmospheric stability provides information on the state of the atmosphere concerned with air diffusion. REMD web-site provides all these data mentioned above as current data, data tables, trend graphs, and additional information. They are updated every hour. The current data are shown with a graphical map around the JNC site. Data tables are shown within 7 days. Daily highest and lowest temperature and precipitation are also shown as a table. There are three kinds of trend graphs of airborne radiation dose rate, the latest 24 hours trend graph, 48 hours, and 7 days. Each graph is shown with a graph of precipitation, so that variation of airborne gamma radiation with rainfall can be seen. Some explanations of this web-site are expressed as additional information. The topics of them are airborne Radiation, Meteorology, Radioactivity and Radiation, and Rainfall and Radiation. A set
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Conceptual design for real time monitoring of electron microbeam
International Nuclear Information System (INIS)
Kim, Ji Seok; Kim, Hyun Ki; Jang, Mee; Choi, Chang Woon; Sun, Gwang Min; Lee, Jai Ki
2008-01-01
It is recognized that the microbeam is powerful system to understand the interaction of ionizing radiation with cells. Especially, electron microbeam system is useful to investigate the effect of low-LET radiation for cells. Electron microbeam has been developed in KIRAMS. It can irradiate the small volume in cell level by collimator and electromagnetic field and give local dose to individual cell by controlling the number of electrons. When the electron microbeam irradiates the individual cell, however, there is a possibility to change the current and intended trajectory of electron beam. Because this possibility introduces the uncertainty of dose, it is necessary to monitor the trajectory and current of electron beam. This study deals with development of real time monitoring device to confirm beam quality and to control if necessary during experiment. Consequently we designed dual monitoring device to solve various factors. And we optimize the design by simulation. (author)
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The time course of location-avoidance learning in fear of spiders.
Rinck, Mike; Koene, Marieke; Telli, Sibel; Moerman-van den Brink, Wiltine; Verhoeven, Barbara; Becker, Eni S
2016-01-01
Two experiments were designed to study the time course of avoidance learning in spider fearfuls (SFs) under controlled experimental conditions. To achieve this, we employed an immersive virtual environment (IVE): While walking freely through a virtual art museum to search for specific paintings, the participants were exposed to virtual spiders. Unbeknown to the participants, only two of four museum rooms contained spiders, allowing for avoidance learning. Indeed, the more SF the participants were, the faster they learned to avoid the rooms that contained spiders (Experiment. 1), and within the first six trials, high fearfuls already developed a preference for starting their search task in rooms without spiders (Experiment 2). These results illustrate the time course of avoidance learning in SFs, and they speak to the usefulness of IVEs in fundamental anxiety research.
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Directory of Open Access Journals (Sweden)
Alberto Corbi
2014-09-01
Full Text Available Recommender systems require input information in order to properly operate and deliver content or behaviour suggestions to end users. eLearning scenarios are no exception. Users are current students and recommendations can be built upon paths (both formal and informal, relationships, behaviours, friends, followers, actions, grades, tutor interaction, etc. A recommender system must somehow retrieve, categorize and work with all these details. There are several ways to do so: from raw and inelegant database access to more curated web APIs or even via HTML scrapping. New server-centric user-action logging and monitoring standard technologies have been presented in past years by several groups, organizations and standard bodies. The Experience API (xAPI, detailed in this article, is one of these. In the first part of this paper we analyse current learner-monitoring techniques as an initialization phase for eLearning recommender systems. We next review standardization efforts in this area; finally, we focus on xAPI and the potential interaction with the LIME model, which will be also summarized below.
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Pichler, Peter; Mazanek, Michael; Dusberger, Frederico; Weilnböck, Lisa; Huber, Christian G; Stingl, Christoph; Luider, Theo M; Straube, Werner L; Köcher, Thomas; Mechtler, Karl
2012-11-02
While the performance of liquid chromatography (LC) and mass spectrometry (MS) instrumentation continues to increase, applications such as analyses of complete or near-complete proteomes and quantitative studies require constant and optimal system performance. For this reason, research laboratories and core facilities alike are recommended to implement quality control (QC) measures as part of their routine workflows. Many laboratories perform sporadic quality control checks. However, successive and systematic longitudinal monitoring of system performance would be facilitated by dedicated automatic or semiautomatic software solutions that aid an effortless analysis and display of QC metrics over time. We present the software package SIMPATIQCO (SIMPle AuTomatIc Quality COntrol) designed for evaluation of data from LTQ Orbitrap, Q-Exactive, LTQ FT, and LTQ instruments. A centralized SIMPATIQCO server can process QC data from multiple instruments. The software calculates QC metrics supervising every step of data acquisition from LC and electrospray to MS. For each QC metric the software learns the range indicating adequate system performance from the uploaded data using robust statistics. Results are stored in a database and can be displayed in a comfortable manner from any computer in the laboratory via a web browser. QC data can be monitored for individual LC runs as well as plotted over time. SIMPATIQCO thus assists the longitudinal monitoring of important QC metrics such as peptide elution times, peak widths, intensities, total ion current (TIC) as well as sensitivity, and overall LC-MS system performance; in this way the software also helps identify potential problems. The SIMPATIQCO software package is available free of charge.
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Evidence of some strategic preservation of episodic learning in patients with schizophrenia.
Thuaire, Flavien; Izaute, Marie; Bacon, Elisabeth
2012-01-30
The aim of this study was to gain a better understanding of schizophrenia patients' strategic use of learning time allocation during encoding, and determine whether they are able to use their monitoring and previous performances to adapt their learning behavior efficiently. Schizophrenia is considered to be a pathology of consciousness as well as being associated with impaired awareness of cognitive processes. In this study, after a learning session, individuals may express a Judgment of Learning (JOL), which reflects their sense of being able to retrieve the information later and which forms the basis for their decision whether or not to carry on learning. The introspective abilities of schizophrenia patients and subsequent strategic control of study time during the encoding of easy or difficult word pairs were investigated in 23 patients and 23 healthy comparison subjects. In spite of their memory impairment, patients were able to judge the difficulty of the word pairs with accuracy and adapt their learning time accordingly. Schizophrenia patients are sensitive to difficulty when rating JOLs and afterwards controlling study time. Monitoring their knowledge at the start helped patients to adapt their learning efficiently. These findings may be of value for cognitive remediation. © 2011 Elsevier Ltd. All rights reserved.
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Combining real-time monitoring and knowledge-based analysis in MARVEL
Schwuttke, Ursula M.; Quan, A. G.; Angelino, R.; Veregge, J. R.
1993-01-01
Real-time artificial intelligence is gaining increasing attention for applications in which conventional software methods are unable to meet technology needs. One such application area is the monitoring and analysis of complex systems. MARVEL, a distributed monitoring and analysis tool with multiple expert systems, was developed and successfully applied to the automation of interplanetary spacecraft operations at NASA's Jet Propulsion Laboratory. MARVEL implementation and verification approaches, the MARVEL architecture, and the specific benefits that were realized by using MARVEL in operations are described.
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ATLAS Tile Calorimeter time calibration, monitoring and performance
AUTHOR|(INSPIRE)INSPIRE-00075913; The ATLAS collaboration
2016-01-01
The Tile Calorimeter (TileCal) is the hadronic calorimeter covering the central region of the ATLAS experiment at the LHC. This sampling device is made of plastic scintillating tiles alternated with iron plates and its response is calibrated to electromagnetic scale by means of several dedicated calibration systems. The accurate time calibration is important for the energy reconstruction, non-collision background removal as well as for specific physics analyses. The initial time calibration with so-called splash events and subsequent fine-tuning with collision data are presented. The monitoring of the time calibration with laser system and physics collision data is discussed as well as the corrections for sudden changes performed still before the recorded data are processed for physics analyses. Finally, the time resolution as measured with jets and isolated muons particles is presented.
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Leisure time activities, parental monitoring and drunkenness in adolescents
Tomcikova, Z.; Veselska, Z.; Madarasova Geckova, A.; van Dijk, J.P.; Reijneveld, S.A.
2012-01-01
Background: The aim of this cross-sectional study was to explore the association between adolescent drunkenness and participation in risky leisure time activities and parental monitoring. Methods: A sample of 3,694 Slovak elementary school students (mean age 14.5 years; 49.0% males) was assessed for
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Leisure Time Activities, Parental Monitoring and Drunkenness in Adolescents
Tomcikova, Zuzana; Veselska, Zuzana; Geckova, Andrea Madarasova; van Dijk, Jitse P.; Reijneveld, Sijmen A.
2013-01-01
Background: The aim of this cross-sectional study was to explore the association between adolescent drunkenness and participation in risky leisure time activities and parental monitoring. Methods: A sample of 3,694 Slovak elementary school students (mean age 14.5 years; 49.0% males) was assessed for
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Directory of Open Access Journals (Sweden)
Meng Lu
2017-10-01
Full Text Available In recent years, sequential tests for detecting structural changes in time series have been adapted for deforestation monitoring using satellite data. The input time series of such sequential tests is typically a vegetation index (e.g., NDVI, which uses two or three bands and ignores all other bands. Being limited to a vegetation index will not benefit from the richer spectral information provided by newly launched satellites and will bring two bottle-necks for deforestation monitoring. Firstly, it is hard to select a suitable vegetation index a priori. Secondly, a single vegetation index is typically affected by seasonal signals, noise and other natural dynamics, which decrease its power for deforestation detection. A novel multispectral time series change monitoring method that combines dimension reduction methods with a sequential hypothesis test is proposed to address these limitations. For each location, the proposed method automatically chooses a “suitable” index for deforestation monitoring. To demonstrate our approach, we implemented it in two study areas: a dry tropical forest in Bolivia (time series length: 444 with strong seasonality and a moist tropical forest in Brazil (time series length: 225 with almost no seasonality. Our method significantly improves accuracy in the presence of strong seasonality, in particular the temporal lag between disturbance and its detection.
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Limerick Nuclear Generating Station vibration monitoring system
International Nuclear Information System (INIS)
Mikulski, R.
1988-01-01
Philadelphia Electric Company utilizes a vibration monitoring computer system at its Limerick Nuclear Generating Station to evaluate machine performance. Performance can be evaluated through instantaneous sampling, online static and transient data. The system functions as an alarm monitor, displaying timely alarm data to the control area. The passage of time since the system's inception has been a learning period. Evaluation through continuous use has led to many enhancements in alarm handling and in the acquisition and display of machine data. Due to the system's sophistication, a routine maintenance program is a necessity. This paper describes the system's diagnostic tools and current utilization. System development and maintenance techniques will also be discussed
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Real-time eye lens dose monitoring during cerebral angiography procedures
Energy Technology Data Exchange (ETDEWEB)
Safari, M.J.; Wong, J.H.D.; Kadir, K.A.A.; Ng, K.H. [University of Malaya, Department of Biomedical Imaging, Faculty of Medicine, Kuala Lumpur (Malaysia); University of Malaya, University of Malaya Research Imaging Centre (UMRIC), Faculty of Medicine, Kuala Lumpur (Malaysia); Thorpe, N.K.; Cutajar, D.L.; Petasecca, M.; Lerch, M.L.F.; Rosenfeld, A.B. [University of Wollongong, Centre for Medical Radiation Physics (CMRP), Wollongong, NSW (Australia)
2016-01-15
To develop a real-time dose-monitoring system to measure the patient's eye lens dose during neuro-interventional procedures. Radiation dose received at left outer canthus (LOC) and left eyelid (LE) were measured using Metal-Oxide-Semiconductor Field-Effect Transistor dosimeters on 35 patients who underwent diagnostic or cerebral embolization procedures. The radiation dose received at the LOC region was significantly higher than the dose received by the LE. The maximum eye lens dose of 1492 mGy was measured at LOC region for an AVM case, followed by 907 mGy for an aneurysm case and 665 mGy for a diagnostic angiography procedure. Strong correlations (shown as R{sup 2}) were observed between kerma-area-product and measured eye doses (LOC: 0.78, LE: 0.68). Lateral and frontal air-kerma showed strong correlations with measured dose at LOC (AK{sub L}: 0.93, AK{sub F}: 0.78) and a weak correlation with measured dose at LE. A moderate correlation was observed between fluoroscopic time and dose measured at LE and LOC regions. The MOSkin dose-monitoring system represents a new tool enabling real-time monitoring of eye lens dose during neuro-interventional procedures. This system can provide interventionalists with information needed to adjust the clinical procedure to control the patient's dose. (orig.)
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Real-time eye lens dose monitoring during cerebral angiography procedures
International Nuclear Information System (INIS)
Safari, M.J.; Wong, J.H.D.; Kadir, K.A.A.; Ng, K.H.; Thorpe, N.K.; Cutajar, D.L.; Petasecca, M.; Lerch, M.L.F.; Rosenfeld, A.B.
2016-01-01
To develop a real-time dose-monitoring system to measure the patient's eye lens dose during neuro-interventional procedures. Radiation dose received at left outer canthus (LOC) and left eyelid (LE) were measured using Metal-Oxide-Semiconductor Field-Effect Transistor dosimeters on 35 patients who underwent diagnostic or cerebral embolization procedures. The radiation dose received at the LOC region was significantly higher than the dose received by the LE. The maximum eye lens dose of 1492 mGy was measured at LOC region for an AVM case, followed by 907 mGy for an aneurysm case and 665 mGy for a diagnostic angiography procedure. Strong correlations (shown as R 2 ) were observed between kerma-area-product and measured eye doses (LOC: 0.78, LE: 0.68). Lateral and frontal air-kerma showed strong correlations with measured dose at LOC (AK L : 0.93, AK F : 0.78) and a weak correlation with measured dose at LE. A moderate correlation was observed between fluoroscopic time and dose measured at LE and LOC regions. The MOSkin dose-monitoring system represents a new tool enabling real-time monitoring of eye lens dose during neuro-interventional procedures. This system can provide interventionalists with information needed to adjust the clinical procedure to control the patient's dose. (orig.)
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Radiographic apparatus and method for monitoring film exposure time
International Nuclear Information System (INIS)
Vatne, R.S.; Woodmansee, W.E.
1981-01-01
In connection with radiographic inspection of structural and industrial materials, method and apparatus are disclosed for automatically determining and displaying the time required to expose a radiographic film positioned to receive radiation passed by a test specimen, so that the finished film is exposed to an optimum blackening (density) for maximum film contrast. A plot is made of the variations in a total exposure parameter (representing the product of detected radiation rate and time needed to cause optimum film blackening) as a function of the voltage level applied to an X-ray tube. An electronic function generator storing the shape of this plot is incorporated into an exposure monitoring apparatus, such that for a selected tube voltage setting, the function generator produces an electrical analog signal of the corresponding exposure parameter. During the exposure, another signal is produced representing the rate of radiation as monitored by a diode detector positioned so as to receive the same radiation that is incident on the film. The signal representing the detected radiation rate is divided, by an electrical divider circuit into the signal representing total exposure, and the resulting quotient is an electrical signal representing the required exposure time. (author)
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Deep learning for automated drivetrain fault detection
DEFF Research Database (Denmark)
Bach-Andersen, Martin; Rømer-Odgaard, Bo; Winther, Ole
2018-01-01
A novel data-driven deep-learning system for large-scale wind turbine drivetrain monitoring applications is presented. It uses convolutional neural network processing on complex vibration signal inputs. The system is demonstrated to learn successfully from the actions of human diagnostic experts...... the fleet-wide diagnostic model performance. The analysis also explores the time dependence of the diagnostic performance, providing a detailed view of the timeliness and accuracy of the diagnostic outputs across the different architectures. Deep architectures are shown to outperform the human analyst...... as well as shallow-learning architectures, and the results demonstrate that when applied in a large-scale monitoring system, machine intelligence is now able to handle some of the most challenging diagnostic tasks related to wind turbines....
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Influence of learning strategy on response time during complex value-based learning and choice.
Directory of Open Access Journals (Sweden)
Shiva Farashahi
Full Text Available Measurements of response time (RT have long been used to infer neural processes underlying various cognitive functions such as working memory, attention, and decision making. However, it is currently unknown if RT is also informative about various stages of value-based choice, particularly how reward values are constructed. To investigate these questions, we analyzed the pattern of RT during a set of multi-dimensional learning and decision-making tasks that can prompt subjects to adopt different learning strategies. In our experiments, subjects could use reward feedback to directly learn reward values associated with possible choice options (object-based learning. Alternatively, they could learn reward values of options' features (e.g. color, shape and combine these values to estimate reward values for individual options (feature-based learning. We found that RT was slower when the difference between subjects' estimates of reward probabilities for the two alternative objects on a given trial was smaller. Moreover, RT was overall faster when the preceding trial was rewarded or when the previously selected object was present. These effects, however, were mediated by an interaction between these factors such that subjects were faster when the previously selected object was present rather than absent but only after unrewarded trials. Finally, RT reflected the learning strategy (i.e. object-based or feature-based approach adopted by the subject on a trial-by-trial basis, indicating an overall faster construction of reward value and/or value comparison during object-based learning. Altogether, these results demonstrate that the pattern of RT can be informative about how reward values are learned and constructed during complex value-based learning and decision making.
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The Role of Personality in a Regular Cognitive Monitoring Program.
Sadeq, Nasreen A; Valdes, Elise G; Harrison Bush, Aryn L; Andel, Ross
2018-02-20
This study examines the role of personality in cognitive performance, adherence, and satisfaction with regular cognitive self-monitoring. One hundred fifty-seven cognitively healthy older adults, age 55+, completed the 44-item Big-Five Inventory and were subsequently engaged in online monthly cognitive monitoring using the Cogstate Brief Battery for up to 35 months (M=14 mo, SD=7 mo). The test measures speed and accuracy in reaction time, visual learning, and working memory tasks. Neuroticism, although not related to cognitive performance overall (P>0.05), was related to a greater increase in accuracy (estimate=0.07, P=0.04) and speed (estimate=-0.09, P=0.03) on One Card Learning. Greater conscientiousness was related to faster overall speed on Detection (estimate=-1.62, P=0.02) and a significant rate of improvement in speed on One Card Learning (estimate=-0.10, Pconscientiousness were observed. Participants volunteering for regular cognitive monitoring may be quite uniform in terms of personality traits, with personality traits playing a relatively minor role in adherence and satisfaction. The more neurotic may exhibit better accuracy and improve in speed with time, whereas the more conscientious may perform faster overall and improve in speed on some tasks, but the effects appear small.
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Mirra, Nicole; Rogers, John
2015-01-01
Allocated classroom time is not the same as time available for learning--a host of economic and social stressors undermine learning time in schools serving low-income students. When time is limited, it is hard to meet rigorous learning standards. The challenge is compounded in high-poverty schools where community stressors place additional demands…
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A real-time virtual delivery system for photon radiotherapy delivery monitoring
Directory of Open Access Journals (Sweden)
Feng Shi
2014-03-01
Full Text Available Purpose: Treatment delivery monitoring is important for radiotherapy, which enables catching dosimetric error at the earliest possible opportunity. This project develops a virtual delivery system to monitor the dose delivery process of photon radiotherapy in real-time using GPU-based Monte Carlo (MC method.Methods: The simulation process consists of 3 parallel CPU threads. A thread T1 is responsible for communication with a linac, which acquires a set of linac status parameters, e.g. gantry angles, MLC configurations, and beam MUs every 20 ms. Since linac vendors currently do not offer interface to acquire data in real time, we mimic this process by fetching information from a linac dynalog file at the set frequency. Instantaneous beam fluence map (FM is calculated based. A FM buffer is also created in T1 and the instantaneous FM is accumulated to it. This process continues, until a ready signal is received from thread T2 on which an in-house developed MC dose engine executes on GPU. At that moment, the accumulated FM is transferred to T2 for dose calculations, and the FM buffer in T1 is cleared. Once the dose calculation finishes, the resulting 3D dose distribution is directed to thread T3, which displays it in three orthogonal planes in color wash overlaid on the CT image. This process continues to monitor the 3D dose distribution in real-time.Results: An IMRT and a VMAT cases used in our patient-specific QA are studied. Maximum dose differences between our system and treatment planning system are 0.98% and 1.58% for the IMRT and VMAT cases, respectively. The update frequency is >10Hz and the relative uncertainty level is 2%.Conclusion: By embedding a GPU-based MC code in a novel data/work flow, it is possible to achieve real-time MC dose calculations to monitor delivery process.------------------------------Cite this article as: Shi F, Gu X, Graves YJ, Jiang S, Jia X. A real-time virtual delivery system for photon radiotherapy delivery
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Learning and Teaching Problems in Part-Time Higher Education.
Trotman-Dickenson, D. I.
1988-01-01
Results of a British survey of the administrations of six universities and six public colleges, employers, and employees who were part-time students are reported and discussed. The survey assessed the perceptions of those groups concerning problems in the instruction and learning of part-time students. (MSE)
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Multi-Isotope Process (MIP) Monitor: A Near-Real-Time Monitor For Reprocessing Facilities
International Nuclear Information System (INIS)
Schwantes, Jon M.; Douglas, Matthew; Orton, Christopher R.; Fraga, Carlos G.; Christensen, Richard
2008-01-01
The threat of protracted diversion of Pu from commercial reprocessing operations is perhaps the greatest concern to national and international agencies tasked with safeguarding these facilities. While it is generally understood that a method for direct monitoring of process on-line and in near-real time (NRT) would be the best defense against protracted diversion scenarios, an effective method with these qualities has yet to be developed. Here, we attempt to bridge this gap by proposing an on-line NRT process monitoring method that should be sensitive to minor alterations in process conditions and compatible with small, easily deployable, detection systems. This Approach is known as the Multi-Isotope Process (MIP) Monitor and involves the determination and recognition of the contaminant pattern within a process stream for a suite of indicator (radioactive) elements present in the spent fuel as a function of process variables. Utilization of a suite of radio-elements, including ones with multiple oxidation states, decreases the likelihood that attempts to divert Pu by altering the ReDox environment within the process would go undetected. In addition, by identifying gamma-emitting indicator isotopes, this Approach might eliminate the need for bulky neutron detection systems, relying instead on small, portable, high-resolution gamma detectors easily deployable throughout the facility
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Reinforcement learning using a continuous time actor-critic framework with spiking neurons.
Directory of Open Access Journals (Sweden)
Nicolas Frémaux
2013-04-01
Full Text Available Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD learning of Doya (2000 to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.
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Fixation and escape times in stochastic game learning
International Nuclear Information System (INIS)
Realpe-Gomez, John; Szczesny, Bartosz; Galla, Tobias; Dall’Asta, Luca
2012-01-01
Evolutionary dynamics in finite populations is known to fixate eventually in the absence of mutation. We here show that a similar phenomenon can be found in stochastic game dynamical batch learning, and investigate fixation in learning processes in a simple 2×2 game, for two-player games with cyclic interaction, and in the context of the best-shot network game. The analogues of finite populations in evolution are here finite batches of observations between strategy updates. We study when and how such fixation can occur, and present results on the average time-to-fixation from numerical simulations. Simple cases are also amenable to analytical approaches and we provide estimates of the behaviour of so-called escape times as a function of the batch size. The differences and similarities with escape and fixation in evolutionary dynamics are discussed. (paper)
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Students' Pressure, Time Management and Effective Learning
Sun, Hechuan; Yang, Xiaolin
2009-01-01
Purpose: This paper aims to survey the status quo of the student pressure and the relationship between their daily time management and their learning outcomes in three different types of higher secondary schools at Shenyang, the capital city of Liaoning Province in mainland China. Design/methodology/approach: An investigation was carried out in 14…
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Representation Learning from Time Labelled Heterogeneous Data for Mobile Crowdsensing
Directory of Open Access Journals (Sweden)
Chunmei Ma
2016-01-01
Full Text Available Mobile crowdsensing is a new paradigm that can utilize pervasive smartphones to collect and analyze data to benefit users. However, sensory data gathered by smartphone usually involves different data types because of different granularity and multiple sensor sources. Besides, the data are also time labelled. The heterogeneous and time sequential data raise new challenges for data analyzing. Some existing solutions try to learn each type of data one by one and analyze them separately without considering time information. In addition, the traditional methods also have to determine phone orientation because some sensors equipped in smartphone are orientation related. In this paper, we think that a combination of multiple sensors can represent an invariant feature for a crowdsensing context. Therefore, we propose a new representation learning method of heterogeneous data with time labels to extract typical features using deep learning. We evaluate that our proposed method can adapt data generated by different orientations effectively. Furthermore, we test the performance of the proposed method by recognizing two group mobile activities, walking/cycling and driving/bus with smartphone sensors. It achieves precisions of 98.6% and 93.7% in distinguishing cycling from walking and bus from driving, respectively.
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A real-time non-contact monitoring method of subsea pipelines
Directory of Open Access Journals (Sweden)
Song Dalei
2015-01-01
Full Text Available To monitoring the subsea pipeline in real-time, a special potentiometric sensor array and a potential prediction model are presented in this paper. Firstly, to measure the potential of seawater, a special potentiometric sensor array with Ag/AgCl all-solid-state reference electrodes is first developed in this paper. Secondly, according to the obtained distribution law of electric field intensities a prediction model of the pipeline potentials is developed. Finally, the potentiometric sensor array is applied in sink experiment and the prediction model is validated by the sink measurements. The maximum error for pipeline potential prediction model is 1.1 mV. The proposed non-contact monitoring method for subsea pipeline can predict the potential of sea pipeline in real-time, thus providing important information for further subsea pipeline maintenance.
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The race to learn: spike timing and STDP can coordinate learning and recall in CA3.
Nolan, Christopher R; Wyeth, Gordon; Milford, Michael; Wiles, Janet
2011-06-01
The CA3 region of the hippocampus has long been proposed as an autoassociative network performing pattern completion on known inputs. The dentate gyrus (DG) region is often proposed as a network performing the complementary function of pattern separation. Neural models of pattern completion and separation generally designate explicit learning phases to encode new information and assume an ideal fixed threshold at which to stop learning new patterns and begin recalling known patterns. Memory systems are significantly more complex in practice, with the degree of memory recall depending on context-specific goals. Here, we present our spike-timing separation and completion (STSC) model of the entorhinal cortex (EC), DG, and CA3 network, ascribing to each region a role similar to that in existing models but adding a temporal dimension by using a spiking neural network. Simulation results demonstrate that (a) spike-timing dependent plasticity in the EC-CA3 synapses provides a pattern completion ability without recurrent CA3 connections, (b) the race between activation of CA3 cells via EC-CA3 synapses and activation of the same cells via DG-CA3 synapses distinguishes novel from known inputs, and (c) modulation of the EC-CA3 synapses adjusts the learned versus test input similarity required to evoke a direct CA3 response prior to any DG activity, thereby adjusting the pattern completion threshold. These mechanisms suggest that spike timing can arbitrate between learning and recall based on the novelty of each individual input, ensuring control of the learn-recall decision resides in the same subsystem as the learned memories themselves. The proposed modulatory signal does not override this decision but biases the system toward either learning or recall. The model provides an explanation for empirical observations that a reduction in novelty produces a corresponding reduction in the latency of responses in CA3 and CA1. Copyright © 2010 Wiley-Liss, Inc.
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Pointillist, Cyclical, and Overlapping: Multidimensional Facets of Time in Online Learning
Directory of Open Access Journals (Sweden)
Pekka Ihanainen
2011-11-01
Full Text Available A linear, sequential time conception based on in-person meetings and pedagogical activities is not enough for those who practice and hope to enhance contemporary education, particularly where online interactions are concerned. In this article, we propose a new model for understanding time in pedagogical contexts. Conceptual parts of the model will be employed as a “cultural technology” to help us relate to evolving phenomena, both physical and virtual. We label these constructs as pointillist, cyclical, and overlapping times.Pointillist time and learning takes place in “dots” of actions that consist of small, discrete moments (e.g., tweeting. Producing, receiving, and sharing ideas in this context are separate points in each actor’s timeline. Cyclical time and learning emerges from intensive periods, which are highly visible in online forums. This construct reveals itself through interactions that often exist in multiple online environments. Overlapping time and learning involves various configurations of linear, pointillist, and cyclical layers, which are mainly evident through the simultaneous uses of social communication technologies.Pointillist, cyclical, and overlapping time constructs enable new orientations for conceptualizing time in pedagogy. In this article we also introduce de-, re-, and en- modes of these pedagogies that connect with approaches to meet the needs of learners for individualization, personalization, and cyborgization.
