71 research outputs found
Enhancing Clinical Learning Through an Innovative Instructor Application for ECMO Patient Simulators
© 2018 The Authors. Reprinted by permission of SAGE PublicationsBackground. Simulation-based learning (SBL) employs the synergy between technology and people to immerse learners in highly-realistic situations in order to achieve quality clinical education. Due to the ever-increasing popularity of extracorporeal membrane oxygenation (ECMO) SBL, there is a pressing need for a proper technological infrastructure that enables high-fidelity simulation to better train ECMO specialists to deal with related emergencies. In this article, we tackle the control aspect of the infrastructure by presenting and evaluating an innovative cloud-based instructor, simulator controller, and simulation operations specialist application that enables real-time remote control of fullscale immersive ECMO simulation experiences for ECMO specialists as well as creating custom simulation scenarios for standardized training of individual healthcare professionals or clinical teams. Aim. This article evaluates the intuitiveness, responsiveness, and convenience of the ECMO instructor application as a viable ECMO simulator control interface. Method. A questionnaire-based usability study was conducted following institutional ethical approval. Nineteen ECMO practitioners were given a live demonstration of the instructor application in the context of an ECMO simulator demonstration during which they also had the opportunity to interact with it. Participants then filled in a questionnaire to evaluate the ECMO instructor application as per intuitiveness, responsiveness, and convenience. Results. The collected feedback data confirmed that the presented application has an intuitive, responsive, and convenient ECMO simulator control interface. Conclusion. The present study provided evidence signifying that the ECMO instructor application is a viable ECMO simulator control interface. Next steps will comprise a pilot study evaluating the educational efficacy of the instructor application in the clinical context with further technical enhancements as per participants’ feedback.Peer reviewedFinal Accepted Versio
Appliance identification using a histogram post-processing of 2D local binary patterns for smart grid applications
Identifying domestic appliances in the smart grid leads to a better power
usage management and further helps in detecting appliance-level abnormalities.
An efficient identification can be achieved only if a robust feature extraction
scheme is developed with a high ability to discriminate between different
appliances on the smart grid. Accordingly, we propose in this paper a novel
method to extract electrical power signatures after transforming the power
signal to 2D space, which has more encoding possibilities. Following, an
improved local binary patterns (LBP) is proposed that relies on improving the
discriminative ability of conventional LBP using a post-processing stage. A
binarized eigenvalue map (BEVM) is extracted from the 2D power matrix and then
used to post-process the generated LBP representation. Next, two histograms are
constructed, namely up and down histograms, and are then concatenated to form
the global histogram. A comprehensive performance evaluation is performed on
two different datasets, namely the GREEND and WITHED, in which power data were
collected at 1 Hz and 44000 Hz sampling rates, respectively. The obtained
results revealed the superiority of the proposed LBP-BEVM based system in terms
of the identification performance versus other 2D descriptors and existing
identification frameworks.Comment: 8 pages, 10 figures and 5 table
Extracorporeal membrane oxygenation simulation-based training: methods, drawbacks and a novel solution
Introduction: Patients under the error-prone and complication-burdened extracorporeal membrane oxygenation (ECMO) are looked after by a highly trained, multidisciplinary team. Simulation-based training (SBT) affords ECMO centers the opportunity to equip practitioners with the technical dexterity required to manage emergencies. The aim of this article is to review ECMO SBT activities and technology followed by a novel solution to current challenges. ECMO simulation: The commonly-used simulation approach is easy-to-build as it requires a functioning ECMO machine and an altered circuit. Complications are simulated through manual circuit manipulations. However, scenario diversity is limited and often lacks physiological and/or mechanical authenticity. It is also expensive to continuously operate due to the consumption of highly specialized equipment. Technological aid: Commercial extensions can be added to enable remote control and to automate circuit manipulation, but do not improve on the realism or cost-effectiveness. A modular ECMO simulator: To address those drawbacks, we are developing a standalone modular ECMO simulator that employs affordable technology for high-fidelity simulation.Peer reviewe
Facilitating Deep Learning for Edge Computing: A Case Study on Data Classification
https://attend.ieee.org/dsc-2022/sicsa-event/Deep Learning (DL) is increasingly empowering technology and engineering in a plethora of ways, especially when big data processing is a core requirement. Many challenges, however, arise when solely depending on cloud computing for Artificial Intelligence (AI), such as data privacy, communication latency, and power consumption. Despite the elevating popularity of edge computing, its overarching issue is not the lack of technical specifications in many edge computing platforms but the sparsity of comprehensive documentation on how to correctly utilize hardware to run ML and DL algorithms. Due to its specialized nature, installing the full version of TensorFlow, a common ML library, on an edge device is a complicated procedure that is seldom successful, due to the many dependent software libraries needed to be compatible with varying architectures of edge computing devices. Henceforth, in this paper, we present a novel technical guide on setting up the TensorFlow Lite, a lightweight version of TensorFlow and demonstrate a complete workflow of model training, validation, and testing on the ODROID-XU4. Results are presented for a case study on energy data classification using the outlined model show almost 7 times higher computational performance compared to cloud-based AI
Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives
Enormous amounts of data are being produced everyday by sub-meters and smart
sensors installed in residential buildings. If leveraged properly, that data
could assist end-users, energy producers and utility companies in detecting
anomalous power consumption and understanding the causes of each anomaly.