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4D Near Real-Time Environmental Monitoring Using Highly Temporal LiDAR
Höfle, Bernhard; Canli, Ekrem; Schmitz, Evelyn; Crommelinck, Sophie; Hoffmeister, Dirk; Glade, Thomas
2016-04-01
The last decade has witnessed extensive applications of 3D environmental monitoring with the LiDAR technology, also referred to as laser scanning. Although several automatic methods were developed to extract environmental parameters from LiDAR point clouds, only little research has focused on highly multitemporal near real-time LiDAR (4D-LiDAR) for environmental monitoring. Large potential of applying 4D-LiDAR is given for landscape objects with high and varying rates of change (e.g. plant growth) and also for phenomena with sudden unpredictable changes (e.g. geomorphological processes). In this presentation we will report on the most recent findings of the research projects 4DEMON (http://uni-heidelberg.de/4demon) and NoeSLIDE (https://geomorph.univie.ac.at/forschung/projekte/aktuell/noeslide/). The method development in both projects is based on two real-world use cases: i) Surface parameter derivation of agricultural crops (e.g. crop height) and ii) change detection of landslides. Both projects exploit the "full history" contained in the LiDAR point cloud time series. One crucial initial step of 4D-LiDAR analysis is the co-registration over time, 3D-georeferencing and time-dependent quality assessment of the LiDAR point cloud time series. Due to the high amount of datasets (e.g. one full LiDAR scan per day), the procedure needs to be performed fully automatically. Furthermore, the online near real-time 4D monitoring system requires to set triggers that can detect removal or moving of tie reflectors (used for co-registration) or the scanner itself. This guarantees long-term data acquisition with high quality. We will present results from a georeferencing experiment for 4D-LiDAR monitoring, which performs benchmarking of co-registration, 3D-georeferencing and also fully automatic detection of events (e.g. removal/moving of reflectors or scanner). Secondly, we will show our empirical findings of an ongoing permanent LiDAR observation of a landslide (Gresten
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Real-time database for high resolution neutron monitor measurements
Energy Technology Data Exchange (ETDEWEB)
Steigies, Christian T.; Rother, Oliver M.; Wimmer-Schweingruber, Robert F.; Heber, Bernd [IEAP, Christian-Albrechts-Universitaet zu Kiel (Germany)
2008-07-01
The worldwide network of standardised neutron monitors is, after 50 years, still the state-of-the-art instrumentation to measure spectral variations of the primary cosmic ray component. These measurements are an ideal complement to space based cosmic ray measurements. Data from the approximately 50 IGY and NM64 neutron monitors is stored locally but also available through data collections sites like the World Data Center (WDC) or the IZMIRAN ftp server. The data from the WDC is in a standard format, but only hourly values are available. IZMIRAN collects the data in the best available time resolution, but the data arrives on the ftp server only hours, sometimes days, after the measurements. Also, the high time-resolution measurements of the different stations do not have a common format, a conversion routine for each station is needed before they can be used for scientific analysis. Supported by the 7th framework program of the European Commission, we are setting up a real-time database where high resolution cosmic ray measurements will be stored and accessible immediately after the measurement. Stations that do not have 1-minute resolution measurements will be upgraded to 1-minute or better resolution with an affordable standard registration system, that will submit the measurements to the database via the internet in real-time.
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Identification of critical time-consuming student support activities in e-learning
De Vries, Fred; Kester, Liesbeth; Sloep, Peter; Van Rosmalen, Peter; Pannekeet, Kees; Koper, Rob
2005-01-01
Please cite the original publication: De Vries, F., Kester, L., Sloep, P., Van Rosmalen, P., Pannekeet, K., & Koper, R. (2005). Identification of critical time-consuming student support activities in e-learning. Research in Learning Technology (ALT-J), 13(3), 219-229.
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Real-time health monitoring of civil infrastructure systems in Colombia
Thomson, Peter; Marulanda Casas, Johannio; Marulanda Arbelaez, Johannio; Caicedo, Juan
2001-08-01
Colombia's topography, climatic conditions, intense seismic activity and acute social problems place high demands on the nations deteriorating civil infrastructure. Resources that are available for maintenance of the road and railway networks are often misdirected and actual inspection methods are limited to a visual examination. New techniques for inspection and evaluation of safety and serviceability of civil infrastructure, especially bridges, must be developed. Two cases of civil structures with health monitoring systems in Colombia are presented in this paper. Construction of the Pereria-Dos Quebradas Viaduct was completed in 1997 with a total cost of 58 million dollars, including 1.5 million dollars in health monitoring instrumentation provided and installed by foreign companies. This health monitoring system is not yet fully operational due to the lack of training of national personnel in system operation and extremely limited technical documentation. In contrast to the Pereria-Dos Quebradas Viaduct monitoring system, the authors have proposed a relatively low cost health monitoring system via telemetry. This system has been implemented for real-time monitoring of accelerations of El Hormiguero Bridge spanning the Cauca River using the Colombian Southwest Earthquake Observatory telemetry systems. This two span metallic bridge, located along a critical road between the cities of Puerto Tejada and Cali in the Cauca Valley, was constructed approximately 50 years ago. Experiences with this system demonstrate how effective low cost systems can be used to remotely monitor the structural integrity of deteriorating structures that are continuously subject to high loading conditions.
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Selection of monitoring times to assess remediation performance
Energy Technology Data Exchange (ETDEWEB)
Kueper, B.H.; Mundle, K. [Queen' s Univ., Kingston, ON (Canada). Dept. of Civil Engineering, Geoengineering Centre
2007-07-01
Several factors determine the time needed for a plume to respond to non-aqueous phase liquid (NAPL) source zone remediation. Most spills of NAPLs (fuels, chlorinated solvents, PCB oils, creosote and coal tar) require mass removal in order to implement remediation technologies such as chemical oxidation, thermal treatments, alcohol flushing, surfactant flushing and hydraulic displacement. While much attention has been given to the development of these remediation technologies, little attention has been given to the response of the plume downstream of the treatment zone and selection of an appropriate monitoring time scale to adequately evaluate the impacts of remediation. For that reason, this study focused on the prevalence of diffusive sinks, the mobility of the contaminant and the hydraulic conductivity of subsurface materials. Typically, plumes in subsurface environments dominated by diffusive sinks or low permeability materials need long periods of time to detach after source removal. This paper presented generic plume response model simulations that illustrated concentration rebound following the use of in-situ chemical oxidation in fractured clay containing trichloroethylene. It was determined that approximately 2 years are needed to reach peak rebound concentration after cessation remedial action. It was concluded that downgradient monitoring well concentrations may be greatly reduced during remedial action due to the fact that oxidant occupies the fracture and because oxidant diffuses into the clay matrix, creating a short period of contaminant reduction in the area of flowing groundwater. 9 refs., 2 tabs., 7 figs.
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Selection of monitoring times to assess remediation performance
International Nuclear Information System (INIS)
Kueper, B.H.; Mundle, K.
2007-01-01
Several factors determine the time needed for a plume to respond to non-aqueous phase liquid (NAPL) source zone remediation. Most spills of NAPLs (fuels, chlorinated solvents, PCB oils, creosote and coal tar) require mass removal in order to implement remediation technologies such as chemical oxidation, thermal treatments, alcohol flushing, surfactant flushing and hydraulic displacement. While much attention has been given to the development of these remediation technologies, little attention has been given to the response of the plume downstream of the treatment zone and selection of an appropriate monitoring time scale to adequately evaluate the impacts of remediation. For that reason, this study focused on the prevalence of diffusive sinks, the mobility of the contaminant and the hydraulic conductivity of subsurface materials. Typically, plumes in subsurface environments dominated by diffusive sinks or low permeability materials need long periods of time to detach after source removal. This paper presented generic plume response model simulations that illustrated concentration rebound following the use of in-situ chemical oxidation in fractured clay containing trichloroethylene. It was determined that approximately 2 years are needed to reach peak rebound concentration after cessation remedial action. It was concluded that downgradient monitoring well concentrations may be greatly reduced during remedial action due to the fact that oxidant occupies the fracture and because oxidant diffuses into the clay matrix, creating a short period of contaminant reduction in the area of flowing groundwater. 9 refs., 2 tabs., 7 figs
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A framework to monitor activities of satellite data processing in real-time
Nguyen, M. D.; Kryukov, A. P.
2018-01-01
Space Monitoring Data Center (SMDC) of SINP MSU is one of the several centers in the world that collects data on the radiational conditions in near-Earth orbit from various Russian (Lomonosov, Electro-L1, Electro-L2, Meteor-M1, Meteor-M2, etc.) and foreign (GOES 13, GOES 15, ACE, SDO, etc.) satellites. The primary purposes of SMDC are: aggregating heterogeneous data from different sources; providing a unified interface for data retrieval, visualization, analysis, as well as development and testing new space weather models; and controlling the correctness and completeness of data. Space weather models rely on data provided by SMDC to produce forecasts. Therefore, monitoring the whole data processing cycle is crucial for further success in the modeling of physical processes in near-Earth orbit based on the collected data. To solve the problem described above, we have developed a framework called Live Monitor at SMDC. Live Monitor allows watching all stages and program components involved in each data processing cycle. All activities of each stage are logged by Live Monitor and shown in real-time on a web interface. When an error occurs, a notification message will be sent to satellite operators via email and the Telegram messenger service so that they could take measures in time. The Live Monitor’s API can be used to create a customized monitoring service with minimum coding.
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Learning from your mistakes: The functional value of spontaneous error monitoring in aphasia
Directory of Open Access Journals (Sweden)
Erica L. Middleton
2014-04-01
Ex. 4.\t(T = umbrella “umbelella, umbrella”: Phonological error; DetCorr We used mixed effects logistic regression to assess whether the log odds of changing from error to correct was predicted by monitoring status of the error (DetCorr vs. NoDet; DetNoCorr vs. NoDet; whether the monitoring benefit interacted with direction of change (forward, backward; and whether effects varied by error type. Figure 1 (top shows that the proportion accuracy change was higher for DetCorr, relative to NoDet, consistent with a monitoring benefit. The difference in log odds was significant for semantic errors in both directions (forward: coeff. = -1.73; z= -7.78; p < .001; backward: coeff = -0.92; z= -3.60; p < .001, and for phonological errors in both directions (forward: coeff. = -0.74; z= -2.73; p=.006; backward : coeff. = -.76; z = -2.73; p = .006. The difference between DetNoCorr and NoDet was not significant in any condition. Figure 1 (bottom shows that for Semantic errors, there was a directional asymmetry favoring the Forward condition (interaction: coeff. = .79; z = 2.32; p = .02. Phonological errors, in contrast, produced comparable effects in Forward and Backward direction. The results demonstrated a benefit for errors that were detected and corrected. This monitoring benefit was present in both the forward and backward direction, supporting the Strength hypothesis. Of greatest interest, the monitoring benefit for Semantic errors was greater in the forward than backward direction, indicating a role for learning.
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Poland, M. P.; Teasdale, R.; Kraft, K.
2010-12-01
Internet-accessible real- and near-real-time Earth science datasets are an important resource for geoscience education, but relatively few comprehensive datasets are available, and background information to aid interpretation is often lacking. In response to this need, the U.S. Geological Survey’s (USGS) Hawaiian Volcano Observatory, in collaboration with the National Aeronautics and Space Administration and the University of Hawai‘i, Mānoa, established the Volcanoes Exploration Project: Pu‘u ‘O‘o (VEPP). The VEPP Web site provides access, in near-real time, to geodetic, seismic, and geologic data from the Pu‘u ‘O‘o eruptive vent on Kilauea Volcano, Hawai‘i. On the VEPP Web site, a time series query tool provides a means of interacting with continuous geophysical data. In addition, results from episodic kinematic GPS campaigns and lava flow field maps are posted as data are collected, and archived Webcam images from Pu‘u ‘O‘o crater are available as a tool for examining visual changes in volcanic activity over time. A variety of background information on volcano surveillance and the history of the 1983-present Pu‘u ‘O‘o-Kupaianaha eruption puts the available monitoring data in context. The primary goal of the VEPP Web site is to take advantage of high visibility monitoring data that are seldom suitably well-organized to constitute an established educational resource. In doing so, the VEPP project provides a geoscience education resource that demonstrates the dynamic nature of volcanoes and promotes excitement about the process of scientific discovery through hands-on learning. To support use of the VEPP Web site, a week-long workshop was held at Kilauea Volcano in July 2010, which included 25 participants from the United States and Canada. The participants represented a diverse cross-section of higher learning, from community colleges to research universities, and included faculty who teach both large introductory non-major classes
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Time-Frequency Methods for Structural Health Monitoring
Directory of Open Access Journals (Sweden)
Alexander L. Pyayt
2014-03-01
Full Text Available Detection of early warning signals for the imminent failure of large and complex engineered structures is a daunting challenge with many open research questions. In this paper we report on novel ways to perform Structural Health Monitoring (SHM of flood protection systems (levees, earthen dikes and concrete dams using sensor data. We present a robust data-driven anomaly detection method that combines time-frequency feature extraction, using wavelet analysis and phase shift, with one-sided classification techniques to identify the onset of failure anomalies in real-time sensor measurements. The methodology has been successfully tested at three operational levees. We detected a dam leakage in the retaining dam (Germany and “strange” behaviour of sensors installed in a Boston levee (UK and a Rhine levee (Germany.
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Autonomous learning by simple dynamical systems with a discrete-time formulation
Bilen, Agustín M.; Kaluza, Pablo
2017-05-01
We present a discrete-time formulation for the autonomous learning conjecture. The main feature of this formulation is the possibility to apply the autonomous learning scheme to systems in which the errors with respect to target functions are not well-defined for all times. This restriction for the evaluation of functionality is a typical feature in systems that need a finite time interval to process a unit piece of information. We illustrate its application on an artificial neural network with feed-forward architecture for classification and a phase oscillator system with synchronization properties. The main characteristics of the discrete-time formulation are shown by constructing these systems with predefined functions.
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A knowledge-based flight status monitor for real-time application in digital avionics systems
Duke, E. L.; Disbrow, J. D.; Butler, G. F.
1989-01-01
The Dryden Flight Research Facility of the National Aeronautics and Space Administration (NASA) Ames Research Center (Ames-Dryden) is the principal NASA facility for the flight testing and evaluation of new and complex avionics systems. To aid in the interpretation of system health and status data, a knowledge-based flight status monitor was designed. The monitor was designed to use fault indicators from the onboard system which are telemetered to the ground and processed by a rule-based model of the aircraft failure management system to give timely advice and recommendations in the mission control room. One of the important constraints on the flight status monitor is the need to operate in real time, and to pursue this aspect, a joint research activity between NASA Ames-Dryden and the Royal Aerospace Establishment (RAE) on real-time knowledge-based systems was established. Under this agreement, the original LISP knowledge base for the flight status monitor was reimplemented using the intelligent knowledge-based system toolkit, MUSE, which was developed under RAE sponsorship. Details of the flight status monitor and the MUSE implementation are presented.
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Leidy, Vivian; And Others
1981-01-01
Five elementary teachers explain how they orient pupils and get learning started on the first day of school--whether or not their supplies or textbooks have arrived--by building learning activities around a common interest like dogs, earthworms, football, or the Statue of Liberty. (Editor/SJL)
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Harmonizing electricity markets with physics : real time performance monitoring using grid-3PTM
International Nuclear Information System (INIS)
Budhraja, V.S.
2003-01-01
The Electric Power Group, LLC provides management and strategic consulting services for the electric power industry, with special emphasis on industry restructuring, competitive electricity markets, grid operations and reliability, power technologies, venture investments and start-ups. The Consortium for Electric Reliability Technology Solutions involves national laboratories, universities, and industry partners in researching, developing, and commercializing electric reliability technology solutions to protect and enhance the reliability of the American electric power system under the emerging competitive electricity market structure. Physics differentiate electric markets from other markets: there is real-time balancing, no storage, interconnected network, and power flows governed by physics. Some issues affecting both grid reliability and market issues are difficult to separate, such as security and congestion management, voltage management, reserves, frequency volatility, and others. The author examined the following investment challenges facing the electricity market: grid solutions, market solutions, and technology solutions. The real time performance monitoring and prediction platform, grid-3P was described and applications discussed, such as ACE-frequency monitoring, performance monitoring for automatic generation control (AGC) and frequency response, voltage/VAR monitoring, stability monitoring using phasor technology, and market monitoring. figs
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On-line, real-time monitoring for petrochemical and pipeline process control applications
Energy Technology Data Exchange (ETDEWEB)
Kane, Russell D.; Eden, D.C.; Cayard, M.S.; Eden, D.A.; Mclean, D.T. [InterCorr International, Inc., 14503 Bammel N. Houston, Suite 300, Houston Texas 77014 (United States); Kintz, J. [BASF Corporation, 602 Copper Rd., Freeport, Texas 77541 (United States)
2004-07-01
Corrosion problems in petroleum and petrochemical plants and pipeline may be inherent to the processes, but costly and damaging equipment losses are not. With the continual drive to increase productivity, while protecting both product quality, safety and the environment, corrosion must become a variable that can be continuously monitored and assessed. This millennium has seen the introduction of new 'real-time', online measurement technologies and vast improvements in methods of electronic data handling. The 'replace when it fails' approach is receding into a distant memory; facilities management today is embracing new technology, and rapidly appreciating the value it has to offer. It has offered the capabilities to increase system run time between major inspections, reduce the time and expense associated with turnaround or in-line inspections, and reduce major upsets which cause unplanned shut downs. The end result is the ability to know on a practical basis of how 'hard' facilities can be pushed before excessive corrosion damage will result, so that process engineers can understand the impact of their process control actions and implement true asset management. This paper makes reference to use of a online, real-time electrochemical corrosion monitoring system - SmartCET 1- in a plant running a mostly organic process media. It also highlights other pertinent examples where similar systems have been used to provide useful real-time information to detect system upsets, which would not have been possible otherwise. This monitoring/process control approach has operators and engineers to see, for the first time, changes in corrosion behavior caused by specific variations in process parameters. Process adjustments have been identified that reduce corrosion rates while maintaining acceptable yields and quality. The monitoring system has provided a new window into the chemistry of the process, helping chemical engineers improve their process
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Off-line and real-time monitoring of acetaminophen photodegradation by an electrochemical sensor.
Berto, Silvia; Carena, Luca; Chiavazza, Enrico; Marletti, Matteo; Fin, Andrea; Giacomino, Agnese; Malandrino, Mery; Barolo, Claudia; Prenesti, Enrico; Vione, Davide
2018-08-01
The photochemistry of N-acetyl-para-aminophenol (acetaminophen, APAP) is here investigated by using differential pulse voltammetry (DPV) analysis to monitor APAP photodegradation upon steady-state irradiation. The purpose of this work is to assess the applicability of DPV to monitor the photochemical behaviour of xenobiotics, along with the development of an electrochemical set-up for the real-time monitoring of APAP photodegradation. We here investigated the APAP photoreactivity towards the main photogenerated reactive transients species occurring in sunlit surface waters (hydroxyl radical HO, carbonate radical CO 3 - , excited triplet state of anthraquinone-2-sulfonate used as proxy of the chromophoric DOM, and singlet oxygen 1 O 2 ), and determined relevant kinetic parameters. A standard procedure based on UV detection coupled with liquid chromatography (HPLC-UV) was used under identical experimental conditions to compare and verify the DPV-based results. The latter were in agreement with HPLC data, with the exception of the triplet-sensitized processes. In the other cases, DPV could be used as an alternative to the well-tested but more costly and time-consuming HPLC-UV technique. We have also assessed the reaction rate constant between APAP and HO by real-time DPV, which allowed for the monitoring of APAP photodegradation inside the irradiation chamber. Unfortunately, real-time DPV measurements are likely to be affected by temperature variations of the irradiated samples. Overall, DPV appeared as a fast, cheap and reasonably reliable technique when used for the off-line monitoring of APAP photodegradation. When a suitable real-time procedure is developed, it could become a very straightforward method to study the photochemical behaviour of electroactive xenobiotics. Copyright © 2018 Elsevier Ltd. All rights reserved.
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A real-time stack radioactivity monitoring system and dose projection program
Energy Technology Data Exchange (ETDEWEB)
Hull, A.P.; Michael, P.A. [Brookhaven National Laboratory, Upton, NY (United States); Bernstein, H.J. [Bernstein & Sons, Bellport, NY (United States)
1995-02-01
At Brookhaven National Laboratory, a commercial Low- and High-Range Air Effluent Monitor has become operational at the 60 Mw (t) High Flux Beam Reactor. Its output data is combined with that from ground-level and elevated meteorological sensors to provide a real-time projection of the down-wind dose rates from noble gases and radioiodines released from the HFBR`s 100 m stack. The output of the monitor, and the meteorological sensors and the dose projections can be viewed at emergency response terminals located in the Reactor Control Room, its Technical Support Center and at the laboratory`s separately located Meteorological Station and Monitoring and Assessment Center.
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Time-Lapse Monitoring of Subsurface Fluid Flow using Parsimonious Seismic Interferometry
Hanafy, Sherif
2017-04-21
A typical small-scale seismic survey (such as 240 shot gathers) takes at least 16 working hours to be completed, which is a major obstacle in case of time-lapse monitoring experiments. This is especially true if the subject that needs to be monitored is rapidly changing. In this work, we will discuss how to decrease the recording time from 16 working hours to less than one hour of recording. Here, the virtual data has the same accuracy as the conventional data. We validate the efficacy of parsimonious seismic interferometry with the time-lapse mentoring idea with field examples, where we were able to record 30 different data sets within a 2-hour period. The recorded data are then processed to generate 30 snapshots that shows the spread of water from the ground surface down to a few meters.
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Experiences and recommendations in deploying a real-time, water quality monitoring system
O'Flynn, B.; Regan, F.; Lawlor, A.; Wallace, J.; Torres, J.; O'Mathuna, C.
2010-12-01
Monitoring of water quality at a river basin level to meet the requirements of the Water Framework Directive (WFD) using conventional sampling and laboratory-based techniques poses a significant financial burden. Wireless sensing systems offer the potential to reduce these costs considerably, as well as provide more useful, continuous monitoring capabilities by giving an accurate idea of the changing environmental and water quality in real time. It is unlikely that the traditional spot/grab sampling will provide a reasonable estimate of the true maximum and/or mean concentration for a particular physicochemical variable in a water body with marked temporal variability. When persistent fluctuations occur, it is likely only to be detected through continuous measurements, which have the capability of detecting sporadic peaks of concentration. Thus, in situ sensors capable of continuous sampling of parameters required under the WFD would therefore provide more up-to-date information, cut monitoring costs and provide better coverage representing long-term trends in fluctuations of pollutant concentrations. DEPLOY is a technology demonstration project, which began planning and station selection and design in August 2008 aiming to show how state-of-the-art technology could be implemented for cost-effective, continuous and real-time monitoring of a river catchment. The DEPLOY project is seen as an important building block in the realization of a wide area autonomous network of sensors capable of monitoring the spatial and temporal distribution of important water quality and environmental target parameters. The demonstration sites chosen are based in the River Lee, which flows through Ireland's second largest city, Cork, and were designed to include monitoring stations in five zones considered typical of significant river systems--these monitor water quality parameters such as pH, temperature, depth, conductivity, turbidity and dissolved oxygen. Over one million data points
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Experiences and recommendations in deploying a real-time, water quality monitoring system
International Nuclear Information System (INIS)
O'Flynn, B; O'Mathuna, C; Regan, F; Lawlor, A; Wallace, J; Torres, J
2010-01-01
Monitoring of water quality at a river basin level to meet the requirements of the Water Framework Directive (WFD) using conventional sampling and laboratory-based techniques poses a significant financial burden. Wireless sensing systems offer the potential to reduce these costs considerably, as well as provide more useful, continuous monitoring capabilities by giving an accurate idea of the changing environmental and water quality in real time. It is unlikely that the traditional spot/grab sampling will provide a reasonable estimate of the true maximum and/or mean concentration for a particular physicochemical variable in a water body with marked temporal variability. When persistent fluctuations occur, it is likely only to be detected through continuous measurements, which have the capability of detecting sporadic peaks of concentration. Thus, in situ sensors capable of continuous sampling of parameters required under the WFD would therefore provide more up-to-date information, cut monitoring costs and provide better coverage representing long-term trends in fluctuations of pollutant concentrations. DEPLOY is a technology demonstration project, which began planning and station selection and design in August 2008 aiming to show how state-of-the-art technology could be implemented for cost-effective, continuous and real-time monitoring of a river catchment. The DEPLOY project is seen as an important building block in the realization of a wide area autonomous network of sensors capable of monitoring the spatial and temporal distribution of important water quality and environmental target parameters. The demonstration sites chosen are based in the River Lee, which flows through Ireland's second largest city, Cork, and were designed to include monitoring stations in five zones considered typical of significant river systems-–these monitor water quality parameters such as pH, temperature, depth, conductivity, turbidity and dissolved oxygen. Over one million data
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Directory of Open Access Journals (Sweden)
Ayse Yarali
Full Text Available Associative learning relies on event timing. Fruit flies for example, once trained with an odour that precedes electric shock, subsequently avoid this odour (punishment learning; if, on the other hand the odour follows the shock during training, it is approached later on (relief learning. During training, an odour-induced Ca(++ signal and a shock-induced dopaminergic signal converge in the Kenyon cells, synergistically activating a Ca(++-calmodulin-sensitive adenylate cyclase, which likely leads to the synaptic plasticity underlying the conditioned avoidance of the odour. In Aplysia, the effect of serotonin on the corresponding adenylate cyclase is bi-directionally modulated by Ca(++, depending on the relative timing of the two inputs. Using a computational approach, we quantitatively explore this biochemical property of the adenylate cyclase and show that it can generate the effect of event timing on associative learning. We overcome the shortage of behavioural data in Aplysia and biochemical data in Drosophila by combining findings from both systems.
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Optimal critic learning for robot control in time-varying environments.
Wang, Chen; Li, Yanan; Ge, Shuzhi Sam; Lee, Tong Heng
2015-10-01
In this paper, optimal critic learning is developed for robot control in a time-varying environment. The unknown environment is described as a linear system with time-varying parameters, and impedance control is employed for the interaction control. Desired impedance parameters are obtained in the sense of an optimal realization of the composite of trajectory tracking and force regulation. Q -function-based critic learning is developed to determine the optimal impedance parameters without the knowledge of the system dynamics. The simulation results are presented and compared with existing methods, and the efficacy of the proposed method is verified.
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Timing of quizzes during learning: Effects on motivation and retention.
Healy, Alice F; Jones, Matt; Lalchandani, Lakshmi A; Tack, Lindsay Anderson
2017-06-01
This article investigates how the timing of quizzes given during learning impacts retention of studied material. We investigated the hypothesis that interspersing quizzes among study blocks increases student engagement, thus improving learning. Participants learned 8 artificial facts about each of 8 plant categories, with the categories blocked during learning. Quizzes about 4 of the 8 facts from each category occurred either immediately after studying the facts for that category (standard) or after studying the facts from all 8 categories (postponed). In Experiment 1, participants were given tests shortly after learning and several days later, including both the initially quizzed and unquizzed facts. Test performance was better in the standard than in the postponed condition, especially for categories learned later in the sequence. This result held even for the facts not quizzed during learning, suggesting that the advantage cannot be due to any direct testing effects. Instead the results support the hypothesis that interrupting learning with quiz questions is beneficial because it can enhance learner engagement. Experiment 2 provided further support for this hypothesis, based on participants' retrospective ratings of their task engagement during the learning phase. These findings have practical implications for when to introduce quizzes in the classroom. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
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Comparison of sensor characteristics of three real-time monitors for organic vapors.
Hori, Hajime; Ishimatsu, Sumiyo; Fueta, Yukiko; Hinoue, Mitsuo; Ishidao, Toru
2015-01-01
Sensor characteristics and performance of three real-time monitors for volatile organic compounds (VOC monitor) equipped with a photo ionization detector (PID), a sensor using the interference enhanced reflection (IER) method and a semiconductor gas sensor were investigated for 52 organic solvent vapors designated as class 1 and class 2 of organic solvents by the Ordinance of Organic Solvent Poisoning Prevention in Japan. Test vapors were prepared by injecting each liquid solvent into a 50 l Tedlar® bag and perfectly vaporizing it. The vapor concentration was from one-tenth to twice the administrative control level for all solvents. The vapor concentration was measured with the monitors and a gas chromatograph equipped with a flame ionization detector simultaneously, and the values were compared. The monitor with the PID sensor could measure many organic vapors, but it could not detect some vapors with high ionization potential. The IER sensor could also detect many vapors, but a linear response was not obtained for some vapors. A semiconductor sensor could detect methanol that could not be detected by PID and IER sensors. Working environment measurement of organic vapors by real-time monitors may be possible, but sensor characteristics and their limitations should be known.
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Directory of Open Access Journals (Sweden)
Barbara M Jones
2015-06-01
Full Text Available This paper engages with literature on flexible learning and teaching in order to explore whether it may be possible, within the South African context, to have flexible learning and teaching provide a third way which goes beyond the current practice of full-time/part-time provision. This binary classification of students is a proxy for day-time/after-hours delivery. The argument is made that effective, flexible learning and teaching requires a fundamental shift in thinking about learning and teaching in higher education that moves us beyond such binaries. The paper proposes that in order to ensure access and success for students, ‘common knowledge’ (Edwards, 2010 will need to be co-constructed which understands flexible learning and teaching in ways which will meet needs of a diversity of students, including working students. It will require ‘resourceful leadership’ (Edwards, 2014 within the university that recognises, enhances and gives purpose to the capability of colleagues at every level of the systems they lead. Also, it will require the building of ‘common knowledge’ between certain sectors of universities and particular workplaces.
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Intelligent data management for real-time spacecraft monitoring
Schwuttke, Ursula M.; Gasser, Les; Abramson, Bruce
1992-01-01
Real-time AI systems have begun to address the challenge of restructuring problem solving to meet real-time constraints by making key trade-offs that pursue less than optimal strategies with minimal impact on system goals. Several approaches for adapting to dynamic changes in system operating conditions are known. However, simultaneously adapting system decision criteria in a principled way has been difficult. Towards this end, a general technique for dynamically making such trade-offs using a combination of decision theory and domain knowledge has been developed. Multi-attribute utility theory (MAUT), a decision theoretic approach for making one-time decisions is discussed and dynamic trade-off evaluation is described as a knowledge-based extension of MAUT that is suitable for highly dynamic real-time environments, and provides an example of dynamic trade-off evaluation applied to a specific data management trade-off in a real-world spacecraft monitoring application.
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All-IP wireless sensor networks for real-time patient monitoring.
Wang, Xiaonan; Le, Deguang; Cheng, Hongbin; Xie, Conghua
2014-12-01
This paper proposes the all-IP WSNs (wireless sensor networks) for real-time patient monitoring. In this paper, the all-IP WSN architecture based on gateway trees is proposed and the hierarchical address structure is presented. Based on this architecture, the all-IP WSN can perform routing without route discovery. Moreover, a mobile node is always identified by a home address and it does not need to be configured with a care-of address during the mobility process, so the communication disruption caused by the address change is avoided. Through the proposed scheme, a physician can monitor the vital signs of a patient at any time and at any places, and according to the IPv6 address he can also obtain the location information of the patient in order to perform effective and timely treatment. Finally, the proposed scheme is evaluated based on the simulation, and the simulation data indicate that the proposed scheme might effectively reduce the communication delay and control cost, and lower the packet loss rate. Copyright © 2014 Elsevier Inc. All rights reserved.
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System-Aware Smart Network Management for Nano-Enriched Water Quality Monitoring
Directory of Open Access Journals (Sweden)
B. Mokhtar
2016-01-01
Full Text Available This paper presents a comprehensive water quality monitoring system that employs a smart network management, nano-enriched sensing framework, and intelligent and efficient data analysis and forwarding protocols for smart and system-aware decision making. The presented system comprises two main subsystems, a data sensing and forwarding subsystem (DSFS, and Operation Management Subsystem (OMS. The OMS operates based on real-time learned patterns and rules of system operations projected from the DSFS to manage the entire network of sensors. The main tasks of OMS are to enable real-time data visualization, managed system control, and secure system operation. The DSFS employs a Hybrid Intelligence (HI scheme which is proposed through integrating an association rule learning algorithm with fuzzy logic and weighted decision trees. The DSFS operation is based on profiling and registering raw data readings, generated from a set of optical nanosensors, as profiles of attribute-value pairs. As a case study, we evaluate our implemented test bed via simulation scenarios in a water quality monitoring framework. The monitoring processes are simulated based on measuring the percentage of dissolved oxygen and potential hydrogen (PH in fresh water. Simulation results show the efficiency of the proposed HI-based methodology at learning different water quality classes.
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Evaluation of a first mine real time diesel particulate matter (DPM) monitor
Energy Technology Data Exchange (ETDEWEB)
Stewart Gillies; Hsin Wei Wu [Gillies Wu Mining Technology (Australia)
2008-04-15
The objective of the study was to develop, test and prove up under mine conditions a Diesel Particulate Matter (DPM) real time atmospheric monitoring unit. The design for the new instrument, termed the D-PDM, is based on the recently developed real time respirable dust PDM. The project's main activities were to undertake through internationally recognised laboratory testing an evaluation of the new design and to undertake a comprehensive underground series of tests to establish the robustness and reliability of the new approach. The phases of design, the international laboratory testing and the underground mine evaluation in five operating mines proved that the monitor is capable in normal mine atmospheres of accurately measuring DPM levels in real time. The monitor has successfully reported data when used as a static or stationary instrument, when placed within the cab of a moving vehicle and when worn on a person's belt. The outcomes of the project provide the industry access to an enhanced tool for understanding the presence of DPM in the mine atmosphere.
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Combination of Robot Simulation with Real-time Monitoring and Control
Directory of Open Access Journals (Sweden)
Jianyu YANG
2014-08-01
Full Text Available The paper mainly focuses in combining virtual reality based operation simulation with remote real-time monitoring and control method for an experimental robot. A system composition framework was designed and relative arm-wheel experimental robot platform was also built. Virtual robots and two virtual environments were developed. To locate the virtual robot within numerical environments, relative mathematical methods is also discussed, including analytic locating methods for linear motion and self-rotation, as well as linear transformation method with homogeneous matrices for turning motion, in order to decrease division calculations. Several experiments were carried out, trajectory errors were found because of relative slides between the wheel and the floor, during the locating experiments. Writing-monitoring experiments were also performed by programming the robotic arm to write a Chinese character, and the virtual robot in monitoring terminal perfectly followed all the movements. All the experiment results confirmed that virtual environment can not only be used as a good supplement to the traditional video monitoring method, but also offer better control experience during the operation.
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Kaendler, Celia; Wiedmann, Michael; Leuders, Timo; Rummel, Nikol; Spada, Hans
2016-01-01
The monitoring by teachers of collaborative, cognitive, and meta-cognitive student activities in collaborative learning is crucial for fostering beneficial student interaction. In a quasi-experimental study, we trained pre-service teachers (N = 74) to notice behavioral indicators for these three dimensions of student activities. Video clips of…
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Eden, Annuschka Salima; Zwitserlood, Pienie; Keuper, Katharina; Junghöfer, Markus; Laeger, Inga; Zwanzger, Peter; Dobel, Christian
2014-01-01
The well-established memory bias for arousing-negative stimuli seems to be enhanced in high trait-anxious persons and persons suffering from anxiety disorders. We monitored the emergence and development of such a bias during and after learning, in high and low trait anxious participants. A word-learning paradigm was applied, consisting of spoken pseudowords paired either with arousing-negative or neutral pictures. Learning performance during training evidenced a short-lived advantage for arousing-negative associated words, which was not present at the end of training. Cued recall and valence ratings revealed a memory bias for pseudowords that had been paired with arousing-negative pictures, immediately after learning and two weeks later. This held even for items that were not explicitly remembered. High anxious individuals evidenced a stronger memory bias in the cued-recall test, and their ratings were also more negative overall compared to low anxious persons. Both effects were evident, even when explicit recall was controlled for. Regarding the memory bias in anxiety prone persons, explicit memory seems to play a more crucial role than implicit memory. The study stresses the need for several time points of bias measurement during the course of learning and retrieval, as well as the employment of different measures for learning success.
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Directory of Open Access Journals (Sweden)
Annuschka Salima Eden
Full Text Available The well-established memory bias for arousing-negative stimuli seems to be enhanced in high trait-anxious persons and persons suffering from anxiety disorders. We monitored the emergence and development of such a bias during and after learning, in high and low trait anxious participants. A word-learning paradigm was applied, consisting of spoken pseudowords paired either with arousing-negative or neutral pictures. Learning performance during training evidenced a short-lived advantage for arousing-negative associated words, which was not present at the end of training. Cued recall and valence ratings revealed a memory bias for pseudowords that had been paired with arousing-negative pictures, immediately after learning and two weeks later. This held even for items that were not explicitly remembered. High anxious individuals evidenced a stronger memory bias in the cued-recall test, and their ratings were also more negative overall compared to low anxious persons. Both effects were evident, even when explicit recall was controlled for. Regarding the memory bias in anxiety prone persons, explicit memory seems to play a more crucial role than implicit memory. The study stresses the need for several time points of bias measurement during the course of learning and retrieval, as well as the employment of different measures for learning success.
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Real-time monitoring of a microbial electrolysis cell using an electrical equivalent circuit model.
Hussain, S A; Perrier, M; Tartakovsky, B
2018-04-01
Efforts in developing microbial electrolysis cells (MECs) resulted in several novel approaches for wastewater treatment and bioelectrosynthesis. Practical implementation of these approaches necessitates the development of an adequate system for real-time (on-line) monitoring and diagnostics of MEC performance. This study describes a simple MEC equivalent electrical circuit (EEC) model and a parameter estimation procedure, which enable such real-time monitoring. The proposed approach involves MEC voltage and current measurements during its operation with periodic power supply connection/disconnection (on/off operation) followed by parameter estimation using either numerical or analytical solution of the model. The proposed monitoring approach is demonstrated using a membraneless MEC with flow-through porous electrodes. Laboratory tests showed that changes in the influent carbon source concentration and composition significantly affect MEC total internal resistance and capacitance estimated by the model. Fast response of these EEC model parameters to changes in operating conditions enables the development of a model-based approach for real-time monitoring and fault detection.
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Bauters, Merja; Purma, Jukka; Leinonen, Teemu
2014-01-01
The aim of this short paper is to look at how mobile video recording devices could support learning related to physical practices or places and situations at work. This paper discusses particular kind of workplace learning, namely learning using short video clips that are related to physical environment and tasks preformed in situ. The paper…
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Real-time monitoring for fast deformations using GNSS low-cost receivers
Directory of Open Access Journals (Sweden)
T. Bellone
2016-03-01
Full Text Available Landslides are one of the major geo-hazards which have constantly affected Italy especially over the last few years. In fact 82% of the Italian territory is affected by this phenomenon which destroys the environment and often causes deaths: therefore it is necessary to monitor these effects in order to detect and prevent these risks. Nowadays, most of this type of monitoring is carried out by using traditional topographic instruments (e.g. total stations or satellite techniques such as global navigation satellite system (GNSS receivers. The level of accuracy obtainable with these instruments is sub-centimetrical in post-processing and centimetrical in real-time; however, the costs are very high (many thousands of euros. The rapid diffusion of GNSS networks has led to an increase of using mass-market receivers for real-time positioning. In this paper, the performances of GNSS mass-market receiver are reported with the aim of verifying if this type of sensor can be used for real-time landslide monitoring: for this purpose a special slide was used for simulating a landslide, since it enabled us to give manual displacements thanks to a micrometre screw. These experiments were also carried out by considering a specific statistical test (a modified Chow test which enabled us to understand if there were any displacements from a statistical point of view in real time. The tests, the algorithm and results are reported in this paper.
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An Accelerometer-Based Sensor System for Real-Time Bridge Scour Monitoring
Directory of Open Access Journals (Sweden)
Yi-Jie Hsieh
2015-11-01
Full Text Available With the fast global climate change, many bridge structures are facing the nature disasters such as earthquakes and floods. The damage of bridges can cause the severe cost of human life and property. The heavy rain brought by the typhoon in July and August in Taiwan causes the bridge scour and makes the damage or collapse for bridges. Since scour is one of the major causes for bridge failure, how to monitor the bridge scour becomes an important task in Taiwan. This paper presents a real-time bridge scour monitoring system based on accelerometer sensors. The presented sensor network consists of a gateway node and under-water sensor nodes with the wired RS-485 communication protocol. The proposed master-slave architecture of the bridge scour monitoring system owns the scalability and flexibility and is setup in the field currently. The experimental results in the field show the presented sensor system can detect the bridge scour effectively with our proposed scour detection algorithm in real time.
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Directory of Open Access Journals (Sweden)
Teguh Febri Sudarma
2013-06-01
Full Text Available Research was aimed to determine: (1 Students’ learning outcomes that was taught with just in time teaching based STAD cooperative learning method and STAD cooperative learning method (2 Students’ outcomes on Physics subject that had high learning activity compared with low learning activity. The research sample was random by raffling four classes to get two classes. The first class taught with just in time teaching based STAD cooperative learning method, while the second class was taught with STAD cooperative learning method. The instrument used was conceptual understanding that had been validated with 7 essay questions. The average gain values of students learning results with just in time teaching based STAD cooperative learning method 0,47 higher than average gain values of students learning results with STAD cooperative learning method. The high learning activity and low learning activity gave different learning results. In this case the average gain values of students learning results with just in time teaching based STAD cooperative learning method 0,48 higher than average gain values of students learning results with STAD cooperative learning method. There was interaction between learning model and learning activity to the physics learning result test in students
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Zhang, Yingying; Wang, Juncheng; Vorontsov, A M; Hou, Guangli; Nikanorova, M N; Wang, Hongliang
2014-01-01
The international marine ecological safety monitoring demonstration station in the Yellow Sea was developed as a collaborative project between China and Russia. It is a nonprofit technical workstation designed as a facility for marine scientific research for public welfare. By undertaking long-term monitoring of the marine environment and automatic data collection, this station will provide valuable information for marine ecological protection and disaster prevention and reduction. The results of some initial research by scientists at the research station into predictive modeling of marine ecological environments and early warning are described in this paper. Marine ecological processes are influenced by many factors including hydrological and meteorological conditions, biological factors, and human activities. Consequently, it is very difficult to incorporate all these influences and their interactions in a deterministic or analysis model. A prediction model integrating a time series prediction approach with neural network nonlinear modeling is proposed for marine ecological parameters. The model explores the natural fluctuations in marine ecological parameters by learning from the latest observed data automatically, and then predicting future values of the parameter. The model is updated in a "rolling" fashion with new observed data from the monitoring station. Prediction experiments results showed that the neural network prediction model based on time series data is effective for marine ecological prediction and can be used for the development of early warning systems.
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Ertuğ, Nurcan; Faydali, Saide
The aims of this study were to determine self-directed learning and time management skills of undergraduate nursing students and to investigate the relationship between the concepts. The use of self-directed learning has increased as an educational strategy in recent years. This descriptive and correlational study was conducted with 383 undergraduate nursing students in Turkey. Data were collected using a sociodemographic questionnaire, the Self-Directed Learning Readiness Scale, and Time Management Questionnaire. Mean scores were as follows: self-directed learning readiness, 159.12 (SD = 20.8); time management, 87.75 (SD = 12.1). A moderate positive correlation was found between self-directed learning readiness and time management values. Time management scores were 78.42 when self-directed learning readiness was ≤149 and 90.82 when self-directed learning readiness was ≥ 150, with a statistically significant difference (p = .000). Level of self-directed learning and academic achievement were higher in students who managed their time well.
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Children benefit differently from night- and day-time sleep in motor learning.
Yan, Jin H
2017-08-01
Motor skill acquisition occurs while practicing (on-line) and when asleep or awake (off-line). However, developmental questions still remain about whether children of various ages benefit similarly or differentially from night- and day-time sleeping. The likely circadian effects (time-of-day) and the possible between-test-interference (order effects) associated with children's off-line motor learning are currently unknown. Therefore, this study examines the contributions of over-night sleeping and mid-day napping to procedural skill learning. One hundred and eight children were instructed to practice a finger sequence task using computer keyboards. After an equivalent 11-h interval in one of the three states (sleep, nap, wakefulness), children performed the same sequence in retention tests and a novel sequence in transfer tests. Changes in the movement time and sequence accuracy were evaluated between ages (6-7, 8-9, 10-11years) during practice, and from skill training to retrievals across three states. Results suggest that night-time sleeping and day-time napping improved the tapping speed, especially for the 6-year-olds. The circadian factor did not affect off-line motor learning in children. The interference between the two counter-balanced retrieval tests was not found for the off-line motor learning. This research offers possible evidence about the age-related motor learning characteristics in children and a potential means for enhancing developmental motor skills. The dynamics between age, experience, memory formation, and the theoretical implications of motor skill acquisition are discussed. Copyright © 2017 Elsevier B.V. All rights reserved.
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Polysynchronous: Dialogic Construction of Time in Online Learning
Oztok, Murat; Wilton, Lesley; Zingaro, Daniel; Mackinnon, Kim; Makos, Alexandra; Phirangee, Krystle; Brett, Clare; Hewitt, Jim
2014-01-01
Online learning has been conceptualized for decades as being delivered in one of two modes: synchronous or asynchronous. Technological determinism falls short in describing the role that the individuals' psychological, social and pedagogical factors play in their perception, experience and understanding of time online. This article explores…
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Neonatal non-contact respiratory monitoring based on real-time infrared thermography
Directory of Open Access Journals (Sweden)
Abbas Abbas K
2011-10-01
Full Text Available Abstract Background Monitoring of vital parameters is an important topic in neonatal daily care. Progress in computational intelligence and medical sensors has facilitated the development of smart bedside monitors that can integrate multiple parameters into a single monitoring system. This paper describes non-contact monitoring of neonatal vital signals based on infrared thermography as a new biomedical engineering application. One signal of clinical interest is the spontaneous respiration rate of the neonate. It will be shown that the respiration rate of neonates can be monitored based on analysis of the anterior naris (nostrils temperature profile associated with the inspiration and expiration phases successively. Objective The aim of this study is to develop and investigate a new non-contact respiration monitoring modality for neonatal intensive care unit (NICU using infrared thermography imaging. This development includes subsequent image processing (region of interest (ROI detection and optimization. Moreover, it includes further optimization of this non-contact respiration monitoring to be considered as physiological measurement inside NICU wards. Results Continuous wavelet transformation based on Debauches wavelet function was applied to detect the breathing signal within an image stream. Respiration was successfully monitored based on a 0.3°C to 0.5°C temperature difference between the inspiration and expiration phases. Conclusions Although this method has been applied to adults before, this is the first time it was used in a newborn infant population inside the neonatal intensive care unit (NICU. The promising results suggest to include this technology into advanced NICU monitors.
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Online gaming for learning optimal team strategies in real time
Hudas, Gregory; Lewis, F. L.; Vamvoudakis, K. G.
2010-04-01
This paper first presents an overall view for dynamical decision-making in teams, both cooperative and competitive. Strategies for team decision problems, including optimal control, zero-sum 2-player games (H-infinity control) and so on are normally solved for off-line by solving associated matrix equations such as the Riccati equation. However, using that approach, players cannot change their objectives online in real time without calling for a completely new off-line solution for the new strategies. Therefore, in this paper we give a method for learning optimal team strategies online in real time as team dynamical play unfolds. In the linear quadratic regulator case, for instance, the method learns the Riccati equation solution online without ever solving the Riccati equation. This allows for truly dynamical team decisions where objective functions can change in real time and the system dynamics can be time-varying.
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Research overview of real-time monitoring system for micro leak of three-dimensional pipe network
Directory of Open Access Journals (Sweden)
Shaofeng WANG
2016-04-01
Full Text Available Aiming at the key technical problems encountered by domestic and foreign scholars in building the real-time monitoring system for the micro leak of three-dimensional pipe networks, the paper classifies the problems into three aspects: 1 in the extraction of fault signal frequency, how to avoid the effect of the mixed echo stack and improve the delay estimation accuracy of the correlation; 2 in network bifurcation structure, how to discern the signal propagation path, and how to locate the leak source; 3 under the uncertainly delay in transmitting and receiving information data, how to ensure the time synchronization accuracy of the real-time monitoring system for the three-dimensional pipe network leakage. Through the comparison of the monitoring technologies for the pipe network leakage at home and abroad, it shows that the acoustic emission sensor network based three-dimensional pipeline leak real-time monitoring has great advantages in detecting the weak leakage of flammable and explosive gas/liquid transportation pipelines.
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Zimmerman, Naomi; Presto, Albert A.; Kumar, Sriniwasa P. N.; Gu, Jason; Hauryliuk, Aliaksei; Robinson, Ellis S.; Robinson, Allen L.; Subramanian, R.
2018-01-01
Low-cost sensing strategies hold the promise of denser air quality monitoring networks, which could significantly improve our understanding of personal air pollution exposure. Additionally, low-cost air quality sensors could be deployed to areas where limited monitoring exists. However, low-cost sensors are frequently sensitive to environmental conditions and pollutant cross-sensitivities, which have historically been poorly addressed by laboratory calibrations, limiting their utility for monitoring. In this study, we investigated different calibration models for the Real-time Affordable Multi-Pollutant (RAMP) sensor package, which measures CO, NO2, O3, and CO2. We explored three methods: (1) laboratory univariate linear regression, (2) empirical multiple linear regression, and (3) machine-learning-based calibration models using random forests (RF). Calibration models were developed for 16-19 RAMP monitors (varied by pollutant) using training and testing windows spanning August 2016 through February 2017 in Pittsburgh, PA, US. The random forest models matched (CO) or significantly outperformed (NO2, CO2, O3) the other calibration models, and their accuracy and precision were robust over time for testing windows of up to 16 weeks. Following calibration, average mean absolute error on the testing data set from the random forest models was 38 ppb for CO (14 % relative error), 10 ppm for CO2 (2 % relative error), 3.5 ppb for NO2 (29 % relative error), and 3.4 ppb for O3 (15 % relative error), and Pearson r versus the reference monitors exceeded 0.8 for most units. Model performance is explored in detail, including a quantification of model variable importance, accuracy across different concentration ranges, and performance in a range of monitoring contexts including the National Ambient Air Quality Standards (NAAQS) and the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. A key strength of the RF approach is that
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Performance results of cooperating expert systems in a distributed real-time monitoring system
Schwuttke, U. M.; Veregge, J. R.; Quan, A. G.
1994-01-01
There are numerous definitions for real-time systems, the most stringent of which involve guaranteeing correct system response within a domain-dependent or situationally defined period of time. For applications such as diagnosis, in which the time required to produce a solution can be non-deterministic, this requirement poses a unique set of challenges in dynamic modification of solution strategy that conforms with maximum possible latencies. However, another definition of real time is relevant in the case of monitoring systems where failure to supply a response in the proper (and often infinitesimal) amount of time allowed does not make the solution less useful (or, in the extreme example of a monitoring system responsible for detecting and deflecting enemy missiles, completely irrelevant). This more casual definition involves responding to data at the same rate at which it is produced, and is more appropriate for monitoring applications with softer real-time constraints, such as interplanetary exploration, which results in massive quantities of data transmitted at the speed of light for a number of hours before it even reaches the monitoring system. The latter definition of real time has been applied to the MARVEL system for automated monitoring and diagnosis of spacecraft telemetry. An early version of this system has been in continuous operational use since it was first deployed in 1989 for the Voyager encounter with Neptune. This system remained under incremental development until 1991 and has been under routine maintenance in operations since then, while continuing to serve as an artificial intelligence (AI) testbed in the laboratory. The system architecture has been designed to facilitate concurrent and cooperative processing by multiple diagnostic expert systems in a hierarchical organization. The diagnostic modules adhere to concepts of data-driven reasoning, constrained but complete nonoverlapping domains, metaknowledge of global consequences of anomalous
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Effects of Business School Student's Study Time on the Learning Process
Tetteh, Godson Ayertei
2016-01-01
Purpose: This paper aims to clarify the relationship between the student's study time and the learning process in the higher education system by adapting the total quality management (TQM) principles-process approach. Contrary to Deming's (1982) constancy of purpose to improve the learning process, some students in higher education postpone their…
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Finite time convergent learning law for continuous neural networks.
Chairez, Isaac
2014-02-01
This paper addresses the design of a discontinuous finite time convergent learning law for neural networks with continuous dynamics. The neural network was used here to obtain a non-parametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties was the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on discontinuous algorithms was used to adjust the weights of the neural network. The adaptive algorithm was derived by means of a non-standard Lyapunov function that is lower semi-continuous and differentiable in almost the whole space. A compensator term was included in the identifier to reject some specific perturbations using a nonlinear robust algorithm. Two numerical examples demonstrated the improvements achieved by the learning algorithm introduced in this paper compared to classical schemes with continuous learning methods. The first one dealt with a benchmark problem used in the paper to explain how the discontinuous learning law works. The second one used the methane production model to show the benefits in engineering applications of the learning law proposed in this paper. Copyright © 2013 Elsevier Ltd. All rights reserved.
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Directory of Open Access Journals (Sweden)
Jude Adekunle Adeleke
2017-04-01
Full Text Available Proactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest. While these applications provide quick response to situations, to minimize their unwanted effects, research efforts are still necessary to provide techniques that can anticipate the future to support proactive control, such that unwanted situations can be averted altogether. This study integrates a statistical machine learning based predictive model in a Semantic Sensor Web using stream reasoning. The approach is evaluated in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM 2 . 5 pollution situations is integrated into the proactive monitoring and control framework. Results show that the proposed approach can effectively predict short term PM 2 . 5 pollution situations: precision of up to 0.86 and sensitivity of up to 0.85 is achieved over half hour prediction horizons, making it possible for the system to warn occupants or even to autonomously avert the predicted pollution situations within the context of Semantic Sensor Web.
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Adeleke, Jude Adekunle; Moodley, Deshendran; Rens, Gavin; Adewumi, Aderemi Oluyinka
2017-04-09
Proactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest. While these applications provide quick response to situations, to minimize their unwanted effects, research efforts are still necessary to provide techniques that can anticipate the future to support proactive control, such that unwanted situations can be averted altogether. This study integrates a statistical machine learning based predictive model in a Semantic Sensor Web using stream reasoning. The approach is evaluated in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM 2 . 5 pollution situations is integrated into the proactive monitoring and control framework. Results show that the proposed approach can effectively predict short term PM 2 . 5 pollution situations: precision of up to 0.86 and sensitivity of up to 0.85 is achieved over half hour prediction horizons, making it possible for the system to warn occupants or even to autonomously avert the predicted pollution situations within the context of Semantic Sensor Web.
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Energy Technology Data Exchange (ETDEWEB)
Mi, J.; Tan, Y.; Zhang, W.
2011-03-28
For years suffering of Booster Injection Kicker transistor bank driver regulator troubleshooting, a new real time monitor system has been developed. A simple and floating circuit has been designed and tested. This circuit monitor system can monitor the driver regulator power limit resistor status in real time and warn machine operator if the power limit resistor changes values. This paper will mainly introduce the power supply and the new designed monitoring system. This real time resistor monitor circuit shows a useful method to monitor some critical parts in the booster pulse power supply. After two years accelerator operation, it shows that this monitor works well. Previously, we spent a lot of time in booster machine trouble shooting. We will reinstall all 4 PCB into Euro Card Standard Chassis when the power supply system will be updated.
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Ubiquitous health monitoring and real-time cardiac arrhythmias detection: a case study.
Li, Jian; Zhou, Haiying; Zuo, Decheng; Hou, Kun-Mean; De Vaulx, Christophe
2014-01-01
As the symptoms and signs of heart diseases that cause sudden cardiac death, cardiac arrhythmia has attracted great attention. Due to limitations in time and space, traditional approaches to cardiac arrhythmias detection fail to provide a real-time continuous monitoring and testing service applicable in different environmental conditions. Integrated with the latest technologies in ECG (electrocardiograph) analysis and medical care, the pervasive computing technology makes possible the ubiquitous cardiac care services, and thus brings about new technical challenges, especially in the formation of cardiac care architecture and realization of the real-time automatic ECG detection algorithm dedicated to care devices. In this paper, a ubiquitous cardiac care prototype system is presented with its architecture framework well elaborated. This prototype system has been tested and evaluated in all the clinical-/home-/outdoor-care modes with a satisfactory performance in providing real-time continuous cardiac arrhythmias monitoring service unlimitedly adaptable in time and space.
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A real-time monitoring system for night glare protection
Ma, Jun; Ni, Xuxiang
2010-11-01
When capturing a dark scene with a high bright object, the monitoring camera will be saturated in some regions and the details will be lost in and near these saturated regions because of the glare vision. This work aims at developing a real-time night monitoring system. The system can decrease the influence of the glare vision and gain more details from the ordinary camera when exposing a high-contrast scene like a car with its headlight on during night. The system is made up of spatial light modulator (The liquid crystal on silicon: LCoS), image sensor (CCD), imaging lens and DSP. LCoS, a reflective liquid crystal, can modular the intensity of reflective light at every pixel as a digital device. Through modulation function of LCoS, CCD is exposed with sub-region. With the control of DSP, the light intensity is decreased to minimum in the glare regions, and the light intensity is negative feedback modulated based on PID theory in other regions. So that more details of the object will be imaging on CCD and the glare protection of monitoring system is achieved. In experiments, the feedback is controlled by the embedded system based on TI DM642. Experiments shows: this feedback modulation method not only reduces the glare vision to improve image quality, but also enhances the dynamic range of image. The high-quality and high dynamic range image is real-time captured at 30hz. The modulation depth of LCoS determines how strong the glare can be removed.
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FPGA implementation of a hybrid on-line process monitoring in PC based real-time systems
Directory of Open Access Journals (Sweden)
Jovanović Bojan
2011-01-01
Full Text Available This paper presents one way of FPGA implementation of hybrid (hardware-software based on-line process monitoring in Real-Time systems (RTS. The reasons for RTS monitoring are presented at the beginning. The summary of different RTS monitoring approaches along with its advantages and drawbacks are also exposed. Finally, monitoring module is described in details. Also, FPGA implementation results and some useful monitoring system applications are mentioned.