Therefore, anomaly detection could stop a minor problem becoming overwhelming.
Moreover, it will aid in better decision-making to reduce wasted energy and
promote sustainable and energy efficient behavior. In this regard, this paper
is an in-depth review of existing anomaly detection frameworks for building
energy consumption based on artificial intelligence. Specifically, an extensive
survey is presented, in which a comprehensive taxonomy is introduced to
classify existing algorithms based on different modules and parameters adopted,
such as machine learning algorithms, feature extraction approaches, anomaly
detection levels, computing platforms and application scenarios. To the best of
the authors' knowledge, this is the first review article that discusses anomaly
detection in building energy consumption. Moving forward, important findings
along with domain-specific problems, difficulties and challenges that remain
unresolved are thoroughly discussed, including the absence of: (i) precise
definitions of anomalous power consumption, (ii) annotated datasets, (iii)
unified metrics to assess the performance of existing solutions, (iv) platforms
for reproducibility and (v) privacy-preservation. Following, insights about
current research trends are discussed to widen the applications and
effectiveness of the anomaly detection technology before deriving future
directions attracting significant attention. This article serves as a
comprehensive reference to understand the current technological progress in
anomaly detection of energy consumption based on artificial intelligence.Comment: 11 Figures, 3 Table
Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations
Recently, tremendous interest has been devoted to develop data fusion
strategies for energy efficiency in buildings, where various kinds of
information can be processed. However, applying the appropriate data fusion
strategy to design an efficient energy efficiency system is not
straightforward; it requires a priori knowledge of existing fusion strategies,
their applications and their properties. To this regard, seeking to provide the
energy research community with a better understanding of data fusion strategies
in building energy saving systems, their principles, advantages, and potential
applications, this paper proposes an extensive survey of existing data fusion
mechanisms deployed to reduce excessive consumption and promote sustainability.
We investigate their conceptualizations, advantages, challenges and drawbacks,
as well as performing a taxonomy of existing data fusion strategies and other
contributing factors. Following, a comprehensive comparison of the
state-of-the-art data fusion based energy efficiency frameworks is conducted
using various parameters, including data fusion level, data fusion techniques,
behavioral change influencer, behavioral change incentive, recorded data,
platform architecture, IoT technology and application scenario. Moreover, a
novel method for electrical appliance identification is proposed based on the
fusion of 2D local texture descriptors, where 1D power signals are transformed
into 2D space and treated as images. The empirical evaluation, conducted on
three real datasets, shows promising performance, in which up to 99.68%
accuracy and 99.52% F1 score have been attained. In addition, various open
research challenges and future orientations to improve data fusion based energy
efficiency ecosystems are explored
Cloud Energy Micro-Moment Data Classification: A Platform Study
Energy efficiency is a crucial factor in the well-being of our planet. In
parallel, Machine Learning (ML) plays an instrumental role in automating our
lives and creating convenient workflows for enhancing behavior. So, analyzing
energy behavior can help understand weak points and lay the path towards better
interventions. Moving towards higher performance, cloud platforms can assist
researchers in conducting classification trials that need high computational
power. Under the larger umbrella of the Consumer Engagement Towards Energy
Saving Behavior by means of Exploiting Micro Moments and Mobile Recommendation
Systems (EM)3 framework, we aim to influence consumers behavioral change via
improving their power consumption consciousness. In this paper, common cloud
artificial intelligence platforms are benchmarked and compared for micro-moment
classification. The Amazon Web Services, Google Cloud Platform, Google Colab,
and Microsoft Azure Machine Learning are employed on simulated and real energy
consumption datasets. The KNN, DNN, and SVM classifiers have been employed.