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Hybrid monitoring scheme for end-to-end performance enhancement of multicast-based real-time media
Park, Ju-Won; Kim, JongWon
2004-10-01
As real-time media applications based on IP multicast networks spread widely, end-to-end QoS (quality of service) provisioning for these applications have become very important. To guarantee the end-to-end QoS of multi-party media applications, it is essential to monitor the time-varying status of both network metrics (i.e., delay, jitter and loss) and system metrics (i.e., CPU and memory utilization). In this paper, targeting the multicast-enabled AG (Access Grid) a next-generation group collaboration tool based on multi-party media services, the applicability of hybrid monitoring scheme that combines active and passive monitoring is investigated. The active monitoring measures network-layer metrics (i.e., network condition) with probe packets while the passive monitoring checks both application-layer metrics (i.e., user traffic condition by analyzing RTCP packets) and system metrics. By comparing these hybrid results, we attempt to pinpoint the causes of performance degradation and explore corresponding reactions to improve the end-to-end performance. The experimental results show that the proposed hybrid monitoring can provide useful information to coordinate the performance improvement of multi-party real-time media applications.
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Development of a real-time extremity dose monitor for personnel in interventional radiology
International Nuclear Information System (INIS)
Ban, Nobuhiko; Kusama, Tomoko; Adachi, Akiko
2000-01-01
Protection of personnel in interventional radiology is one of the most important issues of radiological protection in medicine. Fluoroscopically guided interventional procedures require the operation near X-ray beam, which brings a considerable hand exposure to the operators. For the purpose of effectual control of their extremity doses, we have developed a real-time extremity dose monitor which is worn on a strap around the wrist. The monitor consists of a silicon semiconductor detector, thin lithium battery and a waterproof frame with a four-digit LED display. Experiment was carried out to examine a response of the monitor to diagnostic X-rays. A practical test was also performed to evaluate usability in the actual interventional procedures. In the experiment, the extremity dose monitor was placed on an arm phantom and exposed to diagnostic X-rays. Readings of the monitor were compared to those of Capintec PS-033 shallow chamber. The monitor was highly sensitive to diagnostic X-rays. It showed a linear response down to doses of a few tens of microsieverts. For high dose-rate exposure, however, a slight decrease in the response was observed, about 10% of counting loss for 80 kV, 40 mA X-ray at one meter from the focus. With regard to energy dependence, variation was within 20% for 60 to 100 kV X-rays. The monitor showed a good angular response in general, except lateral geometry facing the far side from a detector center. In the practical test, hand exposures of medical staff were measured with the extremity dose monitor. They were also asked to fill in a questionnaire regarding size and weight of the monitor, clarity of the display and usefulness. The subjects consisted of physicians, technicians and nurses who engaged in angiography, PTCD, CT-biopsy, barium enema and so on. The readings of the monitor were less than 1 mSv in most cases while 93 mSv was recorded in an extreme case due to direct-beam exposure. In some cases, TLD rings were used together with the
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Development of a real-time extremity dose monitor for personnel in interventional radiology
Energy Technology Data Exchange (ETDEWEB)
Ban, Nobuhiko; Kusama, Tomoko [Oita University of Nursing and Health Sciences, Oita (Japan); Adachi, Akiko [Oita Medical University, Oita (JP)] [and others
2000-05-01
Protection of personnel in interventional radiology is one of the most important issues of radiological protection in medicine. Fluoroscopically guided interventional procedures require the operation near X-ray beam, which brings a considerable hand exposure to the operators. For the purpose of effectual control of their extremity doses, we have developed a real-time extremity dose monitor which is worn on a strap around the wrist. The monitor consists of a silicon semiconductor detector, thin lithium battery and a waterproof frame with a four-digit LED display. Experiment was carried out to examine a response of the monitor to diagnostic X-rays. A practical test was also performed to evaluate usability in the actual interventional procedures. In the experiment, the extremity dose monitor was placed on an arm phantom and exposed to diagnostic X-rays. Readings of the monitor were compared to those of Capintec PS-033 shallow chamber. The monitor was highly sensitive to diagnostic X-rays. It showed a linear response down to doses of a few tens of microsieverts. For high dose-rate exposure, however, a slight decrease in the response was observed, about 10% of counting loss for 80 kV, 40 mA X-ray at one meter from the focus. With regard to energy dependence, variation was within 20% for 60 to 100 kV X-rays. The monitor showed a good angular response in general, except lateral geometry facing the far side from a detector center. In the practical test, hand exposures of medical staff were measured with the extremity dose monitor. They were also asked to fill in a questionnaire regarding size and weight of the monitor, clarity of the display and usefulness. The subjects consisted of physicians, technicians and nurses who engaged in angiography, PTCD, CT-biopsy, barium enema and so on. The readings of the monitor were less than 1 mSv in most cases while 93 mSv was recorded in an extreme case due to direct-beam exposure. In some cases, TLD rings were used together with the
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REVIEW OF MONITORING TOOLS FOR E-LEARNING PLATFORMS
Ali Alowayr; Atta Badii
2014-01-01
The advancement of e-learning technologies has made it viable for developments in education and technology to be combined in order to fulfil educational needs worldwide. E-learning consists of informal learning approaches and emerging technologies to support the delivery of learning skills, materials, collaboration and knowledge sharing. E-learning is a holistic approach that covers a wide range of courses, technologies and infrastructures to provide an effective learning environment. The...
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Kean, Ang Chooi; Embi, Mohamed Amin; Yunus, Melor Md
2012-01-01
The paper examines the influence of incorporating information and communication technology (ICT) tools to help learners to promote learning awareness and self-monitoring skills. An open-ended online questionnaire survey was administered to 15 course participants at the conclusion of the course. The data were analysed on the basis of the percentage…
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Energy Technology Data Exchange (ETDEWEB)
Varley, Adam, E-mail: a.l.varley@stir.ac.uk [Department of Biological and Environmental Sciences, University of Stirling, Stirling FK9 4LA (United Kingdom); Tyler, Andrew, E-mail: a.n.tyler@stir.ac.uk [Department of Biological and Environmental Sciences, University of Stirling, Stirling FK9 4LA (United Kingdom); Smith, Leslie, E-mail: l.s.smith@cs.stir.ac.uk [Department of Computing Science and Mathematics, University of Stirling, Stirling FK9 4LA (United Kingdom); Dale, Paul, E-mail: paul.dale@sepa.org.uk [Scottish Environmental Protection Agency, Radioactive Substances, Strathallan House, Castle Business Park, Stirling FK9 4TZ (United Kingdom); Davies, Mike, E-mail: Mike.Davies@nuvia.co.uk [Nuvia Limited, The Library, Eight Street, Harwell Oxford, Didcot, Oxfordshire OX11 0RL (United Kingdom)
2015-07-15
The extensive use of radium during the 20th century for industrial, military and pharmaceutical purposes has led to a large number of contaminated legacy sites across Europe and North America. Sites that pose a high risk to the general public can present expensive and long-term remediation projects. Often the most pragmatic remediation approach is through routine monitoring operating gamma-ray detectors to identify, in real-time, the signal from the most hazardous heterogeneous contamination (hot particles); thus facilitating their removal and safe disposal. However, current detection systems do not fully utilise all spectral information resulting in low detection rates and ultimately an increased risk to the human health. The aim of this study was to establish an optimised detector-algorithm combination. To achieve this, field data was collected using two handheld detectors (sodium iodide and lanthanum bromide) and a number of Monte Carlo simulated hot particles were randomly injected into the field data. This allowed for the detection rate of conventional deterministic (gross counts) and machine learning (neural networks and support vector machines) algorithms to be assessed. The results demonstrated that a Neural Network operated on a sodium iodide detector provided the best detection capability. Compared to deterministic approaches, this optimised detection system could detect a hot particle on average 10 cm deeper into the soil column or with half of the activity at the same depth. It was also found that noise presented by internal contamination restricted lanthanum bromide for this application. - Highlights: • Land contaminated with radium is hazardous to human health. • Routine monitoring permits identification and removal of radioactive hot particles. • Current alarm algorithms do not provide reliable hot particle detection. • Spectral processing using Machine Learning significantly improves detection.
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International Nuclear Information System (INIS)
Varley, Adam; Tyler, Andrew; Smith, Leslie; Dale, Paul; Davies, Mike
2015-01-01
The extensive use of radium during the 20th century for industrial, military and pharmaceutical purposes has led to a large number of contaminated legacy sites across Europe and North America. Sites that pose a high risk to the general public can present expensive and long-term remediation projects. Often the most pragmatic remediation approach is through routine monitoring operating gamma-ray detectors to identify, in real-time, the signal from the most hazardous heterogeneous contamination (hot particles); thus facilitating their removal and safe disposal. However, current detection systems do not fully utilise all spectral information resulting in low detection rates and ultimately an increased risk to the human health. The aim of this study was to establish an optimised detector-algorithm combination. To achieve this, field data was collected using two handheld detectors (sodium iodide and lanthanum bromide) and a number of Monte Carlo simulated hot particles were randomly injected into the field data. This allowed for the detection rate of conventional deterministic (gross counts) and machine learning (neural networks and support vector machines) algorithms to be assessed. The results demonstrated that a Neural Network operated on a sodium iodide detector provided the best detection capability. Compared to deterministic approaches, this optimised detection system could detect a hot particle on average 10 cm deeper into the soil column or with half of the activity at the same depth. It was also found that noise presented by internal contamination restricted lanthanum bromide for this application. - Highlights: • Land contaminated with radium is hazardous to human health. • Routine monitoring permits identification and removal of radioactive hot particles. • Current alarm algorithms do not provide reliable hot particle detection. • Spectral processing using Machine Learning significantly improves detection
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Eide, Ingvar; Westad, Frank
2018-01-01
A pilot study demonstrating real-time environmental monitoring with automated multivariate analysis of multi-sensor data submitted online has been performed at the cabled LoVe Ocean Observatory located at 258 m depth 20 km off the coast of Lofoten-Vesterålen, Norway. The major purpose was efficient monitoring of many variables simultaneously and early detection of changes and time-trends in the overall response pattern before changes were evident in individual variables. The pilot study was performed with 12 sensors from May 16 to August 31, 2015. The sensors provided data for chlorophyll, turbidity, conductivity, temperature (three sensors), salinity (calculated from temperature and conductivity), biomass at three different depth intervals (5-50, 50-120, 120-250 m), and current speed measured in two directions (east and north) using two sensors covering different depths with overlap. A total of 88 variables were monitored, 78 from the two current speed sensors. The time-resolution varied, thus the data had to be aligned to a common time resolution. After alignment, the data were interpreted using principal component analysis (PCA). Initially, a calibration model was established using data from May 16 to July 31. The data on current speed from two sensors were subject to two separate PCA models and the score vectors from these two models were combined with the other 10 variables in a multi-block PCA model. The observations from August were projected on the calibration model consecutively one at a time and the result was visualized in a score plot. Automated PCA of multi-sensor data submitted online is illustrated with an attached time-lapse video covering the relative short time period used in the pilot study. Methods for statistical validation, and warning and alarm limits are described. Redundant sensors enable sensor diagnostics and quality assurance. In a future perspective, the concept may be used in integrated environmental monitoring.
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Teasdale, Rachel; Kraft, Katrien van der Hoeven; Poland, Michael P.
2015-01-01
Training non-scientists in the use of volcano-monitoring data is critical preparation in advance of a volcanic crisis, but it is currently unclear which methods are most effective for improving the content-knowledge of non-scientists to help bridge communications between volcano experts and non-experts. We measured knowledge gains for beginning-(introductory-level students) and novice-level learners (students with a basic understanding of geologic concepts) engaged in the Volcanoes Exploration Program: Pu‘u ‘Ō‘ō (VEPP) “Monday Morning Meeting at the Hawaiian Volcano Observatory” classroom activity that incorporates authentic Global Positioning System (GPS), tilt, seismic, and webcam data from the Pu‘u ‘Ō‘ō eruptive vent on Kīlauea Volcano, Hawai‘i (NAGT website, 2010), as a means of exploring methods for effectively advancing non-expert understanding of volcano monitoring. Learner groups consisted of students in introductory and upper-division college geology courses at two different institutions. Changes in their content knowledge and confidence in the use of data were assessed before and after the activity using multiple-choice and open-ended questions. Learning assessments demonstrated that students who took part in the exercise increased their understanding of volcano-monitoring practices and implications, with beginners reaching a novice stage, and novices reaching an advanced level (akin to students who have completed an upper-division university volcanology class). Additionally, participants gained stronger confidence in their ability to understand the data. These findings indicate that training modules like the VEPP: Monday Morning Meeting classroom activity that are designed to prepare non-experts for responding to volcanic activity and interacting with volcano scientists should introduce real monitoring data prior to proceeding with role-paying scenarios that are commonly used in such courses. The learning gains from the combined
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TEXPLORE temporal difference reinforcement learning for robots and time-constrained domains
Hester, Todd
2013-01-01
This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. Robots have the potential to solve many problems in society, because of their ability to work in dangerous places doing necessary jobs that no one wants or is able to do. One barrier to their widespread deployment is that they are mainly limited to tasks where it is possible to hand-program behaviors for every situation that may be encountered. For robots to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes and could solve the problems of learning and adaptation on robots. This book identifies four key challenges that must be addressed for an RL algorithm to be practical for robotic control tasks. These RL for Robotics Challenges are: 1) it must learn in very few samples; 2) it must learn in domains with continuou...
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Artificial neural network with self-organizing mapping for reactor stability monitoring
International Nuclear Information System (INIS)
Okumura, Motofumi; Tsuji, Masashi; Shimazu, Yoichiro
2009-01-01
In boiling water reactor (BWR) stability monitoring, damping ratio has been used as a stability index. A method for estimating the damping ratio by applying Principal Component Analysis (PCA) to neutron detector signals measured with local power range monitors (LPRMs) had been developed; in this method, measured fluctuating signal is decomposed into some independent components and the signal components directly related to stability are extracted among them to determine the damping ratio. For online monitoring, it is necessary to select stability related signal components efficiently. The self-organizing map (SOM) is one of the artificial neural networks (ANNs) and has the characteristics such that online learning is possible without supervised learning within a relatively short time. In the present study, the SOM was applied to extract the relevant signal components more quickly and more accurately, and the availability was confirmed through the feasibility study. For realizing online stability monitoring only with ANNs, another type of ANN that performs online processing of PCA was combined with SOM. And stability monitoring performance was investigated. (author)
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GSM based real time remote radiation monitoring and mapping system
International Nuclear Information System (INIS)
Dodiya, Kamal; Gupta, Ashutosh; Padmanabhan, N.; Chaudhury, Probal; Pradeepkumar, K.S.
2014-01-01
Mobile Radiological Impact Assessment Laboratory (M-RIAL) has been developed in Radiation Safety Systems Division, Bhabha Atomic Research Centre for carrying out assessment of radioactive contamination following a nuclear or radiological emergency in a nuclear facility or in public domain. During such situations a large area is to be monitored for radiological impact assessment and availability of the monitored data in real-time to a control centre is a great advantage for the decision makers. Development and application of such a system has been described in this paper. The system can transmit real-time radiological data, acquired by the universal counting system of M-RIAL and tagged with positional information, wirelessly to an Emergency Response Centre (ERC) using Global System for Mobile (GSM) communication. The radiological profile of the affected area is then superimposed on Geographical Information System (GIS) at the ERC and which can be used for the generation of radiological impact maps for use as decision support
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Time-series modeling: applications to long-term finfish monitoring data
International Nuclear Information System (INIS)
Bireley, L.E.
1985-01-01
The growing concern and awareness that developed during the 1970's over the effects that industry had on the environment caused the electric utility industry in particular to develop monitoring programs. These programs generate long-term series of data that are not very amenable to classical normal-theory statistical analysis. The monitoring data collected from three finfish programs (impingement, trawl and seine) at the Millstone Nuclear Power Station were typical of such series and thus were used to develop methodology that used the full extent of the information in the series. The basis of the methodology was classic Box-Jenkins time-series modeling; however, the models also included deterministic components that involved flow, season and time as predictor variables. Time entered into the models as harmonic regression terms. Of the 32 models fitted to finfish catch data, 19 were found to account for more than 70% of the historical variation. The models were than used to forecast finfish catches a year in advance and comparisons were made to actual data. Usually the confidence intervals associated with the forecasts encompassed most of the observed data. The technique can provide the basis for intervention analysis in future impact assessments
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Utility machinery vibration monitoring guide: Final report
International Nuclear Information System (INIS)
Moore, T.T.; Thomas, C.C.
1987-08-01
Section I of this guide presents a methodology for developing machinery vibration monitoring programs specifically designed for application within the utility industry. The methodology is designed to enhance a monitoring program and can be used at the outset of program development or as a reference after programs have been started. Section I evaluates all aspects of the monitoring program, including Objectives and Goals, Information Type, Timing and Format, Data Analysis, Data Acquisition, Measurement and Transducer Selection, Personnel and Organization, Program Instrumentation, Program Costs, Program Justification, and Implementation of a Monitoring Program. The methodology is then applied to two host utility plants in Section II, which contains the monitoring programs developed by Gulf States Utilities and Philadelphia Electric Company using this guide. Section III contains the histories of several different types of existing utility monitoring programs. Some of the lessons learned, including the recommendations of these ''mature'' programs for persons starting new programs, are included
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Problem based learning: the effect of real time data on the website to student independence
Setyowidodo, I.; Pramesti, Y. S.; Handayani, A. D.
2018-05-01
Learning science developed as an integrative science rather than disciplinary education, the reality of the nation character development has not been able to form a more creative and independent Indonesian man. Problem Based Learning based on real time data in the website is a learning method focuses on developing high-level thinking skills in problem-oriented situations by integrating technology in learning. The essence of this study is the presentation of authentic problems in the real time data situation in the website. The purpose of this research is to develop student independence through Problem Based Learning based on real time data in website. The type of this research is development research with implementation using purposive sampling technique. Based on the study there is an increase in student self-reliance, where the students in very high category is 47% and in the high category is 53%. This learning method can be said to be effective in improving students learning independence in problem-oriented situations.
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Monitoring external beam radiotherapy using real-time beam visualization
Energy Technology Data Exchange (ETDEWEB)
Jenkins, Cesare H. [Department of Mechanical Engineering and Department of Radiation Oncology, Stanford University, Stanford, California 94305 (United States); Naczynski, Dominik J.; Yu, Shu-Jung S.; Xing, Lei, E-mail: lei@stanford.edu [Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California 94305 (United States)
2015-01-15
Purpose: To characterize the performance of a novel radiation therapy monitoring technique that utilizes a flexible scintillating film, common optical detectors, and image processing algorithms for real-time beam visualization (RT-BV). Methods: Scintillating films were formed by mixing Gd{sub 2}O{sub 2}S:Tb (GOS) with silicone and casting the mixture at room temperature. The films were placed in the path of therapeutic beams generated by medical linear accelerators (LINAC). The emitted light was subsequently captured using a CMOS digital camera. Image processing algorithms were used to extract the intensity, shape, and location of the radiation field at various beam energies, dose rates, and collimator locations. The measurement results were compared with known collimator settings to validate the performance of the imaging system. Results: The RT-BV system achieved a sufficient contrast-to-noise ratio to enable real-time monitoring of the LINAC beam at 20 fps with normal ambient lighting in the LINAC room. The RT-BV system successfully identified collimator movements with sub-millimeter resolution. Conclusions: The RT-BV system is capable of localizing radiation therapy beams with sub-millimeter precision and tracking beam movement at video-rate exposure.
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Directory of Open Access Journals (Sweden)
Vera V. Lyubchenko
2014-12-01
Full Text Available The adoption of Law of Ukraine “On Higher Education” (2014 involves the increase in students’ self-learning activity part in the curriculum. Therefore the self-learning activities’ arrangement in a way augmenting the result quality becomes a top priority task. This research objective consists in elaborating the scenario for organization of the students’ qualitative self-study, based on blended learning models. The author analyzes four blended learning models: the rotation model, flex-model, self-blend model and online driver model, and gives examples of their use. It is shown that first two models are the most suitable for full-time students. A general scenario for the use of blended learning models is described. Although the use of blended learning models causes several difficulties, it also essentially contributes into students’ self-study monitoring and control support.
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Fiber Bragg grating sensors for real-time monitoring of evacuation process
Guru Prasad, A. S.; Hegde, Gopalkrishna M.; Asokan, S.
2010-03-01
Fiber bragg grating (FBG) sensors have been widely used for number of sensing applications like temperature, pressure, acousto-ultrasonic, static and dynamic strain, refractive index change measurements and so on. Present work demonstrates the use of FBG sensors in in-situ measurement of vacuum process with simultaneous leak detection capability. Experiments were conducted in a bell jar vacuum chamber facilitated with conventional Pirani gauge for vacuum measurement. Three different experiments have been conducted to validate the performance of FBG sensor in monitoring vacuum creating process and air bleeding. The preliminary results of FBG sensors in vacuum monitoring have been compared with that of commercial Pirani gauge sensor. This novel technique offers a simple alternative to conventional method for real time monitoring of evacuation process. Proposed FBG based vacuum sensor has potential applications in vacuum systems involving hazardous environment such as chemical and gas plants, automobile industries, aeronautical establishments and leak monitoring in process industries, where the electrical or MEMS based sensors are prone to explosion and corrosion.
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Dedicated real-time monitoring system for health care using ZigBee.
Alwan, Omar S; Prahald Rao, K
2017-08-01
Real-time monitoring systems (RTMSs) have drawn considerable attentions in the last decade. Several commercial versions of RTMS for patient monitoring are available which are used by health care professionals. Though they are working satisfactorily on various communication protocols, their range, power consumption, data rate and cost are really bothered. In this study, the authors present an efficient embedded system based wireless health care monitoring system using ZigBee. Their system has a capability to transmit the data between two embedded systems through two transceivers over a long range. In this, wireless transmission has been applied through two categories. The first part which contains Arduino with ZigBee will send the signals to the second device, which contains Raspberry with ZigBee. The second device will measure the patient data and send it to the first device through ZigBee transceiver. The designed system is demonstrated on volunteers to measure the body temperature which is clinically important to monitor and diagnose for fever in the patients.
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Design of a real-time tax-data monitoring intelligent card system
Gu, Yajun; Bi, Guotang; Chen, Liwei; Wang, Zhiyuan
2009-07-01
To solve the current problem of low efficiency of domestic Oil Station's information management, Oil Station's realtime tax data monitoring system has been developed to automatically access tax data of Oil pumping machines, realizing Oil-pumping machines' real-time automatic data collection, displaying and saving. The monitoring system uses the noncontact intelligent card or network to directly collect data which can not be artificially modified and so seals the loopholes and improves the tax collection's automatic level. It can perform real-time collection and management of the Oil Station information, and find the problem promptly, achieves the automatic management for the entire process covering Oil sales accounting and reporting. It can also perform remote query to the Oil Station's operation data. This system has broad application future and economic value.
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Artificial neural network with self-organizing mapping for reactor stability monitoring
International Nuclear Information System (INIS)
Okumura, Motofumi; Tsuji, Masashi; Shimazu, Yoichiro; Narabayashi, Tadashi
2008-01-01
In BWR stability monitoring damping ratio has been used as a stability index. A method for estimating the damping ratio by applying Principal Component Analysis (PCA) to neutron detector signals measured with local power range monitors (LPRMs) had been developed; In this method, measured fluctuating signal is decomposed into some independent components and the signal component directly related to stability is extracted among them to determine the damping ratio. For online monitoring, it is necessary to select stability related signal component efficiently. The self-organizing map (SOM) is one of the artificial neural networks and has the characteristics such that online learning is possible without supervised learning within a relatively short time. In the present study, the SOM was applied to extract the relevant signal component more quickly and more accurately, and the availability was confirmed through the feasibility study. (author)
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Touchscreen Facilitates Young Children’s Transfer of Learning to Tell Time
Directory of Open Access Journals (Sweden)
Fuxing Wang
2016-11-01
Full Text Available Young children are devoting increasing time to playing on handheld touchscreen devices (e.g., iPads. Though thousands of touchscreen apps are claimed to be educational, there is a lack of sufficient evidence examining the impact of touchscreens on children’s learning outcomes. In the present study, the two questions we focused on were (a whether using a touchscreen was helpful in teaching children to tell time, and (b to what extent young children could transfer what they had learned on the touchscreen to other media. A pre- and posttest design was adopted. After learning to read the time on the iPad touchscreen for 10 minutes, three groups of 5- to 6-year-old children (N = 65 were respectively tested with an iPad touchscreen, a toy clock or a drawing of a clock on paper. The results revealed that posttest scores in the iPad touchscreen test group were significantly higher than those at pretest, indicating that the touchscreen itself could provide support for young children’s learning. Similarly, regardless of being tested with a toy clock or paper drawing, children’s posttest performance was also better than pretest, suggesting that children could transfer what they had learned on an iPad touchscreen to other media. However, comparison among groups showed that children tested with the paper drawing underperformed those tested with the other two media. The theoretical and practical implications of the results, as well as limitations of the present study, are discussed.
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International Nuclear Information System (INIS)
Fontanini, Horacio; Galdoz, Erwin
1989-01-01
A real time monitoring system is described. It was initially developed to be used as a man-machine interface between a basic principles simulator of the Embalse Nuclear Power Plant and the operators. This simulator is under construction at the Bariloche Atomic Center's Process Control Division. Due to great design flexibility, this system can also be used in real plants. The system is designed to be run on a PC XT or AT personal computer with high resolution graphics capabilities. Three interrelated programs compose the system: 1) Graphics Editor, to build static image to be used as a reference frame where to show dynamically updated data. 2) Data acquisition and storage module. It is a memory resident module to acquire and store data in background. Data can be acquired and stored without interference with the operating system, via serial port or through analog-to-digital converter attached to the personal computer. 3) Display module. It shows the acquired data according to commands received from configuration files prepared by the operator. (Author) [es
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The position of teaching materials on the monitor and its effect on the e-learning success
Directory of Open Access Journals (Sweden)
Janko Žufić
2011-06-01
Full Text Available Normal 0 21 false false false MicrosoftInternetExplorer4 Normal 0 21 false false false MicrosoftInternetExplorer4 There are various elements in designing e-teaching materials that could have an impact in raising the efficiency of e-learning. This paper is based on the experiments aiming to investigate whether there are certain positions on the monitor in which students are able to better perceive and/or remember e-teaching materials. Our research was carried out at the Juraj Dobrila University of Pula. Participants were first year students attending the teacher education programme (aged 19.5 – 20.5. The research design included two pre-experimental groups and one experimental group. The monitor was virtually divided into 24 zones. Students read the teaching material displayed on the screen; in each reading four texts in different positions were used. The relative ease/difficulty of remembering the text was taken into account by introducing different ponders to each text. Regarding the memory efficiency, our results show statistically significant differences between certain screen positions (these differences ranged from +29.6% to -42.6% from the average result. Keywords: efficient e-learning, text position on the screen
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Directory of Open Access Journals (Sweden)
Wen-Jye Shyr
2013-02-01
Full Text Available This study develops a novel method for learning mechatronics using remote monitoring and control, based on a programmable logic controller (PLC and WebAccess. A mechatronics module, a Web-CAM and a PLC were integrated with WebAccess software to organize a remote laboratory. The proposed system enables users to access the Internet for remote monitoring and control of the mechatronics module via a web browser, thereby enhancing work flexibility by enabling personnel to control mechatronics equipment from a remote location. Mechatronics control and long-distance monitoring were realized by establishing communication between the PLC and WebAccess. Analytical results indicate that the proposed system is feasible. The suitability of this system is demonstrated in the department of industrial education and technology at National Changhua University of Education, Taiwan. Preliminary evaluation of the system was encouraging and has shown that it has achieved success in helping students understand concepts and master remote monitoring and control techniques.
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Real-time ischemic condition monitoring in normoglycemic and hyperglycemic rats
International Nuclear Information System (INIS)
Choi, Samjin; Kang, Sung Wook; Lee, Gi-Ja; Chae, Su-Jin; Park, Hun-Kuk; Choi, Seok Keun; Chung, Joo-Ho
2010-01-01
An increase in excitotoxic amino acid glutamate (GLU) concentration associated with neuronal damage might be the cause of the ischemic damage observed in stroke patients suffering from hyperglycemia. However, the effect has never been investigated by real-time in vivo monitoring. Therefore, this study examined the effects of the functional responses of ischemia-evoked electroencephalography (EEG), cerebral blood flow (%CBF) and ΔGLU in hyperglycemia through real-time in vivo monitoring. Five Sprague-Dawley rats were treated with streptozocin (hyperglycemia) and five normal rats were used as the controls. Global ischemia was induced using an 11-vessel occlusion model. The experimental protocols consisting of 10 min pre-ischemic, 10 min ischemic and 40 min reperfusion periods were applied to both groups. Under these conditions, the responses of the ischemia-evoked EEG, %CBF and ΔGLU were monitored in real time. The EEG showed flat patterns during ischemia followed by poor recovery during reperfusion. The peak reperfusion %CBF was decreased significantly in the hyperglycemia group compared to the control group (p < 0.05, n = 5). The extracellular ΔGLU releases increased significantly during ischemia (p < 0.0001, n = 5) and reperfusion (p < 0.001, n = 5) in the hyperglycemia group compared to the control group. The decrease in reperfusion %CBF during short-term hyperglycemia might be related to the increased plasma osmolality, decreased adenosine levels and swollen endothelial cells with decreased vascular luminal diameters under hyperglycemic conditions. And, the increase in ΔGLU during short-term hyperglycemia might be related to the neurotoxic effects of the high extracellular concentrations of ΔGLU and the inhibition of GLU uptake
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Real-time alpha monitoring of a radioactive liquid waste stream at Los Alamos National Laboratory
Energy Technology Data Exchange (ETDEWEB)
Johnson, J.D.; Whitley, C.R.; Rawool-Sullivan, M. [Los Alamos National Lab., NM (United States)
1995-12-31
This poster display concerns the development, installation, and testing of a real-time radioactive liquid waste monitor at Los Alamos National Laboratory (LANL). The detector system was designed for the LANL Radioactive Liquid Waste Treatment Facility so that influent to the plant could be monitored in real time. By knowing the activity of the influent, plant operators can better monitor treatment, better segregate waste (potentially), and monitor the regulatory compliance of users of the LANL Radioactive Liquid Waste Collection System. The detector system uses long-range alpha detection technology, which is a nonintrusive method of characterization that determines alpha activity on the liquid surface by measuring the ionization of ambient air. Extensive testing has been performed to ensure long-term use with a minimal amount of maintenance. The final design was a simple cost-effective alpha monitor that could be modified for monitoring influent waste streams at various points in the LANL Radioactive Liquid Waste Collection System.
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Baird, Harriet M; Webb, Thomas L; Martin, Jilly; Sirois, Fuschia M
2018-05-10
Self-monitoring of blood glucose helps people with type 1 diabetes to maintain glycemic control and reduce the risk of complications. However, adherence to blood glucose monitoring is often suboptimal. Like many health behaviors, self-monitoring of blood glucose involves exerting effort in the present to achieve future benefits. As such, the present research explored whether individual differences in time perspective-specifically, the extent to which people have a balanced time perspective-are associated with the frequency with which people with type 1 diabetes monitor their blood glucose and, thus, maintain glycemic control. Adults with type 1 diabetes completed measures of time perspective, feelings associated with monitoring, attitudes toward monitoring, and trait self-control. Objective data regarding the frequency with which participants monitored their blood glucose levels and their long-term glycemic control were extracted from their medical records. Hierarchical regression analyses and tests of indirect effects (N = 129) indicated that having a more balanced time perspective was associated with more frequent monitoring of blood glucose and, as a result, better glycemic control. Further analyses (N = 158) also indicated that there was an indirect relationship between balanced time perspective and monitoring of blood glucose via the feelings that participants associated with monitoring and their subsequent attitudes toward monitoring. These findings point to the importance and relevance of time perspective for understanding health-related behavior and may help to inform interventions designed to promote self-monitoring of blood glucose in people with type 1 diabetes.
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Enrichment in Massachusetts Expanded Learning Time (ELT) Schools. Issue Brief
Caven, Meghan; Checkoway, Amy; Gamse, Beth; Luck, Rachel; Wu, Sally
2012-01-01
This brief highlights key information about enrichment activities, which represent one of the main components of the Massachusetts Expanded Learning Time (ELT) initiative. Over time, the ELT initiative has supported over two dozen schools across the Commonwealth. A comprehensive evaluation of the ELT initiative found that implementation of the…
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Health status monitoring for ICU patients based on locally weighted principal component analysis.
Ding, Yangyang; Ma, Xin; Wang, Youqing
2018-03-01
Intelligent status monitoring for critically ill patients can help medical stuff quickly discover and assess the changes of disease and then make appropriate treatment strategy. However, general-type monitoring model now widely used is difficult to adapt the changes of intensive care unit (ICU) patients' status due to its fixed pattern, and a more robust, efficient and fast monitoring model should be developed to the individual. A data-driven learning approach combining locally weighted projection regression (LWPR) and principal component analysis (PCA) is firstly proposed and applied to monitor the nonlinear process of patients' health status in ICU. LWPR is used to approximate the complex nonlinear process with local linear models, in which PCA could be further applied to status monitoring, and finally a global weighted statistic will be acquired for detecting the possible abnormalities. Moreover, some improved versions are developed, such as LWPR-MPCA and LWPR-JPCA, which also have superior performance. Eighteen subjects were selected from the Physiobank's Multi-parameter Intelligent Monitoring for Intensive Care II (MIMIC II) database, and two vital signs of each subject were chosen for online monitoring. The proposed method was compared with several existing methods including traditional PCA, Partial least squares (PLS), just in time learning combined with modified PCA (L-PCA), and Kernel PCA (KPCA). The experimental results demonstrated that the mean fault detection rate (FDR) of PCA can be improved by 41.7% after adding LWPR. The mean FDR of LWPR-MPCA was increased by 8.3%, compared with the latest reported method L-PCA. Meanwhile, LWPR spent less training time than others, especially KPCA. LWPR is first introduced into ICU patients monitoring and achieves the best monitoring performance including adaptability to changes in patient status, sensitivity for abnormality detection as well as its fast learning speed and low computational complexity. The algorithm
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Building a satellite climate diagnostics data base for real-time climate monitoring
International Nuclear Information System (INIS)
Ropelewski, C.F.
1991-01-01
The paper discusses the development of a data base, the Satellite Climate Diagnostic Data Base (SCDDB), for real time operational climate monitoring utilizing current satellite data. Special attention is given to the satellite-derived quantities useful for monitoring global climate changes, the requirements of SCDDB, and the use of conventional meteorological data and model assimilated data in developing the SCDDB. Examples of prototype SCDDB products are presented. 10 refs
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Home-Learning Practices in Kenya: Views of Parents and Education Officers
Nyatuka, Benard Omenge
2016-01-01
In order for children to acquire meaningful education, families are advised to participate in learning activities at home. Such activities range from monitoring homework, problem-solving to reading with children during leisure time. But home-learning was claimed to receive little attention from key stakeholders among primary schools in Kenya's…
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Vicarious reinforcement learning signals when instructing others.
Apps, Matthew A J; Lesage, Elise; Ramnani, Narender
2015-02-18
Reinforcement learning (RL) theory posits that learning is driven by discrepancies between the predicted and actual outcomes of actions (prediction errors [PEs]). In social environments, learning is often guided by similar RL mechanisms. For example, teachers monitor the actions of students and provide feedback to them. This feedback evokes PEs in students that guide their learning. We report the first study that investigates the neural mechanisms that underpin RL signals in the brain of a teacher. Neurons in the anterior cingulate cortex (ACC) signal PEs when learning from the outcomes of one's own actions but also signal information when outcomes are received by others. Does a teacher's ACC signal PEs when monitoring a student's learning? Using fMRI, we studied brain activity in human subjects (teachers) as they taught a confederate (student) action-outcome associations by providing positive or negative feedback. We examined activity time-locked to the students' responses, when teachers infer student predictions and know actual outcomes. We fitted a RL-based computational model to the behavior of the student to characterize their learning, and examined whether a teacher's ACC signals when a student's predictions are wrong. In line with our hypothesis, activity in the teacher's ACC covaried with the PE values in the model. Additionally, activity in the teacher's insula and ventromedial prefrontal cortex covaried with the predicted value according to the student. Our findings highlight that the ACC signals PEs vicariously for others' erroneous predictions, when monitoring and instructing their learning. These results suggest that RL mechanisms, processed vicariously, may underpin and facilitate teaching behaviors. Copyright © 2015 Apps et al.
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Sensitivity analysis of machine-learning models of hydrologic time series
O'Reilly, A. M.
2017-12-01
Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.
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Geological storage of CO2 : time frames, monitoring and verification
International Nuclear Information System (INIS)
Chalaturnyk, R.; Gunter, W.D.
2005-01-01
In order to ensure that carbon dioxide (CO 2 ) injection and storage occurs in an environmentally sound and safe manner, many organizations pursuing the development of a CO 2 geological storage industry are initiating monitoring programs that include operational monitoring; verification monitoring; and environmental monitoring. Each represents an increase in the level of technology used and the intensity and duration of monitoring. For each potential site, the project conditions must be defined, the mechanisms that control the fluid flow must be predicted and technical questions must be addressed. This paper reviewed some of the relevant issues in establishing a monitoring framework for geological storage and defined terms that indicate the fate of injected CO 2 . Migration refers to movement of fluids within the injection formation, while leakage refers to movement of fluids outside the injection formation, and seepage refers to movement of fluids from the geosphere to the biosphere. Currently, regulatory agencies focus mostly on the time period approved for waste fluid injection, including CO 2 , into depleted hydrocarbon reservoirs or deep saline aquifers, which is in the order of 25 years. The lifetime of the injection operation is limited by reservoir capacity and the injection rate. Monitoring periods can be divided into periods based on risk during injection-operation (10 to 25 years), at the beginning of the storage period during pressure equilibration (up to 100 years), and over the long-term (from 100 to 1000 years). The 42 commercial acid gas injection projects currently in operation in western Canada can be used to validate the technology for the short term, while validation of long-term storage can be based on natural geological analogues. It was concluded that a monitored decision framework recognizes uncertainties in the geological storage system and allows design decisions to be made with the knowledge that planned long-term observations and their
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Chen, Jieshi; Schafer, Benjamin Carrion; Ho, Ivan Wang-Hei
2016-01-01
With the growing demand of real-time traffic monitoring nowadays, software-based image processing can hardly meet the real-time data processing requirement due to the serial data processing nature. In this paper, the implementation of a hardware-based feature detection and networking system prototype for real-time traffic monitoring as well as data transmission is presented. The hardware architecture of the proposed system is mainly composed of three parts: data collection, feature detection,...
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Real-time modeling of primitive environments through wavelet sensors and Hebbian learning
Vaccaro, James M.; Yaworsky, Paul S.
1999-06-01
Modeling the world through sensory input necessarily provides a unique perspective for the observer. Given a limited perspective, objects and events cannot always be encoded precisely but must involve crude, quick approximations to deal with sensory information in a real- time manner. As an example, when avoiding an oncoming car, a pedestrian needs to identify the fact that a car is approaching before ascertaining the model or color of the vehicle. In our methodology, we use wavelet-based sensors with self-organized learning to encode basic sensory information in real-time. The wavelet-based sensors provide necessary transformations while a rank-based Hebbian learning scheme encodes a self-organized environment through translation, scale and orientation invariant sensors. Such a self-organized environment is made possible by combining wavelet sets which are orthonormal, log-scale with linear orientation and have automatically generated membership functions. In earlier work we used Gabor wavelet filters, rank-based Hebbian learning and an exponential modulation function to encode textural information from images. Many different types of modulation are possible, but based on biological findings the exponential modulation function provided a good approximation of first spike coding of `integrate and fire' neurons. These types of Hebbian encoding schemes (e.g., exponential modulation, etc.) are useful for quick response and learning, provide several advantages over contemporary neural network learning approaches, and have been found to quantize data nonlinearly. By combining wavelets with Hebbian learning we can provide a real-time front-end for modeling an intelligent process, such as the autonomous control of agents in a simulated environment.
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Validation of performance of real-time kinematic PPP. A possible tool for deformation monitoring
Martín Furones, Ángel Esteban; Anquela Julián, Ana Belén; DIMAS PAGÉS, ALEJANDRO; Cos-Gayón López, Fernando José
2015-01-01
Structural failures (bridge or building collapses) and geohazards (landslides, ground subsi- dence or earthquakes) are worldwide problems that often lead to significant economic and loss of life. Monitoring the deformation of both natural phenomena and man-made struc- tures is a major key to assessing structural dynamic responses. Actually, this monitoring process is under real-time demand for developing warning and alert systems. One of the most used techniques for real-time deformation m...
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SU-F-P-20: Predicting Waiting Times in Radiation Oncology Using Machine Learning
International Nuclear Information System (INIS)
Joseph, A; Herrera, D; Hijal, T; Kildea, J; Hendren, L; Leung, A; Wainberg, J; Sawaf, M; Gorshkov, M; Maglieri, R; Keshavarz, M
2016-01-01
Purpose: Waiting times remain one of the most vexing patient satisfaction challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick or in pain, to worry about when they will receive the care they need. These waiting periods are often difficult for staff to predict and only rough estimates are typically provided based on personal experience. This level of uncertainty leaves most patients unable to plan their calendar, making the waiting experience uncomfortable, even painful. In the present era of electronic health records (EHRs), waiting times need not be so uncertain. Extensive EHRs provide unprecedented amounts of data that can statistically cluster towards representative values when appropriate patient cohorts are selected. Predictive modelling, such as machine learning, is a powerful approach that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The application of a machine learning algorithm to waiting time data has the potential to produce personalized waiting time predictions such that the uncertainty may be removed from the patient’s waiting experience. Methods: In radiation oncology, patients typically experience several types of waiting (eg waiting at home for treatment planning, waiting in the waiting room for oncologist appointments and daily waiting in the waiting room for radiotherapy treatments). A daily treatment wait time model is discussed in this report. To develop a prediction model using our large dataset (with more than 100k sample points) a variety of machine learning algorithms from the Python package sklearn were tested. Results: We found that the Random Forest Regressor model provides the best predictions for daily radiotherapy treatment waiting times. Using this model, we achieved a median residual (actual value minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes
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SU-F-P-20: Predicting Waiting Times in Radiation Oncology Using Machine Learning
Energy Technology Data Exchange (ETDEWEB)
Joseph, A; Herrera, D; Hijal, T; Kildea, J [McGill University Health Centre, Montreal, Quebec (Canada); Hendren, L; Leung, A; Wainberg, J; Sawaf, M; Gorshkov, M; Maglieri, R; Keshavarz, M [McGill University, Montreal, Quebec (Canada)
2016-06-15
Purpose: Waiting times remain one of the most vexing patient satisfaction challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick or in pain, to worry about when they will receive the care they need. These waiting periods are often difficult for staff to predict and only rough estimates are typically provided based on personal experience. This level of uncertainty leaves most patients unable to plan their calendar, making the waiting experience uncomfortable, even painful. In the present era of electronic health records (EHRs), waiting times need not be so uncertain. Extensive EHRs provide unprecedented amounts of data that can statistically cluster towards representative values when appropriate patient cohorts are selected. Predictive modelling, such as machine learning, is a powerful approach that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The application of a machine learning algorithm to waiting time data has the potential to produce personalized waiting time predictions such that the uncertainty may be removed from the patient’s waiting experience. Methods: In radiation oncology, patients typically experience several types of waiting (eg waiting at home for treatment planning, waiting in the waiting room for oncologist appointments and daily waiting in the waiting room for radiotherapy treatments). A daily treatment wait time model is discussed in this report. To develop a prediction model using our large dataset (with more than 100k sample points) a variety of machine learning algorithms from the Python package sklearn were tested. Results: We found that the Random Forest Regressor model provides the best predictions for daily radiotherapy treatment waiting times. Using this model, we achieved a median residual (actual value minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes
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High-resolution near real-time drought monitoring in South Asia
Aadhar, Saran; Mishra, Vimal
2017-01-01
Drought in South Asia affect food and water security and pose challenges for millions of people. For policy-making, planning, and management of water resources at sub-basin or administrative levels, high-resolution datasets of precipitation and air temperature are required in near-real time. We develop a high-resolution (0.05°) bias-corrected precipitation and temperature data that can be used to monitor near real-time drought conditions over South Asia. Moreover, the dataset can be used to m...
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Incremental Impact of Time on Students' Use of E-Learning via Facebook
Moghavvemi, Sedigheh; Salarzadeh Janatabadi, Hashem
2018-01-01
The majority of studies utilised the cross-sectional method to measure students' intention to learn and investigate their corresponding learning behaviours. Only a few studies have measured the process of change in students' learning behaviour in the context of time. The main purpose of this study is to determine the effects of using a Facebook…
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Learning Bounds of ERM Principle for Sequences of Time-Dependent Samples
Directory of Open Access Journals (Sweden)
Mingchen Yao
2015-01-01
Full Text Available Many generalization results in learning theory are established under the assumption that samples are independent and identically distributed (i.i.d.. However, numerous learning tasks in practical applications involve the time-dependent data. In this paper, we propose a theoretical framework to analyze the generalization performance of the empirical risk minimization (ERM principle for sequences of time-dependent samples (TDS. In particular, we first present the generalization bound of ERM principle for TDS. By introducing some auxiliary quantities, we also give a further analysis of the generalization properties and the asymptotical behaviors of ERM principle for TDS.
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Design and implementation of an interactive web-based near real-time forest monitoring system
Pratihast, Arun Kumar; Vries, de Ben; Avitabile, Valerio; Bruin, De Sytze; Herold, Martin; Bergsma, Aldo
2016-01-01
This paper describes an interactive web-based near real-time (NRT) forest monitoring system using four levels of geographic information services: 1) the acquisition of continuous data streams from satellite and community-based monitoring using mobile devices, 2) NRT forest disturbance detection
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Making time for learning-oriented leadership in multidisciplinary hospital management groups.
Singer, Sara J; Hayes, Jennifer E; Gray, Garry C; Kiang, Mathew V
2015-01-01
Although the clinical requirements of health care delivery imply the need for interdisciplinary management teams to work together to promote frontline learning, such interdisciplinary, learning-oriented leadership is atypical. We designed this study to identify behaviors enabling groups of diverse managers to perform as learning-oriented leadership teams on behalf of quality and safety. We randomly selected 12 of 24 intact groups of hospital managers from one hospital to participate in a Safety Leadership Team Training program. We collected primary data from March 2008 to February 2010 including pre- and post-staff surveys, multiple interviews, observations, and archival data from management groups. We examined the level and trend in frontline perceptions of managers' learning-oriented leadership following the intervention and ability of management groups to achieve objectives on targeted improvement projects. Among the 12 intervention groups, we identified higher- and lower-performing intervention groups and behaviors that enabled higher performers to work together more successfully. Management groups that achieved more of their performance goals and whose staff perceived more and greater improvement in their learning-oriented leadership after participation in Safety Leadership Team Training invested in structures that created learning capacity and conscientiously practiced prescribed learning-oriented management and problem-solving behaviors. They made the time to do these things because they envisioned the benefits of learning, valued the opportunity to learn, and maintained an environment of mutual respect and psychological safety within their group. Learning in management groups requires vision of what learning can accomplish; will to explore, practice, and build learning capacity; and mutual respect that sustains a learning environment.
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Active controllers and the time duration to learn a task
Repperger, D. W.; Goodyear, C.
1986-01-01
An active controller was used to help train naive subjects involved in a compensatory tracking task. The controller is called active in this context because it moves the subject's hand in a direction to improve tracking. It is of interest here to question whether the active controller helps the subject to learn a task more rapidly than the passive controller. Six subjects, inexperienced to compensatory tracking, were run to asymptote root mean square error tracking levels with an active controller or a passive controller. The time required to learn the task was defined several different ways. The results of the different measures of learning were examined across pools of subjects and across controllers using statistical tests. The comparison between the active controller and the passive controller as to their ability to accelerate the learning process as well as reduce levels of asymptotic tracking error is reported here.
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Kivlehan, Francine; Mavré, François; Talini, Luc; Limoges, Benoît; Marchal, Damien
2011-09-21
We described an electrochemical method to monitor in real-time the isothermal helicase-dependent amplification of nucleic acids. The principle of detection is simple and well-adapted to the development of portable, easy-to-use and inexpensive nucleic acids detection technologies. It consists of monitoring a decrease in the electrochemical current response of a reporter DNA intercalating redox probe during the isothermal DNA amplification. The method offers the possibility to quantitatively analyze target nucleic acids in less than one hour at a single constant temperature, and to perform at the end of the isothermal amplification a DNA melt curve analysis for differentiating between specific and non-specific amplifications. To illustrate the potentialities of this approach for the development of a simple, robust and low-cost instrument with high throughput capability, the method was validated with an electrochemical system capable of monitoring up to 48 real-time isothermal HDA reactions simultaneously in a disposable microplate consisting of 48-electrochemical microwells. Results obtained with this approach are comparable to that obtained with a well-established but more sophisticated and expensive fluorescence-based method. This makes for a promising alternative detection method not only for real-time isothermal helicase-dependent amplification of nucleic acid, but also for other isothermal DNA amplification strategies.
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International Nuclear Information System (INIS)
Walsh, P T; Forth, A R; Clark, R D R; Dowker, K P; Thorpe, A
2009-01-01
Real-time, photometric, portable dust monitors have been employed for video exposure monitoring (VEM) to measure and highlight dust levels generated by work activities, illustrate dust control techniques, and demonstrate good practice. Two workplaces, presenting different challenges for measurement, were used to illustrate the capabilities of VEM: (a) poultry farming activities and (b) powder transfer operations in a pharmaceutical company. For the poultry farm work, the real-time monitors were calibrated with respect to the respirable and inhalable dust concentrations using cyclone and IOM reference samplers respectively. Different rankings of exposure for typical activities were found on the small farm studied here compared to previous exposure measurements at larger poultry farms: these were mainly attributed to the different scales of operation. Large variations in the ratios of respirable, inhalable and real-time monitor TWA concentrations of poultry farm dust for various activities were found. This has implications for the calibration of light-scattering dust monitors with respect to inhalable dust concentration. In the pharmaceutical application, the effectiveness of a curtain barrier for dust control when dispensing powder in a downflow booth was rapidly demonstrated.
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Crumpled Molecules and Edible Plastic: Science Learning Activation in Out-of-School Time
Dorph, Rena; Schunn, Christian D.; Crowley, Kevin
2017-01-01
The Coalition for Science After School highlights the dual nature of outcomes for science learning during out-of- school time (OST): Learning experiences should not only be positive in the moment, but also position youth for future success. Several frameworks speak to the first set of immediate outcomes--what youth learn, think, and feel as the…
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Lin, Shu-Hsuan; Lo, Ta-Ju; Kuo, Fang-Yin; Chen, Yu-Chie
2014-01-01
Ultrasonication has been used to accelerate chemical reactions. It would be ideal if ultrasonication-assisted chemical reactions could be monitored by suitable detection tools such as mass spectrometry in real time. It would be helpful to clarify reaction intermediates/products and to have a better understanding of reaction mechanism. In this work, we developed a system for ultrasonication-assisted spray ionization mass spectrometry (UASI-MS) with an ~1.7 MHz ultrasonic transducer to monitor chemical reactions in real time. We demonstrated that simply depositing a sample solution on the MHz-based ultrasonic transducer, which was placed in front of the orifice of a mass spectrometer, the analyte signals can be readily detected by the mass spectrometer. Singly and multiply charged ions from small and large molecules, respectively, can be observed in the UASI mass spectra. Furthermore, the ultrasonic transducer used in the UASI setup accelerates the chemical reactions while being monitored via UASI-MS. The feasibility of using this approach for real-time acceleration/monitoring of chemical reactions was demonstrated. The reactions of Girard T reagent and hydroxylamine with steroids were used as the model reactions. Upon the deposition of reactant solutions on the ultrasonic transducer, the intermediate/product ions are readily generated and instantaneously monitored using MS within 1 s. Additionally, we also showed the possibility of using this reactive UASI-MS approach to assist the confirmation of trace steroids from complex urine samples by monitoring the generation of the product ions. Copyright © 2014 John Wiley & Sons, Ltd.
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Oscillations, Timing, Plasticity, and Learning in the Cerebellum.
Cheron, G; Márquez-Ruiz, J; Dan, B
2016-04-01
The highly stereotyped, crystal-like architecture of the cerebellum has long served as a basis for hypotheses with regard to the function(s) that it subserves. Historically, most clinical observations and experimental work have focused on the involvement of the cerebellum in motor control, with particular emphasis on coordination and learning. Two main models have been suggested to account for cerebellar functioning. According to Llinás's theory, the cerebellum acts as a control machine that uses the rhythmic activity of the inferior olive to synchronize Purkinje cell populations for fine-tuning of coordination. In contrast, the Ito-Marr-Albus theory views the cerebellum as a motor learning machine that heuristically refines synaptic weights of the Purkinje cell based on error signals coming from the inferior olive. Here, we review the role of timing of neuronal events, oscillatory behavior, and synaptic and non-synaptic influences in functional plasticity that can be recorded in awake animals in various physiological and pathological models in a perspective that also includes non-motor aspects of cerebellar function. We discuss organizational levels from genes through intracellular signaling, synaptic network to system and behavior, as well as processes from signal production and processing to memory, delegation, and actual learning. We suggest an integrative concept for control and learning based on articulated oscillation templates.
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Zwart, Fenny S; Vissers, Constance Th W M; van der Meij, Roemer; Kessels, Roy P C; Maes, Joseph H R
2017-09-01
It has been suggested that people with autism spectrum disorder (ASD) have an increased tendency to use explicit (or intentional) learning strategies. This altered learning may play a role in the development of the social communication difficulties characterizing ASD. In the current study, we investigated incidental and intentional sequence learning using a Serial Reaction Time (SRT) task in an adult ASD population. Response times and event related potentials (ERP) components (N2b and P3) were assessed as indicators of learning and knowledge. Findings showed that behaviorally, sequence learning and ensuing explicit knowledge were similar in ASD and typically developing (TD) controls. However, ERP findings showed that learning in the TD group was characterized by an enhanced N2b, while learning in the ASD group was characterized by an enhanced P3. These findings suggest that learning in the TD group might be more incidental in nature, whereas learning in the ASD group is more intentional or effortful. Increased intentional learning might serve as a strategy for individuals with ASD to control an overwhelming environment. Although this led to similar behavioral performances on the SRT task, it is very plausible that this intentional learning has adverse effects in more complex social situations, and hence contributes to the social impairments found in ASD. Autism Res 2017, 10: 1533-1543. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.
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Real Time In-circuit Condition Monitoring of MOSFET in Power Converters
Directory of Open Access Journals (Sweden)
Shakeb A. Khan
2015-03-01
Full Text Available Abstract:This paper presents simple and low-cost, real time in-circuit condition monitoring of MOSFET in power electronic converters. Design metrics requirements like low cost, small size, high power factor, low percentage of total harmonic distortion etc. requires the power electronic systems to operate at high frequencies and at high power density. Failures of power converters are attributed largely by aging of power MOSFETs at high switching frequencies. Therefore, real time in-circuit prognostic of MOSFET needs to be done before their selection for power system design. Accelerated aging tests are performed in different circuits to determine the wear out failure of critical components based on their parametric degradation. In this paper, the simple and low-cost test beds are designed for real time in-circuit prognostics of power MOSFETs. The proposed condition monitoring scheme helps in estimating the condition of MOSFETs at their maximum rated operating condition and will aid the system designers to test their reliability and benchmark them before selecting in power converters.
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da Silva, Thiago Ferreira; Xavier, Guilherme B; Temporão, Guilherme P; von der Weid, Jean Pierre
2012-08-13
By employing real-time monitoring of single-photon avalanche photodiodes we demonstrate how two types of practical eavesdropping strategies, the after-gate and time-shift attacks, may be detected. Both attacks are identified with the detectors operating without any special modifications, making this proposal well suited for real-world applications. The monitoring system is based on accumulating statistics of the times between consecutive detection events, and extracting the afterpulse and overall efficiency of the detectors in real-time using mathematical models fit to the measured data. We are able to directly observe changes in the afterpulse probabilities generated from the after-gate and faint after-gate attacks, as well as different timing signatures in the time-shift attack. We also discuss the applicability of our scheme to other general blinding attacks.
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Kim, Min H; Yoon, Hargsoon; Choi, Sang H; Zhao, Fei; Kim, Jongsung; Song, Kyo D; Lee, Uhn
2016-11-10
Real-time monitoring of extracellular neurotransmitter concentration offers great benefits for diagnosis and treatment of neurological disorders and diseases. This paper presents the study design and results of a miniaturized and wireless optical neurotransmitter sensor (MWONS) for real-time monitoring of brain dopamine concentration. MWONS is based on fluorescent sensing principles and comprises a microspectrometer unit, a microcontroller for data acquisition, and a Bluetooth wireless network for real-time monitoring. MWONS has a custom-designed application software that controls the operation parameters for excitation light sources, data acquisition, and signal processing. MWONS successfully demonstrated a measurement capability with a limit of detection down to a 100 nanomole dopamine concentration, and high selectivity to ascorbic acid (90:1) and uric acid (36:1).