Superb performance has been observed in the selected cloud platforms, showing
relatively close performance. Yet, the nature of some algorithms limits the
training performance.Comment: This paper has been accepted in IEEE RTDPCC 2020: International
Symposium on Real-time Data Processing for Cloud Computin
A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks
Nowadays, analyzing, detecting, and visualizing abnormal power consumption behavior of householders are among the principal challenges in identifying ways to reduce power consumption. This paper introduces a new solution to detect energy consumption anomalies based on extracting micro-moment features using a rule-based model. The latter is used to draw out load characteristics using daily intent-driven moments of user consumption actions. Besides micro-moment features extraction, we also experiment with a deep neural network architecture for efficient abnormality detection and classification. In the following, a novel anomaly visualization technique is introduced that is based on a scatter representation of the micro-moment classes, and hence providing consumers an easy solution to understand their abnormal behavior. Moreover, in order to validate the proposed system, a new energy consumption dataset at appliance level is also designed through a measurement campaign carried out at Qatar University Energy Lab, namely, Qatar University dataset. Experimental results on simulated and real datasets collected at two regions, which have extremely different climate conditions, confirm that the proposed deep micro-moment architecture outperforms other machine learning algorithms and can effectively detect anomalous patterns. For example, 99.58% accuracy and 97.85% F1 score have been achieved under Qatar University dataset. These promising results establish the efficacy of the proposed deep micro-moment solution for detecting abnormal energy consumption, promoting energy efficiency behaviors, and reducing wasted energy. 2020, The Author(s).Open Access funding provided by the Qatar National Library. This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation).Scopu
Using thermochromic ink for medical simulations
© 2017 Alsalemi, Aldisi, Alhomsi, Ahmed, Bensaali, Alinier, Amira, licensee HBKU Press. This is an open access article distributed under the terms of the Creative Commons Attribution license CC BY 4.0, which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. Alsalemi A, Aldisi M, Alhomsi Y, Ahmed I, Bensaali F, Alinier G, Amira A., 'Using thermochromic ink for medical simulations', Qatar Medical Journal, 4th Annual ELSO-SWAC Conference Proceedings 2017:63 http://dx.doi.org/10.5339/qmj.2017.swacelso.63Background: In medical simulation and training, blood is used to exhibit its different behaviors in context. In some cases, blood color differential is an imperative visual effect to ensure high-fidelity training and practical understanding. High simulation realism is usually achieved by using animal or artificial blood (which mimics some biological features of blood), which has high cost, requires disposable equipment such as oxygenators, and entails contamination or infection risks. Methods: A novel method for blood simulation is introduced. Using the thermal properties of thermochromic ink, its color can be altered by adjustment of temperature. 1 The unique red color of blood can be mimicked to a high fidelity using a custom hue of thermochromic ink. Then, by adjusting its temperature, realistic dark and bright red can be employed to simulate the low and high oxygen concentrations of blood, respectively. Although thermochromic ink currently does not imitate other blood properties such as viscosity and clotting, it has superior merits when color change simulation is a paramount priority. The major advantages of the proposed solution are reusability and cost. Thermochromic ink can be used for multiple simulations without any noticeable change in quality. It also costs significantly less than using actual or artificial blood. Results: Testing results of the proposed solution in extracorporeal membrane oxygenation (ECMO) simulation has proven its efficacy as a practical solution for medical simulations (see Figure 1). To prevent membrane occlusion because of the thermochromic ink, the latter needs to be pierced. In addition to ECMO simulation, other medical applications are being considered. Conclusions: The use of thermochromic ink in medical training provides reproducible color change simulation features of blood while maintaining significantly lower equipment costs and contamination risks as all circuit components can be reused.Peer reviewedFinal Published versio
Design and implementation of a modular ECMO simulator
© 2017 The Authors, licensee HBKU Press. This is an open access article distributed under the terms of the Creative Commons Attribution license CC BY 4.0, which permits unrestricted use, distribution and reproduction in any medium, provided theoriginal work is properly cited. Aldisi M, Alsalemi A, Alhomsi Y, Ahmed I, Bensaali F, Alinier G, Amira A., 'Design and implementation of a modular ECMO simulator', Qatar Medical Journal, 4th Annual ELSO-SWAC Conference Proceedings 2017:62 http://dx.doi.org/10.5339/qmj.2017.swacelso.62Extracorporeal membrane oxygenation (ECMO) is a high-complexity life-saving procedure riddled with mechanical complications that can place the patient in a critical state where fast and coordinated actions are required to avoid mortality. Thus, patients on ECMO are supervised round the clock by highly trained nurses and perfusionists. Currently, ECMO training programs include patient emergency simulations performed with different levels of success. Some training facilities use mannequins that have computer-controlled physiological parameters such as heart rate and oxygen saturation. The circuit parameters such as pressure are manually adjusted per scenario; air and artificial blood are manually injected to indicate problems such as air embolism, and hypovolemia. 1 Despite being realistic, using an actual ECMO circuit for simulation training purposes has disadvantages such as the use of expensive disposable equipment (oxygenation membrane), lack of oxygenation color differentials, and manual circuit adjustments and injections.Peer reviewedFinal Published versio
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