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Time-lapse monitoring of soil water content using electromagnetic conductivity imaging
The volumetric soil water content (VWC) is fundamental to agriculture. Unfortunately, the universally accepted thermogravimetric method is labour intensive and time-consuming to use for field-scale monitoring. Electromagnetic (EM) induction instruments have proven to be useful in mapping the spatio-...
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Directory of Open Access Journals (Sweden)
Ingvar Eide
Full Text Available A pilot study demonstrating real-time environmental monitoring with automated multivariate analysis of multi-sensor data submitted online has been performed at the cabled LoVe Ocean Observatory located at 258 m depth 20 km off the coast of Lofoten-Vesterålen, Norway. The major purpose was efficient monitoring of many variables simultaneously and early detection of changes and time-trends in the overall response pattern before changes were evident in individual variables. The pilot study was performed with 12 sensors from May 16 to August 31, 2015. The sensors provided data for chlorophyll, turbidity, conductivity, temperature (three sensors, salinity (calculated from temperature and conductivity, biomass at three different depth intervals (5-50, 50-120, 120-250 m, and current speed measured in two directions (east and north using two sensors covering different depths with overlap. A total of 88 variables were monitored, 78 from the two current speed sensors. The time-resolution varied, thus the data had to be aligned to a common time resolution. After alignment, the data were interpreted using principal component analysis (PCA. Initially, a calibration model was established using data from May 16 to July 31. The data on current speed from two sensors were subject to two separate PCA models and the score vectors from these two models were combined with the other 10 variables in a multi-block PCA model. The observations from August were projected on the calibration model consecutively one at a time and the result was visualized in a score plot. Automated PCA of multi-sensor data submitted online is illustrated with an attached time-lapse video covering the relative short time period used in the pilot study. Methods for statistical validation, and warning and alarm limits are described. Redundant sensors enable sensor diagnostics and quality assurance. In a future perspective, the concept may be used in integrated environmental monitoring.
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Advances in Scientific Possibilities Offered by Real-Time Monitoring Technology.
Kleiman, Evan M; Nock, Matthew K
2017-01-01
There has been a marked increase in research aimed at studying dynamic (e.g., day-to-day, moment-to-moment) changes in mental disorders and related behavior problems. Indeed, the number of scientific papers published that focus on real-time monitoring has been nearly doubling every five years for the past several decades. These methods allow for a more fine-grained description of phenomena of interest as well as for real-world tests of theoretical models of human behavior. Here we comment on the recent study by van Winkel and colleagues (this issue)as an excellent example of the use of real-time monitoring methods to better understand mental disorders. We also discuss the expanding universe of new technologies (e.g., smartphones, wearable biosensors) that can be used to make discoveries about psychopathology and related constructs and describe what we perceive to be some of the most exciting scientific possibilities that can be achieved in the near term by taking advantage of these new and rapidly developing tools.
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Active and passive electrical and seismic time-lapse monitoring of earthen embankments
Rittgers, Justin Bradley
In this dissertation, I present research involving the application of active and passive geophysical data collection, data assimilation, and inverse modeling for the purpose of earthen embankment infrastructure assessment. Throughout the dissertation, I identify several data characteristics, and several challenges intrinsic to characterization and imaging of earthen embankments and anomalous seepage phenomena, from both a static and time-lapse geophysical monitoring perspective. I begin with the presentation of a field study conducted on a seeping earthen dam, involving static and independent inversions of active tomography data sets, and self-potential modeling of fluid flow within a confined aquifer. Additionally, I present results of active and passive time-lapse geophysical monitoring conducted during two meso-scale laboratory experiments involving the failure and self-healing of embankment filter materials via induced vertical cracking. Identified data signatures and trends, as well as 4D inversion results, are discussed as an underlying motivation for conducting subsequent research. Next, I present a new 4D acoustic emissions source localization algorithm that is applied to passive seismic monitoring data collected during a full-scale embankment failure test. Acoustic emissions localization results are then used to help spatially constrain 4D inversion of collocated self-potential monitoring data. I then turn to time-lapse joint inversion of active tomographic data sets applied to the characterization and monitoring of earthen embankments. Here, I develop a new technique for applying spatiotemporally varying structural joint inversion constraints. The new technique, referred to as Automatic Joint Constraints (AJC), is first demonstrated on a synthetic 2D joint model space, and is then applied to real geophysical monitoring data sets collected during a full-scale earthen embankment piping-failure test. Finally, I discuss some non-technical issues related to
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Real Time Radioactivity Monitoring and its Interface with predictive atmospheric transport modelling
International Nuclear Information System (INIS)
Raes, F.
1990-01-01
After the Chernobyl accident, a programme was initiated at the Joint Research Centre of the Commission of the European Communities named 'Radioactivity Environmental Monitoring' (REM). The main aspects considered in REM are: data handling, atmospheric modelling and data quality control related to radioactivity in the environment. The first REM workshop was held in December 1987: 'Aerosol Measurements and Nuclear Accidents: A Reconsideration'. (CEC EUR 11755 EN). These are the proceedings of the second REM workshop, held in December 1989, dealing with real-time radioactivity monitoring and its interface with predictive atmospheric models. Atmospheric transport models, in applications extending over time scales of the order of a day or more become progressively less reliable to the extent that an interface with real-time radiological field data becomes highly desirable. Through international arrangements for early exchange of information in the event of a nuclear accident (European Community, IAEA) such data might become available on a quasi real-time basis. The question is how best to use such information to improve our predictive capabilities. During the workshop the present status of on-line monitoring networks for airborne radioactivity in the EC Member States has been reviewed. Possibilities were discussed to use data from such networks as soon as they become available, in order to update predictions made with long range transport models. This publication gives the full text of 13 presentations and a report of the Round Table Discussion held afterwards
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A Gaussian Process Based Online Change Detection Algorithm for Monitoring Periodic Time Series
Energy Technology Data Exchange (ETDEWEB)
Chandola, Varun [ORNL; Vatsavai, Raju [ORNL
2011-01-01
Online time series change detection is a critical component of many monitoring systems, such as space and air-borne remote sensing instruments, cardiac monitors, and network traffic profilers, which continuously analyze observations recorded by sensors. Data collected by such sensors typically has a periodic (seasonal) component. Most existing time series change detection methods are not directly applicable to handle such data, either because they are not designed to handle periodic time series or because they cannot operate in an online mode. We propose an online change detection algorithm which can handle periodic time series. The algorithm uses a Gaussian process based non-parametric time series prediction model and monitors the difference between the predictions and actual observations within a statistically principled control chart framework to identify changes. A key challenge in using Gaussian process in an online mode is the need to solve a large system of equations involving the associated covariance matrix which grows with every time step. The proposed algorithm exploits the special structure of the covariance matrix and can analyze a time series of length T in O(T^2) time while maintaining a O(T) memory footprint, compared to O(T^4) time and O(T^2) memory requirement of standard matrix manipulation methods. We experimentally demonstrate the superiority of the proposed algorithm over several existing time series change detection algorithms on a set of synthetic and real time series. Finally, we illustrate the effectiveness of the proposed algorithm for identifying land use land cover changes using Normalized Difference Vegetation Index (NDVI) data collected for an agricultural region in Iowa state, USA. Our algorithm is able to detect different types of changes in a NDVI validation data set (with ~80% accuracy) which occur due to crop type changes as well as disruptive changes (e.g., natural disasters).
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The Piston Compressor: The Methodology of the Real-Time Condition Monitoring
International Nuclear Information System (INIS)
Naumenko, A P; Kostyukov, V N
2012-01-01
The methodology of a diagnostic signal processing, a function chart of the monitoring system are considered in the article. The methodology of monitoring and diagnosing is based on measurement of indirect processes' parameters (vibroacoustic oscillations) therefore no more than five sensors is established on the cylinder, measurement of direct structural and thermodynamic parameters is envisioned as well. The structure and principle of expert system's functioning of decision-making is given. Algorithm of automatic expert system includes the calculation diagnostic attributes values based on their normative values, formation sets of diagnostic attributes that correspond to individual classes to malfunction, formation of expert system messages. The scheme of a real-time condition monitoring system for piston compressors is considered. The system have consistently-parallel structure of information-measuring equipment, which allows to measure the vibroacoustic signal for condition monitoring of reciprocating compressors and modes of its work. Besides, the system allows to measure parameters of other physical processes, for example, system can measure and use for monitoring and statements of the diagnosis the pressure in decreasing spaces (the indicator diagram), the inlet pressure and flowing pressure of each cylinder, inlet and delivery temperature of gas, valves temperature, position of a rod, leakage through compression packing and others.
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A Wireless and Real-Time Monitoring System Design for Car Networking Applications
Directory of Open Access Journals (Sweden)
Li Wenjun
2013-01-01
Full Text Available We described a wireless and monitoring system to obtain several classes of vehicle data and send them to the server via General Packet Radio Service (GPRS in real-time. These data are consisted by on-board diagnostic (OBD which get from the vehicle’s OBD interface, Tire-Pressure Monitoring system (TPMS and Global Positioning System (GPS. The main content of this paper is the hardware design of the system, especially RF modules and antennas.
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On-line condition monitoring of nuclear systems via symbolic time series analysis
International Nuclear Information System (INIS)
Rajagopalan, V.; Ray, A.; Garcia, H. E.
2006-01-01
This paper provides a symbolic time series analysis approach to fault diagnostics and condition monitoring. The proposed technique is built upon concepts from wavelet theory, symbolic dynamics and pattern recognition. Various aspects of the methodology such as wavelet selection, choice of alphabet and determination of depth of D-Markov Machine are explained in the paper. The technique is validated with experiments performed in a Machine Condition Monitoring (MCM) test bed at the Idaho National Laboratory. (authors)
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Real-time water quality monitoring at a Great Lakes National Park
Byappanahalli, Muruleedhara; Nevers, Meredith; Shively, Dawn; Spoljaric, Ashley; Otto, Christopher
2018-01-01
Quantitative polymerase chain reaction (qPCR) was used by the USEPA to establish new recreational water quality criteria in 2012 using the indicator bacteria enterococci. The application of this method has been limited, but resource managers are interested in more timely monitoring results. In this study, we evaluated the efficacy of qPCR as a rapid, alternative method to the time-consuming membrane filtration (MF) method for monitoring water at select beaches and rivers of Sleeping Bear Dunes National Lakeshore in Empire, MI. Water samples were collected from four locations (Esch Road Beach, Otter Creek, Platte Point Bay, and Platte River outlet) in 2014 and analyzed for culture-based (MF) and non-culture-based (i.e., qPCR) endpoints using Escherichia coli and enterococci bacteria. The MF and qPCR enterococci results were significantly, positively correlated overall (r = 0.686, p Water quality standard exceedances based on enterococci levels by qPCR were lower than by MF method: 3 and 16, respectively. Based on our findings, we conclude that qPCR may be a viable alternative to the culture-based method for monitoring water quality on public lands. Rapid, same-day results are achievable by the qPCR method, which greatly improves protection of the public from water-related illnesses.
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Tropical Forest Monitoring in Southeast Asia Using Remotely Sensed Optical Time Series
DEFF Research Database (Denmark)
Grogan, Kenneth Joseph
of forest cover using satellite remote sensing technology. Recently, there has been a shift in data protection policy where rich archives of satellite imagery are now freely available. This has spurred a new era in satellite-based forest monitoring leading to advancements in optical time series processing...... markets. At the Landsat 30-m resolution, annual time series coupled with linear segmentation using LandTrendr was found to be an effective approach for monitoring forest disturbance, with moderate to high accuracies, depending on forest type. At the MODIS 250-m resolution, intra-annual time series...... global rubber markets can be linked to forest cover change, the effects of land policy in Cambodia, and beyond, have also had a major influence. It remains to be seen if intervention initiatives such as REDD+ can materialise over the coming years to make a meaningful contribution to tropical forest...
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Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.
Directory of Open Access Journals (Sweden)
Christian Albers
Full Text Available Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP. Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious and strong (teacher spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.
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Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.
Albers, Christian; Westkott, Maren; Pawelzik, Klaus
2016-01-01
Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.
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Luckay, Melanie B.; Laugksch, Rudiger C.
2015-01-01
This article describes the development and validation of an instrument that can be used to assess students' perceptions of their learning environment as a means of monitoring and guiding changes toward social constructivist learning environments. The study used a mixed-method approach with priority given to the quantitative data collection. During…
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Cluster analysis of activity-time series in motor learning
DEFF Research Database (Denmark)
Balslev, Daniela; Nielsen, Finn Å; Futiger, Sally A
2002-01-01
Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel......-time series. The optimal number of clusters was chosen using a cross-validated likelihood method, which highlights the clustering pattern that generalizes best over the subjects. Data were acquired with PET at different time points during practice of a visuomotor task. The results from cluster analysis show...
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Sosnovski, Oleg; Suresh, Pooja; Dudelzak, Alexander E.; Green, Benjamin
2018-02-01
Lubrication oil is a vital component of heavy rotating machinery defining the machine's health, operational safety and effectiveness. Recently, the focus has been on developing sensors that provide real-time/online monitoring of oil condition/lubricity. Industrial practices and standards for assessing oil condition involve various analytical methods. Most these techniques are unsuitable for online applications. The paper presents the results of studying degradation of antioxidant additives in machinery lubricants using Fluorescence Excitation-Emission Matrix (EEM) Spectroscopy and Machine Learning techniques. EEM Spectroscopy is capable of rapid and even standoff sensing; it is potentially applicable to real-time online monitoring.
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Energy Technology Data Exchange (ETDEWEB)
Kane, Russell D. [InterCorr International, Inc., 14503 Bammel N. Houston Road, Suite 300, Houston, TX 77019 (United States); Campbell, Scott [Commercial Microbiology Inc., 10400 Westoffice Drive Suite 107, Houston, TX 77042 (United States)
2004-07-01
An experimental study of microbiologically influenced corrosion (MIC) was conducted involving online, real-time monitoring of a bio-film loop under controlled conditions simulating oil field water handling and injection. Bio-film growth, MIC and biocide efficacy were monitored using an automated, multi-technique monitoring system including linear polarization resistance, electrochemical noise and harmonic distortion analysis. This data was correlated with conventional off-line methods to differentiate conditions of varying MIC activity in real-time to facilitate quick assessment and operator intervention. (authors)
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Learning to stay ahead of time
DEFF Research Database (Denmark)
Staunæs, Dorthe; Raffnsøe, Sverre
2014-01-01
In the context of an ongoing change, management is required to take the form of a leadership that must be reignited over and over again. The article examines a new art of leadership that may be viewed as an attempt to keep up with these challenges and stay ahead of time. It emerges from...... a pilgrimage leadership learning laboratory on the road to Santiago de la Compostela. This moving lab creates situations of extraordinary intensity that border on hyperreality and force the leader to find him/herself anew on the verge of him/herself. Conceived as pilgrimage, leadership moves ahead of time...... as it reaches into and anticipates a future still unknown. In this setting, anticipatory affects and the virtual take up a predominant role. As it emerges here, leadership distinguishes itself not only from leadership in the traditional sense, but also from management and governmentality....
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Pulok, Md Kamrul Hasan
Intelligent and effective monitoring of power system stability in control centers is one of the key issues in smart grid technology to prevent unwanted power system blackouts. Voltage stability analysis is one of the most important requirements for control center operation in smart grid era. With the advent of Phasor Measurement Unit (PMU) or Synchrophasor technology, real time monitoring of voltage stability of power system is now a reality. This work utilizes real-time PMU data to derive a voltage stability index to monitor the voltage stability related contingency situation in power systems. The developed tool uses PMU data to calculate voltage stability index that indicates relative closeness of the instability by producing numerical indices. The IEEE 39 bus, New England power system was modeled and run on a Real-time Digital Simulator that stream PMU data over the Internet using IEEE C37.118 protocol. A Phasor data concentrator (PDC) is setup that receives streaming PMU data and stores them in Microsoft SQL database server. Then the developed voltage stability monitoring (VSM) tool retrieves phasor measurement data from SQL server, performs real-time state estimation of the whole network, calculate voltage stability index, perform real-time ranking of most vulnerable transmission lines, and finally shows all the results in a graphical user interface. All these actions are done in near real-time. Control centers can easily monitor the systems condition by using this tool and can take precautionary actions if needed.
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A Real-Time Data Monitoring and Accumulation System for Dynamic Studies with Radionuclides
Energy Technology Data Exchange (ETDEWEB)
Ammann, W.; Doll, J.; Lorenz, W. J.; Ostertag, H.; Adam, W. E.; Scheer, K. E. [German Cancer Research Centre, Institute of Nuclear Medicine, Heidelberg, Federal Republic of Germany (Germany)
1971-02-15
A multipurpose digital data monitoring and accumulation system is described. The central unit of the system is a PDP-8 computer with a 12K memory. The system contains furthermore a multipurpose digital input/output register for low data rates, a fourfold and a twofold ADC connected to the high-speed multiplexor unit of the PDP-8 and a digital timet. Data from various process peripheries are recorded on a nine-track IBM compatible Ampex tape recorder. When two co-ordinates are recorded the system is used in the ''add-one-to-storage'' mode. In the case of more than two co-ordinates the data are stored in the sequential mode, event by event. A dialogue real-time monitor program in assembler language was developed to control the process peripheries. The 4K-Fortran operating system was modified in such a way that monitor subroutines were called into the Fortran program without loss of the real-time properties of the monitor system during a Fortran run. The use of the system for lung function studies with an Anger-type scintillation camera and {sup 133}Xe is discussed as an example of the application of the system. (author)
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A chest drainage system with a real-time pressure monitoring device.
Chen, Chih-Hao; Liu, Tsang-Pai; Chang, Ho; Huang, Tung-Sung; Liu, Hung-Chang; Chen, Chao-Hung
2015-07-01
Tube thoracostomy is a common procedure. A chest bottle may be used to both collect fluids and monitor the recovery of the chest condition. The presence of the "tidaling phenomenon" in the bottle can be reflective of the extent of patient's recovery. However, current practice essentially depends on gross observation of the bottle. The device used here is designed for a real-time monitoring of change in pleural pressure to allow clinicians to objectively determine when the lung has recovered, which is crucially important in order to judge when to remove the chest tube. The device is made of a pressure sensor with an operating range between -100 to +100 cmH2O and an amplifying using the "Wheatstone bridge" concept. Recording and analysis was performed with LABview software. The data can be shown in real-time on screen and also be checked retrospectively. The device was connected to the second part of a three-bottle drain system by a three-way connector. The test animals were two 40-kg pigs. We used a thoracoscopic procedure to create an artificial lung laceration with endoscopic scissors. Active air leaks could result in vigorous tidaling phenomenon up to 20 cmH2O. In the absence of gross tidaling phenomenon, the pressure changes were around 0.25 cmH2O. This real-time pleural pressure monitoring device can help clinicians objectively judge the extent of recovery of the chest condition. It can be used as an effective adjunct with the current chest drain system.
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International Nuclear Information System (INIS)
Nabeshima, Kunihiko; Suzuki, Katsuo; Shinohara, Yoshikuni; Tuerkcan, E.
1995-11-01
In this paper, the anomaly detection method for nuclear power plant monitoring and its program are described by using a neural network approach, which is based on the deviation between measured signals and output signals of neural network model. The neural network used in this study has three layered auto-associative network with 12 input/output, and backpropagation algorithm is adopted for learning. Furthermore, to obtain better dynamical model of the reactor plant, a new learning technique was developed in which the learning process of the present neural network is divided into initial and adaptive learning modes. The test results at the actual nuclear reactor shows that the neural network plant monitoring system is successfull in detecting in real-time the symptom of small anomaly over a wide power range including reactor start-up, shut-down and stationary operation. (author)
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Real Time Search Algorithm for Observation Outliers During Monitoring Engineering Constructions
Latos, Dorota; Kolanowski, Bogdan; Pachelski, Wojciech; Sołoducha, Ryszard
2017-12-01
Real time monitoring of engineering structures in case of an emergency of disaster requires collection of a large amount of data to be processed by specific analytical techniques. A quick and accurate assessment of the state of the object is crucial for a probable rescue action. One of the more significant evaluation methods of large sets of data, either collected during a specified interval of time or permanently, is the time series analysis. In this paper presented is a search algorithm for those time series elements which deviate from their values expected during monitoring. Quick and proper detection of observations indicating anomalous behavior of the structure allows to take a variety of preventive actions. In the algorithm, the mathematical formulae used provide maximal sensitivity to detect even minimal changes in the object's behavior. The sensitivity analyses were conducted for the algorithm of moving average as well as for the Douglas-Peucker algorithm used in generalization of linear objects in GIS. In addition to determining the size of deviations from the average it was used the so-called Hausdorff distance. The carried out simulation and verification of laboratory survey data showed that the approach provides sufficient sensitivity for automatic real time analysis of large amount of data obtained from different and various sensors (total stations, leveling, camera, radar).
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Real Time Search Algorithm for Observation Outliers During Monitoring Engineering Constructions
Directory of Open Access Journals (Sweden)
Latos Dorota
2017-12-01
Full Text Available Real time monitoring of engineering structures in case of an emergency of disaster requires collection of a large amount of data to be processed by specific analytical techniques. A quick and accurate assessment of the state of the object is crucial for a probable rescue action. One of the more significant evaluation methods of large sets of data, either collected during a specified interval of time or permanently, is the time series analysis. In this paper presented is a search algorithm for those time series elements which deviate from their values expected during monitoring. Quick and proper detection of observations indicating anomalous behavior of the structure allows to take a variety of preventive actions. In the algorithm, the mathematical formulae used provide maximal sensitivity to detect even minimal changes in the object’s behavior. The sensitivity analyses were conducted for the algorithm of moving average as well as for the Douglas-Peucker algorithm used in generalization of linear objects in GIS. In addition to determining the size of deviations from the average it was used the so-called Hausdorff distance. The carried out simulation and verification of laboratory survey data showed that the approach provides sufficient sensitivity for automatic real time analysis of large amount of data obtained from different and various sensors (total stations, leveling, camera, radar.
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Monitoring molecular interactions using photon arrival-time interval distribution analysis
Laurence, Ted A [Livermore, CA; Weiss, Shimon [Los Angels, CA
2009-10-06
A method for analyzing/monitoring the properties of species that are labeled with fluorophores. A detector is used to detect photons emitted from species that are labeled with one or more fluorophores and located in a confocal detection volume. The arrival time of each of the photons is determined. The interval of time between various photon pairs is then determined to provide photon pair intervals. The number of photons that have arrival times within the photon pair intervals is also determined. The photon pair intervals are then used in combination with the corresponding counts of intervening photons to analyze properties and interactions of the molecules including brightness, concentration, coincidence and transit time. The method can be used for analyzing single photon streams and multiple photon streams.
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Enhancement of Online Robotics Learning Using Real-Time 3D Visualization Technology
Richard Chiou; Yongjin (james) Kwon; Tzu-Liang (bill) Tseng; Robin Kizirian; Yueh-Ting Yang
2010-01-01
This paper discusses a real-time e-Lab Learning system based on the integration of 3D visualization technology with a remote robotic laboratory. With the emergence and development of the Internet field, online learning is proving to play a significant role in the upcoming era. In an effort to enhance Internet-based learning of robotics and keep up with the rapid progression of technology, a 3- Dimensional scheme of viewing the robotic laboratory has been introduced in addition to the remote c...
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Liu, Jing-Ying; Liu, Yan-Hui; Yang, Ji-Peng
2014-01-01
The aim of this study was to explore the relationships among study engagement, learning adaptability, and time management disposition in a sample of Chinese baccalaureate nursing students. A convenient sample of 467 baccalaureate nursing students was surveyed in two universities in Tianjin, China. Students completed a questionnaire that included their demographic information, Chinese Utrecht Work Engagement Scale-Student Questionnaire, Learning Adaptability Scale, and Adolescence Time Management Disposition Scale. One-way analysis of variance tests were used to assess the relationship between certain characteristics of baccalaureate nursing students. Pearson correlation was performed to test the correlation among study engagement, learning adaptability, and time management disposition. Hierarchical linear regression analyses were performed to explore the mediating role of time management disposition. The results revealed that study engagement (F = 7.20, P < .01) and learning adaptability (F = 4.41, P < .01) differed across grade groups. Learning adaptability (r = 0.382, P < .01) and time management disposition (r = 0.741, P < .01) were positively related with study engagement. Time management disposition had a partially mediating effect on the relationship between study engagement and learning adaptability. The findings implicate that educators should not only promote interventions to increase engagement of baccalaureate nursing students but also focus on development, investment in adaptability, and time management. Copyright © 2014 Elsevier Inc. All rights reserved.
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[A technological device for optimizing the time taken for blind people to learn Braille].
Hernández, Cesar; Pedraza, Luis F; López, Danilo
2011-10-01
This project was aimed at designing and putting an electronic prototype into practice for improving the initial time taken by visually handicapped people for learning Braille, especially children. This project was mainly based on a prototype digital electronic device which identifies and translates material written by a user in Braille by a voice synthesis system, producing artificial words to determine whether a handicapped person's writing in Braille has been correct. A global system for mobile communications (GSM) module was also incorporated into the device which allowed it to send text messages, thereby involving innovation in the field of articles for aiding visually handicapped people. This project's main result was an easily accessed and understandable prototype device which improved visually handicapped people's initial learning of Braille. The time taken for visually handicapped people to learn Braille became significantly reduced whilst their interest increased, as did their concentration time regarding such learning.
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Remote-Sensing Time Series Analysis, a Vegetation Monitoring Tool
McKellip, Rodney; Prados, Donald; Ryan, Robert; Ross, Kenton; Spruce, Joseph; Gasser, Gerald; Greer, Randall
2008-01-01
The Time Series Product Tool (TSPT) is software, developed in MATLAB , which creates and displays high signal-to- noise Vegetation Indices imagery and other higher-level products derived from remotely sensed data. This tool enables automated, rapid, large-scale regional surveillance of crops, forests, and other vegetation. TSPT temporally processes high-revisit-rate satellite imagery produced by the Moderate Resolution Imaging Spectroradiometer (MODIS) and by other remote-sensing systems. Although MODIS imagery is acquired daily, cloudiness and other sources of noise can greatly reduce the effective temporal resolution. To improve cloud statistics, the TSPT combines MODIS data from multiple satellites (Aqua and Terra). The TSPT produces MODIS products as single time-frame and multitemporal change images, as time-series plots at a selected location, or as temporally processed image videos. Using the TSPT program, MODIS metadata is used to remove and/or correct bad and suspect data. Bad pixel removal, multiple satellite data fusion, and temporal processing techniques create high-quality plots and animated image video sequences that depict changes in vegetation greenness. This tool provides several temporal processing options not found in other comparable imaging software tools. Because the framework to generate and use other algorithms is established, small modifications to this tool will enable the use of a large range of remotely sensed data types. An effective remote-sensing crop monitoring system must be able to detect subtle changes in plant health in the earliest stages, before the effects of a disease outbreak or other adverse environmental conditions can become widespread and devastating. The integration of the time series analysis tool with ground-based information, soil types, crop types, meteorological data, and crop growth models in a Geographic Information System, could provide the foundation for a large-area crop-surveillance system that could identify
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Real-time risk monitoring in business processes : a sensor-based approach
Conforti, R.; La Rosa, M.; Fortino, G.; Hofstede, ter A.H.M.; Recker, J.; Adams, M.
2013-01-01
This article proposes an approach for real-time monitoring of risks in executable business process models. The approach considers risks in all phases of the business process management lifecycle, from process design, where risks are defined on top of process models, through to process diagnosis,
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Development of real-time monitoring system using wired and wireless networks in a full-scale ship
Directory of Open Access Journals (Sweden)
Bu-Geun Paik
2010-09-01
Full Text Available In the present study, the real-time monitoring system is developed based on the wireless sensor network (WSN and power line communication (PLC employed in the 3,000-ton-class training ship. The WSN consists of sensor nodes, router, gateway and middleware. The PLC is composed of power lines, modems, Ethernet gateway and phase-coupler. The basic tests show that the ship has rather good environments for the wired and wireless communications. The developed real-time monitoring system is applied to recognize the thermal environments of main-engine room and one cabin in the ship. The main-engine room has lots of heat sources and needs careful monitoring to satisfy safe operation condition or detect any human errors beforehand. The monitoring is performed in two regions near the turbocharger and cascade tank, considered as heat sources. The cabin on the second deck is selected to monitor the thermal environments because it is close to the heat source of main engine. The monitoring results of the cabin show the thermal environment is varied by the human activity. The real-time monitoring for the thermal environment would be useful for the planning of the ventilation strategy based on the traces of the human activity against inconvenient thermal environments as well as the recognizing the temperature itself in each cabin.
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Potentiometric Sensor for Real-Time Monitoring of Multivalent Ion Concentrations in Molten Salt
International Nuclear Information System (INIS)
Zink, Peter A.; Jue, Jan-Fong; Serrano, Brenda E.; Fredrickson, Guy L.; Cowan, Ben F.; Herrmann, Steven D.; Li, Shelly X.
2010-01-01
Electrorefining of spent metallic nuclear fuel in high temperature molten salt systems is a core technology in pyroprocessing, which in turn plays a critical role in the development of advanced fuel cycle technologies. In electrorefining, spent nuclear fuel is treated electrochemically in order to effect separations between uranium, noble metals, and active metals, which include the transuranics. The accumulation of active metals in a lithium chloride-potassium chloride (LiCl-KCl) eutectic molten salt electrolyte occurs at the expense of the UCl3-oxidant concentration in the electrolyte, which must be periodically replenished. Our interests lie with the accumulation of active metals in the molten salt electrolyte. The real-time monitoring of actinide concentrations in the molten salt electrolyte is highly desirable for controlling electrochemical operations and assuring materials control and accountancy. However, real-time monitoring is not possible with current methods for sampling and chemical analysis. A new solid-state electrochemical sensor is being developed for real-time monitoring of actinide ion concentrations in a molten salt electrorefiner. The ultimate function of the sensor is to monitor plutonium concentrations during electrorefining operations, but in this work gadolinium was employed as a surrogate material for plutonium. In a parametric study, polycrystalline sodium beta double-prime alumina (Na-β(double p rime)-alumina) discs and tubes were subject to vapor-phase exchange with gadolinium ions (Gd3+) using a gadolinium chloride salt (GdCl3) as a precursor to produce gadolinium beta double-prime alumina (Gd-β(double p rime)-alumina) samples. Electrochemical impedance spectroscopy and microstructural analysis were performed on the ion-exchanged discs to determine the relationship between ion exchange and Gd3+ ion conductivity. The ion-exchanged tubes were configured as potentiometric sensors in order to monitor real-time Gd3+ ion concentrations in
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Bailey, Kaitlyn J; Little, Jonathan P; Jung, Mary E
2016-03-01
Exercise helps individuals with prediabetes or type 2 diabetes (T2D) manage their blood glucose (BG); however, exercise adherence in this population is dismal. In this pilot study we tested the efficacy of a self-monitoring group-based intervention using continuous glucose monitors (CGMs) at increasing exercise adherence in individuals with impaired BG. Thirteen participants with prediabetes or T2D were randomized to an 8-week standard care exercise program (CON condition) (n = 7) or self-monitoring exercise intervention (SM condition) (n = 6). Participants in the SM condition were taught how to self-monitor their exercise and BG, to goal set, and to use CGM to observe how exercise influences BG. We hypothesized that compared with the CON condition, using a real-time CGM would facilitate self-monitoring behavior, resulting in increased exercise adherence. Repeated-measures analysis of variance revealed significant Condition × Time interactions for self-monitoring (P goal setting (P = 0.01), and self-efficacy to self-monitor (P = 0.01), such that the SM condition showed greater increases in these outcomes immediately after the program and at the 1-month follow-up compared with the CON condition. The SM condition had higher program attendance rates (P = 0.03), and a greater proportion of participants reregistered for additional exercise programs (P = 0.048) compared with the CON condition. Participants in both conditions experienced improvements in health-related quality of life, waist circumference, and fitness (P values exercise behavior in individuals living with prediabetes or T2D.
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Program Evaluation Metrics for U.S. Army Lifelong Learning Centers
2007-03-01
SPONSORING/ MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. MONITOR ACRONYM ARI U.S. Army Research Institute for the Behavioral and Social Sciences 11. MONITOR ...e.g., technical support and planning); (3) telecommuting (largely the unique features of distance education); and (4) support (e.g., organizational...will be a greater reduction when I learn more about how to use the system. ()Reduced the time to zero ; I did not have to do admin tasks in class, so we
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Time-sensitive Customer Churn Prediction based on PU Learning
Wang, Li; Chen, Chaochao; Zhou, Jun; Li, Xiaolong
2018-01-01
With the fast development of Internet companies throughout the world, customer churn has become a serious concern. To better help the companies retain their customers, it is important to build a customer churn prediction model to identify the customers who are most likely to churn ahead of time. In this paper, we propose a Time-sensitive Customer Churn Prediction (TCCP) framework based on Positive and Unlabeled (PU) learning technique. Specifically, we obtain the recent data by shortening the...
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EEG Eye State Identification Using Incremental Attribute Learning with Time-Series Classification
Directory of Open Access Journals (Sweden)
Ting Wang
2014-01-01
Full Text Available Eye state identification is a kind of common time-series classification problem which is also a hot spot in recent research. Electroencephalography (EEG is widely used in eye state classification to detect human's cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper aims to propose a novel approach for EEG eye state identification using incremental attribute learning (IAL based on neural networks. IAL is a novel machine learning strategy which gradually imports and trains features one by one. Previous studies have verified that such an approach is applicable for solving a number of pattern recognition problems. However, in these previous works, little research on IAL focused on its application to time-series problems. Therefore, it is still unknown whether IAL can be employed to cope with time-series problems like EEG eye state classification. Experimental results in this study demonstrates that, with proper feature extraction and feature ordering, IAL can not only efficiently cope with time-series classification problems, but also exhibit better classification performance in terms of classification error rates in comparison with conventional and some other approaches.
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Ni Mhurchu, Cliona; Roberts, Vaughan; Maddison, Ralph; Dorey, Enid; Jiang, Yannan; Jull, Andrew; Tin Tin, Sandar
2009-11-01
This pilot study evaluated the feasibility (recruitment, retention, and acceptability) and preliminary efficacy of a six-week home-based electronic time monitor intervention on New Zealand children's television watching in 2008. Twenty-nine children aged 9 to 12 years who watched more than 20 h of television per week (62% male, mean age 10.4 years) were randomised to either the intervention or the control group. The intervention group received an electronic TV time monitor for 6 weeks and advice to restrict TV watching to 1 h per day or less. The control group was given verbal advice to restrict TV watching. Participant retention at 6 weeks was 93%. Semi-structured interviews with intervention families confirmed moderate acceptability of TV time monitors and several perceived benefits including better awareness of household TV viewing and improved time planning. Drawbacks reported included disruption to parents' TV watching and increased sibling conflict. Time spent watching television decreased by 4.2 h (mean change [SD]: -254 [536] min) per week in the intervention group compared with no change in the control group (-3 [241] min), but the difference between groups was not statistically significant, p=0.77. Both groups reported decreases in energy intake from snacks and total screen time and increases in physical activity measured by pedometer and between-group differences were not statistically significant. Electronic TV time monitors are feasible to use for home-based TV watching interventions although acceptability varies between families. Preliminary findings from this pilot suggest that such devices have potential to decrease children's TV watching but a larger trial is needed to confirm effectiveness. Future research should be family-orientated; take account of other screen time activities; and employ TV time monitors as just one of a range of strategies to decrease sedentary behaviour.
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Hunter, Louis N; Sison-Williamson, Mitell; Mendoza, Melissa M; McDonald, Craig M; Molitor, Fred; Mulcahey, M J; Betz, Randal R; Vogel, Lawrence C; Bagley, Anita
2008-06-15
Prospective multicenter observation. To determine the validity of 3 commercially available at recording thoracic-lumbar-sacral orthosis (TLSO) wearing time of children with spinal cord injury (SCI) and to assess each monitor's function during daily activities. A major limitation to studies assessing the effectiveness of spinal prophylactic bracing is the patient's compliance with the prescribed wearing time. Although some studies have begun to use objective compliance monitors, there is little documentation of the validity of the monitors during activities of daily life and no comparisons of available monitors. Fifteen children with SCI who wore a TLSO for paralytic scoliosis were observed for 4 days during their rehabilitation stay. Three compliance monitors (2 temperature and 1 pressure sensitive) were mounted onto each TLSO. Time of brace wear from the monitors was compared with the wear time per day recorded in diaries. Observed versus monitored duration of brace wear found the HOBO (temperature sensitive) to be the most valid compliance monitor. The HOBO had the lowest average of difference and variance of difference scores. The correlation between the recorded daily entries and monitored brace wear time was also highest for the HOBO in analysis of dependent and independent scores. Bland-Altman plots showed that the pressure sensitive monitor underestimated wear time whereas the temperature monitors overestimated wear time. Compliance to prescribed wearing schedule has been a barrier to studying TLSO efficacy. All 3 monitors were found to measure TLSO compliance, but the 2 temperature monitors were more in agreement with the daily diaries. Based on its functional advantages compared with the HOBO, the StowAway TidbiT will be used to further investigate the long-term compliance of TLSO bracing in children with SCI.
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Adaptive E-learning System in Secondary Education
Directory of Open Access Journals (Sweden)
Sofija Tosheva
2012-02-01
Full Text Available In this paper we describe an adaptive web application E-school, where students can adjust some features according to their preferences and learning style. This e-learning environment enables monitoring students progress, total time students have spent in the system, their activity on the forums, the overall achievements in lessons learned, tests performed and solutions to given projects. Personalized assistance that teacher provides in a traditional classroom is not easy to implement. Students have regular contact with teachers using e-mail tools and conversation, so teacher get mentoring role for each student. The results of exploitation of the e-learning system show positive impact in acquiring the material and improvement of student’s achievements.
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Johnson, Timothy C.; Slater, Lee D.; Ntarlagiannis, Dimitris; Day-Lewis, Frederick D.; Elwaseif, Mehrez
2012-01-01
Time-lapse resistivity imaging is increasingly used to monitor hydrologic processes. Compared to conventional hydrologic measurements, surface time-lapse resistivity provides superior spatial coverage in two or three dimensions, potentially high-resolution information in time, and information in the absence of wells. However, interpretation of time-lapse electrical tomograms is complicated by the ever-increasing size and complexity of long-term, three-dimensional (3-D) time series conductivity data sets. Here we use 3-D surface time-lapse electrical imaging to monitor subsurface electrical conductivity variations associated with stage-driven groundwater-surface water interactions along a stretch of the Columbia River adjacent to the Hanford 300 near Richland, Washington, USA. We reduce the resulting 3-D conductivity time series using both time-series and time-frequency analyses to isolate a paleochannel causing enhanced groundwater-surface water interactions. Correlation analysis on the time-lapse imaging results concisely represents enhanced groundwater-surface water interactions within the paleochannel, and provides information concerning groundwater flow velocities. Time-frequency analysis using the Stockwell (S) transform provides additional information by identifying the stage periodicities driving groundwater-surface water interactions due to upstream dam operations, and identifying segments in time-frequency space when these interactions are most active. These results provide new insight into the distribution and timing of river water intrusion into the Hanford 300 Area, which has a governing influence on the behavior of a uranium plume left over from historical nuclear fuel processing operations.
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Landfill cover performance monitoring using time domain reflectometry
International Nuclear Information System (INIS)
Neher, E.R.; Cotten, G.B.; McElroy, D.
1998-01-01
Time domain reflectometry (TDR) systems were installed to monitor soil moisture in two newly constructed landfill covers at the Idaho National Engineering and Environmental Laboratory. Each TDR system includes four vertical arrays with each array consisting of four TDR probes located at depths of 15, 30, 45, and 60 cm. The deepest probes at 60 cm were installed beneath a compacted soil layer to analyze infiltration through the compacted layer. Based on the TDR data, infiltration through the two covers between March and October, 1997 ranged from less than measurable to 1.5 cm. However, due to a prohibition on penetrating the buried waste and resulting limits on probe placement depths, deeper percolation was not evaluated. Some of the advantages found in the application of TDR for infiltration monitoring at this site are the relative low cost and rugged nature of the equipment. Also, of particular importance, the ability to collect frequent moisture measurements allows the capture and evaluation of soil moisture changes resulting from episodic precipitation events. Disadvantages include the inability to install the probes into the waste, difficulties in interpretation of infiltration during freeze/thaw periods, and some excessive noise in the data
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Search route decision of environmental monitoring at emergency time
International Nuclear Information System (INIS)
Aoyama, Isao
1979-01-01
The search route decision method is reviewed, especially the adequate arrangement of monitors in view of time in the information-gathering activity by transferring the monitors on the horizontal space after the confirmation of the abnormal release of radioactive material. As for the field of the theory of search, the developmental history is explained, namely the experiences of the naval anti submarine operation in WW-2, the salvage activities and the search problem on the sea. The kinematics for search, the probability theory for detection and the optimum distribution for search are the most important contents of the application of theory of search relating to the environmental monitoring at emergency condition. The combination of a search model consists of the peculiarity of targets, the peculiarity of observers and the standard of optimality. The peculiarity of targets is divided into the space of search, the number of targets, the way of appearance of targets and the motion of targets. The peculiarity of observers is divided into the number of observers, the divisibility of efforts for search, the credibility of search information and the search process. The standard of optimality is divided into the maximum probability of detection, the minimum risk expected and the others. Each item written above of search model is explained. Concerning the formulation of the search model, the theoretical equations for detection probability, discovery potential and instantaneous detection probability, density are derived, and these equations are evaluated and explained. The future plan is to advance the search technology so as to evaluate the detection potential to decide the route of running a monitoring car for a nuclear power plant at accidental condition. (Nakai, Y.)
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A Real-Time Measurement System for Long-Life Flood Monitoring and Warning Applications
Directory of Open Access Journals (Sweden)
Antonio Skarmeta Gómez
2012-03-01
Full Text Available A flood warning system incorporates telemetered rainfall and flow/water level data measured at various locations in the catchment area. Real-time accurate data collection is required for this use, and sensor networks improve the system capabilities. However, existing sensor nodes struggle to satisfy the hydrological requirements in terms of autonomy, sensor hardware compatibility, reliability and long-range communication. We describe the design and development of a real-time measurement system for flood monitoring, and its deployment in a flash-flood prone 650 km2 semiarid watershed in Southern Spain. A developed low-power and long-range communication device, so-called DatalogV1, provides automatic data gathering and reliable transmission. DatalogV1 incorporates self-monitoring for adapting measurement schedules for consumption management and to capture events of interest. Two tests are used to assess the success of the development. The results show an autonomous and robust monitoring system for long-term collection of water level data inmany sparse locations during flood events.
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Gijbels, David; Raemdonck, Isabel; Vervecken, Dries
2010-01-01
Based on the Demand-Control-Support (DCS) model, the present paper aims to investigate the influence of job characteristics such as job demands, job control, social support at work and self-directed learning orientation on the work-related learning behaviour of workers. The present study was conducted in a centre for part-time vocational education…
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High-Resolution Near Real-Time Drought Monitoring in South Asia
Aadhar, S.; Mishra, V.
2017-12-01
Drought in South Asia affect food and water security and pose challenges for millions of people. For policy-making, planning and management of water resources at the sub-basin or administrative levels, high-resolution datasets of precipitation and air temperature are required in near-real time. Here we develop a high resolution (0.05 degree) bias-corrected precipitation and temperature data that can be used to monitor near real-time drought conditions over South Asia. Moreover, the dataset can be used to monitor climatic extremes (heat waves, cold waves, dry and wet anomalies) in South Asia. A distribution mapping method was applied to correct bias in precipitation and air temperature (maximum and minimum), which performed well compared to the other bias correction method based on linear scaling. Bias-corrected precipitation and temperature data were used to estimate Standardized precipitation index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) to assess the historical and current drought conditions in South Asia. We evaluated drought severity and extent against the satellite-based Normalized Difference Vegetation Index (NDVI) anomalies and satellite-driven Drought Severity Index (DSI) at 0.05˚. We find that the bias-corrected high-resolution data can effectively capture observed drought conditions as shown by the satellite-based drought estimates. High resolution near real-time dataset can provide valuable information for decision-making at district and sub- basin levels.
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Probability Learning: Changes in Behavior across Time and Development
Plate, Rista C.; Fulvio, Jacqueline M.; Shutts, Kristin; Green, C. Shawn; Pollak, Seth D.
2018-01-01
Individuals track probabilities, such as associations between events in their environments, but less is known about the degree to which experience--within a learning session and over development--influences people's use of incoming probabilistic information to guide behavior in real time. In two experiments, children (4-11 years) and adults…
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Learning of temporal motor patterns: An analysis of continuous vs. reset timing
Directory of Open Access Journals (Sweden)
Rodrigo eLaje
2011-10-01
Full Text Available Our ability to generate well-timed sequences of movements is critical to an array of behaviors, including the ability to play a musical instrument or a video game. Here we address two questions relating to timing with the goal of better understanding the neural mechanisms underlying temporal processing. First, how does accuracy and variance change over the course of learning of complex spatiotemporal patterns? Second, is the timing of sequential responses most consistent with starting and stopping an internal timer at each interval or with continuous timing?To address these questions we used a psychophysical task in which subjects learned to reproduce a sequence of finger taps in the correct order and at the correct times—much like playing a melody at the piano. This task allowed us to calculate the variance of the responses at different time points using data from the same trials. Our results show that while standard Weber’s law is clearly violated, variance does increase as a function of time squared, as expected according to the generalized form of Weber’s law—which separates the source of variance into time-dependent and time-independent components. Over the course of learning, both the time-independent variance and the coefficient of the time-dependent term decrease. Our analyses also suggest that timing of sequential events does not rely on the resetting of an internal timer at each event.We describe and interpret our results in the context of computer simulations that capture some of our psychophysical findings. Specifically, we show that continuous timing, as opposed to reset timing, is expected from population clock models in which timing emerges from the internal dynamics of recurrent neural networks.
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A Distance Learning Review--The Communicational Module "Learning on Demand--Anywhere at Any Time"
Tatkovic, Nevenka; Ruzic, Maja
2004-01-01
The society of knowledge refers to the society marked with the principle which requires that knowledge, information and life-time learning hold a key to success in the world of IT technology. Internet, World Wide Web, Web Based Education and ever so growing speed of IT and communicational technologies have enabled the application of new modes,…
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Overlay improvements using a real time machine learning algorithm
Schmitt-Weaver, Emil; Kubis, Michael; Henke, Wolfgang; Slotboom, Daan; Hoogenboom, Tom; Mulkens, Jan; Coogans, Martyn; ten Berge, Peter; Verkleij, Dick; van de Mast, Frank
2014-04-01
While semiconductor manufacturing is moving towards the 14nm node using immersion lithography, the overlay requirements are tightened to below 5nm. Next to improvements in the immersion scanner platform, enhancements in the overlay optimization and process control are needed to enable these low overlay numbers. Whereas conventional overlay control methods address wafer and lot variation autonomously with wafer pre exposure alignment metrology and post exposure overlay metrology, we see a need to reduce these variations by correlating more of the TWINSCAN system's sensor data directly to the post exposure YieldStar metrology in time. In this paper we will present the results of a study on applying a real time control algorithm based on machine learning technology. Machine learning methods use context and TWINSCAN system sensor data paired with post exposure YieldStar metrology to recognize generic behavior and train the control system to anticipate on this generic behavior. Specific for this study, the data concerns immersion scanner context, sensor data and on-wafer measured overlay data. By making the link between the scanner data and the wafer data we are able to establish a real time relationship. The result is an inline controller that accounts for small changes in scanner hardware performance in time while picking up subtle lot to lot and wafer to wafer deviations introduced by wafer processing.
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Radiation environmental real-time monitoring and dispersion modeling: A comprehensive solution
International Nuclear Information System (INIS)
Kovacik, A.; Bartokova, I.; Omelka, J.; Melicherova, T.
2014-01-01
The system of real-time radiation monitoring provided by MicroStep-MIS is a turn-key solution for measurement, acquisition, processing, reporting, archiving and displaying of various radiation data. At the level of measurements, the monitoring stations can be equipped with various devices from radiation probes, measuring the actual ambient gamma dose rate, to fully automated aerosol monitors, returning analysis results of natural and manmade radionuclides concentrations in the air. Using data gathered by our radiation probes RPSG-05 integrated into monitoring network of Crisis Management of the Slovak Republic and into monitoring network of Slovak Hydrometeorological Institute, we demonstrate its reliability and long-term stability of measurements. Data from RPSG-05 probes and GammaTracer probes, both of these types are used in the SHI network, are compared. The sensitivity of RPSG-05 is documented on data where changes of dose rate are caused by precipitation. Qualities of RPSG-05 probe are illustrated also on example of its use in radiation monitoring network in the United Arab Emirates. A more detailed information about radioactivity of the atmosphere can be obtained by using spectrometric detectors (e.g. scintillation detectors) which, besides gamma dose rate values, offer also a possibility to identify different radionuclides. However, this possibility is limited by technical parameters of detector like energetic resolution and detection efficiency in given geometry of measurement. A clearer information with less doubts can be obtained from aerosol monitors with a built-in silicon detector of alpha and beta particles and with an electrically cooled HPGe detector dedicated for gamma-ray spectrometry, which is performed during the sampling. Data from a complex radiation monitoring network can be used, together with meteorological data, in radiation dispersion model by MicroStep-MIS. This model serves for simulation of atmospheric propagation of radionuclides
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Correction of time resolution of an ambulatory cardiac monitor (VEST)
International Nuclear Information System (INIS)
Kumita, Shin-ichiro; Nishimura, Tsunehiko; Hayashida, Kohei; Uehara, Toshiisa
1990-01-01
Using ambulatory cardiac monitor (VEST) at exercise study, its time resolution is very important factor. We evaluated the time resolution of VEST using pulsate cardiac baloon phantom. Four analysis were carried out; no smoothing (NS) method, 3 points smoothing (3S) method, short sampling interval (SS) method, and digital filter (DF) method. By comparison of |ΔEF| (|EF:HR120-EF: HR60|) among 4 analysis methods, |ΔEF| by DF method was significant small (NS:3.58±3.01, 3S: 4.46±0.95, SS: 3.35±3.26, DF: 1.11±1.28%). We conclude that correction of time resolution by digital filter is necessary when we use VEST during exercise. (author)
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Love, Jeffrey J.; Finn, Carol A.
2017-07-01
An examination is made of opportunities and challenges for enhancing global, real-time geomagnetic monitoring that would be beneficial for a variety of operational projects. This enhancement in geomagnetic monitoring can be attained by expanding the geographic distribution of magnetometer stations, improving the quality of magnetometer data, increasing acquisition sampling rates, increasing the promptness of data transmission, and facilitating access to and use of the data. Progress will benefit from new partnerships to leverage existing capacities and harness multisector, cross-disciplinary, and international interests.
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Real-Time In Vivo Monitoring of Reactive Oxygen Species in Guard Cells.
Park, Ky Young; Roubelakis-Angelakis, Kalliopi A
2018-01-01
The intra-/intercellular homeostasis of reactive oxygen species (ROS), and especially of superoxides (O 2 .- ) and hydrogen peroxide (O 2 .- ) participate in signalling cascades which dictate developmental processes and reactions to biotic/abiotic stresses. Polyamine oxidases terminally oxidize/back convert polyamines generating H 2 O 2 . Recently, an NADPH-oxidase/Polyamine oxidase feedback loop was identified to control oxidative burst under salinity. Thus, the real-time localization/monitoring of ROS in specific cells, such as the guard cells, can be of great interest. Here we present a detailed description of the real-time in vivo monitoring of ROS in the guard cells using H 2 O 2 - and O 2 .- specific fluorescing probes, which can be used for studying ROS accumulation generated from any source, including the amine oxidases-dependent pathway, during development and stress.
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Real-time monitoring of atom vapor concentration with laser absorption spectroscopy
International Nuclear Information System (INIS)
Fan Fengying; Gao Peng; Jiang Tao
2012-01-01
The technology of laser absorption spectroscopy was used for real-time monitoring of gadolinium atom vapor concentration measurement and the solid state laser pumped ring dye laser was used as optical source. The optical fiber was taken to improve the stability of laser transmission. The multi-pass absorption technology combined with reference optical signal avoided the influence of laser power fluctuation. The experiment result shows that the system based on this detection method has a standard error of 4%. It is proved that the monitoring system provides reliable data for atom vapor laser isotope separation process and the separation efficiency can be improved. (authors)
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Meyer, F. J.; Webley, P.; Dehn, J.; Arko, S. A.; McAlpin, D. B.
2013-12-01
Volcanic eruptions are among the most significant hazards to human society, capable of triggering natural disasters on regional to global scales. In the last decade, remote sensing techniques have become established in operational forecasting, monitoring, and managing of volcanic hazards. Monitoring organizations, like the Alaska Volcano Observatory (AVO), are nowadays heavily relying on remote sensing data from a variety of optical and thermal sensors to provide time-critical hazard information. Despite the high utilization of these remote sensing data to detect and monitor volcanic eruptions, the presence of clouds and a dependence on solar illumination often limit their impact on decision making processes. Synthetic Aperture Radar (SAR) systems are widely believed to be superior to optical sensors in operational monitoring situations, due to the weather and illumination independence of their observations and the sensitivity of SAR to surface changes and deformation. Despite these benefits, the contributions of SAR to operational volcano monitoring have been limited in the past due to (1) high SAR data costs, (2) traditionally long data processing times, and (3) the low temporal sampling frequencies inherent to most SAR systems. In this study, we present improved data access, data processing, and data integration techniques that mitigate some of the above mentioned limitations and allow, for the first time, a meaningful integration of SAR into operational volcano monitoring systems. We will introduce a new database interface that was developed in cooperation with the Alaska Satellite Facility (ASF) and allows for rapid and seamless data access to all of ASF's SAR data holdings. We will also present processing techniques that improve the temporal frequency with which hazard-related products can be produced. These techniques take advantage of modern signal processing technology as well as new radiometric normalization schemes, both enabling the combination of
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An efficient flow-based botnet detection using supervised machine learning
DEFF Research Database (Denmark)
Stevanovic, Matija; Pedersen, Jens Myrup
2014-01-01
Botnet detection represents one of the most crucial prerequisites of successful botnet neutralization. This paper explores how accurate and timely detection can be achieved by using supervised machine learning as the tool of inferring about malicious botnet traffic. In order to do so, the paper...... introduces a novel flow-based detection system that relies on supervised machine learning for identifying botnet network traffic. For use in the system we consider eight highly regarded machine learning algorithms, indicating the best performing one. Furthermore, the paper evaluates how much traffic needs...... to accurately and timely detect botnet traffic using purely flow-based traffic analysis and supervised machine learning. Additionally, the results show that in order to achieve accurate detection traffic flows need to be monitored for only a limited time period and number of packets per flow. This indicates...
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Real Time Environmental Radiation Monitoring System in the Philippines
International Nuclear Information System (INIS)
Garcia, Teofilo Y.
2015-01-01
The widespread release of radioactive materials caused by the Fukushima Daiichi Nuclear Power Plant Accident that occurred on 11 March 2011 raised concerns on the environmental radiation monitoring Presently, the Philippine Nuclear Research Institute (PNRI) can only perform limited incident. Country-wide radiation measurements by carrying out field-works in the different provinces of the country. This is due to limitation in the availability of appropriate equipment to carry-out the task of conducting radiation measurements, especially in remote and hart to access areas of the country. Although no nuclear reactor is currently operating in the Philippines, it is situated in a region surrounded by neighboring countries with several existing or planned nuclear power plants. While nuclear power has tremendous benefits in meeting the electricity needs of growing populations, and does not have the adverse environmental effects associated with burning of fossil fuels, there are potential risks from releases of radio nuclides into the environment. The PNRI, through the support of the International Atomic Energy Agency (IAEA), is establishing an on-line environmental radiation monitoring system that can provide real-time environmental during emergencies that lead to extensive spread of radioactive materials, such as nuclear power plant accidents, an on-line radiation monitoring system will enable the immediate detection of radiological emergencies affecting the country and will provide important information of authorities for appropriate emergency response. (author)
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Improving GNSS time series for volcano monitoring: application to Canary Islands (Spain)
García-Cañada, Laura; Sevilla, Miguel J.; Pereda de Pablo, Jorge; Domínguez Cerdeña, Itahiza
2017-04-01
The number of permanent GNSS stations has increased significantly in recent years for different geodetic applications such as volcano monitoring, which require a high precision. Recently we have started to have coordinates time series long enough so that we can apply different analysis and filters that allow us to improve the GNSS coordinates results. Following this idea we have processed data from GNSS permanent stations used by the Spanish Instituto Geográfico Nacional (IGN) for volcano monitoring in Canary Islands to obtained time series by double difference processing method with Bernese v5.0 for the period 2007-2014. We have identified the characteristics of these time series and obtained models to estimate velocities with greater accuracy and more realistic uncertainties. In order to improve the results we have used two kinds of filters to improve the time series. The first, a spatial filter, has been computed using the series of residuals of all stations in the Canary Islands without an anomalous behaviour after removing a linear trend. This allows us to apply this filter to all sets of coordinates of the permanent stations reducing their dispersion. The second filter takes account of the temporal correlation in the coordinate time series for each station individually. A research about the evolution of the velocity depending on the series length has been carried out and it has demonstrated the need for using time series of at least four years. Therefore, in those stations with more than four years of data, we calculated the velocity and the characteristic parameters in order to have time series of residuals. This methodology has been applied to the GNSS data network in El Hierro (Canary Islands) during the 2011-2012 eruption and the subsequent magmatic intrusions (2012-2014). The results show that in the new series it is easier to detect anomalous behaviours in the coordinates, so they are most useful to detect crustal deformations in volcano monitoring.
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Study of weld quality real-time monitoring system for auto-body assembly
Xu, Jun; Li, Yong-Bing; Chen, Guan-Long
2005-12-01
Resistance spot welding (RSW) is widely used for the auto-body assembly in automotive industry. But RSW suffers from a major problem of inconsistent quality from weld to weld. The major problem is the complexity of the basic process that may involve material coatings, electrode force, electrode wear, fit up, etc. Therefore weld quality assurance is still a big challenge and goal. Electrode displacement has proved to be a particularly useful signal which correlates well with weld quality. This paper introduces a novel auto-body spot weld quality monitoring system which uses electrode displacement as the quality parameter. This system chooses the latest laser displacement sensor with high resolution to measure the real-time electrode displacement. It solves the interference problem of sensor mounting by designing special fixture, and can be successfully applied on the portable welding machine. It is capable of evaluating weld quality and making diagnosis of process variations such as surface asperities, shunting, worn electrode and weld expansion with real-time electrode displacement. As proved by application in the workshop, the monitoring system has good stability and reliability, and is qualified for monitoring weld quality in process.
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Furlong, K. P.; Benz, H.; Hayes, G. P.; Villasenor, A.
2010-12-01
Although most would agree that the occurrence of natural disaster events such as earthquakes, volcanic eruptions, and floods can provide effective learning opportunities for natural hazards-based courses, implementing compelling materials into the large-enrollment classroom environment can be difficult. These natural hazard events derive much of their learning potential from their real-time nature, and in the modern 24/7 news-cycle where all but the most devastating events are quickly out of the public eye, the shelf life for an event is quite limited. To maximize the learning potential of these events requires that both authoritative information be available and course materials be generated as the event unfolds. Although many events such as hurricanes, flooding, and volcanic eruptions provide some precursory warnings, and thus one can prepare background materials to place the main event into context, earthquakes present a particularly confounding situation of providing no warning, but where context is critical to student learning. Attempting to implement real-time materials into large enrollment classes faces the additional hindrance of limited internet access (for students) in most lecture classrooms. In Earth 101 Natural Disasters: Hollywood vs Reality, taught as a large enrollment (150+ students) general education course at Penn State, we are collaborating with the USGS’s National Earthquake Information Center (NEIC) to develop efficient means to incorporate their real-time products into learning activities in the lecture hall environment. Over time (and numerous events) we have developed a template for presenting USGS-produced real-time information in lecture mode. The event-specific materials can be quickly incorporated and updated, along with key contextual materials, to provide students with up-to-the-minute current information. In addition, we have also developed in-class activities, such as student determination of population exposure to severe ground
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Zhang, Runkun; Huang, Wanting; Li, Gongke; Hu, Yufei
2017-03-21
The development of noninvasive methods for real-time in vivo analysis is of great significant, which provides powerful tools for medical research and clinical diagnosis. In the present work, we described a new strategy based on cataluminescence (CTL) for real-time in vivo clinical breath analysis. To illustrate such strategy, a homemade real-time CTL monitoring system characterized by coupling an online sampling device with a CTL sensor for sevoflurane (SVF) was designed, and a real-time in vivo method for the monitoring of SVF in exhaled breath was proposed. The accuracy of the method was evaluated by analyzing the real exhaled breath samples, and the results were compared with those obtained by GC/MS. The measured data obtained by the two methods were in good agreement. Subsequently, the method was applied to real-time monitoring of SVF in exhaled breath from rat models of the control group to investigate elimination pharmacokinetics. In order to further probe the potential of the method for clinical application, the elimination pharmacokinetics of SVF from rat models of control group, liver fibrosis group alcohol liver group, and nonalcoholic fatty liver group were monitored by the method. The raw data of pharmacokinetics of different groups were normalized and subsequently subjected to linear discriminant analysis (LDA). These data were transformed to canonical scores which were visualized as well-clustered with the classification accuracy of 100%, and the overall accuracy of leave-one-out cross-validation procedure is 88%, thereby indicating the utility of the potential of the method for liver disease diagnosis. Our strategy undoubtedly opens up a new door for real-time clinical analysis in a pain-free and noninvasive way and also guides a promising development direction for CTL.
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Design and development of a run-time monitor for multi-core architectures in cloud computing.
Kang, Mikyung; Kang, Dong-In; Crago, Stephen P; Park, Gyung-Leen; Lee, Junghoon
2011-01-01
Cloud computing is a new information technology trend that moves computing and data away from desktops and portable PCs into large data centers. The basic principle of cloud computing is to deliver applications as services over the Internet as well as infrastructure. A cloud is a type of parallel and distributed system consisting of a collection of inter-connected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resources. The large-scale distributed applications on a cloud require adaptive service-based software, which has the capability of monitoring system status changes, analyzing the monitored information, and adapting its service configuration while considering tradeoffs among multiple QoS features simultaneously. In this paper, we design and develop a Run-Time Monitor (RTM) which is a system software to monitor the application behavior at run-time, analyze the collected information, and optimize cloud computing resources for multi-core architectures. RTM monitors application software through library instrumentation as well as underlying hardware through a performance counter optimizing its computing configuration based on the analyzed data.
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Design and Development of a Run-Time Monitor for Multi-Core Architectures in Cloud Computing
Directory of Open Access Journals (Sweden)
Junghoon Lee
2011-03-01
Full Text Available Cloud computing is a new information technology trend that moves computing and data away from desktops and portable PCs into large data centers. The basic principle of cloud computing is to deliver applications as services over the Internet as well as infrastructure. A cloud is a type of parallel and distributed system consisting of a collection of inter-connected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resources. The large-scale distributed applications on a cloud require adaptive service-based software, which has the capability of monitoring system status changes, analyzing the monitored information, and adapting its service configuration while considering tradeoffs among multiple QoS features simultaneously. In this paper, we design and develop a Run-Time Monitor (RTM which is a system software to monitor the application behavior at run-time, analyze the collected information, and optimize cloud computing resources for multi-core architectures. RTM monitors application software through library instrumentation as well as underlying hardware through a performance counter optimizing its computing configuration based on the analyzed data.
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Why Hong Kong students favour more face-to-face classroom time in blended learning
Directory of Open Access Journals (Sweden)
James Henri
Full Text Available A three year study in student characteristics, needs and learning styles guided instructors at the University of Hong Kong Faculty of Education to improve teaching and learning in a core module: Information Literacy. A mixed-method approach analyzed data collected from undergraduate, in-service teachers in a BEd program, and helped instructors in the program to gain insight into the Hong Kong teacher working, post-service towards a BEd in Library and Information Science. Part-time students indicated a preference for a combination of online and face-to-face teaching, with more face-to-face class time in that mix. These findings would also be informative for other part-time programs using blended teaching and learning models.
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A high-resolution mini-microscope system for wireless real-time monitoring.
Wang, Zongjie; Boddeda, Akash; Parker, Benjamin; Samanipour, Roya; Ghosh, Sanjoy; Menard, Frederic; Kim, Keekyoung
2017-09-04
Compact, cost-effective and high-performance microscope that enables the real-time imaging of cells and lab-on-a-chip devices is highly demanded for cell biology and biomedical engineering. This paper aims to present the design and application of an inexpensive wireless mini-microscope with resolution up to 2592 × 1944 pixels and speed up to 90 fps. The mini-microscope system was built on a commercial embedded system (Raspberry Pi). We modified a camera module and adopted an inverse dual lens system to obtain the clear field of view and appropriate magnification for tens of micrometer objects. The system was capable of capturing time-lapse images and transferring image data wirelessly. The entire system can be operated wirelessly and cordlessly in a conventional cell culturing incubator. The developed mini-microscope was used to monitor the attachment and proliferation of NIH-3T3 and HEK 293 cells inside an incubator for 50 hours. In addition, the mini-microscope was used to monitor a droplet generation process in a microfluidic device. The high-quality images captured by the mini-microscope enabled us an automated analysis of experimental parameters. The successful applications prove the great potential of the developed mini-microscope for monitoring various biological samples and microfluidic devices. This paper presents the design of a high resolution mini-microscope system that enables the wireless real-time imaging of cells inside the incubator. This system has been verified to be a useful tool to obtain high-quality images and videos for the automated quantitative analysis of biological samples and lab-on-a-chip devices in the long term.
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The Application of Foundation Pit Monitoring Technology to the Excavation
Directory of Open Access Journals (Sweden)
Qiu Jin
2015-01-01
Full Text Available The foundation pit monitoring plays an important role in the foundation pit supporting projects especially in those deep foundation pit projects. Through the whole monitoring of the foundation pit construction from the excavation to the backfill, we can learn about the forcing and deforming process of the foundation pit supporting system, and grasp the impact of external condition changes on the foundation pit. This paper takes a project in Jinan as an example to establish a specific monitoring program, and then conducts the analysis and evaluation of the monitoring data; the real-time grasp of the foundation pit deformation and internal force changes can help to further ensure the security status of the foundation pit, thus better guiding the construction.
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Eden, Annuschka Salima; Zwitserlood, Pienie; Keuper, Katharina; Junghöfer, Markus; Laeger, Inga; Zwanzger, Peter; Dobel, Christian
2014-01-01
The well-established memory bias for arousing-negative stimuli seems to be enhanced in high trait-anxious persons and persons suffering from anxiety disorders. We monitored the emergence and development of such a bias during and after learning, in high and low trait anxious participants. A word-learning paradigm was applied, consisting of spoken pseudowords paired either with arousing-negative or neutral pictures. Learning performance during training evidenced a short-lived advantage for arou...
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Monitoring Acidophilic Microbes with Real-Time Polymerase Chain Reaction (PCR) Assays
Energy Technology Data Exchange (ETDEWEB)
Frank F. Roberto
2008-08-01
Many techniques that are used to characterize and monitor microbial populations associated with sulfide mineral bioleaching require the cultivation of the organisms on solid or liquid media. Chemolithotrophic species, such as Acidithiobacillus ferrooxidans and Leptospirillum ferrooxidans, or thermophilic chemolithotrophs, such as Acidianus brierleyi and Sulfolobus solfataricus can grow quite slowly, requiring weeks to complete efforts to identify and quantify these microbes associated with bioleach samples. Real-time PCR (polymerase chain reaction) assays in which DNA targets are amplified in the presence of fluorescent oligonucleotide primers, allowing the monitoring and quantification of the amplification reactions as they progress, provide a means of rapidly detecting the presence of microbial species of interest, and their relative abundance in a sample. This presentation will describe the design and use of such assays to monitor acidophilic microbes in the environment and in bioleaching operations. These assays provide results within 2-3 hours, and can detect less than 100 individual microbial cells.
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Learning motion concepts using real-time microcomputer-based laboratory tools
Thornton, Ronald K.; Sokoloff, David R.
1990-09-01
Microcomputer-based laboratory (MBL) tools have been developed which interface to Apple II and Macintosh computers. Students use these tools to collect physical data that are graphed in real time and then can be manipulated and analyzed. The MBL tools have made possible discovery-based laboratory curricula that embody results from educational research. These curricula allow students to take an active role in their learning and encourage them to construct physical knowledge from observation of the physical world. The curricula encourage collaborative learning by taking advantage of the fact that MBL tools present data in an immediately understandable graphical form. This article describes one of the tools—the motion detector (hardware and software)—and the kinematics curriculum. The effectiveness of this curriculum compared to traditional college and university methods for helping students learn basic kinematics concepts has been evaluated by pre- and post-testing and by observation. There is strong evidence for significantly improved learning and retention by students who used the MBL materials, compared to those taught in lecture.
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Definition of a near real-time microbiological monitor for application in space vehicles
Kilgore, Melvin V., Jr.; Zahorchak, Robert J.; Arendale, William F.; Woodward, Samuel S.; Pierson, Duane L.
1989-01-01
The concepts and methodologies for microbiological monitoring in space are examined, focusing on the determination of the requirements of a near real-time microbiological monitor. Results are presented from the technical evaluation of five microbiological monitor concepts, including cultural methods, single cell detection, biomolecular detection, specific product detection, and general molecular composition. Within these concepts, twenty-eight specific methodolgies were assessed and the five candidate methodologies with the highest engineering and feasibility scores were selected for further evaluations. The candidate methodologies are laser light scattering, primary fluorescence, secondary fluorescence, volatile product detection, and electronic particle detection. The advantages and disadvantages of these five candidate methodologies are discussed.
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Cerebellar motor learning versus cerebellar motor timing: the climbing fibre story
Llinás, Rodolfo R
2011-01-01
Abstract Theories concerning the role of the climbing fibre system in motor learning, as opposed to those addressing the olivocerebellar system in the organization of motor timing, are briefly contrasted. The electrophysiological basis for the motor timing hypothesis in relation to the olivocerebellar system is treated in detail. PMID:21486816
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EDUCATIONAL LEAPFROGGING IN THE mLEARNING TIME
Directory of Open Access Journals (Sweden)
Abdel Rahman IBRAHIM SULEIMAN
2014-07-01
Full Text Available In this theoretical study, researcher tries to shed light on the modern strategy of education, Mobile learning is this strategy, which has become a reality exists in the educational institutions and aims researcher of this study. Trying to figure out the reality of Mobil Determining if the mobile learning part of the E-Learning. Trying for identify future of mobile learning. And the researcher collect the information and the data from previous research in addition to what has been published on websites and blogs and has reached the researcher to achieve the successes of Mobile learning at the level of the educational process now , and that this strategy of mobile learning is not part of the e-learning, and generation of generations , but a new way for the development of the educational process educational , researcher is expected to evolve Mobile learning expands even at the all levels of educational.
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Pellerin, Brian A.; Bergamaschi, Brian A.; Murdoch, Peter S.; Downing, Bryan D.; Saraceno, John Franco; Aiken, George R.; Striegl, Robert G.
2011-01-01
Floods, hurricanes, and longer-term changes in climate and land use can have profound effects on water quality due to shifts in hydrologic flow paths, water residence time, precipitation patterns, connectivity between rivers and uplands, and many other factors. In order to understand and respond to changes in hydrology and water quality, resource managers and policy makers have a need for accurate and early indicators, as well as the ability to assess possible mechanisms and likely outcomes. In-situ optical sensors-those making continuous measurements of constituents by absorbance or fluorescence properties in the environment at timescales of minutes to years-have a long history in oceanography for developing highly resolved concentrations and fluxes, but are not commonly used in freshwater systems. The United States Geological Survey (USGS) has developed the Aquatic Real-Time Monitoring Network, with high-resolution optical data collection for organic carbon, nutrients, and sediment in large coastal rivers, along with continuous measurements of discharge, water temperature, and dissolved inorganic carbon. The collecting of continuous water-quality data in the Nation?s waterways has revealed temporal trends and spatial patterns in constituents that traditional sampling approaches fail to capture, and will serve a critical role in monitoring, assessment and decision-making in a rapidly changing landscape.
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The new Athens center on data processing from the neutron monitor network in real time
Directory of Open Access Journals (Sweden)
Mavromichalaki
2005-11-01
Full Text Available The ground-based neutron monitors (NMs record galactic and solar relativistic cosmic rays which can play a useful key role in space weather forecasting, as a result of their interaction with interplanetary disturbances. The Earth's-based neutron monitor network has been used in order to produce a real-time prediction of space weather phenomena. Therefore, the Athens Neutron Monitor Data Processing Center (ANMODAP takes advantage of this unique multi-directional device to solve problems concerning the diagnosis and forecasting of space weather. At this moment there has been a multi-sided use of neutron monitors. On the one hand, a preliminary alert for ground level enhancements (GLEs may be provided due to relativistic solar particles and can be registered around 20 to 30 min before the arrival of the main part of lower energy particles responsible for radiation hazard. To make a more reliable prognosis of these events, real time data from channels of lower energy particles and X-ray intensity from the GOES satellite are involved in the analysis. The other possibility is to search in real time for predictors of geomagnetic storms when they occur simultaneously with Forbush effects, using hourly, on-line accessible neutron monitor data from the worldwide network and applying a special method of processing. This chance of prognosis is only being elaborated and considered here as one of the possible uses of the Neutron Monitor Network for forecasting the arrival of interplanetary disturbance to the Earth. The achievements, the processes and the future results, are discussed in this work.
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Improvements in real time {sup 222}Rn monitoring at Stromboli volcano
Energy Technology Data Exchange (ETDEWEB)
Lavagno, A., E-mail: andrea.lavagno@polito.it [Dipartimento di Scienze Applicata e Tecnologia, Politecnico di Torino (Italy); INFN, Sezione di Torino (Italy); Laiolo, M. [Dipartimento di Scienze della Terra, Università di Torino (Italy); Gervino, G. [Dipartimento di Fisica, Università di Torino (Italy); INFN, Sezione di Torino (Italy); Cigolini, C.; Coppola, D.; Piscopo, D. [Dipartimento di Scienze della Terra, Università di Torino (Italy); Marino, C. [Dipartimento di Fisica, Università di Torino (Italy); INFN, Sezione di Torino (Italy)
2013-08-01
Monitoring gas emissions from soil allow to get information on volcanic activity, hidden faults and hydrothermal dynamics. Radon activities at Stromboli were collected by means of multi-parametric real-time stations, that measure radon as well as environmental parameters. The last improvements on the detection system are presented and discussed.
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Time-series modeling of long-term weight self-monitoring data.
Helander, Elina; Pavel, Misha; Jimison, Holly; Korhonen, Ilkka
2015-08-01
Long-term self-monitoring of weight is beneficial for weight maintenance, especially after weight loss. Connected weight scales accumulate time series information over long term and hence enable time series analysis of the data. The analysis can reveal individual patterns, provide more sensitive detection of significant weight trends, and enable more accurate and timely prediction of weight outcomes. However, long term self-weighing data has several challenges which complicate the analysis. Especially, irregular sampling, missing data, and existence of periodic (e.g. diurnal and weekly) patterns are common. In this study, we apply time series modeling approach on daily weight time series from two individuals and describe information that can be extracted from this kind of data. We study the properties of weight time series data, missing data and its link to individuals behavior, periodic patterns and weight series segmentation. Being able to understand behavior through weight data and give relevant feedback is desired to lead to positive intervention on health behaviors.
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Time-place learning and memory persist in mice lacking functional Per1 and Per2 clock genes.
Mulder, C; Van Der Zee, E A; Hut, R A; Gerkema, M P
2013-12-01
With time-place learning, animals link a stimulus with the location and the time of day. This ability may optimize resource localization and predator avoidance in daily changing environments. Time-place learning is a suitable task to study the interaction of the circadian system and memory. Previously, we showed that time-place learning in mice depends on the circadian system and Cry1 and/or Cry2 clock genes. We questioned whether time-place learning is Cry specific or also depends on other core molecular clock genes. Here, we show that Per1/Per2 double mutant mice, despite their arrhythmic phenotype, acquire time-place learning similar to wild-type mice. As well as an established role in circadian rhythms, Per genes have also been implicated in the formation and storage of memory. We found no deficiencies in short-term spatial working memory in Per mutant mice compared to wild-type mice. Moreover, both Per mutant and wild-type mice showed similar long-term memory for contextual features of a paradigm (a mild foot shock), measured in trained mice after a 2-month nontesting interval. In contrast, time-place associations were lost in both wild-type and mutant mice after these 2 months, suggesting a lack of maintained long-term memory storage for this type of information. Taken together, Cry-dependent time-place learning does not require Per genes, and Per mutant mice showed no PER-specific short-term or long-term memory deficiencies. These results limit the functional role of Per clock genes in the circadian regulation of time-place learning and memory.
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Learning Goals and Strategies in the Self-regulation of Learning
Gaeta Gonzalez, Martha Leticia
2013-01-01
In order to self-regulate their learning, students need to use different strategies to plan, monitor, and evaluate their learning activities (meta-cognitive strategies), as well as to control their motivation and emotion (volitional strategies). Students' effectiveness in their self-regulated learning process also varies depending on the academic…
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Real-time monitoring of cisplatin-induced cell death.
Directory of Open Access Journals (Sweden)
Hamed Alborzinia
Full Text Available Since the discovery of cisplatin more than 40 years ago and its clinical introduction in the 1970s an enormous amount of research has gone into elucidating the mechanism of action of cisplatin on tumor cells. With a novel cell biosensor chip system allowing continuous monitoring of respiration, glycolysis, and impedance we followed cisplatin treatment of different cancer cell lines in real-time. Our measurements reveal a first effect on respiration, in all cisplatin treated cell lines, followed with a significant delay by interference with glycolysis in HT-29, HCT-116, HepG2, and MCF-7 cells but not in the cisplatin-resistant cell line MDA-MB-231. Most strikingly, cell death started in all cisplatin-sensitive cell lines within 8 to 11 h of treatment, indicating a clear time frame from exposure, first response to cisplatin lesions, to cell fate decision. The time points of most significant changes were selected for more detailed analysis of cisplatin response in the breast cancer cell line MCF-7. Phosphorylation of selected signal transduction mediators connected with cellular proliferation, as well as changes in gene expression, were analyzed in samples obtained directly from sensor chips at the time points when changes in glycolysis and impedance occurred. Our online cell biosensor measurements reveal for the first time the time scale of metabolic response until onset of cell death under cisplatin treatment, which is in good agreement with models of p53-mediated cell fate decision.
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Cui, Yi-an; Liu, Lanbo; Zhu, Xiaoxiong
2017-08-01
Monitoring the extent and evolution of contaminant plumes in local and regional groundwater systems from existing landfills is critical in contamination control and remediation. The self-potential survey is an efficient and economical nondestructive geophysical technique that can be used to investigate underground contaminant plumes. Based on the unscented transform, we have built a Kalman filtering cycle to conduct time-lapse data assimilation for monitoring the transport of solute based on the solute transport experiment using a bench-scale physical model. The data assimilation was formed by modeling the evolution based on the random walk model and observation correcting based on the self-potential forward. Thus, monitoring self-potential data can be inverted by the data assimilation technique. As a result, we can reconstruct the dynamic process of the contaminant plume instead of using traditional frame-to-frame static inversion, which may cause inversion artifacts. The data assimilation inversion algorithm was evaluated through noise-added synthetic time-lapse self-potential data. The result of the numerical experiment shows validity, accuracy and tolerance to the noise of the dynamic inversion. To validate the proposed algorithm, we conducted a scaled-down sandbox self-potential observation experiment to generate time-lapse data that closely mimics the real-world contaminant monitoring setup. The results of physical experiments support the idea that the data assimilation method is a potentially useful approach for characterizing the transport of contamination plumes using the unscented Kalman filter (UKF) data assimilation technique applied to field time-lapse self-potential data.
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Remote monitoring in international safeguards
International Nuclear Information System (INIS)
Dupree, S.A.; Sonnier, C.S.; Johnson, C.S.
1996-01-01
In recent years, technology that permits the integration of monitoring sensors and instruments into a coherent network has become available. Such integrated monitoring systems provide a means for the automatic collection and assessment of sensor signals and instrument readings and for processing such signals and readings in near real time. To gain experience with the new monitoring system technology, the US Department of energy, through bilateral agreements with its international partners, has initiated a project to emplace demonstration systems in various nuclear facilities and conduct field trials of the technology. This effort is the International Remote Monitoring Project. Under this project, remote monitoring systems are being deployed around the world in an incremental manner. Each deployment is different and each offers lessons for improving the performance and flexibility of the technology. Few problems were encountered with the operation of the installations to date, and much has been learned about the operation and use of the new technology. In the future, the authors believe systems for safeguards applications should be capable of being monitored remotely, emphasize the use of sensors, and utilize selective triggering for recording of images. Remote monitoring across national borders can occur only in the context of a cooperative, nonadversarial implementation regime. However, significant technical and policy work remains to be done before widespread safeguards implementation of remote monitoring should be considered. This paper shows that an abundance of technology supports the implementation of integrated and remote monitoring systems. Current field trials of remote monitoring systems are providing practical data and operational experience to aid in the design of tomorrow's systems
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Time domain spectroscopy to monitor the condition of cable insulation
International Nuclear Information System (INIS)
Mopsik, F.I.; Martzloff, F.D.
1989-01-01
The use of Time Domain Spectroscopy, the measurement of dielectric constant and loss using time-domain response, the monitoring the aging of reactor cable insulation is examined. The method is presented, showing its sensitivity, accuracy and wide frequency range. The method's ability to acquire a great deal of information in a short time and its superiority to conventional single frequency data is shown. Different cable samples are examined before and after exposure to radiation and changes with exposure are clearly seen to occur. Also it is shown that a wide range of behavior can be found in different insulation systems. The requirements for performing valid measurements is presented. The need for controlled samples and correlation with other criteria for aging is discussed. 14 refs., 9 figs
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Quantitative real-time monitoring of dryer effluent using fiber optic near-infrared spectroscopy.
Harris, S C; Walker, D S
2000-09-01
This paper describes a method for real-time quantitation of the solvents evaporating from a dryer. The vapor stream in the vacuum line of a dryer was monitored in real time using a fiber optic-coupled acousto-optic tunable filter near-infrared (AOTF-NIR) spectrometer. A balance was placed in the dryer, and mass readings were recorded for every scan of the AOTF-NIR. A partial least-squares (PLS) calibration was subsequently built based on change in mass over change in time for solvents typically used in a chemical manufacturing plant. Controlling software for the AOTF-NIR was developed. The software collects spectra, builds the PLS calibration model, and continuously fits subsequently collected spectra to the calibration, allowing the operator to follow the mass loss of solvent from the dryer. The results indicate that solvent loss can be monitored and quantitated in real time using NIR for the optimization of drying times. These time-based mass loss values have also been used to calculate "dynamic" vapor density values for the solvents. The values calculated are in agreement with values determined from the ideal gas law and could prove valuable as tools to measure temperature or pressure indirectly.
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Cahill, Paul; Michalis, Panagiotis; Solman, Hrvoje; Kerin, Igor; Bekic, Damir; Pakrashi, Vikram; McKeogh, Eamon
2017-04-01
With the effects of climate change becoming more apparent, extreme weather events are now occurring with greater frequency throughout the world. Such extreme events have resulted in increased high intensity flood events which are having devastating consequences on hydro-structures, especially on bridge infrastructure. The remote and often inaccessible nature of such bridges makes inspections problematic, a major concern if safety assessments are required during and after extreme flood events. A solution to this is the introduction of smart, low cost sensing solutions at locations susceptible to hydro-hazards. Such solutions can provide real-time information on the health of the bridge and its environments, with such information aiding in the mitigation of the risks associated with extreme weather events. This study presents the development of an intelligent system for remote, real-time monitoring of hydro-hazards to bridge infrastructure. The solution consists of two types of remote monitoring stations which have the capacity to monitor environmental conditions and provide real-time information to a centralized, big data database solution, from which an intelligent decision support system will accommodate the results to control and manage bridge, river and catchment assets. The first device developed as part of the system is the Weather Information Logging Device (WILD), which monitors rainfall, temperature and air and soil moisture content. The ability of the WILD to monitor rainfall in real time enables flood early warning alerts and predictive river flow conditions, thereby enabling decision makers the ability to make timely and effective decisions about critical infrastructures in advance of extreme flood events. The WILD is complemented by a second monitoring device, the Bridge Information Recording Device (BIRD), which monitors water levels at a given location in real-time. The monitoring of water levels of a river allows for, among other applications
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Development of a real time chemistry monitoring and diagnostic system
International Nuclear Information System (INIS)
Gaudreau, T.M.; Millett, P.J.; Bates, J.; Burns, G.
1998-01-01
EPRI has developed SMART chem WORKS, which is capable of operating as a real time chemistry diagnostic and monitoring system. A high degree of plant-specific customization is possible which allows discrimination between normal chemistry and off-normal conditions. The initial implementation of the system has been very successful. State of the art technology has been employed which allows remote administration of the system, a flexible, web page display of the output from the system and instant notification of excursions using email and pagers. The second installation of SMART chem WORKS is currently underway at a BWR plant, Grand Gulf. The SMART chem WORKS techniques can be applied to monitor PWR Primary Chemistry, PWR Secondary Chemistry and BWR steam cycle chemistry. A fossil steam cycle simulator will also be developed for application to fossil plants. (J.P.N.)
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Blended learning pedagogy: the time is now!
Pizzi, Michael A
2014-07-01
Pedagogy is rapidly changing. To develop best practice in academia, it is important that we change with the changing needs of students. This article suggests that blended learning is one of the most important pedagogical formats that can enhance student learning, optimize the use of active learning strategies, and potentially improve student learning outcomes.